DETAILED ACTION
Status of the Application
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 .
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
This action is a Final Action on the merits in response to the application filed on 12/10/2025.
Claims 1 and 12 have been amended.
Claims 1-22 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office have been maintained.
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 1-11 are directed towards a method; claims 12-22 are directed towards a system, all of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including training and updating data models. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-22, the independent claims (claims 1 and 12) are directed to managing the completions and completed forms, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, A computer-implemented method comprising:
identifying, using the trained machine learning model, structured user inputs, wherein the structured user inputs include form fields, user-specified categories, or dynamically recognized hierarchical objects;
collecting, by the processor, factor data for each of a plurality of users, wherein the factor data comprises at least one of:
(i) historical user interaction data,
(ii) session interruptions and resumption metrics,
(iii) structured content elements, including form fields, loan numbers, and user-generated categorizations, and
(iv) real-time engagement signals from chat-based user interactions;
calculating, by the trained machine learning model, a workflow efficiency score for each of the plurality of users based on:
(i) the parsed workflow,
(ii) the collected factor data, and
(iii) predicted abandonment likelihood or satisfaction trends, wherein the workflow efficiency score reflects a dynamic correlation between user behaviors and structured task progression;
rendering, within the conversational interface, a real-time progress indicator representing a user's advancement through a multi-step application;
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes managing personal behavior such as social activities and following rules or instructions (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier. The claims recite the steps are performed by the processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier.
The limitations of
parsing, by a processor, a workflow for completing an electronic form within a conversational interface;
training, by a processor, a machine learning model with a plurality of training observations of historic user factor data and historic user satisfaction data, wherein the machine learning model comprises at least one of:
(i) a neural network comprising a plurality of input nodes, a plurality of hidden nodes, and at least one output node,
(ii) a rule-based system,
(iii) a linear or non-linear regression system,
(iv) a fuzzy logic system,
(v) a decision tree,
(vi) a nearest neighbor classifier, and
(vii) a statistical pattern recognition classifier;
automatically adapting, by the processor, at least one interactive parameter in the form interface based on the workflow efficiency score, wherein the adaptation comprises:
(i) context-sensitive input guidance,
(ii) dynamically switching between predictive responses, scrollable options, or manual entry
(iii) transitioning to human assistant based on complexity thresholds, and
(iv) modifying visibility of progress- tracking elements according to inferred user engagement;
reactivating, upon user return, a previously paused session by restoring structured input data and resuming from the last completed workflow step; and
updating the trained machine learning model based on continuous interaction feedback, including abandonment signals, completion times, and correction patterns, to refine future workflow predictions.
are mere data gathering and processing recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier. The processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier are recited at a high level of generality. In limitation (a), the machine learning model is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The machine learning model is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites machine learning model. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and processing. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0050 “the AA may use machine learning algorithms trained on existing HA and user interaction data to predict how the user is likely to feel about any particular topic or line of questioning”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
parsing, by a processor, a workflow for completing an electronic form within a conversational interface;
training, by a processor, a machine learning model with a plurality of training observations of historic user factor data and historic user satisfaction data, wherein the machine learning model comprises at least one of:
(i) a neural network comprising a plurality of input nodes, a plurality of hidden nodes, and at least one output node,
(ii) a rule-based system,
(iii) a linear or non-linear regression system,
(iv) a fuzzy logic system,
(v) a decision tree,
(vi) a nearest neighbor classifier, and
(vii) a statistical pattern recognition classifier;
automatically adapting, by the processor, at least one interactive parameter in the form interface based on the workflow efficiency score, wherein the adaptation comprises:
(i) context-sensitive input guidance,
(ii) dynamically switching between predictive responses, scrollable options, or manual entry
(iii) transitioning to human assistant based on complexity thresholds, and
(iv) modifying visibility of progress- tracking elements according to inferred user engagement;
reactivating, upon user return, a previously paused session by restoring structured input data and resuming from the last completed workflow step; and
updating the trained machine learning model based on continuous interaction feedback, including abandonment signals, completion times, and correction patterns, to refine future workflow predictions.
are recited at a high level of generality. These elements amount to transmitting data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-11 and 13-22 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 8-9, 19, 21 recite chat interface to display information; claims 11, 22 recite machine learning model for processing data; claim 13 recite processors for parsing forms; claims 15, 16 recite processors for predicting input data; . The dependent claims 2-11 and 13-22 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-11 and 13-22 recites processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-11 and 13-22 recites processor, machine learning, interface, neural network, nodes, regression system, fuzzy logic, decision tree, classifier, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-11 and 13-22 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1 and 12. Therefore claims 2-11 and 13-22 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Page 9 of 13
Response dated January 5, 2021
Attorney Docket No. 132247-US
Response to Arguments
Applicant’s arguments filed 12/10/2025 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 12/10/2025.
Regarding the 35 U.S.C. 101 rejection, at pg. 11-19 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
parsing, by a processor, a workflow for completing an electronic form within a conversational interface;
training, by a processor, a machine learning model with a plurality of training observations of historic user factor data and historic user satisfaction data, wherein the machine learning model comprises at least one of:
(i) a neural network comprising a plurality of input nodes, a plurality of hidden nodes, and at least one output node,
(ii) a rule-based system,
(iii) a linear or non-linear regression system,
(iv) a fuzzy logic system,
(v) a decision tree,
(vi) a nearest neighbor classifier, and
(vii) a statistical pattern recognition classifier;
identifying, using the trained machine learning model, structured user inputs, wherein the structured user inputs include form fields, user-specified categories, or dynamically recognized hierarchical objects;
collecting, by the processor, factor data for each of a plurality of users, wherein the factor data comprises at least one of:
(i) historical user interaction data,
(ii) session interruptions and resumption metrics,
(iii) structured content elements, including form fields, loan numbers, and user-generated categorizations, and
(iv) real-time engagement signals from chat-based user interactions;
calculating, by the trained machine learning model, a workflow efficiency score for each of the plurality of users based on:
(i) the parsed workflow,
(ii) the collected factor data, and
(iii) predicted abandonment likelihood or satisfaction trends, wherein the workflow efficiency score reflects a dynamic correlation between user behaviors and structured task progression;
automatically adapting, by the processor, at least one interactive parameter in the form interface based on the workflow efficiency score, wherein the adaptation comprises:
(i) context-sensitive input guidance,
(ii) dynamically switching between predictive responses, scrollable options, or manual entry
(iii) transitioning to human assistant based on complexity thresholds, and
(iv) modifying visibility of progress- tracking elements according to inferred user engagement;
rendering, within the conversational interface, a real-time progress indicator representing a user's advancement through a multi-step application;
reactivating, upon user return, a previously paused session by restoring structured input data and resuming from the last completed workflow step; and
updating the trained machine learning model based on continuous interaction feedback, including abandonment signals, completion times, and correction patterns, to refine future workflow predictions.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations regarding the managing the completions of form, which constitutes methods related to managing personal behavior such as social activities and following rules or instructions which are still considered an abstract idea under the 2019 PEG. The managing the completions of form is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Next, regarding “parses a workflow for an electronic form inside a conversational interface, uses a trained machine learning model to derive a workflow efficiency score from multi-dimensional behavioral factor data, and then automatically adapts interactive parameters of the form interface in real time, renders a dynamic progress indicator, supports session resumption from the last completed workflow step, and updates the model from continuous feedback.”. The Examiner would like to direct the Applicant to the MPEP 2106.05(i) explicitly states that:
“Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:
iv. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017)”, as the Applicant case or claimed technical improvement is not akin to said Westlake case. Additionally, all improvements recited in the claim (managing the completions of form) are directed towards an existing business process that does not integrate the judicial exception into a practical application because the claim recites additional elements at a high-level of generality used to execute mere instructions in order to implement the abstract idea on a general purpose computer.
Furthermore, for the following key limitations and supporting arguments:
“1. Technical parsing and structuring of conversational input”
“2. Multi-factor workflow efficiency scoring for each user”
“3. Real-time adaptation of interactive parameters based on the score”
“4. Dynamic progress tracking and session resumption at the interface
layer ”
These key limitation and their respective arguments are an admittance that the application is directed to improving the user’s experience and not the computer itself, specifically:
Pg. 12, “identifying, using the trained machine learning model, structured
user inputs, wherein the structured user inputs include form fields, user-specified
categories, or dynamically recognized hierarchical objects”
Pg. 13, “collecting, by the processor, factor data for each of a plurality of
users, wherein the factor data comprises at least one of: (i) historical user
interaction data, (ii) session interruptions and resumption metrics, (iii) structured
content elements, including form fields, loan numbers, and user-generated
categorizations, and (iv) real-time engagement signals from chat-based user
interactions”
Pg. 14, "automatically adapting, by the processor, at least one interactive
parameter in the form interface based on the workflow efficiency score, wherein
the adaptation comprises:
- (iii) transitioning to human assistant based on complexity thresholds, and
- (iv) modifying visibility of progress-tracking elements according to inferred user engagement;
- switching between different input modalities (predictive responses,
scroll wheel, free text),
- selectively surfacing or hiding progress indicators,
- and escalating to a human assistant when complexity thresholds
are detected.”
Pg. 15, “-rendering, within the conversational interface, a real-time progress
indicator representing the user's advancement through the multi-step
application;
-reactivating, upon user return, a previously paused session by
restoring structured input data and resuming from the last completed workflow
step”
Then, regarding claims are significantly more and/or practical application, the Examiner respectfully disagree. By, the Applicant claiming pre-trained machine learning model, the Applicant is admitting to using an already trained machine learning model which is not significantly more or an improvement to the machine learning model.
Although the additional element machine learning model limits the identified judicial exceptions “The claim is rooted in improving the computer-implemented conversational interface and its workflow control, not in a business method such as underwriting, marketing, or loan approval. The "electronic form" in the claim is expressly generic and not limited to any financial or commercial process. The improvement lies in how the system parses, scores, adapts, renders, resumes, and learns at the interface level.” this type of limitation in the claims merely confines the use of the abstract idea to a particular technological environment (i.e. neural networks) and thus fails to add an inventive concept to the claims, thus the use of the machine learning 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
Lastly, the Examiner finds the Chat Interface is a Visual Feedback displayed which is considered an insignificant post-solution activity of delivering data; see MPEP 2106.05(g). Then, the Examiner notes that whether or not the claims are novel or obvious is evaluated with respect to satisfying the conditions of 35 USC 102 / 35 USC 103, not necessarily determining whether the claims are eligible under 35 USC 101.
For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hodges et al., W.O. Pub. 2007090161, (discussing the analyzing of workflow applications).
Folmer et al., Architecturally sensitive usability patterns, https://www.researchgate.net/profile/Eelke_Folmer/publication/2892617_Architectural_Sensitive_Usability_Patterns/links/56abf92d08aeaa696f2a0160/Architectural-Sensitive-Usability-Patterns.pdf, Department of Mathematics and Computing Science, University of Gronigen, Netherlands, 2003 (discussing the determining of users patterns.).
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 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.
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UCHE BYRD
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Art Unit 3624
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