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 .
The claim rejections related to 35 USC § 101 regarding to claims 1-20 are withdrawn.
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.
Claims 1-5, 7-12, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over KULKARNI et al. (KULKARNI) US 2024/0134682 in view of Golani et al. (Golani) US 8265979
In regard to claim 1, Kulkarni disclose A computer system, ([0011]-[0013] a workflow management system) comprising:
one or more processors; one or more machine-readable medium coupled to the one or more processors and storing computer program code comprising sets of instructions executable by the one or more processors to: ([0002]-[0004] processor, memory and instructions)
a) obtain, from a process model of a process comprising a plurality of process steps, the process model comprising inputs, outputs, and attributes for the plurality of process steps, ([0019]-[0029][0036]-[0055] obtain a predictor model for predicting a workflow for a process and the model include inputs, outputs and attributes for steps for the process) , the attributes comprising input attributes and time-based attributes determinative of the outputs ([0015] [0019]-[0029][0036]-[0055] [0100]-[0165] features set form the input, output (time elapsed at each step of the process to determine the predicted output)
wherein the executable decision logic is obtained from the one or more unique branches and conditions by traversing the process model; ([0020]-[0035] [0100]-[0105] generate a workflow by defining a workflow process, adding process nodes and edges to the workflow process, define process branches between the steps with a set of nodes and edges (the process nodes with edges which corresponding to steps for completing the task with branches and relationships therewith) and define relationships between the process branches by traversing the paths based on rules with attributes associated with, such as trigger points for taking an action to moving to a new step, for example)
c) receive an identifier of a current step of an instance of the process and a previous path traversed for the instance of the process; (Fig. 2, [0024-[0025]] [0032]- [0047][0100]-[0105] extract step title “1. Identify Errors…” for the current step of a task of the process and a path traversed for the process based on observation)
d) generate, based on the identifier of the current step and the previous path traversed for the instance, a machine learning (ML) prediction of the next step of the instance of the process, wherein the ML prediction comprises a predicted output value of a plurality of predicted output values; ([0022]-[0034] [0036]-[0050][0100]-[0105] generate predictions of the next step of the task based on the value of the current step and path traversed from the ML model and the ML predict includes a target value from the various predicted outputs)
e) execute the decision logic on the ML prediction of the next step of the instance of the process; ([0022]-[0034] [0040]-[0050][0100]-[0105] execute the workflow on the predicted next step of the task of the process) and
f) repeat steps c through e for each subsequent step of the instance of the process until the end of the instance is reached, wherein after the end of the instance is reached a process flow based on each subsequent step of the instance is presented in a user interface to the user along with the plurality of predicted output values and the one or more conditions. ([0013]-[0034] [0040]-[0054][0100]-[0105] determine a predicted workflow through the steps for the process using a feedback loop to retrain and update the ML model to determine the next new steps and using the ML model to predicting the target value and determine the new step using the generated workflow until the workflow is completed and displaying the process completion progression and with predicted target output value and triggering points, rules, etc. on the GUI)
But Kulkarni fail to explicitly disclose “a) extract, one or more unique branches between particular steps of the process and one or more conditions associated with the unique branches, the conditions comprising time-based conditions and attribute-based conditions; b) convert the extracted one or more unique branches and conditions associated with the unique branches to executable decision logic,”
Golani disclose a) extract, one or more unique branches between particular steps of the process and one or more conditions associated with the unique branches, (Fig. 2, col. 2, line 27- col. 4, line 15, col.5, line 55-col. 7, line 41, read different flows between steps of the process and conditions associated with the respective flows stored in the memory to construct the workflow graph) the conditions comprising time-based conditions and attribute-based conditions; (Fig. 2, col. 2, line 27- col. 3, line 8, col.5, line 55-col. 7, line 41, the conditions have dependency constraint, for example, time, location, etc.)
b) convert the extracted one or more unique branches and conditions associated with the unique branches to executable decision logic, (col. 2, line 27- col. 4, line 15, line 15, col.5, line 55-col. 8, line 64, generate the workflow graph to an execution graph with the different flows and conditions)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Huang ‘s automated generation of workflows of into Kulkarni’s invention as they are related to the same field endeavor of automatic workflow generation and optimization. The motivation to combine these arts, as proposed above, at least because Huang ‘s automated generation of workflows with training data generation would help to provide more training data to train a ML model into Kulkarni’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more training data to train the ML model would help to improve training efficiency of workflow generation.
In regard to claim 2, Kulkarni and Golani disclose The computer system of claim 1,
Kulkarni disclose wherein the computer program code further comprises sets of instructions executable by the one or more processors to:
obtain a user interface (UI) input via a user interface to adjust the attributes. ([0016] [0023]-[0027] [0038]-[0048] [0070] receive user input regarding steps of the workflow from GUI, with user feedback, etc.)
In regard to claim 3, Kulkarni and Golani disclose The computer system of claim 2,
Kulkarni disclose wherein the UI input adjusts the attributes of the instance of the process without modifying the process model. ([0016] [0023]-[0027] [0038]-[0048] [0070] receive user input regarding steps of the workflow from GUI, with user feedback, etc. about feature, value, etc. without changing the generated workflow)
In regard to claim 4, Kulkarni and Golani disclose The computer system of claim 1,
Kulkarni disclose wherein the ML prediction is generated using a trained machine learning model, ([0037]-[0054] the prediction is generated based on the trained ML model)
wherein the computer program code further comprises sets of instructions executable by the one or more processors to:
obtain execution logs and historical data for a plurality of historical process instances of the process model; ([0016]-[0027][0037]-[0045] obtain logs and historical workflow data of the stored workflow) and
initiate training of the machine learning model using the execution logs and historical data. ([0016]-[0027][0034]-[0045][0097]-[0100] retraining the ML model using the logs and historical workflow data of the stored workflow based on receiving vegetive response, for example)
In regard to claim 5, Kulkarni and Golani disclose The computer system of claim 4,
Kulkarni disclose wherein the execution logs and historical data for the plurality of historical process instances include statuses, timestamps, input values, and output values for the plurality of process steps. ([0016]-[0027][0032]-[0045] [0047]-[0054] [0100]-[105] obtain logs and historical workflow data of the stored workflow, include workflow status, time elapsed at each step of the process , input and output values for the process steps)
In regard to claim 7, Kulkarni and Golani disclose The computer system of claim 1,
Kulkarni disclose wherein the computer program code further comprises sets of instructions executable by the one or more processors to:
provide a notification regarding the instance of the process in the user interface. ([0023]-[0027][0034]-[0047] providing notifications for the predicted workflow for the process in the GUI)
In regard to claims 8-12, 14, claims 8-12, 14 are medium claims corresponding to the system claims 1-5, 7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-5, 7.
In regard to claims 15-20, claims 15-20 are method claims corresponding to the system claims 1-5, 7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-5, 7.
Claims 6, 13 are rejected under 35 U.S.C. 103 as being unpatentable over KULKARNI et al. (KULKARNI) US 2024/0134682 and Golani et al. (Golani) US 8265979 as applied to claim 4, further in view of Huang US 2019/0205792
In regard to claim 6, Kulkarni and Golani disclose The computer system of claim 4,
But Kulkarni and Golani fail to explicitly disclose “wherein the computer program code further comprises sets of instructions executable by the one or more processors to: remove duplicates and incomplete instances from the execution logs and historical data to obtain cleansed training data; and group the cleansed training data per process step of the plurality of process step, wherein the training of the machine learning model uses the cleansed training data grouped per process step.”
Huang disclose wherein the computer program code further comprises sets instructions executable by the one or more processors to: remove duplicates and incomplete instances from the execution logs and historical data to obtain cleansed training data; ([0043]-[0048] [0093] [0126][0156] remove the duplicate records and delete undesired data from the data file to obtain the updated data) and
group the cleansed training data per process step of the plurality of process step, wherein the training of the machine learning model uses the cleansed training data grouped per process step. ([0041]-[0048][0087]-[0089] [0126] [0174]-[0183] grouping the data for steps in the process and training the ML model use the grouped data)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Huang ‘s automated generation of workflows of into Golani and Kulkarni’s invention as they are related to the same field endeavor of automatic workflow generation and optimization. The motivation to combine these arts, as proposed above, at least because Huang ‘s automated generation of workflows with training data generation would help to provide more training data to train a ML model into Golani and Kulkarni’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more training data to train the ML model would help to improve training efficiency of workflow generation.
In regard to claim 13, claim 13 is a medium claim corresponding to the system claim above and, therefore, is rejected for the same reasons set forth in the rejections of claim 6.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 filed on 8/10/2026 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20210157640 A1 2021-05-27 MOLTZAN et al.
BRANCH PREDICTION FOR USER INTERFACES IN WORKFLOWS
MOLTZAN et al. disclose Systems, methods, and other embodiments associated with branch prediction in workflows are described. In one embodiment, a method includes inputting a workflow and serially progressing through the workflow in a flow sequence and in response to the flow sequence encountering a first decision element in the workflow that includes a plurality of branch paths: (i) executing a prediction that predicts a resulting path of the first decision element to predict a first user interface from the plurality of user interfaces that may be encountered subsequently in the flow sequence as part of a first terminal element; and (ii) pre-building the first user interface that is predicted prior to encountering the first terminal element. In response to the flow sequence reaching the first terminal element, displaying the pre-built first user interface on a display device… see abstract.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/ Primary Examiner, Art Unit 2143
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