DETAILED ACTION
This is in response to the applicant’s communication filed on 3/16/26, wherein:
Claims 1-7 are currently pending; and
Claims 8-20 are cancelled.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The claims were filed on 3/27/24 and are a CIP of 17/903356, filed on 9/6/22. However, the claims include subject matter not present in 17/903356; specifically, at least the portions of the claims which address the AI model. Therefore, the claims receive the priority date of 3/27/24.
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 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites a system and therefore, falls into a statutory category.
Step 2A – Prong 1 (Is a Judicial Exception Recited?): The following underlined limitations identify the abstract limitations which are considered certain methods of organizing human activity
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the system to:
receive scanned certification documents associated with a tenant through a networked computing device;
extract structured data from the scanned certification documents using one or more document processing techniques comprising optical character recognition (OCR), optical mark recognition (OMR), natural language processing (NLP), and named entity recognition (NER);
validate the extracted structured data by automatically comparing extracted values to predetermined data formats and value ranges associated with certification requirements;
train a machine learning model to produce an artificial intelligence (Al) model using labeled training data derived from previously processed certification documents, the machine learning model configured to detect inconsistencies between tenant-provided responses and extracted document data;
generate, through a graphical user interface, a dynamically generated certification questionnaire based at least in part on the extracted structured data;
receive tenant responses to the certification questionnaire through the graphical user interface;
automatically detect discrepancies between the tenant responses and the extracted structured data using the trained machine learning model;
generate a discrepancy report identifying detected inconsistencies in the tenant responses;
automatically populate certification form fields with validated structured data corresponding to the tenant responses;
determine whether third-party verification documentation associated with the tenant is required based on the validated structured data;
when third-party verification documentation is required, automatically transmit digital verification requests to third-party systems over a network;
receive third-party verification documentation electronically through the network;
classify the received third-party verification documentation using the trained machine learning model to determine a verification category including income verification, asset verification, or employment verification;
store the classified verification documentation and completed certification forms in a structured certification record database; and
generate a graphical interface displaying verification status indicators associated with the certification record.
These limitations constitute a system for remotely completing a certification process for an affordable housing program (Specification ¶2), which are processes that, under their broadest reasonable interpretation, are considered certain methods of organizing human activity – commercial or legal interactions (including agreements in the form of contracts and marketing or sales activities or behaviors) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea.
The claim further recites “train a machine learning algorithm.” When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning algorithm train a machine learning model to produce an artificial intelligence (Al) model using labeled training data derived from previously processed certification documents represents the creation of mathematical interrelationships between data. See Specification at ¶117-118, 121, indicating the use of machine learning algorithms such as gradient descent, which is a mathematical algorithm. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?): This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more processors; memory; a networked computing device; a graphical user interface; third-party systems; a network; a structured certification record database; optical character recognition (OCR), optical mark recognition (OMR), natural language processing (NLP), and named entity recognition (NER). The computer is recited at a high-level of generality (i.e., as a generic processing device performing generic computer functions), such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the receive and store limitations may be considered insignificant extra-solution activity (see MPEP 2106.05(g)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea when considered both individually and as a whole. The claim is directed to an abstract idea.
The limitations reciting “extract structured data from the scanned certification documents using one or more document processing techniques comprising optical character recognition (OCR), optical mark recognition (OMR), natural language processing (NLP), and named entity recognition (NER),” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the computers are invoked merely as a tool to perform existing processes (“extract structured data from the scanned certification documents using one or more document processing techniques comprising optical character recognition (OCR), optical mark recognition (OMR), natural language processing (NLP), and named entity recognition (NER)”). See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the steps of the abstract idea amount to no more than mere instructions to apply the exception using a generic computer component. Further, the claims simply append well-understood, routine, and conventional (WURC) activities previously known to the industry, specified at a high level of generality, to the judicial exception, in the form of the extra-solution activity. The courts have recognized that the computer functions claimed (the receive and store limitations) as WURC (see 2106.05(d), identifying receiving or transmitting data over a network as WURC, as recognized by Symantec, identifying storing information in memory as WURC, as recognized by Versata). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible, as when viewed individually, and as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent claims 2-7 merely recite further embellishments of the abstract idea of independent claim 1 as discussed above with respect to integration of the abstract idea into a practical application, and these features only serve to further limit the abstract idea of independent claim 1; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits.
In light of the detailed explanation and evidence provided above, the Examiner asserts that the claimed invention, when the limitations are considered individually and as whole, is directed towards an abstract idea.
Subject Matter Distinguished From Prior Art
The prior art of record neither anticipates nor supports a conclusion of obviousness without the use of impermissible hindsight with respect to claims 1-14.
As to claim 1, the prior art of record neither anticipates not fairly and reasonable teach a computerized document verification system for processing digital certification records for an affordable/low-income housing program, the system comprising:
one or more processors; and
memory storing instructions that, when executed by the one or more processors, cause the system to:
receive scanned certification documents associated with a tenant through a networked computing device;
extract structured data from the scanned certification documents using one or more document processing techniques comprising optical character recognition (OCR), optical mark recognition (OMR), natural language processing (NLP), and named entity recognition (NER);
validate the extracted structured data by automatically comparing extracted values to predetermined data formats and value ranges associated with certification requirements;
train a machine learning model to produce an artificial intelligence (Al) model using labeled training data derived from previously processed certification documents, the machine learning model configured to detect inconsistencies between tenant-provided responses and extracted document data;
generate, through a graphical user interface, a dynamically generated certification questionnaire based at least in part on the extracted structured data;
receive tenant responses to the certification questionnaire through the graphical user interface;
automatically detect discrepancies between the tenant responses and the extracted structured data using the trained machine learning model;
generate a discrepancy report identifying detected inconsistencies in the tenant responses;
automatically populate certification form fields with validated structured data corresponding to the tenant responses;
determine whether third-party verification documentation associated with the tenant is required based on the validated structured data;
when third-party verification documentation is required, automatically transmit digital verification requests to third-party systems over a network;
receive third-party verification documentation electronically through the network;
classify the received third-party verification documentation using the trained machine learning model to determine a verification category including income verification, asset verification, or employment verification;
store the classified verification documentation and completed certification forms in a structured certification record database; and
generate a graphical interface displaying verification status indicators associated with the certification record.
Examiner notes that the underlined limitations above, in combination with the other limitations found within the independent claims are not found in the prior art.
Response to Arguments
Claim Rejections – 35 USC 101
Step 2A: Prong One: The Claims Recite a Judicial Exception
Applicant argues that claim 1 recites “computer-implemented technical processes involving document digitization, machine learning classification, and automated document validation,” cannot be “practically performed as a mental process and therefore do not fall within the enumerated abstract idea groupings.” Remarks 10. Examiner respectfully disagrees that the claims do not fall within the abstract idea groupings. The claims have not been grouped into the mental processes subgrouping, but the certain methods of organizing human activity subgrouping.
Step 2A: Prong 2
Applicant argues that the operations of claim 1 “replace manual document review and verification procedures with automated computer-implemented processing, improving both accuracy and processing efficiency of certification records,” provides a practical application in the field of automated document processing systems. Remarks 10. Applicant asserts that claim 1 provides specific improvements in the form of (1) automated document data extraction, (2) machine learning based discrepancy detection, (3) automated classification of verification documentation, and (4) structured population of certification forms. Examiner respectfully disagrees that any of these alleged improvements provides a practical application. Applicant seems to have combined the abstract idea with the additional elements in order to assert the improvements. However, the improvement must be in the additional elements themselves. The additional elements are identified above as: one or more processors; memory; a networked computing device; a graphical user interface; third-party systems; a network; a structured certification record database; optical character recognition (OCR), optical mark recognition (OMR), natural language processing (NLP), and named entity recognition (NER). Applicant has neither alleged improvement in the additional elements or provided evidence of such improvement.
Step 2B: The Claims Do Not Provide Significantly More than the Abstract Idea
Applicant argues that claim 1 recites specific technical architecture, which “represent specific technological solutions implemented using computer processing techniques, not generic computer implementation.” Remarks 12. The Supreme Court has identified a number of considerations as relevant to the evaluation of whether the claimed additional elements amount to an inventive concept. Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include: (i) improvements to the functioning of a computer, (ii) improvements to any other technology or technical field, (iii) applying the judicial exception with, or by use of, a particular machine, (iv) effecting a transformation or reduction of a particular article to a different state or thing, (v) adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016), and (vi) other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05. Applicant has not alleged any of these considerations apply, or provided evidence that the invention provides significantly more than the abstract idea. The use of known computer processing techniques does not provide significantly more than the abstract idea, as there is no improvement to the functioning of a computer or to any other technology or technical field.
Claim Rejections – Double Patenting
Examiner acknowledges receipt of the terminal disclaimer and withdraws the rejection accordingly.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARRIE S GILKEY whose telephone number is (571)270-7119. The examiner can normally be reached Monday-Thursday 7:30-4:30 CT and Friday 7:30-12 CT.
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/CARRIE S GILKEY/Primary Examiner, Art Unit 3626