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 .
DETAILED ACTION
This action is in response to papers filed on 1/23/2026.
Claims 21-23, 25, 27-37, 39, 41, and 42 have been amended.
Claims 1-20, 24, and 38 have been cancelled.
Claims 41 and 42 have been added.
Claims 21-23, 25-37, and 39-42 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/23/2026 has been entered.
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 21-23, 25-37, and 39-42 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claims are directed to a process (method as introduced in Claim 21), and/or system (Claim 35), and/or non-transitory computer-readable storage medium with executable instructions (Claim 31), thus Claims 21-23, 25-37, and 39-42 fall within one of the four statutory categories. See MPEP 2106.03.
Step 2A, Prong 1:
The claimed invention recites an abstract idea according to MPEP §2106.04. The independent claims which recite the following claim limitations as an abstract idea, are underlined below.
Claims 21, 31, and 35 recite (as represented by the language of Claim 21):
accessing, by one or more processors coupled with memory, one or more models that are trained using machine learning using training data related to correlations between intents of historical natural language requests and a plurality of workflow operations formed from a plurality of actionable tasks related to at least one of a navigation or an executable operation in an application, each of the one or more models trained to correlate the historical natural language requests to one or more of the plurality of workflow operations for the application, wherein the plurality of workflow operations are configured to cause the application to execute at least one of a time-off request, a profile update request, or a report generation request;
generating, by the one or more processors, for display via an interactive graphical user interface of a client device, a query element interactable by the client device;
receiving, by the one or more processors, via an interaction with the query element from the client device, a natural language request input using the client device to perform a task within the application;
inputting, by the one or more processors, the natural language request to the one or more models trained using the machine learning to correlate the natural language request to the one or more of the plurality of workflow operations for the application;
determining, by the one or more processors, in response to the one or more models correlating the natural language request to one of the plurality of workflow operations for the application, a probability that the natural language request corresponds to one of the plurality of workflow operations;
determining, by the one or more processors, based on the natural language request and the probability from the one or more models, a workflow operation from the plurality of workflow operations to perform in the application;
inputting, by the one or more processors, in response to the determination of the workflow operation, the natural language request to at least one machine learning classifier;
determining, by the one or more processors, based on an output of the at least one machine learning classifier generated in response to the input natural language request, a probability that the natural language request includes action parameters used to complete execution of the workflow operation in the application;
executing, by the one or more processors, in response to the probability satisfying a threshold, the workflow operation in the application based on the at least one of the action parameters in the natural language request; and
outputting, by the one or more processors, via the interactive graphical user interface of the client device in response to executing the workflow operation, an interactive graphical user interface element associated with a display page embedded in the application, wherein the display page corresponds to the executed workflow operation, the interactive graphical user interface element contains the at least one action parameter, and the at least one input parameter controls a duration associated with the execution of the workflow operation.
The underlined claim limitations as emphasized above, as drafted, recite a process that, under its broadest reasonable interpretation covers the p managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Other than reciting a computer implementation, nothing in the claim elements precludes the step from encompassing the managing personal behavior or relationships or interactions between people which represents the abstract idea of certain methods of organizing human activity. But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for using natural language requests to determine what task a user wants to perform and then providing an interface element for performing a task.
Outside of the computer environment, the interactable query element, under broadest reasonable interpretation, could be simply an index (or series of indexes) to identify the needed data/fields (navigational task). The model used to identify the data/files could be trained on [designed and tested using] historical data such as common words and terms being matched to the concepts to which they are commonly associated within the context of the file system. For example, see Fig. 5A and 5B in the instant specification. A user looking for a “weekly check-ins” for an employee could be guided to the file (of a different name) in which the weekly check-ins are kept and then further guided to a specific employee’s records or additional reports.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims recite additional elements such as:
one or more models that are trained using machine learning using training data related to correlations between data, each of the one or more models trained to correlate the data and machine learning classifier;
tasks and operation performed in or by an application;
processors;
memory;
executable operation by the application; and
generating and using a display via an interactive graphical user interface of a client device including a query element interactable by the client device.
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
Accordingly, since the specification describes the additional elements in general terms, without describing the particulars, the additional elements may be broadly but reasonably construed as generic computing components being used to perform the judicial exception (see specification at [0036]). Furthermore, the trained model used to perform the recited steps is recited at a high-level of generality and is only nominally and generically recited as a tool for performing these steps. These claimed additional elements merely recite the words “apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e)). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
Step 2B:
The claims do not include additional elements, individually or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept at Step 2B. Thus, the claim is not patent eligible.
Dependent Claims:
Claims 22-23, 25-30, 32-34, 36, 37, and 39-42 recite further elements related to the steps of the parent claims. These activities fail to differentiate the claims from the related activities in the parent claims and fail to provide any material to render the claimed invention to be significantly more than the identified abstract ideas, as outlined below.
Claims 22, 23, 32, 33, 36, and 37 recite additional interactions (performing tasks and inputs) with the interface, but does not lead toward eligibility. The additional interactions merely provide additional steps for inputting data into the interface and thus does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 25 and 39 recite additional steps for determining probability and selecting output, but does not lead toward eligibility.
Claims 26 and 40 recite the use of a human resources application type”, but does not lead toward eligibility. The specific type of application does not integrate the abstract idea into a practical application or provide an inventive concept.
Claims 27-30, and 34 recite types of tasks to be identified and performed, but does not lead toward eligibility. The specific type of tasks identified or performed does not integrate the abstract idea into a practical application or provide an inventive concept. Furthermore, Claims 27 and 34 recite further elements related to the use of a web page and uniform resource locator for displaying data. The web page and uniform resource locator used in these steps is recited at a high-level of generality and is only nominally and generically recited as a tool for performing these steps. Furthermore, Claims 30 and 34 recite further elements related to a menu-driven application ecosystem to perform activities by the parent claims, addressed above. The menu-driven application ecosystem used in these steps is recited at a high-level of generality and is only nominally and generically recited as a tool for performing these steps.
Claims 41 and 42 recite the parsing of the natural language request and to determine consecutive words, but does not lead toward eligibility. Merely reciting that the results are put into a machine learning classifier does not integrate the abstract idea into a practical application or provide an inventive concept.
The claims do not provide any new additional limitations or meaningful limits beyond abstract idea that are not addressed above in the independent claims therefore, they do not integrate the abstract idea into a practical application nor do they provide significantly more to the abstract idea. Thus, after considering all claim elements, both individually and as a whole, it has been determined that the claims do not integrate the judicial exception into a practical application or provide an inventive concept. Therefore, Claims 22-23, 25-30, 32-34, 36, 37, and 39-42 are ineligible.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-23, 25-37, and 39-42 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
In regards to Claims 21-23, 25-37, and 39-42, Claims 21, 31 ,and 35 recite the limitation "action parameters”. The depending claims still use the original “input parameters” terminology. The specification, as originally filed, does not include the term “action parameters”, nor does it differentiate “action parameters” from “input parameters”. For further consideration of the claim merits, under broadest reasonable consideration in light of the specification, these terms will be considered as equivalent (see also specification at [0018], “…and the user can specify in the query any parameters necessary to perform the desired action that are used to prefill input parameters on the page for the desired action.”).
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 21, 25, 29, 31, 34, 35, and 39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al. (US 2021/0173718 A1) in view of Shapira et al. (Pub. No. US 2014/0250147 A1).
In regards to Claims 21, 31, and 35, Patel discloses:
A method/system, comprising:
accessing, by one or more processors coupled with memory, one or more models that are trained using machine learning using training data related to correlations between intents of historical natural language requests and a plurality of workflow operations formed from a plurality of actionable tasks related to at least one of a navigation or an executable operation in an application, each of the one or more models trained to correlate the historical natural language requests to one or more of the plurality of workflow operations for the application, wherein the plurality of workflow operations are configured to cause the application to execute at least one of a time-off request, a profile update request, or a report generation request; ([0012]; [0021]; [0032]-[0034], the models used for determining intent of the natural language Request (NLR) are trained on historical data (intent classification of previous NLR, intent classifications are based on the indications/intents/purposes of the request, such as tasks to be performed, also indicates at least one example of an executable operation performed as an identified task (identified tasks performed by the system are discussed throughout the reference (see also [0003]; [0004]; [0025]); [0018]; [0020], inputs for workflow request can include providing status updates (updates, status, feedback representing reports); [0038]; [0039], shows workflow operations formed from a plurality of actionable tasks, for example a workflow operation of “deploy to UAT” (an operation performed in the application), is formed by a plurality of actionable tasks (tasks assigned as part of the workflow request/operation [Note: as the workflow operations are made up of tasks, and the requests are analyzed to determine intent and correlate the operations using NLP, both the workflow operations and the included tasks would be analyzed and correlated, therefore the process applied to the actionable tasks is also applied to the workflow operation (that is made up of the actionable tasks)])
generating, by the one or more processors, for display via an interactive graphical user interface of a client device, a query element interactable by the client device; ([0019]; [0030]; [0031], shows a chat interface for inputting NLR (chat window represents an interactable query element displayed on an interactive graphical user interface (this is just one example, other examples of input methods that would also represent interactable query element displayed on a graphical user interface are also disclosed throughout the reference));
receiving, by the one or more processors, via an interaction with the query element from the client device, a natural language request input using the client device to perform a task within the application; ([0019]; [0030]; [0031], shows a chat interface for inputting NLR (chat window represents an interactable query element displayed on a graphical user interface (this is just one example, other examples of input methods that would also represent interactable query element displayed on a graphical user interface are also disclosed throughout the reference); [0032]-[0034], determines task(s) being requested in the request);
inputting, by the one or more processors, the natural language request to the one or more models trained using the machine learning to correlate the natural language request to the one or more of the plurality of workflow operations for the application; ([0012]; [0021]; [0032]-[0034], uses the trained model to determine the intent of the NLR and correlate the NLR to one or more intended workflow operations (and related tasks), see also [0003]);
determining, by the one or more processors, in response to the one or more models correlating the natural language request to one of the plurality of workflow operations for the application, a probability that the natural language request corresponds to one of the plurality of workflow operations; ([0003]; [0034]; [0035], for each intent classification (representing workflow operations and related tasks that are potentially included in the request), a confidence score is determined that measures how likely that intent classification/workflow operations (and related tasks) are related to the request, this confidence score represents a probability that the workflow operations (and related tasks) are included in the request);
determining, by the one or more processors, based on the natural language request and the probability from the one or more models, a workflow operation from the plurality of workflow operations to perform in the application; ([0003], at least one workflow operation (and related tasks) are identified as intended by the NLR based on the confidence score (probability) and that workflow operation is executed/performed in the DevOps virtual assistant platform (application));
inputting, by the one or more processors, in response to the determination of the workflow operation, the natural language request to at least one machine learning classifier; ([0012]; [0021]; [0032]-[0034], the models used for determining intent of the natural language Request (NLR) are trained on historical data (intent classification of previous NLR, intent classifications are based on the indications/intents/purposes of the request, such as tasks to be performed, also indicates at least one example of an executable operation performed as an identified task (identified tasks performed by the system are discussed throughout the reference, see also [0003]; [0004]; [0025])
[Claims 21 and 35] executing, by the one or more processors, in response to the probability satisfying a threshold, the workflow operation in the application based on the at least one of the action parameters in the natural language request; ([0003], identified workflow operation (and related tasks) from the NLR are executed/performed by the DevOps virtual assistant platform (see also [0012]; [0021]; [0030]-[0035], includes comparing the confidence score to a threshold to determines if the workflow operations (and related tasks) are processed) and
[Claim 31] send, in response to the probability satisfying a threshold, the workflow operation based on at least one of the action parameters in the natural language request to a cloud-based service hosting the application to execute the workflow operation in the application; ([0003], identified workflow operation (and related tasks) from the NLR are executed/performed by the DevOps virtual assistant platform, see also [0012]; [0021]; [0030]-[0035], includes comparing the confidence score to a threshold to determines if workflow operations (and related tasks) are processed); [0065]-[0067], additionally the system can incorporate cloud resources, including “…a group of cloud resources, such as one or more applications…”, see also Fig. 2; [0068], “…application 234-1 may include software associated with DevOps virtual assistant platform 230 and/or any other software capable of being provided via cloud computing environment…”); and
outputting/generate, by the one or more processors, via the interactive graphical user interface of the client device in response to executing the workflow operation, an interactive graphical user interface element associated with a display page embedded in the application, wherein the display page corresponds to the executed workflow operation, the interactive graphical user interface element contains the at least one action parameter, and the at least one action parameter controls a duration associated with the execution of the workflow operation ([0050]-[0052], an interactive interface is provided for interacting with the bots performing the tasks related to the NLR, the interface/dashboard is associated with the virtual assistance platform (application) and can be used to provide input via a user device; [0051], users can manually activate or deactivate a bot performing a workflow operation (and related tasks), indicating that one or more interactive elements allow controlling of the duration of the workflow operation (and related tasks)).
Patel discloses all of the above limitations. Patel does not explicitly disclose, but Shapira teaches:
determining, by the one or more processors, based on an output of the at least one machine learning classifier generated in response to the input natural language request, a probability that the natural language request includes action parameters used to complete execution of workflow operations in the application ([0088], “The search module 312 may be further configured to score each entity type/third party application match by estimating a probability of whether the entity match is a good match.”; [0040]; [0089], demonstrates that the match uses text (terms, keywords, etc.), Patel uses natural language, so the text in the request used to determine “actionable tasks” (as part of workflow operations) and “parameters required by the actionable tasks” would be represented terms, keywords, etc. in the natural language request; [0081]; [0089], example shows the probability of a match between the text and the applications, the plurality of applications in Shapira being comparable to the application platform/ecosystem in Patel; [0080], regarding “actionable tasks” and “parameters required by the actionable tasks”, shows the consideration of more than one text portion of the input request, however only one is required by this claim; [0057], example of the natural language query including input parameters that would be used to perform and complete workflow operations (and related tasks) in a third party application, such as an application that has functionality to make a reservation for “French Laundry” (input parameter) and “7:00” (input parameter); [0086]; [0087], machine learning models are used to calculate result scores, the use of culling and/or outputting “a set of third party applications based on the consideration set” would all represent forms of classification (separating into one or more groups))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Patel so as to have included determining, by the one or more processors, based on an output of the at least one machine learning classifier generated in response to the input natural language request, a probability that the natural language request includes input parameters used to perform the workflow operation in the application, as taught by Shapira in order to ensure that the best application is provided to the user for performing their task (Shapira, [0081], “…provides the user with a list of the best available applications based on his/her inputted query terms.”).
In regards to Claims 25 and 39, Patel discloses the above method for determining workflow operations from natural language inputs. Patel does not explicitly disclose, but Shapira teaches:
determining, by the one or more processors, a plurality of probabilities that the natural language request includes the workflow operation and input parameters used by the workflow operation, each of the plurality of probabilities indicating a probability that the natural language request includes the workflow operation and the input parameters used by the workflow operation for the application; ([0088], “The search module 312 may be further configured to score each entity type/third party application match by estimating a probability of whether the entity match is a good match.”; [0040]; [0089], demonstrates that the match uses text (terms, keywords, etc.), Patel uses natural language, so the text in the request used to determine “actionable tasks” (workflow operations with related tasks) and “parameters” would be represented terms, keywords, etc. in the natural language request; [0081]; [0089], example shows the probability of a match between the text and the applications, the probability that a keyword matches the criteria for selecting an application would also represent the probability that the criteria for being related to the application are identified in the text (i.e. “the actionable task and the input parameters required by the actionable task for an application of the plurality of applications” would be compared to the natural language text of the request to determine the “probability that the natural language request includes the [text]”, the text representing “actionable task” and “input parameters” in Patel)
ranking, by the one or more processors, the plurality of probabilities that the natural language request includes the workflow operation and the input parameters used by the workflow operation; ([0086], the applications are ranked based on the results score; [0088]; [0089], shows examples of the scores based on the probability determination, discussed above)
selecting, by the one or more processors, the workflow operation and the input parameters used by the workflow operation with a highest probability ([0081], “…provides the user with a list of the best available applications based on his/her inputted query terms.”, although the refence teaches a list of the best references, one of ordinary skill in the art would understand that the ability to identify the multiple best results (based on the probabilities presented above) would also indicate the ability to identify/select a single best and/or highest probability score using the same comparisons).
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention to have modified the system of Patel so as to have included determining, by the computing device, a plurality of probabilities that the natural language request includes the workflow operation and the input parameters required by the workflow operation, each of the plurality of probabilities indicating the probability that the natural language request includes the workflow operation and the input parameters required by the workflow operation for an application of the plurality of applications and ranking, by the computing device, the plurality of probabilities that the natural language request includes the workflow operation and the input parameters required by the workflow operation, as taught by Shapira in order to ensure that the best application is provided to the user for performing their task (Shapira, [0051]; [0081], “…provides the user with a list of the best available applications based on his/her inputted query terms.”).
In regards to Claim 29, Patel discloses:
wherein the workflow operation is the executional operation to perform a function of the application in the application; ([0012]; [0021]; [0032]-[0034], indicates at least one example of an executable operation performed as an identified task (identified tasks performed by the system are discussed throughout the reference, see also [0003]; [0004]; [0025]).
In regards to Claim 34, Patel discloses:
wherein the workflow operation is at least one of the navigational task to display a web page of a uniform resource locator in a web-based application, the navigational task to display an embedded page of a workflow in the application, the executional operation to perform a function of the application in the application, or the navigational task to display the interactive graphical user interface comprising a menu-driven application ecosystem; ([0012]; [0021]; [0032]-[0034], at least one example of an executable operation performed as an identified task (identified tasks performed by the system are discussed throughout the reference, see also [0003]; [0004]; [0025]).
Claim(s) 22, 23, 26-28, 30, 32, 33, 36, 37, and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Shapiro in further view of in view of Pakiman et al. (Pub. No. US 2017/0322782 A1).
In regards to Claims 22, 32, and 36, Patel/Shapiro discloses the above method for determining workflow operations from natural language inputs. Patel/Shapiro does not explicitly disclose, but Pakiman teaches:
further comprising sending to the client device, by the one or more processors, the interactive graphical user interface to perform the task with action parameters used to perform the task populated in elements of the interactive graphical user interface derived from the natural language request (Fig. 6G; Fig. 6H, shows notes entered in natural language format (see also [0055]); [0082]; [0083], process for using natural language (NL), the NL notes are used to determine actionable tasks (workflow operations with related tasks) that can be performed in the application ecosystem (see Fig. 6G; Fig. 6H, the notes regarding activities to perform and the related actionable tasks that are supplied represent a request and response/result), in this example, the natural language note is used to identify an actionable task (such as “convert to a new task”), then selecting the actionable task (“convert to a new task”) creates a graphical user interface with populated elements (646), example of a populated element is the date (11/13/2015 or “before Wednesday meeting”)).
Patel/Shapiro discloses a “base” method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman teaches a comparable method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman also teaches an embodiment in which a graphical user interface is provided to perform the task with input parameters used to perform the task derived from the natural language request and populated in elements of the graphical user interface, as shown above.
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention, to have further modified the system of Patel/Shapiro so as to have included sending to the client device, by the one or more processors, the graphical user interface to perform the task with input parameters used to perform the task populated in elements of the graphical user interface derived from the natural language request, as taught by Pakiman. One of ordinary skill in the art would have recognized the adaptation of sending to the client device, by the one or more processors, the graphical user interface to perform the task with input parameters used to perform the task populated in elements of the graphical user interface derived from the natural language request to Patel could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.")
In regards to Claims 23, 33, and 37, Patel/Shapiro discloses the above method for determining workflow operations from natural language inputs. Patel/Shapiro does not explicitly disclose, but Pakiman teaches:
further comprising receiving, by the one or more processors, an indication from the client device to perform the task with input parameters used to perform the task populated in elements of the interactive graphical user interface derived from the natural language request (Fig. 6G; Fig. 6H; [0055]; [0082]; [0083], the populated task (“convert to a new task”) can be performed using the populated data (see 646, “done” button and 647, “converted to task”), the populated data derived from the natural language)
Patel/Shapiro discloses a “base” method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman teaches a comparable method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman also teaches an embodiment in which an indication from the client device to perform the task with input parameters populated in elements of the graphical user interface derived from the natural language request, as shown above.
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention, to have further modified the system of Patel/Shapiro so as to have included receiving, by the one or more processors, an indication from the client device to perform the task with input parameters used to perform the task populated in elements of the graphical user interface derived from the natural language request, as taught by Pakiman. One of ordinary skill in the art would have recognized the adaptation of receiving, by the one or more processors, an indication from the client device to perform the task with input parameters used to perform the task populated in elements of the graphical user interface derived from the natural language request to Patel could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.")
In regards to Claims 26 and 40, Patel/Shapiro discloses the above method for determining workflow operations from natural language inputs. Patel/Shapiro does not explicitly disclose, but Pakiman teaches:
wherein the application is a human resources application ([0039], “…an application may have an enterprise focus or orientation… a human resources system”)
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention to have modified the system of Patel/Shapiro so as to have included wherein the application of a plurality of applications is a human resources application, as taught by Pakiman in order to ensure that tasks and related applications are related to the activities they need to perform (Pakiman, [0037]; [0039], “…in support of work and activities carried out by the user...”).
Although rejected using the prior art reference, it is also noted for future reference, that the type or purpose of the ecosystem/applications does not have any significant effect on the explicit or implicit functioning or performance of the claimed invention.
In regards to Claim 27, Patel/Shapiro discloses the above method for determining workflow operations from natural language inputs. Patel/Shapiro does not explicitly disclose, but Pakiman teaches:
wherein the workflow operations is the navigational task to display a web page of a uniform resource locator in a web-based application ([0005], “…where the user interface aspects of the generated application are operable on…a web-based environment…”, web-based environment would include uniform resource locators (see also Fig. 4A; [0060], further shows HTTP for accessing webpages), since the application is web-based and uses pages (see [0064]), the application pages are interpreted as web pages).
Patel/Shapiro discloses a “base” method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman teaches a comparable method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman also teaches an embodiment in which the workflow operation is a navigational task to display a web page of a uniform resource locator in a web-based application, as shown above.
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention, to have further modified the system of Patel/Shapiro so as to have included wherein the workflow operation is the navigational task to display a web page of a uniform resource locator in a web-based application, as taught by Pakiman. One of ordinary skill in the art would have recognized the adaptation of wherein the workflow operation is the navigational task to display a web page of a uniform resource locator in a web-based application to Patel could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.")
In regards to Claim 28, Patel/Shapiro discloses the above method for determining workflow operations from natural language inputs. Patel/Shapiro does not explicitly disclose, but Pakiman teaches:
wherein the workflow operation is the navigational task to display an embedded page of a workflow in the application (Fig. 6G; Fig. 6H; [0082]; [0083], an actionable task is selected to navigate directly to the relevant page and template of the application (the actionable tasks, page, etc. indicating a workflow for a task); Note: This is being interpreted in light of Applicant’s specification. The specification does not specifically describe or define workflow in regards to navigating embedded pages in the application ecosystem, however, [0021]-[0023] appears to indicate that the workflow represents a direct access to tasks (automation) that avoids the need to learn and use navigational and executional user interface. The interface examples cited for Pakiman only uses the initial/single menu wherein each selection navigates directly to a task page and not a menu-driven navigational and executional user interface that would need to be learned in order to find task pages).
Patel/Shapiro discloses a “base” method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman teaches a comparable method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman also teaches an embodiment in which the workflow operation is a navigational task to display a web page of a uniform resource locator in a web-based application, as shown above.
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention, to have further modified the system of Patel/Shapiro so as to have included wherein the workflow operation is the navigational task to display an embedded page of a workflow in the application, as taught by Pakiman. One of ordinary skill in the art would have recognized the adaptation of wherein the workflow operation is the navigational task to display an embedded page of a workflow in the application to Patel could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.")
In regards to Claim 30, Patel/Shapiro discloses the above method for determining workflow operations from natural language inputs. Patel/Shapiro does not explicitly disclose, but Pakiman teaches:
wherein the workflow operation is the navigational task to display the interactive graphical user interface comprising a menu-driven application ecosystem (Fig. 6G; 642, actionable items are presented in a menu, the selection of an item navigates to an application page (see also [0023]; [0101]; [0154], additional references to using menus as part of application design)).
Patel/Shapiro discloses a “base” method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman teaches a comparable method/system in which natural language processing is used to determine tasks to be performed in an application, as shown above. Pakiman also teaches an embodiment in which the workflow operation is a navigational task to display the graphical user interface comprising a menu-driven application ecosystem, as shown above.
It would have been obvious to one of ordinary skill in the art, before to the effective filing date of the claimed invention, to have further modified the system of Patel/Shapiro so as to have included wherein the workflow operation is the navigational task to display the graphical user interface comprising a menu-driven application ecosystem, as taught by Pakiman. One of ordinary skill in the art would have recognized the adaptation of wherein the workflow operation is the navigational task to display the graphical user interface comprising a menu-driven application ecosystem to Patel could be performed with the technical expertise demonstrated in the applied references. (See KSR [127 S Ct. at 1739] "The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.")
Claim(s) 41 and 42 is/are rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Shapira in further view of Lubowich et al. (Pub. No. US 2010/0246799 A1).
In regards to Claims 41 and 42, Patel/Shapira discloses the parsing of an NLP request and inputting the NLP request into a machine learning model for processing, as disclosed above. Patel discloses the ability to parse the NLP request into separate phrases that can be used by the machine learning process to identify intent ([0032]; [0033]; [0091]; Claim 2, NLP request can be parsed to determine one or more phrases (consecutive words) that is used to identify intent and those phrases can be classified using the machine learning process, it is noted that Patel can use spoken word or voice-based inputs). Shapira additionally discloses the parsing a query (request) into terms for processing ([0103]).
Patel/Shapira does not explicitly disclose that the request is parsed into non-overlapping partitioned parts, each non-overlapping partitioned part containing a predetermined number of consecutive words, however, Lubowich teaches:
parse into non-overlapping partitioned parts, each non-overlapping partitioned part containing a predetermined number of consecutive words; and
inputting, by the one or more processors, in response to parsing the natural language request, for each non-overlapping partitioned part, inputting the partitioned part into at least one machine-learning classifier ([0035], the natural language input can be parsed into non-overlapping sequences of a predetermined number of words (“…non-overlapping sequences of X words each…”), the segments represent “context units” and feature vectors, each context unit/feature vector can be input to the machine learning model separately for analysis (see Abstract; [0008]; [0036]; [0052]))
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Patel/Shapira so as to have included parse into non-overlapping partitioned parts, each non-overlapping partitioned part containing a predetermined number of consecutive words; and inputting, by the one or more processors, in response to parsing the natural language request, for each non-overlapping partitioned part, inputting the partitioned part into at least one machine-learning classifier, as taught by Lubowich in order to provide additional granularity in determining context for determining intent and insight for NLP inputs (Lubowich, Abstract; [0008]; [0036]; [0052]; [0035]; Patel, [0012]; [0021]; [0032]-[0034]; [0091]; Claim 2).
Additional Relevant Prior Art Identified but not Relied Upon
Casalaina et al. (Pub. No. US 2016/0266755 A1). Discloses cloud-based services for hosting and interacting with applications (see at least [0224]; Claim 21; Claim 36).
Ding et al. (CN 113570114 A). Discloses machine learning algorithms for predicting probabilities including matching users to services, accessing one or more prediction models that are trained using machine learning using training data related to correlations between historical user characteristics of historical requests, each of the one or more models trained to correlate the historical requests to one or more of the plurality of tasks in applications, to make predictions (see at least Abstract; page 2, lines 47-53; page 3; page 10, lines 8-12; page 13, lines 13-31; page 15, lines 12-31).
Ernest (WO 2022155450 A1). Discloses making specific predictions/matching to criteria derived from natural language requests including historical usage and behavior data (see at least [0046]; [0047]; [0049]; [0052]; [0059]; [0060]).
Kalns et al. (Pub. No. US 2014/0310001 A1). Discloses natural language requests and retaining historical intent information (see at least [0029]; [0051]).
Liu (WO 2012054462 A2). Discloses receiving a natural language request input by a user to perform a task in an application ecosystem of a plurality of applications; sending the natural language request to perform the task to a cloud-based service providing the application ecosystem; displaying a user interface screen to perform the task with input parameters required to perform the task populated in elements of the user interface screen derived from the natural language request; and sending an indication from the user to perform [a] task to the cloud-based service providing the application ecosystem (as applied in parent application 17/704900, see at least Abstract; Fig. 6G; Fig. 6H; [0023]; [0031]; [0033]; [0055]; ; [0069] [0082]; [0083]; [0171]; [0182]).
Thomas, III et al. (Pub. No. US 2021/0337008 A1). Discloses cloud-based services for hosting and interacting with applications (see at least [00012]; [0023]; [0028]; [0032]).
Rachevsky et al. (WO 2015127370 A1). Discloses trained classifiers and parsing text samples for consecutive words (see at least [0033]).
Response to Arguments
Applicant’s arguments filed 1/23/2026 have been fully considered but they are not persuasive.
I. Rejection of Claims under 35 U.S.C. §101:
Applicant argues that the claimed invention the claimed technology cannot reasonably be performed by a person or between people. Indeed, the technical problem addressed by this technical solution involves "a problem arising from integration of many applications into an entire ecosystem of applications used by an enterprise, their customers and partners.” Even if the alleged problem is the focus of the claimed invention, the activities performed in the claims are recited much broader than the problem described. There is not sufficient material to demonstrate a process or system for integrating of many applications into an entire ecosystem of applications in a manner that cannot be performed by humans or would require a non-generic machine. Applicant does not provide sufficient evidence to demonstrate that the claims involve technical challenges that cannot be performed by a human or the alleged technical improvement.,
This was also addressed in the previous office action:
Applicant asserts that the claimed invention cannot reasonably be performed by person or between people and states that it addresses a technical solution and improves the machine learning process (page 13). Applicant provides [descriptions] of the claimed invention that are narrower than what is claimed and discusses intended benefits, however, these assertions are not supported by sufficient evidence or background. Applicant asserts such improvements and solutions such as learning and adapting to users, faster access, no need for in depth familiarity, etc. However, the specification, including the cited paragraphs, merely make broad assertions to these alleged benefits without providing any background, evidence, or explanation demonstrating how such would be achieved in a meaningful manner beyond the abstract ideas. For example, in [0024], Applicant asserts that “Unlike conventional systems where a user learns and adapts to a menu-driven user interface and application ecosystem workflow to perform desired tasks, a computer system implementing the technological improvements of the present disclosure adapts to the user and learns how a user describes the actions they want to perform and helps the user execute the action.”. However, Applicant provides no detail regarding the alleged deficiencies of the conventional systems, such as why conventional systems cannot or will not provide these functions, what the deficiencies of the conventional systems are, how the claimed invention provides these functions in a meaningful manner over the conventional systems, etc. Additionally, the claimed invention uses natural language processing to identify task and provide interface elements, etc., but it is not clear how the system learns and adapts to the user. The system merely identifies a user’s intent and finds results, but there Is no discussion of any process for leaning a specific users’ intents or adapting to a specific users’ intents (other than merely asserting that it does).
As previously, Applicant asserts that the claims are similar to MCRO, but merely recites the claims language and asserts that they are similar, without any analysis or comparisons. This was also addressed in the previous office action:
In regards to MCRO, Applicant asserts that the claimed invention is similar and then describes the claim, but does not provide any analysis or comparison to demonstrate how/why the claimed invention would be comparable to the findings of the MCRO decision.
The index example was used to provide an example of a non-computer environment form of performing these activities functions. Examiner was not alleging that the index itself, in the example, is an interactive graphical user interface element (computer environment), but that the interactive graphical user interface element merely performs its intended, generic functions.
For reference and clarification, form the previous office action: In regards to the interactable element, indexes can be displayed and interacted with [not a computer implemented index, activities such as adding data and choosing what activities to execute/perform can be performed using a non-computer index]. The use of an application and interface to interact with an element merely provides a tool for inputting data. The interactions with the element includes general activities such as inputting data and choosing to execute an action. The application, interface, and interface elements merely provide tools for performing these functions in a generic computer environment. The use of the technological elements in the claims merely apply the activities to a computer environment for automation.
See MPEP 2106.05(a), Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field (“If it is asserted 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. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. 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.”).
II. Rejection of Claims under 35 U.S.C. §103:
Applicant argues that Shapira only ranks applications and does not action parameters used to execute operations in the applications. Additionally, Applicant argues that Shapira does not determine whether all parameters for a specific workflow operation are present nor does it use this probability to decide whether to execute a task or prompt for more info. However, Shapira uses parameters that will be used for the operations to determine and rank the applications that best fit those operations (as per the example provide di the rejections). These parameters are used to determine the probability that those applications are a match based on their ability to perform those tasks and parameters not just merely ranking applications and entities. Additionally, Shapiro was not used for the limitations of executing based on the probabilities (as this was applied to Patel).
It is also noted that the claims do not recite “determine whether all parameters for a specific workflow operation are present” or “decide whether to execute a task or prompt for more info”.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Conclusion
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/S.D.S/Examiner, Art Unit 3629 April 3, 2026
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629