Information processing device, information processing method, and program

Abstract

The information processing device 1 has an acquisition unit 131 that acquires document data, which is electronic data of documents related to a transaction, a summary generation unit 132 that 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 a plurality of different types of documents, and a cluster generation unit 133 that generates clusters by dividing the plurality of summary data into related cases. The summary generation unit 132 may generate summary data by extracting information indicating transaction details contained in the document data and for associating the transaction details with transaction details in other document data indicating the same transaction.

Description

[Technical field]

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

[0002] There is a known accounting processing device that performs accounting entries using AI (Artificial Intelligence) that has previously performed machine learning based on training data and has learned to select combinations of account items that correspond to transaction detail information (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-165967 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the accounting procedures covered by the prior art, accounting staff had to refer to related documents to confirm the fact of delivery as a preparatory step for journal entry, as well as confirm that the contract or order details, delivery details, and invoice details match, and check for any improper accounting procedures. This required a lot of time and effort to check documents scattered throughout the company.

[0005] The present invention has been made in consideration of these points, and aims to make it possible to easily link related documents in a transaction so as to reduce the effort required for checking transaction documents. [Means for solving the problem]

[0006] The information processing device of the first aspect of the present invention has an acquisition unit that acquires document data, which is electronic data of documents related to a transaction, a summary generation unit that generates a plurality of summary data indicating summaries extracted from each of a plurality of document data including document data of a plurality of different types of documents, and a cluster generation unit that generates clusters by dividing the plurality of summary data into related cases.

[0007] The summary generation unit may generate summary data by extracting information indicating transaction details contained in the document data, the information being used to associate the transaction details with transaction details 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 document, and the information processing device may further have a memory unit that stores a machine learning model that generates the cluster when the type of document indicated by the document data and the summary data extracted from the document data are input, and the cluster generation unit may input the multiple summary data into the machine learning model to generate the cluster.

[0009] The acquisition unit may further acquire target document data, which is document data for determining related cases, and the summary generation unit may generate target summary data, which is the summary data corresponding to the target document data, and the information processing device may further have an identification unit that identifies cases to which the document corresponding to the target summary data is related based on the clusters generated by the cluster generation unit, and an output unit that outputs document data related to the cases identified by the identification unit.

[0010] The information processing device may further have a reception unit that receives a selection of document data to be displayed, and may further have an identification unit that identifies document data that has a predetermined relationship with the cluster to which the document data accepted by the reception unit belongs as candidate document data that is a candidate related to the document data, and an output unit that associates the document data to be displayed with the candidate document data and outputs them.

[0011] The system may further include a reception unit that receives a selection of document data to be journalized, and a journal data generation unit that generates journal data that journalizes transactions indicated by the document data based on information contained in the document data to be journalized and other document data corresponding to the cluster to which the document data belongs.

[0012] The journal data generation unit may generate journal data that journalizes transactions indicated by the document data based on items contained in the document data to be journalized and specified items contained in other document data corresponding to the cluster to which the document data belongs, the specified items corresponding to the document type of the document data.

[0013] The system may further include a memory unit that associates and stores document data with journal entry data indicating the journal entry for the transaction represented by the document data, a reception unit that accepts a selection of document data to be journalized, and a journal entry data generation unit that generates the journal entry data for the document data to be journalized based on the document data to be journalized and the journal entry data associated with document data that belongs to a cluster that has a predetermined relationship with the cluster to which the document data belongs.

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

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

[0016] According to the present invention, documents related to a transaction can be easily linked. [Brief description of the drawings]

[0017] [Figure 1] 1 is a diagram for explaining an overview of an information processing system S. [Diagram 2] 1 is a block diagram showing a configuration of an information processing device 1. FIG. [Diagram 3] FIG. 11 is a diagram illustrating an example of summary data. [Figure 4] FIG. 4 is a diagram illustrating an example of a data structure of cluster information. [Diagram 5] 3 is a flowchart showing a process flow in the information processing device 1. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] [Outline of Information Processing System S] FIG. 1 is a diagram for explaining an overview of an information processing system S. The configuration of the information processing system S will be explained with reference to FIG. 1(a). The information processing system S is a system for receiving and sending transaction documents and processing the received transaction documents. The information processing system S has an information processing device 1 and an information terminal 2.

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

[0020] The information terminal 2 is a terminal used by an accountant. As an example, the information terminal 2 is a smartphone, a tablet, or a personal computer. As an example, the information terminal 2 transmits a document to be processed to the information processing device 1. As an example, the information terminal 2 displays a screen based on control from the information processing device 1, and transmits the contents of a user's operation to the information processing device 1.

[0021] The processing in the information processing system S will be described with reference to Fig. 1(b). The information processing device 1 acquires document data. The document data is electronic data obtained by digitizing documents related to transactions. Examples of the document data include electronic data of estimates, approval documents, contracts, purchase orders, delivery notes, invoices, etc.

[0022] Document data includes information that is not necessary for linking transaction documents. As an example, a quotation includes information such as information about the person making the quotation, the quotation number, and the validity period of the quotation, but this information is not necessarily necessary for linking transaction documents. In addition, a contract may include various clauses (e.g., clauses regarding compensation for damages, late payment penalties, risk of loss, termination, etc.) that do not affect the linking of transaction documents. Therefore, by removing such information and determining the relationship between documents, the accuracy of linking documents can be improved.

[0023] The information processing device 1 generates summary data D2 based on the acquired document data. Summary data D2 is information obtained by extracting information included in the document data, which is the content of the transaction and is to be associated with other document data. Summary data D2 is data generated by extracting information for associating different types of document data issued for the same transaction. That is, summary data D2 includes information for associating with transaction content in other document data issued for the same transaction. Summary data D2 includes information for associating each document from a plurality of document data including document data of a plurality of different types of documents. 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 device 1 generates clusters (C1, C2, C3, C4) divided for each related case based on the generated summary data. The information processing device 1 generates clusters based on the similarity between the summary data generated from the transaction documents. As a result, documents with the same parties and objects in the transaction and similar dates are presumed to be related transaction documents, so the information processing device 1 can generate clusters divided from the transaction documents (summary data) for each series of transactions (also called cases).

[0025] Based on the selection of document data accepted from information terminal 2, information processing device 1 may display on information terminal 2 document data belonging to the same cluster as the selected document data in association with the selected document data.

[0026] By configuring the information processing system S in this way, it is possible to easily link documents related to a transaction.

[0027] [Configuration of information processing device 1] 2 is a block diagram showing the configuration of the information processing device 1. The information processing device 1 has a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 has an acquisition unit 131, a summary generation unit 132, a cluster generation unit 133, an identification 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 read only memory (ROM), a random access memory (RAM), a solid state drive (SSD), a hard disk drive, etc. The storage unit 12 stores in advance a program to be executed by the control unit 13. The storage unit 12 stores a machine learning model that, when summary data extracted from each of a plurality of document data is input, generates a cluster to which each of the plurality of summary data belongs. The machine learning model may further receive, as an input, a document type indicated by the document data.

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

[0030] Acquisition unit 131 acquires document data, which is electronic data of a document related to a transaction. Acquisition unit 131 may acquire document data from an external device (not shown), for example, or may acquire document data stored in storage unit 12. Acquisition unit 131 may acquire document data by information terminal 2 uploading the document data. Acquisition unit 131 may acquire document data in association with a document type, or may identify the acquired document type based on items included in the document data.

[0031] The summary generating 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 a plurality 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 the 5W2H such as the purpose of the transaction (Why), document number, scheduled delivery date (When), the other party of the transaction (Who), the object of the transaction (What), the transaction amount (How many), and the transaction amount (How much). The summary data may include different items depending on the type of document received. The summary data may include information for associating documents related to the transaction with each other such as an estimate 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 for each document type may be predefined. In this case, storage unit 12 stores item information that associates the document type with an item to be extracted in the summary data for each document type. Summary generation unit 132 refers to the item information, extracts an item corresponding to the document data acquired by acquisition unit 131, and generates summary data.

[0033] Storage unit 12 may also store a trained model that has been trained using document data including document types and items to be extracted for each document type as training data, and that generates summary data when document data is input. In this case, summary generation unit 132 generates summary data by inputting document data to the trained model.

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

[0035] Cluster generation unit 133 may store in storage unit 12 cluster information that associates document data with a cluster to which the document data belongs. FIG. 4 is a diagram showing an example of a data structure of 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 the 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] The information processing device 1 configured in this way has the effect of easily linking documents related to a transaction.

[0037] If the input document data lacks information for generating summary data, summary generation unit 132 may generate data for the lacking item based on other items. In this case, the item information stored in storage unit 12 stores a definition of the relationship between items included in the summary data (e.g., a calculation formula). For example, the definition of the relationship between items defines that the consumption tax amount is the subtotal amount multiplied by the consumption tax rate. If the acquired document data lacks an item, summary generation unit 132 generates data to supplement the lacking item based on the definition of the relationship between items included in the item information. For example, if the item for 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 is generated by adding the item for the calculated consumption tax amount.

[0038] The cluster generation unit 133 may filter the summary data based on conditions such as the counterparty of the transaction, the transaction amount, the time of the transaction, etc. 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. By configuring the cluster generation unit 133 in this way, the accuracy of linking 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, summary data extracted from a "contract" includes a trading partner, a transaction object, a contract unit price, a contract period, etc. Summary data extracted from a "purchase order" includes an order date, a trading partner, a transaction object, and an order quantity. Summary data extracted from an "invoice" includes an order date, a delivery date, a trading partner, a transaction object, a transaction quantity, a unit price, an invoice amount, etc. The cluster generation unit 133 inputs the summary data including the document type into a machine learning model to generate clusters.

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

[0041] The acquiring unit 131 acquires target document data, which is document data to be judged for related cases. The summary generating unit 132 generates target summary data, which is summary data corresponding to the target document data, as described above.

[0042] The identification unit 134 identifies cases to which the document corresponding to the target summary data is related, based on the clusters generated by the cluster generation unit 133. As an example, the identification unit 134 inputs the target summary data into the machine learning model that generated the clusters, thereby outputting the cluster to which the summary data belongs, and identifies the cluster to which the target summary data belongs.

[0043] The output unit 135 outputs document data related to the case identified by the identification 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 identified by the identification unit 134 as a cluster belonging to the target summary data.

[0044] The information processing device 1 may be configured to allow a user to view document data that belongs to a similar cluster.

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

[0046] Identification unit 134 identifies document data having a predetermined relationship with a cluster to which the document data accepted by acceptance unit 136 belongs as candidate document data that is a candidate related to the document data. Specifically, identification unit 134 identifies document data that belongs to a cluster within a predetermined distance from the selected document to be displayed as the candidate document data.

[0047] The output unit 135 outputs the document data to be displayed and the candidate document data in association with each other. 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 identification unit 134.

[0048] The reception unit 136 receives the selection of the document data to be journalized. The reception unit 136 displays a screen for receiving the selection of the document to be journalized on the information terminal 2 and receives the selection operation of the user. The journal data generation unit 137 generates journal data by journalizing a transaction indicated by the document data based on information contained in the document data to be journalized and other document data corresponding to the cluster to which the document data belongs. Specifically, the journal data generation unit 137 generates journal data by journalizing a transaction indicated by the document data based on an item contained in the document data to be journalized and a predetermined item contained in other document data corresponding to the cluster to which the document data belongs, the predetermined item corresponding to the document type of the document data. As an example, the storage unit 12 stores items to be extracted from other documents when creating the journal data. As an example, the storage unit 12 stores the acquisition of an accounting code and a purpose of a contract contained in a request form. The journal data generation unit 133 refers to the storage unit 12, identifies items to be extracted from other documents, and acquires the items by referring to document data belonging to the same cluster as the document data to be journalized. The journal data generation unit 137 then creates journal data based on items such as amounts included in the document data to be journalized and items such as accounting codes acquired from the other documents. The output unit 135 may display the created journal data on the information terminal 2.

[0049] As an example, the storage unit 12 may store a trained model that has been trained using multiple different types of document data and correct journalization data as teacher data, and that has been trained to output journalization data using multiple different types of document data as input. The journalization data generation unit 137 may input document data to be journalized and document data that belongs to the same cluster as the document data to the trained model, and output the journalization data.

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

[0051] In this case, the storage unit 12 stores the document data in association with the journal entry data indicating the journal entry for the transaction indicated by the document data. As an example, the storage unit 12 stores the document data ID in association with the journal entry data created for the document data indicated by the document data ID. As an example, the journal entry data includes a summary, an account title, various accounting codes, etc.

[0052] The journal data generation unit 137 generates journal data for the document data to be journalized based on the document data to be journalized and the journal data associated with the document data belonging to a cluster that has a predetermined relationship with the cluster to which the document data belongs. The journal data generation unit 137 refers to the storage unit 12 and determines whether or not there is journal data already created for the document data belonging to the same cluster as the selected document data. If there is journal data created for the document data belonging to the same cluster, the journal data creation unit 137 creates journal data for the transaction indicated by the selected document data to be journalized based on the journal data already created for the document data belonging to the same cluster stored in the storage unit 12. The journal data generation unit 137 may create journal data for the transaction indicated by the document data to be journalized based on the journal data created for the document data belonging to clusters whose clusters are within a predetermined threshold distance in addition to the document data belonging to the same cluster.

[0053] By configuring the information processing device 1 in this way, it is possible to reduce the effort required for performing accounting operations.

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

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

[0056] Accepting unit 136 accepts the selection of document data to be displayed (S04). Output unit 135 associates the selected document data with document data belonging to the same cluster and displays them on information terminal 2 (S05). Information processing device 1 then ends the process.

[0057] [Effects of this embodiment] As described above, the information processing device 1 provides an advantage in that related documents in a transaction can be easily linked.

[0058] Although the present invention has been described above 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 of the present invention. For example, all or part of the device can be configured by distributing or integrating functionally or physically in any unit. In addition, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effect of the new embodiment resulting from the combination combines the effect of the original embodiment. [Explanation of symbols]

[0059] 1. Information processing device 2. Information terminal 11 Communications Department 12 Storage section 13 Control section 131 Acquisition Department 132 Summary generator 133 Cluster Generation Unit 134 Specific part 135 Output section 136 Reception 137 Journal entry data generation unit

Claims

1. an acquisition unit that acquires document data, which is electronic data of documents related to a transaction; a summary generation unit that generates a plurality of summary data pieces representing summaries obtained by extracting information for associating each document from a plurality of document data pieces including document data pieces of a plurality of different types of documents, the summary data pieces including different types of information for each of the document types; a storage unit that stores a machine learning model that generates a cluster when a document type indicated by document data and the summary data extracted from the document data are input; a cluster generation unit that generates clusters by dividing the plurality of summary data into related cases by inputting the plurality of summary data into the machine learning model; An information processing device having the above.

2. an acquisition unit that acquires document data, which is electronic data of documents related to a transaction, and acquires target document data, which is document data that is a target for determining a related case; a summary generation unit that generates a plurality of summary data representing summaries by extracting information for associating each document from each of a plurality of document data including document data of a plurality of different types of documents, and generates target summary data, which is the summary data corresponding to the target document data; a cluster generation unit that generates clusters by dividing the plurality of pieces of summary data into related cases; an identification unit that identifies a case related to a document corresponding to the target summary data based on the clusters generated by the cluster generation unit; an output unit that outputs document data related to the case identified by the identification unit; An information processing device having the above.

3. an acquisition unit that acquires document data, which is electronic data of documents related to a transaction; a summary generation unit that generates a plurality of summary data pieces indicating summaries obtained by extracting information for associating each document from each of a plurality of document data pieces including document data pieces of a plurality of different types of documents; a cluster generation unit that generates clusters by dividing the plurality of pieces of summary data into related cases; a reception unit that receives a selection of document data to be displayed; an identification unit that identifies document data that has a predetermined relationship with a cluster to which the document data accepted by the acceptance unit belongs as candidate document data that is a candidate related to the document data; an output unit that outputs the document data to be displayed and the candidate document data in association with each other; An information processing device having the above.

4. an acquisition unit that acquires document data, which is electronic data of documents related to a transaction; a summary generation unit that generates a plurality of summary data pieces indicating summaries obtained by extracting information for associating each document from each of a plurality of document data pieces including document data pieces of a plurality of different types of documents; a cluster generation unit that generates clusters by dividing the plurality of pieces of summary data into related cases; a reception unit that receives a selection of document data to be journalized; a journal data generation unit that generates journal data by journalizing a transaction indicated by the document data to be journalized based on information contained in the document data to be journalized and other document data corresponding to the cluster to which the document data belongs; An information processing device having the above.

5. The journal data generation unit generates journal data by journalizing transactions indicated by the document data to be journalized based on items included in the document data to be journalized and predetermined items included in other document data corresponding to the cluster to which the document data belongs, the predetermined items corresponding to the document types of the other document data. The information processing device according to claim 4 .

6. an acquisition unit that acquires document data, which is electronic data of documents related to a transaction; a summary generation unit that generates a plurality of summary data pieces indicating summaries obtained by extracting information for associating each document from each of a plurality of document data pieces including document data pieces of a plurality of different types of documents; a cluster generation unit that generates clusters by dividing the plurality of pieces of summary data into related cases; a storage unit that stores document data and journal data indicating journal entries for transactions indicated by the document data in association with each other; a reception unit that receives a selection of document data to be journalized; a journal data generation unit that generates journal data for the document data to be journalized based on the document data to be journalized and the journal data associated with document data that belongs to a cluster that has a predetermined relationship with the cluster to which the document data belongs; An information processing device having the above.

7. The computer executes A step of acquiring document data, which is electronic data of documents related to the transaction; generating a plurality of summary data representing summaries obtained by extracting information for associating each document from a plurality of document data including document data of a plurality of different types of documents, the summary data including different types of information for each of the document types; generating clusters by dividing the plurality of summary data into related cases by inputting the plurality of summary data into a machine learning model that generates clusters when it receives input of a document type indicated by the document data and the summary data extracted from the document data; An information processing method comprising:

8. The computer executes A step of acquiring document data, which is electronic data of documents related to the transaction; generating 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 a plurality of different types of documents; generating clusters by dividing the plurality of summary data into related cases; acquiring target document data, which is document data for which a related case is to be determined; generating target abstract data, the abstract data corresponding to the target document data; Identifying a case to which a document corresponding to the target summary data is related based on the clusters generated in the cluster generating step; outputting document data related to the identified case; An information processing method comprising:

9. On the computer, A step of acquiring document data, which is electronic data of documents related to the transaction; generating a plurality of summary data representing summaries obtained by extracting information for associating each document from a plurality of document data including document data of a plurality of different types of documents, the summary data including different types of information for each of the document types; generating clusters by dividing the plurality of summary data into related cases by inputting the plurality of summary data into a machine learning model that generates clusters when it receives input of a document type indicated by the document data and the summary data extracted from the document data; A program that executes the following.

10. On the computer, A step of acquiring document data, which is electronic data of documents related to the transaction; generating 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 a plurality of different types of documents; generating clusters by dividing the plurality of summary data into related cases; acquiring target document data, which is document data for which a related case is to be determined; generating target abstract data, the abstract data corresponding to the target document data; Identifying a case to which a document corresponding to the target summary data is related based on the clusters generated in the cluster generating step; outputting document data related to the identified case; A program that executes the following.