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 .
This action is responsive to claims filed 07/08/2026 and Applicant’s communication regarding application 19/094344 filed 07/08/2026.
Claims 1-20 have been examined with this office action.
Claim Interpretation
machine learning model prompt – [Figure 7, element 702] [0091] As portrayed in FIG. 7, in some implementations, the visual deposit system 106 the visual deposit system 106 trains a mobile check deposit model 708 based on a check image dataset. Specifically, the check image dataset includes digital images of checks. For example, the visual deposit system 106 utilizes a machine learning model prompt 702 including a check image 704 of the check image dataset and historical data 706 of a client account (e.g., mobile check deposit history, general account behavior, transaction history, etc.) with the mobile check deposit model 708 to generate a transaction decision 710.
[0093] As further illustrated in FIG. 7, in one or more implementations, the visual deposit system 106 utilizes the check image training dataset to train the mobile check deposit model 708 to determine a transaction decision 710 based on the check image 704 in addition to the historical data 706. Specifically, the visual deposit system 106 utilizes the prompt 702 including both the check image 704 and the historical data 706 with the mobile check deposit model 708 to generate the transaction decision 710. For example, the visual deposit system 106 utilizes the mobile check deposit model 708 to generate the transaction decision 710 indicating to automatically accept 712, automatically reject 714, or flag for manual review 716 the deposit request.
Thus, a “machine learning model prompt” is interpreted as specific features pertaining to a check to be analyzed by the mobile check deposit model. The examiner contends that specific features pertaining to a check are part of the abstract idea of the claims.
Specification
The specification is objected to because the Background of the Invention and Brief Summary of the Invention: section is missing from the specification:
Content of Specification
(a) Title of the Invention: See 37 CFR 1.72(a) and MPEP § 606. The title of the invention should be placed at the top of the first page of the specification unless the title is provided in an application data sheet. The title of the invention should be brief but technically accurate and descriptive, preferably from two to seven words may not contain more than 500 characters.
(b) Cross-References to Related Applications: See 37 CFR 1.78 and MPEP § 201.11.
(c) Statement Regarding Federally Sponsored Research and Development: See MPEP § 310.
The Names Of The Parties To A Joint Research Agreement: See 37 CFR 1.71(g).
(e) Incorporation-By-Reference Of Material Submitted On a Compact Disc: The specification is required to include an incorporation-by-reference of electronic documents that are to become part of the permanent United States Patent and Trademark Office records in the file of a patent application. See 37 CFR 1.52(e) and MPEP § 608.05. Computer program listings (37 CFR 1.96(c)), “Sequence Listings” (37 CFR 1.821(c)), and tables having more than 50 pages of text were permitted as electronic documents on compact discs beginning on September 8, 2000.
(f) Background of the Invention: See MPEP § 608.01(c). The specification should set forth the Background of the Invention in two parts:
(1) Field of the Invention: A statement of the field of art to which the invention pertains. This statement may include a paraphrasing of the applicable U.S. patent classification definitions of the subject matter of the claimed invention. This item may also be titled "Technical Field."
(2) Description of the Related Art including information disclosed under 37 CFR 1.97 and 37 CFR 1.98: A description of the related art known to the applicant and including, if applicable, references to specific related art and problems involved in the prior art which are solved by the applicant's invention. This item may also be titled "Background Art."
(g) Brief Summary of the Invention: See MPEP § 608.01(d). A brief summary or general statement of the invention as set forth in 37 CFR 1.73. The summary is separate and distinct from the abstract and is directed toward the invention rather than the disclosure as a whole. The summary may point out the advantages of the invention or how it solves problems previously existent in the prior art (and preferably indicated in the Background of the Invention). In chemical cases it should point out in general terms the utility of the invention. If possible, the nature and gist of the invention or the inventive concept should be set forth. Objects of the invention should be treated briefly and only to the extent that they contribute to an understanding of the invention.
(h) Brief Description of the Several Views of the Drawing(s): See MPEP § 608.01(f). A reference to and brief description of the drawing(s) as set forth in 37 CFR 1.74.
(i) Detailed Description of the Invention: See MPEP § 608.01(g). A description of the preferred embodiment(s) of the invention as required in 37 CFR 1.71. The description should be as short and specific as is necessary to describe the invention adequately and accurately. Where elements or groups of elements, compounds, and processes, which are conventional and generally widely known in the field of the invention described and their exact nature or type is not necessary for an understanding and use of the invention by a person skilled in the art, they should not be described in detail. However, where particularly complicated subject matter is involved or where the elements, compounds, or processes may not be commonly or widely known in the field, the specification should refer to another patent or readily available publication which adequately describes the subject matter.
(j) Claim or Claims: See 37 CFR 1.75 and MPEP § 608.01(m). The claim or claims must commence on separate sheet or electronic page (37 CFR 1.52(b)(3)). Where a claim sets forth a plurality of elements or steps, each element or step of the claim should be separated by a line indentation. There may be plural indentations to further segregate subcombinations or related steps. See 37 CFR 1.75 and MPEP § 608.01(i)-(p).
(k) Abstract of the Disclosure: See MPEP § 608.01(f). A brief narrative of the disclosure as a whole in a single paragraph of 150 words or less commencing on a separate sheet following the claims. In an international application which has entered the national stage (37 CFR 1.491(b)), the applicant need not submit an abstract commencing on a separate sheet if an abstract was published with the international application under PCT Article 21. The abstract that appears on the cover page of the pamphlet published by the International Bureau (IB) of the World Intellectual Property Organization (WIPO) is the abstract that will be used by the USPTO. See MPEP § 1893.03(e).
(l) Sequence Listing, See 37 CFR 1.821-1.825 and MPEP §§ 2421-2431. The requirement for a sequence listing applies to all sequences disclosed in a given application, whether the sequences are claimed or not. See MPEP § 2421.02.
Appropriate correction is required.
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 an abstract idea of check deposit fraud detection without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v.CLS Bank International, et al., 573 U.S. _ (2014) as provided by the interim guidelines FR 12/16/2014 Vol. 79 No. 241.
Analysis
Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. In this case independent claim 1 and all claims which depend from it are directed toward a method, and independent claim 8 and all claims which depend from it are directed toward a computer readable medium storing instruction to perform functions/steps, and independent claim 15 all claims which depend from it are directed toward a system. As such, all claims fall within one of the four categories of invention deemed to be the appropriate subject matter.
Step 2A Prong 1, Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 PEG.
Specifically, claim 1 comprises inter alia the functions or steps of “A computer-implemented method comprising: generating, by one or more servers in response to a deposit request comprising a check image from a client device, a machine learning model prompt comprising the check image and instructions for extracting features of the check image; extracting, by the one or more servers utilizing a vision-language model, the features of the check image indicated in the instructions of the machine learning model prompt; determining, by the one or more servers utilizing a deposit decision model comprising a set of check deposit rules, a transaction decision indicating to accept or reject the deposit request according to the extracted features of the check image; and causing, by the one or more servers, a transaction system to accept or reject the deposit request based on the transaction decision”.
Claim 8 comprises inter alia the functions or steps of “A non-transitory computer-readable medium storing instructions that, when executed by at least one processing device, cause a computing device to: generating, by one or more servers in response to a deposit request comprising a check image from a client device, a machine learning model prompt comprising the check image and instructions for extracting features of the check image; extracting, by the one or more servers utilizing a vision-language model, the features of the check image indicated in the instructions of the machine learning model prompt; determining, by the one or more servers utilizing a deposit decision model comprising a set of check deposit rules, a transaction decision indicating to accept or reject the deposit request according to the extracted features of the check image; and causing, by the one or more servers, a transaction system to accept or reject the deposit request based on the transaction decision”.
Claim 15 comprises inter alia the functions or steps of “A system comprising:at least one processing device; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processing device, cause the system to: generating, by one or more servers in response to a deposit request comprising a check image from a client device, a machine learning model prompt comprising the check image and instructions for extracting features of the check image; extracting, by the one or more servers utilizing a vision-language model, the features of the check image indicated in the instructions of the machine learning model prompt; determining, by the one or more servers utilizing a deposit decision model comprising a set of check deposit rules, a transaction decision indicating to accept or reject the deposit request according to the extracted features of the check image; and causing, by the one or more servers, a transaction system to accept or reject the deposit request based on the transaction decision”.
Those claim limits in bold are identified as claim limitations which recite the abstract idea, while those that are un-bolded are identified as additional elements.
The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Check deposit fraud detection is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2, Next, it is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223—24. “[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer.” Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way.
In particular, the claims contain the following additional elements: a computer; one or more servers; a client device; a vision-language model; a non-transitory computer-readable medium storing instructions; a system. However, the specification description of the additional elements a computer ([0132] “…general-purpose computer including computer hardware…”); one or more servers ([0159] “…Servers may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein …”); a client device ([0117] “…the client device(s) 1010 may include, but are not limited to, mobile devices (e.g., smartphones, tablets) or other types of computing devices, including those explained below with reference to FIGS. 12 & 13. Additionally, the client device(s) 1010 can include computing devices associated with (and/or operated by) user accounts for the internetwork facilitation system 1004 …”); a vision-language model ([0018] “…the vision-language model 108 includes one or more of a Large Language and Vision Assistant (LLa Va) model, a Contrastive Language-Image Pretraining (CLIP) model, or other multimodel models for handling both text and image inputs….”); a non-transitory computer-readable medium storing instructions; a system; ([0132]) are at a high level of generality using exemplary language or as part of a generic technological environment and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more that the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the abstract idea(s) amounts to no more than mere instructions to apply the exaction using a generic computer component. Mere instruction to apply an exertion using a generic computer component cannot provide an inventive concept. These generic computer components are claimed at a high level of generality to perform their basic functions which amount to no more than generally linking the use of the judicial exception to the particular technological environment of field of use (Specification as cited above for additional elements) and further see insignificant extra-solution activity MPEP § 2106.05 I. A. iii, 2106.05(b), 2106.05(b) III, 2106.05(g). Thus, the claims are not patent eligible.
As for dependent claims 2-7, 9-14, and 16-20 these claims recite limitations that further define the same abstract idea using previously identified additional elements noted from the respective independent claims from which they depend. Therefore, the cited dependent claims are considered patent ineligible for the reasons given above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bueche (U.S. Patent No. 11900755) in view of Anandkumar (PGPub Document No. 20240265690).
As per claim 1, Bueche teaches a computer-implemented method ([column 5, lines 7-10]) comprising: generating, by one or more servers ([column 3, lines 41-45] “The image processing steps for detecting and extracting the check image from the background portions may be implemented in whole, or in parts, on either a user device having captured the digital image, a remote server running an application for remote deposit, or both…”) in response to a deposit request comprising a check image from a client device, a machine learning model prompt (parameter set from check image data and user history) comprising the check image and instructions for extracting features of the check image ([column 10, lines 1-23] “Referring to FIG. 6, which illustrates one example of a machine learning model repository 129, several different trained machine learning models (ML models 1 through N) 602 may be included. Each of the ML models 602 in the model repository 129 may consist of a model parameter set 604 and the functional relationship component 606. As described below, the parameter set 604 contains the parameter data, that was achieved by training the model 602, and the parameter set 604 is used to process the input data ( e.g. check image data from the captured image depicting the check) according to the functional relationship component 606. The functional relationship component 606 may include the type of matrix manipulation or convolution algorithm to be applied to the model parameter set 604 and the input data, and the basic instructions for how the input data and model parameters 604 are to be applied to the input data by the image processing circuitry 128. The model parameter set 604 is data that has been trained in a separate model training process prior to being made available for download to, and storage on, the computing device 109. For example, each ML model 602 may be trained at a central location and then downloaded as part of the remote deposit app from a 3rd party app store or the institution” [column 25, lines 53-65] “…stores previously captured checks and images from that user that is used to build a history for that user and that may be used to train a machine learning/artificial intelligence type model to profile the user for authentication and fraud prevention in future remote deposit transactions”);
determining, by the one or more servers utilizing a deposit decision model comprising a set of check deposit rules (fraud processing using machine learning), a transaction decision (prevent processing / deposit funds) indicating to accept or reject the deposit request according to the extracted features of the check image ([column 24, line 43 - column 25, lines 23-65] “…profile the user for authentication and fraud prevention in future remote deposit transaction” [claim 6] :… prevent transmission of the data from the captured check to the remote server in response to a determination by the trained machine learning duplicate model that the check depicted in the digital check image based is a duplicate check” or if no fraud detected” [Figure 13, 1309] “Institution receives digital image file and processes digital image, processes check depicted in digital image file, and deposits funds into account for deposit” [Figure 14, 1407] “Deposit funds into account based on processed check”); and causing, by the one or more servers, a transaction system to accept or reject the deposit request based on the transaction decision ([column 24, line 43 - column 25, lines 23-65] “…profile the user for authentication and fraud prevention in future remote deposit transaction” [claim 6] :… prevent transmission of the data from the captured check to the remote server in response to a determination by the trained machine learning duplicate model that the check depicted in the digital check image based is a duplicate check” or if no fraud detected” [Figure 13, 1309] “Institution receives digital image file and processes digital image, processes check depicted in digital image file, and deposits funds into account for deposit” [Figure 14, 1407] “Deposit funds into account based on processed check”).
Bueche further teaches extracting, by the one or more servers utilizing, the features of the check image indicated in the instructions of the machine learning model prompt ([column 14, lines 37-41] “…Any image processing technology, software, or other application(s) may be used to retrieve the digital image of the check 107 from the 40 digital image file 135 and to obtain the relevant data of the check 107 from the digital image file 135…” ([column 20, lines 39-50] “At 1406, the institution system 205 processes the check depicted in the check image to extract the check data contained therein. Processing of the digital image file may include retrieving financial information regarding the check. The financial information may comprise the MICR number, the routing number, a check amount, or other information 45 found on the check. The processing step may include implementation of one of more of ML models such as the dewarping 800, or denoising 900, models discussed above, or may implement deterministic techniques using known algorithms, before extracting financial information from the image”). Bueche could be broadly interpreted as teaches a vision-language model since the models extract data from both images and text using machine learning and the system/method may implement deterministic techniques using known algorithms, where a vision-language model is a known algorithm. However, Bueche does not explicitly indicate the use of a vision-language model.
Anandkumar teaches a vision-language model used for extracting the features of the check image ([0002] “Vision language models rely on massive amounts of image-text data for training a single monolithic model…” [0124] “…Examples of inference (the process through which a DNN extracts useful information from a given input) include identifying handwritten numbers on checks deposited into ATM machines, identifying images of friends in photos, delivering movie recommendations to over fifty million users, identifying and classifying different types of automobiles, pedestrians, and road hazards in driverless cars, or translating human speech in real-time”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted as one of the known algorithms deterministic techniques in Bueche the vision-language model as found in Anandkumar for the since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the vision-language model of Anandkumar for the known algorithms deterministic techniques in Bueche. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
As per claim 2,
Bueche teaches the computer-implemented method of claim 1, wherein extracting the features of the check image indicated in the instructions of the machine learning model prompt comprises extracting, utilizing the vision-language model, check information features comprising alphanumeric information regarding a transaction indicated by a check in the check image ([column 3, lines 13-23] text which the examiner takes the position includes both letters and numbers).
As per claim 3,
Bueche teaches the computer-implemented method of claim 2, wherein determining the transaction decision indicating to accept or reject the deposit request according to the extracted features of the check image comprises: comparing the extracted check information features with account information of a client account submitting the deposit request to determine whether a text-based fraud indicator is present in the check image; and determining, utilizing the deposit decision model, the transaction decision to indicate to reject the deposit request in response to determining that the text-based fraud indicator is present ([column 24, line 43 - column 25, lines 23-65] “…profile the user for authentication and fraud prevention in future remote deposit transaction” [claim 6] :… prevent transmission of the data from the captured check to the remote server in response to a determination by the trained machine learning duplicate model that the check depicted in the digital check image based is a duplicate check” or if no fraud detected [column 25, lines 53-65] “…stores previously captured checks and images from that user that is used to build a history for that user and that may be used to train a machine learning/artificial intelligence type model to profile the user for authentication and fraud prevention in future remote deposit transactions”).
As per claim 4,
Bueche teaches the computer-implemented method of claim 2, wherein determining, by the one or more servers utilizing the deposit decision model comprising the set of check deposit rules, the transaction decision indicating to accept or reject the deposit request according to the extracted features of the check image comprises: comparing the extracted check information features with transaction information of the check to determine whether a fraud indicator is present in the check image; and determining, utilizing the deposit decision model, the transaction decision to indicate to reject the deposit request in response to determining that the fraud indicator is present ([column 24, line 43 - column 25, lines 23-65] “…profile the user for authentication and fraud prevention in future remote deposit transaction” [claim 6] :… prevent transmission of the data from the captured check to the remote server in response to a determination by the trained machine learning duplicate model that the check depicted in the digital check image based is a duplicate check” or if no fraud detected [column 25, lines 53-65] “…stores previously captured checks and images from that user that is used to build a history for that user and that may be used to train a machine learning/artificial intelligence type model to profile the user for authentication and fraud prevention in future remote deposit transactions”).
As per claim 5,
Bueche teaches the computer-implemented method of claim 1, wherein: extracting the features of the check image indicated in the instructions of the machine learning model prompt comprises extracting physical features of the check image; and determining the transaction decision indicating to accept or reject the deposit request according to the extracted features of the check image comprises determining the transaction decision to accept or reject the deposit request in response to determining that the extracted physical features of the check image indicate one or more physical fraud indicators ([column 24, line 43 - column 25, lines 23-65] “…profile the user for authentication and fraud prevention in future remote deposit transaction” [claim 6] “… prevent transmission of the data from the captured check to the remote server in response to a determination by the trained machine learning duplicate model that the check depicted in the digital check image based is a duplicate check” or if no fraud detected” [Figure 13, 1309] “Institution receives digital image file and processes digital image, processes check depicted in digital image file, and deposits funds into account for deposit” [Figure 14, 1407] “Deposit funds into account based on processed check”).
As per claim 6,
Bueche teaches the computer-implemented method of claim 5, wherein determining the transaction decision to accept or reject the deposit request in response to determining that the extracted physical features of the check image indicate the one or more physical fraud indicators comprises: detecting, utilizing the deposit decision model and based on the extracted physical features of the check image, the one or more physical fraud indicators comprising at least one of an object in addition to a check in the check image (background noise) or a predetermined check design pattern ([column 3, lines 13-23] “…the printed text on a check often cannot be read correctly due to background noise…”); and determining the transaction decision to indicate to reject the deposit request based on detecting a presence of the one or more physical fraud indicators ([column 24, line 43 - column 25, lines 23-65] “…profile the user for authentication and fraud prevention in future remote deposit transaction” [claim 6] :… prevent transmission of the data from the captured check to the remote server in response to a determination by the trained machine learning duplicate model that the check depicted in the digital check image based is a duplicate check” or if no fraud detected” [Figure 13, 1309] “Institution receives digital image file and processes digital image, processes check depicted in digital image file, and deposits funds into account for deposit” [Figure 14, 1407] “Deposit funds into account based on processed check”).
As per claim 7,
Bueche teaches the computer-implemented method of claim 5, wherein determining the transaction decision to accept or reject the deposit request in response to determining that the extracted physical features of the check image indicate the one or more physical fraud indicators comprises: detecting, utilizing the deposit decision model and based on the extracted physical features of the check image, the one or more physical fraud indicators comprising at least one of a physical alteration to a check in the check image, a prior deposit indicator (duplicate check) , or a fraudulent check source indicator; and determining the transaction decision to indicate to reject the deposit request based on detecting a presence of the one or more physical fraud indicators ([column 24, line 43 - column 25, lines 23-65] “…profile the user for authentication and fraud prevention in future remote deposit transaction” [claim 6] :… prevent transmission of the data from the captured check to the remote server in response to a determination by the trained machine learning duplicate model that the check depicted in the digital check image based is a duplicate check” or if no fraud detected” [Figure 13, 1309] “Institution receives digital image file and processes digital image, processes check depicted in digital image file, and deposits funds into account for deposit” [Figure 14, 1407] “Deposit funds into account based on processed check”).
As per claim 8, Bueche teaches a non-transitory computer-readable medium storing instructions that, when executed by at least one processing device ([column 26, lines 35-58] [claim 1]).
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 1.
As per claim 9,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 2.
As per claim 10,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 3.
As per claim 11,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 4.
As per claim 12,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 5.
As per claim 13,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 6.
As per claim 14,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 7.
As per claim 15, Bueche teaches a system comprising: at least one processing device; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processing device ([Figure 1] [column 4, line 42 – column 6, line 24] [claim 1, “a processor; memory; and instructions stored on the computer-readable medium”]).
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 1.
As per claim 16,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 2.
As per claim 17,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 3.
As per claim 18,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 4.
As per claim 19,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 5.
As per claim 20,
The limits of this claim are rejected using the same prior art and rationale as previously addressed in Claim 7.
Response to Arguments
Applicant's arguments with regards to claims have been fully considered but they are not persuasive.
EXAMINER’S RESPONSE TO APPLICANT REMARKS CONCERNING Claim Rejections - 35 USC § 101: Applicant's arguments with regards to 35 USC § 101 have been fully considered but are not persuasive. Regarding applicant’s argument that the claims are not directed toward a “fundamental economic practice long prevalent in commerce”, check deposit fraud detection is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The additional elements of the claim (a computer; one or more servers; a client device; a vision-language model; a non-transitory computer-readable medium storing instructions; a system) are not improved and are merely applied to the abstract idea of the claim. As such, the examiner maintains the rejection.
EXAMINER’S RESPONSE TO APPLICANT REMARKS CONCERNING Claim Rejections - 35 USC § 103: Applicant's arguments with regards to 35 USC § 101 have been fully considered but are not persuasive. Regarding arguments directed toward the claim limitation “generating, by one or more servers in response to a deposit request comprising a check image from a client device, a machine learning model prompt comprising the check image and instructions for extracting features of the check image”, the applicant does not identify the difference between the cited prior arts mapping to a machine learning model prompt and instructions for extracting features of the check image and the interpretation of the structural features as found in the spec. As such, the examiner maintains that Bueche teaches the claim limit where the prompt as a parameter set from check image data and user history and instructions for extracting features of the check image map to ([column 10, lines 1-23] “Referring to FIG. 6, which illustrates one example of a machine learning model repository 129, several different trained machine learning models (ML models 1 through N) 602 may be included. Each of the ML models 602 in the model repository 129 may consist of a model parameter set 604 and the functional relationship component 606. As described below, the parameter set 604 contains the parameter data, that was achieved by training the model 602, and the parameter set 604 is used to process the input data ( e.g. check image data from the captured image depicting the check) according to the functional relationship component 606. The functional relationship component 606 may include the type of matrix manipulation or convolution algorithm to be applied to the model parameter set 604 and the input data, and the basic instructions for how the input data and model parameters 604 are to be applied to the input data by the image processing circuitry 128. The model parameter set 604 is data that has been trained in a separate model training process prior to being made available for download to, and storage on, the computing device 109. For example, each ML model 602 may be trained at a central location and then downloaded as part of the remote deposit app from a 3rd party app store or the institution” [column 25, lines 53-65] “…stores previously captured checks and images from that user that is used to build a history for that user and that may be used to train a machine learning/artificial intelligence type model to profile the user for authentication and fraud prevention in future remote deposit transactions”).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Regarding applicants’ argument directed toward claims 5-7, 12-14, and 19-20, the scope of the claims are broader than applicant’s interpretation of the claims. The presently claim invention does not require physical fraud indicators which include a physical alteration to the check, an object in addition to the check in the check image, a predetermined check design pattern, a prior deposit indicator, or a fraudulent check source indicator. As such, the examiner maintains the rejection.
Conclusion
For prior art made of record and not relied upon is considered pertinent to applicant's disclosure see Notice of References Cited items A-D submitted 05/04/2026 used as prior art and in the conclusion section in the office action submitted 05/04/2026.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory A Pollock whose telephone number is (571) 270-1465. The examiner can normally be reached M-F 8 AM - 4 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas can be reached on 571 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gregory A Pollock/Primary Examiner, Art Unit 3691
09/30/2026
