DETAILED ACTION
This is a Final Office Action in response to the amendment filed 09/30/2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Status of Claims
Claims 1/8/15 have been amended. Claims 2-7, 9-14, 16-20 have been canceled. Claims 1/8/15 are currently pending in the application and have been examined.
Response to Amendment
The amendment filed 9/30/2025 has been entered.
Response to Arguments
Claim Rejections 35 USC § 101:
Applicant submits on page 12 of the remarks that the claims do not recite a judicial exception. Examiner respectfully disagrees and notes that according to the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG) and under step 2A of the analysis of claims per the Alice framework, if a claim limitation covers observations or evaluations then it falls within the “mental process” grouping of abstract ideas. Under the 2019 PEG, the “mental processes” grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Per the October 2019 Updated Guidance examples of claims that recite mental processes include: a claim directed to “collecting information, analyzing it, and displaying certain results of the collection and analysis” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. Claims can recite a mental process even if they are claimed as being performed on a computer. As the Federal Circuit has explained, "Courts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016). See MPEP 2106.04(a)(2).
Applicant submits on page 16 of the remarks that the amended claims integrate a judicial exception into a practical application. Examiner respectfully disagrees and notes that the present claims do not integrate the judicial exception into a practical application in a matter that imposes meaningful limit to the judicial exception. The additional elements recited in the claims are just applying the use of a generic computer environment to perform the abstract idea. These additional elements do not provide improvement to the computer technology and do not provide a meaningful link of the abstract idea to a practical application.
Claim Rejections 35 USC § 103:
Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1,8, 15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
With respect to claims 1, 8, 15 the independent claims (claims 1, 18 and 15) are directed, in part, to a method and a system and a non-transitory computer-readable medium for collecting and processing transactional data. Step 1 – First pursuant to step 1 in the January 2019 Guidance, claims 1 is directed to a method comprising a series of steps which falls under the statutory category of a process claim 8 is directed to a system which falls under the statutory category of a machine and claim 15 is directed to a non-transitory computer readable medium, which falls under the statutory category of an article of manufacture. However, these claim elements are considered to be abstract ideas because they are directed to a mental process which includes observations or evaluations.
As per Step 2A - Prong 1 of the subject matter eligibility analysis, the claims are directed, in part, to receiving… a document; transforming… the document via optical character recognition (OCR) into characters; determining… a plurality of transactions recorded in the document; identifying… a stock-keeping unit (SKU) level data from the document; categorizing… the plurality of transactions into categorized transactional data; storing the categorized transactional data…; publishing consumption application programming interfaces (APIs); comparing… the document and a digital receipt comprising the SKU to determine a discrepancy; identifying… fraud based on the discrepancy; accessing… a consumer insight based on the prediction and through the consumption APIs; and providing the categorized transactional data and the consumer insight for consumption APIs ... If a claim limitation, under its broadest reasonable interpretation covers an observation or evaluation, then it falls under the “mental process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As per Step 2A - Prong 2 of the subject matter eligibility analysis, this judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional elements: a provider technology backend executed by one or more processors, a capture module, an aggregation engine, a fraud module, a storage module, application programming interfaces, a user device; claim 8 recites the additional elements: a system, one computer, a processor; claim 15 recites the additional elements: a non-transitory computer readable storage medium, computer processors. These additional elements are recited at a high-level of generality (i.e., as a generic device performing a generic computer function of receiving and storing data) such that these elements amount no more than mere instructions to apply the exception using a generic computer component. Examiner looks to Applicant’s specification in at least figures 1 and 3 and related text and [0057-0058] to understand that the invention may be implemented in a generic environment that “Computing device 300 includes a processor 303 coupled to a memory 306. Memory 306 may include volatile memory and/or persistent memory. The processor 303 executes computer-executable program code stored in memory 306, such as software programs 315. Software programs 315 may include one or more of the logical steps disclosed herein as a programmatic instruction, which can be executed by processor 303. Memory 306 may also include data repository 305, which may be nonvolatile memory for data persistence. The processor 303 and the memory 306 may be coupled by a bus 309. In some examples, the bus 309 may also be coupled to one or more network interface connectors 317, such as wired network interface 319, and/or wireless network interface 321. Computing device 300 may also have user interface components, such as a screen for displaying graphical user interfaces and receiving input from the user, a mouse, a keyboard and/or other input/output components (not shown). The various processing steps, logical steps, and/or data flows depicted in the figures and described in greater detail herein may be accomplished using some or all of the system components also described herein. In some implementations, the described logical steps may be performed in different sequences and various steps may be omitted. Additional steps may be performed along with some, or all of the steps shown in the depicted logical flow diagrams. Some steps may be performed simultaneously. Accordingly, the logical flows illustrated in the figures and described in greater detail herein are meant to be exemplary and, as such, should not be viewed as limiting. These logical flows may be implemented in the form of executable instructions stored on a machine-readable storage medium and executed by a micro-processor and/or in the form of statically or dynamically programmed electronic circuitry.” In addition, [0034] describes machine learning: “The storage module can have APIs that allow services to access the data stored in the storage module. Exemplary services are aggregation services, machine learning services, etc. The storage module can act as a repository of data for machine learning algorithms and modules (referred to herein as “ML models”). ML models can use the data in the storage model for processing and can derive relationships based on the processed data.” As described, the machine learning system is just being applied as a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are mere instructions to implement the abstract idea on a computer.
As per Step 2B of the subject matter eligibility analysis, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are mere instructions to apply the abstract idea on a computer. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements and the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that amount to significantly more than the abstract idea itself. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
The dependent claims further refine the abstract idea. These claims, including the recitation of machine learning, do not provide a meaningful linking to the judicial exception. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as by describing the nature and content of the data that is received/sent. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not significantly more than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 8, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2018/0189786 (hereinafter; Poole) in view of US Pub. No. 2023/0245152 (hereinafter; Zhu. Further in view of US Pub. No. 2021/0103937 (hereinafter; Joglekar).
Regarding claims 1/8/15, Poole discloses:
A method for collection and processing of transactional data; a system; A non-transitory computer readable storage medium comprising: (Poole [0005-0008] disclose transaction information and transaction record.) receiving, at a capture module of a provider technology backend executed by one or more processors, a document; (Poole [0076] discloses electronic image capture of a paper receipt for the transaction; system 300 may include a user device 302, which may be similar to a mobile device such as client device 120, a network 304, which may be similar to network 110, a front-end controlled domain 306, a back-end controlled domain 312, and a backend system 318.) transforming, by the capture module, the document via optical character recognition (OCR) into characters; (Poole [0037] discloses an OCR device that may convert images of text into characters.) determining, by an aggregation engine of the provider technology backend, a plurality of transactions recorded in the document; (Poole [0038] discloses the OCR image may be utilized to match a transaction receipt to a particular transaction record for a user stored on account provider system 150.) identifying, by the aggregation engine, a stock-keeping unit (SKU) level data from the document; (Poole [0037] discloses data extraction processor 140 may enhance extract transaction receipt data from a paper receipt, which may include identifying store keeping unit (SKU) level inventory information; [0039] discloses the identifying information may include SKU level inventory information.)
executing, by the capture module of the provider technology backend, a secure authentication protocol for each access request to the categorized transactional data via the consumption application programming interfaces (APIs); (Poole [0037] discloses an authentication server.
publishing, by the provider technology backend, consumption application programming interfaces (APIs) through a public API gateway that is accessible without authenticating a third-party service accessing the public API gateway; (Poole [0025] discloses Device control functionality may include connection creation, frequency-hopping sequence selection and timing, power control, security control, polling, packet processing, and the like. The device control functionality and other Bluetooth™-related functionality may be supported using a Bluetooth™ API provided by the platform associated with the client device 120 (e.g., The Android™ platform, the iOS™ platform). Using a Bluetooth™ API, an application stored on a client device 120 (e.g., a banking application, a financial account application, price matching application etc.) or the device may be able to scan for other Bluetooth™ devices, query the local Bluetooth™ adapter for paired Bluetooth™ devices, establish RFCOMM channels, connect to other devices through service discovery, transfer data to and from other devices, and manage multiple connections. A Bluetooth™ API used in the methods, systems, and devices described herein may include an API for Bluetooth Low Energy™ (BLE) to provide significantly lower power consumption and allow a mobile device 120 to communicate with BLE devices that have low power requirements; [0090] discloses Application server(s) 316 may include hardware and/or software that is dedicated to the efficient execution of procedures (e.g., programs, routines, scripts) for supporting its applied applications. Application server(s) 316 may comprise one or more application server frameworks, including, for example, Java application servers (e.g., Java platform, Enterprise Edition (Java EE), the .NET framework from Microsoft®, PHP application servers, and the like). The various application server frameworks may contain a comprehensive service layer model. Also, application server(s) 316 may act as a set of components accessible to, for example, a financial institution, or other entity implementing system 400, through an API defined by the platform itself.)
comparing, by a fraud module of the provider technology backend, the document and a digital receipt comprising the SKU to determine a discrepancy; (Poole [0038]; [0097] disclose comparing receipts and using SKU number for comparison.)
accessing, by the third-party service executed by one or more processors, a consumer insight based on the prediction and through the consumption APIs, a consumer insight based on the prediction; (Poole [0091] discloses Backend 318 may be associated with various databases, including account databases that maintain, for example, cardholder information (e.g., demographic data, credit data, cardholder profile data, and the like), transaction card databases that maintain transaction card data (e.g., transaction history, account balance, spending limit, budget categories, budget spending, budget limits, and the like), connection information (e.g., public/private key pairs, UUIDs, device identifiers, and the like) and the like.)
Although Poole discloses systems and methods for processing transactional data, Poole does not specifically discloses categorizing transactions and using machine learning for predicting transactional data. However, Zhu discloses the following limitations:
categorizing, by the aggregation engine of the provider technology backend, the plurality of transactions into categorized transactional data; (Zhu [0018] discloses For example, in some implementations, each transaction represented in the transaction data may be associated with a consumer or purchaser, a merchant or seller, an amount of the transaction, a product or service category (e.g., based on the merchant or seller), a date and/or time when the transaction occurred, and/or a location where the transaction occurred.) storing, by the provider technology backend, the categorized transactional data in a storage module; (Zhu [0049] discloses the transaction device 320 includes a transaction card (or another physical medium with integrated circuitry) capable of storing and communicating account information.)
determining, by a machine learning model of a data services module of the provider technology backend, a prediction based on the transactional data, the machine learning model being trained on a pattern of the user's previous transactions stored in the storage module; (Zhu [0022] discloses As shown in FIG. 1B, and by reference number 120, the trend prediction system may model patterns in historical consumer trends (e.g., using one or more machine learning models) based on the consumer trend data, the real-time transaction data, and/or the product-level data.)
identifying, by the fraud module, fraud based on the discrepancy; (Zhu [0052] discloses using the information stored by the transaction backend system for fraud detection.)
and providing, by the provider technology backend, the categorized transactional data and the consumer insight for consumption via the consumption APIs by a user device. (Zhu [0056] discloses Additionally, or alternatively, the client system 370 may include a client device or a user device, such as a wireless communication device, a mobile phone, a user equipment, a laptop computer, a tablet computer, a desktop computer, a wearable communication device (e.g., a smart wristwatch, smart eyeglasses, a head mounted display, or a virtual reality headset), or a similar device.)
It would have been obvious to one of ordinary skill in the art to combine the system for price matching of Poole with the trend identification of Zhu in order to provide trend information associated with a specific client (Zhu abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Although Poole discloses systems and methods for processing transactional data, Poole does not specifically disclose cryptographically signed information or a machine learning model based on feedback. However, Joglekar discloses the following limitations:
wherein the fraud module utilizes a non-transitory memory to store a cryptographically signed audit log of each comparison and fraud determination, and wherein the machine learning model is configured to update its prediction parameters in real time based on feedback from the fraud module, (See at least Joglekar [0136] using a feedback loop from a machine learning model; [0103] accessing cryptographic keys.)
wherein the aggregation engine is further configured to receive transactional data from at least two distinct sources selected from: a point-of- sale system, a third-party API, and an email application, and to associate SKU-level data with each transaction regardless of source. (Joglekar discloses Point of Sale systems in at least [0084]; [0094]; accessing emails in at least [0101]; [0135]; [0146])
It would have been obvious to one of ordinary skill in the art to combine the system for price matching of Poole with the systems and methods for generating profiles for entities of Joglekar in order to generate profiles based on feature data (Joglekar abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCIS Z SANTIAGO-MERCED whose telephone number is (571)270-5562. The examiner can normally be reached M-F 7am-4:30pm EST.
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/FRANCIS Z. SANTIAGO MERCED/Examiner, Art Unit 3625