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 .
DETAILED ACTION
INFORMATION DISCLOSURE STATEMENT
The information disclosure statement (IDS) submitted on 02/14/24 & 07/14/25 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to as ineligible under subject eligibility test. In the Subject Matter Eligibility Test for Products and Processes (Federal Register, Vol. 79, No. 241, dated Tuesday, December 16, 2014, page 74621), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional device elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1 & 19-20
Step 1
This step inquires “is the claim to a process, article of machine, manufacture or composition of matter?” Yes,
Claim 1 – “Method” is a process.
Claims 19 & 20 - “Systems” or “Non-Transitory CRM” are machines.
Step 2A - Prong 1
This step inquires “does the claim recite an abstract idea, law or natural phenomenon”. This claim appears to directed to an abstract idea.
The limitation of “categorizing a collection of check images into a first plurality of check images that have successfully been processed via optical character recognition (OCR) to obtain deposit data and a second plurality of check images that have failed OCR processing; associating categorization data with each of the first plurality of check images and each of the second plurality of check images; providing the first plurality of check images, the second plurality of check images, and the categorization data to an untrained or partially trained machine learning (ML) model to obtain a further trained ML model; providing a deposit check image to the further trained ML model; receiving a confidence score from the further trained ML model, the confidence score indicating a likelihood the deposit check image will be successfully processed via OCR to obtain deposit data; in response to the confidence score meeting a predetermined threshold, forwarding the deposit check image for OCR processing; determining the OCR processing of the deposit check image has failed; in response to the OCR processing of the deposit check image having failed, providing the deposit check image to the further trained ML model to further train the further trained ML model.”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity) but for the recitation of generic computer components. That is, other than reciting “at least one memory; and at least one processor coupled to the at least one memory,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “at least one memory; and at least one processor coupled to the at least one memory,” language, “categorizing; associating; providing; receiving; determining” in the context of this claim encompasses covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity).
Step 2A - Prong 2
This step inquires “does the claim recite additional elements that integrate the judicial exception into a practical application”. This judicial exception is not integrated into a practical application. In particular, the claim recites two additional element – using a “at least one memory; and at least one processor coupled to the at least one memory,” to perform “categorizing; associating; providing; receiving; determining” steps. The “at least one memory; and at least one processor coupled to the at least one memory,” are recited at a high-level of generality (i.e., as a generic processor) “categorizing a collection of check images into a first plurality of check images that have successfully been processed via optical character recognition (OCR) to obtain deposit data and a second plurality of check images that have failed OCR processing; associating categorization data with each of the first plurality of check images and each of the second plurality of check images; providing the first plurality of check images, the second plurality of check images, and the categorization data to an untrained or partially trained machine learning (ML) model to obtain a further trained ML model; providing a deposit check image to the further trained ML model; receiving a confidence score from the further trained ML model, the confidence score indicating a likelihood the deposit check image will be successfully processed via OCR to obtain deposit data; in response to the confidence score meeting a predetermined threshold, forwarding the deposit check image for OCR processing; determining the OCR processing of the deposit check image has failed; in response to the OCR processing of the deposit check image having failed, providing the deposit check image to the further trained ML model to further train the further trained ML model.” such that it amounts no more than mere instructions to apply the exception using a generic computer component.
STEP 2A – PRONG 2 - CONCLUSION
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The critical inquiry here is does the claim recite additional elements that amount to “significantly more” than the judicial exception? The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a “at least one memory; and at least one processor coupled to the at least one memory,” to perform “categorizing; associating; providing; receiving; determining” steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As to claim 2, this claim is directed to generic computer components (“trained ML model”), and insignificant extra-solution activity (“Field of Use/Technological Environment”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 3, this claim is directed to generic computer components (“mobile device”) and insignificant extra-solution activity (“Output/Reporting Activity ancillary to the core decisioning”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 4, this claim is directed to generic computer components (“mobile device, untrained or partially ML model”) and insignificant extra-solution activity (“data transfer/distribution or field of use - ancillary”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 5, this claim is directed to mental process (“providing user instructions to a user”) and insignificant extra-solution activity (“user messaging/remediation – ancillary to abstract decisioning”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 6, this claim is directed to mental process (“identifying parameters and determining weights indicating importance or “evaluation/judgment””). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 7, this claim is directed to generic computer components (“Deep learning model”), mental process (“identifying parameters and determining weights indicating value e.g. evaluation/judgment”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 8, this claim is directed to generic computer components (“DL model, mobile device”) and insignificant extra-solution activity (“field of use/ environment of use”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 9, this claim is directed to generic computer components (“generic ML model”) and mental process (“updating weights based on other weights e.g. evaluation/optimization”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 10, this claim is directed to mental process (“providing user instructions based on evaluated parameter values e.g. observation/evaluation/judgment and following rules/instructions”) and insignificant extra-solution activity (“ancillary guidance – extra solution activity”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 11, this claim is directed to generic computer components (“onboard sensors”), and insignificant extra-solution activity (“data gathering/adding inputs”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 12, this claim is directed to generic computer components (“onboard sensor, accelerometer, gyroscope”) and insignificant extra-solution activity (“data gathering specificity”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 13, this claim is directed to insignificant extra-solution activity (“narrows what data features are considered however this generally feeds features into the predictive model”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 14, this claim is directed to generic computer components (“onboard sensor, ML model”), and insignificant extra-solution activity (“data gathering/extra inputs”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 15, this claim is directed to mental process (“adjusting a decision threshold based on observed outcomes is evaluation/judgment”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 16, this claim is directed to mental process (“in response to the confidence score meeting the predetermined threshold is core judgment/decision rule”) and insignificant extra-solution activity (“data gathering”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 17, this claim is directed to insignificant extra-solution activity (“data gathering/field of use”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claim 18, this claim is directed to generic computer components (“mobile device; camera”) and insignificant extra-solution activity (“use a camera to get the image is data acquisition”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
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 1-5, 7-8, 10-11, 14 & 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Zagaynov et al. (U.S. Publication 2022/0366179) in view of Rapoport et al. (U.S. Patent 10,192,089)
As to claims 1 & 19-20, Zagaynov discloses categorizing a collection of images into a first plurality of images that have successfully been processed via optical character recognition (OCR) to obtain deposit data and a second plurality of images that have failed OCR processing; ([0038-0039])
associating categorization data with each of the first plurality of images and each of the second plurality of images; ([0038-0040])
providing the first plurality of images, the second plurality of images, and the categorization data to an untrained or partially trained machine learning (ML) model to obtain a further trained ML model; ([0040-0043])
providing a deposit image to the further trained ML model;
receiving a confidence score from the further trained ML model, the confidence score indicating a likelihood the deposit image will be successfully processed via OCR to obtain deposit data; ([0063-0065])
in response to the confidence score meeting a predetermined threshold, forwarding the deposit image for OCR processing; ([0082-0084])
determining the OCR processing of the deposit image has failed; in response to the OCR processing of the deposit image having failed, providing the deposit image to the further trained ML model to further train the further trained ML model. ([0082-0086])
Zagaynov [0033] discloses Image 102 may be an image of any document, such as a commercial or government application, a contract, a research paper, a memorandum, a medical document, a government-issued identification, a newspaper article, a business card, a letter, or any other type of a document.
Zagaynov is silent to implementing OCR systems in the task of OCRing checks.
However, Rapoport’s Background discloses implementing OCR systems in the task of OCRing checks.
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zagaynov’s disclosure to include the above limitations in order to incorporate dynamic security features to enhance authentication capabilities (See Background)
As to claim 2, Zagaynov in view of Rapoport discloses everything as disclosed in claim 1. In addition, Zagaynov discloses wherein the further trained ML model is implemented on a mobile device. ([0030, 0032, 0079])
As to claim 3, Zagaynov in view of Rapoport discloses everything as disclosed in claim 2. In addition, Zagaynov discloses providing, via the mobile device, a status of the deposit check image to a user prior to forwarding the deposit check image for OCR processing. ([0080-0083])
As to claim 4, Zagaynov in view of Rapoport discloses everything as disclosed in claim 2. In addition, Zagaynov discloses wherein the untrained or partially trained ML model is trained on a remote platform and provided to the mobile device. ([0030, 0037])
As to claim 5, Zagaynov in view of Rapoport discloses everything as disclosed in claim 1. In addition, Zagaynov discloses in response to the confidence score not meeting the predetermined threshold, instructions to a user to re-take the deposit check image. ([0023, 0093])
As to claim 7, Zagaynov in view of Rapoport discloses everything as disclosed in claim 1. In addition, Zagaynov discloses providing the deposit check image to a deep learning (DL) model, wherein the DL model is configured to identify a plurality of parameters associated with a check image and determine a plurality of weights associated with the parameters, each of the plurality of weights indicating an importance of a corresponding parameter in predicting whether a check image can be successfully processed via OCR to obtain deposit data. (Fig. 5)
As to claim 8, Zagaynov in view of Rapoport discloses everything as disclosed in claim 7. In addition, Zagaynov discloses wherein the DL model is implemented on a mobile device used to capture the deposit check image. ([0030, 0032, 0079]) ([0033, 0079])
As to claim 10, Zagaynov in view of Rapoport discloses everything as disclosed in claim 7. In addition, Zagaynov discloses providing instructions to a user to modify a condition of image capture based on a value of a parameter associated with a check image captured prior to the deposit check image. (804, Fig, 8)
As to claim 11, Zagaynov in view of Rapoport discloses everything as disclosed in claim 7. In addition, Zagaynov discloses providing onboard sensor data associated with the deposit check image to the DL model with the deposit check image. (810, Fig. 4)
As to claim 14, Zagaynov in view of Rapoport discloses everything as disclosed in claim 11. In addition, Zagaynov discloses providing the onboard sensor data associated with the deposit check image to the further trained ML model with the deposit check image. (810, Fig. 4)
As to claim 17, Zagaynov in view of Rapoport discloses everything as disclosed in claim 1. In addition, Zagaynov discloses wherein the deposit check image is manually captured by a user. (834, Fig. 8)
As to claim 18, Zagaynov in view of Rapoport discloses everything as disclosed in claim 2. In addition, Zagaynov discloses wherein the deposit check image is captured using a camera of the
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Zagaynov et al. (U.S. Publication 2022/0366179) in view of Rapoport et al. (U.S. Patent 10,192,089) as applied in claims 1, above further in view of Roach et al. (U.S. Publication 2014/0032406)
As to claim 6, Zagaynov in view of Rapoport discloses everything as disclosed in claim 1 but is silent to wherein the first plurality of check images comprises a check image comprising a blurry portion.
However, Roach discloses wherein the first plurality of check images comprises a check image comprising a blurry portion. ([0107, 0109] discloses decreasing the chance that the captured image will be blurred. Capture Images of a check or other financial instrument. )
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zagaynov in view of Rapoport’s disclosure to include the above limitations in order to OCR failures on blurred check images.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Zagaynov et al. (U.S. Publication 2022/0366179) in view of Rapoport et al. (U.S. Patent 10,192,089) as applied in claims 1, above further in view of HARALDSON et al. (WO 2022/060264)
As to claim 9, Zagaynov in view of Rapoport discloses everything as disclosed in claim 7 but is silent to updating a plurality of weights of the further trained ML model based on the plurality of weights determined by the DL model.
However, HARALDSON discloses updating a plurality of weights of the further trained ML model based on the plurality of weights determined by the DL model. ([0039-0040, 0091, 0098, 0123, 0161-0162, 0177] discloses changing one or more parameters of the second ML based on the calculated output average or calculated weighted output. )
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zagaynov in view of Rapoport’s disclosure to include the above limitations in order to improve OCR success prediction accuracy and calibration, speed convergence/reduce required retraining data and stabilizing updates when new capture conditions or devices are used.
Claims 12-13 & 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over
Zagaynov et al. (U.S. Publication 2022/0366179) in view of Rapoport et al. (U.S. Patent 10,192,089) as applied in claim 11, above further in view of Prentice (U.S. Publication 2011/0050954)
As to claim 12, Zagaynov in view of Rapoport discloses everything as disclosed in claim 11 but is silent to wherein the onboard sensor data comprises at least one of accelerometer data from a time of the deposit check image capture or gyroscope data from the time of the deposit check image capture.
However, Prentice discloses wherein the onboard sensor data comprises at least one of accelerometer data from a time of the deposit check image capture or gyroscope data from the time of the deposit check image capture. ([0007] discloses a gyroscope is a device that can measure angular acceleration and is found in digital still cameras to record camera movement only during image capture.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zagaynov in view of Rapoport’s disclosure to include the above limitations in order to detect/mitigate motion/blur and improve deposit image quality (e.g. fewer failed captures/ better OCR).
As to claim 13, Zagaynov in view of Rapoport discloses everything as disclosed in claim 12 but is silent to wherein the plurality of parameters comprises at least one of acceleration or angular velocity.
However, Roach discloses wherein the plurality of parameters comprises at least one of acceleration or angular velocity. ([0105[ discloses accelerometer and gyro sensor)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zagaynov in view of Rapoport’s disclosure to include the above limitations in order to better prevent forwarding images likely to fail OCR due to device motion.
As to claim 15, Zagaynov in view of Rapoport discloses everything as disclosed in claim 1 but is silent to adjusting the predetermined threshold based on a failure rate of OCR processing of forwarded deposit check images.
However, Roach discloses adjusting the predetermined threshold based on a failure rate of OCR processing of forwarded deposit check images. ([0319] discloses thresholds can be lowered after repeated rejections/resubmissions.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zagaynov in view of Rapoport’s disclosure to include the above limitations in order to reduce unnecessary recaptures/false rejections while maintaining a desired OCR success performance level.
As to claim 16, Zagaynov in view of Rapoport discloses everything as disclosed in claim 2 but is silent to automatically capturing the deposit check image in response to the confidence score meeting the predetermined threshold, wherein the deposit check image comprises an image frame of a live stream of image frames.
However, Roach discloses automatically capturing the deposit check image in response to the confidence score meeting the predetermined threshold, wherein the deposit check image comprises an image frame of a live stream of image frames. ([0099] discloses automatically capture the image when requirements or thresholds are met. [0476] discloses camera in video mode; frames buffered; determine suitability for OCR; select best frame;)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zagaynov in view of Rapoport’s disclosure to include the above limitations in order to automatically obtain a high quality check image frame with minimal user intervention and improved OCR success.
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen P Coleman whose telephone number is (571)270-5931. The examiner can normally be reached Monday-Thursday 8AM-5PM.
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Stephen P. Coleman
Primary Examiner
Art Unit 2675
/STEPHEN P COLEMAN/Primary Examiner, Art Unit 2675