DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Claim Interpretation - 35 USC § 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or preAIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1:
“a digital document authenticity validation engine configured to…”; and
“a SVM document classifier configured to…”.
Claim 4:
“intelligent document processing engine ... configured to…”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or preAIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1, 6-9, 11-12, 14-16 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wells et al (US20240221413) in view of Dang et al (US10402641).
Regarding claim 1, 9 and 16, Wells teaches a system for identification of digital document tampering, the system comprising:
(Wells, " Fraudsters may leverage technology to automate a series of repeated, fraudulent attempts to mislead an entity ... The document evaluator 226 described herein may beneficially detect such fraudulent documents.", [0036]; a document evaluation system is designed to detect fraudulent/fake documents, which constitutes a system for identification of digital document tampering)
a first computing platform including a first memory, and one or more first computing processor devices in communication with the first memory,
(Wells, Figs. 1 and 2; "The server 122 is a computing device that includes a hardware and/or virtual server that includes a processor, a memory", [0041]; "the computing device 200 includes a processor 202, a memory 204", [0044]; a first computing platform having a processor and memory in communication)
Wells does not expressly disclose but Dang teaches:
wherein the first memory stores a Support Vector Machine (SVM) platform comprising one or more SVM algorithms, executable by at least one of the one or more first computing processor devices and including:(Dang; "the image classifying engine may use a support vector machine (SVM) classifier technique to generate a non-linear boundary between data points in the training set.", c6:50-55; Dang teaches the specific implementation of utilizing a Support Vector Machine (SVM) algorithm to generate boundaries to classify documents; on the other hand, Wells teaches the memory storing evaluation algorithms executable by the computing processor (“the memory 204 may store an instance of the document evaluator 226”, [0046]))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to implement the document evaluation algorithms stored in Wells' memory using the SVM classifier techniques taught by Dang to provide robust, non-linear classification boundaries. The combination of Wells and Dang also teaches other enhanced capabilities.
The combination of Wells and Dang further teaches:
a digital document authenticity validation engine configured to:
receive a batch of digital documents,
(Wells, "The image preprocessor 302 receives one or more images representing a document", [0054]; "obtaining a set of test images representing multiple instances of the first document", [0006]; a validation engine receives images representing documents, including obtaining batches/sets of test images representing multiple instances of a document)
implement at least one of the one or more SVM algorithms to verify authenticity of barcodes present within one or more of the digital documents in the batch of digital documents,
(Wells, "The object detection engine 308 detects one or more objects in a document image.", [0083], "identifies that the field is a human-readable zone (HRZ), as opposed to a machine-readable zone, such as a barcode or QR code.", [0098]; Dang; "the image classifying engine may use a support vector machine (SVM) classifier technique to generate a non-linear boundary between data points in the training set.", c6:50-55; Wells teaches detecting objects within document images, including acknowledging machine-readable zones such as barcodes; Dang provides the SVM classification technique; it would have been obvious to apply Dang's SVM algorithm within Wells's object evaluation engine to specifically verify the authenticity of detected barcodes)
implement at least one of the one or more SVM algorithms including at least one image classifier model to classify images present within one or more of the digital documents in the batch of digital documents and verify authenticity of the images,
(Wells, "The object detection engine 308 detects one or more objects in a document image”, “detect the facial image 510 and ghost image 520", [0083], "evaluates whether a bounding box associated with content is present or absent.", [0118]; verifying the authenticity and presence of images, such as facial and ghost images, within the documents; Dang, " The one or more processors may determine, for the document and using a first machine learning model, a first classification of one of a plurality of document types", c17:1-5 "the image classifying engine may use a support vector machine (SVM) classifier technique to generate a non-linear boundary between data points in the training set.", c6:50-55; using an SVM classifier model to classify image data; applying an SVM image classifier to verify the authenticity of the images present in the documents)
implement at least one of the one or more SVM algorithms to verify authenticity of signatures provided by a signatory and present within one or more of the digital documents in the batch of digital documents, and
(Wells, “the object detection engine 308 detects one or more objects in a document image ... Examples of object may include one or more of a hole punched in the document (often indicating that the document is expired or invalid), the overall shape of the document (e.g., a clipped bottom right corner may be used by the system 100 to quickly determine invalidity for certain jurisdictions/issuers), signatures, facial images, ghost images, holograms, watermarks, kinegrams, seals, symbols, laser perforations", [0083]; "performing a similarity check between the signature 1438 and a signature on the back", [0123]; "a purported signature is consistent with a font", [0126]; verifying the authenticity of signatures provided by a signatory on the documents by performing similarity checks; Dang, "the image classifying engine may use a support vector machine (SVM) classifier technique to generate a non-linear boundary between data points in the training set.", c6”50-55; Dang teaches the SVM algorithm; it would have been obvious to apply Dang's SVM technique to execute Wells' signature authenticity verification)
a SVM document classifier configured to:
receive results of barcode, image, and signature authenticity validation from the digital document authenticity validation engine, and
(Wells, Fig. 13; "The verification determiner 1314 obtains at least a subset of the intermediary results generated by one or more of the bounding box presence/absence evaluator 1308, the inter-bounding box evaluator 1310 or its subcomponent(s), and the intra-bounding box evaluator 1312 or its subcomponent(s)", [0141]; a verification determiner receives intermediate results from the evaluations of bounding boxes containing the barcodes, images, and signatures; Dang, "the image classifying engine may use a support vector machine (SVM) classifier technique to generate a non-linear boundary between data points in the training set.", c6:50-55; implementing the classifier using SVM)
implement at least one of the one or more SVM algorithms to classify each digital document in the batch of digital documents as (i) valid document or (ii) invalid document based, at least, on the results of barcode, image, and signature authenticity validation; and
(Wells, "based on at least a subset of the intermediary results, determines whether the document under test is a valid instance of the document.", [0141], "returns a verification result ... indicating that the document (e.g., the imaged photo ID) is not verified/invalid or is valid.", [0144]; classifying the document as valid or invalid based on the intermediate evaluation results of the images, signatures, and document zones; Dang, "the image classifying engine may use a support vector machine (SVM) classifier technique to generate a non-linear boundary between data points in the training set.", c6:50-55; utilizing an SVM algorithm for classification)
a second computing platform including a second memory, and one or more second computing processor devices in communication with the second memory,
(Wells, "The memory 204 may be included in a single computing device or distributed among a plurality of computing devices.", [0046]; "various entities of the system may be integrated into a single computing device or system or divided into additional computing devices or systems", [0042]; the system can be distributed across additional computing platforms having their own processors and memories)
wherein the second memory stores a neuro-symbolic Artificial Intelligence (AI) analyzer executable by at least one of the one or more second computing processor devices and configured to:
(Wells, "The verification determiner 1314 may apply one or more of heuristics, statistical analysis, and AI/ML model(s) to determine whether the document under test is verified.", [0143]; combining heuristics (rules) and AI/ML models; Dang, "supervised training procedure that includes receiving input to the model from a subject matter expert ... the image classifying engine may perform an artificial neural network processing technique", c7: 5-20; "the thresholding engine may apply a set of rules to the classification", c8:40-45; "supervised training procedure that includes receiving input to the model from a subject matter expert"; combining artificial neural networks ("neuro-"), rules-based logic ("symbolic"), and expert knowledge to evaluate document classification, which collectively teaches a neuro-symbolic AI analyzer stored in memory and executable by the processor)
perform symbolic logical reasoning based at least on expert knowledge and neural network analysis to verify, for each digital document in the batch of documents, a correctness of
(i) the valid document or
(ii) the invalid document classification rendered by the SVM document classifier.
(Wells, "layered with machine learning ... to perform additional validity checks or modify the evaluations performed by the decision engine 310", [0167]; " be layer with human auditors or reviewers, who may confirm and/or reject an intermediate or overall result", [0166]; verifying the correctness (confirm/reject) of the classification results by layering additional machine learning and human expert auditing; Dang, "determine whether to accept the classification of the document ... based on the confidence score ... the thresholding engine may apply a set of rules to the classification", c8:35-45; "supervised training procedure that includes receiving input to the model from a subject matter expert ... the image classifying engine may perform an artificial neural network processing technique", c7: 5-20; verifying and accepting document classification by applying logical reasoning (a set of rules) and neural network analysis informed by subject matter expert knowledge; it obvious to verify the valid/invalid classification rendered by the primary classifier using neuro-symbolic reasoning)
Regarding claim 6, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the digital document authenticity validation engine is further configured to verify authenticity of the images including:
(Wells, "evaluates whether a bounding box associated with content is present or absent.", [0118]; configuring the validation engine to verify the authenticity of images/objects by evaluating their presence and attributes)
detect at least one image in one or more of the digital documents in the batch of digital documents,
(Wells, "The object detection engine 308 detects one or more objects in a document image.", "detect the facial image 510 and ghost image 520", [0083]; detecting at least one image, such as a facial or ghost image, within the documents)
extract the at least one image from the one or more of the digital documents in the batch of digital documents, and
(Wells, "crop the (e.g., post-processed) image of the document and generate a snippet of the associated text or object contained therein", [0084]; "snippet 910 corresponds to the portion of the CADL 500 in bounding box 510", [0085]; extracting the detected images by cropping them into isolated snippets)
verify (i) alignment of the least one image and (ii) position of the least one image, (iii) size of the least image in comparison to a known reference image,
(Wells, " determines whether the relative positions of the bounding boxes for a field prefix and corresponding field are consistent with the bounding box template of the document assembly object", [0121]; " he templating engine 414, by encoding the coordinates of a bounding box from a sample, creates a derived check, i.e., to check that a corresponding bounding box in the document under test is (1) present, (2) present at that location, (3) has the same size within a margin of error", [0106]; "slight variations in alignment", [0113]; "the background/microprint evaluator 1342 compares a snippet of the document under test to a corresponding snippet from the reconstructed background/microprint", [0133]; verifying the position, size, and alignment of the extracted images by comparing them against bounding box templates and reconstructed reference images)
wherein the alignment, position, and size of the least one image are specific to the image classification.
(Wells, "The document assembly object obtainer 1304 obtains the document assembly object associated with that class or set of labels.", [0116]; "The templating engine 414 may generate a template based on the derived information from one or more valid instance of the document. In some implementations, the templating engine 414 generates a bounding box template describing valid instances of the document", [0087]; the templates specifying the expected alignment, size, and position for comparison are dynamically obtained based directly on the specific document classification)
Regarding claim 7, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the digital document authenticity validation engine is further configured to verify authenticity of the signatures including:
detect at least one signature in one or more of the digital documents in the batch of digital documents,
wherein the at least signature comprises
(i) a physical signature, or
(ii) an electronic signature (e-signature),
extract at least one signature from the one or more of the digital documents in the batch of digital documents, and
verify at least one of (i) shape of the least one signature, (ii) smoothness of the least of signature, and (iii) line thickness of the least one signature in comparison to a known reference signature.
(Wells, "Examples of object may include one or more of a hole punched in the document (often indicating that the document is expired or invalid), the overall shape of the document (e.g., a clipped bottom right corner may be used by the system 100 to quickly determine invalidity for certain jurisdictions/issuers), signatures, facial images, ghost images, holograms, watermarks, kinegrams, seals, symbols, laser perforations, etc.", [0083]; detecting signatures as objects; "As another example, the cardholder's signature below the facial image may be associated with a bounding box (not shown) to conduct a comparison to a signature on the back of the card (not shown) and/or to computer-generated fonts, e.g., Lucidia Console, posing as human written text/signature.", [0089]; verifying signatures by comparison to reference or fonts, which can include shape, smoothness, thickness via bounding box analysis; obvious for physical or electronic signatures)
Regarding claim 8, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the system of claim 1, wherein the SVM document classifier is configured to classify each digital document in the batch of digital documents as (i) valid document or (ii) invalid document including:
(Wells, "determines whether the document under test is a valid instance of the document.", [0141]; " the verification determiner 1314 returns a result to a requesting customer, such as a bank, indicating that the document (e.g., the imaged photo ID) is not verified/invalid or is valid", [0144]; the classifier determines and classifies the document as valid or invalid)
assigning a validity value to each digital document based at least on the results of barcode, image, and signature authenticity validation,
(Wells, " assigning a value of “1” to an intermediate result that indicates a match/similarity/consistency and a “0” to an intermediary result that indicates an anomaly/mismatch/inconsistency is detected and determining whether an average or weighted average satisfies a verification threshold", [0143]; assigning numerical validity values (1 or 0) based on the intermediate evaluation results of the images, barcodes, and signatures, and then combining them into a weighted validity average/score)
comparing each of the validity values to a corresponding predetermined validity threshold value,
(Wells, "determining whether an average or weighted average satisfies a verification threshold", [0143; comparing the assigned average or weighted validity values against a predetermined verification threshold value)
wherein the corresponding predetermined validity threshold value is based on document type, and
(Wells, "the threshold may be present in and modified in the document assembly object", [0108]; "The document assembly object obtainer 1304 obtains the document assembly object associated with that class or set of labels.", [0116]; the threshold is stored inside the document assembly object, which is inherently based on the document classification/type)
classify each documents in the batch of digital documents as (i) valid document based on the validity value being at or above the corresponding predetermined validity threshold value and (ii) invalid document based on the validity value being below the corresponding predetermined validity threshold value.
(Wells, "determining whether an average or weighted average satisfies a verification threshold", [0143]; " The verification determiner 1314 returns a verification result ... indicating that the document (e.g., the imaged photo ID) is not verified/invalid or is valid.", [0144]; classifying the document as a valid instance if the validity threshold is satisfied (met or exceeded), or invalid if it fails to satisfy the threshold; Dang; "the thresholding engine may apply a set of rules to the classification of the image classifying engine. For example, the set of rules may prescribe that a confidence score associated with the classification determined by the image classifying engine satisfies a certain threshold value in order for the thresholding engine to accept the classification of the document. In some implementations, the threshold value may represent a minimum confidence score that produces a reliable document classification.", c8:40-50; assigning confidence scores (validity values) based on validations and comparing to thresholds for acceptance (valid) or rejection (invalid), threshold based on document type; obvious to extend to barcode, image, signature from combination)
Regarding claim 11, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the computer-implemented method of claim 9, further comprising:
capturing an image of each digital document in the batch of documents;
(Wells, "The image preprocessor 302 receives one or more images representing a document", [0054]; "obtaining a set of test images representing multiple instances of the first document", [0006]; capturing and receiving images representing the digital documents within a batch/set)
implementing Artificial Intelligence (AI) including Machine Learning (ML) on the captured image to classify each digital document and extract data from each digital document in the batch of documents based on the classification; and
(Wells, "The document classifier 1302 obtains an image of a document and determines a document classification associated with the document under test.", [0115]; "The document assembly object obtainer 1304 obtains the document assembly object associated with that class or set of labels.", [0116]; "The document under test derived information obtainer 1306 obtains derived information associated with the document under test.", [0117]; "OCR engine 306 converts text in an image into machine-readable text.", [0080]; determining a classification and extracting data based strictly on that document's specific classification assembly object; Dang, "the image classifying engine may use one or more artificial intelligence techniques, such as machine learning, deep learning (e.g., convolutional neural networks), and/or the like to determine a confidence score that a document is classified as one or more of the plurality of document types.", c5:45-55; the specific implementation of using AI/ML techniques to classify each digital document)
implementing a rules engine to verify authenticity of each digital document in the batch documents by applying classification-specific rules to the extracted data, and
(Wells, "heuristic rules are included as checks ... The intra-bounding box evaluator 1312 may use these rules from the document assembly object to generate the intermediate results", [0139]; "The document assembly object obtainer 1304 obtains the document assembly object associated with that class or set of labels.", [0116]; verifying authenticity by applying heuristic rules stored within the document assembly object, which are classification-specific because the object is obtained based on the document's determined class)
wherein implementing the at least one SVM algorithm to classify each digital document in the batch of digital documents as
(i) valid document or
(ii) invalid document is based further on the results of document authenticity validation performed by the intelligent document processing engine.
(Wells, Dang, see comments on claim 1)
Regarding claim 12, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the computer-implemented method of claim 11, wherein implementing the rules engine to verify authenticity of each digital document further comprises:
implementing the rules engine to verify authenticity of each digital document in the batch documents by applying classification-specific rules to the extracted data,
(Wells, "heuristic rules are included as checks. In some implementations, the intra-bounding box evaluator 1312 may use these rules from the document assembly object to generate the intermediate results of whether the document number is the correct length and alphanumeric composition", [0139]; "The document assembly object obtainer 1304 obtains the document assembly object associated with that class or set of labels.", [0116]; verifying authenticity by applying rules obtained from the classification-specific document assembly object)
wherein the classification-specific rules are based on at least one of (i) text spelling, (ii) font style, (iii) font size, (iv) color, (v) alignment, and (vi) clarity.
(Wells, " determine other or additional characteristics of the text such as, but not limited to, one or more of a font size (e.g., 8 pt.), font color (e.g., using the red, green, blue (RGB) or cyan, magenta, yellow, black (CMYK) or other color representation model), font style (e.g., italic, bold, underlined), orientation (e.g., horizontal or vertical), and the capitalization scheme (e.g., all caps, caps and small caps, or caps and lower case letters", [0138]; "slight variations in alignment", [0113]; applying rules based on font style, font size, font color, and alignment)
Regarding claim 14, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the computer-implemented method of claim 9, wherein implementing the at least one SVM algorithm to verify authenticity of the images further comprises:
detecting at least one image in one or more of the digital documents in the batch of digital documents;
extracting the at least one image from the one or more of the digital documents in the batch of digital documents, and
verifying (i) alignment of the least one image and (ii) position of the least one image, (iii) size of the least image in comparison to a known reference image,
wherein the alignment, position, and size of the least one image are specific to the image classification.
(Wells, Dang, see comments on claim 6)
Regarding claim 15, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the computer-implemented method of claim 9, wherein implementing the at least one SVM algorithm to verify authenticity of signatures further comprises:
detecting at least one signature in one or more of the digital documents in the batch of digital documents,
wherein the at least signature comprises
(i) a physical signature, or
(ii) an electronic signature (e-signature);
extracting at least one signature from the one or more of the digital documents in the batch of digital documents; and
verifying at least one of (i) shape of the least one signature, (ii) smoothness of the least of signature, and (iii) line thickness of the least one signature in comparison to a known reference signature.
(Wells, Dang, see comments on claim 7)
Regarding claim 18, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the computer program product of claim 16, wherein the computer-readable medium further comprises:
a seventh set of codes for causing a computing device to capture an image of each digital document in the batch of documents;
an eighth set of codes for causing a computer device to implement Artificial Intelligence (AI) including Machine Learning (ML) on the captured image to classify each digital document and extract data from each digital document in the batch of documents based on the classification; and
a ninth set of codes for causing a computing device to implement a rules engine to verify authenticity of each digital document in the batch documents by applying classification-specific rules to the extracted data, and
wherein the fifth set of codes are further configured to cause the computing device to implement the at least one SVM algorithm to classify each digital document in the batch of digital documents as
(i) valid document or
(ii) invalid document is based further on the results of document authenticity validation performed by the intelligent document processing engine.
(Wells, Dang, see comments on claims 1 and 3)
Regarding claim 19, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the computer program product of claim 18,
wherein the seventh set of codes are further configured to cause the computer to implement the rules engine to verify authenticity of each digital document in the batch documents by applying classification-specific rules to the extracted data,
wherein the classification-specific rules are based on at least one of (i) text spelling, (ii) font style, (iii) font size, (iv) color, (v) alignment, and (vi) clarity.
(Wells, see comments on claim 12; same rationale as used for claim 12)
Regarding claim 20, the combination of Wells and Dang teaches its/their respective base claim(s).
The combination further teaches the computer program product of claim 16,
wherein the second set of codes are further configured to cause the computer to
(i) detect at least one barcode in one or more of the digital documents in the batch of digital documents,
(ii) extract the at least one barcode from the one or more of the digital documents in the batch of digital documents, and
(iii) verify (a) a pattern of the least one barcode and (b) a position of the least one barcode, wherein the pattern and position of the least one barcode are specific to a document type, and
wherein the third set of codes are further configured to cause the computer to
(i) detect at least one image in one or more of the digital documents in the batch of digital documents,
(ii) extract the at least one image from the one or more of the digital documents in the batch of digital documents, and
(iii) verifying (a) alignment of the least one image and (b) position of the least one image, and (c) size of the least image in comparison to a known reference image, wherein the alignment, position and size of the least one image are specific to the image classification, and
wherein the fourth set of codes are further configured to cause the computer to
(i) detect at least one signature in one or more of the digital documents in the batch of digital documents, wherein the at least one signature comprises (a) a physical signature, or (b) an electronic signature (e-signature),
(ii) extract at least one signature from the one or more of the digital documents in the batch of digital documents, and
(iii) verifying at least one of (a) shape of the least one signature, (b) smoothness of the least one signature, and (c) line thickness of the least one signature in comparison to a known reference signature.
(Wells, Dang, see comments on claims 6-7)
Allowable Subject Matter
Claim(s) 2-5, 10, 13 and 17 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening Claim(s).
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 2, 5, 10, 13 and 17 recite(s) limitation(s) related to extracting and analyzing document metadata, like creation and modification dates, to improve SVM classification; verifying barcode authenticity by detecting, extracting, and checking barcode patterns and positions against specific document types; extracting and analyzing metadata, like date comparisons, to further inform SVM document classification. There are no explicit teachings to the above limitation(s) found in the prior art cited in this office action and from the prior art search.
Claim(s) 3-4 depend on claim 2.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 5/2/2026
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