Information processing device, information processing method, and program

Abstract

To provide an information processing device capable of easily associating related documents in a transaction with each other so that the time and effort required to verify transaction documents can be reduced.SOLUTION: In an information processing system comprising an information processing device 1 and an information terminal, the information processing device comprises: an acquisition unit 131 which acquires document data, which is electronic data of documents related to transactions; a summary generation unit 132 which generates a plurality of pieces of summary data indicating a summary created by extracting information for associating each document from among a plurality of pieces of document data including documents of different types; and a cluster generation unit 133 which generates a cluster in which the plurality of pieces of summary data have been divided for each related case. The summary generation unit 132 generates summary data by extracting information indicating transaction contents included in the document data and for associating the transaction with the transaction contents in other document data indicating the same transaction.SELECTED DRAWING: Figure 2

Description

Technical Field

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

Background Art

[0002] There is known an accounting processing apparatus that performs journal entry using an AI (Artificial Intelligence) that has learned to perform machine learning based on teacher data in advance and select a combination of ledger items corresponding to transaction detail information (for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the accounting processing targeted by the prior art, as preprocessing for journal entry, the person in charge of accounting refers to relevant documents to confirm the fact of delivery, and also confirms that the contract or order details, delivery details, and billing details match, and checks whether there is any improper accounting processing. It has been time-consuming to check the documents scattered within the company.

[0005] Therefore, the present invention has been made in view of these points, and an object thereof is to be able to easily link related documents in a transaction so as to reduce the labor of checking transaction documents.

Means for Solving the Problems

[0006] In the information processing apparatus according to the first aspect of the present invention, an acquisition unit that acquires document data, which is electronic data of a document related to a transaction, a summary generation unit that generates a plurality of summary data indicating summaries obtained by extracting information for associating each document from each of the plurality of document data including document data of different types of documents, and a cluster generation unit that generates clusters obtained by dividing the plurality of summary data for each related case.

[0007] The summary generation unit may generate summary data obtained by extracting information indicating the transaction content included in the document data and associating it with the transaction content in other document data indicating the same transaction.

[0008] The summary generation unit generates the summary data including different types of information for each type of the document, and the information processing apparatus further has a storage unit that stores a machine learning model for generating the cluster, and when the type of the document indicated by the document data and the summary data extracted from the document data are input, the cluster generation unit may input the plurality of summary data into the machine learning model and generate the cluster.

[0009] The acquisition unit further acquires target document data, which is document data to be determined for related cases, the summary generation unit generates target summary data, which is the summary data corresponding to the target document data, and the information processing apparatus further has a specifying unit that specifies a case related to the document corresponding to the target summary data based on the cluster generated by the cluster generation unit, and an output unit that outputs document data related to the case specified by the specifying unit.

[0010] The information processing apparatus further has a reception unit that receives a selection of document data to be displayed, and a specifying unit that specifies, as candidate document data, which is a candidate related to the document data, document data having a predetermined relationship with the cluster to which the document data received by the reception unit belongs, and an output unit that outputs the document data to be displayed and the candidate document data in association with each other.

[0011] A reception unit that receives selection of document data to be translated, and a translation data generation unit that generates translation data obtained by translating a transaction indicated by the document data based on information included in the document data to be translated and other document data corresponding to a cluster to which the document data belongs may be further provided.

[0012] The translation data generation unit may generate translation data obtained by translating a transaction indicated by the document data based on an item included in the document data to be translated and a predetermined item included in other document data corresponding to the cluster to which the document data belongs, the predetermined item corresponding to the type of the document of the document data.

[0013] A storage unit that stores in association document data and translation data indicating a translation of a transaction indicated by the document data, a reception unit that receives selection of document data to be translated, and a translation data generation unit that generates the translation data for the document data to be translated based on the document data to be translated, the cluster to which the document data belongs, and the translation data associated with document data belonging to a cluster having a predetermined relationship with the cluster may be further provided.

[0014] In the information processing method according to the second aspect of the present invention, the computer executes steps of: acquiring document data which is electronic data of a document related to a transaction; generating a plurality of summary data indicating summaries obtained by extracting information for associating each document from each of the plurality of document data including document data of a plurality of different types of documents; and generating clusters obtained by dividing the plurality of summary data for each related case.

[0015] In the program according to the third aspect of the present invention, the computer is made to execute steps of: acquiring document data which is electronic data of a document related to a transaction; generating a plurality of summary data indicating summaries obtained by extracting information for associating each document from each of the plurality of document data including document data of a plurality of different types of documents; and generating clusters obtained by dividing the plurality of summary data for each related case.

Advantages of the Invention

[0016] According to the present invention, related documents in a transaction can be easily associated.

Brief Description of the Drawings

[0017]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Embodiments for Carrying Out the Invention

[0018] [Outline of Information Processing System S] FIG. 1 is a diagram for explaining the outline of the information processing system S. The configuration of the information processing system S will be described with reference to FIG. 1(a). The information processing system S is a system for exchanging transaction documents and processing the exchanged transaction documents. The information processing system S includes an information processing apparatus 1 and an information terminal 2.

[0019] The information processing apparatus 1 is a device for electronically exchanging transaction documents and processing the transaction documents. The information processing apparatus 1 is, for example, a server. The information processing apparatus 1 accumulates transaction documents and clusters the accumulated transaction documents to associate documents related to the same transaction. The information processing apparatus 1 may journalize a received invoice or the like based on information of other transaction documents belonging to the cluster.

[0020] The information terminal 2 is a terminal used by the person in charge of accounting processing. The information terminal 2 is, for example, a smartphone, a tablet, or a personal computer. The information terminal 2 transmits, as an example, documents to be processed to the information processing apparatus 1. As an example, the information terminal 2 displays a screen based on the control from the information processing apparatus 1 and transmits the operation content of the user to the information processing apparatus 1.

[0021] Referring to FIG. 1(b), the processing in the information processing system S will be described. The information processing apparatus 1 acquires document data. The document data is electronic data obtained by digitizing documents related to transactions. The document data is, for example, data obtained by digitizing estimates, requests for approval, contracts, purchase orders, delivery notes, invoices, and the like.

[0022] The document data contains information that is not necessary for associating transaction documents. For example, in an estimate, information such as the information of the person in charge of the estimate, the estimate number, and the validity period of the estimate is included, but these are not necessarily necessary information for linking transaction documents. Also, in a contract, various terms that do not affect the linking of transaction documents (for example, terms such as damages, damage delay payments, risk bearing, and cancellation) may be included. Therefore, by removing such information and determining the relevance between documents, the accuracy of associating documents can be improved.

[0023] The information processing apparatus 1 generates summary data D2 based on the acquired document data. The summary data D2 is information that extracts, from the information contained in the document data, the content of the transaction and the information for associating with other document data. The summary data D2 is data generated by extracting information for associating different types of document data issued for the same transaction. That is, the summary data D2 includes information for associating with the transaction content in other document data issued for the same transaction. The summary data D2 includes information for associating each document from each of a plurality of document data including document data of a plurality of different types. The summary data D2 includes, for example, information such as the counterparty of the transaction, the transaction date, the document issue date, the transaction object, and the quantity.

[0024] The information processing apparatus 1 generates clusters (C1, C2, C3, C4) divided for each related case based on the generated summary data. The information processing apparatus 1 generates clusters based on the similarity between the summary data generated from the transaction documents. As a result, since documents with the same transaction parties or objects and close dates are presumed to be related transaction documents, the information processing apparatus 1 can generate clusters by dividing the transaction documents (summary data) for each series of transactions (also referred to as cases).

[0025] Based on the selection of the document data received from the information terminal 2, the information processing apparatus 1 may associate the document data belonging to the same cluster as the selected document data with the selected document data and display it on the information terminal 2.

[0026] With the information processing system S configured in this way, related documents in a transaction can be easily linked, which has the effect of achieving this.

[0027] [Configuration of Information Processing Apparatus 1] FIG. 2 is a block diagram showing the configuration of the information processing apparatus 1. The information processing apparatus 1 includes a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 includes an acquisition unit 131, a summary generation unit 132, a cluster generation unit 133, a specification unit 134, an output unit 135, a reception unit 136, and a journal data generation unit 137.

[0028] The communication unit 11 is a communication interface for transmitting and receiving data to and from other devices via a network. The storage unit 12 is a storage medium including a ROM (Read Only Memory), a RAM (Random Access Memory), an SSD (Solid State Drive), a hard disk drive, and the like. The storage unit 12 stores in advance a program executed by the control unit 13. When the storage unit 12 inputs summary data extracted from each of a plurality of document data, it stores a machine learning model that generates clusters to which each of the plurality of summary data belongs. The machine learning model may receive, as an additional input, the type of document indicated by the document data.

[0029] The control unit 13 is a processor such as a CPU (Central Processing Unit). By executing the program stored in the storage unit 12, the control unit 13 functions as an acquisition unit 131, a summary generation unit 132, a cluster generation unit 133, a specification unit 134, an output unit 135, a reception unit 136, and a journal data generation unit 137.

[0030] The acquisition unit 131 acquires document data which is electronic data of documents related to transactions. As an example, the acquisition unit 131 may acquire document data from an external device (not shown), or may acquire document data stored in the storage unit 12. The acquisition unit 131 may acquire document data when the information terminal 2 uploads the document data. The acquisition unit 131 may acquire document data in association with the type of document, or may specify the type of document acquired based on the items included in the document data.

[0031] The summary generation unit 132 generates a plurality of summary data indicating summaries obtained by extracting information for associating each document from each of a plurality of document data including document data of different types of documents. FIG. 3 is a diagram showing an example of the summary data. As an example, the summary data includes information indicating 5W2H such as the purpose (Why) of the transaction, document number, scheduled delivery date (When), counterparty of the transaction (Who), object of the transaction (What), quantity of the transaction (How many), and transaction amount (How much). The summary data may include different items depending on the type of received document. The summary data may include information for associating documents related to the transaction such as a quotation number, a contract number, a proposal number, a settlement number, a delivery number, and an invoice number.

[0032] As an example, information to be extracted as summary data may be predetermined for each type of document. In this case, the storage unit 12 stores item information associating the type of document with the items to be extracted in the summary data for each type of document. The summary generation unit 132 refers to the item information, extracts the items corresponding to the document data acquired by the acquisition unit 131, and generates summary data.

[0033] Further, the storage unit 12 may store a learned model that has learned document data including document types and items to be extracted for each document type as teacher data, and that generates summary data when the document data is input. In this case, the summary generation unit 132 generates summary data by inputting the document data to the learned model.

[0034] The cluster generation unit 133 generates clusters obtained by dividing a plurality of pieces of summary data for each related case. As an example, the cluster generation unit 133 extracts feature amounts from respective pieces of summary data, performs clustering based on the similarity between the extracted feature amounts, and generates clusters divided for each case. As an example, the cluster generation unit 133 generates clusters based on known cosine similarity. The feature amount is vector data indicating the features of each piece of summary data. As an example, the cluster generation unit 133 divides clusters so that the distance between the feature amount of the document and the center of the cluster in the feature amount space is within a predetermined threshold value. As an example, when inputting a plurality of pieces of summary data, the cluster generation unit 133 performs clustering using a machine learning model that divides each piece of summary data into clusters. The cluster generation unit 133 may output by associating document data with the cluster to which the document data belongs.

[0035] The cluster generation unit 133 may store in the storage unit 12 cluster information associating document data with the cluster to which the document data belongs. FIG. 4 is a diagram showing an example of the data structure of the cluster information. The cluster information includes a "document data ID", a "cluster ID", and a "document type". The cluster information may include information included in the summary data of each document. The "document data ID" is an ID (Identification) for identifying document data. The "cluster ID" is an ID for identifying the cluster to which the document data belongs. The "document type" is the type of the document data.

[0036] With the information processing apparatus 1 configured in this way, there is an effect that related documents in a transaction can be easily associated with each other.

[0037] When there is information lacking for generating summary data in the input document data, the summary generation unit 132 may generate data for the lacking items based on other items. In this case, in the item information stored in the storage unit 12, the definition of the relationship between the items included in the summary data (for example, a calculation formula) is stored. In the definition of the relationship between items, for example, it is defined that the consumption tax amount is the product of the subtotal amount and the consumption tax rate. And when there are lacking items in the acquired document data, the summary generation unit 132 generates data for supplementing the lacking items based on the definition of the relationship between the items included in the item information. For example, when the item of the consumption tax amount is lacking in the document data, the consumption tax amount is calculated by multiplying the subtotal amount by the consumption tax rate, and summary data with the calculated item of the consumption tax amount added is generated.

[0038] The cluster generation unit 133 may filter the summary data based on conditions such as the counterparty of the transaction, the transaction amount, and the transaction time. As an example, the cluster generation unit 133 extracts summary data corresponding to the same counterparty of the transaction from the target summary data, and performs clustering on the extracted summary data. With the cluster generation unit 133 configured in this way, the accuracy of associating related cases is improved.

[0039] Since different information is included for each type of document, the summary data may include different information for each type of document. The summary generation unit 132 generates summary data including different types of information for each type of document. For example, the summary data extracted from the "contract" includes the counterparty of the transaction, the object of the transaction, the contract unit price, the contract period, etc. The summary data extracted from the "purchase order" includes the purchase order date, the counterparty of the transaction, the object of the transaction, the purchase order quantity. The summary data extracted from the "invoice" includes the purchase order date, the delivery date, the counterparty of the transaction, the object of the transaction, the transaction quantity, the unit price, the invoice amount, etc. The cluster generation unit 133 inputs the summary data including the document type into the machine learning model to generate clusters.

[0040] The information processing apparatus 1 may be configured to output document data related to the document data obtained after the generation of the cluster.

[0041] The acquisition unit 131 acquires target document data which is the document data to be determined for related cases. The summary generation unit 132 generates target summary data which is the summary data corresponding to the target document data as described above.

[0042] The specifying unit 134 specifies the cases related to the documents corresponding to the target summary data based on the clusters generated by the cluster generation unit 133. As an example, the specifying unit 134 inputs the target summary data to the machine learning model that generated the cluster, outputs the cluster to which the summary data belongs, and specifies the cluster to which the target summary data belongs.

[0043] The output unit 135 outputs the document data related to the cases specified by the specifying unit 134. As an example, the output unit 135 causes the information terminal 2 to display a screen for displaying other document data belonging to the cluster specified by the specifying unit 134 as the cluster to which the target summary data belongs.

[0044] The information processing apparatus 1 may be configured to allow the user to view document data belonging to similar clusters.

[0045] The reception unit 136 receives the selection of the document data to be displayed. The reception unit 136 causes the information terminal 2 to display a screen for receiving the selection of the document data to be displayed. The user operates the information terminal 2 to perform an operation for selecting the document data to be displayed. The reception unit 136 acquires information indicating the document data selected by the user from the information terminal 2.

[0046] The specific unit 134 identifies document data that has a predetermined relationship with the cluster to which the document data received by the reception unit 136 belongs, as candidate document data that is a candidate related to the said document data. Specifically, the specific unit 134 identifies, as candidate document data, document data belonging to a cluster within a predetermined distance from the selected document to be displayed.

[0047] The output unit 135 outputs, in association with each other, the document data to be displayed and the candidate document data. The output unit 135 causes the information terminal 2 to display a screen for displaying the document data to be displayed and the candidate document data identified by the specific unit 134.

[0048] The reception unit 136 receives the selection of the document data to be translated. The reception unit 136 causes the information terminal 2 to display a screen for receiving the selection of the document to be translated, and receives the user's selection operation. The translation data generation unit 137 generates translation data obtained by translating the transaction indicated by the document data based on the information included in the document data to be translated and other document data corresponding to the cluster to which the said document data belongs. Specifically, the translation data generation unit 137 generates translation data obtained by translating the transaction indicated by the document data based on the items included in the document data to be translated and predetermined items included in other document data corresponding to the cluster to which the said document data belongs, where the predetermined items correspond to the type of the document of the said document data. As an example, when creating translation data, the storage unit 12 stores the items to be extracted from other documents. As an example, the storage unit 12 stores acquiring the accounting code and the purpose of the contract included in the request for approval. The translation data generation unit 133 refers to the storage unit 12 to identify the items to be extracted from other documents, refers to the document data belonging to the same cluster as the document data to be translated, and acquires the said items. Then, the translation data generation unit 137 creates translation data based on items such as the amount included in the document data to be translated and items such as the accounting code acquired from other documents. The output unit 135 may cause the created translation data to be displayed on the information terminal 2.

[0049] As an example, the storage unit 12 may store a learned model that has learned a plurality of different types of document data and correct translation data as teacher data, and has been learned to output translation data with the plurality of different types of document data as input. The translation data generation unit 137 may input the document data to be translated and the document data belonging to the same cluster as the said document data into the learned model, and cause the learned model to output translation data.

[0050] In the case of consecutive transactions, invoices that have already been translated may belong to the same cluster. Therefore, the information processing apparatus 1 may be configured to perform translation processing of the document data based on other document data belonging to the same cluster.

[0051] In this case, the storage unit 12 stores the document data and the translation data indicating the translation of the transaction indicated by the document data in association with each other. As an example, the storage unit 12 stores the document data ID and the translation data created for the document data indicated by the document data ID in association with each other. In the translation data, as an example, it includes an abstract, an account item, various accounting codes, and the like.

[0052] The translation data generation unit 137 generates translation data for the document data to be translated based on the document data to be translated, the cluster to which the document data belongs, and the translation data associated with the document data belonging to a cluster having a predetermined relationship with the said cluster. The translation data generation unit 137 refers to the storage unit 12 to determine whether there is any created translation data for the document data belonging to the same cluster as the selected document data. If there is translation data created for the document data belonging to the same cluster, the translation data creation unit 137 creates translation data for the transaction indicated by the selected document data to be translated based on the created translation data for the document data belonging to the same cluster stored in the storage unit 12. In addition to the document data belonging to the same cluster, the translation data generation unit 137 may create translation data for the transaction indicated by the document data to be translated based on the translation data created for the document data belonging to clusters whose distance between clusters is within a predetermined threshold value.

[0053] With the information processing apparatus 1 configured in this way, the labor for performing the translation work can be reduced.

[0054] [Flow of processing in the information processing apparatus 1] FIG. 5 is a flowchart showing the flow of processing in the information processing apparatus 1. The flowchart shown in FIG. 5 starts from the point in time when an instruction to perform clustering is received.

[0055] The acquisition unit 131 acquires a plurality of document data (S01). The summary generation unit 132 generates summary data for each of the acquired plurality of document data (S02). The cluster generation unit 133 generates clusters obtained by dividing the summary data generated by the summary generation unit 132 for each related case (S03).

[0056] The reception unit 136 receives the selection of the document data to be displayed (S04). The output unit 135 associates the document data belonging to the same cluster as the selected document data and displays it on the information terminal 2 (S05). Then the information processing apparatus 1 ends the processing.

[0057] [Effects of the Present Embodiment] As described above, the information processing apparatus 1 has an effect that related documents in a transaction can be easily associated with each other.

[0058] Although the present invention has been described using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist. For example, all or part of the device can be configured by functionally or physically dispersing and integrating it in any unit. Also, new embodiments resulting from an arbitrary combination of a plurality of embodiments are included in the embodiments of the present invention. The effects of the new embodiments resulting from the combination have the effects of the original embodiments combined.

Explanation of Reference Numerals

[0059] 1 Information processing apparatus 2 Information terminal 11 Communication unit 12 Storage unit 13 Control unit 131 Acquisition unit 132 Summary generation unit 133 Cluster generation unit 134 Identification unit 135 Output unit 136 Reception unit 137 Journal data generation unit

Claims

1. An acquisition unit that acquires document data, which is electronic data of documents related to transactions; An abstract generation unit that generates a plurality of abstract data indicating an abstract obtained by extracting information for associating each document from each of the plurality of document data including document data of a plurality of different types of documents; A cluster generation unit that generates clusters obtained by dividing the plurality of abstract data for each related case; An information processing apparatus having the above.

2. The abstract generation unit generates abstract data that is information indicating the transaction content included in the document data and that is obtained by extracting information for associating with the transaction content in other document data indicating the same transaction. The information processing apparatus according to claim 1.

3. The abstract generation unit generates the abstract data including different types of information for each type of the documents. The information processing apparatus further includes a storage unit that stores a machine learning model for generating the cluster, and when the type of the document indicated by the document data and the abstract data extracted from the document data are input, The cluster generation unit inputs the plurality of abstract data into the machine learning model and generates the cluster. The information processing apparatus according to claim 1.

4. The acquisition unit further acquires target document data, which is document data to be determined for related cases. The abstract generation unit generates target abstract data, which is the abstract data corresponding to the target document data. The information processing apparatus A specifying unit that specifies a case related to the document corresponding to the target abstract data based on the cluster generated by the cluster generation unit; An output unit that outputs document data related to the case specified by the specifying unit. The information processing apparatus according to any one of claims 1 to 3, further having the above.

5. The information processing apparatus further includes a reception unit that receives a selection of document data to be displayed, The information processing apparatus includes a specifying unit that specifies, as candidate document data that is a candidate related to the document data, document data that has a predetermined relationship with the cluster to which the document data received by the reception unit belongs; An output unit that outputs the document data to be displayed and the candidate document data in association with each other. The information processing apparatus according to any one of claims 1 to 3, further having the above.

6. A reception unit that receives a selection of document data to be journalized; A journal data generation unit that generates journal data by journalizing the transaction indicated by the document data based on the information included in the document data to be journalized and other document data corresponding to the cluster to which the document data belongs. The information processing apparatus according to any one of claims 1 to 3, further comprising the above.

7. The journal data generation unit generates journal data by journalizing the transaction indicated by the document data based on an item included in the document data to be journalized and a predetermined item included in other document data corresponding to the cluster to which the document data belongs, the predetermined item corresponding to the type of the document of the document data. The information processing apparatus according to claim 6.

8. A storage unit that stores in association document data and journal data indicating the journalization of the transaction indicated by the document data. A reception unit that receives selection of document data to be journalized. A journal data generation unit that generates the journal data for the document data to be journalized based on the document data to be journalized, the cluster to which the document data belongs, and the journal data associated with the document data belonging to a cluster having a predetermined relationship with the cluster. The information processing apparatus according to any one of claims 1 to 3, further comprising the above.

9. An information processing method executed by a computer, comprising: acquiring document data that is electronic data of a document related to a transaction; generating a plurality of summary data indicating summaries obtained by extracting information for associating each document from each of the plurality of document data including document data of a plurality of different types of documents; generating clusters obtained by dividing the plurality of summary data for each related case. An information processing method having the above steps.

10. A program for causing a computer to: acquire document data that is electronic data of a document related to a transaction; generate a plurality of summary data indicating summaries obtained by extracting information for associating each document from each of the plurality of document data including document data of a plurality of different types of documents; generate clusters obtained by dividing the plurality of summary data for each related case. A program for causing a computer to execute the above steps.

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