DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 12/21/2023 for application number 18/391,821.
Claims 1-20 are pending.
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 .
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 an abstract idea without significantly more. Independent claim 1 (representative of independent claim 18) recites:
A computer-implemented method, comprising: receiving data related to a periodic event for an organization, the data including a number of activities corresponding to one or more processes to be completed in the periodic event, wherein each process includes one or more sub-process and each sub-process includes one or more tasks to be completed in the periodic event; generating a graph representing a calendar for completing each task included in the periodic event, wherein the graph includes one or more process nodes representing the one or more processes, one or more sub-process nodes representing the one or more sub-processes included in each process, and one or more task nodes representing the one or more tasks included in each sub-process; and inputting the graph and the data associated with the graph into a multi-layer neural network, to cause the multi-layer neural network to generate one or more simulated events, wherein the graph governs dataflow of the data through the multi-layer neural network, and wherein each of the one or more simulated events includes at least one task to be completed according to an alternative procedure that is different from an existing procedure for completing the at least one task, and wherein each of the one or more simulated events flattens the periodic event by reducing efforts to be placed during a predefined time range when completing each task included in the periodic event.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process. A human, with pen and paper, can create a graph representing a calendar with processes for an event, and mentally judge an alternative procedure for task completion that is more efficient.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional elements of [a] receiving process data, [b] a multi-layer neural network, and [c] generic computer components like a processor and memory (for claim 18). Additional element [a] is insignificant extra-solution activity because it is mere data gathering for use in the abstract idea. Element [b] is a mere instruction to apply because it merely recites an outcome (of generating an alternative procedure for a task) without how to accomplish the outcome (i.e. details of how the neural network operates to generate the alternative procedure). Element [c] is also a mere instruction to apply because it merely adds generic computer components after-the-fact to the abstract idea. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements only add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional element [a] is well-understood, routine, and conventional, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Additional elements [b] and [c] are both mere instructions to apply the exception, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements only add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the abstract idea.
With respect to dependent claims 3-5, 11-12, 14-17, and 20 these claims add additional mental process steps. Claims 3-5 and 20 recite the graph is a tree indicating relationships between process / sub-process and sub-process / task, indicating sequential relationships between tasks including one task is to be performed first; a human can create the claimed tree structure. Claims 11-12 recites the alternative procedure causes the task to be completed outside the predefined time range; a human can mentally judge a task would be better performed at a different time. Claim 14 recites re-sequencing the tasks; a human can mentally judge tasks should be performed in a different order. Claim 15 recites the event is weekly, biweekly, monthly, etc.; a human can mentally break down a weekly, monthly, etc., task into processes and tasks. Claims 16-17 recite that a subset of tasks are dependent on each other and another subset is independent of each other. A human can think about tasks that are dependent and independent.
With respect to dependent claims 2 and 19, (2A, prong 2) these claims adds the additional element of the neural network being a graph neural network. This additional element does not integrate the abstract idea into a practical application because it a mere instruction to apply; this element merely recites an outcome (a graph neural network generates the alternative procedure) without how to accomplish the outcome (i.e. details of how the graph neural network operates). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements only add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea. (2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the graph neural network is a mere instruction to apply the exception, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements only add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea.
With respect to dependent claims 6-10, (2A, prong 2) these claims recite the additional element of training the neural network using historical data from different organizations with different procedures, and training the network to identify a problematic task and determine an alternative procedure. This additional element does not integrate the abstract idea into a practical application because it a mere instruction to apply; this element merely recites an outcome (that the neural network is trained with historical data to predict problematic tasks) without how to accomplish the outcome (i.e. details of how the training works).
With respect to dependent claim 13, (2A, prong 2) this claim recites the additional element of completing a task using process automation. This additional element does not integrate the abstract idea into a practical application because it a mere instruction to apply; the limitation merely states a desired solution (that a task is automated with increased efficiency) without how to accomplish the solution (how the process automation works to perform a task with increased efficiency). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements only add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea. (2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional element of the graph neural network is a mere instruction to apply the exception, as explained above. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements only add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 8-12, 14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2017/0337492 A1) in view of Mitra et al. (US 2022/0343155 A1).
In reference to claim 1, Chen teaches a computer-implemented method (para. 0005), comprising: receiving data related to a periodic event for an organization (cyclical workflow data is retrieved, para. 0040-41), the data including a number of activities corresponding to one or more processes to be completed in the periodic event (workflow process, see fig. 6, correspond to different activities like approval from a department, para. 0065-67), wherein each process includes one or more sub-process and each sub-process includes one or more tasks to be completed in the periodic event (workflow has plurality of tiers, or sub-processes, and each tier has one or more tasks, see fig. 6, para. 0065-67); generating a graph representing a calendar for completing each task included in the periodic event, wherein the graph includes one or more process nodes representing the one or more processes, one or more sub-process nodes representing the one or more sub-processes included in each process, and one or more task nodes representing the one or more tasks included in each sub-process (see graph in fig. 6, para. 0065-67); and inputting the graph and the data associated with the graph into [a machine learning model] … to generate one or more simulated events (task / calendar data is input to ML model, para. 0063-69, 0076), wherein the graph governs dataflow of the data … (the graph governs the order of the workflow, para. 0065-67), and wherein each of the one or more simulated events includes at least one task to be completed according to an alternative procedure that is different from an existing procedure for completing the at least one task (a proposed schedule, or sequence of tasks, is created, para. 0069-78), and wherein each of the one or more simulated events flattens the periodic event by reducing efforts to be placed during a predefined time range when completing each task included in the periodic event (event increases speed and probability of task success, para. 0074-81).
However, Chen does not explicitly teach inputting the graph and the data associated with the graph into a multi-layer neural network, to cause the multi-layer neural network to generate one or more simulated events, wherein the graph governs dataflow of the data through the multi-layer neural network.
Mitra teaches inputting the graph and the data associated with the graph into a multi-layer neural network, to cause the multi-layer neural network to generate one or more simulated events, wherein the graph governs dataflow of the data through the multi-layer neural network (multi-layer graph neural network creates task schedule, para. 0054-65).
It would have been obvious to one of ordinary skill in art, having the teachings of Chen and Mitra before the earliest effective filing date, to modify the machine learning model of Chen to include the neural network of Mitra.
One of ordinary skill in the art would have been motivated to modify the machine learning model of Chen to include the neural network of Mitra because it can help make scheduling tasks more efficient (Mitra, para. 0002-04).
In reference to claim 2, Mitra teaches the computer-implemented method of claim 1, wherein the multi-layer neural network is a graph neural network (graph neural network, para. 0054-65).
In reference to claim 3, Chen teaches the computer-implemented method of claim 1, wherein the graph is a tree type graph that includes a first type of linkage indicating a first relationship between each process and a sub-process included in each process, and a second type of linkage indicating a second relationship between each sub-process and a task included in each sub-process (graph indicates links between process and tiers, or sub-process, as well as the tasks in each tier, see fig. 6, para. 0065-67).
In reference to claim 4, Chen teaches the computer-implemented method of claim 3, wherein the tree type graph further includes one or more linkages each indicating a sequential relationship between a pair of tasks (graph indicates sequence of tasks, para. 0065-67).
In reference to claim 5, Chen teaches the computer-implemented method of claim 4, wherein the sequential relationship between the pair of tasks indicates that one task is to be completed before the other task included in the pair of tasks (graph indicates that a certain task has to be completed before another task, para. 0065-67).
In reference to claim 6, Chen does not explicitly teach the computer-implemented method of claim 1, wherein the multi-layer neural network is trained by using historical data related to the periodic event from one or more organizations.
Mitra teaches the computer-implemented method of claim 1, wherein the multi-layer neural network is trained by using historical data related to the periodic event from one or more organizations (graph neural network trained on historical data, para. 0055-59).
It would have been obvious to one of ordinary skill in art, having the teachings of Chen and Mitra before the earliest effective filing date, to modify the machine learning model of Chen to include the trained neural network of Mitra.
One of ordinary skill in the art would have been motivated to modify the machine learning model of Chen to include the trained neural network of Mitra because it can help make scheduling tasks more efficient (Mitra, para. 0002-04).
In reference to claim 8, Mitra further teaches the computer-implemented method of claim 6, wherein the historical data include various possible procedures for completing a task included in the periodic event (historical data has different procedures, i.e. how to schedule the task and who to schedule the task to, para. 0054-56).
In reference to claim 9, Chen teaches the computer-implemented method of claim 6, wherein the multi-layer neural network is configured to identify the at least one task to be problematic when flatting the periodic event (a bottleneck task can be identified, para. 0081).
In reference to claim 10, Chen teaches the computer-implemented method of claim 9, wherein the multi-layer neural network is configured to automatically determine one or more alternative procedures for the at least one task identified to be problematic (a different procedure, like a different person or different due date, is determined, para. 0081).
In reference to claim 11, Chen teaches the computer-implemented method of claim 1, wherein the alternative procedure causes the at least one task to be completed according to a different timeline (different due date for approval, para. 0081).
In reference to claim 12, Chen teaches the computer-implemented method of claim 11, wherein the alternative procedure causes the at least one task to be completed outside the predefined time range (for a predefined time range, like 3 weeks, alternative procedures can also be generated outside the time range, like 8 weeks, para. 0075).
In reference to claim 14, Chen does not explicitly teach the computer-implemented method of claim 1, wherein the alternative procedure causes the at least one task to be completed through a re-sequence of tasks to be completed when completing the one or more processes.
Mitra teaches the computer-implemented method of claim 1, wherein the alternative procedure causes the at least one task to be completed through a re-sequence of tasks to be completed when completing the one or more processes (tasks can be re-sequenced, para. 0031-32).
It would have been obvious to one of ordinary skill in art, having the teachings of Chen and Mitra before the earliest effective filing date, to modify the scheduling of Chen to include the re-sequencing of Mitra.
One of ordinary skill in the art would have been motivated to modify the scheduling of Chen to include the re-sequencing of Mitra because it can help efficiently schedule when there are disruptions and changes to tasks (Mitra, para. 0005).
In reference to claim 16, Chen teaches the computer-implemented method of claim 1, wherein the one or more tasks include a subset of tasks that are dependent on each other (graph indicates that some tasks must be completed before others, para. 0065-67).
In reference to claim 17, Chen teaches the computer-implemented method of claim 1, wherein the one or more tasks include a subset of tasks that are independent of each other (graph indicates that some tasks may be completed independently, para. 0065-67).
In reference to claim 18, this claim is directed to a system associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a system associated with the method claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a system associated with the method claimed in claim 3 and is therefore rejected under a similar rationale.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2017/0337492 A1) in view of Mitra et al. (US 2022/0343155 A1) as applied to claim 1 above, and in further view of Wooters et al. (US 2024/0087187 A1).
In reference to claim 7, Chen and Mitra do not explicitly teach the computer-implemented method of claim 6, wherein the one or more organizations are from different industrial fields.
Wooters teaches the computer-implemented method of claim 6, wherein the one or more organizations are from different industrial fields (para. 0044).
It would have been obvious to one of ordinary skill in art, having the teachings of Chen, Mitra, and Wooters before the earliest effective filing date, to modify the training data of Mitra to include the different industries of Wooters.
One of ordinary skill in the art would have been motivated to modify the training data of Mitra to include the different industries of Wooters because it would allow the scheduling of Chen and Mitra to be trained in different tasks in different industries.
Claim(s) 13 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 2017/0337492 A1) in view of Mitra et al. (US 2022/0343155 A1) as applied to claim 1 above, and in further view of Smith et al. (US 6,850,643 B1).
In reference to claim 13, Chen and Mitra do not explicitly teach the computer-implemented method of claim 1, wherein the alternative procedure causes the at least one task to be completed through a process automation with increased efficiency.
Smith teaches the computer-implemented method of claim 1, wherein the alternative procedure causes the at least one task to be completed through a process automation with increased efficiency (some tasks are completed using business process automation, col. 2, lines 43-44; col. 5, lines 25-34).
It would have been obvious to one of ordinary skill in art, having the teachings of Chen, Mitra, and Smith before the earliest effective filing date, to modify the tasks of Chen to include the automation of Smith.
One of ordinary skill in the art would have been motivated to modify the tasks of Chen to include the automation of Smith because it would allow the some tasks to be accomplished more efficiently (Smith, col. 1, lines 30-33).
In reference to claim 15, Chen and Mitra do not explicitly teach the computer-implemented method of claim 1, wherein the periodic event is one of a weekly event, biweekly event, monthly weekly, quarterly event, or annual event.
Smith teaches the computer-implemented method of claim 1, wherein the periodic event is one of a weekly event, biweekly event, monthly weekly, quarterly event, or annual event (weekly or monthly events, col. 5, lines 17-24).
It would have been obvious to one of ordinary skill in art, having the teachings of Chen, Mitra, and Smith before the earliest effective filing date, to modify the event of Chen to include the times of Smith.
One of ordinary skill in the art would have been motivated to modify the event of Chen to include the times of Smith because it would allow the task scheduling of Chen and Mitra to be used with more types of tasks, like regularly scheduled financial reports (Smith, col. 5, lines 11-24).
Conclusion
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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