DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
This action is in reply to Application 19/009,837 filed on 3 January 2025.
Claims 1-10 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
With regard to claim 1, the claim recites an abbreviation (e.g., CIR) without first reciting the actual term that the abbreviation represents. As such ambiguity of the abbreviated term exists. Accordingly the claim is indefinite. Dependent claims 2-10 are rejected based on dependency on a rejected base claim.
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.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, representative method claim 1 is directed towards facilitating fraud detection associated with a check image deposit across accounts. Claim 1 is directed to the abstract idea of using rules and/or instructions to implement an economic- /commercial-related concept/practice of facilitating fraud detection associated with a check across accounts comprising the steps of extracting, comparing, calculating, retrieving, presenting (displaying), and transmitting (providing) data/information associated with a financial-related transaction, grouped under the certain methods of organizing human activity – fundamental economic principles, practices or concepts; sales activity; following set of instructions; commercial or legal interactions (agreements in the form of contracts; business relations); managing interactions between people (including social activities, teachings, following rules or instructions) grouping in prong one of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance).
Claim 1 recites: “receiving a new incoming check image associated with an account; extracting from the incoming check its features, wherein the features are associated with a plurality of detectors; comparing the features with corresponding features associated with profile check images stored in a CIR; developing a fraud score based on the comparisons, for each of the plurality of detectors; retrieving information related to one or more other accounts linked with the account related to and then either update or develop the fraud score considering the retrieved information; displaying …, the new incoming check image with at least one highlighted feature, based on business rules, along with confidence indicators for the at least one highlighted feature that illustrate the risk, based on the associated fraud score, for the at least one highlighted feature; and providing, …, inputs that allow a reviewer to quickly approve or decline the new incoming check image”.
Accordingly, the claim recites an abstract idea (See 2019 Revised Patent Subject Matter Eligibility Guidance).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance), the additional elements of the claim such as a “user interface”, “CIR”, represent the use of a computer as a tool (intermediary) to perform an abstract idea and/or does no more than generally link the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e. automate) implement the acts of using rules and/or instructions to implement an economic- /commercial-related concept/practice of facilitating fraud detection associated with a check across accounts comprising the steps of extracting, comparing, calculating, retrieving, presenting (displaying), and transmitting (providing) data/information associated with a financial-related transaction.
When analyzed under step 2B (See 2019 Revised Patent Subject Matter Eligibility Guidance), the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of using rules and/or instructions to implement an economic- /commercial-related concept/practice of facilitating fraud detection associated with a check across accounts comprising the steps of extracting, comparing, calculating, retrieving, presenting (displaying), and transmitting (providing) data/information associated with a financial-related transaction using computer technology. Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Hence, claim 8 is not patent eligible.
Dependent claims 2-10 add further details and contain limitations that narrow the scope of the invention. However, these details do not result in significantly more than the abstract idea itself. As explained in the December 16, 2014 Interim Eligibility Guidance from the USPTO (in reference to the BuySAFE, Inc. v. Google, Inc. decision), further narrowing the details of an abstract idea does not change the § 101 analysis since a more narrow abstract idea does not make it any less abstract.
Viewed individually and in combination, these additional elements do not provide meaningful limitations to transform the abstract idea such that the claims amount to significantly more than the abstraction itself.
Accordingly, the present pending claims are not patent eligible and are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
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 of this title, 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.
Claims 1-10 are rejected under 35 U.S.C. § 103 as being unpatentable over Vadlamudi et al., US 2022/0366513 A1 (“Vadlamudi”), in view of Kolavennu et al., US 2024/0144204 A1 (“Kolavennu”).
Re Claim 1: Vadlamudi discloses a method comprising using at least one hardware processor to:
receiving a new incoming check image associated with an account; (FIG. 5: “RECEIVE A DIGITAL IMAGE OF A CHECK THAT INCLUDES .A SIGNATURE OF THE ACCOUNT HOLDER S504”)
extracting from the incoming check its features, wherein the features are associated with a plurality of detectors; (¶[0104]: “model may first extract the signatures with a localizer, then feed the two extracted signatures …”)
Regarding the limitation feature comprising:
comparing the features with corresponding features associated with profile check images stored in a CIR;
Kolavennu makes these teachings in a related endeavor (¶[0015]: “The check fraud detection system 102 can communicate, via a network 104, with a check database 106 and a check submission system 108. In an example implementation, the check fraud detection system 102 includes an extraction processor 110, an object detection processor 112, a comparison processor 114, and a recognition processor 116.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolavennu with the invention of Vadlamudi as disclosed above, for the motivation of facilitating performing automatic check fraud detection.
Vadlamudi further discloses:
developing a fraud score based on the comparisons, for each of the plurality of detectors; (¶[0099]: “the identification module 422 may be configured to identify that the received check is fraudulent based on a determination that the similarity score is a value that is below a predetermined threshold value, and authorization module 424 may be configured to automatically deny processing of the received check.”)
retrieving information related to one or more other accounts linked with the account related to and then either update or develop the fraud score considering the retrieved information; (¶[0023]: “… cause a processor to perform the following: accessing a database that stores preauthorized historical reference 128-dimensional embedding of a signature relating to an account holder”; ¶[0107]: “a database may be provided that stores preauthorized historical reference 128-dimensional embedding of a signature relating to an account holder.”; ¶[0111]: “At step S510, the new 128-dimensional embedding of the signature may be compared with the preauthorized historical reference 128-dimensional embedding of the signature by accessing the database.”)
displaying via a user interface, the new incoming check image with at least one highlighted feature, based on business rules, along with confidence indicators for the at least one highlighted feature that illustrate the risk, based on the associated fraud score, for the at least one highlighted feature; (¶[0093]: “… application module 416 may be configured to apply a machine learning model ( e.g., a pre-trained model) to generate a new 128-dimensional embedding of the signature of the account holder parsed from the received digital image of the check by the parsing module 414.”)
providing, via the user interface, inputs that allow a reviewer to quickly approve or decline the new incoming check image. (¶[0007]: “… method may further include: identifying that the received check is not fraudulent based on a determination that the similarity score is a value that is at or above a predetermined threshold value; and automatically authorizing processing of the received check.”)
Re Claim 2: Vadlamudi in view of Kolavennu discloses the method of claim 1. Vadlamudi further discloses:
wherein the inputs further allow the reviewer to escalate the review. (¶[0005]: “implementing a fraud check detection module for automatically detecting and identifying fraudulent checks through check image analysis by utilizing neural networks, machine learning models, and graphics processing units (GPUs), thereby identifying whether a check is fraudulent in real-time upon receiving a digital image of the check.”)
Re Claim 3: Vadlamudi in view of Kolavennu discloses the method of claim 1. Vadlamudi further discloses:
wherein the new incoming check image is displayed in the user interface if the result of the comparison is the fraud score that is higher than a threshold. (¶[0007]: “… method may further include: identifying that the received check is not fraudulent based on a determination that the similarity score is a value that is at or above a predetermined threshold value; and automatically authorizing processing of the received check.”)
Re Claim 4: Vadlamudi in view of Kolavennu discloses the method of claim 1. Vadlamudi further discloses:
wherein business alerts associated with the account are presented through the user interface along with the new incoming check image. (¶[0101]: “… notification module 432 may be configured to automatically notify the account holder via an electronic message or voice message that the received check has been denied for further processing.”)
Re Claim 5: Vadlamudi in view of Kolavennu discloses the method of claim 1. Regarding the limitation feature comprising:
wherein an image of a profile check that can be used to visually contrast and compare with the new incoming check image is present within the user interface along with the new incoming check image.
Kolavennu makes these teachings in a related endeavor (¶[0015]: “The check fraud detection system 102 can communicate, via a network 104, with a check database 106 and a check submission system 108. In an example implementation, the check fraud detection system 102 includes an extraction processor 110, an object detection processor 112, a comparison processor 114, and a recognition processor 116.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolavennu with the invention of Vadlamudi as disclosed above, for the motivation of facilitating performing automatic check fraud detection.
Re Claim 6: Vadlamudi in view of Kolavennu discloses the method of claim 1. Vadlamudi further discloses:
wherein the user interface includes at least some of a notes section, a global score associated with the plurality of detectors associated with the new incoming check image, the channel on which new incoming check image was received, and the number of profile checks in the related CIR. (¶[0099]: “the identification module 422 may be configured to identify that the received check is fraudulent based on a determination that the similarity score is a value that is below a predetermined threshold value, and authorization module 424 may be configured to automatically deny processing of the received check.”)
Re Claim 7: Vadlamudi in view of Kolavennu discloses the method of claim 5. Regarding the limitation feature comprising:
wherein the user interface is configured to allow the reviewer to overlay the new incoming check image on the profile check image.
Kolavennu makes these teachings in a related endeavor (¶[0015]: “The check fraud detection system 102 can communicate, via a network 104, with a check database 106 and a check submission system 108. In an example implementation, the check fraud detection system 102 includes an extraction processor 110, an object detection processor 112, a comparison processor 114, and a recognition processor 116.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolavennu with the invention of Vadlamudi as disclosed above, for the motivation of facilitating performing automatic check fraud detection.
Re Claim 8: Vadlamudi in view of Kolavennu discloses the method of claim 1. Vadlamudi further discloses:
wherein multiple new incoming check images are displayed for review in the user interface at the same time. (¶[0005]: “implementing a fraud check detection module for automatically detecting and identifying fraudulent checks through check image analysis by utilizing neural networks, machine learning models, and graphics processing units (GPUs), thereby identifying whether a check is fraudulent in real-time upon receiving a digital image of the check.”; ¶[0054]: “As described herein, various embodiments provide optimized processes of implementing a check fraud detection module in which the generated proprietary real-time fraud detection model (FDM) may handle a large number of received digital images of checks and multiple data sources required by machine learning models, executing the FDMs in real-time using deployment of multiple neural networks in one platform built using a cluster of GPUs and CPUs.”)
Re Claim 9: Vadlamudi in view of Kolavennu discloses the method of claim 1. Regarding the limitation feature comprising:
wherein the one or more other accounts can include checking accounts, credit card accounts, or other financial accounts.
Kolavennu makes these teachings in a related endeavor (¶[0013]: “… comparing the incoming check stock image and a reference check stock image using a machine learning algorithm; determining an image pattern score based on the comparison of the incoming check stock image and the reference check stock image …”; ¶[0015]: “The check fraud detection system 102 can communicate, via a network 104, with a check database 106 and a check submission system 108. In an example implementation, the check fraud detection system 102 includes an extraction processor 110, an object detection processor 112, a comparison processor 114, and a recognition processor 116.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolavennu with the invention of Vadlamudi as disclosed above, for the motivation of facilitating performing automatic check fraud detection.
Re Claim 10: Vadlamudi in view of Kolavennu discloses the method of claim 1. Regarding the limitation feature comprising:
wherein the retrieved information can include transaction channel information, transaction frequency/velocity information, transaction location information and/or transaction amount information.
Kolavennu makes these teachings in a related endeavor (¶[0012]: “The method may include detecting a date and an amount of the incoming check using character recognition; and determining a double presentment score based on the detected date and amount …”; ¶[0013]: “… comparing the incoming check stock image and a reference check stock image using a machine learning algorithm; determining an image pattern score based on the comparison of the incoming check stock image and the reference check stock image …”; ¶[0015]: “The check fraud detection system 102 can communicate, via a network 104, with a check database 106 and a check submission system 108. In an example implementation, the check fraud detection system 102 includes an extraction processor 110, an object detection processor 112, a comparison processor 114, and a recognition processor 116.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolavennu with the invention of Vadlamudi as disclosed above, for the motivation of facilitating performing automatic check fraud detection.
Conclusion
The prior art(s) made of record and not relied upon is/are considered pertinent to applicant's disclosure.
Bueche, Jr. (US 12,260,700 B1) discloses a system, computing device, and method for document detection and deposit processing. An image of a check is captured by an imaging device and processing of the digital image of the check for deposit at a remote server may be accomplished with a downloaded software application on a portable computing device associated with the imaging device. The downloaded application may include one or more trained machine learning models for processing the captured image. The portable computing device may utilize deterministic algorithms for certain
image processing tasks and machine learning models for others. The selection between machine learning and deterministic processing may be made locally on the portable device or in response to instructions from an institution server to use a particular processing method.
Foster et al. (US 12,039,504 B1) discloses a mobile check deposit. Methods and systems for remote check deposit are disclosed. A check image captured by an image capture device of a mobile device in response to receiving a user actuation causing the image capture device to capture the check image
is received. The mobile device is caused to perform optical character recognition (OCR) on the check image to generate OCR data. The OCR data generated from the check image is verified to determine whether it includes required predetermined check data to process the check for remote deposit. The OCR data is provided to a financial institution server for validation processing. In response to receiving a confirmation from a user, the check image is provided to the financial instruction server with instructions to process the check for remote deposit. The mobile device receives a deposit receipt notification from the financial institution server after the check is deposited.
Claims 1-10 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Clifford Madamba whose telephone number is 571-270-1239. The examiner can normally be reached on Mon-Thu 7:30-5:00 EST Alternate Fridays.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon, can be reached at 571-272-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLIFFORD B MADAMBA/Primary Examiner, Art Unit 3692