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 .
Status of Claims
This communication is a Final Office Action in response to Applicant’s amendment for application number 18/711,386 received on 10/28/2025.
In accordance with Applicant’s amendment, claims 1-23 are amended. Claims 1-23 are currently pending and have been examined.
Priority
Applicants claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 11/06/2025 has been considered.
Response to Amendment
The amendment filed on 10/28/2025 has been entered.
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Examiner notes that upon review of the amended claims, the §112(f) claim interpretations applied to the original claims are withdrawn.
Upon review of the amended claims, the previous §112(b) rejections are withdrawn.
Upon review of the amended claims, the previous §112(a) rejections are withdrawn.
Response to Arguments
Response to §101 arguments – Applicant’s arguments (Remarks at pgs. 9-14) with respect to the §101 rejections previously applied to the claims have been considered and are not persuasive.
Applicant argues (Remarks at pg. 9) – “As none of the features recited in the claims are directed towards any of the enumerated categories of abstract ideas, the claims cannot be properly interpreted as being abstract. Because the amended claims do not recite an abstract idea, the present claims are subject-matter eligible under Step 2A, Prong 1 of the eligibility analysis.”. In response, Examiner respectfully disagrees and notes that the claims, as currently presented, recite an abstract idea directed to “Mental Processes” by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). For example, the independent claims recite the limitation for receiving remarks associated with one or more field operations. One of ordinary skill in the art would be able to reasonably perform this step in their mind, or with the help of pen and paper through observation. Therefore, the claims recite an abstract idea directed to the “Mental Processes” abstract idea grouping.
Applicant argues (Remarks at pg. 11-12) – “At least the claimed "processing the remarks for event detection using a machine learning model to detect the event, an output of the rule-based model being an input to the machine learning model, the machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data comprising labels generated for events that had previously been detected by the rule-based model" and "incorporating any event detected by the rule-based model into the labeled training data for the WESTCLASS model" are practical technical operations that integrate the exception into a practical application.”. In response, Examiner respectfully disagrees and reminds Applicant that as can be seen in the Step 2A, Prong 1 analysis below (See MPEP 2106.04), the additional elements recited in the claims are excluded from the decision whether the claims recite abstract limitations. The additional elements are analyzed in Step 2A, Prong 2 (i.e. to determine whether the additional elements integrate the judicial exception into a practical application), and Step 2B (i.e. to determine whether the additional elements add significantly more.).
Applicant argues (Remarks at pg. 12-13) – “Furthermore, the improvement of the technological field in accordance with M.P.E.P. § 2106.05(I)(A)(ii) renders the claim "significantly more" than a judicial exception. The Federal Circuit has ruled in numerous cases that claims directed towards technological solutions to technological problems are not abstract under the two-step Alice test. Applicant submits that the amended claims are similarly directed towards a technological solution to a technological problem.
At least the claimed "processing the remarks for event detection using a machine learning model to detect the event, an output of the rule-based model being an input to the machine learning model, the machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data comprising labels generated for events that had previously been detected by the rule-based model" and "incorporating any event detected by the rule-based model into the labeled training data for the WESTCLASS model" renders the claims "significantly more" than the judicial exception.
The claim is a technical improvement and shows an inventive concept in that it provides a hybrid system that includes a rule-based model and a machine learning model that are implemented such that the machine learning model is a fallback mechanism that ensures that only edge/ambiguous cases consume machine learning resources, which increases throughput and reduces error.”. In response, Examiner respectfully disagrees and notes that as currently recited, the computing additional elements in the claims do not add significantly more to the abstract idea because they amount to using generic computing elements (computer hardware) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment, the internet, online) and does not amount to significantly more than the abstract idea itself. Applicant’s specification recites the computing additional elements at a high level of generality. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Additionally, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
to show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) for more information about mere instructions to apply an exception. Furthermore, with respect to the limitations for using an ensemble model of ubiquitous technology (rule-based model, and WESTCLASS model) to identify events, these do not add significantly more because they merely provide nothing more than instructions to implement an abstract idea on a generic computer using a “computer-implemented method”, which does not add significantly more to the abstract idea. See MPEP 2106.05(f). Moreover, the items to increase throughput and reduce error – discussed in Applicant’s argument – are irrelevant to the analysis because these features are not recited or required by the claim. For example, the claims do not recite or require increasing throughput and/or reducing error. Applicant’s argument lacks merit because is relies on limitations not required by the claims and it would be improper to import such limitations from the Specification. See Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also, CollegeNet, Inc. v. Apply Yourself Inc., 418 F.3d 1225, 1231 (Fed. Cir. 2005) (while the specification can be examined for proper context of a claim term, limitations from the specification will not be imported into the claims). Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Additionally, as noted above, as well as in the 101 rejections below, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.
Applicant argues (Remarks at pg. 13-14) – “The amended features include integral use of a machine to achieve performance of a method that integrates the recited judicial exception into a practical application. See M.P.E.P. § 2106.05(b)(II). The use of the hybrid model - a rule-based model as a first step in event detection, and a machine learning model as a second step that is used only when the rule-based model did not detect an event, works faster and sues fewer resources than machine learning models alone, but is still able to detect and determine an event when the rule-based model alone is insufficient. As such, responses can be given "in real-time or near real-time" (see [0093]) and "reliance on a domain expert can be reduced" (see [0095]). See also [00104]: [00104] A challenge to automated event detection in remarks in DMRs and/or other types of reports can be the lack of an annotated dataset for use as labeled training data for supervised learning. As an example, a hybrid NLP technique can be implemented to help address such a challenge. For example, consider use of a rule-based model for identifying specific events where the identified specific events can be considered labeled training data for use in training a ML model. In such an example, the labeled training data from the rule-based model may supplement additional labeled training data. Once the ML model is trained, it may be utilized for purposes of event detection in unlabeled data (e.g., remarks that have not been annotated by a domain expert). As an example, an ML model may be part of a weakly-supervised training workflow that can generate so-called pseudo- documents for purposes of training in a manner with a reduced amount of labeled training data. As an example, a workflow can include, for training, using a rule- based model to enlarge a labeled training data and followed by training of a weakly- supervised text classification (WESTCLASS) model based on the enlarged training set. Once an ML model is trained, a framework may implement a hybrid approach that utilizes a rule-based approach in combination with an ML model-based approach for event detection. In such an example, where the rule-based model does not perform well on some cases, cases where no event is detected by the rule-based model can be input into the trained ML model for final event assignment. In addition, [00146] discloses (emphasis added): [00146] As explained, a hybrid approach that utilizes a rule-based model and a ML model can improve accuracy compared to use of a rule-based model alone. As explained, a ML model can work in situations where a corpus of labeled training data tends to be quite small. For example, the ML model 1040 in the hybrid system 1000 can be trained with a set of labeled training data that maybe small fraction of the amount of data required for some NLP techniques (e.g., consider 10 percent or less, 5 percent or less, 1 percent or less of a total corpus used for a NLP technique).
M.P.E.P. § 2106.05(a) states that "the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification." Such technical explanation is provided in the present specification, for example, at least at [0093], [0095], [00104], [00136]-[00138], and [00149]. In the claimed invention, event detection uses a hybrid model combining rule-based and machine learning techniques, with a fallback mechanism, which improves the functioning of the underlying technology. The claims recite a specific way of achieving a desired outcome or result, which is not a mere instruction to apply the exception using generic computer components." See M.P.E.P. § 2106.05(f). In response, Examiner reiterates that as currently presented, the limitations for using an ensemble model of ubiquitous technology (rule-based model, and WESTCLASS model) to identify events, they do not add significantly more because they merely provide nothing more than instructions to implement an abstract idea on a generic computer using a “computer-implemented method”, which does not add significantly more to the abstract idea. See MPEP 2106.05(f). Furthermore, the item hybrid system 1000 can be trained with a set of labeled training data that maybe small fraction of the amount of data required for some NLP techniques (e.g., consider 10 percent or less, 5 percent or less, 1 percent or less of a total corpus used for a NLP technique) – discussed in Applicant’s argument – is irrelevant to the analysis because these features are not recited or required by the claim. For example, the claims do not recite or require using 10 percent or less, 5 percent or less, 1 percent or less of a total corpus used for a NLP technique to train the model. Applicant’s argument lacks merit because is relies on limitations not required by the claims and it would be improper to import such limitations from the Specification. See Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004). See also, CollegeNet, Inc. v. Apply Yourself Inc., 418 F.3d 1225, 1231 (Fed. Cir. 2005) (while the specification can be examined for proper context of a claim term, limitations from the specification will not be imported into the claims). Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Additionally, as noted above, as well as in the 101 rejections below, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.
Response to §103 arguments – Applicant’s arguments (Remarks at pgs. 15-18) with respect to the §103 rejections previously applied to the claims are considered moot given the new grounds of rejections set forth in the instant office action, which required new prior art to be applied to the independent claims. See §103 rejections below for details.
In response to Applicant’s argument regarding the interview (i.e., “During the interview, the Examiner agreed that the amendments overcome this rejection” – Remarks at pg. 16), Examiner would like to clarify the record and note that as documented in the Examiner Interview Summary Record (PTOL – 413) dated 10/29/2025, the Examiner and Primary Examiner notified the Attorney that the proposed amendments are significant and that the prior art applied will be reconsidered pending full review of the claims. Further, the Examiner and Primary Examiner informed Applicant’s Attorney that the amendments would likely necessitate expanding the search. Examiner further reminds Applicant that expanding the search as necessitated by Applicant’s amendments doesn’t necessarily constitute overcoming a rejection, and the previously applied prior art could still be considered relevant and/or analogous. Examiner further points Applicant to Applicant’s own interview summary record, dated 10/28/2025, where no such agreement is evidenced.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claim(s) 1-18, and 21-23 is/are directed to a method (i.e., Process), and claim(s) 19 is/are directed to a system (i.e., Machine), and claim(s) 20 is/are directed to a non-transitory computer-readable storage media (i.e., Manufacture). Therefore, claims 1-23 are directed to patent eligible categories of invention. Accordingly, the claims satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claims 1, 19, and 20 recite a method, a system and one or more non-transitory computer-readable storage media for processing field operations data. As drafted, the limitations recited by the independent claims fall under the “Mental Processes” abstract idea group by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Independent claim 1 recites a method for processing field operations data with the following limitations:
receiving remarks associated with one or more field operations; (But for the additional elements – underlined – recited in this limitation, the step for “receiving remarks” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
processing the remarks, using a rule-based model, for event detection using dependency matching to detect an event among a plurality of predefined events, the event being detected by the dependency matching using one or more patterns to match the remarks to the event; (But for the additional elements – underlined – recited in this limitation, the step for “processing the remarks” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
detecting that the dependency matching of the rule-based model failed to detect the event; (But for the additional elements – underlined – recited in this limitation, the step for “detecting that the dependency matching failed to detect the event” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
subsequently and responsive to the dependency matching of the rule-based model failing to detect the event, further processing the remarks for event detection using a machine learning model to detect the event, an output of the rule-based model being an input to the machine learning model, the machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data comprising labels generated for events that had previously been detected by the rule-based model; outputting at least the detected event; (But for the additional elements – underlined – recited in this limitation, the step for “further processing the remarks”, and “outputting at least the detected event” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, the “outputting at least the detected event” step amounts to insignificant extra-solution activity as insignificant application.);
and incorporating any event detected by the rule-based model into the labeled training data for the WESTCLASS model. (But for the additional elements – underlined – recited in this limitation, the step for “incorporating events detected into training data” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
Independent claims 19 and 20 recite a system and one or more non-transitory computer-readable storage media with limitations that are substantially similar to the limitations of independent claim 1. Therefore, the same analysis applies. The additional elements beyond the abstract idea for consideration under Step 2A, Prong 2, and Step 2B recited by the independent claims are: using a rule-based model, using a machine learning model, machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data comprising labels generated for events that had previously been detected by the rule-based model, one or more processors, memory, and one or more non-transitory computer-readable storage media.
The dependent claims further narrow the abstract idea and introduce the following additional elements for consideration under Step 2A, Prong 2, and Step 2B:
From claim 9: the deep neural network model comprises one or more of a convolution neural network model, or recurrent neural network model
From claim 13: rendering a graphical user interface to a display
From claim 14: training the machine learning model using the feedback
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely one or more processors, memory, and one or more non-transitory computer-readable storage media, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (generic computing environment).
With respect to the limitations for using a rule-based model, using a machine learning model, machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data comprising labels generated for events that had previously been detected by the rule-based model, the deep neural network model comprises one or more of a convolution neural network model, or recurrent neural network model, rendering a graphical user interface to a display, and training the machine learning model using the feedback, they provide nothing more than mere instructions to implement an abstract idea on a generic computer, which does not integrate the judicial exception into a practical application. 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.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely one or more processors, memory, and one or more non-transitory computer-readable storage media, these additional element(s) has/have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment, the internet, online) and does not amount to significantly more than the abstract idea itself. Applicant’s specification recites the computing additional elements at a high level of generality. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Additionally, use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
With respect to the limitations for using a rule-based model, using a machine learning model, machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data comprising labels generated for events that had previously been detected by the rule-based model, the deep neural network model comprises one or more of a convolution neural network model, or recurrent neural network model, rendering a graphical user interface to a display, and training the machine learning model using the feedback, they provide nothing more than mere instructions to implement an abstract idea on a generic computer, which does not add significantly more to the abstract idea. 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.
Furthermore, even if the receiving remarks and outputting at least the detected event steps are interpreted as additional elements, these activities at most amount to insignificant extra-solution activity (i.e., mere data gathering and insignificant application), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Additionally, the receiving remarks extra-solution activity has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself. The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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 (i.e., changing from AIA to pre-AIA ) 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 application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-2, 10-12, 19-20, and 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (US 20210285321 A1, hereinafter “Verma”), in view of Kryscinski et al. (US 20210124876 A1, hereinafter “Kryscinski”).
Regarding claims 1/19/20: Verma teaches a hybrid computer-implemented method, a hybrid computer-implemented system comprising one or more processors, and a memory with processor-executable instructions, and one or more non-transitory computer-readable storage media ([0040] In some aspects, a system, a method, or a non-transitory computer-readable medium for detecting and warning for a lost circulation event are provided; [0020] The processing device 202 can execute instructions stored in the memory device 208 to perform the operations.) with limitations for:
receiving remarks associated with one or more field operations; ([0036] Real-time drilling data 602 can be received and undergo a data ingestion process 604. The data ingestion process 604 can pre-process the real-time drilling data 602 using operations such as temporary caching 610, resampling and cleaning 612, and feature selection and scaling 614 based on training parameters. The ingested data can also undergo a real-time outlier check process 606.);
processing the remarks, using a rule-based model, for event detection ([0037] [0037] The pre-processed data can be used in an automated mud loss detection process 608. The automated mud loss detection process 608 may include applying a first model 622 to make a judgment if the data suggests that a lost circulation event has begun.);
using dependency matching to detect an event among a plurality of predefined events, the event being detected by the dependency matching using one or more patterns to match the remarks to the event; ([0037] If the detection is made continuously for a set amount of consecutive data rows, an alarm can be raised to alert the drilling operator 624.);
detecting that the dependency matching of the rule-based model failed to detect the event; subsequently and responsive to the dependency matching of the rule-based model failing to detect the event, further processing the remarks for event detection using a machine learning model to detect the event, an output of the rule-based model being an input to the machine learning model,… ([0037] If the analysis on the data suggests a normal operation, the data can be passed to a second model 626 to identify early warning precursors in the data that may indicate that a lost circulation event is about to occur. The second model 626 may be more probabilistic than the first model 622 as it is predicting whether a lost circulation event is about to occur.);
comprising labels generated for events that had previously been detected by the rule-based model; outputting at least the detected event; ([0035] The outlier removed data can be used in a model training process 508. The model training process 508 may be a semi-supervised learning process that uses unsupervised spectral clustering based automated mud loss zone warning 526 or unsupervised spectral clustering based precursor learning 528, which can then be used to train classifiers. For example, unlabeled drilling data can be segmented into different zones that can correspond to normal zones and lost circulation zones. The identification can occur in two steps: (1) segmenting between normal zone data and event data and (2) apply the clustering to the normal zone data and identify precursors across the aggregated dataset. The resulting dataset can be used to train supervised classifiers that can detect mud loss and the associated precursors.);
and incorporating any event detected by the rule-based model into the labeled training data… ([0035] The identification can occur in two steps: (1) segmenting between normal zone data and event data and (2) apply the clustering to the normal zone data and identify precursors across the aggregated dataset. The resulting dataset can be used to train supervised classifiers that can detect mud loss and the associated precursors. After a system testing process 510, the models can be deployed 512 for raising alarms and predicting lost circulation events based on incoming real-time drilling data.).
Verma doesn’t explicitly teach:
…the machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data
…for the WESTCLASS model.
Kryscinski teaches:
…the machine learning model comprising a weakly-supervised text classification (WESTCLASS) model, the WESTCLASS model being trained on labeled training data ([Abstract] A weakly-supervised, model-based approach is provided for verifying or checking factual consistency and identifying conflicts between source documents and a generated summary. In some embodiments, an artificially generated training dataset is created by applying rule-based transformations to sentences sampled from one or more unannotated source documents of a dataset.);
…for the WESTCLASS model. ([Abstract] A weakly-supervised, model-based approach is provided for verifying or checking factual consistency and identifying conflicts between source documents and a generated summary. In some embodiments, an artificially generated training dataset is created by applying rule-based transformations to sentences sampled from one or more unannotated source documents of a dataset.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Verma with Kryscinski’s feature(s) listed above. One would’ve been motivated to do so in order to evaluate the factual consistency of abstractive text summarization (Kryscinski; [0003]). By incorporating the teachings of Kryscinski, one would’ve been able to use a westclass model for detection.
Regarding claim 2: Verma teaches
wherein the dependency matching matcher comprises discriminating, the discriminating comprising excluding one or more of negations or forecasts in the remarks such that, responsive to the dependency matching finding a match for the remarks that is negated or predicted as corresponding to a possible future event, the event is not detected. ([0044] resampling and cleaning the real-time data to remove data that is unrelated to detecting the lost circulation event. Examiner notes that one of ordinary skill in the art would reasonably consider excluding one or more negations or forecasts in the remarks as equivalent to ignoring data that is not indicative of an event or potential event, equivalent to removing data that is unrelated to detecting the lost circulation event, as disclosed by Verma.).
Regarding claim 10: Verma teaches
wherein the one or more field operations comprise at least one oil and gas field operation. ([0016] FIG. 1 is a schematic diagram of a drilling rig 100 for drilling a wellbore 102 into a subterranean formation 101 with fractures according to one example of the present disclosure. In this example, drilling rig 100 is depicted for a well, such as an oil or gas well, for extracting fluids from a subterranean formation 101.).
Regarding claim 11: Verma teaches
wherein the remarks comprise drilling remarks. ([0014] the trained system can be used in connection with a drilling operation by receiving real-time drilling data and other parameters about the drilling operation.).
Regarding claim 12: Verma teaches
wherein the remarks comprise drilling fluid remarks. ([0012] Certain aspects and examples of the present disclosure relate to identifying a lost circulation event occurring and identifying the likelihood of a lost circulation event occurring in connection with a drilling operation by using a machine-learning process and monitoring real-time data in connection with the drilling operation. Lost circulation includes the loss to the formation being drilled of drilling fluids that are used in connection with the drilling operation.).
Regarding claim 22: Verma teaches:
wherein the finding a match for the remarks that is negated comprises distinguishing between the event not occurring and the failing to detect the event. ([0037] The pre-processed data can be used in an automated mud loss detection process 608. The automated mud loss detection process 608 may include applying a first model 622 to make a judgment if the data suggests that a lost circulation event has begun. If the detection is made continuously for a set amount of consecutive data rows, an alarm can be raised to alert the drilling operator 624. If the analysis on the data suggests a normal operation, the data can be passed to a second model 626 to identify early warning precursors in the data that may indicate that a lost circulation event is about to occur. The second model 626 may be more probabilistic than the first model 622 as it is predicting whether a lost circulation event is about to occur. If the signature is found to be above a pre-set threshold that may be based on the system training phase, an early warning indicator can be outputted 628 so that the drilling operator can determine the problem and take preventive issues. The system may provide some indications, such as by comparing the signature to historic precursors, as to why the alarm is raised. The system may use the incoming data to update its models so that the system can continuously improve the models. If no precursors are detected, the system moves to the next row of data 630.).
Regarding claim 23: Verma teaches:
wherein the finding a match for the remarks that is negated comprises determining that a matched event did not occur. ([Fig. 6] Step 630: If no precursors detected, move to next data row.).
Claims 3, 8-9, 13, 15-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (US 20210285321 A1, hereinafter “Verma”), in view of Kryscinski et al. (US 20210124876 A1, hereinafter “Kryscinski”), as applied to claims 1 and 2 above, in further view of Wheatley et al. (WO 2020072720 A1, hereinafter “Wheatley”).
Regarding claim 3: Verma teaches
wherein the forecasts pertain to possible occurrence of a future event recorded in the remarks ([0037] The second model 626 may be more probabilistic than the first model 622 as it is predicting whether a lost circulation event is about to occur.).
Verma doesn’t explicitly teach:
using temporal language.
Wheatley teaches:
using temporal language. ([00204] a method can include utilizing a combination of time- series and model data with one or more of equipment, asset, and reservoir specific details. For example, consider an analysis that assesses pressures in view of what data are available about a reservoir (e.g., as to possible damage to a reservoir, as to pump performance, expected duration of artificial lift employment, etc.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s feature(s) listed above. One would’ve been motivated to do so in order to detect an event and/or associate an action with an event (Wheatley; [00204]). By incorporating the teachings of Wheatley, one would’ve been able to detect events by analyzing temporal remarks (e.g., time series).
Regarding claim 8: Verma doesn’t explicitly teach:
wherein the machine learning model comprises a deep neural network model.
Wheatley teaches:
wherein the machine learning model comprises a deep neural network model. ([00185] As an example, a convolution neural network system (CNNS) can be implemented using one or more platforms. Examiner notes that CNNs are known examples of deep neural networks as they include multiple hidden layers in the neural network.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s feature(s) listed above. One would’ve been motivated to do so in order to include an analysis engine that can include one or more features of the TENSORFLOW platform (Wheatley; [00185]). By incorporating the teachings of Wheatley, one would’ve been able to use deep neural networks to detect events.
Regarding claim 9: Verma doesn’t explicitly teach:
wherein the deep neural network model comprises one or more of a convolution neural network model or a recurrent neural network model.
Wheatley teaches:
wherein the deep neural network model comprises one or more of a convolution neural network model or a recurrent neural network model. ([00185] As an example, a convolution neural network system (CNNS) can be implemented using one or more platforms. Examiner notes that CNNs are known examples of deep neural networks as they include multiple hidden layers in the neural network.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s feature(s) listed above. One would’ve been motivated to do so in order to include an analysis engine that can include one or more features of the TENSORFLOW platform (Wheatley; [00185]). By incorporating the teachings of Wheatley, one would’ve been able to use deep neural networks to detect events.
Regarding claim 13: Verma doesn’t explicitly teach:
rendering a graphical user interface to a display, wherein the graphical user interface comprises a remark among the remarks and the detected event.
Wheatley teaches:
rendering a graphical user interface to a display, wherein the graphical user interface comprises a remark among the remarks and the detected event. ([Fig. 25] GUI 2500 – Comment Log, Events).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s feature(s) listed above. One would’ve been motivated to do so in order to commence a workflow, issue one or more control actions, etc. (Wheatley; [00197]). By incorporating the teachings of Wheatley, one would’ve been able to use a GUI to display information.
Regarding claim 15: Verma doesn’t explicitly teach:
further comprising adjusting at least one ongoing field operation based at least in part on the detected event.
Wheatley teaches:
further comprising adjusting at least one ongoing field operation based at least in part on the detected event. ([00173] one or more displays can provide content (e.g., adjust ESP speed set point or choke position, etc.) that can depend on particulars of a workflow. In such an example, the content may be associated with a selected event such as one or more actions to mitigate the event.; [00204] As an example, a method can include utilizing a combination of time- series and model data with one or more of equipment, asset, and reservoir specific details. For example, consider an analysis that assesses pressures in view of what data are available about a reservoir (e.g., as to possible damage to a reservoir, as to pump performance, expected duration of artificial lift employment, etc.); or, for example, an analysis that assesses motor temperature in view of the maximum operating temperature of the motor (e.g., where a higher operating temperature may shorten operational life, shorten time to maintenance, etc.); or, for example, an analysis that assesses pump operating point in view of a manufacturer’s operating curve (e.g., deviation from manufacturer’s operating curve, etc.). Such approaches can, for example, help to detect an event and/or associate an action with an event.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s feature(s) listed above. One would’ve been motivated to do so, so that a sandface pressure related event that can be mitigated (Wheatley; [00204]). By incorporating the teachings of Wheatley, one would’ve been able to adjust operations based on a detected event.
Regarding claim 16: Verma doesn’t explicitly teach:
wherein the detected event comprises a stuck pipe event.
Wheatley teaches:
wherein the detected event comprises a stuck pipe event. (([0062] As an example, one or more portions of a drillstring may become stuck. One of ordinary skill in the art would recognize a pipe as part of a drillstring.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s feature(s) listed above. One would’ve been motivated to do so in order to rotate pipe or lower it back into a bore (Wheatley; [0062]). By incorporating the teachings of Wheatley, one would’ve been able to detect a stuck pipe event.
Regarding claim 18: Verma doesn’t explicitly teach:
rendering a graphical user interface to a display, wherein the graphical user interface comprises at least one graphic that associates the detected event with one or more of a location or a rig.
Wheatley teaches:
rendering a graphical user interface to a display, wherein the graphical user interface comprises at least one graphic that associates the detected event with one or more of a location or a rig. ([Fig. 17] AED Type (e.g., pump off, insufficient lift), Client, Field, Well).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s feature(s) listed above. One would’ve been motivated to do so in order to allow a user to discern differences in wells given triage scores where a user may understand components that make up a triage score (Wheatley; [00201]). By incorporating the teachings of Wheatley, one would’ve been able to detect a stuck pipe event.
Claims 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (US 20210285321 A1, hereinafter “Verma”), in view of Kryscinski et al. (US 20210124876 A1, hereinafter “Kryscinski”), as applied to claim 1 above, in further view of Basu et al. (US 10364662 B1, hereinafter “Basu”), in further view of New (US 20180322111 A1, hereinafter “New”).
Regarding claim 4: Verma doesn’t teach:
wherein the dependency matching comprises parsing phrases in the remarks to generate a pattern,
the parsing comprising examining dependency structures by identifying a head word, a dependent word, and a dependency link in the remarks.
Basu teaches:
wherein the dependency matching comprises parsing phrases in the remarks to generate a pattern, ([Column 17, Lines 32-45] The analytics system can further include a parser to assist with subsystem access to the items of the configuration script. In an example, the parser can construct objects accessible to the various subsystems. The analytics system, associated subsystems, configuration script, parser, and data sources can be implemented on one or more computational devices connected through various communications protocols. In particular, access between a subsystem and another subsystem can be facilitated by direct communication or by communication through data storage accessible to both subsystems. Similarly, access from a subsystem to the configuration script can be facilitated by direct access to the script or to data objects parsed from the configuration script.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Basu’s additional feature(s) listed above. One would’ve been motivated to do so, so that access from a subsystem to the configuration script can be facilitated by direct access to the script or to data objected parsed from the configuration script (Basu; [Column 17, Lines 42-45]). By incorporating the teachings of Basu, one would’ve been able to use a parser.
Basu doesn’t explicitly teach:
the parsing comprising examining dependency structures by identifying a head word, a dependent word, and a dependency link in the remarks.
New teaches:
the parsing comprising examining dependency structures by identifying a head word, a dependent word, and a dependency link in the remarks. ([0009] All words of an utterance are contained in one or more of these dependent relationships. In each of these dependency links, one word is dependent on the other. The dependent word can come before or after the word it depends on. When an utterance is parsed with this type of parse, a single word is said to be the head of the utterance. All other words in the utterance directly depend on the head word or indirectly depend on the head word by depending on one or more intermediary words that depend on the head word. The term Dependency Grammar has been used to describe such a system for parsing utterances.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with New’s feature(s) listed above. One would’ve been motivated to do so, so that dependent links can be found between all words of an utterance (New; [0009]). By incorporating the teachings of New, one would’ve been able to use patterns with a head word, a dependent word, and a dependency link.
Regarding claim 5: Verma doesn’t explicitly teach:
wherein the pattern comprises a head word, a dependent wordy and a dependency link.
New teaches:
wherein the pattern comprises a head word, a dependent wordy and a dependency link. ([0009] All words of an utterance are contained in one or more of these dependent relationships. In each of these dependency links, one word is dependent on the other. The dependent word can come before or after the word it depends on. When an utterance is parsed with this type of parse, a single word is said to be the head of the utterance. All other words in the utterance directly depend on the head word or indirectly depend on the head word by depending on one or more intermediary words that depend on the head word. The term Dependency Grammar has been used to describe such a system for parsing utterances.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with New’s feature(s) listed above. One would’ve been motivated to do so, so that dependent links can be found between all words of an utterance (New; [0009]). By incorporating the teachings of New, one would’ve been able to use patterns with a head word, a dependent word, and a dependency link.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (US 20210285321 A1, hereinafter “Verma”), in view of Kryscinski et al. (US 20210124876 A1, hereinafter “Kryscinski”), as applied to claim 1 above, in further view of Katz et al. (US 20210117456 A1, hereinafter “Katz”).
Regarding claim 6: Verma doesn’t explicitly teach:
wherein the dependency matching comprises stemming and lemmatization.
Katz teaches:
wherein the dependency matching comprises stemming and lemmatization. (([0035] Lemmatization may also incorporate stemming or other types of analysis that reduce inflectional forms and derivatively related forms to a common base form, which may vary based on the language to which the analysis applies. For example, lemma replacement controller 250 may apply a Porter Stemmer lemmatization analysis for identifying a lemma of a head word in an answer in English.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Katz’s feature(s) listed above. One would’ve been motivated to do so in order to identify the lemma of the head word in the answer (Katz; [0035]). By incorporating the teachings of Katz, one would’ve been able to analyze text data using stemming and lemmatization.
Regarding claim 7: Verma doesn’t explicitly teach:
wherein the dependency matching uses the stemming and lemmatization to replace a word with its root form.
Katz teaches:
wherein the dependency matching uses the stemming and lemmatization to replace a word with its root form. (([0035] lemma replacement controller 250 may apply a more complex lemmatization analysis for more morphologically complex languages, such as a lookup table from surface forms to root forms. [0037] lemma replacement controller 250 replaces the non-matching answer with the identified surface form in replaced answers 254).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Katz’s additional feature(s) listed above. One would’ve been motivated to do so in order to modify answers to match the grammatical properties of the question (Katz; [0037]). By incorporating the teachings of Katz, one would’ve been able to replace words with its root form.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (US 20210285321 A1, hereinafter “Verma”), in view of Kryscinski et al. (US 20210124876 A1, hereinafter “Kryscinski”), in further view of Wheatley et al. (WO 2020072720 A1, hereinafter “Wheatley”), as applied to claim 13 above, in further view of Basu et al. (US 10364662 B1, hereinafter “Basu”),
Regarding claim 14: Verma doesn’t explicitly teach:
comprising receiving input via the graphical user interface as feedback
and comprising training the machine learning model using the feedback.
Wheatley teaches:
comprising receiving input via the graphical user interface as feedback ([Fig. 25] GUI 2500 – Comment Log; [00260] The GUI 2500 also shows various comments in a comment log, which can include various concerns, learnings, etc.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wheatley’s additional feature(s) listed above. One would’ve been motivated to do so in order to guide a user in deciding whether or not to cause the system to issue the control instruction(s) (Wheatley; [00260]). By incorporating the teachings of Wheatley, one would’ve been able to receiving input via the GUI as feedback.
Wheatley doesn’t explicitly teach:
and comprising training the machine learning model using the feedback.
Basu teaches:
and comprising training the machine learning model using the feedback. ([Column 28, Lines 54-62] the system can include a report analysis engine 5710 trained to analyze drill reports, such as historical drill reports or current drill reports. Such a report analysis engine 5710 can provide feedback to further enhance the field specific model 5708 or to provide information useful by other engines in determining parameters and recipes to be used on a given well or throughout a given field. The report analysis engine 5710 can be trained as described above and can incorporate structures, such as a neural network or classification.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Basu’s additional feature(s) listed above. One would’ve been motivated to do so in order to include a field specific model and engines derived from such a field specific model (Basu; [Column 28, Lines 32-33]). By incorporating the teachings of Basu, one would’ve been able to fine tune the model with feedback.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (US 20210285321 A1, hereinafter “Verma”), in view of Kryscinski et al. (US 20210124876 A1, hereinafter “Kryscinski”), as applied to claim 1, in further view of Basu et al. (US 10364662 B1, hereinafter “Basu”),
Regarding claim 17: Verma doesn’t explicitly teach:
further comprising planning at least one field operation based at least in part on the detected event.
Basu teaches:
further comprising planning at least one field operation based at least in part on the detected event. ([Column 20, Lines 29-33] The proposed system incorporates data, regardless of source, structure, size, or format, to prescribe actionable recipes for drilling, completing, and producing wells that maximize their economic value at every point over the course of their serviceable lifetimes.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Basu’s additional feature(s) listed above. One would’ve been motivated to do so in order to help operators anticipate and improve wells (Basu; [Column 20, Lines 28-29]). By incorporating the teachings of Basu, one would’ve been able to plan operations based on detected events.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Verma et al. (US 20210285321 A1, hereinafter “Verma”), in view of Kryscinski et al. (US 20210124876 A1, hereinafter “Kryscinski”), as applied to claim 1 above, in further view of Wolverton et al. (US 20140136187 A1, hereinafter “Wolverton”).
Regarding claim 21: Verma doesn’t explicitly teach:
wherein the rule-based model and the machine learning model each operate on a phrase-by-phrase basis or a sentence-by- sentence basis to process the remarks.
Wolverton teaches:
wherein the rule-based model and the machine learning model each operate on a phrase-by-phrase basis or a sentence-by- sentence basis to process the remarks. (Par. [0046] teaches an input recognizer/interpreter 130 may include a standard (now existing or later-developed) natural language processor. The natural language processor may apply syntactic, grammar, and/or semantic rules; [0052] The input classifier 134 analyzes the computer-readable representations of the inputs 102 as prepared by the input recognizer/interpreter 130, and classifies the inputs 102 according to rules or templates that may be stored in the vehicle-specific conversation model 132 or the vehicle context model 116; [0053] Such classifications are, in general, based on the degree of specificity of the inputs 102 as well as the type of inquiry associated with the inputs 102. For example, direct, fact-based questions for which an answer can be provided fairly quickly (such as questions that have a yes or no answer, or "what is?" questions) may be classified differently than questions that require a more complicated explanation (such as "how-to" questions) or questions that are possibly ambiguous unless further information is obtained. The manner in which inputs 102 are classified may be used to determine whether a suitable response can be quickly found in the vehicle-specific user's guide knowledge base 140, or whether sources in the vehicle-related search realm 142 may need to be searched, alternatively or in addition to the knowledge base 140.; [0054] If the classifier 134 determines that an input 102 is "situation-aware;" that is, the input 102 may have a different meaning depending on the current context, the reasoner 136 considers the current context as determined by the context analyzer 126, described below, and incorporates the most likely relevant aspects of the current context into the interpretation of the input 102 to infer the user's most probable intended meaning of the input 102. To do this, the reasoner 136 applies a probabilistic or statistical model (using, e.g., Bayesian modeling). The probabilistic or statistical model includes data relating to the likelihood, probability, or degree of confidence or certainty with which particular inputs 102 are associated with particular meanings based on the context.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Verma with Wolverton’s feature(s) listed above. One would’ve been motivated to do so in order to process conversational and other inputs generated by a person (Wolverton; [0031]). By incorporating the teachings of Wolverton, one would’ve been able to process the natural language remarks by 2 separate models.
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.
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/G.J.T./Examiner, Art Unit 3625
/TIMOTHY PADOT/Primary Examiner, Art Unit 3625
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