system

Abstract

The system according to the embodiment aims to automatically extract the main points from the contents of an email and input them into a spreadsheet. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an extraction unit, an input unit, and a determination unit. The reception unit receives emails. The analysis unit analyzes the content of the emails received by the reception unit. The extraction unit extracts key points from the content analyzed by the analysis unit. The input unit inputs the key points extracted by the extraction unit into a spreadsheet. The determination unit determines a next action based on the information input by the input unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of being cumbersome and inefficient, as it requires users to manually extract the main points from emails and enter them into a spreadsheet.

[0005] The system according to the embodiment aims to automatically extract the main points from the contents of an email and input them into a spreadsheet. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an extraction unit, an input unit, and a determination unit. The reception unit receives emails. The analysis unit analyzes the content of the emails received by the reception unit. The extraction unit extracts key points from the content analyzed by the analysis unit. The input unit inputs the key points extracted by the extraction unit into a spreadsheet. The determination unit determines a next action based on the information input by the input unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically extract the main points from the contents of an email and input them into a spreadsheet. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The automated sales support system according to an embodiment of the present invention automatically summarizes the key points of customer email requests, uploads them to a spreadsheet, and even records the actions that salespeople should take. This system first inputs information about internal procedures. When a customer email arrives, the generation AI analyzes the email content using natural language processing technology to extract important keywords and phrases. The extracted key points are categorized into categories such as project type (e.g., networking, digital marketing), response items (e.g., sending an order form, obtaining approval), and project size (sales, profits). This information is automatically entered into a spreadsheet. Furthermore, the generation AI creates a table summarizing the next actions that salespeople should take and their progress. This table lists the project's progress and the next actions to be taken. This allows salespeople to work more efficiently.

[0029] The automated sales support system according to the embodiment includes a reception unit, an analysis unit, an extraction unit, an input unit, and a determination unit. The reception unit receives emails. Examples of emails include, but are not limited to, text emails, HTML emails, and emails with attachments. The reception unit, for example, obtains emails from a mail server and imports them into the system. The reception unit can also analyze the format of the emails and convert them into an appropriate format. For example, the reception unit can convert HTML emails into text format. The analysis unit uses natural language processing technology to analyze the content of the emails received by the reception unit. For example, the analysis unit can segment the content of the emails using morphological analysis and perform grammatical analysis. The analysis unit can also perform semantic analysis to understand the content of the emails. For example, the analysis unit extracts important keywords and phrases from the content of the emails. The extraction unit extracts key points from the content analyzed by the analysis unit. For example, the extraction unit extracts key points based on important keywords and phrases. The extraction unit can also summarize the content of the emails and extract key points. For example, the extraction unit summarizes the content of the email and extracts important information. The input unit inputs the key points extracted by the extraction unit into a spreadsheet. Examples of spreadsheets include, but are not limited to, Excel and Google (registered trademark) Sheets. For example, the input unit inputs the key points into specific cells of the spreadsheet. The input unit can also input information according to a spreadsheet format. For example, the input unit inputs information into fields such as case type, response item, and case size. The determination unit determines a next action based on the information input by the input unit. For example, the determination unit determines the next action by referring to past data. The determination unit can also determine the next action based on the current situation. For example, the determination unit determines the progress of the case and the next action to be taken. This allows the automated sales support system according to the embodiment to improve the work efficiency of sales representatives.

[0030] The analysis unit can analyze the content of the email using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit, for example, uses morphological analysis to divide the content of the email and perform grammatical analysis. The analysis unit can also perform semantic analysis to understand the content of the email. For example, the analysis unit extracts important keywords and phrases from the content of the email. This allows the content of the email to be accurately analyzed using natural language processing technology.

[0031] The determination unit can determine the next action by referring to past data. Past data includes, for example, past email history and past action history, but is not limited to these examples. The determination unit, for example, refers to past email history to determine an action for a similar case. The determination unit can also refer to past action history to determine an optimal next action. For example, the determination unit determines the next action based on past success cases. In this way, by referring to past data, a more appropriate next action can be determined.

[0032] The reception unit can analyze the user's past email reception history and select the optimal reception method. The reception unit, for example, analyzes the user's past email reception history and selects the optimal reception method. For example, the reception unit prioritizes the selection of a reception method for emails that the user has frequently received in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past email reception history. For example, the reception unit analyzes the user's past email reception history and selects the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past email reception history.

[0033] The reception unit can filter emails based on the user's current project or area of ​​interest when receiving emails. For example, the reception unit preferentially receives emails related to a project the user is currently working on. For example, the reception unit filters emails related to the user's area of ​​interest and receives only important emails. The reception unit can also preferentially receive related emails based on the progress of the user's current project. For example, the reception unit preferentially receives emails related to the user's current project. In this way, important emails can be preferentially received by filtering emails based on the user's current project or area of ​​interest.

[0034] When receiving emails, the reception unit can prioritize receiving highly relevant emails by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving emails related to that area. For example, the reception unit filters highly relevant emails based on the user's current location and receives only important emails. Furthermore, when the user is traveling, the reception unit can also prioritize receiving emails related to the user's current location. For example, when the user is in a specific area, the reception unit prioritizes receiving emails related to that area. In this way, by taking into account the user's geographical location information, highly relevant emails can be prioritized.

[0035] The reception unit can analyze the user's social media activity when receiving emails and receive related emails. For example, the reception unit preferentially receives emails related to topics in which the user has shown interest on social media. For example, the reception unit filters important emails from the user's social media activity and receives them. The reception unit can also preferentially receive emails from accounts the user follows on social media. For example, the reception unit preferentially receives emails related to topics in which the user has shown interest on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related emails.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis on emails with a high level of importance. For example, the analysis unit performs a concise analysis on emails with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis on emails with a high level of importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the email.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of email. For example, the analysis unit applies a network-specific analysis algorithm to network-related emails. For example, the analysis unit applies a marketing-specific analysis algorithm to digital marketing-related emails. The analysis unit can also select and apply an appropriate analysis algorithm to emails in other categories. For example, the analysis unit applies a network-specific analysis algorithm to network-related emails. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of email.

[0038] During analysis, the analysis unit can determine the priority of analysis based on the time when the email was received. For example, the analysis unit prioritizes the analysis of recently received emails. For example, the analysis unit may analyze emails that were received earlier later. The analysis unit can also dynamically adjust the priority of analysis based on the time when the email was received. For example, the analysis unit prioritizes the analysis of recently received emails. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the email was received.

[0039] During analysis, the analysis unit can adjust the order of analysis based on the relevance of emails. For example, the analysis unit prioritizes analysis of highly relevant emails. For example, the analysis unit may analyze less relevant emails later. The analysis unit can also dynamically adjust the order of analysis based on the relevance of emails. For example, the analysis unit prioritizes analysis of highly relevant emails. In this way, adjusting the order of analysis based on the relevance of emails enables efficient analysis.

[0040] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between emails during extraction. The extraction unit, for example, analyzes the interrelationships between emails and extracts related main points. For example, the extraction unit takes into account the interrelationships between emails and preferentially extracts important main points. The extraction unit can also improve the accuracy of extraction based on the interrelationships between emails. For example, the extraction unit analyzes the interrelationships between emails and extracts related main points. In this way, the accuracy of extraction is improved by taking into account the interrelationships between emails.

[0041] The extraction unit can perform extraction while taking into consideration the attribute information of the sender of the email. The extraction unit, for example, extracts important points based on the attribute information of the sender. For example, the extraction unit improves the accuracy of extraction by taking into consideration the attribute information of the sender. The extraction unit can also preferentially extract related points based on the attribute information of the sender. For example, the extraction unit extracts important points based on the attribute information of the sender. In this way, by taking into consideration the attribute information of the sender of the email, important points can be preferentially extracted.

[0042] The extraction unit can perform extraction while taking into account the geographical distribution of emails. The extraction unit, for example, extracts related main points based on the geographical distribution of emails. For example, the extraction unit takes into account the geographical distribution of emails and preferentially extracts important main points. The extraction unit can also improve the accuracy of extraction based on the geographical distribution of emails. For example, the extraction unit extracts related main points based on the geographical distribution of emails. In this way, by taking into account the geographical distribution of emails, related main points can be preferentially extracted.

[0043] The extraction unit can improve the accuracy of extraction by referring to the related literature of the email during extraction. For example, the extraction unit extracts important main points based on the related literature of the email. For example, the extraction unit improves the accuracy of extraction by referring to the related literature of the email. The extraction unit can also preferentially extract related main points based on the related literature of the email. For example, the extraction unit extracts important main points based on the related literature of the email. In this way, by referring to the related literature of the email, the accuracy of extraction is improved.

[0044] The input unit can adjust the level of detail of the input based on the importance of the email when inputting. For example, the input unit performs detailed input for emails with high importance. For example, the input unit performs concise input for emails with low importance. The input unit can also dynamically adjust the level of detail of the input according to the importance of the email. For example, the input unit performs detailed input for emails with high importance. This allows for efficient input by adjusting the level of detail of the input according to the importance of the email.

[0045] The input unit can apply different input algorithms depending on the category of email during input. For example, the input unit applies a network-specific input algorithm to network-related emails. For example, the input unit applies a marketing-specific input algorithm to digital marketing-related emails. The input unit can also select and apply an appropriate input algorithm to emails of other categories. For example, the input unit applies a network-specific input algorithm to network-related emails. In this way, input accuracy is improved by applying an input algorithm according to the category of email.

[0046] The input unit can determine the priority of input based on the time when the email was received when inputting. For example, the input unit inputs emails that were received recently with priority. For example, the input unit inputs emails that were received earlier later. The input unit can also dynamically adjust the priority of input based on the time when the email was received. For example, the input unit inputs emails that were received recently with priority. In this way, efficient input is possible by determining the priority of input based on the time when the email was received.

[0047] The input unit can adjust the order of input based on the relevance of emails during input. For example, the input unit inputs emails with high relevance first. For example, the input unit inputs emails with low relevance later. The input unit can also dynamically adjust the order of input based on the relevance of emails. For example, the input unit inputs emails with high relevance first. This allows for efficient input by adjusting the order of input based on the relevance of emails.

[0048] When making a decision, the decision unit can select the optimal next action by referring to past data. The decision unit, for example, selects the optimal next action based on past data. For example, the decision unit selects the optimal next action by referring to past success cases. The decision unit can also select the optimal next action to avoid past failure cases. For example, the decision unit selects the optimal next action based on past data. In this way, the optimal next action can be selected by referring to past data.

[0049] When making a decision, the decision unit can apply different decision algorithms depending on the category of the email. For example, the decision unit applies a network-specific decision algorithm to network-related emails. For example, the decision unit applies a marketing-specific decision algorithm to digital marketing-related emails. The decision unit can also select and apply an appropriate decision algorithm to emails in other categories. For example, the decision unit applies a network-specific decision algorithm to network-related emails. In this way, the accuracy of the decision is improved by applying a decision algorithm depending on the category of the email.

[0050] At the time of determination, the determination unit can determine the priority of the next action based on the time of receipt of the email. The determination unit determines the priority of the next action based on, for example, the most recently received email. For example, the determination unit determines the priority of the next action by giving a later priority to emails that were received earlier. The determination unit can also dynamically adjust the priority of the next action based on the time of receipt. For example, the determination unit determines the priority of the next action based on the time of receipt of the email. In this way, by determining the priority of the next action based on the time of receipt of the email, efficient actions can be taken.

[0051] When making a decision, the decision unit can adjust the order of next actions based on the relevance of the emails. The decision unit, for example, determines the order of next actions based on highly relevant emails. For example, the decision unit determines the order of next actions by leaving less relevant emails at a later date. The decision unit can also dynamically adjust the order of next actions based on the relevance of emails. For example, the decision unit determines the order of next actions based on highly relevant emails. This allows for efficient actions by adjusting the order of next actions based on the relevance of emails.

[0052] When making a decision, the decision unit can determine details of the next action based on the content of the email. The decision unit, for example, analyzes the content of the email and determines a specific next action. For example, the decision unit identifies required resources based on the content of the email and determines the next action. The decision unit can also determine a specific step to be taken next based on the content of the email. For example, the decision unit analyzes the content of the email and determines a specific next action. In this way, a specific action can be suggested by determining details of the next action based on the content of the email.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the user's past response times to emails and predict the optimal response time. For example, the reception unit can identify time periods in which the user responded quickly in the past and prioritize receiving emails during those time periods. The reception unit can also achieve efficient email reception by avoiding time periods in which the user responded slowly in the past. Furthermore, the reception unit can refer to the user's calendar information and receive emails during time periods when the user does not have any meetings or appointments. This makes it possible to predict the optimal email reception time based on the user's past response patterns and improve business efficiency.

[0055] The analysis unit can refer to relevant external databases based on the content of the email to obtain additional information. For example, the analysis unit can search external product review sites and technical literature databases based on the product or service name stated in the email to obtain related information. The analysis unit can also obtain market trends and competitive information related to the content of the email and provide it to sales representatives. Furthermore, the analysis unit can refer to relevant laws and regulations and industry standards based on the content of the email and provide necessary compliance information. This makes it possible to obtain relevant external information based on the content of the email and support sales activities.

[0056] The reception unit can select the optimal email reception server based on the user's geographical location information. For example, if the user is overseas, the reception unit can prioritize the use of a local email server to minimize email delays. Also, if the user is traveling, the reception unit can select the nearest server to ensure prompt email reception. Furthermore, the reception unit can prioritize the reception of emails related to a specific region based on the user's geographical location information. This allows the optimal email reception server to be selected taking into account the user's geographical location information, thereby enabling efficient email reception.

[0057] The extraction unit can refer to relevant internal databases and extract additional information based on the content of the email. For example, the extraction unit can search the company's internal customer management system or project management system based on the customer name or case number contained in the email and extract relevant information. The extraction unit can also extract past case history and response history related to the email content and provide it to sales representatives. Furthermore, the extraction unit can extract related internal technical documents and manuals based on the email content and provide the necessary information. This makes it possible to extract relevant internal information based on the email content and support sales activities.

[0058] The determination unit can cooperate with a related task management system based on the content of the email and automatically register the next action as a task. For example, the determination unit can automatically add a new task to the task management system based on the task described in the email. The determination unit can also update the progress of an existing task based on the content of the email. Furthermore, the determination unit can automatically adjust the priority of a task related to the content of the email to achieve efficient task management. This makes it possible to cooperate with a related task management system based on the content of the email and support efficient task management.

[0059] The analysis unit can refer to related social media information based on the content of the email and obtain additional information. For example, the analysis unit can search for related posts and reviews on social media based on the company name or product name mentioned in the email and obtain related information. The analysis unit can also obtain trend information and user feedback related to the content of the email and provide it to sales representatives. Furthermore, the analysis unit can also obtain opinions and comments from related influencers based on the content of the email and provide necessary information. This makes it possible to obtain related social media information based on the content of the email and support sales activities.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives emails. Emails can be in text format, HTML format, or emails with attachments. The reception unit retrieves emails from the email server and imports them into the system. It can also analyze the email format and convert it into an appropriate format. For example, it can convert HTML emails into text format. Step 2: The analysis unit uses natural language processing technology to analyze the content of the email received by the reception unit. The analysis unit uses morphological analysis to divide the email content and perform grammatical analysis. It also performs semantic analysis to understand the content of the email. For example, it extracts important keywords and phrases from the email content. Step 3: The extraction unit extracts key points from the content analyzed by the analysis unit. The extraction unit extracts key points based on important keywords and phrases. It can also summarize the content of the email and extract important information. Step 4: The input unit inputs the key points extracted by the extraction unit into a spreadsheet. Spreadsheets include Excel and Google Sheets. The input unit inputs the key points into specific cells in the spreadsheet and enters information according to a format. For example, information is entered into fields such as the type of project, the action to be taken, and the size of the project. Step 5: The decision unit determines the next action based on the information input by the input unit. The decision unit determines the next action by referring to past data. The decision unit also determines the next action based on the current situation. For example, it determines the progress of the case and the next action to be taken.

[0062] (Example 2) The automated sales support system according to an embodiment of the present invention automatically summarizes the key points of customer email requests, uploads them to a spreadsheet, and even records the actions that salespeople should take. This system first inputs information about internal procedures. When a customer email arrives, the generation AI analyzes the email content using natural language processing technology to extract important keywords and phrases. The extracted key points are categorized into categories such as project type (e.g., networking, digital marketing), response items (e.g., sending an order form, obtaining approval), and project size (sales, profits). This information is automatically entered into a spreadsheet. Furthermore, the generation AI creates a table summarizing the next actions that salespeople should take and their progress. This table lists the project's progress and the next actions to be taken. This allows salespeople to work more efficiently.

[0063] The automated sales support system according to the embodiment includes a reception unit, an analysis unit, an extraction unit, an input unit, and a determination unit. The reception unit receives emails. Examples of emails include, but are not limited to, text emails, HTML emails, and emails with attachments. The reception unit, for example, obtains emails from a mail server and imports them into the system. The reception unit can also analyze the format of the emails and convert them into an appropriate format. For example, the reception unit can convert HTML emails into text format. The analysis unit uses natural language processing technology to analyze the content of the emails received by the reception unit. For example, the analysis unit can segment the content of the emails using morphological analysis and perform grammatical analysis. The analysis unit can also perform semantic analysis to understand the content of the emails. For example, the analysis unit extracts important keywords and phrases from the content of the emails. The extraction unit extracts key points from the content analyzed by the analysis unit. For example, the extraction unit extracts key points based on important keywords and phrases. The extraction unit can also summarize the content of the emails and extract key points. For example, the extraction unit summarizes the content of the email and extracts important information. The input unit inputs the key points extracted by the extraction unit into a spreadsheet. Examples of spreadsheets include, but are not limited to, Excel and Google Sheets. For example, the input unit inputs the key points into specific cells of the spreadsheet. The input unit can also input information according to a spreadsheet format. For example, the input unit inputs information into fields such as case type, response item, and case size. The determination unit determines a next action based on the information input by the input unit. For example, the determination unit determines the next action by referring to past data. The determination unit can also determine the next action based on the current situation. For example, the determination unit determines the progress of the case and the next action to be taken. This allows the automated sales support system according to the embodiment to improve the work efficiency of sales representatives.

[0064] The analysis unit can analyze the content of the email using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit, for example, uses morphological analysis to divide the content of the email and perform grammatical analysis. The analysis unit can also perform semantic analysis to understand the content of the email. For example, the analysis unit extracts important keywords and phrases from the content of the email. This allows the content of the email to be accurately analyzed using natural language processing technology.

[0065] The determination unit can determine the next action by referring to past data. Past data includes, for example, past email history and past action history, but is not limited to these examples. The determination unit, for example, refers to past email history to determine an action for a similar case. The determination unit can also refer to past action history to determine an optimal next action. For example, the determination unit determines the next action based on past success cases. In this way, by referring to past data, a more appropriate next action can be determined.

[0066] The reception unit can estimate the user's emotions and adjust the timing of email reception based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the reception unit delays the reception of emails and waits until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can immediately receive emails and respond quickly. For example, if the user is busy, the reception unit temporarily suspends the reception of emails and waits until the user is calm. This allows the user's stress to be reduced by adjusting the timing of email reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The reception unit can analyze the user's past email reception history and select the optimal reception method. The reception unit, for example, analyzes the user's past email reception history and selects the optimal reception method. For example, the reception unit prioritizes the selection of a reception method for emails that the user has frequently received in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past email reception history. For example, the reception unit analyzes the user's past email reception history and selects the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past email reception history.

[0068] The reception unit can filter emails based on the user's current project or area of ​​interest when receiving emails. For example, the reception unit preferentially receives emails related to a project the user is currently working on. For example, the reception unit filters emails related to the user's area of ​​interest and receives only important emails. The reception unit can also preferentially receive related emails based on the progress of the user's current project. For example, the reception unit preferentially receives emails related to the user's current project. In this way, important emails can be preferentially received by filtering emails based on the user's current project or area of ​​interest.

[0069] The reception unit can estimate the user's emotions and determine the priority of emails to be received based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, when the user is feeling stressed, the reception unit postpones emails of low importance and prioritizes emails of high importance. The reception unit can also receive all emails equally when the user is relaxed. For example, when the user is busy, the reception unit prioritizes emails of high urgency. In this way, by determining the priority of emails according to the user's emotions, important emails can be processed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] When receiving emails, the reception unit can prioritize receiving highly relevant emails by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving emails related to that area. For example, the reception unit filters highly relevant emails based on the user's current location and receives only important emails. Furthermore, when the user is traveling, the reception unit can also prioritize receiving emails related to the user's current location. For example, when the user is in a specific area, the reception unit prioritizes receiving emails related to that area. In this way, by taking into account the user's geographical location information, highly relevant emails can be prioritized.

[0071] The reception unit can analyze the user's social media activity when receiving emails and receive related emails. For example, the reception unit preferentially receives emails related to topics in which the user has shown interest on social media. For example, the reception unit filters important emails from the user's social media activity and receives them. The reception unit can also preferentially receive emails from accounts the user follows on social media. For example, the reception unit preferentially receives emails related to topics in which the user has shown interest on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related emails.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. The analysis unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, if the user is excited, the analysis unit provides analysis results that add visually stimulating effects. This allows the analysis to be presented in a way that is optimal for the user by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis on emails with a high level of importance. For example, the analysis unit performs a concise analysis on emails with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis on emails with a high level of importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the email.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of email. For example, the analysis unit applies a network-specific analysis algorithm to network-related emails. For example, the analysis unit applies a marketing-specific analysis algorithm to digital marketing-related emails. The analysis unit can also select and apply an appropriate analysis algorithm to emails in other categories. For example, the analysis unit applies a network-specific analysis algorithm to network-related emails. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of email.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is in a hurry, the analysis unit provides a short, to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is excited, the analysis unit provides an analysis result with a visually stimulating effect. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] During analysis, the analysis unit can determine the priority of analysis based on the time when the email was received. For example, the analysis unit prioritizes the analysis of recently received emails. For example, the analysis unit may analyze emails that were received earlier later. The analysis unit can also dynamically adjust the priority of analysis based on the time when the email was received. For example, the analysis unit prioritizes the analysis of recently received emails. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the email was received.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of emails. For example, the analysis unit prioritizes analysis of highly relevant emails. For example, the analysis unit may analyze less relevant emails later. The analysis unit can also dynamically adjust the order of analysis based on the relevance of emails. For example, the analysis unit prioritizes analysis of highly relevant emails. In this way, adjusting the order of analysis based on the relevance of emails enables efficient analysis.

[0078] The extraction unit can estimate the user's emotions and determine the priority of key points to be extracted based on the estimated user emotions. The extraction unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the extraction unit prioritizes extracting key points of high importance. The extraction unit can also extract all key points equally if the user is relaxed. For example, if the user is busy, the extraction unit prioritizes extracting key points of high urgency. In this way, by determining the priority of key points according to the user's emotions, important key points can be preferentially extracted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between emails during extraction. The extraction unit, for example, analyzes the interrelationships between emails and extracts related main points. For example, the extraction unit takes into account the interrelationships between emails and preferentially extracts important main points. The extraction unit can also improve the accuracy of extraction based on the interrelationships between emails. For example, the extraction unit analyzes the interrelationships between emails and extracts related main points. In this way, the accuracy of extraction is improved by taking into account the interrelationships between emails.

[0080] The extraction unit can perform extraction while taking into consideration the attribute information of the sender of the email. The extraction unit, for example, extracts important points based on the attribute information of the sender. For example, the extraction unit improves the accuracy of extraction by taking into consideration the attribute information of the sender. The extraction unit can also preferentially extract related points based on the attribute information of the sender. For example, the extraction unit extracts important points based on the attribute information of the sender. In this way, by taking into consideration the attribute information of the sender of the email, important points can be preferentially extracted.

[0081] The extraction unit can estimate the user's emotions and adjust the display method of the extracted key points based on the estimated user emotions. The extraction unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is nervous, the extraction unit provides a simple, highly visible display method. The extraction unit can also provide a display method including detailed information if the user is relaxed. For example, if the user is in a hurry, the extraction unit provides a display method that focuses on the main points. This allows the display method of the key points to be adjusted according to the user's emotions, thereby enabling the optimal display for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The extraction unit can perform extraction while taking into account the geographical distribution of emails. The extraction unit, for example, extracts related main points based on the geographical distribution of emails. For example, the extraction unit takes into account the geographical distribution of emails and preferentially extracts important main points. The extraction unit can also improve the accuracy of extraction based on the geographical distribution of emails. For example, the extraction unit extracts related main points based on the geographical distribution of emails. In this way, by taking into account the geographical distribution of emails, related main points can be preferentially extracted.

[0083] The extraction unit can improve the accuracy of extraction by referring to the related literature of the email during extraction. For example, the extraction unit extracts important main points based on the related literature of the email. For example, the extraction unit improves the accuracy of extraction by referring to the related literature of the email. The extraction unit can also preferentially extract related main points based on the related literature of the email. For example, the extraction unit extracts important main points based on the related literature of the email. In this way, by referring to the related literature of the email, the accuracy of extraction is improved.

[0084] The input unit can estimate a user's emotions and adjust the input method into the spreadsheet based on the estimated user emotions. The input unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is stressed, the input unit provides a simple input method and minimizes input steps. Furthermore, if the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the input unit prioritizes voice input to enable quick input. This allows the input method to be adjusted according to the user's emotions, thereby providing the optimal input method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The input unit can adjust the level of detail of the input based on the importance of the email when inputting. For example, the input unit performs detailed input for emails with high importance. For example, the input unit performs concise input for emails with low importance. The input unit can also dynamically adjust the level of detail of the input according to the importance of the email. For example, the input unit performs detailed input for emails with high importance. This allows for efficient input by adjusting the level of detail of the input according to the importance of the email.

[0086] The input unit can apply different input algorithms depending on the category of email during input. For example, the input unit applies a network-specific input algorithm to network-related emails. For example, the input unit applies a marketing-specific input algorithm to digital marketing-related emails. The input unit can also select and apply an appropriate input algorithm to emails of other categories. For example, the input unit applies a network-specific input algorithm to network-related emails. In this way, input accuracy is improved by applying an input algorithm according to the category of email.

[0087] The input unit can estimate the user's emotion and adjust the length of the input based on the estimated user emotion. The input unit, for example, analyzes the user's facial expressions and voice to estimate the emotion. For example, if the user is in a hurry, the input unit provides short, to-the-point input. The input unit can also provide detailed input if the user is relaxed. For example, if the user is excited, the input unit provides input with a visually stimulating effect. This allows the input to be optimized for the user by adjusting the length of the input according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The input unit can determine the priority of input based on the time when the email was received when inputting. For example, the input unit inputs emails that were received recently with priority. For example, the input unit inputs emails that were received earlier later. The input unit can also dynamically adjust the priority of input based on the time when the email was received. For example, the input unit inputs emails that were received recently with priority. In this way, efficient input is possible by determining the priority of input based on the time when the email was received.

[0089] The input unit can adjust the order of input based on the relevance of emails during input. For example, the input unit inputs emails with high relevance first. For example, the input unit inputs emails with low relevance later. The input unit can also dynamically adjust the order of input based on the relevance of emails. For example, the input unit inputs emails with high relevance first. This allows for efficient input by adjusting the order of input based on the relevance of emails.

[0090] The decision unit can estimate the user's emotions and adjust the next action determination method based on the estimated user emotions. The decision unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, the decision unit can suggest a detailed next action if the user is relaxed. The decision unit can also suggest a concise and quick next action if the user is in a hurry. For example, the decision unit can suggest a next action with a visually stimulating effect if the user is excited. In this way, the next action determination method can be adjusted according to the user's emotions, thereby suggesting the optimal action for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When making a decision, the decision unit can select the optimal next action by referring to past data. The decision unit, for example, selects the optimal next action based on past data. For example, the decision unit selects the optimal next action by referring to past success cases. The decision unit can also select the optimal next action to avoid past failure cases. For example, the decision unit selects the optimal next action based on past data. In this way, the optimal next action can be selected by referring to past data.

[0092] When making a decision, the decision unit can apply different decision algorithms depending on the category of the email. For example, the decision unit applies a network-specific decision algorithm to network-related emails. For example, the decision unit applies a marketing-specific decision algorithm to digital marketing-related emails. The decision unit can also select and apply an appropriate decision algorithm to emails in other categories. For example, the decision unit applies a network-specific decision algorithm to network-related emails. In this way, the accuracy of the decision is improved by applying a decision algorithm depending on the category of the email.

[0093] The determination unit can estimate the user's emotions and determine the priority of next actions based on the estimated user emotions. The determination unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the determination unit prioritizes next actions with high importance. The determination unit can also determine all next actions equally if the user is relaxed. For example, if the user is busy, the determination unit prioritizes next actions with high urgency. In this way, by determining the priority of next actions according to the user's emotions, important actions can be executed preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] At the time of determination, the determination unit can determine the priority of the next action based on the time of receipt of the email. The determination unit determines the priority of the next action based on, for example, the most recently received email. For example, the determination unit determines the priority of the next action by giving a later priority to emails that were received earlier. The determination unit can also dynamically adjust the priority of the next action based on the time of receipt. For example, the determination unit determines the priority of the next action based on the time of receipt of the email. In this way, by determining the priority of the next action based on the time of receipt of the email, efficient actions can be taken.

[0095] When making a decision, the decision unit can adjust the order of next actions based on the relevance of the emails. The decision unit, for example, determines the order of next actions based on highly relevant emails. For example, the decision unit determines the order of next actions by leaving less relevant emails at a later date. The decision unit can also dynamically adjust the order of next actions based on the relevance of emails. For example, the decision unit determines the order of next actions based on highly relevant emails. This allows for efficient actions by adjusting the order of next actions based on the relevance of emails.

[0096] When making a decision, the decision unit can determine details of the next action based on the content of the email. The decision unit, for example, analyzes the content of the email and determines a specific next action. For example, the decision unit identifies required resources based on the content of the email and determines the next action. The decision unit can also determine a specific step to be taken next based on the content of the email. For example, the decision unit analyzes the content of the email and determines a specific next action. In this way, a specific action can be suggested by determining details of the next action based on the content of the email. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, extraction unit, input unit, and determination unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive emails via the communication I / F 44 of the smart device 14. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the content of the emails using natural language processing technology. The extraction unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, extracts important keywords and phrases. The input unit, implemented, for example, by the control unit 46A of the smart device 14, inputs the extracted key points into a spreadsheet. The determination unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, determines the next action. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, extraction unit, input unit, and determination unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive emails via the communication I / F 44 of the smart glasses 214. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the email using natural language processing technology. The extraction unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and extracts important keywords and phrases. The input unit, for example, is realized by the control unit 46A of the smart glasses 214 and inputs the extracted key points into a spreadsheet. The determination unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and determines a next action. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, extraction unit, input unit, and determination unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive emails via the communication I / F 44 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the emails using natural language processing technology. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important keywords and phrases. The input unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and inputs the extracted key points into a spreadsheet. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the next action. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, extraction unit, input unit, and determination unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive emails via the communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the content of the emails using natural language processing technology. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts important keywords and phrases. The input unit is realized, for example, by the control unit 46A of the robot 414 and inputs the extracted key points into a spreadsheet. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the next action.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit can analyze the user's past response times to emails and predict the optimal response time. For example, the reception unit can identify time periods in which the user responded quickly in the past and prioritize receiving emails during those time periods. The reception unit can also achieve efficient email reception by avoiding time periods in which the user responded slowly in the past. Furthermore, the reception unit can refer to the user's calendar information and receive emails during time periods when the user does not have any meetings or appointments. This makes it possible to predict the optimal email reception time based on the user's past response patterns and improve business efficiency.

[0099] The analysis unit can refer to relevant external databases based on the content of the email to obtain additional information. For example, the analysis unit can search external product review sites and technical literature databases based on the product or service name stated in the email to obtain related information. The analysis unit can also obtain market trends and competitive information related to the content of the email and provide it to sales representatives. Furthermore, the analysis unit can refer to relevant laws and regulations and industry standards based on the content of the email and provide necessary compliance information. This makes it possible to obtain relevant external information based on the content of the email and support sales activities.

[0100] The determination unit can estimate the user's emotions and adjust the notification method for the next action based on the estimated user's emotions. For example, if the user is feeling stressed, the determination unit can provide a simple, highly visible notification method. If the user is relaxed, the determination unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the determination unit can prioritize voice notification or push notification to quickly convey information. This makes it possible to optimally convey information by adjusting the notification method according to the user's emotions.

[0101] The reception unit can select the optimal email reception server based on the user's geographical location information. For example, if the user is overseas, the reception unit can prioritize the use of a local email server to minimize email delays. Also, if the user is traveling, the reception unit can select the nearest server to ensure prompt email reception. Furthermore, the reception unit can prioritize the reception of emails related to a specific region based on the user's geographical location information. This allows the optimal email reception server to be selected taking into account the user's geographical location information, thereby enabling efficient email reception.

[0102] The analysis unit can estimate the user's emotions and adjust the display format of the analysis results based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide analysis results including detailed graphs and charts. If the user is in a hurry, the analysis unit can provide analysis results in a concise text format that focuses on the main points. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This makes it possible to provide optimal information by adjusting the display format of the analysis results according to the user's emotions.

[0103] The extraction unit can refer to relevant internal databases and extract additional information based on the content of the email. For example, the extraction unit can search the company's internal customer management system or project management system based on the customer name or case number contained in the email and extract relevant information. The extraction unit can also extract past case history and response history related to the email content and provide it to sales representatives. Furthermore, the extraction unit can extract related internal technical documents and manuals based on the email content and provide the necessary information. This makes it possible to extract relevant internal information based on the email content and support sales activities.

[0104] The input unit can estimate the user's emotions and adjust the layout of the spreadsheet based on the estimated user's emotions. For example, if the user is feeling stressed, the input unit can provide a simple, highly visible layout. If the user is feeling relaxed, the input unit can also provide a layout including detailed information. Furthermore, if the user is in a hurry, the input unit can also provide a layout that highlights important information. In this way, by adjusting the layout of the spreadsheet according to the user's emotions, optimal information display is possible.

[0105] The determination unit can cooperate with a related task management system based on the content of the email and automatically register the next action as a task. For example, the determination unit can automatically add a new task to the task management system based on the task described in the email. The determination unit can also update the progress of an existing task based on the content of the email. Furthermore, the determination unit can automatically adjust the priority of a task related to the content of the email to achieve efficient task management. This makes it possible to cooperate with a related task management system based on the content of the email and support efficient task management.

[0106] The reception unit can estimate the user's emotions and adjust the email filtering criteria based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can filter out emails of low importance and accept only emails of high importance. Also, if the user is relaxed, the reception unit can accept all emails equally. Furthermore, if the user is busy, the reception unit can prioritize accepting emails of high urgency. This allows optimal email reception by adjusting the email filtering criteria according to the user's emotions.

[0107] The analysis unit can refer to related social media information based on the content of the email and obtain additional information. For example, the analysis unit can search for related posts and reviews on social media based on the company name or product name mentioned in the email and obtain related information. The analysis unit can also obtain trend information and user feedback related to the content of the email and provide it to sales representatives. Furthermore, the analysis unit can also obtain opinions and comments from related influencers based on the content of the email and provide necessary information. This makes it possible to obtain related social media information based on the content of the email and support sales activities.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit receives emails. Emails can be in text format, HTML format, or emails with attachments. The reception unit retrieves emails from the email server and imports them into the system. It can also analyze the email format and convert it into an appropriate format. For example, it can convert HTML emails into text format. Step 2: The analysis unit uses natural language processing technology to analyze the content of the email received by the reception unit. The analysis unit uses morphological analysis to divide the email content and perform grammatical analysis. It also performs semantic analysis to understand the content of the email. For example, it extracts important keywords and phrases from the email content. Step 3: The extraction unit extracts key points from the content analyzed by the analysis unit. The extraction unit extracts key points based on important keywords and phrases. It can also summarize the content of the email and extract important information. Step 4: The input unit inputs the key points extracted by the extraction unit into a spreadsheet. Spreadsheets include Excel and Google Sheets. The input unit inputs the key points into specific cells in the spreadsheet and enters information according to a format. For example, information is entered into fields such as the type of project, the action to be taken, and the size of the project. Step 5: The decision unit determines the next action based on the information input by the input unit. The decision unit determines the next action by referring to past data. The decision unit also determines the next action based on the current situation. For example, it determines the progress of the case and the next action to be taken.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] 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.

[0173] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0175] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving emails; an analysis unit that analyzes the content of the email accepted by the acceptance unit; an extraction unit that extracts key points from the content analyzed by the analysis unit; an input unit that inputs the key points extracted by the extraction unit into a spreadsheet; a determination unit that determines a next action based on the information input by the input unit. A system characterized by:

2. The analysis unit Analyzing email content using natural language processing technology The system of claim 1 .

3. The determination unit Referencing past data to determine next actions The system of claim 1 .

4. The reception unit Estimate user emotions and adjust the timing of email acceptance based on the estimated user emotions The system of claim 1 .

5. The reception unit Analyze the user's past email reception history and select the optimal reception method The system of claim 1 .

6. The reception unit Filter incoming email based on your current projects and interests The system of claim 1 .

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

8. The reception unit When accepting emails, prioritize relevant emails by taking into account the user's geographic location. The system of claim 1 .

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