DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action. In response to Examiner’s Non-Final Rejection of 11/17/2025, Applicant, on 02/13/2026, amended claims 1, 11, and 16. Claims 1-20 are pending in this application and have been rejected below.
Response to Arguments
Applicant's arguments filed 02/13/2026 have been fully considered, but they are not fully persuasive. The updated 35 USC § 103 and 101 rejection of claims 1-21 are applied in light of Applicant's amendments.
The Applicant argues “the features newly-introduced to the claim are non-abstract elements that integrate the alleged mental processes into a practical application of computer user interface development. With amended claim 1 passing Step 2A, Prong One, it is not necessary to perform the Step 2B analysis.” (Remarks 02/13/2026)
In response, the Examiner respectfully disagrees. The claimed subject matter, is directed to an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The claimed subject matter is merely claims a method for calculating and analyzing information regarding datasets. Although it may be intended to be performed in a digital environment, the claimed subject matter (as currently claimed in the independent claim) speaks to the calculating and analyzing data. Such steps are not tied to the technological realm, but rather utilizing technology to perform the abstract ideas (human activity). Additionally, the claimed subject matter can also be categorized as a Mental Process as it recites concepts performed in the human mind (observation and evaluation). The steps of calculating data, training/updating models, and generating a model/trend line can be performed by a human (mental process/pen and paper). The practice of calculating information and constructing models with set parameters and timelines can be performed without computers, and thus are not tied to technology nor improving technology.
The solution mentioned in the amended limitation is not implemented/integrated into technology and thus not an improvement to the technical field. Further, there is no integration into a practical application as the claims can be interpreted as humans per se, as the claims fail to tie the steps to technology; insignificant extra solution activities (which are merely calculating and/or analyzing data).
The 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 an 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).
The steps relied upon by the Applicant as recited does not improve upon another technology, the functioning of the computer itself, or allow the computer to perform a function not previously performable by a computer. The claims do not mention to any use of a specialized computer and/or processor. The Applicant is using generic computing components (processors) to perform in a generic/expected way (obtaining and analyzing data).The abstract idea is not particular to a technological environment, but is merely being applied to a computer realm. The process of calculating and analyzing data specifically for dataset information, and performing additional analysis can be done without a computer, and thus the claims are not “necessarily rooted", but rather they are utilizing computer technology to perform the abstract idea. The Examiner does not recognize any elements of the Applicant's claims and/or specification that would improve or allow the computer to perform a function(s) not previously performable by the computer, or improve the functioning of the computer itself. It is insufficient to indicate that the claims are novel and non-obvious, and thus contain “something more.” Just because the components may perform a specialized function does not mean that that the computer components are specialized. As such the application of the abstract idea of collecting and analyzing data regarding datasets information, and performing correlation analysis is insufficient to demonstrate an improvement to the technology.
Applicant’s arguments with respect to the rejection to the claims for the 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the current combination of references being used in the current rejection. In light of Applicants amendments and arguments the Examiner updated the search and provided new art to reject the claim limitations.
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 1-20 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is 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.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the method (claims 1-10), computer program product (claims 11-15), and system (claims 16-20) are directed to potentially eligible categories of subject matter (i.e., process, machine, and article of manufacture respectively). Thus, Step 1 is satisfied.
With respect to Step 2, and in particular Step 2A Prong One, it is next noted that the claims recite an abstract idea by reciting concepts performed in the human mind (including an observation, evaluation, judgment, opinion), which falls into the “Mental Process” group; and by reciting fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which falls into the “Certain methods of organizing human activity” within the enumerated groupings of abstract ideas. The mere nominal recitation of a generic computer does not take the claim limitation out of methods of organizing human activity or the mental processes grouping. Thus, the claim recites a mental process for performing certain methods of organizing human activity.
The limitations reciting the abstract idea(s) (Mental process and Certain methods of organizing human activity), as set forth in exemplary claim 1, are: obtaining, for a plurality of instances of performance of a monitored workflow, user flow data sets, each of the user flow data sets indicating a set of interactions initiated by a user during a corresponding instance of the plurality of instances of the performance of the monitored workflow, and each of the user flow data sets being based on corresponding eye tracking of the user and user input provided by the user during the corresponding instance; obtaining, for each user flow data set of the user flow data sets, metadata comprising at least one quantification usable to order the user flow data sets; obtaining, using the metadata and the user flow data sets, an ordering of the user flow data sets; generating, based on the ordering, a visual representation of the interactions between the user and a user interface;
obtaining, based on the visual representation, a modification to a program code associated with the user interface; implementing the modification to the program code to obtain an updated user interface; updating, using the updated user interface at least the ordering, operation of the data processing system to obtain an updated data processing system updating, using operation of the data processing system to obtain an updated data processing system; and providing computer implemented services using the updated data processing system. Independent claims 11 and 16 recite the CRM and system for performing the method of independent claim 1 without adding significantly more. Thus, the same rationale/analysis is applied.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are directed to: A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing operation of a data processing system… generating, based on the ordering, a visual representation of the interactions; a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing operation of the data processing system… (as recited in claims 11 and 16). However, these elements fail to integrate the abstract idea into a practical application because they 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.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitation(s) is/are directed to: A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for managing operation of a data processing system… generating, based on the ordering, a visual representation of the interactions; a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for managing operation of the data processing system… (as recited in claims 11 and 16) for implementing the claim steps/functions. These elements have been considered, but merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation), though at a very high level of generality and without imposing meaningful limitation on the scope of the claim.
The 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 an 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, Applicant’s Specification (paragraph [0134]) describes generic off-the-shelf computer-based elements for implementing the claimed invention, and which does not amount to significantly more than the abstract idea, which is not enough to transform an abstract idea into eligible subject matter. Such generic, high-level, and nominal involvement of a computer or computer-based elements for carrying out the invention merely serves to tie the abstract idea to a particular technological environment, which is not enough to render the claims patent-eligible, as noted at pg. 74624 of Federal Register/Vol. 79, No. 241, citing Alice, which in turn cites Mayo. See, e.g., 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. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide conventional computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself. Further, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)).
The dependent claims (3-7, 9-13, and 15-20) are directed to the same abstract idea as recited in the independent claims, and merely incorporate additional details that narrow the abstract idea via additional details of the abstract idea. For example claims 2-7 “for a user flow data set of the user flow data sets: counting a number of the interactions that the user performed to complete a corresponding instance of the plurality of instances of the performance of the monitored workflow to obtain an interaction count; for the user flow data set of the user flow data sets: for each of the number of interactions: estimating a transition cost for transitioning between two of the number of interactions; for the user flow data set of the user flow data sets: for each of the number of interactions: identifying whether the respective interaction of the number of interactions lead to a desired outcome for the user; for the user flow data set of the user flow data sets: for each of the number of interactions: identifying a type of the respective interaction of the number of interactions; for each of the user flow data sets: estimating a user time cost based on a portion of the metadata corresponding to the respective user flow data set of the user flow data sets; and ordering the user flow data sets based on the corresponding user time costs; wherein the user time cost is an estimated duration of time for completing a corresponding user flow data set of the user flow data sets when performed in a prescribed manner by the user; wherein the ordering of the user flow data sets is a ranking order of the user flow data sets from a lowest user time cost to a highest user time cost; wherein each interaction of the set of interactions identifies a user interface element that the user has focused attention on for a duration of time exceeding a threshold and an outcome of the interaction; wherein the monitored workflow is a user orientated task having a defined start, a defined end, and that, when performed, results in a predetermined outcome, the monitored workflow may be performed using different sets of actions between the defined start and the defined end, and the monitored workflow comprising preliminary actions performed prior to the defined start and that, when performed, initiate the defined start”, without additional elements that integrate the abstract idea into a practical application and without additional elements that amount to significantly more to the claims. The remaining dependent claims (12-15 and 17-20) recite the CRM and system for performing the method of claims 2-10. Thus, the same rationale/analysis is applied. Thus, all dependent claims have been fully considered, however, these claims are similarly directed to the abstract idea itself, without integrating it into a practical application and with, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims.
The ordered combination of elements in the dependent 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 conventional 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 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.
Claims 1, 9-10, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20210110345 (hereinafter “Iyer”) et al., in view of U.S. PGPub 20160240050 to (hereinafter “Block”) et al., in further view of U.S. PGPub 20220335031 to (hereinafter “Eberlein”) et al.
As per claim 1, Iyer teaches a method for managing operation of a data processing system, the method comprising:
obtaining, for a plurality of instances of performance of a monitored workflow, user flow data sets, each of the user flow data sets indicating a set of interactions initiated by a user during a corresponding instance of the plurality of instances of the performance of the monitored workflow, …and user input provided by the user during the corresponding instance; obtaining, for each user flow data set of the user flow data sets, metadata comprising at least one quantification usable to order the user flow data sets; obtaining, using the metadata and the user flow data sets, an ordering of the user flow data sets; updating, using at least the ordering, operation of the data processing system to obtain an updated data processing system; Examiner note: Iyer teaches the ability to track/obtain user workflow data and utilizes robotic process automation (RPA) workflows using machine learning (ML). The ML model may then be trained/retrained (updated), and used to match the stored actions with stored workflow sequences of actions in order to predict and complete the workflow (leveraging data and metadata). Support can be found below.
Iyer 0027: “the user/developer may be presented with these sequences as options to potentially complete one or more next steps in the workflow. In certain embodiments, the sequences are ranked in order of their respective confidence thresholds. The user/developer may then select the pertinent next sequence, which is automatically added to the workflow…0069: By way of nonlimiting example, consider a workflow in which a user of an RPA designer application includes sequences for opening a web browser and searching for certain information on the Internet where the browsed webpage contains a table. The user may then add activities to open an Excel® workbook and copy-and-paste this table into an Excel® spreadsheet. In the background, the designer application may track the actions taken by the user as the user creates workflows and consult one or more ML models after each activity or a sequence of activities. If the user tends to include this sequence of activities repeatedly following adding a certain activity, the ML model(s) may learn to predict that the user will likely perform this sequence of actions based on a certain context and beginning activity (e.g., when the user adds an activity that launches a web browser, the user then adds activities to visit the website and copy-and-paste the table into the Excel® spreadsheet)…0075: The process begins with a developer creating a workflow in a designer application, which the designer application saves as an XAML workflow as the user adds and modifies activities in the workflow. When the user adds or modifies an activity, the current XAML workflow is sent to an ML model for preprocessing. During preprocessing, the relevant data from the XAML file is extracted, and irrelevant data is stripped. In certain embodiments, the preprocessing may include adding or deriving relevant data for consideration by the ML model to further improve accuracy (e.g., adding more relevant metadata variables)…0079: Whether the user accepts or rejects the suggestion(s), and which suggestion was selected (if any), may be used to update metrics (4) pertaining to predicted activities (e.g., probability scores for given metrics) providing an indication as to how a given ML model is performing. If user rejects the suggested activity or sequence of activities, the user can continue to build his or her own workflow. The designer application then continues to monitor the user's workflow, and after completion thereof, sends the completed workflow (5) to a training database as a feedback that will be used as training data in the future. In some embodiments, this data may be used to retrain the local ML model, the global ML model, or both.”
generating, based on the ordering, a visual representation of the interactions between the user and a user interface; 0027: “ It is possible that more than one possible next sequences of activities may exceed the suggestion confidence threshold. If this is the case, the user/developer may be presented with these sequences as options to potentially complete one or more next steps in the workflow. In certain embodiments, the sequences are ranked in order of their respective confidence thresholds. The user/developer may then select the pertinent next sequence, which is automatically added to the workflow.”
Iyer may not explicitly teach the following. However, Block teaches:
and each of the user flow data sets being based on corresponding eye tracking of the user; Block 0073: “In some example embodiments banking resources such as automated teller machines, the teller terminal 22, and the banking computers 20 are operative to run workflow analysis software, customer awareness software, and image tracking software. In some example embodiments portions of each of the workflow analysis software, customer awareness software, and image tracking software may reside on different computerized banking resources in the banking computer system 100 operable to execute software instructions…0134: The eye tracking software may operate to provide inputs into the terminal. The eye tracking software may also be used by the terminal-presented teller or avatar so that the teller appears to look at the user when “speaking.” In addition, eye tracking software may operate to permit the customer to communicate with the terminal and complete banking transaction using only the customer's eyes and eye movements. For example, the user may control the functions of the terminal with the movement of their eyes, whereby the user's eyes may act as a pointer or cursor on the screen.”
Iyer and Block are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Iyer with the aforementioned teachings from Block with a reasonable expectation of success, by adding steps that allow the software to track eye data with the motivation to more efficiently and accurately organize and analyze user information [Block 0134].
Iyer and Block may not explicitly teach the following. However, Eberlein teaches:
obtaining, based on the visual representation, a modification to a program code associated with the user interface;Eberlein 0070-0071: “ It is determined whether at least one extension is to be recommended (306). For example, and as described herein, if a frequency meets or exceeds a threshold frequency, it can be determined to recommend that a field extension be added for a respective data type. If it is determined not to recommend an extension, the example process 300 loops back. If it is determined to recommend at least one extension, a recommendation is provided (308). As described by way of example herein, it can be determined that the first frequency exceeds the threshold frequency and that the second frequency does not exceed the threshold frequency. In response, the extension recommendation system provides a proposal to add a URL field to the “customer data” and provides input validation (e.g., checking URL conventions). A user (e.g., citizen developer) is presented with the proposed field, the type, and the first frequency (estimating how often it would be used). In some examples, the user can also be presented with example content (e.g., website addresses determined from the free-text fields). It is determined whether a recommendation is accepted (310). For example, and as described herein, the user can accept the proposal or reject the proposal. If a recommendation is not accepted, the example process 300 loops back. If a recommendation is accepted, extension code is provided (312). For example, and as described herein, the user can provide a label for the UI element that will be created to enable input of the data. For example, the extension recommendation system can propose a label (e.g., URL) and the user can either accept the proposed label or edit the label (e.g., delete URL and input Customer Website).”
implementing the modification to the program code to obtain an updated user interface; updating, using the updated user interface; and providing computer implemented services using the updated data processing system;Eberlein 0072: “Default values are determined (314). For example, and as described herein, default values can be determined using one or more of a default value proposal generator to transfer data from related DOs, a ML model deployment to generate default values, and a default-value proposal generator to extract default values from an attached document. The extension is deployed (316). For example, and as described herein, the extension code is executed to deploy the field extension and corresponding UI update for production use. For example, and as described herein, the field extension (e.g., URL field) is added to a corresponding table and the UI is updated to include the input element (e.g., labeled as Customer Website). In some examples, default values are displayed in the UI as users interact with the extended application…0066: users do not need to enter these fields manually and benefit from the generated extension.”
Iyer, Block, and Eberlein are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Iyer and Block with the aforementioned teachings from Eberlein with a reasonable expectation of success, by adding steps that allow the software to utilize code with the motivation to more efficiently and accurately organize and analyze user information [Eberlein 0072].
As per claim 9, Iyer, Block, and Eberlein teach all the limitations of claim 1.
In addition, Iyer teaches:
wherein each interaction of the set of interactions identifies a user interface element that the user has focused attention on for a duration of time exceeding a threshold and an outcome of the interaction; Iyer 0020-0031: “In some embodiments, a developer may start building his or her workflow from scratch. As a step (i.e., an activity) is added to the workflow, the ML model (local, global, or both) may analyze the step, and potentially one or more preceding step(s) in a sequence, and check whether one or more sequences may potentially be desired following that step that meet at least a predetermined probabilistic threshold. Once the user adds an activity to the workflow, the last N activities, including this newly added activity, may be considered by the ML model to check whether a next logical sequence of activities can be predicted and autocompleted. This possibility may be determined by the threshold confidence level of the model prediction, which may be above 90% in some embodiments. If the confidence level for stored sequences to be suggested based on the current sequence of activities in the workflow is less than the threshold confidence level, then no suggestion may be provided. The ML model may then be run again when the next activity is added until the suggestion confidence threshold is met. Thus, there are both confidence thresholds determined for each possible sequence to potentially be suggested and a suggestion confidence threshold that these sequences must meet in order to be suggested.”
As per claim 10, Iyer, Block, and Eberlein teach all the limitations of claim 1.
In addition, Iyer teaches:
wherein the monitored workflow is a user orientated task having a defined start, a defined end, and that, when performed, results in a predetermined outcome, the monitored workflow may be performed using different sets of actions between the defined start and the defined end, and the monitored workflow comprising preliminary actions performed prior to the defined start and that, when performed, initiate the defined start; Iyer 0020-0031: “In some embodiments, a developer may start building his or her workflow from scratch. As a step (i.e., an activity) is added to the workflow, the ML model (local, global, or both) may analyze the step, and potentially one or more preceding step(s) in a sequence, and check whether one or more sequences may potentially be desired following that step that meet at least a predetermined probabilistic threshold. Once the user adds an activity to the workflow, the last N activities, including this newly added activity, may be considered by the ML model to check whether a next logical sequence of activities can be predicted and autocompleted. This possibility may be determined by the threshold confidence level of the model prediction, which may be above 90% in some embodiments. If the confidence level for stored sequences to be suggested based on the current sequence of activities in the workflow is less than the threshold confidence level, then no suggestion may be provided. The ML model may then be run again when the next activity is added until the suggestion confidence threshold is met. Thus, there are both confidence thresholds determined for each possible sequence to potentially be suggested and a suggestion confidence threshold that these sequences must meet in order to be suggested… The communication between agent 214 and conductor 230 is always initiated by agent 214 in some embodiments. In the notification scenario, agent 214 may open a WebSocket channel that is later used by conductor 230 to send commands to the robot (e.g., start, stop, etc.)… FIG. 8 is an autocompletion architectural diagram 800 for both a personalized and a generalized flow, according to an embodiment of the present invention. When a user starts developing the workflow and after one or more activities are added to the workflow, the initial XAML workflow is passed (1) from the designer application to one or more retrieved (2) ML models to predict one or more potential next sequences of activities for suggestion to the user. In some embodiments, the pretrained ML models are personalized (local) and generalized (global). If the local ML model fails to find a sequence for suggestion that exceeds a suggestion confidence interval, the global ML model may be used. If no suggestions meet the suggestion confidence threshold, the designer application may continue to send XAML workflows as the user adds to and/or modifies the workflow.”
Claims 11 and 16 are directed to the system and CRM for performing the method of claim 1 above. Since Iyer, Block, and Eberlein teach the system and CRM, the same art and rationale apply.
Claims 2-5, 12-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20210110345 (hereinafter “Iyer”) et al., in view of U.S. PGPub 20160240050 to (hereinafter “Block”) et al., in further view of U.S. PGPub 20220335031 to (hereinafter “Eberlein”) et al. , and in further view of U.S. PGPub 20080059294 (hereinafter “Schauser”) et al.
As per claim 2, Iyer, Block, and Eberlein teach all the limitations of claim 1.
Iyer, Block, and Eberlein may not explicitly teach the following. However, Schauser teaches:
wherein obtaining the metadata comprises: for a user flow data set of the user flow data sets: counting a number of the interactions that the user performed to complete a corresponding instance of the plurality of instances of the performance of the monitored workflow to obtain an interaction count; Schauser 0069: “In one embodiment, a workflow server may count and analyze user clicks for purposes of advertisement targeting. In another embodiment, a workflow server may count and analyze time that a user has spent accessing or viewing a given advertisement. In still other embodiments, a workflow server may count and analyze the number of completed sales an advertisement has generated.”
Iyer, Block, and Schauser are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Iyer and Block with the aforementioned teachings from Schauser with a reasonable expectation of success, by adding steps that allow the software to count data with the motivation to more efficiently and accurately organize and analyze user information [Schauser 0069].
As per claim 3, Iyer, Block, Eberlein, and Schauser teach all the limitations of claim 2.
Iyer, Block, and Eberlein may not explicitly teach the following. However, Schauser teaches:
wherein obtaining the metadata further comprises: for the user flow data set of the user flow data sets: for each of the number of interactions: estimating a transition cost for transitioning between two of the number of interactions; Schauser 0003-0069: “advertising to users of workflow software to better enable workflow software to generate advertising revenue…To solve this problem, a maker of workflow software may try to reduce costs to the industries by generating revenue through advertisements displayed within the workflows, in addition to or rather than simply selling the software…In one embodiment, a workflow server may count and analyze user clicks for purposes of advertisement targeting. In another embodiment, a workflow server may count and analyze time that a user has spent accessing or viewing a given advertisement. In still other embodiments, a workflow server may count and analyze the number of completed sales an advertisement has generated.”
Iyer, Block, Eberlein, and Schauser are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Iyer, Block, and Eberlein with the aforementioned teachings from Schauser with a reasonable expectation of success, by adding steps that allow the software to count data with the motivation to more efficiently and accurately organize and analyze user information [Schauser 0069].
As per claim 4, Iyer, Block, Eberlein, and Schauser teach all the limitations of claim 2.
In addition, Iyer teaches:
wherein obtaining the metadata further comprises: for the user flow data set of the user flow data sets: for each of the number of interactions: identifying whether the respective interaction of the number of interactions lead to a desired outcome for the user; Iyer 0020-0040: “] Some embodiments pertain to automatic completion of RPA workflows using ML. As used herein ML may refer to deep learning (DL) (e.g., deep learning neural networks (DLNNs)), shallow learning (e.g., shallow learning neural networks (SLNNs)), any other suitable type of machine learning, or any combination thereof without deviating from the scope of the invention. Such embodiments may intelligently and automatically predict and complete the next series of activities in workflows (e.g., one, a few, many, the remainder of the workflow, etc.) using ML techniques. Activities that users create and/or modify while creating workflows may be captured and stored in a database over a period of time. An ML model may then be trained on a suitable dataset (e.g., an extensible application markup language (XAML) file dataset) that includes the workflows containing sequences of activities created by RPA developers. XAML files may contain the information used to create RPA workflows (e.g., activities, parameters, activity flow, etc.).”
As per claim 5, Iyer, Block, Eberlein, and Schauser teach all the limitations of claim 2.
In addition, Iyer teaches:
wherein obtaining the metadata further comprises: for the user flow data set of the user flow data sets: for each of the number of interactions: identifying whether the respective interaction of the number of interactions lead to a desired outcome for the user; Iyer 0020-0040: “In some embodiments, local models may be trained for each RPA developer to take into account individual developer styles and preferences. For example, a developer may prefer to send a certain email after a sequence of activities, may prefer certain variable types, etc. Once trained, global and local ML models could be pushed to the RPA developer application or made available to the RPA application remotely (e.g., executed on the server side at the request of the RPA developer application). If no local ML model has been developed for that user, the global ML model can be used. In some embodiments, the local ML model may be applied first, and if no next sequence is predicted (e.g., the confidence threshold for the local model is not met), the global ML model may then be applied to attempt to find a sequence for suggestion. In certain embodiments, the local ML model and the global ML model may have different confidence thresholds… this may include adding the activities to the workflow, setting declarations and usage of variables (i.e., programming variables), reading from/writing to certain files, and/or any other desired pertinent steps to logically conclude a sequence in a workflow without deviating from the scope of the invention. An RPA workflow, somewhat similar to a programming language, typically has variables of different types that are used during execution of the workflow. If these variables are not declared as a proper datatype, the workflow may run into errors. Thus, correct data types of a variables to hold numbers (e.g., Integer), text (e.g., String), etc. should be selected. Thus, some embodiments both perform autocompletion of workflows and internally declare the associated variables of the correct type intelligently.”
Claims 12-15 and 17-20 are directed to the system and CRM for performing the method of claims 2-5 above. Since Iyer, Block, Eberlein, and Schauser teach the system and CRM, the same art and rationale apply.
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20210110345 (hereinafter “Iyer”) et al., in view of U.S. PGPub 20160240050 to (hereinafter “Block”) et al., in further view of U.S. PGPub 20220335031 to (hereinafter “Eberlein”) et al., and in further view of U.S. PGPub 20080059294 (hereinafter “Schauser”) et al., and in even further view of U.S. PGPub 20220067599 (hereinafter “Yurovsky”) et al.
As per claim 6, Iyer, Block, Eberlein, and Schauser teach all the limitations of claim 2.
Iyer, Block, Eberlein, and Schauser may not explicitly teach the following. However, Yurovsky teaches:
wherein obtaining the ordering comprises: for each of the user flow data sets: estimating a user time cost based on a portion of the metadata corresponding to the respective user flow data set of the user flow data sets; and ordering the user flow data sets based on the corresponding user time costs; Yurovsky 0042-0050: “the measures of interest include metrics representing a return on investment of the RPA. For example, the measures of interest may include a costs or money saved metric, a time saved metric, and a number of robot hours metric representing the number of hours RPA robots spent executing a workflow. In one embodiment, the time saved metric may be determined as the sum of a baseline time to manually complete the process, the time to rework the process due to errors, and the time to audit/review the process. The costs saved metric may be determined as the product of the time saved (e.g., in hours) by automating the workflow and the cost (e.g., per hour) of a user to manually perform the process. The number of robot hours metric may be determined as the total execution time of RPA robots… FIG. 5 shows a dashboard 500 visualizing a cumulative time saved metric, in accordance with one or more embodiments. Dashboard 500 depicts bar graph 502 visualizing a relationship between cumulative hours saved and time (in weeks) for workflows 504. Process baselines 506 defines the manual time (in minutes) required for a user to complete workflows 504 and hourly costs for a user to complete workflows 504. Process baselines 506 may be received as user input and are used to calculate the time saved metric.”
Iyer, Block, Eberlein, Schauser, and Yurovsky are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Iyer, Block, Eberlein, and Schauser with the aforementioned teachings from Yurovsky with a reasonable expectation of success, by adding steps that allow the software to utilize cost data with the motivation to more efficiently and accurately organize and analyze user information [Yurovsky 0042].
As per claim 7, Iyer, Block, Schauser, and Yurovsky teach all the limitations of claim 6.
Iyer, Block, Eberlein, and Schauser may not explicitly teach the following. However, Yurovsky teaches:
wherein the user time cost is an estimated duration of time for completing a corresponding user flow data set of the user flow data sets when performed in a prescribed manner by the user; Yurovsky 0040-0050: “The execution time of the long running workflow is calculated as the time from the start of the execution of the long running workflow to the completion of the execution of the long running workflow by one or more RPA robots, excluding time that the execution of the long running workflow was suspended (i.e., the time that the long running workflow was suspended and resumed)… The run time of the workflow is calculated based on RPA data such as, e.g., times at which execution of the long running workflow has started and completed, which may be extracted from event logs of execution of workflows. The run time of the workflow is calculated as the time from the start of the execution of the workflow to the completion of the execution of the workflow (including the time that the workflow was suspended)…the measures of interest include metrics representing a return on investment of the RPA. For example, the measures of interest may include a costs or money saved metric, a time saved metric, and a number of robot hours metric representing the number of hours RPA robots spent executing a workflow. In one embodiment, the time saved metric may be determined as the sum of a baseline time to manually complete the process, the time to rework the process due to errors, and the time to audit/review the process. The costs saved metric may be determined as the product of the time saved (e.g., in hours) by automating the workflow and the cost (e.g., per hour) of a user to manually perform the process. The number of robot hours metric may be determined as the total execution time of RPA robots… FIG. 5 shows a dashboard 500 visualizing a cumulative time saved metric, in accordance with one or more embodiments. Dashboard 500 depicts bar graph 502 visualizing a relationship between cumulative hours saved and time (in weeks) for workflows 504. Process baselines 506 defines the manual time (in minutes) required for a user to complete workflows 504 and hourly costs for a user to complete workflows 504. Process baselines 506 may be received as user input and are used to calculate the time saved metric.”
Iyer, Block, Eberlein, Schauser, and Yurovsky are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Iyer, Block, Eberlein, and Schauser with the aforementioned teachings from Yurovsky with a reasonable expectation of success, by adding steps that allow the software to utilize cost data with the motivation to more efficiently and accurately organize and analyze user information [Yurovsky 0042].
As per claim 8 Iyer, Block, Eberlein, Schauser, and Yurovsky teach all the limitations of claim 6.
Iyer, Block, Eberlein, and Schauser may not explicitly teach the following. However, Yurovsky teaches:
wherein the ordering of the user flow data sets is a ranking order of the user flow data sets from a lowest user time cost to a highest user time cost; Yurovsky 0034: “FIG. 3 is an architectural diagram illustrating a simplified deployment example of RPA system 300, in accordance with one or more embodiments. In some embodiments, RPA system 300 may be, or may include, RPA systems 100 and/or 200 of FIGS. 1 and 2, respectively. RPA system 300 includes multiple client computing systems 302 running robots. Computing systems 302 are able to communicate with a conductor computing system 304 via a web application running thereon. Conductor computing system 304, in turn, communicates with database server 306 and an optional indexer server 308. With respect to FIGS. 2 and 3, it should be noted that while a web application is used in these embodiments, any suitable client/server software may be used without deviating from the scope of the invention. For instance, the conductor may run a server-side application that communicates with non-web-based client software applications on the client computing systems. Embodiments described herein provide for a unified platform for end-to-end evaluation of RPA for a suite of RPA products of an RPA platform. The RPA products each perform different RPA functions, such as, e.g., process mining, task capture, or automation, for the RPA platform. Embodiments described herein enable end-to-end evaluation of RPA by calculating one or more measures of interest based on RPA data associated with a plurality of RPA related data sources. Advantageously, embodiments described herein provide for a unified evaluation of the suite of RPA products as a whole, enabling users to determine the costs saved, the time saved, and other metrics relating to the return on investment of RPA.”
Iyer, Block, Eberlein, Schauser, and Yurovsky are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Iyer, Block, Eberlein, and Schauser with the aforementioned teachings from Yurovsky with a reasonable expectation of success, by adding steps that allow the software to utilize cost data with the motivation to more efficiently and accurately organize and analyze user information [Yurovsky 0042].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Crews; James B.. Diagnostic Lateral Wellbores And Methods Of Use, .U.S. PGPub 20160326859 The present invention relates to methods of obtaining information about subterranean formations and features therein using multiple wellbores, and more particularly relates, in one non-limiting embodiment, to methods of obtaining information about unconventional shale subterranean formations and features thereof using multiple wellbores comprising at least one primary lateral wellbore and at least one diagnostic lateral wellbore adjacent thereto.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
/Arif Ullah/
Primary Examiner, Art Unit 3625
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