INTELLIGENT SYSTEM FOR AUTOMATED CLASSIFICATION AND ENUMERATION OF ALPHANUMERIC DATA

Abstract

This specification provides an automated expense enumeration system that leverages optical character recognition (OCR) and machine learning algorithms to facilitate the detection, extraction, and refinement of transaction enumeration from digital receipt images. The system begins with an OCR scan to identify transaction-related text, which is then analyzed using machine learning to accurately classify and segment the data into individual expense entries, with a particular focus on VAT classification and organizing expenses into subcategories. A user interface allows for the verification, adjustment, and confirmation of these entries, incorporating user feedback to continuously improve the accuracy of the machine learning model.Stored in a database, the finalized entries allow for improved reporting, accounting practices, and efficient VAT recovery.

Description

Title of the invention: INTELLIGENT SYSTEM FOR AUTOMATED CLASSIFICATION AND ENUMERATION OF ALPHANUMERIC DATA DOMAIN

[0001] This specification generally relates to the processing of alphanumeric data and more particularly to the automated classification of alphanumeric data. CONTEXT

[0002] The integration of digital technology into financial management processes has progressed significantly, driven by the development of artificial intelligence (AI) and improvements in computer hardware. In particular, the field of expense management has seen transformative potential through these technologies. Optical character recognition (OCR) technology, for example, has evolved to accurately identify, extract, and organize financial data from digital images of receipts and invoices, providing a reliable method for automating expense documentation and reporting.

[0003] Despite these advances, the complexity of expenditure statements, particularly those involving multiple items and varying tax rates across different jurisdictions, presents challenges that often require human intervention. While ITA and OCR technologies have greatly reduced the manual workload associated with enumerating and classifying expenditures, the nuanced requirements for accurate VAT categorization and the organization of expenditures into specific subcategories still necessitate a certain level of oversight. SUMMARY

[0004] One aspect of the specification provides a method for the automatic intelligent enumeration of expenditures in an electronic expense management system, the method including: receiving a digital image of a receipt by an electronic device, wherein the receipt includes one or more detailed transactions; initiating an optical character recognition (OCR) process on the received digital image to detect the text indicating said one or more detailed transactions; applying a machine learning algorithm to analyze the detected text to identify the characteristics of the detailed transactions, wherein the characteristics include at least the item description, the amount, and the applicable tax rate; and segmenting the identified detailed transactions into separate expenditure entries, each expenditure entry corresponding to a a separate detailed transaction, identified on the receipt; the generation of a user interface (UI) display presenting the segmented expense entries, in which the UI display includes options for a user to confirm, modify, or reject the segmentation and characteristics of each expense entry; receiving user input via the UI display regarding the confirmation, modification, or rejection of the segmented expense entries and their characteristics; updating the machine learning algorithm based on the received user input to improve the accuracy of future OCR processes and the identification of characteristics in detailed transactions;and the storage of confirmed expenditure entries in a database associated with the electronic expenditure management system, in which each stored expenditure entry is linked to a corresponding expenditure status and is accessible for report generation, accounting processes, and VAT recovery.

[0005] One aspect of the specification provides a method, in which the OCR process further includes the preprocessing of the digital image for noise reduction and contrast enhancement in order to improve the accuracy of text detection.

[0006] One aspect of the specification provides a method, further comprising the automatic categorization of each of the segmented expenditure entries into predefined expenditure categories based on the identified characteristics and using a classification algorithm in the machine learning algorithm.

[0007] One aspect of the specification provides a method, in which the generation of the user interface display also includes the display of a graphical representation of the receipt as well as segmented expense entries to facilitate verification and modification by the user.

[0008] One aspect of the specification provides a method, including the calculation of a total amount of expenditure from the sum of the amounts of the confirmed expenditure entries and its comparison to a total amount shown on the receipt to ensure the completeness of the enumeration process.

[0009] One aspect of the specification provides a method, in which the machine learning algorithm is further configured to learn the user's corrections over time, up to the categorization of expense entries, thereby improving the automatic categorization of future receipts based on the user's past entries.

[0010] One aspect of the specification provides a method, further comprising the application of a validation rule which verifies the accuracy of the VAT rate applied to each expenditure entry based on the geographical location of the expenditure and the applicable tax laws.

[0011] One aspect of the specification provides a method in which the machine learning algorithm uses user feedback on the UI screen to train the model specifically to detect detailed lines that are often misrecognized, thereby improving the algorithm's ability to accurately identify and extract detailed transaction data from various receipt formats.

[0012] One aspect of the specification provides a method, including the integration of confirmed expenditure entries with an enterprise resource planning (ERP) system for the automation of financial reporting and accounting processes.

[0013] One aspect of the specification provides a method, further including the generation of detailed reports which include detailed expenditures for each receipt, the reports being configurable to include specific data fields based on user or business requirements.

[0014] One aspect of the specification provides a computer-implemented method for the automatic enumeration of expenses in an electronic expense management system, the method comprising: (a) the receipt, by the electronic expense management system, of a digital image of a receipt uploaded by a user; (b) the initiation, by the electronic expense management system, of an enumeration detection service configured to check the uploaded digital image for itemized lines using pattern analysis and machine learning techniques; (c) prompting, by a user interface (UI) of the electronic expense management system, the user to confirm whether the receipt contains itemized lines;(d) updating a machine learning model of the electronic expense management system, based on the user's confirmation response, in which the response includes a positive or negative confirmation regarding the presence of itemized lines on the receipt; (e) in response to a positive confirmation from the user, the extraction, by an optical character recognition (OCR) enumeration service of the electronic expense management system, of data from the itemized lines on the digital image; (f) the generation, by the UI, of a display of the extracted itemized lines for review by the user and the receipt of user input regarding corrections to the itemized lines; (g) updating the machine learning model based on user input to refine the accuracy of itemized receipt detection and data extraction by OCR;(h) the processing of the expense reimbursement process in the electronic expense management system, which includes detailed receipts, for reimbursement and VAT recovery; and (i) providing an option in the electronic expense management system for auditors to initiate the enumeration service by; OCR using a "detail receipt" feature, further improving the machine learning model with data from listener interactions; wherein the machine learning model improves the enumeration detection service and the OCR enumeration service by: (i) continuously learning from user and listener interactions to improve the detection of detailed receipts, and (ii) fine-tuning the OCR data extraction process to improve the accuracy of data extraction from detailed lines on receipts.

[0015] One aspect of the specification provides a server-implemented method for the intelligent automatic enumeration of expenses in an electronic expense management system, the system configured to: (a) receive a digital image of a receipt, the receipt including one or more detailed transactions, the receipt being downloaded by a user via an electronic device; (b) run an optical character recognition (OCR) module on the server to process the received digital image and detect the text indicating said one or more transactions; (c) apply a machine learning algorithm, run on the server, to analyze the detected text and identify the characteristics of the detailed transactions, the characteristics including at least the item description, the amount, and the applicable tax rate;(d) segment the identified detailed transactions into separate expenditure entries on the server, each expenditure entry corresponding to a unique detailed transaction, identified on the receipt; (e) generate a user interface (UI) that is provided to the electronic device to present the segmented expenditure entries, the UI including options for the user to confirm, modify, or reject the segmentation and identified features of each expenditure entry; (f) receive, at the server level, user input from the UI regarding the user's confirmation, modification, or rejection of the segmented expenditure entries and their features; (g) update the machine learning algorithm on the server based on the user input to improve the accuracy of subsequent OCR processes and the identification of features in detailed transactions;and (h) store confirmed expense entries in a server-managed database associated with the electronic expense management system, each stored expense entry being linked to a corresponding expense report and made accessible for activities such as report generation, accounting processes, and VAT recovery. BRIEF DESCRIPTION OF FIGURES;

[0016] Fig. 1 is a schematic diagram of a system for the intelligent automatic enumeration of expenses.

[0017] The [Fig.2] is an example of a classification server structure of the [Fig. 1].

[0018] Figure 3 shows a diagram representing an intelligent automatic enumeration method for expenses.

[0019] Fig. 4 is an example of a receipt.

[0020] Fig. 5 is another example of a receipt, as entered into the system.

[0021] The [Fig.6] is an example of a user interface.

[0022] The [Fig.7] is another example of a user interface.

[0023] Figure 8 shows a diagram illustrating the feedback loop of the machine learning process of Figure 3. DETAILED DESCRIPTION

[0024] Figure 1 shows an intelligent automatic expense enumeration system, usually denoted as 100. The system 100 includes a classification server 104. In the system 100, the classification server 104 connects to a network 108 such as the Internet. The network 108 interconnects the classification server 104 with: a) a payment processing engine 112; b) a plurality of client devices 116; and d) an administrator workstation 120. As will be seen below, the classification server 104 performs a number of processing functions for the system 100.

[0025] Note that, collectively, customer devices 116-1, 116-2...116-n are referred to generically as device 116, device 116. This nomenclature is used elsewhere in this document.

[0026] Figure 2 shows a schematic diagram of a non-limiting example of internal components of the classification server 104. In this example, the classification server 104 includes at least one input device 204. The input from device 204 is received by a processor 208, which in turn controls an output device 212. The input device 204 can be a traditional keyboard and / or a mouse to provide physical input. Similarly, the output device 212 can be a display. In variations, additional input devices 204 and / or other output devices 212 are considered or can be omitted entirely, depending on the context.

[0027] The processor 208 can be implemented as a plurality of processors or several multi-core processors. The processor 208 can be configured to execute different programming instructions in response to the input received via said one or more input devices 204 to control one or more output devices 212 to generate the output on these devices.

[0028] To perform its programming functions, the processor 208 is configured to communicate with one or more memory units, including non-DRAM memory Volatile 216 and volatile 220 memory. Non-volatile 216 memory can be based on persistent memory technology, such as electrically erasable programmable read-only memory (EEPROM), flash memory, solid-state drives (SSDs), other types of hard drives, or combinations thereof. Non-volatile 216 memory can also be described as non-transient, computer-readable storage media. Furthermore, more than one type of non-volatile 216 memory may be available.

[0029] Volatile memory 220 is based on random access memory (RAM) technology. For example, volatile memory 220 can be based on double data rate (DDR) synchronous dynamic random access memory (SDRAM). Other types of volatile memory 220 are envisaged.

[0030] The processor 208 also connects to the network 108 via a network interface 232. The network interface 232 can also be used to connect another computing device which has an input and output device, thus avoiding the need to have the input device 204 and / or the output device 212.

[0031] Programming instructions in the form of applications 224 are typically stored persistently in non-volatile memory 216 and used by the processor 208, which reads from and writes to volatile memory 220 during the execution of the applications 224. Various processes discussed herein can be coded as one or more applications 224. One or more arrays or databases 228 are stored in non-volatile memory 216 for use by the applications 224.

[0032] The infrastructure of the classification server 104, or a variant thereof, can be used to implement any computer node in the system 100, including the payment processing engine 112. In addition, the classification server 104 and the payment processing engine 112 can also be implemented as virtual machines and / or mirror images to balance the load.

[0033] Furthermore, a person skilled in the art will recognize that the essential elements of the processor 208, the input device 204, the output device 212, the non-volatile memory 216, the volatile memory 220, and the network interface 232, as described in relation to the environment of the classification server 104, have analogies in the various form factors of client machines such as those that can be used to implement the client devices 116 and the workstation 120. The client devices 116 and the workstation 120 can be based on any combination of computer workstations, laptops, electronic tablets, mobile phone devices, or the like.

[0034] Each device 116 and its user 124 are thus associated with a user identification object 128. A person skilled in the art will recognize that the The form of an identification object 128 is not particularly limited, and in a simple embodiment, it can simply be an alphanumeric sequence that is entirely unique with respect to other identification objects in the system 100. Identification objects can also be more complex, in that they can be combinations of account identification data (e.g., username, name, password, two-factor authentication token, etc.) that uniquely identify a given user 124. Identification objects themselves can also be indexes that point to other identification objects, such as accounts. The essential point is that they are uniquely identifiable within the system 100 in association with what they represent.The user identification object 128 can therefore be used as part of the authentication of an account and / or a session with the classification server 104 and / or the payment processing engine 112.

[0035] According to this embodiment, the client devices 116 rely on any suitable client computing platform, operated by the users 124 to submit expense reimbursement requests to be submitted for reimbursement by their employer or other entity. Reimbursement requests include travel expenses, such as transportation, accommodation, and meals. Reimbursement requests may also include purchases of small equipment or similar items. The nature of the reimbursement request itself is not particularly limited and is provided for illustrative purposes, although it is not necessarily relevant to the technical aspects of this specification.

[0036] As discussed in more detail below, classification server 104 hosts an expense reimbursement application 224-1. The application 224-1 enables an expense reimbursement process which includes a user 124 logging into an account hosted by an engine 104 via identification data associated with their respective identification object 128. The user 124 can then interact with graphical interfaces generated by the engine 104 on the screen of a respective device 116 to receive detailed alphanumeric information articulating the details of the expense reimbursement request and also to upload images of detailed documents such as receipts or other documentation to support the alphanumeric information.In due course, the expense reimbursement request can be approved (or rejected), in whole or in part, by engine 104, either automatically or with human supervision manifested at workstation level 120 by an administrator 132. Payment for the approved parts of the request can be made by the payment processing engine 112 which oversees the transfer of funds to a financial account associated with the respective user.

[0037] For the fabrication, [Fig. 3] shows a diagram presenting a process of Intelligent automated expense validation, typically designated as 300. Process 300 can be implemented on System 100. Business users can choose to implement Process 300 on System 100 or variations thereof, or with certain blocks omitted, executed in parallel, or in a different order than shown. Process 300 can therefore also be modified. However, for explanatory purposes, Process 300 will be described in relation to its execution on System 100, with a specific focus on Processing Method 300, such as, for example, a portion of a 224-1 expense claim stored on the 104 classification server and its interactions with other nodes in System 100.

[0038] Block 310 includes receiving a digital image of a receipt, the receipt including one or more detailed transactions. Receipt 404 in [Fig. 4] shows an example of such a receipt. Receipt 404 can be captured by a camera in a device 116 or otherwise digitized in any manner now envisaged or in the future. The classification server 104 can receive receipt 404 via the network 108 for further processing.

[0039] In particular, receipt 404 is a striking example for this specification insofar as it is a hotel bill which includes a total charge but also a plurality of items including room charges, meals and other services up to restaurant receipts listing the various items purchased.

[0040] Block 320 initiates an optical character recognition (OCR) process on the received digital image to detect the text that indicates one or more detailed transactions. OCR technology is used to scan the digital image of the receipt in order to identify and extract the textual information related to the transactions listed on the receipt. This step transforms the visual data of the receipt into machine-readable text, enabling further analysis of the detailed transactions.

[0041] The information on receipt 404 as a result of block 320 is reproduced below:

[0042] Grand Sundance Hotel 7760 Pennington St. Bardstown, KY 40004

[0043] Received

[0044] Customer Information Name: David Address: 390 West Pearl St. Commack, NY 11725 Contact Number: 1-202-555-0145

[0045] Room No.: 16 Nights: 2 Check-in time: 12:30 Check-out time: 11:30 [Tables 1] Date Description Taxes Fees Credit 03 / 29 / 19 Room Accommodation 5% $3000.00 03 / 29 / 19 Meals 1% $1000.00 03 / 29 / 19 Telephone Bill 1% $280.00 03 / 29 / 19 Linens 1% $150.00 03 / 29 / 19 Car Rental 1% $450.00

[0046] Total tax: 9% Total fees: $4880.00 Tax rate: $439.20

[0047] Grand total: $5,319.20

[0048] Customer signature:

[0049] Cashier's signature:

[0050] Referring now to [Fig. 5] to continue the discussion of an example of process 300, another example of receipt 504 will be mentioned. (Receipt 504 having already been entered into classification server 104). As shown in [Fig. 5], the OCR process of block 320 is complete, but the information on receipt 504 is reproduced below.

[0051] Hotel bill - 0 16 / 10 / 2023 Germany - 550.00 EUR

[0052] Service fee: 20 EUR

[0053] Parking fee: 50 EUR

[0054] Internet expenses: EUR 30

[0055] Total amount: EUR 650.00

[0056] Block 330 includes the application of a machine learning algorithm to analyze the detected text in order to identify the characteristics of detailed transactions, including, for example, the item description, the amount, and any applicable tax rates. The use of machine learning algorithms allows the analysis of the extracted text to discern various attributes of each transaction, such as descriptions, costs, and taxes. This algorithmic approach is designed to learn and improve over time, adapting to new data and user feedback to enhance its ability to accurately classify and interpret transaction details.

[0057] Block 340 includes the segmentation of detailed transactions identified as separate expense entries, each corresponding to a distinct detailed transaction, identified on the receipt. The system segments the extracted transaction details into individual expense entries. This segmentation is performed to provide a structured breakdown included on a single receipt, facilitating expense management and tracking by categorizing each item separately.

[0058] Block 350 includes the generation of a user interface (UI) display (such as on a device 116) showing segmented expenditure entries, the display of The user interface (UI) includes options for users to confirm, modify, or reject the segmentation and characteristics of each expense entry. The UI is designed to present segmented expense entries in a clear and navigable way, allowing users to review, confirm, or adjust the details of each transaction. Options to modify or reject specific entries are provided to ensure the expense report accurately reflects actual transactions.

[0059] Figure 6 shows an example of the user interface (UI) 604 for display on device 116 illustrating the execution of blocks 330, 340, and 350, where, in relation to receipt 504: Service charge: EUR 20; Parking charge: EUR 50; and Internet expense: EUR 30 are extracted as separate itemized charges from the room charge of EUR 550. In particular, *Service charge: EUR 20; Parking charge: EUR 50; and Internet expense: EUR 30* are identified as separate itemized charges subordinate to the room charge itself. These separate itemized charges subordinate to the room charge may be referred to as "sub-items," which are broken down into individual items for proper evaluation, identification, auditing, and / or reimbursement purposes.

[0060] To this end, UI 604 contains a section entitled "Create Receipt," in which users can add digital images or electronic receipt files. This area is bordered by "Attachments," which provides an interactive element labeled "Click or Drag to Add Attachment," allowing users to easily upload their receipt documents. Adjacent to the attachments area 612, there is a categorization header dividing the input fields into two categories: "To be Reimbursed" and "Already Paid by Company," allowing users to classify expenses accordingly. In the "To be Reimbursed" section, users are prompted to complete the expense details. This includes "Period" fields, indicating the date of the expense, and "Country," specifying the location where the expense was incurred. Below this, there is a subsection entitled "Receipt Items."The subsection titled "Receipt Items" provides a dynamic form where users can enter multiple items associated with a single receipt. It includes dropdown menus for "Receipt Type," where users can select the nature of the expense, and "Tax Rate," where users can specify the tax rate as "standard," "reduced," or "none" for each item. Adjacent to the dropdown menu are fields for users to enter the monetary "Amount" for each item and select the "Currency" from a dropdown menu, ensuring accurate financial documentation in different currencies. For each detailed entry, a line is provided where users can enter a description, such as "Expenses." for Internet,” “Parking Fee,” or “Service Fee,” with corresponding fields to enter, and view the amounts clearly and in an organized manner. At the bottom of the “Receipt Items” subsection, a running total is displayed, titled “Total Amount,” summarizing the costs of all entered items. Below the item list, there is an additional text entry field titled “Description” allowing users to include a detailed description of the expense, if needed. The final section of UI 604 is titled “Additional Information,” containing a text box with a checkbox. This box allows users to enter additional details about the expense, which can be marked for inclusion in the Expense Report for further clarification or to support multi-line entry.A prominent "Save and Close" button is displayed at the bottom of the UI, indicating the action users will take to save entered data and exit the receipt creation process. The UI 604 is designed to facilitate the organized entry of expense data, promoting a streamlined reimbursement process and providing a user-friendly experience for the efficient management of financial records within the electronic expense management system.

[0061] The 360 ​​block includes receiving user input via the UI display regarding the confirmation, modification, or rejection of segmented expense entries and their characteristics. Users interact with the UI to finalize the details of the expense entries. This interaction includes confirming the accuracy of the segmented entries, necessitating modifications to match actual transactions, or rejecting incorrect segments. This step can be used to confirm that the expense status is accurate and reflects the user's verification and adjustments.

[0062] The 360 ​​block can be implemented via a suitable UI such as UI 604. Another example for the 360 ​​block is UI 704 in [Fig.7] which is substantially the same as UI 604 but shows some fields in "edit" mode, including field 708.

[0063] Block 370 includes an update to the machine learning algorithm based on user input received to improve the accuracy of future OCR processes and the identification of detailed transaction features. The system incorporates user feedback to refine the machine learning algorithm, making it an expert in identifying and classifying transaction details in future scenarios. This adaptive learning process is designed to progressively improve system performance by reducing errors and increasing the efficiency of processing expenditure enumerations.

[0064] Fig. 8 shows a diagram 804 which illustrates the machine learning feedback loop 808 envisaged in block 370.

[0065] Block 380 comprises the storage of confirmed expenditure entries in a database associated with the electronic expenditure management system, in which each stored expenditure entry is linked to a corresponding expenditure report and is accessible for report generation, accounting processes, and VAT recovery. The confirmed expenditure entries are stored in a structured manner in the system's database, facilitating access and retrieval for various purposes such as generating expenditure statements, integrating with accounting systems, and processing VAT recovery. This structured storage keeps expenditure data organized, secure, and readily available for financial management and reporting purposes.

[0066] As discussed, System 100 is an expert in extracting specific sub-items from expense receipts. In particular, VAT is a very important type of sub-item. The specification provides a specialized solution for extracting and managing value-added tax (VAT) information from itemized receipts, a process of particular importance due to the variable nature of VAT in different jurisdictions. The system recognizes that businesses often encounter a multitude of VAT rates and rules depending on the location and type of services purchased. It is specifically designed to accurately capture and extract this information. Using advanced optical character recognition (OCR) and machine learning algorithms, the system can identify and separate the VAT amounts for each item listed on a receipt.This capability can be used by companies operating internationally or in regions with diverse tax regulations, as it enables accurate VAT recovery and compliance with local tax laws. Automating VAT extraction simplifies the refund process, ensures accurate financial reporting, and reduces the administrative burden associated with manual VAT calculation, particularly when dealing with receipts that combine multiple transactions, each potentially subject to different VAT rates.

[0067] In general, machine learning, neural networks or other artificial intelligence techniques can be employed at the loop 808 level, with various manual interventions being successively used to train a machine learning model to determine which types of inputs are sub-items, including the appropriate identification of the tax (e.g. VAT).

[0068] In light of the foregoing, it now appears that variants, combinations, and subsets of the preceding embodiments are envisaged. For example, the classification server 104 can be avoided, or its function can be distributed in a variant on the system 100 and / or executed entirely locally on the device 116.

[0069] It should also be noted that process 300 can be implemented or omitted with certain blocks, executed in parallel, or in a different order than that shown. For example, process 300 can begin at a modified version of block 350, where the user interacts with a user interface to start the process of providing receipts to block 310. Alternatively, the process can begin with the user capturing a digital image of a receipt using the expense reimbursement application 224-1, followed by navigation to a GUI that visualizes the expenses, or by having a pop-up in the expense reimbursement application 224-1 that immediately displays a breakdown of the sub-items extracted from the photographed receipt.

[0070] As discussed, one or more applications 224 may incorporate machine learning or artificial intelligence, including but not limited to deep learning-based algorithms and neural networks. These technologies may be trained to improve the machine learning functions described herein. The machine learning applications 224 may be operated by the processor 208 in a training mode, in which machine learning, deep learning algorithms, and neural networks are trained in accordance with the teachings herein.

[0071] Said one or more machine learning algorithms and / or deep learning algorithm and / or neural networks of machine learning applications 224 may include, but not be limited to: a generalized linear regression algorithm; a random forest algorithm; an automatic support vector algorithm; a gradient reinforcement regression algorithm; a decision tree algorithm; a generalized additive model; neural network algorithms; deep learning algorithms; evolutionary programming algorithms; Bayesian inference algorithms; reinforcement learning algorithms, and the like.Preferences for generalized linear regression algorithms, random forest algorithms, automatic support vector algorithms, gradient reinforcement regression algorithms, decision tree algorithms, generalized additive models, neural networks, and deep learning algorithms may be due to factors such as computational efficiency, scalability, data requirements, or suitability for a specific application.

[0072] In some implementations, the machine learning algorithm is designed to learn from user corrections over time. As users provide corrections related to the categorization of expenditure entries, the algorithm can adapt and refine its processes, improving potentially the automatic categorization of future receipts based on the user's accumulated entries.

[0073] Furthermore, in certain embodiments, the machine learning algorithm uses user feedback from the FUI display to train the model to detect detailed lines that are generally poorly recognized. This training can improve the algorithm's ability to accurately identify and extract data from various receipt formats.

[0074] In some embodiments, the OCR process for extracting text from digital images of receipts includes preprocessing steps for noise reduction and contrast enhancement. This preprocessing can improve the accuracy of text detection, enabling more efficient extraction of text data for subsequent analysis by machine learning algorithms.

[0075] In another embodiment, the process can automatically categorize each of the segmented expenditure entries into predefined expenditure categories based on the identified characteristics. This categorization is facilitated by a classification algorithm within the framework of machine learning, which analyzes the characteristics to assign the appropriate expenditure categories.

[0076] Furthermore, some embodiments include a user interface display that presents a graphical representation of the original receipt along with the segmented expense entries. This feature can help users automatically verify and modify the segmented entries by providing a visual comparison, which can improve user confidence in the accuracy of the segmentation.

[0077] Other improvements to the process include calculating the total expenditure amount from the confirmed expenditure entries and comparing this calculated total to the total amount shown on the receipt. This step can improve the completeness and accuracy of the enumeration process, helping to verify that all transactions have been recorded and properly documented.

[0078] Another aspect involves a validation rule that verifies the accuracy of the VAT rate applied to each expenditure entry, taking into account the geographical location of the expenditure and the applicable tax laws. This approach can contribute to compliance and accuracy in the treatment of tax-related expenditures.

[0079] Furthermore, the system can integrate confirmed expenditure entries into an enterprise resource planning (ERP) system. This integration can facilitate the automation of financial reporting and accounting processes, simplify financial operations, and improve data accuracy in enterprise systems.

[0080] In addition, the system can generate detailed reports that include the expenses listed for each receipt. These reports can be configured to include specific data fields based on user or company requirements, enabling customized financial reports that meet diverse organizational needs.

[0081] Furthermore, this specification provides an embodiment in which the system incorporates a risk assessment module specifically designed to evaluate the potential for fraud or error in the listed transactions. In this embodiment, the machine learning algorithm can be trained to categorize and list expenses and also assign a risk level to each transaction, based on predefined criteria. These criteria can include unusual spending patterns, discrepancies between entries of similar items, and non-compliance with company spending policies. For example, transactions involving unapproved types of expenses, such as personal expenses like cigarettes or luxury items typically not covered by standard company policies, are automatically flagged as high-risk.

[0082] The system further enhances its risk assessment capabilities by identifying out-of-policy expenses such as luxury accommodations or upgrades during travel that exceed company-approved limits. It can also detect duplicate claims where the same receipt is submitted multiple times, either by an employee or in different reports, indicating potential fraudulent activity. In addition, expenses incurred on dates or at locations inconsistent with planned business activities can trigger alerts, suggesting inaccuracies or misuse.

[0083] Currency mismatches in transactions—expenses for which reimbursement is requested in currencies not associated with the reported location of the expense—also constitute red flags for potential review. Similarly, non-compliant VAT refund claims, in which VAT recovery is requested for ineligible expenses or incorrect tax rates, are identified and highlighted for administrative action.

[0084] Each of these risk factors, and others that will appear to people in the field, can be visually represented in a dashboard of the workstation 120 for the administrator 132, or as an icon in the interface of a device 116 for the user 124, allowing for quick and efficient identification and prioritization of transactions that may require further examination.

[0085] A person skilled in the art will now appreciate that the teachings of this document can improve technological efficiency and the use of computer and communication resources throughout the entire System 100 by automatically identifying items and their types on documents such as receipts during entry, assessments and audits, optionally coupled with machine learning, and generating a risk level for an administrator or user in the form of a dashboard or icon.In this way, expenses can be allocated appropriately and anomalies in expense reports can be managed more automatically to reduce the system resources required to send reports in both directions between the user and the administrator, increasing the throughput of automatic expense reimbursements while also providing a graphical interface that facilitates machine learning.

[0086] It must be acknowledged that the characteristics and aspects of the various examples provided above can be combined in other examples that also fall within the scope of the present invention. Furthermore, the figures are not to scale and may be exaggerated in size and shape for illustrative purposes.

Claims

Demands

1. A method for intelligent automatic enumeration of expenditures in an electronic expenditure management system, the method comprising: receiving a digital image of a receipt by an electronic device, in which the receipt includes one or more itemized transactions; initiating an optical character recognition (OCR) process on the received digital image to detect the text indicating said one or more itemized transactions; applying a machine learning algorithm to analyze the detected text to identify the features of the itemized transactions, in which the features include at least the item description, the amount, and the applicable tax rate; segmenting the identified itemized transactions into separate expenditure entries, each expenditure entry corresponding to a distinct itemized transaction, identified on the receipt;the generation of a user interface (UI) display presenting segmented expense entries, in which the UI display includes options for a user to confirm, modify, or reject the segmentation and characteristics of each expense entry; the receipt of user input via the UI display regarding the confirmation, modification, or rejection of expense entries and their characteristics; the updating of the machine learning algorithm based on the received user input to improve the accuracy of future OCR processes and the identification of detailed transaction characteristics;and the storage of confirmed expenditure entries in a database associated with the electronic expenditure management system, in which each stored expenditure entry is linked to a corresponding expenditure statement and is accessible for report generation, accounting processes and VAT recovery.

2. The method according to claim 1, wherein the OCR process further includes the preprocessing of the digital image for the noise reduction and contrast enhancement to improve text detection accuracy.

3. The method according to claim 1 further comprising the automatic categorization of each of the segmented expenditure entries into predefined expenditure categories based on the identified characteristics and using a classification algorithm in the machine learning algorithm.

4. The method according to claim 1, wherein the generation of the user interface display further includes the display of a graphical representation of the receipt as well as segmented expense entries to facilitate verification and modification by the user.

5. The method according to claim 1, further comprising calculating a total amount of expenditure from the sum of the amounts of confirmed expenditure entries and comparing it to a total amount shown on the receipt to ensure the completeness of the enumeration process.

6. The method according to claim 3, wherein the machine learning algorithm is further configured to learn user corrections over time, up to the categorization of expense entries, thereby improving the automatic categorization of future receipts, based on past user entries.

7. The method according to claim 1 further comprising the application of a validation rule which verifies the accuracy of the VAT rate applied to each expenditure entry, based on the geographical location of the expenditure and the applicable tax laws.

8. The method according to claim 1, wherein the machine learning algorithm uses user feedback on the UI screen to train the model specifically to detect detailed lines that are often misrecognized, thereby improving the algorithm's ability to accurately identify and extract detailed transaction data in various receipt formats.

9. The method according to claim 1, further comprising the integration of confirmed expenditure entries into an enterprise resource planning (ERP) system for the automation of financial reporting and accounting processes.

10. The method according to claim 1, further comprising the generation of detailed reports which include detailed expenditures for each receipt, the reports being configurable to include specific data fields, based on user or business requirements.

11. A computer-implemented method for the automatic enumeration of expenses in an electronic expense management system, the method comprising: (a) the receipt, by the electronic expense management system, of a digital image of a receipt uploaded by a user; (b) the initiation, by the electronic expense management system, of an enumeration detection service configured to check the uploaded digital image for itemized lines using pattern analysis and machine learning techniques; (c) prompting, via a user interface (UI) of an electronic expense management system, the user to confirm whether the receipt contains itemized lines;(d) updating a machine learning model of the electronic expense management system, based on the user's confirmation response, in which the response includes a positive or negative confirmation regarding the presence of itemized lines on the receipt; (e) in response to a positive confirmation from the user, the extraction, by an optical character recognition (OCR) enumeration service of the electronic expense management system, of data from the itemized lines on the digital image; (f) the generation, by FUI, of a display of the extracted itemized lines for review by the user and the receipt of user input regarding corrections to the itemized lines; (g) updating the machine learning model based on user input to refine the accuracy of itemized receipt detection and data extraction by OCR;(h) the processing of the expense reimbursement process in the electronic expense management system, which includes detailed receipts, for reimbursement and VAT recovery; and; (i) providing an option in the electronic expenditure management system for auditors to launch the OCR enumeration service using a "Detail Receipt" feature, further improving the machine learning model with data from auditor interactions; wherein the machine learning model improves the enumeration detection service and the OCR enumeration service by: (i) continuously learning from auditor and user interactions to improve the detection of detailed receipts; and (ii) fine-tuning the OCR data extraction process to improve the accuracy of data extraction from detailed lines on receipts.

12. A server-implemented system for the intelligent automatic enumeration of expenditures in an electronic expense management system, the system being configured to: (a) receive a digital image of a receipt, the receipt including one or more itemized transactions, the receipt being downloaded by a user via an electronic device; (b) run an optical character recognition (OCR) module on the server to process the received digital image and detect the text indicating said one or more itemized transactions; (c) apply a machine learning algorithm, run on the server, to analyze the detected text and identify the characteristics of the itemized transactions, the characteristics including at least the item description, the amount, and the applicable tax rate;(d) segment the identified detailed transactions into separate expense entries on the server, each expense entry corresponding to a unique detailed transaction, distinguished on the receipt; (e) generate a user interface (UI) for the electronic device to display the segmented expense entries, the UI including options for the user to confirm, modify, or reject the segmentation and characteristics of each expense entry; (f) receive, at the server level, user input from the UI regarding the confirmation, modification, or rejection of the; segmented expense entries and their characteristics, by the user; (g) update the machine learning algorithm on the server based on received user input to improve the accuracy of subsequent OCR processes and the identification of detailed transaction features; and (h) store confirmed expenditure entries in a server-managed database associated with the electronic expenditure management system, each stored expenditure entry being linked to a corresponding expenditure statement and made accessible for activities such as report generation, accounting processes and VAT recovery.