Information processing system, information processing method, and program

Abstract

The objective is to provide a system that can appropriately acquire information contained in text information from a generation AI device. [Solution] The system includes a text information acquisition unit that acquires text information including characters, an item acquisition unit that acquires extraction items related to extraction information to be extracted from the text information based on a user's operational input, a query information identification unit that identifies extraction query information associated with the setting candidate items corresponding to the extraction items based on a query database that associates and stores setting candidate items that can be set as targets for extraction and query information to be input to a generation AI device learned to extract the extraction information from the text information, a transmission unit that transmits the extraction query information and the text information to the generation AI device, and an extraction information acquisition unit that acquires the extraction information included in the text information from the generation AI device.

Description

[Technical field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] An accounting processing system has been disclosed that uses a generative AI device to automatically generate accounting entries from text data (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6895632 Summary of the Invention [Problem to be solved by the invention]

[0004] The accounting processing system described in Patent Document 1 extracts transaction information from supporting data by image recognition processing, and generates text information including transaction detail information from the extracted transaction information by character recognition processing. The accounting processing system also generates journal entry data from the text information using a journal entry generation AI that has learned to select a combination of account items corresponding to the transaction detail information. This enables the accounting processing system to execute appropriate automatic journal entries.

[0005] However, in the accounting processing system described in Patent Document 1, although a journal entry generation AI is used, if the query information for generating journal entry data to be input into the journal entry generation AI is inappropriate, a problem arises in that journal entry data cannot be appropriately generated from text information.

[0006] Therefore, in order to solve the above problem, an object of the present invention is to provide a system that can appropriately acquire information contained in text information from a generation AI device. [Means for solving the problem]

[0007] An information processing system according to one embodiment of the present invention includes a text information acquisition unit that acquires text information including characters; an item acquisition unit that acquires extraction items related to extraction information to be extracted from the text information based on a user's operational input; a query information identification unit that identifies extraction query information associated with a setting candidate item corresponding to the extraction item based on a query database that associates and stores setting candidate items that may be set as targets for extraction and query information to be input to a generation AI device learned to extract the extraction information from the text information; a transmission unit that transmits the extraction query information and the text information to the generation AI device; and an extraction information acquisition unit that acquires the extraction information contained in the text information from the generation AI device.

[0008] An information processing method according to one embodiment of the present invention includes a computer that executes the following steps: a text information acquisition unit that acquires text information including characters; acquires extraction items related to extraction information to be extracted from the text information based on a user's operational input; identifies extraction query information associated with the candidate setting items corresponding to the extraction items based on a query database that associates and stores candidate setting items that may be set as targets for extraction and query information to be input to a generation AI device learned to extract the extraction information from the text information; transmits the extraction query information and the text information to the generation AI device; and acquires the extraction information included in the text information from the generation AI device.

[0009] A program according to one embodiment of the present invention causes a computer to execute the following operations: a text information acquisition unit that acquires text information including characters; acquires extraction items related to extraction information to be extracted from the text information based on a user's operational input; identifies extraction query information associated with the candidate setting items corresponding to the extraction items based on a query database that associates and stores candidate setting items that may be set as targets for extraction and query information to be input to a generation AI device learned to extract the extraction information from the text information; transmits the extraction query information and the text information to the generation AI device; and acquires the extraction information included in the text information from the generation AI device. Effect of the Invention

[0010] According to the present invention, a system can be provided that can appropriately acquire information contained in text information from a generation AI device. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing an overview of an extraction system. [Diagram 2] 2 is a database showing an example of a query database 101a. [Diagram 3] 2 is a database showing an example of a text database 101b. [Figure 4] 2 is a database showing an example of a label database 101c. [Diagram 5] 1 is a flowchart showing a processing procedure of the extraction system. [Figure 6] FIG. 11 is a diagram showing an example of target text information. [Figure 7] FIG. 13 is a diagram showing an example of extracted query information and extracted block text information input to a generation AI device; [Figure 8] FIG. 13 is a diagram showing an example of extraction information output from a generation AI device. [Figure 9] FIG. 2 illustrates an example of a hardware configuration of a computer. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] An extraction system 10 according to one embodiment of the present invention will be described in detail below with reference to the drawings. However, the embodiment described below is merely an example, and is not intended to exclude the application of various modifications and techniques not specifically described below. In other words, the present invention can be implemented by modifying it in various ways or combining each embodiment without departing from the spirit of the invention. In addition, in the following description of the drawings, the same or similar parts are denoted by the same or similar reference numerals.

[0013] In this embodiment, the terms "part", "device", and "system" do not simply mean physical means, but also include cases where the functions of the "part", "device", and "system" are realized by software. The functions of one "part", "device", or "system" may be realized by two or more physical means or devices, and the functions of two or more "parts", "devices", and "systems" may be realized by one physical means or device. Furthermore, the various functions described below of each of the multiple devices constituting the extraction system 10 may be configured to be executed by other devices in the multiple devices.

[0014] === Overview of Extraction System 10 === <<Configuration Overview>> An overview of the extraction system 10 will be described with reference to Fig. 1. Fig. 1 is a diagram showing an overview of the extraction system 10.

[0015] The extraction system 10 is a system that extracts text desired by a user from text contained in a digital document that is generated based on a target image using an optical character recognition device.

[0016] The target image is, for example, an image of various contracts, papers, etc. The target image is, for example, an image specified in a graphic format such as JPEG (Joint Photographic Experts Group), TIFF (Tagged Image File Format), PNG (Portable Network Graphics), or an image of PDF (Portable Document Format) data.

[0017] The extraction system 10 includes, for example, an extraction device 100, an optical character recognition device 200, and a user terminal 300.

[0018] The extraction device 100 is a device that outputs the result of character recognition of a target image based on the result of character recognition of the target image by an optical character recognition device.

[0019] The optical character recognition device 200 is a device that performs character recognition on a target image.

[0020] The extraction device 100 and the optical character recognition device 200 may be, for example, a cloud computer, a server computer, a personal computer (e.g., a desktop, a laptop, a tablet, etc.), a media computer platform (e.g., a cable or satellite set-top box, a digital video recorder), a handheld computer device (e.g., a PDA, an email client, etc.), or other types of computers or communication platforms. At least a part of the processing in the extraction device 100 and the optical character recognition device 200 may be realized by one or more computers (for example, but not limited to, cloud computing configured by one or more computers).

[0021] The user terminal 300 is a device that receives operational inputs from a user and displays various types of information.

[0022] The user terminal 300 may be, for example, a smartphone, a mobile phone (feature phone), a personal computer (e.g., desktop, laptop, tablet, etc.), a media computing platform (e.g., cable, satellite set-top box, digital video recorder), a handheld computing device (e.g., PDA (Personal Digital Assistant), email client, etc.), a wearable device (glasses-type device, watch-type device, etc.), or another type of computer or communication platform.

[0023] <<Processing Overview>> An overview of the processing of the extraction system 10 will be described with reference to FIG.

[0024] First, in step S10, the extraction device 100 acquires, from the optical character recognition device 200, text information generated based on a target image (hereinafter, referred to as “target text information”).

[0025] In step S11, the extraction device 100 identifies text information of a predetermined group of characters included in the target text information (hereinafter referred to as "block text information"). The extraction device 100 assigns a label indicating a classification of the block text information (hereinafter referred to as "extraction label") to each piece of block text information. The extraction device 100 associates the block text information with the extraction label and stores them in the storage unit 101 (a label database D101c described below).

[0026] The block text information may be, for example, a set of text information consisting of a plurality of sentences, or a set of text information consisting of a single sentence consisting of a plurality of words. Specifically, the block text information may be, for example, in a contract or a thesis, at least one sentence may be a set of text information, at least one article, clause or paragraph may be a set of text information, at least one chapter may be a set of text information, or information in a table format may be a set of text information.

[0027] In step S12, the user terminal 300 receives an operation input from the user and acquires items (hereinafter referred to as “extracted items”) that are categories related to the information to be extracted from the text information (hereinafter referred to as “extracted information”). The user terminal 300 transmits the extracted items to the extraction device 100.

[0028] In step S13, the extraction device 100 refers to the label database D111 to identify an extraction label corresponding to the extraction item. Then, the extraction device 100 refers to the text database D101b to identify block text information related to the identified extraction label (hereinafter, referred to as "extracted block text information").

[0029] In step S14, the extraction device 100 refers to the query database D112 to identify candidate setting items corresponding to the identified extraction label, and identifies query information (hereinafter referred to as "extracted query information") to be input to the generation AI (Artificial Intelligence) device based on the candidate setting items.

[0030] The generative AI device is a device that has the function of a large-scale language model (hereinafter referred to as "generative AI") that has a huge data set based on a deep learning model. The generative AI is, for example, a text generation AI that is trained to extract extracted information based on query information, such as ChatGPT or Google Bard.

[0031] The query information is a sentence to be input to the AI ​​generation device, i.e., a prompt, which is an instruction sentence to be given to the AI ​​generation device. In other words, the extraction query information is an instruction sentence to be input to the AI ​​generation device in order to extract specific text information from the block text information.

[0032] In step S15, the extraction device 100 transmits the extraction block text information and the extraction query information to the generation AI device.

[0033] In step S16, the extraction device 100 obtains, from the generation AI device, extraction information that is the result of extracting text information from the block text information.

[0034] This enables the extraction device 100 to appropriately extract information desired by the user from the target text information using the generation AI device.

[0035] ===Extraction device 100=== As shown in FIG. 1, the extraction device 100 includes a memory unit 101, a text information acquisition unit 102, a label setting determination unit 103, an item acquisition unit 104, a block text information identification unit 105, a query information identification unit 106, a transmission unit 107, an extraction information acquisition unit 108, and a display processing unit 109.

[0036] The storage unit 101 includes, for example, a query database D101a, a text database D101b, and a label database D101c.

[0037] The query database D101a will be described with reference to Fig. 2. Fig. 2 is a database showing an example of the query database 101a. The query database D101a is a database that stores candidate setting items, which are items that can be set as extraction targets, in association with query information. The query database D101a is a database for identifying queries to be input to the generation AI device.

[0038] As shown in Fig. 2, the query database D101a includes items such as [candidate ID], [candidate setting item], and [query information]. [Candidate ID] stores identification information that can uniquely identify a candidate setting item. [Candidate setting item] stores text information indicating a candidate setting item. [Query information] stores query information to be input to the generation AI device.

[0039] The query information is a prompt optimized to allow the generation AI device to extract extraction information related to the setting candidate items from the text information. The query information includes items such as extraction requirements and extraction examples. The extraction requirements are information for extracting appropriate information, such as information on the characteristics of the information to be extracted and the extraction method (for example, extraction requirements T21 in FIG. 7 described later). The extraction examples are information for improving the accuracy of extraction, such as information on examples of extraction results for input (for example, extraction examples T22 in FIG. 7 described later).

[0040] By using the query database D101a, the extraction device 100 makes it possible for a user to input query information for obtaining desired extraction information from the generation AI to the generation AI device.

[0041] The text database D101b will be described with reference to Fig. 3. Fig. 3 shows an example of the text database D101b. The text database 101b is a database that stores block text information and extraction labels indicating classifications of the block text information in association with each other.

[0042] As shown in Fig. 3, the text database D101b includes items such as [Text ID], [Text Information], [Block ID], [Block Text Information], and [Extraction Label]. [Text ID] stores identification information that can uniquely identify the target text information. [Text Information] stores the target text information. [Block ID] stores identification information that can uniquely identify the block text information. [Block Text Information] stores a predetermined group of block text information from the target text information. [Extraction Label] stores a label that indicates the classification of the block text information.

[0043] By using the text database D101b, the extraction device 100 can appropriately extract, from the query database D101a, query information for obtaining extraction information desired by the user from the generation AI.

[0044] The label database D101c will be described with reference to Fig. 4. Fig. 4 shows an example of the label database D101c. The label database 101c is a database that stores labels indicating classifications and text information in association with each other.

[0045] As shown in FIG. 4, the label database D101c includes items such as [Label ID], [Extraction Label], and [Text Features]. [Label ID] stores identification information that can uniquely identify a label. [Label] stores an extraction label to be assigned to block text information. [Text Features] stores text features of text included in block text information to which an extraction label is associated. Text features are information indicating, for example, a word, a combination of multiple words, or a word or combination of words included in a tabular region. Specifically, for example, a text feature corresponding to the extraction label "Company Name" is a condition that "text including all of 'Party A', 'Party B', and 'Contract'".

[0046] The extraction device 100 can generate the text database D101b by appropriately assigning extraction labels to the block text information included in the target text information by using the label database D101c. That is, the extraction device 100 (the label setting determination unit 103 described later) can assign extraction labels to each of the block text information including a word (e.g., "Article 2"), a combination of multiple words (e.g., a combination of "A", "B", and "Contract"), and a tabular region (e.g., a combination of a ruled line and a word surrounded by the ruled line) included in the block text information.

[0047] The text information acquisition unit 102 acquires text information generated by the optical character recognition device 200 based on a target image from the optical character recognition device 200, for example.

[0048] The label setting determination unit 103 identifies block text information included in the target text information. Then, the label setting determination unit 103 associates the extraction label with the block text information based on the correspondence between the extraction label and the text features of the text included in the text information. The label setting determination unit 103 associates the extraction label with the block text information and stores it in the text database D101b.

[0049] Specifically, first, the label setting determination unit 103 performs an analysis of the text included in the target text information, for example, by morphological analysis, and classifies the target text information into block text information. For example, when the target image is a contract, and the label setting determination unit 103 identifies the text of "Article 2" in the target text information, it classifies the sentence stipulated in "Article 2" of the contract as block text information.

[0050] The label setting determination unit 103 may then associate an extraction label with the block text information based on, for example, the label database D101c. Specifically, the label setting determination unit 103 refers to the label database D101c and associates, with block text information that satisfies any of the text features, a label corresponding to the text feature as an extraction label. The label setting determination unit 103 associates the extraction label with the block text information and stores it in the text database D101b. Note that the method for analyzing the text included in the target text information is, for example, a method such as morphological analysis.

[0051] The label setting determination unit 103 may, for example, input block text information to a trained model trained using the extraction label and text features included in the text information as teacher data, thereby associating the extraction label with the block text information. Specifically, the label setting determination unit 103, for example, inputs the block text information to the trained model, thereby acquiring at least one extraction label that indicates a probability of being associated with the block text information equal to or higher than a predetermined threshold value from the trained model. The label setting determination unit 103 associates, for example, the extraction label that indicates the highest probability among the acquired extraction labels with the block text information, and stores the associated label in the text database D101b.

[0052] The item acquiring unit 104 acquires extracted items based on an operation input by a user to the user terminal 300. Specifically, for example, when the target image is a contract, the item acquiring unit 104 acquires the "company name" input by the user to the user terminal 300 from the user terminal 300 as an extracted item.

[0053] The block text information identifying unit 105 refers to the text database D101b to identify extracted block text information that is block text information that corresponds to the extraction item. Specifically, the block text information identifying unit 105 identifies, for example, "Company Name" that is an extraction label that corresponds to the extraction item "Company Name" in the text database D101b. Then, the block text information identifying unit 105 refers to the text database D101b to identify "Article 2..." that is block text information related to the extraction label "Company Name".

[0054] The query information identifying unit 106 refers to the query database D101a to identify extracted query information. Specifically, the query information identifying unit 106 identifies, for example, a setting candidate item "company name" corresponding to the extracted item "company name" in the query database D101a. The query information identifying unit 106 identifies, in the query database D101a, extracted query information related to the setting candidate item "company name".

[0055] The transmission unit 107 transmits extraction query information for acquiring extraction information from the generation AI device. Specifically, the transmission unit 107 transmits the extraction query information and the block text information to the generation AI device.

[0056] The extraction information acquisition unit 108 acquires the extraction information included in the text information from the generation AI device. Specifically, when the target image is a contract, the extraction information acquisition unit 108 acquires extraction information indicating, for example, the extraction item "company name" from the contract, such as "A Co., Ltd."

[0057] The display processing unit 109 causes the display unit to display various types of information. For example, the display processing unit 109 causes the display unit to display information shown in Fig. 7 and Fig. 8, which will be described later.

[0058] <<First Modification>> When the target text information includes data in a table format, the label setting determination unit 103 may determine whether or not there is text information corresponding to an extracted item in the text information included in the area in the table format.

[0059] Specifically, the label setting determination unit 103 identifies the text information in the table format of the block B30 shown in Fig. 6. The label setting determination unit 103 associates an extraction label with the block B30 based on the text features of the label database D101c. At this time, the label setting determination unit 103 may associate the extraction label with the title of the block B30 ("Rental Requirements" in Fig. 6). The label setting determination unit 103 determines whether the block B30 is related to the extraction label corresponding to the extraction item, and whether the text information included in the block B30 includes text information corresponding to the extraction information. At this time, the label setting determination unit 103 may identify the ruled lines of the block B30, and determine whether the text information written in each area surrounded by the ruled lines corresponds to the extraction item.

[0060] Then, when the label setting determination unit 103 determines that there is text information (item) corresponding to the extraction item, the extraction device 100 acquires, as extraction information, information related to the item corresponding to the extraction item in the tabular data.

[0061] On the other hand, if the label setting determination unit 103 determines that there is no text information (item) corresponding to the extracted item, the extraction device 100 transmits the extracted query information and the extracted block text information to the generation AI device as described above, and obtains the extracted information from the generation AI device.

[0062] In this way, the extraction device 100 may be configured to obtain extraction information on a rule basis when the target text information contains information that clearly indicates the correspondence between items and text contents, and to obtain extraction information using the generation AI device when the rule-based extraction information cannot be obtained. This enables the extraction device 100 to reduce the frequency of using the generation AI device and to improve the accuracy of obtaining appropriate extraction information.

[0063] <<Second Modification>> The extraction device 100 may specify the extraction information based on a comparison result between the first extraction information acquired when the label setting determination unit 103 determines that there is text information (item) corresponding to the extraction item and the second extraction information acquired from the generation AI device when the label setting determination unit 103 determines that there is no text information (item) corresponding to the extraction item. Specifically, the extraction device 100 may transmit the comparison result to the user terminal 300 and specify the extraction information based on a result of selection of the first extraction information or the second extraction information based on a user's operation input. In addition, the extraction device 100 may specify the extraction information that indicates a higher index of the extraction accuracy indicated by the first extraction information and the extraction accuracy indicated by the second extraction information. This enables the extraction device 100 to improve the accuracy of acquiring appropriate extraction information.

[0064] <<Third Modification>> In the above, the label setting determination unit 103 has been described as generating the text database D101b, but this is not limited thereto. When the text database D101b is not generated, the label setting determination unit 103 identifies a "text feature" related to the label in the label database D101c corresponding to the extraction item. Then, based on the identified text feature, the block text information identification unit 105 associates an extraction label corresponding to the text feature with the block text information that satisfies the text feature among the block text information included in the target text information, and identifies the block text information as extracted block text information. That is, the extraction device 100 does not need to include the text database D101b, and may be configured to identify the extracted block text information based on the identified text feature. This makes it possible to identify the extracted block text information with a simple system configuration.

[0065] ===Optical character recognition device 200=== Returning to Fig. 1, a description will be given of the configuration of optical character recognition device 200. Optical character recognition device 200 is a device that, for example, when a target image is input, recognizes characters included in the target image and generates text information, an accuracy index, and coordinates (hereinafter, simply referred to as "generated information") for each recognized character, for example.

[0066] In addition, optical character recognition device 200 may be configured to include multiple optical character recognition devices, and may be configured to transmit to extraction device 100 generated information that is a character recognition result with high accuracy among the character recognition results of each of the multiple optical character recognition devices.

[0067] As shown in FIG. 1, the optical character recognition device 200 includes, for example, a storage unit 210, a transmission / reception unit 220, and a processing unit 230. The storage unit 210 stores various information. The processing unit 230 executes processing for character recognition. The transmission / reception unit 220 transmits and receives various information to and from the extraction device 100. The processing unit 230 analyzes an image using, for example, a neural network trained to distinguish between characters. The neural network includes, for example, a plurality of convolutional network layers and a recurrent network layer. The processing unit 230 segments, for example, a target image into pages, blocks, lines, or characters. Character recognition is performed on the characters included in the segmented image to generate, for example, first generated information for each character. The processing unit 230 may generate generated information for, for example, a segmented image (for example, a target image, a block image, or a line image) as a single group.

[0068] ===User Terminal 300=== The configuration of the user terminal 300 will be described with reference to Fig. 1. As shown in Fig. 1, the user terminal 300 includes functional units, for example, a storage unit 310, a transmission / reception unit 320, and a display processing unit 330. Each functional unit is a function realized by, for example, a processor 1001 reading out a program stored in a memory 1002.

[0069] The memory unit 410 stores various information. The user information 311 stores user names, attribute information, etc. in association with a user ID as a primary key. The user name is a name registered by the user himself / herself and is arbitrary. The attribute information is information such as the user's name and address. The transmission / reception unit 320 transmits and receives various information to and from the extraction device 100. The various information acquired by the transmission / reception unit 320 is stored in the memory unit 310. The display processing unit 330 displays the various information acquired from the extraction device 100 on the display unit.

[0070] ===Processing Procedure=== The processing procedure of the extraction system 10 will be described with reference to Figs. 5, 6, 7, and 8. Fig. 5 is a flowchart showing the processing procedure of the extraction system 10. Fig. 6 is a diagram showing an example of target text information. Fig. 7 is a diagram showing an example of extraction query information and extraction block text information input to the generating AI device. Fig. 8 is a diagram showing an example of extraction information output from the generating AI device.

[0071] In step S100, the extraction device 100 acquires a target image from a predetermined device. The extraction device 100 transmits the target image to the optical character recognition device 200.

[0072] In step S101, the optical character recognition device 200 segments a target image to generate target text information of the target image. The optical character recognition device 200 transmits the target text information to the extraction device 100.

[0073] In step S102, the extraction device 100 stores the target text information in the text database D101b. At this time, the extraction device 100 divides the target text information into block text information and stores the block text information.

[0074] Specifically, as shown in FIG. 6, the block text information is information indicating, for example, block B10 which is block text information contained in "Article 2," block B20 which is block text information contained in the bullet points of "Property Description," and block B30 which is a tabular area of ​​"Rental Requirements" and the block text information contained in that tabular area.

[0075] In step S103, the extraction device 100 associates an extraction label with each block of text information that satisfies the text feature in the label database D101c based on the text feature in the label database D101c. The extraction device 100 associates the extraction label with the block of text information and stores it in the text database D101b.

[0076] Specifically, the extraction device 100 identifies block text information of block ID "1001002" in the text database D101b shown in Fig. 3, which satisfies the text feature "text including all of 'Party A', 'Party B', and 'Contract'" of label ID "2002" in the label database D101c shown in Fig. 4. When the extraction device 100 can identify the block text information, the extraction device 100 associates the block text information with the extraction label "company name" corresponding to the text feature in the label database D101c, and stores the block text information in the text database D101b.

[0077] In step S104, the extraction device 100 acquires extraction items, which are categories related to the extraction information, from the user terminal 300. Specifically, the extraction device 100 acquires extraction items indicating "company name" and "address", etc. That is, the extraction device 100 acquires information indicating the item contents that the user desires to extract.

[0078] In step S105, the extraction device 100 identifies extracted block text information related to the extraction label in the text database D101b corresponding to the extraction item.

[0079] Specifically, the extraction device 100 identifies, for example, an extraction label "company name" in the text database D101b corresponding to the extraction item "company name." The extraction device 100 identifies, as extracted block text information, the block text information "Article 2..." in the text database D101b related to the extraction label "company name."

[0080] In step S106, the extraction device 100 identifies candidate setting items in the query database D101a that correspond to the extraction items. The extraction device 100 identifies query information that corresponds to the identified candidate setting items as extracted query information.

[0081] Specifically, the extraction device 100 identifies a setting candidate item "company name" in the query database D101a corresponding to the extraction item "company name", for example. The extraction device 100 identifies query information in the query database D101a corresponding to the setting candidate item "company name" as extracted query information (information shown in "extraction requirement" and "extraction example" in FIG. 2).

[0082] In step S107, the extraction device 100 transmits the extracted block text information and the extracted query information to the generating AI device. Hereinafter, the extracted block text information and the extracted query information transmitted to the generating AI device will be described with reference to FIG.

[0083] As shown in FIG. 7, the block text information T10 is a predetermined range of text information included in the target text information, and in this case, the block text information is "Article 2...". The extraction query information T20 includes extraction requirements T21 and extraction examples T22. As shown in FIG. 7, the extraction requirements T21 are information for extracting appropriate information, and here, they specify the specific descriptions of the extraction target ("Lender" and "Lesser") and the output format (array format on the JSON side). The extraction examples T22 are information for improving the convenience of users who use the extracted information, and here, they provide examples of input sentences and specify the extraction format of the extraction results for the examples.

[0084] In step S108, the extraction device 100 acquires the extraction information from the generation AI device. For example, the extraction device 100 displays the extraction information shown in Fig. 8 on the display unit of the user terminal 300. As shown in Fig. 8, the extraction device 100 displays the extraction target (here, the company name of the "lender" and the company name of the "borrower") on the display unit in the output format (here, JSON format) specified in the extraction requirement T21.

[0085] If the user desires to obtain a plurality of pieces of extraction information, the extraction device 100 repeats the processes from step S104 to step S108.

[0086] In this way, in the extraction system 10, a prompt can be input to the generation AI device to enable the user to properly obtain the information desired by the user from the generation AI device, thereby enabling the user to properly obtain the information.

[0087] ===Hardware Configuration=== An example of a hardware configuration for implementing the extraction device 100, the optical character recognition device 200, and the user terminal 300 on a computer will be described with reference to Fig. 9. Fig. 9 is a diagram showing an example of a hardware configuration of a computer.

[0088] As shown in FIG. 9, a computer 1000 includes a processor 1001, a memory 1002, a storage device 1003, an input I / F unit 1004, a data I / F unit 1005, a communication I / F unit 1006, and a display unit 1007.

[0089] The processor 1001 is a control unit that controls various processes in the computer 1000 by executing programs stored in the memory 1002 .

[0090] The memory 1002 is a storage medium such as a RAM (Random Access Memory), etc. The memory 1002 temporarily stores the program code of the program executed by the processor 1001 and data required when the program is executed.

[0091] The storage device 1003 is a non-volatile storage medium such as a hard disk drive (HDD), a flash memory, etc. The storage device 1003 stores an operating system and various programs for implementing the above-mentioned components.

[0092] The input I / F unit 1004 is a device for receiving input from a user. Specific examples of the input I / F unit 1004 include a keyboard, a mouse, a touch panel, various sensors, a wearable device, etc. The input I / F unit 1004 may be connected to the computer 1000 via an interface such as a USB (Universal Serial Bus).

[0093] The data I / F unit 1005 is a device for inputting data from outside the computer 1000. A specific example of the data I / F unit 1005 is a drive device for reading data stored in various storage media. The data I / F unit 1005 may be provided outside the computer 1000. In that case, the data I / F unit 1005 is connected to the computer 1000 via an interface such as a USB.

[0094] The communication I / F unit 1006 is a device for performing data communication via the Internet N, either wired or wirelessly, with an external device of the computer 1000. The communication I / F unit 1006 may be provided outside the computer 1000. In that case, the communication I / F unit 1006 is connected to the computer 1000 via an interface such as a USB.

[0095] The display unit 1007 is a device for displaying various types of information. Specific examples of the display unit 1007 include a liquid crystal display, an organic EL (Electro-Luminescence) display, a display of a wearable device, and the like. The display unit 1007 may be provided outside the computer 1000. In that case, the display unit 1007 is connected to the computer 1000 via, for example, a display cable. In addition, when a touch panel is adopted as the input I / F unit 1004, the display unit 1007 can be configured integrally with the input I / F unit 1004.

[0096] ===Summary=== <1> The extraction system 10 in this embodiment includes a text information acquisition unit 102 that acquires text information including characters, an item acquisition unit that acquires extraction items related to the extraction information to be extracted from the text information based on a user's operation input, a query information identification unit 106 that identifies extraction query information associated with a setting candidate item corresponding to the extraction item based on a query database D101a that associates and stores setting candidate items that can be set as the extraction target with query information to be input to a generation AI device that has been trained to extract extraction information from text information, a transmission unit 107 that transmits the extraction query information and the text information to the generation AI device, and an extraction information acquisition unit 108 that acquires the extraction information included in the text information from the generation AI device. This allows the extraction system 10 to appropriately extract information desired by the user from the target text information from the generation AI device.

[0097] <2> The extraction system 10 in this embodiment further includes a block text information identification unit 105 that identifies extracted block text information associated with an extraction label corresponding to an extraction item, the block text information being a predetermined group of characters included in the text information, and the transmission unit 107 transmits the extraction query information and the extracted block text information to the generation AI device, and the extraction information acquisition unit 108 acquires the extraction information included in the extracted block text information from the generation AI device. This allows the extraction system 10 to appropriately extract information desired by the user from the target text information from the generation AI device with less processing load.

[0098] <3> The extraction system 10 in this embodiment further includes a block text information identification unit 105 that identifies extracted block text information associated with an extraction label corresponding to an extraction item based on a text database D101b that associates and stores block text information, which is a predetermined group of characters included in text information, and an extraction label, which is a classification of the predetermined group of characters, and the transmission unit 107 transmits the extraction query information and the extracted block text information to the generation AI device, and the extraction information acquisition unit 108 acquires the extraction information included in the extracted block text information from the generation AI device. This allows the extraction system 10 to appropriately extract information desired by the user from the target text information from the generation AI device with less processing.

[0099] <4> In addition, the extraction system 10 in this embodiment further includes a label setting determination unit 103 (label setting unit) that associates the extraction label with the block text information based on the correspondence between the extraction label and the text features of the text included in the text information (for example, the estimation result by the label database D101c and the trained model, etc.). This allows the extraction system 10 to appropriately extract information desired by the user from the target text information from the generation AI device with a smaller amount of processing.

[0100] <5> In addition, the extraction system 10 in this embodiment further includes a label setting determination unit 103 (determination unit) that determines whether or not there is an item corresponding to the extraction item in the tabular data when the text information includes tabular data, and the extraction information acquisition unit 108 acquires information related to the item corresponding to the extraction item in the tabular data as extraction information when the label setting determination unit 103 (determination unit) determines that there is an item corresponding to the extraction item. This allows the extraction system 10 to appropriately extract information desired by the user from the target text information from the generation AI device with less processing load.

[0101] <6> In addition, when the label setting determination unit 103 (determination unit) determines that there is no item corresponding to the extraction item, the transmission unit 107 in the extraction system 10 in this embodiment transmits the extraction query information and the extraction block text information to the generation AI device. This makes it possible for the extraction system 10 to reduce the frequency of using the generation AI device and to improve the accuracy of acquiring appropriate extraction information. [Explanation of symbols]

[0102] 10...extraction system, 100...extraction device, 101...memory unit, 102...text information acquisition unit, 103...label setting determination unit, 104...item acquisition unit, 105...block text information identification unit, 106...query information identification unit, 107...transmission unit, 108...extraction information acquisition unit, 109...display processing unit, 200...optical character recognition device, 300...user terminal.

Claims

1. a text information acquisition unit for acquiring text information including characters; an item acquisition unit that acquires extraction items related to extraction information to be extracted from the text information based on an operation input by a user; A query information identification unit that identifies extraction query information associated with the candidate setting item corresponding to the extraction item based on a query database that associates and stores the candidate setting item that can be set as a target for extraction and query information to be input to a generating AI device that has been trained to extract the extraction information from the text information; and a block text information identification unit that identifies extracted block text information associated with the extraction label corresponding to the extraction item based on a text database that associates and stores block text information, which is a predetermined group of characters included in the text information, and extraction labels, which are classifications of the predetermined group of characters; A transmission unit that transmits the extracted query information and the extracted block text information to the generating AI device; An extraction information acquisition unit that acquires the extraction information included in the extraction block text information from the generation AI device; An information processing system comprising:

2. a label setting unit that associates the extraction label with the block text information based on a correspondence relationship between the extraction label and a text feature that is a feature of text included in the text information; The information processing system according to claim 1 .

3. a determination unit that, when the text information includes table-format data, determines whether or not an item corresponding to the extracted item exists in the table-format data; when the determination unit determines that there is an item corresponding to the extraction item, the extraction information acquisition unit acquires, as the extraction information, information related to the item corresponding to the extraction item in the tabular data.

3. The information processing system according to claim 1 or 2.

4. The transmission unit transmits the extracted query information and the extracted block text information to the generation AI device when the determination unit determines that there is no item corresponding to the extracted item. The information processing system according to claim 3 .

5. The computer a text information acquisition unit for acquiring text information including characters; acquiring an extraction item related to extraction information to be extracted from the text information based on an operation input by a user; Identifying extraction query information associated with the candidate setting item corresponding to the extraction item based on a query database that associates and stores a candidate setting item that can be set as a target for extraction and query information to be input to a generating AI device that has been trained to extract the extraction information from the text information; Identifying extracted block text information associated with the extraction label corresponding to the extraction item based on a text database that associates and stores block text information, which is a predetermined group of characters included in the text information, and extraction labels, which are classifications of the predetermined group of characters; Sending the extracted query information and the extracted block text information to the generating AI device; Obtaining the extracted information included in the extracted block text information from the generating AI device; An information processing method for performing the above.

6. On the computer, a text information acquisition unit for acquiring text information including characters; acquiring an extraction item related to extraction information to be extracted from the text information based on an operation input by a user; Identifying extraction query information associated with the candidate setting item corresponding to the extraction item based on a query database that associates and stores a candidate setting item that can be set as a target for extraction and query information to be input to a generating AI device that has been trained to extract the extraction information from the text information; Identifying extracted block text information associated with the extraction label corresponding to the extraction item based on a text database that associates and stores block text information, which is a predetermined group of characters included in the text information, and extraction labels, which are classifications of the predetermined group of characters; Sending the extracted query information and the extracted block text information to the generating AI device; Obtaining the extracted information included in the extracted block text information from the generating AI device; A program that executes the following.

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