DETAILED ACTION
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 .
THIS ACTION IS MADE FINAL.
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 mailing date of this final action.
Status of the Application
The following is a Final Office Action in response to Examiner's communication of 12/02/2025, Applicant, on 03/02/2026.
Status of Claims
Claims 1 and 11 are currently amended.
Claims 1-22 are currently pending following this response.
New matter
No new matter has been added to the amended claims.
Response to Arguments - 35 USC § 101
The arguments have been fully considered, but they are not persuasive.
The examiner respectfully disagrees.
Arguments pages 12-13.
Claims can recite abstract ideas even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer’).
Collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory in Content Extraction is according to the court an abstract idea that is similar to other concepts that have been identified as abstract by the courts. Present claim 1 is collecting and analyzing data using a generic computer processor even if the processor is executing data analysis routines (Applicant’s arguments page 12) and providing an interactive user interface. Therefore, it is reasonable to conclude based on the similarity of the idea described in this claim to several abstract ideas found by the courts that claim 1 is directed to an abstract idea. The present claims mirror cases like Billing v. United States or FairWarning IP v. latric Systems where the court ruled that collecting, analyzing, and displaying data for risk or compliance purposes is an abstract idea, regardless of how complex the scoring algorithm is. Further, simply invoking “machine learning” to select routines or calculate scores is often treated as a black box by the court unless the claims specify a technical improvement to the ML architecture itself. This concept is viewed as a tool used to automate a manual concept.
The Examiner separated the additional elements in the claims from the abstract limitations (please see 35 USC § 101 rejection below) and evaluated the claims according to 2019 PEG (Applicant’s arguments page 11). The Examiner considered the identified limitations individually and in combination. The Examiner followed the 2019 PEG 35 USC § 101 steps (see below) and did not jump to conclusionary statement.
Arguments pages 13-14.
The additional elements, identified by the Examiner, such as “A computer-implemented system that”, “the system comprising: a data input that”, “a memory component that”, “and a computer processor that is coupled to the data input and further programmed to”, “to interface with a data analysis routine processor”, “automatically”, “generating an interactive interface comprising dashboards, Excel output templates, and revenue journal entry pairing interfaces, wherein the interactive interface”, “wherein the interactive interface”, and “wherein the interactive interface enables users to” are generic computer elements recited at a high level of generality and used to apply the abstract idea and nothing more. The recited additional elements do not integrate the claims into a practical application because as a whole, the additional elements do not improve any existing technology nor they improve the function of a computer. Applicant’s arguments page 12 regarding “use the judicial exception in a manner that imposes a meaningful limit on the judicial exception” are not persuasive because of the lack of technical improvements in the claims and the relying on generic computer processor to perform the claimed steps. The interactive user interface as claimed does not provide any technical improvements over the prior art systems because the user interface simply display analysis results even with drill- through capabilities providing user-selectable hierarchical data access to display underlying journal entries and includes dynamic curated filtering and interactive data manipulation capabilities based on user selection for transactions. As a result, the present claims are not eligible under 101.
Arguments page 16.
Because the Examiner has determined that the judicial exception is not integrated into a practical application, the Examiner proceeds to Step 2B of the Eligibility Guidelines, which asks whether there is an inventive concept. In making this Step 2B determination, the Examiner must consider whether there are specific limitations or elements recited in the claim “that are not well - understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present” or whether the claim “simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, indicative that an inventive concept may not be present.” Eligibility Guidance, 84 Fed. Reg. 56 (footnote omitted). The Examiner must also consider whether the combination of steps perform “in an unconventional way and therefore include an ‘inventive step,’ rendering the claim eligible at Step 2B” Id. In this part of the analysis, the Examiner considers “the elements of each claim both individually and ‘as an ordered combination’” to determine “whether the additional elements ‘transform the nature of the claim’ into a patent-eligible application.” Alice, 134 S. Ct. at 2354. As discussed above, there is no evidence in the record that the steps of scoring transactions and general ledger for auditing is accomplished in a non-conventional way. The Examiner therefore concludes that the claims used generic, conventional, technology to implement the abstract idea of scoring transactions and general ledger for auditing and that there is no improvement to an “existing technology.”
The Examiner does not see in the present claims and in the original disclosure any evidence of an improved machine learning algorithm. Claim limitation “to interface with a data analysis routine processor to selectively determine a set of data analysis routines for execution, the machine learning model automatic determination based at least in part on an analysis of the input data for the presence of a plurality of required relevant data elements for each data analysis routine; determining, by the machine learning model, (1) a highest possible score for the determined set of analysis routines and (2) that the highest possible score is capable of providing an appropriate level of audit evidence” (arguments page 5) does not imply an improved machine learning algorithm simply by determining an optimal set of routines for execution. Machine learning algorithms are increasingly being used to determine and optimize routines in various applications, particularly those involving planning, scheduling, and personalization. This is done by Data Collection and Analysis, Pattern Recognition and Prediction, Optimization and Decision Making. Some examples of algorithms: Reinforcement Learning, Gradient Descent, Genetic Algorithms, Bayesian Optimization, K-Means Clustering, Decision Trees, and Random Forest.
The Examiner submits that the above discussion is within the scope/guidance of the August 4, 2025 Memorandum. The above discussion shows that the Examiner Distinguished claims that recite a judicial exception from claims that merely involve a judicial exception, analyzed the claims as a whole, “Apply it” versus improvement consideration, and finally, the Examiner did not oversimplified the present claims in ant way.
In conclusion, the Examiner maintains the rejections of the pending claims under 35 USC § 101 in the present office action.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-22 are directed to an abstract idea without additional elements to integrate the claims into a practical application or to amount to significantly more than the abstract idea.
Claims 1-22 are directed to a process, machine, or manufacture (Step 1), however the claims are directed to the abstract idea of scoring transactions and general ledger for auditing.
With respect to Step 2A Prong One of the frameworks, claim 1 recites an abstract idea. Claim 1 includes limitations for “implements a data analysis risk assessment and substantive procedure for revenue transactions, receives one or more of: transactional data and general ledger data for an entity; stores and manages the one or more: transactional data and the general ledger data; perform the steps of: receiving input data comprising one or more of: current year and previous year transactional data and general ledger data; detecting and addressing formula errors including DIV/0!, #N/A!, #NAME?,#NULL, #NUM!, #REF!, and #VALUE! errors, and excluding transactions that do not meet engagement criteria from the data analysis routines; implementing a machine learning model to selectively determine a set of data analysis routines for execution, the machine learning model automatic determination based at least in part on an analysis of the input data for the presence of a plurality of required relevant data elements for each data analysis routine; determining, by the machine learning model, (1) a highest possible score for the determined set of analysis routines and (2) that the highest possible score is capable of providing an appropriate level of audit evidence; upon determining that the highest possible score is capable of providing an appropriate level of audit evidence, executing the set of data analysis routines with the input data to analyze one or more of: revenue general ledger and transaction-level-data; calculating a score for each of the set of data analysis routines applied to each of the one or more of: transaction and general ledger data using a points model; based at least in part of the score, categorizing each of the one or more of: transaction and general ledger data into one of a set of at least three groups, each group comprising a relative residual audit risk level, wherein the at least three groups determine whether a set of expectations are met and a categorization into a higher residual audit risk group indicates one or more of the existence of systemic risk, the categorized transaction and general ledger data is not homogenous with a population of the transaction and general ledger data, and the categorized transaction and general ledger data exhibits greater risk than the population of the transaction and general ledger data; based on the set of groups, accumulating audit evidence accordingly; testing one or more details of transaction and general ledger data that is categorized into the highest residual risk group; instituting a journal entries (JE) pairing routine to the set of analysis routines, comprising: applying a JE pairing segregation logic to the transactional data and general ledger data, wherein the JE pairing segregation logic comprises: determining whether journal entries debit or credit primary and secondary specified accounts; determining whether lines within the journal entry fully offset and net to zero without including specified accounts; and splitting lines that impact or offset specified accounts from lines that do not, wherein lines that offset the specified accounts are tagged in the output;; and generating an interactive JE pairing user interface; and generating and transmitting an output based at least in part of the score and the set of groups, wherein generating and transmitting the output comprises, provides drill- through capabilities providing user-selectable hierarchical data access to display underlying journal entries, wherein the drill-through capabilities enable a user to drill through a line item to view the full underlying journal entry and display a summary of items that did not obtain sufficient and appropriate audit evidence within the selected item and includes dynamic curated filtering and interactive data manipulation capabilities based on user selection for transactions, customers, and products, and perform additional audit procedures over items categorized as requiring additional audit procedures”
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the limitations above recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for scoring transactions and general ledger for auditing. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 11 recites substantially similar limitations to those presented with respect to claim 1. As a result, claim 11 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1. Similarly, claims 2-10 and 12-22 recite certain methods of organizing human activity associated with managing personal behavior or relationships or interactions between people because the claimed elements describe a process for scoring transactions and general ledger for auditing. As a result, claims 2-10 and 12-22 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “A computer-implemented system that”, “the system comprising: a data input that”, “a memory component that”, “and a computer processor that is coupled to the data input and further programmed to”, “to interface with a data analysis routine processor”, “automatically”, “generating an interactive interface comprising dashboards, Excel output templates, and revenue journal entry pairing interfaces, wherein the interactive interface”, “wherein the interactive interface”, and “wherein the interactive interface enables users to”. When considered in view of the claim as a whole, the steps of “receiving and transmitting” do not integrate the abstract idea into a practical application because “receiving and transmitting” are an insignificant extra solution activity to the judicial exception. When considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 11 recites substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A computer-implemented method”, when considered in view of the claim as a whole, the recited computer elements do not integrate the abstract idea into a practical application because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 11 does not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-10 and 12-22 do not include any additional elements beyond those recited by independent claims 1 and 11. As a result, claims 22-10 and 12-22 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea. The additional elements of claim 1 include “A computer-implemented system that”, “the system comprising: a data input that”, “a memory component that”, “and a computer processor that is coupled to the data input and further programmed to”, “to interface with a data analysis routine processor”, “automatically”, “generating an interactive interface comprising dashboards, Excel output templates, and revenue journal entry pairing interfaces, wherein the interactive interface”, “wherein the interactive interface”, and “wherein the interactive interface enables users to”. The steps of “receiving and transmitting” do not amount to significantly more than the abstract idea because “receiving and transmitting” are well-understood, routine, and conventional computer function in view of MPEP 2106.05(d)(ll). The recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. As a result, claim 1 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
As noted above, claim 11 recites substantially similar limitations to those recited with respect to claim 1. Although claim 11 further recites “A computer-implemented method”, the recited computer elements do not amount to significantly more than the abstract idea because the computer elements are generic computer elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 11 does not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-10 and 12-22 do not include any additional elements beyond those recited by independent claims 1 and 11. As a result, claims 2-10 and 12-22 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-22 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendments and arguments dated 03/02/2026 necessitated the updating of the 35 USC § 101 rejections of the pending claims presented in the present 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).
Any inquiry concerning this communication from the examiner should be directed to Abdallah El-Hagehassan whose contact information is (571) 272-0819 and Abdallah.el-hagehassan@uspto.gov The examiner can normally be reached on Monday- Friday 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/ABDALLAH A EL-HAGE HASSAN/
Primary Examiner, Art Unit 3623
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