Document management system and document management method using ai

Abstract

To provide a mechanism for increasing business efficiency using summary generation by AI.SOLUTION: A document management system comprises: document information acquisition means for acquiring first document information; summary generation means for inputting the first document information acquired by the document information acquisition means to a trained machine learning model that has been trained using machine learning so as to generate a summary using document information as an input, thereby acquiring a summary for the first document information from the machine learning model; and output processing means for outputting the summary acquired by the summary generation means.SELECTED DRAWING: Figure 24

Description

[Technical field]

[0001] The present invention relates to a document management system and a document management method using AI. [Background technology]

[0002] Background of this technical field is JP 2013-190991 A (Patent Document 1). This publication states that "in a support method for streamlining response monitoring (listening to and evaluating speech) in a call center, the method makes it possible to streamline monitoring work even for a set of responses that deviate from the dialogue flow of the talk script. The voice dialogue summarization device extracts multiple utterances from a set of dialogue texts, and extracts utterances that are mutually linked by implication relationships from the extracted multiple utterances as utterance clusters" (see abstract). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2013-190991 A Summary of the Invention [Problem to be solved by the invention]

[0004] The above-mentioned Patent Document 1 describes a mechanism of a voice dialogue summarization device that extracts multiple utterances from a collection of dialogue texts in a call center and extracts utterance clusters. However, this patent document does not consider a method of improving the efficiency of various types of customer service operations using telephone, chat, email, etc. by using AI-based summary generation. Therefore, the present invention provides a mechanism for improving business efficiency by using AI-based summary generation. [Means for solving the problem]

[0005] In order to solve the above problems, for example, the configurations described in the claims are adopted. The present application includes multiple means for solving the above-mentioned problems, and one example is characterized by having a document information acquisition means for acquiring first document information, a summary generation means for inputting the first document information acquired by the document information acquisition means into a trained machine learning model that has performed machine learning to generate a summary using document information as input, thereby acquiring a summary for the first document information from the machine learning model, and an output processing means for outputting the summary acquired by the summary generation means. Effect of the Invention

[0006] According to the present invention, it is possible to provide a mechanism for improving business efficiency by using summary generation by AI. Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is an example of a configuration diagram of an entire customer response management system 1. As shown in FIG. [Diagram 2] FIG. 2 is a diagram showing an example of the hardware configuration of the management server 101. As shown in FIG. [Diagram 3] FIG. 3 is a diagram showing an example of the hardware configuration of the user terminal 102. As shown in FIG. [Figure 4] FIG. 4 is a diagram showing an example of the hardware configuration of the administrator terminal 103. As shown in FIG. [Diagram 5] FIG. 5 is an example of a system configuration diagram of the summary generation module 214. [Figure 6] FIG. 6 is an example of customer information 600. [Figure 7] FIG. 7 is an example of category information 700. [Figure 8] FIG. 8 is an example of document information 800. [Figure 9] FIG. 9 is an example of summary information 900. [Figure 10] FIG. 10 is an example of memo information 1000. [Figure 11] FIG. 11 is an example of the response record information 1100. [Figure 12] FIG. 12 is an example of FAQ information 1200. [Figure 13] FIG. 13 is an example of auxiliary input information 1300. [Figure 14] FIG. 14 is an example of a customer response process flow 1400. [Figure 15] FIG. 15 shows an example of a document information acquisition process flow 1500. [Figure 16] FIG. 16 is an example of a summary generation process flow 1600. [Figure 17] FIG. 17 shows an example of a detailed summary generation process flow 1700. [Figure 18] FIG. 18 is an example of a process flow 1800 for generating a summary by category. [Figure 19] FIG. 19 is an example of another process flow 1900 for generating summaries by category. [Figure 20] FIG. 20 shows an example of a highlighting process flow 2000. [Figure 21] FIG. 21 is an example of a customer information search screen 2100. [Figure 22] FIG. 22 is an example of a customer information inquiry screen 2200. [Diagram 23] FIG. 23 is an example of a category selection input screen 2300. [Figure 24] FIG. 24 is an example of a summary display screen 2400. [Diagram 25] FIG. 25 is an example of another summary display screen 2500. [Figure 26] FIG. 26 is an example of a summary settings screen 2600. [Figure 27] FIG. 27 shows an example of a reference information call setting screen 2700. [Figure 28] FIG. 28 is an example of a feedback reception screen 2800. [Figure 29] FIG. 29 is an example of a work status display screen 2900. [Diagram 30] FIG. 30 shows an example of a work status details display screen 3000. [Diagram 31] FIG. 31 is an example of another feedback reception screen 3100. [Diagram 32] FIG. 32 shows an example of a summary generation instruction screen 3200. [Diagram 33] FIG. 33 is an example of a summary display screen 3300. [Diagram 34] FIG. 34 is an example of a customer response process flow 3400 using category information. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, the embodiments will be described with reference to the drawings. This embodiment allows users, such as operators working at a call center, to easily perform customer service operations. For example, we provide a system that allows a call center operator to easily execute a series of processes after a customer interaction by inputting information about the call content into the system and reflecting it in a CRM (Customer Relationship Management) system. This type of post-customer interaction processing is called ACW (After-Call Work). Examples of ACW that the system can handle include the process of entering and selecting various category information about the content of the inquiry, the process of creating a summary of the content of the call, and the process of storing the response status and method in the CRM system in association with customer information confirmed during the call.

[0009] Using this system makes it possible to centralize customer service operations and dramatically reduce ACW time, thereby streamlining call center operations. This system is not limited to telephone response work at a call center, but can also be applied to customer response work using chat, e-mail, etc., and can also be applied to any document management work.

[0010] The customer response management system 1 comprises a plurality of user terminals 102 and a plurality of manager terminals 103, each of which is connected to a management server 101 via a network. Note that the network may be wired or wireless, and each terminal can transmit and receive information via the network. The user terminal 102 is a terminal used by a user, such as an operator who handles customer service at a call center. The administrator terminal 103 is a terminal that manages the operations of a plurality of user terminals 102 and a large number of operators operating in a call center. The administrator terminal 103 may also have a function for managing the management server 101, such as changing the settings of the management server 101.

[0011] Each terminal of the customer response management system 1 and the management server 101 may be, for example, a portable terminal (mobile terminal) such as a smartphone, tablet, mobile phone, or personal digital assistant (PDA), or a wearable terminal such as glasses / goggles, wristwatch, or clothing. They may also be stationary or portable computers, or servers located on the cloud or network. In terms of functionality, they may be a VR (Virtual Reality) terminal, an AR (Augmented Reality) terminal, or an MR (Mixed Reality) terminal. Or they may be a combination of a plurality of these terminals. For example, a combination of one smartphone and one wearable terminal may function logically as one terminal. They may also be other information processing terminals.

[0012] Each terminal and management server 101 of the customer response management system 1 includes a processor that executes an operating system, applications, programs, etc., a main storage device such as a RAM (Random Access Memory), an auxiliary storage device such as an IC card, a hard disk drive, an SSD (Solid State Drive), a flash memory, etc., a communication control unit such as a network card, a wireless communication module, a mobile communication module, etc., input devices such as a touch panel, a keyboard, a mouse, voice input, and input by motion detection by imaging of a camera unit, and an output device such as a monitor or display. Note that the output device may be a device or terminal that transmits information to be output to an external monitor, display, printer, device, etc.

[0013] The main memory stores various programs and applications (modules), and the processor executes these programs and applications to realize the various functional elements of the overall system. Each of these modules may be implemented in hardware by integration or the like. Each module may be an independent program or application, or may be implemented as a subprogram or function within a single integrated program or application.

[0014] In this specification, each module is described as an entity (subject) that performs processing, but in reality, a processor that processes various programs, applications, etc. (modules) executes the processing. Various databases (DB) are stored in the auxiliary storage device. A "database" is a functional element (storage unit) that stores a data set so that it can handle any data operation (e.g., extraction, addition, deletion, overwriting, etc.) from a processor or an external computer. The method of implementing the database is not limited, and may be, for example, a database management system, a file management system, spreadsheet software, or a text file such as XML or JSON.

[0015] FIG. 2 is a diagram showing an example of the hardware configuration of the management server 101. As shown in FIG. The management server 101 is configured, for example, as a server placed on a cloud. However, the management server 101 may be constructed as an on-premise type in the same base as the user terminal 102 and / or the administrator terminal 103. Also, the management server 101 may be configured integrally with the user terminal 102 and / or the administrator terminal 103. The main memory device 201 stores programs and applications such as a customer response processing module 211, a category selection module 212, a document information acquisition module 213, a summary generation module 214, a memo information acquisition module 215, a highlighting module 216, a feedback module 217, and a data search module 218. The processor 203 executes these programs and applications to realize each functional element of the management server 101.

[0016] The customer response processing module 211 provides a function for managing the overall customer response processing performed by a user (operator, etc.). In this embodiment, a telephone response in a call center will be described as an example, but the present invention is not limited to this and can also be applied to customer requests received via chat, email, inquiry forms, message transmission and reception services, etc. Furthermore, the customer response processing module 211 can display the document information acquired by the document information acquisition module 213 and the summary generated by the summary generation module 214 together on one screen in a corresponding relationship. In addition to the configuration in which two panels are displayed on one screen, it is also possible to display the document information and the summary by switching between them using tabs or the like. In this case as well, it can be said that the document information and the summary are displayed in a corresponding relationship. Alternatively, it is also possible to simply display the contents of one panel.

[0017] The category selection module 212 identifies which category the customer inquiry belongs to. Examples of categories include the following: Target business categories: finance, insurance, e-commerce, public energy (electricity, gas, water, etc.), tourism, bridal, etc. Applicable product categories: - For the bridal category, wedding venues, reception parties, honeymoons, etc. Insurance categories include property insurance, life insurance, education insurance, car insurance, fire insurance, etc. Content category: application / procedures, questions (inquiries), consultations, requests, etc. By designing the category information hierarchically, it becomes possible to flexibly respond to various types of customer service processing. The category information is stored in the category information 700, but it may be registered in advance or may be freely written and registered by the user, etc. Also, the category selection module 212 may be configured to newly register, as category information, a character string extracted from terms used in the dialogue text. The category information may be meta-information. Meta-information for a document or the like can be treated as category information.

[0018] The document information acquisition module 213 acquires the contents of a conversation with a customer as text data and stores it as document information. For example, the document information is generated by analyzing the voice of a telephone conversation with a customer and converting it into text. In the case of communication with customers through chat, e-mail, inquiry forms, message sending and receiving services, etc., the contents of the text sent and received are stored as document information. In addition, it is not limited to the contents of the communication with customers, as long as any document information can be obtained.

[0019] The summary generation module 214 inputs document information and generates a summary of it. When AI is used, the summary generation module 214 generates a summary for the input document information by inputting the document information into a trained machine learning model that has been trained to generate summaries using document information as input. At this time, by inputting category information together with the document information, it is possible to improve the accuracy of the generated summary and adjust the summary content. The memo information acquisition module 215 acquires and stores memos entered by the user while serving a customer. The memo may be a blank screen into which text is entered, or may have a predetermined format into which information is entered. Any text information may be entered. The memo may be acquired before or after serving a customer.

[0020] The highlighting module 216 makes it easier to handle customer inquiries by highlighting parts of document information, summaries, and memo information that satisfy certain conditions. Highlighting may be achieved, for example, by making the portion of a document that meets a condition bold, underlining, changing the color, italics, shading, changing the font, blinking, surrounding the text with a line, changing the background color, or overlaying a color layer on top of the text. Highlighting may be achieved by making the text more noticeable or distinct than other portions of the text. The text to be highlighted may also be displayed on a separate screen or window.

[0021] The feedback module 217 receives user evaluations and corrections for the generated summary. The feedback module 217 may also receive corrections for document information and memo information. In this embodiment, these evaluations and corrections are collectively called feedback from the user. The data search module 218 searches for the customer information 600, the FAQ information 1200, the input assistance information 1300, and the like during customer response operations.

[0022] The auxiliary storage device 202 includes various databases for storing various types of information. Each database stores, for example, customer information 600, category information 700, document information 800, summary information 900, memo information 1000, and response record information 1100. Each piece of information will be described in detail later.

[0023] FIG. 3 is a diagram showing an example of the hardware configuration of the user terminal 102. As shown in FIG. The user terminal 102 is composed of a terminal such as a smartphone, a tablet, a notebook PC, or a desktop PC. The main memory device 301 stores programs and applications such as a management server cooperation module 310, and the processor 303 executes these programs and applications to realize the various functional elements of the user terminal 102.

[0024] The management server cooperation module 310 cooperates with the customer service processing module 211 of the management server 101 to execute the customer service processing provided by the management server 101. The management server cooperation module 310 displays various screens such as Figs. 21 to 33 provided by the management server 101 on a display which is one of the output devices 205.

[0025] The customer service processing module 211 of the management server 101 provides, for example, a customer service function from the cloud, and the management server cooperation module 310 displays screen information transmitted from the management server 101 on a display. However, the configuration may be such that all of the program and application functions provided by the management server 101 are implemented on the user terminal 102, and the user terminal 102 realizes the customer response function alone, or the configuration may be such that some of the program and application functions are implemented on the user terminal 102, and the user terminal 102 and the management server 101 work together to realize the customer response function.

[0026] The auxiliary storage device 302 stores, for example, user terminal management information 320. The user terminal management information 320 stores, for example, information for identifying an operator who is a user using the user terminal 102 . Furthermore, in the case where a part of the functions of the management server 101 is implemented on the user terminal 102 side, the same information as the various information stored in the management server 101 may be stored in the user terminal management information 320 .

[0027] FIG. 4 is a diagram showing an example of the hardware configuration of the administrator terminal 103. As shown in FIG. The administrator terminal 103 is configured as a terminal such as a smartphone, a tablet, a notebook PC, or a desktop PC. The main memory device 401 stores programs and applications such as a user terminal management module 410 and a management server management module 411, and each functional element of the administrator terminal 103 is realized by the processor 403 executing these programs and applications.

[0028] The user terminal management module 410 collectively manages a plurality of user terminals 102 operating in the call center. The user terminal management module 410 displays a screen that allows the efficiency of ACW work to be monitored, for example, as shown in Fig. 29 or 30. A list of each call (interaction) can be confirmed and filtered, and the input of each interaction can be displayed. The information displayed on these screens is obtained by the user terminal management module 410 in cooperation with the customer response processing module 211 of the management server 101.

[0029] In addition, the user terminal management module 410 calculates and displays the distribution and statistics of the time spent on ACW for each important attribute, such as by time (month, week, etc.), by user, by category of customer service content, etc. Furthermore, the user terminal management module 410 displays the ACW work log of each user, for example, as shown in FIG.

[0030] The management server management module 411 performs various settings for the management server 101. For example, the management server management module 411 can perform settings for summary generation using a summary setting screen 2600 in FIG. It is also possible to configure the system so that all the functions of the programs and applications provided by the management server 101 are implemented on the administrator terminal 103, and the administrator terminal 103 realizes the customer response function. Also, it is also possible to configure the system so that some of the functions of the programs and applications are implemented on the administrator terminal 103, and the user terminal 102 and the administrator terminal 103 work together to realize the customer response function.

[0031] The auxiliary storage device 402 stores, for example, administrator terminal information 420 . The manager terminal information 420 stores, for example, information for identifying the manager who uses the manager terminal 103, information for identifying the user terminal 102 and each user that is managed, and the like. Furthermore, in the case where some of the functions of the management server 101 are implemented on the administrator terminal 103 side, the same information as the various information stored in the management server 101 may be stored in the administrator terminal information 420 .

[0032] FIG. 5 is an example of a system configuration diagram of the summary generation module 214. There are various possible ways to implement the summary generation module 214, but FIG. 5 shows an example of a specific implementation using a machine learning module. An API (Application Programming Interface) module 501 receives a request for generating a summary from the user terminal 102, acquires document information, and starts the summary generation process. The API module 501 also transmits the generated summary to the user terminal 102 via a Web server module 505. The API module 501 may be implemented as an independent API server.

[0033] The message queuing module 502 temporarily stores document information in a queue, and inputs the information to the ML module 503 in sequence. The ML (Machine Learning) module acquires document information stored in the queue, generates a summary, and passes it to the queue. The ML module 503 may be implemented by an independent ML server. The worker module 504 stores the summary received from the message queuing module 502 in the DB 511 in association with the document ID. The Web server module 505 displays the summary results and the like as a UI (User Interface). The storage device 512 stores multiple machine learning trained models, which can be read into the ML module 503 to change the content of the generated summary. A detailed summary generation process flow by the summary generation module 214 in FIG. 5 will be described later with reference to FIG.

[0034] 6 to 13 show various types of information stored in the management server 101. FIG. FIG. 6 is an example of customer information 600. The customer information 600 stores information about customers who call the call center. The customer information 600 includes information to identify the customer, such as a user ID 601, a user name, a phone number, an email address, a date of birth, a gender, and an address, and values ​​such as those shown as sample values ​​620 are entered for each item 610. The user ID 601 is a unique ID, such as an automatically generated hash value, and is a primary key referenced by other information. The user display (sometimes called a user title or user display name) is a user name that is displayed on the screen of the user terminal 102, the administrator terminal 103, or the like.

[0035] FIG. 7 is an example of category information 700. The category information 700 is information selected according to the content of customer service, and stores information indicating the content of customer service. The category information 700 has a category ID 701, a subcategory ID 702, a subcategory ID2 703, subcategory contents 704, subcategory contents 705, and subcategory contents 2 706, and values ​​such as those shown as sample values ​​720 are input for each item 710. A category ID 701, a subcategory ID 702, and a subcategory ID2 703 are IDs that, either alone or in combination, uniquely identify the content of a customer response.

[0036] In the example of FIG. 7, Bridal001 of category ID 701 indicates wedding-related category content 704, Ceremony001 of subcategory ID 702 indicates wedding hall-related subcategory content 705, and Inquiry002 of subcategory ID2 703 indicates inquiry-related subcategory content 2 706. In this example, under the category "Wedding," for example, there are subcategories related to wedding venues, as well as subcategories related to reception parties, honeymoons, and the like. In addition to inquiries, the wedding hall-related subcategory also includes a sub-subcategory called applications.

[0037] In another example, under the category of insurance, there are subcategories such as property insurance, life insurance, education insurance, automobile insurance, and fire insurance. Furthermore, under the non-life insurance subcategory, there are sub-subcategories such as inquiries, applications, and insurance claims. In this way, by designing the category information hierarchically, it becomes possible to flexibly respond to various types of customer service processing.

[0038] In the example of Fig. 7, the categories are structured in three hierarchical levels, but they may be structured in two hierarchical levels, or in four or more hierarchical levels. Also, there may be no hierarchy, and all categories may be managed in only one level of category ID. In addition, although the above description is based on the structure in which the hierarchy goes down in the order of weddings, wedding venue-related, and inquiries, this order may be reversed, for example, the hierarchy may go down in the order of inquiries, weddings, and wedding venue-related.

[0039] FIG. 8 is an example of document information 800. The document information 800 stores, for example, information about documents acquired by customer service. The document information 800 includes a document ID 801, a document display 802, a recording date and time 803, and a document information storage URL 804, and values ​​such as those shown as sample values ​​820 are input to each item 810.

[0040] The document ID 801 is identification information that uniquely identifies a stored document. The document display 802 (sometimes called the document title 802 or document display name 802 ) stores an explanation of the document title or the like displayed on the user terminal 102 or the administrator terminal 103 . The recording date and time 803 indicates the date and time when the document was stored in the storage device. In the example of Fig. 8, "20220401141201" indicates "April 1, 2022, 14:12:01". Furthermore, when a document is updated, the update date and time of the document and the last update date and time may also be stored. The document information storage URL 804 indicates the location where the actual document is stored. Note that, in addition to a method of specifying a path to data in the form of a URL, a method of specifying a path to a file in storage inside the server may also be used. Also, if the data size is not large, the information may be written directly.

[0041] In this embodiment, the document information stored in the document information storage URL 804 of the document information 800 may be referred to as "document information." For example, text data stored in the document information storage URL 804 may be referred to as "document information." Furthermore, text data before being stored in the document information 800 may also be called "document information."

[0042] The text data stored here may be, for example, dialogue text, which is a text version of the content of a conversation between a customer and an operator during customer service. In addition, in the case of communication with a customer via chat, e-mail, an inquiry form, a message sending / receiving service, etc., the content of the text sent and received is stored as document information. In addition, any document information related to the content of the communication with the customer may be acquired and stored. In addition, when the present embodiment is applied to a wide range of business operations other than call centers, it is sufficient to acquire and store some document information. Customer service is called by various names, such as support, inquiries, customer success, CX (Customer Experience), and contact centers.

[0043] FIG. 9 is an example of summary information 900. The summary information 900 stores the summaries generated by the summary generation module 214 . The summary information 900 includes a summary ID 901, a summary display 902, a document ID 903, a recording date and time 904, a last update date and time 905, and a summary information storage URL 906. Values ​​such as those shown as sample values ​​920 are input to each item 910.

[0044] The abstract ID 901 is identification information that uniquely identifies the stored abstract. The summary display 902 (sometimes called summary title 902 or summary display name 902 ) stores an explanation such as the title of the summary to be displayed on the user terminal 102 or the administrator terminal 103 . The document ID 903 stores the value of the document ID 801 of the document information 800, and identifies which document information 800 the summary corresponds to. Recorded date and time 904 indicates the date and time the summary was stored in storage.

[0045] The last update date and time 905 indicates the date and time when the summary was last updated after being modified. Note that in the case where the summary is updated multiple times, not only the last update date and time but also the update date and time of the summary may be stored. Summary information storage URL 906 indicates the location where the actual summary is stored. Note that instead of specifying the path to the data in the form of a URL, a path to a file in storage within the server may be specified. Also, if the data size is not large, the information may be written directly. In this embodiment, when a summary identified by one summary ID 901 is updated multiple times, the update history of the summary or all versions of the summary can be stored in association with the same summary ID 901. However, a new summary ID may also be assigned each time the summary is updated and stored.

[0046] FIG. 10 is an example of memo information 1000. The memo information 1000 includes a memo ID 1001, a memo display 1002, a document ID 1003, a recording date and time 1004, a last update date and time 1005, and a memo information storage URL 1006. Values ​​such as those shown as sample values ​​1020 are input to each item 1010. The memo ID 1001 is identification information that uniquely identifies the stored memo information. The memo display 1002 (sometimes called memo title 1002 or memo display name 1002 ) stores an explanation such as a title of memo information displayed on the user terminal 102 or the administrator terminal 103 . The document ID 1003 stores the value of the document ID 802 of the document information 800, and specifies which document information 800 the memo information corresponds to. The recording date and time 1004 indicates the date and time when the memo information was stored in the storage device.

[0047] The last update date and time 1005 indicates the last update date and time after the memo information is modified. Note that in addition to the last update date and time, the update date and time of the memo information when it is updated multiple times may also be stored. The memo information storage URL 1006 indicates the location where the actual memo information is stored. In this embodiment, when multiple updates are made to memo information identified by one memo ID 1001, the update history can be stored in association with the same memo ID 1001, and a new memo ID can also be reassigned each time an update is made.

[0048] FIG. 11 is an example of the response record information 1100. The content of the response when the user responds to a customer is stored as a response record. The response record information 1100 includes a response ID 1101, a response start date and time 1102, a response end date and time 1103, a person in charge 1104, a customer ID 1105, a category ID 1106, a summary ID 1107, an evaluation score 1108, and a customer input data storage URL 1109, and values ​​such as those shown as sample values ​​1120 are entered for each item 1110.

[0049] The response ID 1101 is identification information that uniquely identifies a customer response. The service start date / time 1102 and the service end date / time 1103 indicate the date / time when the customer service started and ended. The person in charge 1104 stores information for identifying the user (operator) who attended to the customer. The customer ID stores information that identifies the customer served.

[0050] Category ID 1106 is information indicating the content of the customer service, and stores category ID 701 of category information 700 in Fig. 7. Note that, in subcategory ID and subcategory ID2 below category ID 1106, information of subcategory ID 702 and subcategory ID2 703 in Fig. 7 are stored, respectively. The summary ID 1107 stores the information of the summary ID 901 in FIG. 9, and stores in association with what kind of summary has been generated based on the customer response.

[0051] The evaluation score given to the generated summary by the user or the administrator is stored in the evaluation score 1108. The evaluation score 1108 may be stored in the summary information 900 instead of in the response record information 1100. Customer input data storage URL 1109 stores information that identifies or explains the content of the customer response and the content of the processing that was input as a result. For example, in the case of a response to an application from a customer, information about the resulting application is written in customer input data storage URL 1109.

[0052] FIG. 12 is an example of FAQ information 1200. The FAQ information 1200 stores a list of frequently asked questions and their answers. The FAQ information 1200 includes an FAQ ID 1201, an overview, a category ID 1202, a subcategory ID, and an FAQ data storage URL 1203, and values ​​such as those shown as sample values ​​1220 are input for each item 1210.

[0053] The FAQ ID 1201 is identification information that uniquely identifies an FAQ. The summary stores information indicating the contents, such as the title of the FAQ. FAQ data storage URL 1203 indicates the location where the actual FAQ contents are stored. Note that a path to the data may be specified in the form of a URL, or a path to a file in storage within the server may be specified. If the data size is not large, the information may be written directly. For example, text information with questions and answers in separate columns may be stored as FAQ data.

[0054] Category ID 1202 is information indicating the content of the customer response, and stores category ID 701 of category information 700 in Fig. 7. Note that information on subcategory ID 702 and subcategory ID2 703 in Fig. 7 is stored in subcategory ID and subcategory ID2 below category ID 1202, respectively. In this way, by specifying and storing a customer service category for the FAQ information 1200, it is possible to obtain FAQ information related to the customer service category and display it on the user terminal 102 or the like.

[0055] FIG. 13 is an example of auxiliary input information 1300. The auxiliary input information 1300 stores auxiliary input information for retrieving input contents to be input for each customer when serving the customer. The auxiliary input information 1300 has an auxiliary input information ID 1301, an overview, a category ID 1302, a subcategory ID, and an input content storage URL 1303, and values ​​such as those shown as sample values ​​1320 are input for each item 1310. The auxiliary input information ID 1301 is identification information that uniquely identifies auxiliary input information such as a form to be input. The summary stores information indicating the contents, such as the title of the input auxiliary information.

[0056] Category ID 1302 is information indicating the content of the customer service, and stores category ID 701 of category information 700 in Fig. 7. Note that information on subcategory ID 702 and subcategory ID2 703 in Fig. 7 is stored in subcategory ID and subcategory ID2 below category ID 1302, respectively. In this way, by specifying and storing the customer service category for the auxiliary input information 1300, auxiliary input information related to the customer service category can be obtained and displayed on the user terminal 102 or the like.

[0057] FIG. 14 is an example of a customer response process flow 1400. The customer service processing module 211 starts customer service (step 1410). Specifically, when an operator (user) receives a call and starts customer service, the operator may be notified of the start of customer service, or the operator may input the start of customer service. The document information acquisition module 213 converts the contents of the call into text during the call and acquires the document information (step 1420). The converted text of the call is sometimes called a dialogue text. The document information acquisition process will be described later.

[0058] Next, the customer interaction processing module 211 identifies the customer on the other end of the call (step 1430). Specifically, the customer response processing module 211 calls the data search module 218, and the data search module 218 displays the contents of the customer search field 2140 on the customer information search screen 2100 in FIG. The operator searches for customer information while looking at the contents of the call and the dialogue text, and if a corresponding customer is found, the operator specifies that customer as the call partner. If a corresponding customer is not found and a new customer is registered, this will be described later.

[0059] FIG. 21 is an example of a customer information search screen 2100. In the dialogue text field 2110, the contents of the call that have been converted into text in the document information acquisition process 1420 are displayed in almost real time. The memo field 2120 is a section where the operator inputs memos when responding to a customer. The memo information entered and acquired here is sometimes called second document information.

[0060] The customer search field 2140 is a display area where the operator can input keywords for searching for a customer and search for customer information registered in the customer information 600 . The customer response processing module 211 acquires information on the telephone number of the customer who is responding and displays it in the incoming call information 2141. Note that the configuration may be such that the operator inputs the telephone number displayed on the telephone into the incoming call information 2141.

[0061] Methods for identifying customer information include, for example: The customer response processing module 211 automatically inputs customer information from the telephone number. · Operators input customer information by searching and selecting. - Manually entered by the operator. It is possible that this is the case.

[0062] On the other hand, the customer response processing module 211 may be configured to assist with input based on the contents of the dialogue text. For example, the customer response processing module 211 may input, into the name field of the search condition 2142, name information 2111 that is likely to be a customer among information 2111, 2112 relating to personal names in the dialogue text displayed in the dialogue text field 2110. For example, the name information 2111 that appears first among the words uttered by the customer is input into the name field. Note that the configuration may also be such that the customer's name heard by the operator during the dialogue is input into the name field of the search condition 2142. The customer response processing module 211 can also extract named entities from the dialogue text and infer and input customer information from them. For example, customer information can be inferred and input from the name, phone number, content, etc. displayed in the dialogue text using a rule-based technique using a dictionary or the like, or a machine learning-based technique.

[0063] In addition, as search conditions 2142, input of information about the customer, such as name, date of birth, telephone number, and postal code, can also be accepted. The highlighting module 216 of the management server 101 highlights parts of the dialogue text displayed in the dialogue text field 2110 that relate to attributes that are likely to be important for a customer search, such as name, date, time, phone number, location, contract number, etc. Furthermore, if there is an item in the highlighted content that corresponds to the search condition 2142, the highlighting module 216 inputs the name, phone number, etc. that corresponds to the relevant part to complete it. Conversely, when the operator enters information such as a name and telephone number heard over the phone into the search conditions 2142, the highlighting module 216 can highlight the corresponding portion in the dialogue text.

[0064] When the data search module 218 detects that the operator has selected the search button 2143, it searches the customer information 600 using the incoming call information 2141 and the information entered in the search conditions 2142 to check whether a corresponding record exists. If a corresponding record exists in the customer information 600, the data search module 218 displays the customer information inquiry screen 2200 shown in FIG.

[0065] The customer information display field 2150 displays customer information registered in the customer information 600, and when customer information is found by a search, the customer information found in the customer information display field 2150 may be displayed and highlighted. If no corresponding customer information is found in the customer information 600, the customer response processing module 211 accepts the selection of a new customer registration button 2160, and displays a registration screen for new customer information, taking over the information entered in the incoming call information 2141 and the search condition field 2142. The new customer registration may also be provided with a function for completing the information from the dialogue text, and a function for highlighting the completed portion in the dialogue text. In this embodiment, the data search module 218 displays information acquired from the customer information 600 in the customer search field 2140, but this display may be entirely performed by the customer response processing module 211.

[0066] FIG. 22 is an example of a customer information inquiry screen 2200. The data search module 218 displays the customer information 2241 found by the search in a customer introduction section 2240 . The highlighting module 216 highlights information corresponding to the customer information displayed in the displayed customer information 2241 on the dialogue text in the dialogue text field 2210 . With this configuration, it is possible to easily confirm where in the dialogue text the customer information found by the search or the customer information to be newly registered is described.

[0067] The customer response processing module 211 stores a customer ID corresponding to the call partner customer identified by search or new registration. It is also possible to configure the data search module 218 to search the customer information 600 based on the incoming telephone number or the name extracted from the dialogue text to identify the customer without the user performing the search. Also, the data search module 218 may be configured to display candidate customer names, etc., and the customer may be specified by the user inputting a confirmation.

[0068] FIG. 32 shows an example of a summary generation instruction screen 3200. When the customer response processing module 211 detects that the summary start button 3235 has been pressed, it transmits the dialogue text, which is document information, to the summary generation module 214, and the summary generation module 214 executes the summary generation process (step 1440). The summary generation process will be specifically described in detail with reference to FIG.

[0069] 33 is an example of a summary display screen 3300. The summary generated by the summary generation module 214 is displayed. In the example of the displayed summary 3330, the customer's name 3332 and the person who handled the message 3331 are highlighted, and important matters in the generated summary 3333 are also highlighted. Figures 32 and 33 show examples in which the input document information and the summary generated from it are each displayed on one panel, but these may be displayed on two panels in one window. Also, Figures 32 and 33, each showing only one panel, may be displayed side by side or vertically in one window.

[0070] The customer response processing module 211 associates the summary generated by the summary generating module 214 with the content of the customer response and stores them in the response record information 1100 (step 1450). Specifically, the customer response processing module 211 generates a response ID 1101 for each customer response, and stores in the response record information 1100 the staff ID of the staff who responded, the customer ID indicating the customer identified in step 1430, the summary ID indicating the summary generated in step 1440, the response start date and time, and the response end date and time in association with each other.

[0071] If the feedback module 217 receives a rating for the summary, the rating information may also be stored. Furthermore, when an application or the like occurs as a result of customer interaction, the input data for the procedure of the application or the like can be reflected in a CRM system, etc. In such a case, information that identifies or explains the content of the customer interaction and the content of the processing that was input as a result can be stored in the customer input data storage URL 1109 of the interaction record information 1100. When an operator makes corrections to the summary or updates the memo, the history of such changes may be stored in the interaction record information 1100, the summary information 900, the memo information 1000, or the like.

[0072] FIG. 34 is an example of a customer response process flow 3400 using category information. A step 3440 for accepting selection of category information is added to the customer response process flow 1400 in FIG. The processing of steps 3410 to 3430 by the customer response processing module 211 is similar to steps 1410 to 1430 in FIG. 14, and therefore a description thereof will be omitted.

[0073] Next, the customer response processing module 211 displays a category selection input screen 2300 shown in FIG. 23, and receives a selection of a category for the response content from the operator (step 3440). Categories are classifications of customer service contents, and are stored in category information 700 shown in Fig. 7. In response to the selected category, customer service processing module 211 acquires and stores information for identifying the category, such as the corresponding category ID, subcategory ID, and subcategory ID2. As described above, category information can be freely entered by the user, or meta information can be registered, added, or used as category information.

[0074] FIG. 23 is an example of a category selection input screen 2300. The inquiry content input field 2350 displays fields for inputting or selecting categories such as case classification, content, and response method. The caller classification is information for identifying what kind of person the customer is, such as an applicant, spouse, father, mother, contract holder, insured person, prospective customer, other party involved in an accident, dealer, financial institution, etc. The caller classification may be treated as a category. Specifically, the customer response processing module 211 calls the category selection module 212, and the category selection module 212 displays the contents of the inquiry content input 2350 on the category selection input screen 2300 in FIG.

[0075] In the example of Fig. 23, according to the content of the customer response, "bridal" is selected as the category, "ceremony hall" as the subcategory, and "inquiry" as the sub-subcategory. When the setting button 2352 is pressed after the category selection, the customer response processing module 211 acquires and stores the category ID 701, subcategory ID 702, and subcategory ID2 703 shown in the example of Fig. 7 as IDs corresponding to these categories.

[0076] The "content" and "type of response method" in the inquiry content input 2350 in Fig. 23 are displayed differently depending on the selected "case category." For example, when "insurance" is selected as the case category, "automobile" and "fire" are displayed, which indicate automobile insurance and fire insurance, which are subcategories of this insurance.

[0077] The highlighting module 216 can highlight character strings such as keywords and sentences related to the category in the dialogue text in the dialogue text field 2310. For example, in the example of Figure 23, keywords related to bridal, such as bridal, wedding in Hawaii, bride and groom, are highlighted. By checking these highlighted characters, the operator can easily determine which category the content of the customer service belongs to, which has the effect of facilitating category selection.

[0078] Furthermore, keywords and sentences related to the categories registered in the category information 700 can be registered in advance, or related keywords and sentences can be acquired each time. The category selection module 212 analyzes the keywords and sentences in the dialogue text, determines which category registered in advance in the category information 700 they relate to, and automatically selects the input in the inquiry content input field 2350 according to the keywords and sentences displayed in the dialogue text. It is also possible to input dialogue text information and classify the entire sentence into categories on a rule-based basis using dictionary information or the like, or using a machine learning model.

[0079] Furthermore, by linking the category selection module 212 and the highlighting module 216, for example, when the operator selects "application" as the type of response method, keywords related to "application" are highlighted in the dialogue text, and when the operator switches the dialogue method type to "inquiry," keywords related to "inquiry" are highlighted in the dialogue text. Similarly, if the case category is changed from bridal to insurance, insurance-related keywords will be highlighted.

[0080] The category selection module 212 and the highlighting module 216 can cooperate with each other to implement various functions, such as the following: The category selection module 212 can extract character strings such as keywords and sentences related to categories, which are classifications of the contents of customer service, from the dialogue text, and automatically select a category based on the extracted character strings. It is also possible to input dialogue text information and classify the entire text into categories on a rule-based basis using dictionary information or the like, or using a machine learning model.

[0081] In addition, the highlighting module 216 highlights character strings such as keywords and sentences related to a category from within the dialogue text, and the category selection module 212 can automatically select a related category based on the highlighted character strings. In addition, the highlighting module 216 can highlight character strings related to the category contents in response to the selection of the category contents. When the selection of the category contents is changed, the highlighting module 216 can highlight character strings related to the changed category contents. It is also possible to input dialogue text information and select a category from the entire text. It is also possible to input dialogue text information and select a category from the entire text on a rule-based basis using dictionary information or the like, or using a machine learning model.

[0082] In this embodiment, the category selection module 212 acquires the category information stored in the category information 700 and displays the contents in the inquiry content input field 2350 based on the acquired category information. However, this display may be entirely performed by the customer response processing module 211.

[0083] When the customer response processing module 211 detects that the summary start button 2335 has been pressed after the category has been selected, it transmits the dialogue text, which is document information, to the summary generation module 214, and the summary generation module 214 executes the summary generation process (step 3450). The summary generation process will be specifically described in detail with reference to FIG. It should be noted that the selection of a category is not essential, and the summary generation process may be executed even if no category is selected. Also, a category may be automatically selected in the background by the category selection module 212 based on the contents of the document information, without displaying the category selection input screen 2300 as shown in Fig. 23.

[0084] The customer response processing module 211 associates the summary generated by the summary generating module 214 with the content of the customer response and stores them in the response record information 1100 (step 3460). Specifically, the customer response processing module 211 generates a response ID 1101 for each customer response, and stores in the response record information 1100 the staff member ID of the staff member who responded, the customer ID indicating the customer identified in step 3430, the category ID indicating the category selected in step 3440, etc., the summary ID indicating the summary generated in step 3450, the response start date and time, and the response end date and time in association with each other.

[0085] If the feedback module 217 receives a rating for the summary, the rating information may also be stored. Furthermore, when an application or the like occurs as a result of customer interaction, the input data for the procedure of the application or the like can be reflected in a CRM system, etc. In such a case, information that identifies or explains the content of the customer interaction and the content of the processing that was input as a result can be stored in the customer input data storage URL 1109 of the interaction record information 1100. When an operator makes corrections to the summary or updates the memo, the history of such changes may be stored in the interaction record information 1100, the summary information 900, the memo information 1000, or the like.

[0086] FIG. 15 shows an example of a document information acquisition process flow 1500. Although telephone customer service is assumed, the present invention is not limited to this, and the document information acquisition process can be applied to anything that can acquire document information. The document information acquisition module 213 receives input of telephone voice during telephone answering (step 1510). The document information acquisition module 213 analyzes the voice information and converts it into text information (step 1520). Note that the content of the dialogue with the customer converted into text is called a dialogue text. The document information acquisition module 213 displays the text information as dialogue text in, for example, the dialogue text field 2110 in FIG. 21 (step 1530).

[0087] If there is no need to store the text information, it is discarded (No in step 1540), and if there is a need to store it (Yes in step 1540), the document information acquisition module 213 generates a document ID, associates it with the text information, and stores it in the document information 800 (step 1550).

[0088] The text information stored in document information 800 is not limited to dialogue text, but the content of text sent and received in response to interactions with customers via chat, email, inquiry forms, message sending and receiving services, etc., is stored as text information. Any other document information related to the content of interactions with customers may be acquired and stored. Document information may also be acquired by linking with some other service, by being copied and brought over after a phone call, etc. Document information may also be acquired by linking with some other service, by being copied and brought over after a phone call, etc. Furthermore, when this embodiment is widely applied to business other than call centers, it is sufficient to acquire and store some document information.

[0089] FIG. 16 is an example of a summary generation process flow 1600. The summary generation module 214 obtains document information (step 1610). Specifically, the summary generating module 214 obtains text information (called document information) from a location referenced by the document information storage URL 804 of the document information 800 . Alternatively, the document information acquisition module 213 may acquire the dialogue text acquired, or any other document information may be acquired.

[0090] The summary generation module 214 inputs the acquired document information (text information) into the trained machine learning model and generates a summary (step 1620). The summary generation module 214 associates the generated summary with the generated document ID and stores them in the summary information 900 (step 1630). Although the input for generating a summary is assumed to be the dialogue text or memos, it may also be possible to refer to other FAQs referred to during the call or logs of other services used.

[0091] FIG. 17 shows an example of a detailed summary generation process flow 1700. There are various possible ways to implement the summary generation module 214, but FIG. 5 shows an example of a specific implementation using a machine learning module, and the summary generation process using this configuration will be described. The API module 501 obtains the text information (step 1705). The API module 501 generates a document ID and stores the document ID and the text information in the document information 800 (step 1710). The API module sends a message including the document ID and the text information to the message queuing module 502 (step 1715).

[0092] The ML module 503 inputs the message obtained from the message queuing module 502 into the machine learning model obtained from the storage device 512 and generates a summary (step 1720). The ML module 503 sends a pair of the document ID and the summary result to the message queuing module 502 (step 1725). The worker module 504 stores the document ID and the summary result in the summary information 900 (step 1735).

[0093] The API module 501 acquires the summary results (called summary information) stored in the summary information 900 (step 1740). The Web server module 505 obtains the summary information from the API module 501 (step 1745). The web server module 505 outputs the summary information (step 1750). As the machine learning model, a large-scale language model can be used, which is a highly accurate generative model, but is not limited to this. For example, a machine learning model that generates summaries by character string extraction may be used.

[0094] FIG. 18 is an example of a process flow 1800 for generating a summary by category. In the category-specific summary generation processing flow 1800, when generating a summary, by further adding category information as an input, a summary suitable for the category can be generated. The summary generation module 214 obtains document information (step 1810). The summary generation module 214 obtains 1820 the category information obtained by the category selection module 212 . The summary generation module 214 inputs the acquired document information (text information) and category information into a trained machine learning model, thereby generating a summary related to the category information for the input document information (step 1830). The summary generation module 214 associates the generated summary with the generated document ID and stores them in the summary information 900 (step 1840).

[0095] The trained machine learning model can be a trained machine learning model that has been subjected to machine learning using category information indicating the category to which the document information belongs and multiple pieces of document information belonging to this category as training data. In the example of selecting category information in step 3440 of the customer response processing flow 3400 using category information in Fig. 34, a category related to the dialogue text has already been selected. Therefore, by inputting document information of the dialogue text together with category information to this machine learning model, the summary generation module 214 can generate a summary optimized for this document information. This processing flow uses a machine learning model that uses machine learning to train a set of category information and document information, and a single machine learning model can output a summary that corresponds to the input category.

[0096] As mentioned above, the advance selection of a category is not essential, and the summary generation process may be executed even if no category is selected. Also, a category may be automatically selected in the background by the category selection module 212 based on the contents of the document information, without displaying the category selection input screen 2300 as shown in Fig. 23. Alternatively, instead of using category information itself, document information may be input into a machine learning model to determine the document category and then generate a summary corresponding to that category.

[0097] FIG. 19 is an example of another process flow 1900 for generating summaries by category. In another category-specific summary generation process flow 1900, the machine learning model to be used is switched depending on the category information, thereby making it possible to generate summaries suitable for the category. The summary generation module 214 obtains document information (step 1910). The summary generation module 214 obtains the category information obtained by the category selection module 212 (step 1920).

[0098] The summary generation module 214 selects a machine learning model corresponding to the category information (step 1930). For example, if a separate server is prepared for each machine learning model, the summary generation module 214 transmits the document information to the machine learning server corresponding to the category information.

[0099] The summary generation module 214 inputs the acquired document information (text information) into a machine learning model selected corresponding to the category information, thereby generating a summary for the input document information (step 1940). The summary generation module 214 associates the generated summary with the generated document ID and stores them in the summary information 900 (step 1950).

[0100] The trained machine learning model can be a trained machine learning model that has undergone machine learning using multiple document information belonging to a specific category as training data, and a trained machine learning model can be prepared for each category. These trained machine learning models are stored in the storage device 512 in FIG. 5, and in response to the selection of a machine learning model, the selected machine learning model is read from the storage device 512 to the ML module 503. Note that a configuration in which an ML module 503 is prepared for each machine learning model optimized for a category and the ML module 503 is switched may also be used.

[0101] As described in the selection of category information in step 3440 of the customer response processing flow 3400 in Fig. 34, the category related to the dialogue text has already been selected. Therefore, by inputting the document information of the dialogue text to the machine learning model optimized for each category selected based on the category information, the summary generation module 214 can generate a summary optimized for this document information. In this processing flow, a summary corresponding to the input category can be output by switching the machine learning model trained for each category based on the category information.

[0102] It should be noted that the selection of a category in step 3440 of the customer response processing flow 3400 in Fig. 34 is not essential, and the summary generation processing may be executed even if no category is selected. Also, a configuration may be adopted in which a category is automatically selected in the background by the category selection module 212 based on the contents of the document information, without displaying the category selection input screen 2300 as shown in Fig. 23. That is, according to this embodiment, it is possible to provide a mechanism for explicitly switching summaries depending on the category information of the contents of a call.

[0103] FIG. 20 shows an example of a highlighting process flow 2000. The highlighting module 216 obtains text information (called document information) from the document information 800 (step 2010). The highlighting module 216 obtains a summary (called summary information) from the summary information 900 (step 2020). The highlighting module 216 obtains the entered memo information from the memo information 1000 (step 2030).

[0104] The highlighting module 216 performs analysis, for example, by natural language processing, on each piece of acquired information, and extracts one or more characteristic character strings (step 2040). The highlighting module 216 highlights a characteristic character string that satisfies various conditions described below, or a character string that includes this characteristic character string (step 2050). A characteristic character string is a word or a character string containing a word in text data, and does not include, in principle, a character string containing only a particle. However, it is not limited to this, and may be a combination of words containing a particle, a character string containing a particle, or any other character string or sentence. A keyword is also one type of characteristic character string. However, for example, it is also possible to highlight only particles in parts where there is a possibility of a particle error or where it is desired to emphasize a grammatical error.

[0105] Note that highlighting may be achieved, for example, by highlighting the portion of a document that meets a condition in bold, underlining, changing the color, italics, shading, changing the font, blinking, surrounding the text with a line, changing the background color, overlaying a color layer on the text, etc. Highlighting may be achieved by making the text stand out or distinguishable from other portions of the text. In addition to highlighting characters in the text in some way, you can also highlight them by displaying them in a separate frame. For example, you can display a space on a separate screen or window that contains only the date information you want to highlight.

[0106] In the above description, the highlighting module 216 acquires information from the document information 800, summary information 900, and memo information 1000 stored in the storage device and performs highlighting, but this is not limiting. For example, the document information acquisition module 213, the summary generation module 214, and the memo information acquisition module 215 may acquire information from the document information 800, the summary information 900, and the memo information 1000, respectively, and the highlighting module may perform the highlighting process. Also, the customer response processing module 211 may acquire various information, and the highlighting module may perform the highlighting process.

[0107] Furthermore, the highlighting module 216 may be configured to highlight the text information (dialogue text) acquired by the document information acquisition module 213, the summary information generated by the summary generation module 214, and the memo information acquired by the memo information acquisition module 215, instead of the document information 800, summary information 900, and memo information 1000 already stored in the storage device. Further, the customer response processing module 211 may be configured to perform highlighting in cooperation with the highlighting module 216, or the customer response processing module 211 itself may have the functionality of the highlighting module 216.

[0108] FIG. 24 is an example of a summary display screen 2400. After selecting a category in FIG. 23, a summary is generated by pressing the summary start button 2335 and the summary is displayed in the summary text field 2430. The summary displayed here was generated by inputting the document information displayed in the dialogue text field 2410 into a machine learning model that generates a summary corresponding to the category selection.

[0109] The customer response processing module 211 displays three types of information, the first document information (dialogue text) acquired by the document information acquisition module 213, the summary acquired by the summary generation module 214, and the second document information (memo information) acquired by the memo information acquisition module 215, in a dialogue text field 2410, a summary text field 2430, and a memo field 2420, respectively, and outputs them in a corresponding manner using a three-panel display. The description in the dialogue text 2410 is produced by converting the audio information of a telephone conversation into text, and so there are some parts that have not been transcribed accurately. However, even when dialogue text containing such errors is used as input, the summary generation module 214 can generate a correct summary 2430.

[0110] It should be noted that all three panels are not necessary, and it is also possible to configure the system so that no dialogue, no summary, or no memo column is displayed, in which case any two of the dialogue text column 2410, summary column 2430, and memo column 2420 are displayed in association with each other. Also, each of the dialogue, summary, and memo column can be displayed independently. Furthermore, it is not necessarily required to use a three-panel or two-panel display. For example, even if three types of information or any two types of information are displayed by switching between them using tabs or the like, it can be said that the configuration displays each piece of information in a corresponding manner. Note that memo information may be used as the first document information, and dialogue text, chat, e-mail, an inquiry form, a message transmission / reception service, or the like may be used as the second document information.

[0111] The highlighting module 216 highlights the common name of the person in the dialogue text and the summary sentence, “Patent Jiro,” by underlining in 2411 and 2431 . Even though they are not exactly the same, "Eliza Hanako" 2432 in the summary and "Eliza Hanako" 2422 in the memo section are highlighted with an underline in comparison with the corresponding name "Eliza Hanako" 2412 in the dialogue text. In addition, in the dialogue text, the statement 2413 "Airfare, hotel fees, etc. for other persons are not included" and the corresponding statement 2433 "Hotel fees, etc. for other persons are not included" are both highlighted in color. Additionally, the date February 2, 2022 appears in both the dialogue and the note, and is underlined in each.

[0112] In this example, keywords in the dialogue text that are common to the summary are highlighted. In other words, the highlighting module 216 performs highlighting when a characteristic character string corresponding to the first document information (dialogue text) and the summary exists as a condition. The highlighting module 216 highlights the characteristic character string that corresponds to the summary among one or more characteristic character strings included in the first document information (dialogue text). Additionally, it is also possible to highlight characteristic character strings that are commonly present in the dialogue text, summary text, or notes.

[0113] It is also possible to highlight corresponding keywords in the summary and the memo. In other words, the highlighting module 216 performs highlighting when a characteristic string corresponding to the second document information (memo information) and the summary is present as a condition. The highlighting module 216 highlights a characteristic string that corresponds to the second document information (memo information) and the summary among one or more characteristic strings included in at least one of the first document information (dialogue text), the second document information (memo information), and the summary. Also, the dialogue text and the summary may be expressed differently, but the corresponding parts can be highlighted.

[0114] Pressing the setting button 2436 will take you to the summary generation setting screen of Fig. 26. By changing the settings here, such as changing the category selection, and pressing the summary start button 2435 again, a summary will be generated again based on the changed settings. The summary generation setting screen may be configured to be displayed as a separate screen as shown in FIG. 26, or may be displayed on the summary output screen as shown in FIGS.

[0115] FIG. 25 is an example of another summary display screen 2500. The parts that differ from Figure 24 are highlighted. In this example, the following statement 2511 in the dialogue text 2310, "Airfare for the bride and groom is included, but airfare and hotel costs for other persons are not included," and the corresponding statement 2531 in the summary sentence 2530, "Airfare for the bride and groom is included, but hotel costs for other persons are not included," are highlighted with an underline.

[0116] Additionally, keywords that are present in the notes but not in the summary can be highlighted in the dialogue information or notes. That is, the condition is that the characteristic character string exists in the second document information (memo information) but does not exist in the summary, and when this condition is satisfied, the highlighting module 216 highlights the characteristic character string that exists in the second document information (memo information) but does not exist in the summary, out of one or more characteristic character strings included in at least either the first document information (dialogue text) or the second document information (memo information).

[0117] In the example of Fig. 25, a statement 2522 "I would like to add two more people" written in the memo field 2520 exists in the memo, but does not exist in the summary text 2530. Therefore, the highlighting module 216 highlights the statements 2512 and 2522 "I would like to add two more people" corresponding to this statement in the memo and the dialogue text.

[0118] It can also highlight keywords in the summary that are present in the summary but not in the dialogue information or notes. That is, the highlighting is performed when a characteristic character string exists in the summary but does not exist in the first document information (dialogue text) or the second document information (memo information) under the condition that this condition is met. The highlighting module 216 highlights, of one or more characteristic character strings included in the summary, characteristic character strings that exist in the summary but do not exist in the first document information (dialogue text) or the second document information (memo information). In the example of Fig. 25, the statement 2538 "We decided to change the wedding plan" exists in the summary but does not exist in the dialogue text or the memo. Depending on the machine learning model, a sentence that does not exist in the input dialogue text may be generated, but since a sentence containing such a character string may be different from the facts, highlighting the sentence containing such a character string gives the user an opportunity to correct it.

[0119] 25, the statement 2533 "future policy direction" written in the summary section 2530 exists in the summary, but does not exist in the memo section 2520. Therefore, the highlighting module 216 highlights the statement 2513 "future policy direction" corresponding to this statement in the dialogue text. It can also highlight strings of characters that are present in the summary and notes but not in the dialogue text. It can also highlight strings of characters that are present in the summary and notes but not in the dialogue text. It can also highlight other areas where the AI ​​is unsure.

[0120] In other words, the condition is that among the summaries generated by the summary generation module 214, there is a possibility that the accuracy of the summaries is not high, and if this condition is met, highlighting is performed. For example, the judgment can be made according to the probability distribution of the model that outputs the summary. For example, highlighting is performed when the probability distribution is constant and there is no significant difference in vocabulary selection. In addition, a separate learning model for discrimination can be created to make the judgment. The highlighting module 216 highlights one or more characteristic strings contained in the first document information (dialogue text), the second document information (memo information), or the summary generated by the summary generation module 214 that may be of low accuracy.

[0121] Furthermore, summary sentence selection tag 2537 is a tab for comparing and displaying multiple summary sentences output by the machine learning model. The display method may be to display at least two or more summary sentences in parallel, or to switch by selecting a button or to switch in a pull-down format. The display method may be to display at least two or more summary sentences in parallel, or to switch by selecting a button or to switch in a pull-down format. In addition, summaries generated by multiple machine learning models may be displayed for each tab, or summaries generated according to different categories may be displayed.

[0122] The other highlighting module 216 provides a mechanism for making it easier for people to review input and output information by highlighting them. The following are mechanisms that make it easier for people to review input and output individually. · Highlight important details (dates, names, contract numbers, etc.) -Highlight areas where the AI ​​is unsure. Provide a mechanism for making it easier to refer to and review input and output. Highlight parts that are in the summary but have no similar input (dialogue text, notes, etc.). Whether or not the points are close to each other can be determined by the following method. - Judgment by rules - Judgment based on the degree of match or similarity in some units such as strings or words - Judgment by dictionary etc. - Judgment based on machine learning technology - Similarity of distributed representation (vector representation) - Judgment based on machine learning model -Highlight where you used Input in the summary. ·Highlight what is in your notes that is not in your summary. -Highlight parts of the dialogue that may be useful for summarizing In addition, you can determine whether it would be a good idea to include it using the following method. - Generate multiple summaries and identify strings in those summaries as candidates - Use machine learning models and rules to determine the importance separately

[0123] After review, features for suggesting modifications include: - Suggests corrections for words and phrases using a thesaurus and similarity calculation between words and phrases using machine learning. For example, the word "meeting" is highlighted in the summary, and correction candidates such as "conference," "meeting," "MTG," and "meeting" are displayed in a selectable form. Possible display formats include popping up correction candidates, displaying them in a separate tab or screen, or superimposing correction candidates when the mouse cursor is moved over the highlighted portion. In addition, the system may be configured to display correction suggestions for parts where the AI ​​is not confident. In addition, the system may be configured to display correction candidates when the user specifies a location by clicking, etc. -For words, phrases, and other expressions that are frequently confused, conversion rules are created and suggested using past data or manually. - Suggest other candidates that the AI ​​could have output in various units such as words, phrases, and sentences. For example, suggest the next most likely one to be generated. For example, from among the candidates that the machine learning model would generate, display other candidates, such as the next most likely one, as suggestions for correction. The correction points can be, for example, points where the AI ​​is not confident, or a user can specify any point by clicking, etc. For example, in the summary example of FIG. 24, for the expression "A quote for Hawaii was provided," a candidate such as "A quote for Hawaii was recently mailed" is presented. This correction suggestion may have a function for accepting feedback if the suggestion is incorrect or not very good. For example, a "check mark" indicating that the suggestion is accepted or a "cross mark" indicating that the suggestion is not accepted may be displayed below the correction suggestion, and a selection from the user may be accepted. Alternatively, instead of accepting or rejecting a correction suggestion, a "good" or "bad" mark may be displayed to receive an evaluation of whether the suggestion is good or bad, and evaluation from the user may be received.

[0124] After review, features for suggesting modifications include: - Suggests corrections for words and phrases using a thesaurus and similarity calculation between words and phrases using machine learning. For example, the word "meeting" is highlighted in the summary, and correction candidates such as "conference," "meeting," "MTG," and "meeting" are displayed in a selectable form. Possible display formats include popping up correction candidates, displaying them in a separate tab or screen, or superimposing correction candidates when the mouse cursor is moved over the highlighted portion. In addition, the system may be configured to display correction suggestions for parts where the AI ​​is not confident. In addition, the system may be configured to display correction candidates when the user specifies a location by clicking, etc. -For words, phrases, and other expressions that are frequently confused, conversion rules are created and suggested using past data or manually. - Suggest other candidates that the AI ​​could have output in various units such as words, phrases, and sentences. For example, suggest the next most likely one to be generated. For example, from among the candidates that the machine learning model would generate, display other candidates, such as the next most likely one, as suggestions for correction. The correction points can be, for example, points where the AI ​​is not confident, or a user can specify any point by clicking, etc. For example, in the summary example of FIG. 24, for the expression "A quote for Hawaii was provided," a candidate such as "A quote for Hawaii was recently mailed" is presented. This correction suggestion may have a function for accepting feedback if the suggestion is incorrect or not very good. For example, a "check mark" indicating that the suggestion is accepted or a "cross mark" indicating that the suggestion is not accepted may be displayed below the correction suggestion, and a selection from the user may be accepted. Alternatively, instead of accepting or rejecting a correction suggestion, a "good" or "bad" mark may be displayed to receive an evaluation of whether the suggestion is good or bad, and evaluation from the user may be received. If a character string such as a word is modified, the modified character string may be input again into the machine learning model, and the entire summary or the summary of the part following the modified character string may be regenerated by the machine learning model.

[0125] The following are examples of UI (User Interface) features that make it easy for people to edit the summary alone. A UI is provided that allows users to compare and edit the summary output by AI with the text of the revised summary. The feedback module 217 accepts edits to the summary generated by the summary generation module 214. The summaries before and after revision can be displayed in a form that allows comparison. For example, the summaries before and after revision can be displayed by switching between tabs, or displayed side by side. It is also possible to highlight edited parts by changing their color, etc. Provides a function for modifying the summary generated by the summary generation module 214 using templates. Provide a function to compare the outputs of multiple summaries and select one. Note that a configuration in which the outputs of multiple machine learning models can be compared and selected may be provided. Note that a configuration in which the outputs of multiple machine learning models can be compared and selected may be provided.

[0126] FIG. 26 is an example of a summary settings screen 2600. This is a screen for setting the output of a summary. The summary setting screen is displayed by selecting the summary setting button 2436 in Fig. 24 or the summary setting button 2536 in Fig. 25. The summary setting screen 2600 may be displayed as a separate screen, may be displayed in parallel with another UI, or may be displayed in an overlay (all or part) format. 26 shows an example of a summary setting screen that is displayed when "insurance" has already been selected as the case category. Insurance type 2601 is displayed as a subcategory, and automobile insurance has been selected. As a sub-subcategory, a response method type 2602 is displayed, and procedure is selected.

[0127] The keyword specification 2603 specifies the terms to be used when generating a summary. The summary generation module 214 generates a summary including the terms specified here. Length adjustment 2604 adjusts the length of the summary to be generated, and in this example, a summary of up to 180 characters is generated. Note that, although an upper limit for the length of the summary is specified in this example, a lower limit may also be specified, or a guideline for the length of the summary may also be specified. Selecting bullet points 2605 generates a summary of bullet points instead of long sentences.

[0128] When the function 2606 for correcting variations in date and time expressions is selected, even if various formats for date and time expressions are used in the dialogue text, a summary is generated in which the date and time expressions are in a unified format. In addition, the system may have a function for correcting variations in the spelling of expressions. For example, a setting can be made to correct all instances of "kyo" to "kyoto". The location of input of dialogue text, memos, etc. to be summarized can be specified in summary portion specification 2607. For example, by clicking on summary portion specification 2607, a screen for accepting specification of a partial area of ​​dialogue text or memos is displayed, and the specified area can be used as the target for summarization. By configuring these settings and pressing the summary start button 2435, the summary generation module 214 generates a summary from the dialogue text with the configured contents.

[0129] As mentioned above, we provide a variety of features to vary the summary output: - Ability to control summary output by specifying category information of inquiry contents. - Ability to specify keywords and have those words reflected in the output. -Ability to adjust summary length. - Ability to output summaries in bullet point format. -A function to correct variations in the spelling of expressions. -A function to specify the summary section from input such as dialogue text or notes.

[0130] FIG. 27 shows an example of a reference information call setting screen 2700. When the customer response processing module 211 accepts the selection of the dialogue text button 2701 at the top left of the screen, it displays the contents of the dialogue text. When the customer response processing module 211 accepts the selection of the FAQ button 2702, it calls the data search module 218. The data search module 218 acquires the category information that has already been selected, and acquires the FAQ data specified by the corresponding category ID, etc. 1202 from the FAQ information 1200 in Fig. 12, from the location specified by the FAQ data storage URL 1203. In addition, it is also possible to display FAQs used during customer interactions, as well as FAQs searched based on the dialogue text.

[0131] As mentioned above, the category information can be specified by the operator, but the category information can also be automatically selected from the contents of the dialogue text. Therefore, when the operator selects the FAQ button 2702, the related FAQ data associated with these dialogue texts via the category information is obtained and displayed.

[0132] The external link 2703 is a button for calling other external systems. In addition to the dialogue text, FAQs referred to during the call, logs of other services used, such as Web systems, etc., can also be used as input for generating a summary. FIG. 28 is an example of a feedback reception screen 2800. The feedback module 217 receives ratings 2801 for the generated summaries. The feedback module 217 may also be referred to as a rating acquisition module. The feedback module 217 can also accept edits or modifications from the user to the summary displayed in the summary text field 2830. Note that instead of the feedback module 217, a separate summary editing module that accepts edits to the summary may be provided.

[0133] The feedback module 217 accumulates highly rated summaries, the document information (dialogue text) on which the highly rated summaries are based, and summaries revised by the user, and can use these to retrain the machine learning model and improve the machine learning model. Other information that can be stored in a database, for example, includes summaries generated by the AI, summaries revised by the user, evaluation scores, and indicators of the difference between the user's summary and the AI's summary.

[0134] When the text modified by the user is reflected in a CRM or the like using an API, the text is reflected when the operation is performed, and when pasting the text separately using copy and paste, the text can be reflected when the copy button is pressed. Also, instead of a copy button, a button called update or reflect may be used. Also, updates may be performed automatically in the background, without the need to explicitly press a button. In this way, the accuracy of the model can be improved by using a human-in-the-loop mechanism in which the user repeatedly evaluates and corrects the generated summary. In addition, re-learning can use the score of the difference between a summary generated by AI and a summary revised by a human, as well as information such as how much the revisions have changed.

[0135] In addition to manually retraining a model on a regular basis, a pipeline can be built to perform retraining on a regular basis. As for the data to be learned, in addition to a method of relearning using all data in the database, a method of preferentially learning data in the database that did not have very good scores can also be considered. It is also possible to provide a system in which correction work for the results of model generation can be carried out through BPO (Business Process Outsourcing), and the results can be improved before being provided.

[0136] FIG. 31 is an example of another feedback reception screen 3100. The user can output the template and start writing a new summary from there. For example, when the feedback module 217 accepts the selection of the "template output" button 3131 displayed next to the summary text 3130, the feedback module 217 displays a template for optional input in the summary text. The feedback module 217 can change the content of the template to be output based on the content of the category (procedural, automobile insurance, etc.) previously input.

[0137] Furthermore, in cases where category information has not been input in advance, the feedback module 217 may display a template selection panel 3132 in accordance with the selection of the template output, and may accept the selection of the template within this panel. Also, categories may be in a hierarchical structure, and a template may be automatically selected based on the contents of the dialogue, or a candidate with a high probability may be recommended. Alternatively, the summary generation module 214 may be configured to output the template.

[0138] FIG. 29 shows an example of a work status display screen 2900 displayed on the manager terminal 103. The user terminal management module 410 of the administrator terminal 103 provides a function that can monitor the efficiency of ACW work of multiple users who use each user terminal. The user terminal management module 410 displays distribution and statistics of the time spent in ACW for each important attribute. For example, it is possible to display the ACW work log for each user while sorting and filtering by attributes such as time (month, week, etc.), user (operator), and customer service content category. In the example of Fig. 29, the list of each call (interaction) can be confirmed and filtered. When each item is selected, the user terminal management module 410 displays a work status detail display screen 3000 of Fig. 30.

[0139] FIG. 30 shows an example of a work status details display screen 3000. On the work status detail display screen 3000, text information of the dialogue text is displayed in a dialogue text field 3010, and notes entered by the user are displayed in a memo field 3020. Moreover, information on the selected category is displayed in an inquiry content input field 3050, and the selected category is confirmed by pressing a set button 3051. The summary column 3030 displays a summary generated from the dialogue text. If the category selection is corrected, the summary can be generated again by pressing the summary start button 2031 again.

[0140] . When the user presses the Reflect in CRM button 3060, the entire content displayed here is finally stored in the CRM system. According to this embodiment, it is possible to improve the efficiency of ACW work, which requires a large number of man-hours. Also, a highly accurate summary is automatically generated and can be displayed immediately on the Web. The generated summaries can be displayed in an easy-to-edit UI, making it possible to carry out all ACW operations on a single, simple customer response management system1.

[0141] The present invention is not limited to the above-described embodiments, and includes various modified examples. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the configurations described. It is also possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0142] In addition, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as the program, table, file, etc. that realizes each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0143] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. The above-described embodiments disclose at least the configurations described in the claims. [Explanation of symbols]

[0144] 1...customer response management system, 102...user terminal, 103...administrator terminal, 211...customer response processing module, 212...category selection module, 213...document information acquisition module, 214...summary generation module, 215...memo information acquisition module, 216...highlighting module, 217...feedback module, 218...data search module

Claims

1. A document information acquisition means for acquiring first document information; a summary generating means for inputting the first document information acquired by the document information acquiring means into a trained machine learning model that has performed machine learning to generate a summary using document information as an input, and acquiring a summary for the first document information from the machine learning model; an output processing means for outputting the summary obtained by the summary generating means; A document management system having:

2. A category specification unit for specifying a category to which the first document information belongs. The document management system according to claim 1 .

3. The summary generating means selects a second machine learning model corresponding to the category, and inputs the first document information to the second machine learning model, thereby obtaining a summary for the first document information from the second machine learning model. The document management system according to claim 2 .

4. The second machine learning model is a trained machine learning model that has been trained by performing machine learning using multiple pieces of document information belonging to the category as training data. The document management system according to claim 3 .

5. The summary generating means receives information indicating the category and the first document information, and obtains a summary related to the category for the first document information from the machine learning model. The document management system according to claim 2 .

6. The machine learning model is a trained machine learning model that has been subjected to machine learning using information indicating a category to which document information belongs and a plurality of pieces of document information belonging to the category as training data. The document management system according to claim 5.

7. The summary generating means outputs a template of a summary related to the category. The document management system according to claim 2 .

8. a summary editing means for accepting edits to the summary; The document management system according to any one of claims 1 to 7.

9. The summary generating means receives a part of the edited summary and regenerates at least a part of the summary. The document management system according to claim 8.

10. The summary editing means outputs a template for modifying the summary. The document management system according to claim 8.

11. 8. The document management system according to claim 1, further comprising an evaluation acquisition unit for receiving an evaluation of the summary.

12. The document management system according to claim 11 , wherein the machine learning model is retrained using document information from which the received highly-rated summary has been generated.

13. the summary generating means receives input of setting values ​​for the summary to be generated; The output contents of the generated summary are changed according to the set value. The document management system according to any one of claims 1 to 7.

14. The summary generating means receives second document information related to the first document acquired by the second information acquiring means and the first document information, and generates a summary. The document management system according to any one of claims 1 to 7.

15. A highlighting means for highlighting a characteristic character string that satisfies a predetermined condition among one or more characteristic character strings included in the summary is provided. The document management system according to any one of claims 1 to 7.

16. The predetermined condition is that there is information regarding an attribute including at least one of a date, a time, a name, a location, a telephone number, a subscriber number, a customer number, an address, a quantity, and an address; The highlighting means highlights a character string including information related to the attribute among one or more of the characteristic character strings included in the summary. The document management system of claim 15.

17. the predetermined condition is that the characteristic character string is present in the abstract but not in the first document information; The highlighting means highlights, of the one or more characteristic character strings included in the summary, a characteristic character string that is present in the summary but not present in the first document information. The document management system of claim 15.

18. the predetermined condition is that a corresponding characteristic character string exists between the summary and the first document information; The highlighting means highlights a corresponding characteristic character string between the summary and the first document information out of one or more characteristic character strings included in the summary. The document management system of claim 15.

19. (Summary accuracy is low = highlights parts where the AI ​​is not confident) the predetermined condition is that a character string that may possibly result in a low accuracy of the summary generated by the summary generating means is present in the summary; The highlighting means highlights a character string that may possibly be a character string that is not highly accurate in the summary generated by the summary generating means, out of one or more of the characteristic character strings included in the summary. The document management system of claim 15.

20. The output processing means outputs the summary obtained by the summary generating means and the first document information obtained by the document information obtaining means in association with each other. The document management system according to any one of claims 1 to 7.

21. A highlighting unit is provided for highlighting one or more characteristic character strings included in at least one of the summary and the first document information, the characteristic character strings satisfying a predetermined condition. The document management system of claim 20.

22. The predetermined condition is that there is information regarding an attribute including at least one of a date, a time, a name, a place, an address, a quantity, and an address; The highlighting means highlights a character string including information related to the attribute among one or more of the characteristic character strings included in at least one of the summary and the first document information.

22. The document management system of claim 21.

23. the predetermined condition is that the characteristic character string is present in the abstract but not in the first document information; The highlighting means highlights, of the one or more characteristic character strings included in the summary, a characteristic character string that is present in the summary but not present in the first document information.

22. The document management system of claim 21.

24. the predetermined condition is that a corresponding characteristic character string exists between the summary and the first document information; The highlighting means highlights the characteristic character string that corresponds between the abstract and the first document information, out of the one or more characteristic character strings included in at least one of the abstract and the first document information.

22. The document management system of claim 21.

25. the predetermined condition is that a character string that may possibly result in a low accuracy of the summary generated by the summary generating means is present in the summary; The highlighting means highlights a character string that may be a low-accuracy character string in the summary generated by the summary generating means, out of one or more of the characteristic character strings included in at least one of the summary and the first document information.

22. The document management system of claim 21.

26. The output processing means outputs the summary obtained by the summary generating means and the second document information obtained by the second information obtaining means in association with each other. The document management system according to any one of claims 1 to 7.

27. A highlighting unit is provided for highlighting one or more characteristic character strings included in at least one of the summary and the second document information, the characteristic character strings satisfying a predetermined condition.

27. The document management system of claim 26.

28. The predetermined condition is that there is information regarding an attribute including at least one of a date, a time, a name, a place, an address, a quantity, and an address; The highlighting means highlights a character string including information related to the attribute among one or more of the characteristic character strings included in at least one of the summary and the second document information.

28. The document management system of claim 27.

29. the predetermined condition is that the characteristic character string is present in the second document information but not in the summary; The highlighting means highlights, among the one or more characteristic character strings included in the second document information, a characteristic character string that is present in the second document information but not in the summary.

28. The document management system of claim 27.

30. the predetermined condition is that a corresponding characteristic character string exists between the summary and the second document information; The highlighting means highlights the characteristic character strings that correspond between the abstract and the second document information, among the one or more characteristic character strings included in at least one of the abstract and the second document information.

28. The document management system of claim 27.

31. the predetermined condition is that a character string that may possibly result in a low accuracy of the summary generated by the summary generating means is present in the summary; The highlighting means highlights a character string that may be a low-accuracy character string in the summary generated by the summary generating means, out of one or more of the characteristic character strings included in at least one of the summary and the second document information.

28. The document management system of claim 27.

32. The output processing means outputs the first document information acquired by the document information acquisition means, the summary acquired by the summary generation means, and the second document information acquired by the second information acquisition means in association with each other. The document management system according to any one of claims 1 to 7.

33. A second information acquisition means for acquiring second document information, A highlighting unit is provided for highlighting one or more characteristic character strings included in at least one of the summary, the first document information, and the second document information, the characteristic character string satisfying a predetermined condition.

33. The document management system of claim 32.

34. the predetermined condition is that the characteristic character string corresponding to the second document information and the summary exists; The highlighting means highlights the characteristic character string corresponding to the second document information and the abstract, among the one or more characteristic character strings included in at least one of the first document information, the second document information, and the abstract.

34. The document management system of claim 33.

35. the predetermined condition is that the characteristic character string is present in the second document information but not in the summary; The highlighting means highlights, among the one or more characteristic character strings included in at least one of the first document information and the second document information, a characteristic character string that exists in the second document information but does not exist in the summary.

34. The document management system of claim 33.

36. the predetermined condition is that the characteristic character string is present in the abstract but not present in the first document information or the second document information; The highlighting means highlights, of the one or more characteristic character strings included in the summary, a characteristic character string that is present in the summary but not present in the first document information or the second document information.

34. The document management system of claim 33.

37. The document information acquisition means receives input of voice information, The speech information is analyzed and converted into text to obtain the first document information. The document management system according to any one of claims 1 to 7.

38. A program for causing a management server to function as each of the means of the document management system according to any one of claims 1 to 37.

39. Obtaining first document information; inputting the first document information into a trained machine learning model that has performed machine learning to generate a summary using document information as an input, thereby obtaining a summary for the first document information from the machine learning model; Output the summary Document management methods.

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