Information processing device, information processing method, and program

Abstract

Previously, we were unable to accurately predict the arrival date of invoices, which hindered the speed of our monthly closing process. [Solution] An information processing device 1 comprises an arrival date prediction unit 135 that predicts the next arrival date and obtains an arrival date prediction, by referring to an arrival management unit 112 which stores one or more receipt information having the past one or more arrival dates of invoices, associated with one or more connection information having the first contact person identifier of the recipient of the invoice and the second contact person identifier of the issuer of the invoice, and an arrival date output unit 141 which outputs the arrival date prediction obtained by the arrival date prediction unit 135 to the first contact person identified by the first contact person identifier. As a result, the arrival date of invoices can be predicted with high accuracy, thus supporting the rapid processing of monthly settlements.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus and the like for predicting the arrival date of a claim document.

Background Art

[0002] Conventionally, there has been a claim document management apparatus that aims to reduce the risk of payment omission, identifies the scheduled acquisition date of claim document image data, and presents warning information to at least one of the claimant who bills the transaction amount or the respondent who is billed for the transaction amount when the claim document image data has not been acquired by the time the scheduled acquisition date has passed (see Patent Document 1).

[0003] Also, in Patent Document 1, it is described that the scheduled acquisition date is identified based on the acquisition interval of a plurality of claim document image data corresponding to regularly issued claim documents.

[0004] Also, in Patent Document 1, when the fluctuation range of the acquisition interval of a plurality of related image data in which the issuer of the claim document and the claim target name included in the claim document image data match among a plurality of claim document image data acquired in the past is within a predetermined range, it is described that the scheduled acquisition date is identified. Note that the issuer of the claim document is, for example, a company. Also, the claim target name is at least one of the subject of the claim document and the product names described in the claim document.

[0005] Furthermore, in Patent Document 1, it is described that the scheduled payment date is identified based on the payment deadline of the amount included in each of a plurality of claim document image data corresponding to regularly issued claim documents.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

[0007] However, in conventional technology, the expected acquisition date is determined using multiple invoice image data associated with the issuer of the invoice (e.g., a company), and because the expected arrival date of the invoice is not predicted for each pair of the first person receiving the invoice and the second person issuing the invoice, it was often impossible to determine an appropriate expected acquisition date. More specifically, in conventional technology, for example, in cases where multiple people exchange various types of invoices, such as between large companies, the expected acquisition date is sometimes determined using invoice image data, and it was often impossible to determine an appropriate expected acquisition date.

[0008] In other words, with conventional technology, it was not possible to accurately predict the arrival date of invoices, which meant that monthly closing processes could not be completed quickly. [Means for solving the problem]

[0009] The first information processing device of the present invention is an information processing device comprising: an arrival date prediction unit that predicts the next arrival date and obtains an arrival date by referring to an arrival management unit which stores one or more receipt information having one or more past arrival dates of an invoice, associated with one or more connection information having a first contact person identifier of the recipient of the invoice and a second contact person identifier of the issuer of the invoice; and a prediction date output unit which outputs the arrival date obtained by the arrival date prediction unit to the first contact person identified by the first contact person identifier.

[0010] This configuration allows for highly accurate prediction of invoice arrival dates, thus supporting the rapid processing of monthly financial statements.

[0011] Furthermore, the information processing device of the second invention, compared to the first invention, comprises an arrival date prediction unit which obtains the dates of three or more arrival dates associated with connection information, obtains the median value when the dates are considered as data distributed on a circle, and obtains a first arrival date prediction date which is the median value, and an arrival date output unit which outputs the first arrival date prediction date obtained by the prediction unit, or an arrival date prediction date based on the first arrival date prediction date obtained by the prediction unit.

[0012] This configuration allows for highly accurate prediction of invoice arrival dates, thus supporting the rapid processing of monthly financial statements.

[0013] Furthermore, the information processing device of this third invention, compared to the first or second invention, comprises an arrival date prediction unit comprising a determination means for determining whether the first arrival date prediction date acquired by the prediction means is a non-business day, and a correction means for acquiring an arrival date prediction date that is the business day immediately following the first arrival date prediction date or the business day immediately preceding the first arrival date prediction date if the determination means determines that it is a non-business day, and an arrival date output unit which outputs the arrival date prediction date acquired by the correction means if the determination means determines that it is a business day, and outputs the first arrival date prediction date acquired by the prediction means.

[0014] This configuration allows for highly accurate prediction of invoice arrival dates by taking into account non-business days and business days of the invoice issuer, thereby supporting the rapid processing of monthly financial statements.

[0015] Furthermore, the information processing device of the fourth invention further comprises a frequency acquisition unit that acquires frequency information that identifies the frequency at which invoices are received using two or more past arrival dates associated with connection information, with respect to any one of the first to third inventions, and a frequency output unit that outputs the frequency information acquired by the frequency acquisition unit in association with connection information.

[0016] This configuration allows for the acquisition of frequency information that identifies the frequency of invoice arrival for each connection.

[0017] Further, for any one of the first to fourth inventions, in the information processing apparatus of the fifth invention, the received information includes a reference total amount which is the total amount of the amounts corresponding to one or more past invoices that have arrived within a predetermined period, and is one or more invoices that form a pair with the connection information associated with the received information. The information processing apparatus further includes a warning determination unit that acquires the total amount of the amounts corresponding to one or more invoices that have arrived in the current month, and determines whether the difference between the total amount and the reference total amount is large enough to satisfy a warning condition, and a warning output unit that outputs warning information when the warning determination unit determines that the warning condition is satisfied.

[0018] With such a configuration, it is possible to warn of the non-arrival of invoices based on the amounts of the invoices, and thus it is possible to assist in quickly performing the monthly settlement process.

[0019] Further, for any one of the first to fifth inventions, in the information processing apparatus of the sixth invention, the information processing apparatus further includes an invoice reception unit that receives invoice information associated with connection information, an arrival date acquisition unit that acquires the arrival date which is the date when the invoice reception unit receives the invoice information, and an arrival date accumulation unit that accumulates the arrival date acquired by the arrival date acquisition unit in a reception management unit in association with the connection information.

[0020] With such a configuration, it is possible to accumulate arrival dates for accurately predicting the arrival date of invoices.

Advantages of the Invention

[0021] According to the information processing apparatus of the present invention, since the arrival date of an invoice can be accurately predicted, it is possible to assist in quickly performing the monthly settlement process.

Brief Description of the Drawings

[0022] [Figure 1] Conceptual diagram of information system A in Embodiment 1 [Figure 2] Block diagram of the same information system A [Figure 3] Block diagram of the same information processing apparatus 1 [Figure 4]Flowchart for explaining the operation example of the information processing device 1 [Figure 5] Flowchart for explaining an example of frequency acquisition processing [Figure 6] Flowchart for explaining the first example of arrival date prediction processing [Figure 7] Flowchart for explaining the second example of arrival date prediction processing [Figure 8] Flowchart for explaining the operation example of the learning process [Figure 9] Flowchart for explaining an example of teacher data acquisition processing [Figure 10] Diagram showing an example of the claim management table [Figure 11] Diagram showing an example of the connection management table [Figure 12] Diagram showing an example of the arrival date prediction table [Figure 13] Diagram showing an example of the frequency condition management table [Figure 14] Block diagram of the computer system

Mode for Carrying Out the Invention

[0023] Hereinafter, embodiments of an information processing device and the like will be described with reference to the drawings. In the embodiments, components denoted by the same reference numerals perform the same operations, and thus the description may be omitted again.

[0024] (Embodiment 1)[[ID=四十三]] In the present embodiment, in a situation where one or more received information having information (hereinafter referred to as "connection information") linking the first person in charge of the recipient of the claim and the second person in charge of the issuer of the claim and one or more arrival dates of past claims are stored, an information processing device that predicts and outputs the next arrival date for each connection information will be described.

[0025] Furthermore, in this embodiment, we will describe an information processing device that obtains the dates associated with two or more arrival dates corresponding to the connection information, and obtains the median value when these dates are considered as data distributed on a circle as the predicted arrival date.

[0026] Furthermore, in this embodiment, we will describe an information processing device that acquires an estimated arrival date, taking into account non-business days and business days.

[0027] Furthermore, this embodiment describes an information processing device that acquires frequency information indicating the frequency of invoice arrival using the arrival dates of two or more invoices, and associates this frequency information with connectivity information.

[0028] Furthermore, in this embodiment, we will describe an information processing device that warns that future invoices may arrive if the total amount of one or more invoices in the most recent predetermined period is small enough to meet the warning conditions compared to the total amount of one or more invoices in the past within a predetermined period (total payment amount) within that period.

[0029] Furthermore, in this embodiment, we will describe an information processing device that receives invoice information in association with connection information and stores the arrival date together with the receipt information in association with the connection information.

[0030] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is irrelevant. Information X and information Y may be linked, may reside in the same buffer, may information X be contained in information Y, or information Y may be contained in information X, and so on.

[0031] Furthermore, in this specification, selecting or determining information Z means obtaining information Z, obtaining a pointer to information Z, obtaining the ID of information Z, setting a flag on information Z, etc., and it is sufficient to be able to access information Z.

[0032] Figure 1 is a conceptual diagram of information system A in this embodiment. Information system A comprises an information processing device 1 and one or more terminal devices 2.

[0033] Information processing device 1 is a device that predicts the arrival date of invoices. Here, information processing device 1 has the function of receiving and storing invoice information. Information processing device 1 is usually a server, but it may also be a terminal. Information processing device 1 may be, for example, a so-called cloud server or ASP server, but its type and location are not specified.

[0034] Terminal device 2 is a terminal used by the user. The user is, for example, the primary contact person at the recipient of the invoice, or the secondary contact person at the issuer of the invoice. Terminal device 2 can be, for example, a personal computer, smartphone, tablet, etc., and the type is not limited.

[0035] The information processing device 1 and one or more terminal devices 2 can communicate with each other via a network such as the Internet.

[0036] Figure 2 is a block diagram of information system A in this embodiment. Figure 3 is a block diagram of information processing device 1.

[0037] The information processing device 1 comprises a storage unit 11, a receiving unit 12, a processing unit 13, and an output unit 14. The storage unit 11 comprises an invoice management unit 111 and a receipt management unit 112. The receiving unit 12 comprises an invoice receiving unit 121. The processing unit 13 comprises an invoice storage unit 131, a frequency acquisition unit 132, an arrival date acquisition unit 133, an arrival date storage unit 134, an arrival date prediction unit 135, and a warning determination unit 136. The arrival date prediction unit 135 comprises a prediction means 1351, a determination means 1352, and a correction means 1353. The output unit 14 comprises a predicted date output unit 141, a frequency output unit 142, and a warning output unit 143.

[0038] The terminal device 2 includes a terminal storage unit 21, a terminal receiving unit 22, a terminal processing unit 23, a terminal transmission unit 24, a terminal receiving unit 25, and a terminal output unit 26.

[0039] The storage unit 11, which constitutes the information processing device 1, stores various types of information. These types of information include, for example, invoice information, receipt information, and non-business day information, which will be described later.

[0040] Non-business day information is information used to identify non-business days. It is preferable that non-business days be managed for each secondary contact person. Non-business days may be common to two or more secondary contact persons. Non-business days may be common to all secondary contact persons. Non-business day information may also be information that identifies business days. Information that identifies business days can also be information that identifies non-business days. Non-business days are, for example, Saturdays, Sundays, and public holidays. It is preferable that non-business day information is managed in conjunction with secondary contact persons who are not default non-business days. Default non-business days are, for example, Saturdays, Sundays, and public holidays. The secondary contact person is the person in charge of issuing the invoice. The primary contact person is the person in charge of receiving the invoice.

[0041] The invoice management unit 111 stores one or more invoice information. Invoice information refers to information about an invoice. Invoice information includes information that indicates the contents of the invoice. For example, invoice information includes the name of the recipient of the invoice, the name of the issuer of the invoice, and the amount. Invoice information includes, for example, an image of the invoice. Invoice information is associated with, for example, connection information.

[0042] Connection information is information that identifies the recipient of an invoice (first contact person) and the issuer of the invoice (second contact person). Connection information typically has a first contact person identifier and a second contact person identifier. The first contact person identifier is information that identifies the first contact person. The second contact person identifier is information that identifies the second contact person. The contact person identifier may be, for example, the contact person's ID, the contact person's name, the name of the organization to which the contact person belongs and the contact person's name, or the ID of the organization to which the contact person belongs and the contact person's name. Connection information is associated with, for example, the connection identifier. The connection identifier is information that identifies the connection information. The connection identifier may be, for example, the ID of the connection information or the name of the connection information.

[0043] The receipt management unit 112 stores one or more receipt information entries. Receipt information is information for managing the date on which an invoice corresponding to the connection information was received (the invoice arrival date). The receipt information has the past arrival dates of one or more invoices corresponding to the connection information. The receipt information may also include, for example, the base total amount corresponding to the connection information. The base total amount is the sum of the amounts corresponding to one or more past invoices that arrived during a predetermined period (for example, one month). These one or more invoices are invoices corresponding to the same connection information. The receipt information is associated with the connection information. The receipt information is associated with, for example, the connection identifier. The arrival date is, for example, information indicating the year, month, and day, or information indicating the month and day. The predetermined period is, for example, four months, but is not limited to that.

[0044] The reception unit 12 receives information and instructions. Examples of information and instructions include invoice information, forecast date output instructions, frequency output instructions, and output instructions.

[0045] A forecast date output instruction is an instruction to output the forecast date of arrival of an invoice. A forecast date output instruction may include, for example, a first-person contact identifier. A forecast date output instruction may also include, for example, an organization identifier. An organization identifier is information that identifies an organization. An organization identifier may include, for example, an organization ID or organization name. An organization may be, for example, a company, a local government, or a sole proprietor.

[0046] A frequency output instruction is an instruction to output frequency information. A frequency output instruction may include, for example, a first-person responsible identifier. Another frequency output instruction may include, for example, an organization identifier.

[0047] An output instruction is an instruction to output various information, including the estimated arrival date of an invoice. This information may include, for example, frequency information. An output instruction may also include, for example, a primary contact person identifier. An output instruction may also include, for example, an organization identifier.

[0048] The reception unit 12 normally receives information and instructions from the terminal device 2. However, the reception unit 12 may also receive information and instructions from the user. In such cases, the means of inputting information and instructions can be anything, such as a touch panel, keyboard, mouse, or menu screen.

[0049] The invoice receiving unit 121 receives invoice information corresponding to the connection information. The invoice receiving unit 121 usually receives invoice information from the terminal device 2.

[0050] The processing unit 13 performs various processes. These various processes include, for example, those performed by the invoice storage unit 131, frequency acquisition unit 132, arrival date acquisition unit 133, arrival date storage unit 134, and arrival date prediction unit 135.

[0051] The invoice storage unit 131 stores the receipt information received by the invoice reception unit 121 in the invoice management unit 111. It is preferable for the invoice storage unit 131 to store the receipt information received by the invoice reception unit 121 in the invoice management unit 111, associating it with the corresponding linkage information.

[0052] The frequency acquisition unit 132 acquires frequency information that identifies how often invoices are received, using two or more past arrival dates associated with the connection information. These two or more past arrival dates associated with the connection information are usually stored in the receipt management unit 112. Examples of frequency information include "monthly," "once every two months," "once a quarter," "once every six months," and "once a year."

[0053] For example, the frequency acquisition unit 132 acquires two or more arrival dates (month and day) associated with the connection information. Next, for example, the frequency acquisition unit 132 plots these two or more arrival dates on a circle with a year of 360 degrees. Then, the frequency acquisition unit 132 acquires frequency information "monthly" if arrival dates are detected in 30-degree units on the circle, frequency information "once a quarter" if arrival dates are detected in 90-degree units, and frequency information "once every six months" if arrival dates are detected in 180-degree units.

[0054] The arrival date acquisition unit 133 acquires the arrival date, which is the date on which the invoice reception unit 121 received the invoice information. When the invoice reception unit 121 receives the invoice information, the arrival date acquisition unit 133 acquires the arrival date, which is the current date, from, for example, a clock (not shown).

[0055] The arrival date storage unit 134 associates the arrival date acquired by the arrival date acquisition unit 133 with the connection information and stores it in the receipt management unit 112.

[0056] The arrival date prediction unit 135 refers to the receipt management unit 112 and predicts the next arrival date for each of the one or more connection information items, and obtains the predicted arrival date. The prediction means 1351, which constitutes the arrival date prediction unit 135, predicts the next arrival date of the invoice for each of the one or more connection information items, and obtains the first predicted arrival date. The first predicted arrival date is a predicted date that may be changed by the correction means 1353.

[0057] The prediction means 1351, which constitutes the arrival date prediction unit 135, can use various algorithms to obtain the predicted arrival date. For example, the prediction means 1351 can obtain the predicted arrival date using circular median. For example, the prediction means 1351 can obtain the predicted arrival date using machine learning prediction processing. For example, the prediction means 1351 can obtain the predicted arrival date using an interval of two or more arrival dates. For example, the prediction means 1351 can obtain the predicted arrival date using generative AI. (1) Method using circular median

[0058] The prediction means 1351 obtains the circular median for each of the two or more arrival dates associated with the connection information. More specifically, the prediction means 1351 obtains the days for each of the two or more arrival dates associated with the connection information, obtains the median when those days are considered as data distributed on a circle, and obtains the first predicted arrival date, which is the first future date and has that median as its day.

[0059] For example, the prediction means 1351 obtains the days (e.g., 1st, 31st, 2nd) associated with each of two or more arrival dates (e.g., June 1st, June 31st, August 2nd) corresponding to the connection information, obtains the median (1st) when considering the data (31st, 1st, 2nd) distributed on the circumference, and obtains September 1st, the next future date with the median as the first predicted arrival date.

[0060] For example, the prediction means 1351 obtains two or more arrival dates (month and day) corresponding to the connection information. Next, the prediction means 1351 plots these two or more arrival dates on a circle with a year of 360 degrees. Then, the prediction means 1351 determines that if arrival dates are detected in 30-degree increments on the circle, invoices will arrive monthly; if arrival dates are detected in 90-degree increments, invoices will arrive quarterly; and if arrival dates are detected in 180-degree increments, invoices will arrive every six months. Next, the prediction means 1351 obtains the date after the obtained interval from the last arrival date of the invoice as the first predicted arrival date. (2) Method using machine learning prediction processing (2-1) When there is only one learning model

[0061] The prediction means 1351 obtains N (where N is usually a natural number greater than or equal to 2) past arrival dates associated with a single connection information. Next, the prediction means 1351 obtains the day associated with each of the N arrival dates as an explanatory variable, provides this explanatory variable and the learning model to a machine learning prediction processing module, executes the module, and obtains the predicted date. Next, the prediction means 1351 obtains the first predicted arrival date, which is the first future date that has that day.

[0062] A learning model is information constructed through the learning process of machine learning, and is used in the prediction process of machine learning. A learning model can also be called a learner, classifier, or classification model. In this context, the learning model is a model obtained by feeding two or more training data sets, where the dates of the past N arrival dates are the explanatory variables and the dates of the next arrival dates after the past N arrival dates are the dependent variables, into a machine learning learning module, and then running that module.

[0063] The machine learning algorithm can be anything, including deep learning, random forests, and decision trees. Furthermore, various existing machine learning functions and libraries can be used, such as the TensorFlow® library, the R language's random forest module, and fastText. (2-1) When there are two or more learning models

[0064] If there are two or more learning models, it means that there is a learning model for each number of days (number of explanatory variables) of past arrival dates.

[0065] The prediction means 1351 obtains past arrival dates associated with a single connection information. The prediction means 1351 also obtains the number of past arrival dates. Next, the prediction means 1351 obtains a learning model associated with the number of past arrival dates. Next, the prediction means 1351 obtains the day associated with each of the N arrival dates as an explanatory variable, provides the explanatory variable and the obtained learning model to a machine learning prediction processing module, executes the module, and obtains the predicted date. Next, the prediction means 1351 obtains the first predicted arrival date, which is the first future date that has that day.

[0066] The learning model used here is a model obtained by feeding two or more training data sets to a machine learning module, where the date of the previous arrival date is used as the explanatory variable and the date of the next arrival date after the previous arrival date is used as the dependent variable, for each number of past arrival dates, and then running that module. (3) Method using an interval of two or more arrival dates

[0067] The prediction means 1351 uses the frequency information acquired by the frequency acquisition unit 132 to obtain the next predicted arrival date after the last past arrival date. For example, if the acquired arrival dates are "January 31st, April 30th, July 31st, October 31st", the prediction means 1351 determines that the interval is 3 months and obtains the next predicted arrival date "January 31st". (4) Method using a generated AI

[0068] The prediction means 1351, for example, substitutes one or more past arrival dates into the prompt template in the storage unit 11 to construct a prompt, provides the prompt to the generating AI, and obtains the next predicted arrival date from the generating AI.

[0069] The prompt template is, for example, "You received an invoice on the following arrival dates. Please tell me when the next invoice will arrive. Arrival Date: <Arrival Date>". Furthermore, an example of a prompt obtained by the prediction means 1351 by substituting one or more past arrival dates into the prompt template is, for example, "You received an invoice on the following arrival dates. Please tell me when the next invoice will arrive. Arrival Date: January 31, 2024, April 30, 2024, July 31, 2024, October 31, 2024".

[0070] Then, when the prediction means 1351 gives the above prompt to the generating AI, it obtains the predicted arrival date "January 30, 2025" from the generating AI.

[0071] Note that the generation AI is a function of a generation AI device (not shown) different from the information processing device 1, but it may also be possessed by the information processing device 1. The generation AI may also be called a generation AI device. The generation AI here usually has the functionality of a text generation AI. The generation AI may be ChatGPT or Google Bard, but is not limited to these. Note that Google is a registered trademark. The generation AI device may be a cloud server or an ASP server, but is not limited to these types.

[0072] The determination means 1352 determines whether the first predicted arrival date obtained by the prediction means 1351 is a non-business day. The determination means 1352 uses the non-business day information from the storage unit 11 to determine whether the first predicted arrival date obtained by the prediction means 1351 is a non-business day.

[0073] The determination means 1352, for example, acquires non-business day information corresponding to one connection information (in this case, the second person in charge) and determines whether the first arrival forecast date acquired by the prediction means 1351 is a non-business day.

[0074] If the determination means 1352 determines that the first predicted arrival date obtained by the prediction means 1351 is a non-business day, the correction means 1353 obtains a predicted arrival date that is either the business day immediately following the first predicted arrival date or the business day immediately preceding the first predicted arrival date.

[0075] The correction means 1353 may always obtain the business day immediately following the first predicted arrival date as the predicted arrival date. The correction means 1353 may always obtain the business day immediately preceding the first predicted arrival date as the predicted arrival date.

[0076] If the determination means 1352 determines that the first predicted arrival date is a non-business day, for example, the correction means 1353 obtains a flag corresponding to the second person in charge from the storage unit 11 and obtains a predicted arrival date that is the business day immediately following or immediately preceding the first predicted arrival date as indicated by the flag. The flag indicates whether to use the immediately following business day or the immediately preceding business day.

[0077] The warning determination unit 136 obtains the total amount corresponding to one or more invoices that are paired with the linked information associated with the receipt information and that have arrived in the current month, and determines whether the difference between this total amount and the standard total amount is large enough to satisfy the warning conditions. The standard total amount is the information contained in the receipt information.

[0078] A warning condition is a condition used to determine if there may be undelivered invoices. A warning condition is when the discrepancy is equal to or greater than a certain threshold.

[0079] The output unit 14 outputs various types of information. These types of information include, for example, the predicted arrival date, frequency information, and invoice information. In this context, output usually refers to transmission to the terminal device 2. However, output may also be a concept that includes, for example, display on a screen, projection using a projector, printing with a printer, sound output, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0080] The forecast date output unit 141 outputs the predicted arrival date obtained by the arrival date forecast unit 135 to the first person in charge identified by the first person in charge identifier. The forecast date output unit 141 transmits the predicted arrival date to the terminal device 2 of the first person in charge identified by the first person in charge identifier.

[0081] The forecast date output unit 141 outputs the first arrival forecast date acquired by the forecasting means 1351, or the arrival forecast date based on the first arrival forecast date acquired by the forecasting means 1351. The arrival forecast date based on the first arrival forecast date is, for example, the arrival forecast date acquired by the correction means 1353.

[0082] It is preferable that the predicted date output unit 141 outputs the predicted arrival date acquired by the correction unit 1353 when the determination unit 1352 determines that it is a non-business day, and outputs the first predicted arrival date acquired by the prediction unit 1351 when the determination unit 1352 determines that it is a business day.

[0083] The frequency output unit 142 outputs the frequency information acquired by the frequency acquisition unit 132, associating it with the connection information.

[0084] The warning output unit 143 outputs warning information when the warning determination unit 136 determines that the warning conditions are met. Warning information is information that indicates there may be undelivered invoices. For example, the warning output unit 143 sends the warning information to the person in charge at the recipient's address (first person in charge) whose connection information corresponds to the receipt information that the warning determination unit 136 has determined to meet the warning conditions.

[0085] The terminal storage unit 21, which constitutes the terminal device 2, stores various types of information. These types of information include, for example, the person in charge identifier. The person in charge identifier is either the first person in charge identifier or the second person in charge identifier.

[0086] The terminal reception unit 22 receives various types of information and instructions. These types of information and instructions include, for example, invoice information, forecast date output instructions, frequency output instructions, and output instructions. The input method for these types of information and instructions can be anything, such as a touch panel, keyboard, mouse, or menu screen. The terminal reception unit 22 can be implemented using device drivers for input means such as touch panels and keyboards, or control software for menu screens.

[0087] The terminal processing unit 23 performs various processes. These processes include, for example, converting the information received by the terminal receiving unit 22 into a data structure for transmission, and converting the information received by the terminal receiving unit 25 into a data structure for display.

[0088] The terminal transmission unit 24 transmits various information and instructions to the information processing device 1. These various information include, for example, invoice information, forecast date output instructions, frequency output instructions, and output instructions.

[0089] The terminal receiving unit 25 receives various types of information from the information processing device 1. These types of information include, for example, invoice information, predicted arrival date, and frequency information.

[0090] The terminal output unit 26 outputs information received by the terminal receiving unit 25, information acquired by the terminal processing unit 23, information received by the terminal reception unit 22, etc. Here, output is a concept that includes display on a screen, projection using a projector, printing with a printer, sound output, transmission to an external device, storage on a recording medium, and handover of processing results to other processing devices or other programs.

[0091] The storage unit 11, the invoice management unit 111, the receipt management unit 112, and the terminal storage unit 21 are preferably made of non-volatile recording media, but can also be made of volatile recording media.

[0092] The process by which information is stored in the storage unit 11, etc. is not relevant. For example, information may be stored in the storage unit 11, etc. via a recording medium, information transmitted via a communication line, etc. may be stored in the storage unit 11, etc., or information input via an input device may be stored in the storage unit 11, etc.

[0093] The reception unit 12 and the invoice reception unit 121 are usually implemented by wireless or wired communication means, but they may also be implemented by means of receiving broadcasts, device drivers for input means such as touch panels and keyboards, or control software for menu screens.

[0094] The processing unit 13, invoice storage unit 131, frequency acquisition unit 132, arrival date acquisition unit 133, arrival date storage unit 134, arrival date prediction unit 135, warning judgment unit 136, prediction means 1351, judgment means 1352, and correction means 1353 can typically be implemented using a processor, memory, etc. The processing procedures of the processing unit 13, etc., are typically implemented in software, and this software is recorded on a recording medium such as ROM. However, it may also be implemented in hardware (dedicated circuitry). The processor can be a CPU, MPU, GPU, etc., and the type is not limited.

[0095] The output unit 14, the predicted date output unit 141, the frequency output unit 142, and the warning output unit 143 are usually implemented by wireless or wired communication means, but they may also be implemented by driver software for an output device such as a display or speaker, or by driver software for an output device and the output device itself.

[0096] The terminal reception unit 22 can be implemented using device drivers for input means such as touch panels and keyboards, or control software for menu screens, etc.

[0097] The terminal transmission unit 24 is usually implemented by wireless or wired communication means, but it may also be implemented by broadcasting means.

[0098] The terminal receiving unit 25 is usually implemented by wireless or wired communication means, but it may also be implemented by means of receiving broadcasts.

[0099] The terminal output unit 26 may or may not be considered to include output devices such as a display or speakers. The terminal output unit 26 can be implemented using driver software for an output device, or driver software for an output device and an output device.

[0100] Next, an example of the operation of the information processing device 1 will be explained using the flowchart in Figure 4. Here, the information processing device 1 is a server that receives instructions and other data from the terminal device 2. However, the information processing device 1 may also be a standalone terminal.

[0101] (Step S401) The invoice receiving unit 121 determines whether or not it has received invoice information. If invoice information has been received, it proceeds to step S402; otherwise, it proceeds to step S406. The invoice information received here is associated with a connection identifier. Associating with a connection identifier may also mean associating with connection information, possessing connection information, etc.

[0102] (Step S402) The processing unit 13 obtains a connection identifier that corresponds to the invoice information received in step S401.

[0103] (Step S403) The arrival date acquisition unit 133 acquires the arrival date, which is today's date, from a clock (not shown).

[0104] (Step S404) The arrival date storage unit 134 stores the arrival date obtained in step S403 in the receipt management unit 112, associating it with the connection identifier obtained in step S402.

[0105] (Step S405) The invoice storage unit 131 stores the invoice information received in step S401 in the invoice management unit 111. Return to step S401.

[0106] (Step S406) The reception unit 12 determines whether or not it has received a frequency acquisition instruction. If it has received a frequency acquisition instruction, it proceeds to step S407; otherwise, it proceeds to step S413.

[0107] (Step S407) The frequency acquisition unit 132 assigns 1 to counter i.

[0108] (Step S408) The frequency acquisition unit 132 determines whether or not the i-th connection information exists. If the i-th connection information exists, the unit proceeds to step S409; otherwise, it returns to step S401.

[0109] (Step S409) The frequency acquisition unit 132 acquires the arrival date corresponding to the i-th connection information from the receipt management unit 112. The frequency acquisition unit 132 acquires, for example, all arrival dates corresponding to the i-th connection information. Next, the frequency acquisition unit 132 acquires frequency information using these arrival dates. An example of such frequency acquisition processing will be explained using the flowchart in Figure 5. Note that the arrival date corresponding to the i-th connection information is the actual arrival date.

[0110] (Step S410) The frequency output unit 142 determines whether or not it was able to obtain frequency information in step S409. If it was able to obtain frequency information, it proceeds to step S411; if it was not able to obtain frequency information, it proceeds to step S412.

[0111] (Step S411) The frequency output unit 142 stores the frequency information obtained in step S409 in the receipt management unit 112, associating it with the i-th connection information.

[0112] (Step S412) The frequency acquisition unit 132 increments the counter i by 1. Return to step S408.

[0113] (Step S413) The reception unit 12 determines whether or not it has received an output instruction. If it has received a frequency acquisition instruction, it proceeds to step S407; otherwise, it proceeds to step S413.

[0114] (Step S414) The arrival date prediction unit 135 obtains all connection information corresponding to the output instruction from the receipt management unit 112.

[0115] (Step S415) The arrival date prediction unit 135 assigns 1 to counter i.

[0116] (Step S416) The arrival date prediction unit 135 determines whether or not the i-th connection information exists among the connection information obtained in step S414. If the i-th connection information exists, the unit proceeds to step S417; otherwise, it returns to step S401.

[0117] (Step S417) The arrival date prediction unit 135 obtains the next predicted arrival date using the arrival date paired with the i-th connection information. An example of such arrival date prediction processing will be explained using the flowcharts in Figures 6 and 7.

[0118] (Step S418) The arrival date storage unit 134 determines whether or not the predicted arrival date was obtained in step S417. If the predicted arrival date was obtained, the process proceeds to step S419; otherwise, the process proceeds to step S422.

[0119] (Step S419) The predicted date output unit 141 stores the predicted arrival date in the receipt management unit 112, associating it with the i-th connection information.

[0120] (Step S420) The predicted date output unit 141 determines whether the predicted arrival date satisfies the output conditions. If the output conditions are met, the unit proceeds to step S421; otherwise, it proceeds to step S422. The output conditions are, for example, "the predicted arrival date is in this month" and "the predicted arrival date is within a threshold date from today."

[0121] (Step S421) The forecast date output unit 141 associates the forecast arrival date with the connection information to be output.

[0122] (Step S422) The frequency output unit 142 determines whether or not frequency information corresponding to the i-th connection information exists in the receiving management unit 112. If frequency information exists, proceed to step S423; otherwise, proceed to step S424.

[0123] (Step S423) The frequency output unit 142 obtains the frequency information corresponding to the i-th connection information from the receiving management unit 112 and associates the frequency information with the connection information to be output.

[0124] (Step S424) The arrival date prediction unit 135 increments counter i by 1. Return to step S416.

[0125] Furthermore, in the flowchart of Figure 4, at a predetermined timing (for example, at the end of the month, the beginning of the month), or when an instruction is received from terminal device 2, the warning determination unit 136 may obtain the total amount corresponding to one or more invoices that are paired with the connection information associated with the receipt information and that have arrived in the current month, for each of the one or more receipt information, and determine whether the difference between this total amount and the standard total amount is large enough to satisfy the warning conditions. For each of the one or more receipt information that the warning determination unit 136 determines to satisfy the warning conditions, the warning output unit 143 may send warning information to the first person in charge associated with the receipt information.

[0126] Furthermore, in the flowchart of Figure 4, processing is terminated by power off or processing termination interrupts.

[0127] Next, an example of the frequency acquisition process in step S409 will be explained using the flowchart in Figure 5.

[0128] (Step S501) The frequency acquisition unit 132 determines whether there are two or more arrival dates among the arrival dates paired with the connection information of interest. If there are two or more arrival dates, the unit proceeds to step S502; otherwise, the unit proceeds to step S509.

[0129] (Step S502) The frequency acquisition unit 132 acquires two or more arrival dates that are paired with the connection information of interest from the receipt management unit 112, and acquires one or more intervals (number of days) between those two or more arrival dates.

[0130] (Step S503) The frequency acquisition unit 132 determines whether the difference between one or more intervals acquired in step S502 is within a threshold. If this condition is met, the process proceeds to step S504; otherwise, the process proceeds to step S509.

[0131] (Step S504) The frequency acquisition unit 132 assigns 1 to counter i.

[0132] (Step S505) The frequency acquisition unit 132 determines whether the i-th interval condition exists in the storage unit 11. If the i-th interval condition exists, the unit proceeds to step S506; otherwise, it proceeds to step S509. The interval condition is a condition relating to the interval between arrival dates. Each of the one or more interval conditions in the storage unit 11 corresponds to frequency information. The interval condition is, for example, the attribute value of "interval condition" in Figure 13, which will be described later.

[0133] (Step S506) The frequency acquisition unit 132 determines whether or not the i-th interval condition is met. If the i-th interval condition is met, the unit proceeds to step S507; otherwise, the unit proceeds to step S509.

[0134] (Step S507) The frequency acquisition unit 132 acquires frequency information from the storage unit 11 that is paired with the i-th interval condition.

[0135] (Step S508) The frequency acquisition unit 132 increments the counter i by 1. Return to step S505.

[0136] (Step S509) The frequency acquisition unit 132 sets the frequency information to "NULL". It returns to the higher-level processing.

[0137] Next, we will explain the first example of the arrival date prediction process in step S417 using the flowchart in Figure 6. The first example is the case using circular median.

[0138] (Step S601) The arrival date prediction unit 135 obtains one or more arrival dates from the receipt management unit 112 that correspond to the connection information of interest.

[0139] (Step S602) The arrival date prediction unit 135 determines whether the arrival dates obtained in step S601 satisfy the prediction conditions (for example, "the number of arrival dates is equal to or greater than a threshold (for example, 2)"). If the prediction conditions are met, the unit proceeds to step S603; otherwise, the unit proceeds to step S608.

[0140] (Step S603) The arrival date prediction unit 135 arranges the days of all arrival dates obtained in step S601 on the circumference. Note that the days are only those that make up the year, month, and day.

[0141] (Step S604) The arrival date prediction unit 135 obtains the median value of all days arranged on the circumference.

[0142] (Step S605) The arrival date prediction unit 135 obtains an arrival date prediction that is a future day and has the date obtained in step S604. The arrival date prediction may be a year, month, day, or day.

[0143] (Step S606) The determination means 1352 determines whether the predicted arrival date obtained in step S605 is a non-business day or not. If it is a non-business day, the process proceeds to step S607; if it is a business day, the process returns to the higher level.

[0144] (Step S607) The correction means 1353 obtains the next business day or the business day immediately preceding the predicted arrival date obtained in step S605.

[0145] (Step S608) The arrival date prediction unit 135 assigns "NULL" to the arrival date prediction date. It returns to the higher-level processing.

[0146] Next, a second example of the arrival date prediction process in step S417 will be explained using the flowchart in Figure 7. The second example involves prediction using machine learning.

[0147] (Step S701) The arrival date prediction unit 135 obtains one or more arrival dates from the receipt management unit 112 that correspond to the connection information of interest.

[0148] (Step S702) The arrival date prediction unit 135 determines whether the arrival dates obtained in step S701 satisfy the prediction conditions (for example, "the number of arrival dates is equal to or greater than a threshold (for example, 2)"). If the prediction conditions are met, the unit proceeds to step S703; otherwise, the unit proceeds to step S708.

[0149] (Step S703) The arrival date prediction unit 135 acquires a learning model. It is preferable for the arrival date prediction unit 135 to acquire a learning model from the storage unit 11 that is paired with the number of arrival dates acquired in step S701.

[0150] (Step S704) The arrival date prediction unit 135 assigns one or more arrival dates and the learning model to a machine learning prediction processing module, executes the module, and obtains the predicted date.

[0151] (Step S705) The arrival date prediction unit 135 obtains an arrival date prediction that is a future day and has the date obtained in step S704. The arrival date prediction may be a year, month, day, or day.

[0152] (Step S706) The determination means 1352 determines whether the predicted arrival date obtained in step S705 is a non-business day or not. If it is a non-business day, the process proceeds to step S707; if it is a business day, the process returns to the higher level.

[0153] (Step S707) The correction means 1353 obtains the next business day or the business day immediately preceding the predicted arrival date obtained in step S705. It returns to the higher-level processing.

[0154] (Step S708) The arrival date prediction unit 135 assigns "NULL" to the arrival date prediction date. It returns to the higher-level processing.

[0155] Next, an example of the operation of a learning process performed by a learning device (not shown) will be explained using the flowchart in Figure 8. Note that the learning device may also be a learning unit (not shown) of the information processing device 1.

[0156] (Step S801) The learning device assigns 1 to counter i. Counter i is the number of explanatory variables that make up the training data. Note that counter i may start from 2.

[0157] (Step S802) The learning device determines whether i satisfies the learning condition. If i satisfies the learning condition, it proceeds to step S803; otherwise, it returns to the higher-level process. The learning condition is, for example, "i <= 4".

[0158] (Step S803) The learning device assigns 1 to counter j.

[0159] (Step S804) The learning device determines whether the j-th connection information exists in the receiving management unit 112. If the j-th connection information exists, proceed to step S805; otherwise, proceed to step S809.

[0160] (Step S805) The learning device obtains all arrival dates that are paired with the j-th connection information.

[0161] (Step S806) The learning device determines whether the number of arrival dates obtained in step S805 is greater than or equal to (i+1). If it is greater than or equal to (i+1), proceed to step S807; otherwise, proceed to step S808.

[0162] (Step S807) The learning device uses all the arrival dates obtained in step S805 to acquire one or more training data points. An example of this training data acquisition process will be explained using the flowchart in Figure 9.

[0163] (Step S808) The learning device increments counter j by 1. Return to step S804.

[0164] (Step S809) The learning device provides the training data acquired in step S807 to a module that performs machine learning training, executes the module, and obtains a learning model.

[0165] (Step S810) The learning device stores the learned model in the storage unit 11, associating it with the number of explanatory variables i.

[0166] (Step S811) The learning device increments counter i by 1. Return to step S802.

[0167] Next, an example of the training data acquisition process in step S807 will be explained using the flowchart in Figure 9.

[0168] (Step S901) The learning device obtains the number of explanatory variables (E). Note that the number of explanatory variables (E) is i in step S802 in Figure 8.

[0169] (Step S902) The learning device obtains the number of arrival dates (R) that it has acquired.

[0170] (Step S903) The learning device assigns 1 to counter i.

[0171] (Step S904) The learning device determines whether or not the condition "(i+E)<=R" is satisfied. If "(i+E)<=R" is satisfied, the device proceeds to step S905; otherwise, it returns to the higher-level process.

[0172] (Step S905) The learning device constructs training data using the i-th to E-th arrival dates from the acquired arrival dates as explanatory variables and the i-th to (E+1)th arrival date as the target variable.

[0173] (Step S906) The learning device increments counter i by 1. Return to step S904.

[0174] The following describes a specific example of the operation of information system A in this embodiment.

[0175] Currently, the invoice management unit 111 of the information processing device 1 stores the invoice management table shown in Figure 10. The invoice management table is a table that manages received invoice information, etc. The invoice management table manages records that have "ID", "Invoice Information", "Sender Identifier", "Recipient Identifier", "Arrival Date", "Account", "Account Information", and "Invoice Amount". "ID" is information that identifies the record. "Invoice Information" is assumed to be a PDF file of the invoice. "Sender Identifier" has "Company" and "Contact Person". "Company" is information that identifies the organization (company, sole proprietor, government office, etc.) that sent the invoice information, and in this case, it is the company name. "Contact Person" is information that identifies the contact person at the organization that sent the invoice information, and in this case, it is the contact person's name. "Recipient Identifier" has "Company" and "Contact Person". "Company" is information that identifies the organization (company, sole proprietor, government office, etc.) that received the invoice information, and in this case, it is the company name. "Contact Person" is information that identifies the contact person at the organization that received the invoice information, and in this case, it is the contact person's name. "Arrival Date" is the date the invoice information was received. "Account Information" refers to the account information of the sender of the invoice. "Account Information" is the account information entered by the recipient for the amount on the invoice. Here, "Account Information" refers to bank account information, typically including the bank name and account number. "Invoice Amount" is the amount being invoiced. Note that invoice information is linked to associated information.

[0176] Furthermore, the receipt management unit 112 stores the connection management table shown in Figure 11. The connection management table is a table for managing connection information. The connection management table has "Connection ID," "Sender Identifier," "Recipient Identifier," "Sender Email Address," "Recipient Email Address," "Standard Total Amount," and "Frequency Information." The "Sender Email Address" is the email address of the person sending the invoice. The "Recipient Email Address" is the email address to which the invoice is sent. Note that the "Sender Email Address" can also be considered as the Sender Identifier. Similarly, the "Recipient Email Address" can also be considered as the Recipient Identifier. In this case, the "Sender Identifier" includes "Company" and "Contact Person." In this case, the "Recipient Identifier" includes "Company" and "Contact Person." Furthermore, records with and without "Frequency Information" may be mixed. The "Standard Total Amount" in Figure 11 is information obtained and stored by the processing unit 13, which acquires the total amount of one or more invoice information items associated with the connection information over a predetermined past period (for example, one month). Furthermore, the processing unit 13 may obtain the standard total amount only for connection information in which two or more invoices have been received during a predetermined period in the past. The processing unit 13 may also obtain the standard total amount only for connection information in which the frequency information satisfies predetermined conditions (for example, "monthly," "yearly," or "regularly").

[0177] Furthermore, the receipt management unit 112 stores an arrival date forecast table having the structure shown in Figure 12. The arrival date forecast table is a table that manages the actual arrival date for each connection ID and the next predicted arrival date of the invoice for each connection ID.

[0178] Furthermore, the storage unit 11 stores the frequency condition management table shown in Figure 13. The frequency condition management table is a table that manages the conditions used when determining frequency information. The frequency condition management table has "ID", "interval condition", and "frequency information".

[0179] In light of the above situation, the following two specific examples will be explained. Specific example 1 is a process for obtaining the predicted arrival date, and specific example 2 is a process for obtaining frequency information.

[0180] (Specific example 1) Assume that employee Z of Company X entered the output instruction "Company X" into terminal device 2. Furthermore, assume that the output instruction "Company X" is a command to obtain and output the estimated arrival date for all connection information of Company X.

[0181] Next, employee Z's terminal device 2 receives the output instruction and transmits the output instruction to the information processing device 1.

[0182] Next, the reception unit 12 of the information processing device 1 receives the output instruction "Company X". Then, the arrival date prediction unit 135 obtains all connection IDs (here, "1", "2", "3", "5", "6", etc.) that are paired with the output instruction "Company X" from the connection management table (Figure 11).

[0183] Next, the arrival date prediction unit 135 retrieves all arrival dates from the arrival date prediction table (Figure 12) for each connection ID. Then, for each connection ID, the arrival date prediction unit 135 retrieves the arrival date prediction using the retrieved arrival dates, for example, through the process described using the flowchart in Figure 6.

[0184] For example, based on the arrival date of "Connection ID=1", the arrival date prediction unit 135 obtains the days "3,1,1,2", arranges these days on a circle to obtain "1,1,2,3", and obtains the median value "1" or "2" (in this case, "1") of the sorted days. Next, the determination means 1352 obtains October 1st, which is after the final arrival date (2024 / 9 / 2) and is the next month and day that includes day "1". Then, the determination means 1352 refers to a calendar (not shown) to obtain the day of the week for October 1st (Tuesday) and determines that the day of the week is not a Saturday, Sunday, or public holiday. As a result, the correction means 1353 does not perform any correction processing. The predicted date output unit 141 then assigns "1" to the predicted arrival date of "Connection ID=1" (see the first record in Figure 12).

[0185] For example, based on the arrival date of "Connection ID=5", the arrival date prediction unit 135 obtains the days "28, 31, 2, 15, 29", arranges these days on a circle to obtain "15, 28, 29, 31, 2", and obtains the median of these sorted days, "29". Next, the determination means 1352 obtains September 29th, which is after the final arrival date (2024 / 8 / 29) and includes the day "29". Then, the determination means 1352 refers to a calendar (not shown) to obtain the day of the week for September 29th (Sunday) and determines that the day of the week is either Saturday, Sunday, or a public holiday. As a result, the correction means 1353 performs a correction process. That is, the correction means 1353 obtains the date of the Monday immediately following September 29th, "September 30th". Then, the prediction date output unit 141 substitutes "30" for the arrival prediction date of "Connection ID=5" (see the 4th record in Figure 12).

[0186] The arrival date prediction unit 135 performs the above processing for all connection IDs that are paired with the output instruction "X Company". Then, the arrival date prediction table for the predicted sunrise is completed.

[0187] Furthermore, the predicted date output unit 141 may also transmit the arrival date prediction table shown in Figure 12 to employee Z's terminal device 2. Such transmission may be performed in response to instructions from employee Z's terminal device 2, or it may be performed when the predicted arrival date is obtained.

[0188] (Specific example 2) The frequency acquisition unit 132, following the operation of the frequency acquisition process explained using the flowchart in Figure 5, refers to the frequency condition management table in Figure 13 for each connection ID, obtains the interval (number of days) from two consecutive arrival days, and acquires frequency information from the frequency condition management table that matches the interval condition that one or more intervals satisfy. As a result, the frequency acquisition unit 132 obtains, for example, the frequency information for each connection ID in the connection management table in Figure 11. Note that the frequency acquisition unit 132 may not be able to acquire frequency information for some connection IDs.

[0189] As described above, according to this embodiment, the arrival date of invoices can be predicted with high accuracy, thus supporting the rapid processing of monthly financial statements.

[0190] Furthermore, according to this embodiment, by taking into account the non-business days and business days of the invoice issuer, the arrival date of the invoice can be predicted with high accuracy, and as a result, it is possible to support the rapid processing of monthly financial statements.

[0191] Furthermore, according to this embodiment, frequency information can be obtained that identifies the frequency of invoice arrival for each connection information.

[0192] Furthermore, according to this embodiment, since it is possible to warn of undelivered invoices based on the invoice amount, it is possible to support the rapid processing of monthly financial statements.

[0193] The processing in this embodiment may be implemented by software. This software may be distributed by software download or the like. Alternatively, this software may be recorded on a recording medium such as a CD-ROM and distributed. This also applies to other embodiments in this specification. The software that implements information system A in this embodiment is the following program. In other words, this program causes a computer to function as an arrival date prediction unit that associates one or more connection pieces of information having a first contact person identifier for the recipient of the invoice and a second contact person identifier for the issuer of the invoice, refers to a receipt management unit that stores one or more receipt pieces of information having one or more past arrival dates for the invoice, predicts the next arrival date and obtains the predicted arrival date, and a predicted date output unit that outputs the predicted arrival date obtained by the arrival date prediction unit to the first contact person identified by the first contact person identifier.

[0194] Figure 14 is a block diagram of a computer system 300 that executes the program described herein to realize the various embodiments of the information processing device 1 described above.

[0195] In Figure 14, the computer system 300 includes a computer 301 with a CD-ROM drive, a keyboard 302, a mouse 303, and a monitor 304.

[0196] In Figure 14, the computer 301 includes, in addition to the CD-ROM drive 3012, an MPU 3013, a bus 3014 connected to the CD-ROM drive 3012, a ROM 3015 for storing programs such as boot-up programs, a RAM 3016 connected to the MPU 3013 for temporarily storing application program instructions and providing temporary storage space, and a hard disk 3017 for storing application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card for providing connectivity to a LAN.

[0197] The program that causes the computer system 300 to execute the functions of the information processing device 1, etc., as described above, may be stored on the CD-ROM 3101, inserted into the CD-ROM drive 3012, and then transferred to the hard disk 3017. Alternatively, the program may be transmitted to the computer 301 via a network (not shown) and stored on the hard disk 3017. The program is loaded into the RAM 3016 during execution. The program may also be loaded directly from the CD-ROM 3101 or the network.

[0198] The program does not necessarily have to include an operating system (OS) or third-party program that causes the computer 301 to execute the functions of the information processing device 1, etc., as described above. The program only needs to include the instruction portion that calls the appropriate function (module) in a controlled manner and obtains the desired result. How the computer system 300 operates is well known, so a detailed explanation is omitted.

[0199] In the above program, steps such as sending information and receiving information do not include hardware-based processing, such as processing performed by a modem or interface card in the transmission step (processing that can only be performed by hardware).

[0200] Furthermore, the computer running the above program may be a single computer or multiple computers. In other words, it may perform centralized processing or distributed processing.

[0201] Furthermore, it goes without saying that in each of the above embodiments, two or more communication means present in a single device may be physically implemented in a single medium.

[0202] Furthermore, in each of the above embodiments, each process may be implemented by centralized processing by a single device, or by distributed processing by multiple devices.

[0203] It goes without saying that the present invention is not limited to the embodiments described above, and various modifications are possible, all of which are also included within the scope of the present invention. [Industrial applicability]

[0204] As described above, the information processing device 1 according to the present invention has the effect of being able to support the rapid processing of monthly settlements because it can predict the arrival date of invoices with high accuracy, and is useful as a server for managing invoices. [Explanation of Symbols]

[0205] A Information Systems 1. Information Processing Device 2 Terminal devices 11 Storage Unit 12 Reception Department 13 Processing Unit 14 Output section 21 Terminal storage section 22 Terminal Reception Section 23 Terminal Processing Unit 24 Terminal transmission unit 25 Receiving part of the terminal 26 Terminal output section 111 Bill Management Department 112 Receipt Management Department 121 Invoice Reception Department 131 Invoice Storage Department 132 Frequency acquisition unit 133 Arrival Date Acquisition Section 134 Arrival Date Storage Section 135 Arrival Date Forecast Section 136 Warning judgment section 141 Prediction Day Output Section 142 Frequency output section 143 Warning output section 1351 Prediction means 1352 Judgment means 1353 Correction means

Claims

1. An arrival date prediction unit refers to a receiving management unit that stores one or more receipt information items having one or more past arrival dates for the invoice, associated with one or more connection information items having the first contact person identifier of the recipient of the invoice and the second contact person identifier of the issuer of the invoice, predicts the next arrival date, and obtains the predicted arrival date. An information processing device comprising: an arrival date prediction unit and a prediction date output unit that outputs the arrival date prediction unit obtained to the first person in charge identified by the first person in charge identifier.

2. The aforementioned arrival date prediction unit, The system includes a prediction means that obtains the dates of three or more arrival dates associated with the aforementioned connection information, obtains the median value when those dates are considered as data distributed on a circle, and obtains the first predicted arrival date using that median value as the date. The aforementioned forecast date output unit is, The information processing apparatus according to claim 1, which outputs the first predicted arrival date acquired by the prediction means, or a predicted arrival date based on the first predicted arrival date acquired by the prediction means.

3. The aforementioned arrival date prediction unit, A determination means for determining whether the first predicted arrival date obtained by the prediction means is a non-business day, If the determination means determines that it is a non-business day, the system includes a correction means for obtaining an arrival forecast date which is the business day immediately following the first arrival forecast date or the business day immediately preceding the first arrival forecast date. The aforementioned forecast date output unit is, The information processing apparatus according to claim 1 or 2, wherein if the determination means determines that it is a non-business day, it outputs the predicted arrival date acquired by the correction means, and if the determination means determines that it is a business day, it outputs the first predicted arrival date acquired by the prediction means.

4. A frequency acquisition unit acquires frequency information that identifies how often invoices are received, using two or more past arrival dates associated with the aforementioned connection information. The information processing apparatus according to any one of claims 1 to 3, further comprising: a frequency output unit that outputs the frequency information acquired by the frequency acquisition unit in association with the connection information.

5. The aforementioned received information is, The base total amount includes the sum of the amounts corresponding to one or more past invoices received within a specified period, A warning determination unit obtains the total amount of one or more invoices that correspond to the linked information associated with the aforementioned receipt information and that have arrived in the current month, and determines whether the difference between the said total amount and the aforementioned standard total amount is large enough to satisfy the warning conditions. The information processing apparatus according to any one of claims 1 to 4, further comprising a warning output unit that outputs warning information when the warning determination unit determines that the warning conditions are met.

6. The invoice receiving department receives invoice information that corresponds to the connection information, The aforementioned invoice receiving unit obtains the arrival date, which is the date on which it received the invoice information; The information processing apparatus according to any one of claims 1 to 5, further comprising: an arrival date storage unit that stores the arrival date acquired by the arrival date acquisition unit in the receipt management unit in association with the aforementioned connection information.

7. An information processing method implemented by an information processing device comprising an arrival date prediction unit and a prediction date output unit, The arrival date prediction unit refers to a receiving management unit which stores one or more receiving records having one or more past arrival dates for the invoice, associated with one or more connection records having a first contact person identifier for the recipient of the invoice and a second contact person identifier for the issuer of the invoice, and predicts the next arrival date and obtains the predicted arrival date. An information processing method comprising: a prediction date output step in which the prediction date output unit outputs the prediction date obtained by the arrival date prediction unit to the first person in charge identified by the first person in charge identifier.

8. Computers, An arrival date prediction unit refers to a receiving management unit that stores one or more receipt information items having one or more past arrival dates for the invoice, associated with one or more connection information items having the first contact person identifier of the recipient of the invoice and the second contact person identifier of the issuer of the invoice, predicts the next arrival date, and obtains the predicted arrival date. A program to function as a prediction date output unit that outputs the predicted arrival date acquired by the arrival date prediction unit to the first person in charge identified by the first person in charge identifier.

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