CTFR 18/669,968 CTFR 85117 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Drawings The drawings as submitted by Applicant on 05/21/2024 have been accepted. Disposition of Claims Claims 1-24 are pending in the instant application. Claims 7, 13, 15, and 16 have been cancelled. Claims 21-24 have been added. Claims 1, 2, 5, 8, 9, 17, 17, and 20 have been amended. The rejection of the pending claims is hereby made final. Response to Remarks The examiner has considered Applicant’s arguments regarding the rejection of the pending claims under 35 USC 101, and has found said arguments to be persuasive. The rejection of the pending claims under 35 USC 101 is hereby withdrawn. The examiner has considered Applicant’s arguments regarding the rejection of the pending claims under 35 USC 102, and has found said arguments to be persuasive. The rejection of the pending claims under 35 USC 102 is hereby withdrawn. Double Patenting 08-33 AIA The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a non-statutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. Claims 1-6, 8-12, 14, and 17-24 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-22 of United States Patent Number 12,026,716. Although the claims at issue are not identical, they are not patentably distinct from each other because both applications are directed to systems and methods for the autonomous loading and unloading of pallets, as outlined below: Application number 18/669968 US Patent Number 12,026,716 1. (Currently Amended) A computer-implemented method for detecting potentially fraudulent documents using optical character recognition and machine learning, comprising: receiving a digital image of a document associated with a pending transaction; determining, based at least in part on application of an optical character recognition algorithm to the digital image, first document data including location and content of a field present in[[of]] the document; accessing second document data associated with a user account corresponding to the first document data; generating, using a machine learning program trained with labelled image data corresponding to historical document data of a plurality of users, classification rules for classifying documents as fraudulent based on document characteristics; providing applying the classification rules to the first document data and the second document data, as inputs, to a trained machine learning program, the trained machine learning program generating to generate a score, based on the first document data and the second document data, indicating whether the field is potentially fraudulent; determining, based on the score, that the at least one of the field or the content is potentially fraudulent; and based on determining that at least one of the field or the content is potentially fraudulent, causing a notification to halt processing of the pending transaction to be output to a computing device. 2. (Currently Amended) The computer-implemented method of claim 1, wherein determining the first document data comprises: applying an optical character recognition algorithm to the digital image to identify the first document data, wherein the first document data comprises one or more of a document type, an originating entity, a printed name, or a handwritten signature. 3. (Original) The computer-implemented method of claim 1, wherein the second document data comprises at least one of a previous handwritten signature, a known handwritten signature, or one or more expected fields. 4. (Original) The computer-implemented method of claim 1, wherein causing the notification to halt the processing of the pending transaction to be output includes causing a point- of-sale computing device associated with a merchant to display the notification. 5. (Currently Amended) The computer-implemented method of claim 1, wherein both (i) determining that the field or the content is potentially fraudulent, and (ii) causing the notification to be output, occur substantially in real-time. 6. (Original) The computer-implemented method of claim 1, wherein the score is determined based on at least one of: a font included in the first document data being different from an acceptable font; a pattern included in the first document data being different from an acceptable pattern; a color included in the first document data being different from an acceptable color; handwriting included in the first document data being outside of an acceptable tolerance; or a format of the handwriting included in the first document data being different than an expected format. 1. A computer-implemented method for detecting potentially fraudulent physical documents, comprising: receiving an image of a physical document of a first document type; determining a fraudulent document detection rule associated with the first document type, wherein the fraudulent document detection rule is output by a machine learning model trained based on training data including image data corresponding to a plurality of physical documents having the first document type and fraud determinations associated with the plurality of physical documents, wherein the fraudulent document detection rule includes: a first document factor; and a first score value associated with the first document factor; applying the fraudulent document detection rule to the physical document, wherein the fraudulent document detection rule is configured to output: a document score associated with the physical document, based at least in part on the first score value, wherein the document score is indicative that the physical document is potentially fraudulent; and a fraud classification indicative of a type of fraud associated with the physical document; determining that the physical document is potentially fraudulent, based at least in part on the document score associated with the physical document; and causing an indication that the physical document is potentially fraudulent to be output to one or more computing devices. 2. The computer-implemented method of claim 1, wherein applying the fraudulent document detection rule further comprises: determining a second score value associated with a second document factor, wherein the document score associated with the physical document is computed based at least in part on the first score value and the second score value. 3. The computer-implemented method of claim 2, wherein the first document factor is a forgery factor and the second document factor is a counterfeit factor. 4. The computer-implemented method of claim 1, wherein applying the fraudulent document detection rule further comprises determining that a location of a document field on the physical document is within an acceptable tolerance of an expected location of the document field. 5. The computer-implemented method of claim 1, wherein both (i) the determining that the physical document is potentially fraudulent, and (ii) the causing the indication that the physical document is potentially fraudulent to be output, occur substantially in real-time upon receiving the image of the physical document. 6. The computer-implemented method of claim 5, wherein causing the indication that the physical document is potentially fraudulent to be output includes causing a point-of-sale computing device associated with a merchant to display the indication. 7. The computer-implemented method of claim 1, wherein the plurality of physical documents corresponds to government identification documents, and wherein the method further comprises: training a second machine learning model, using a second plurality of physical documents and second associated fraud determinations, to determine a second fraudulent document detection rule associated with a second document type, wherein the first document type corresponds to identification documents associated with a first government entity, and the second document type corresponds to identification documents associated with a second government entity. 8. The computer-implemented method of claim 1, wherein the plurality of physical documents corresponds to financial instruments, and wherein the method further comprises: training a second machine learning model, using a second plurality of physical documents and second associated fraud determinations, to determine a second fraudulent document detection rule associated with a second document type, wherein the first document type corresponds to financial instruments associated with a first financial institution, and the second document type corresponds to financial instruments associated with a second financial institution. 9. The computer-implemented method of claim 1, wherein the first document factor of the fraudulent document detection rule comprises at least one of: a first tolerance value associated with a dimension of the physical document; a second tolerance value associated with a color of the physical document; a third tolerance value associated with a line thickness of the physical document; or a fourth tolerance value associated with a font of the physical document. The examiner submits that the language as recited in the pending application is similar to that as recited US Patent Number 12,026,716 as shown in the table above, which shows exemplary claims 1-6 of the pending application in view of claims 1-22 of US Patent Number 12,026,716 . Conclusion 07-40 AIA 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. 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The examiner has considered all references listed on the Notice of References Cited, PTO-892. The examiner has considered all references cited on the Information Disclosure Statement submitted by Applicant, PTO-1449. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TALIA F CRAWLEY whose telephone number is (571)270-5397. The examiner can normally be reached on Monday thru Thursday; 8:30 AM-4:30 PM EST. 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, Fahd A Obeid can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TALIA F CRAWLEY/Primary Examiner, Art Unit 3627 Application/Control Number: 18/669,968 Page 2 Art Unit: 3627 Application/Control Number: 18/669,968 Page 3 Art Unit: 3627 Application/Control Number: 18/669,968 Page 4 Art Unit: 3627 Application/Control Number: 18/669,968 Page 5 Art Unit: 3627 Application/Control Number: 18/669,968 Page 6 Art Unit: 3627
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