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 . Claims 1, 2, 7-17, and 19-20 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant, on 105/15/2026, amended claims and previously cancelled claims 3-6, and 18. Claims 1, 2, 7-17, and 19-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The 103 Rejections are overcome and withdrawn in the Final Office action dated 12/11/2024.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that the claims are not directed towards mental processes as the claims are performed by retraining machine learning models (using event log data), automatically collect… and are not operations performed in the human mind.
Examiner respectfully disagrees. The claims recite the mental processes of process mining, determining input-related/rule-related zero-touch quotient, determine accounting factors, and determining zero-touch processing quotient. While the functions are performed using the additional elements such as the tools, engines, retraining a machine learning model (recited at a high level of generality), etc. there additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f). The Examiner suggests adding an initial training step and further technical details with respect to data collection, model training, and automatically adjusting data-collection tools.
Applicant further contends that the claims are not directed towards methods of organizing human activity as claims are directed towards identifying features, adjusting data-collection tools, etc.
Examiner respectfully disagrees as the claims are directed towards determining processes for zero-touch potential (i.e. automation) in business/accounting processes (see Applicant’s Specification, [at least 02, 03, 22]).
Applicant contends that the claims improve the operation of zero-touch monitoring system and further points to the Specification [03-04, 17-18] at Prong 2. Applicant further asserts that the claims improve how the system handles process data and points to Specification [40, 46, 50].
Examiner respectfully disagrees. The additional elements are analyzed both individually as well as in combination and determined to be performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The cited improvement merely improves upon the abstract idea itself and not the technology as a whole. Further the Examiner notes what additional elements or combination is being relied upon to facilitate the cited improvement.
Applicant further points to amendments regarding retraining a machine learning model and asserts the claims are not generic AI and corresponds to the specification’s technical description. Applicant further points to the specification and argues that similar to Desjardins the claims adjust one or mor model weights until an error signal falls below a threshold.
Examiner respectfully disagrees. The claims recite the use of machine learning at a high level of generality and as such are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The Examiner suggests adding an initial training step and further technical details with respect to data collection, model training, and automatically adjusting data-collection tools.
Applicant contends that the claims recite significantly more than any abstract idea. Applicant further points to limitations regarding retraining a machine learning model (using event log data) and using the retrained model to generate values and further asserts the claimed combination recites a specific technological implementation that improves the functioning of computerized zero-touch monitoring system and machine learning components.
Examiner respectfully disagrees. The claims recite the use of machine learning at a high level of generality and as such are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Examiner further points to Appeal 2025-003304 Application 17/304,491 contrasts DesJardins and REcentive: As the Federal Circuit explained, “[i]terative training using selected training material and dynamic adjustments based on real-time changes [is] incident to the very nature of machine learning” and “do[es] not represent a technological improvement.” Recentive at 1212
The 101 Rejections are updated and maintained below.
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, 2, 7-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) 1, 2, 7-17, and 19-20 is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims 1, 16, and 20 recite a series of steps for zero-touch potential for an event to entry process.
Regarding Claims 1, 16, and 20:
A system for driving zero-touch potential for an event-to- entry process, comprising:
a processor; and a memory, coupled to the processor, configured to store executable instructions that, when executed by the processor, cause the processor to/A computer-implemented method for driving zero-touch potential for an event-to-entry process, the method comprising/ A computer program product for driving zero-touch potential for an event-to-entry process, the computer program product comprising a non- transitory computer-readable medium having computer-readable program code stored thereon, the computer-readable program code configured to:
perform, by a process-mining engine, process-mining on an event-to-entry process associated with an organization to collect data information associated with the process;
determine, by the process-mining engine, an extent of human intervention in the event-to-entry process based on an image processing included in the event-to-entry process;
determine, by an input-related zero-touch evaluation engine, an input-related zero-touch quotient for the event-to-entry process based on the data information including the extent of human intervention;
determine, by a rule-related zero-touch evaluation engine, a rule-related zero- touch quotient for the event-to-entry process based on the data information;
determine, by a predictive accounting factor component, a zero-touch potential predictive accounting factor (ZTP PAF) value for the event-to-entry process, the ZTP PAF value quantifying an incremental zero-touch potential for the event-to-entry process and corresponding attributes;
generate, by a zero-touch processing engine, a zero-touch processing quotient for the event-to-entry process based on the input-related zero-touch quotient, the rule-related zero- touch quotient, and the ZTP PAF value; and
responsive to the determined zero-touch processing quotient being over a predefined threshold, automatically adjust data-collection tools (i.e. automatically implies the use of a general purpose computer; adjust data-collection tools recited at a high level of generality) to be utilized to improve data structure of data to be collected for the event-to-entry process by examining a data structure for specific data already used in the event-to-entry process and by using one or more of enterprise resource planning (ERP) or customer relationship management (CRM) software application tools to automatically collect more structured data related to the event-to-entry process in a relational database,
wherein the input-related zero-touch quotient for the event-to-entry process is determined by:
determining a first set of data components included in the event-to-entry process, wherein the first set of data components include one or more of a structuredness, repetitiveness, or intervention associated with the event-to-entry process;
allocating a maturity value for each of first set of data components, wherein a maturity value of the structuredness has a proportionate and positive relationship to the input-related zero-touch quotient, and a maturity value of the intervention has an inverse relationship to the input-related zero-touch quotient; and
determine the input-related zero-touch quotient for the event-to-entry process based on the maturity value for each of first set of data components
retrain at least one machine learning model (i.e. recited at a high level of generality) used by the predictive accounting factor component using updated event log data received through the adjusted data-collection tools, including comparing model outputs to labeled outputs, generating error signals based on differences between the model outputs and the labeled outputs, and adjusting one or more model weights until the error signals fall below a threshold; and
use the retrained at least one machine learning model to generate an updated ZTP PAF value for each of the first set of data components.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed to process mining, determining input-related/rule-related zero-touch quotient, determine accounting factors, and determining zero-touch processing quotient.
Further the claims are directed to “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards determining processes for zero-touch potential (i.e. automation) in business/accounting processes (see Applicant’s Specification, [at least 02, 03, 22]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least the additional elements bolded above. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does 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 elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claim(s) 2, 7-11, 14-15, 17, and 19, the claim further narrows the abstract idea or recite additional elements previously addressed in the independent claims.
Regarding Claim(s) 12, the claim further recite the additional element(s) of using a machine learning model and using a predictive machine learning model (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Regarding Claim(s) 13, the claim further recite the additional element(s) of the predictive machine learning model has been trained and tested over data (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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/JEREMY L GUNN/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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