ThatThNotice 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 .
Applicant’s arguments with respect to claim(s) 1, 14, and 18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
2. 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.
3. Claims 1-4, 8-16, 18, 19, and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Vijayaraghavan et al. Pub No. US-20220357929-A1 (hereafter Vijayaraghavan) in view of ORESHKIN et al. Pub. No. US 2020/0372329 A1 (hereafter Oreshkin) and Zhang et al. Pub. No. CN 108875928 A (hereafter Zhang).
4. Regarding claim 1, Vijayaraghavan teaches “A computer-implemented method comprising: ([0013] teaches computer-implemented method)
determining intent information associated with at least a portion of a task by processing data related to the task using at least a first set of artificial intelligence techniques ([0036] introduces data related to certain tasks. [0040-0042] teaches a set of artificial intelligence technique; the complexity embedding engine used to determine intent associated with a task (i.e. convert user text messages of an NLP interface to create a vector using NLP techniques such as CENV and WENV to determine what the user needs. See example in [0045-0048]. It is then inputted into a complexity classification engine. See [0055-0062] for more details of the classification engine);
determining task execution workflow data based at least in part on the intent information associated with at least a portion of the task ([0008-0010] teaches the output of the complexity classification engine fed into an estimation engine which can then predict project delivery details. See [0063] for more specifics of workflow and the estimation engine);
predicting one or more efforts associated with executing the task by processing at least a portion of the task execution workflow data using at least a second set of artificial intelligence techniques ([0034] introduces the second set of artificial intelligence techniques used to predict effort. [0063-0067] teaches an estimation engine along with a what-if analysis engine that predicts efforts (e.g. hours, costs, person-days) associated with the task using the project parameters), wherein processing the at least a portion of the task execution workflow data comprises processing the at least a portion of the task execution workflow data using a neural network having an input layer and multiple … network branches ([0063-0064] teaches a neural network with an input layer and multiple hidden layer regression neural model), … the multiple … network branches having an input coupled to an output of the input layer ([0063-0064]), …, and … the multiple parallel network branches having at least one … hidden layer and a … output layer ([0063-0064]), wherein a first network branch of the multiple … network branches predicts an amount of resources needed to execute the task, and wherein a second network branch of the multiple … network branches predicts an amount of time needed to execute the task ([0063-0064] teaches of a multiple hidden layer based regression neural model such that branches within the neural model may be used to predict the outcomes of cost and person-days together);
and performing one or more automated actions based at least in part on at least one of the one or more predicted efforts associated with executing the task ([0001] introduces automated what-if analysis. [0034], [0066-0068] teaches a what-if analysis engine that allows users to simulate different scenarios based on predicted efforts. Also see Fig. 7);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory ([0075] teaches processor that uses memory).”
Vijayaraghavan may not explicitly teach a neural network having multiple parallel network branches with their own respective layers and outputs.
Oreshkin teaches multiple parallel branches such that it teaches the limitations “a neural network having an input layer and multiple parallel network branches, each of the multiple parallel network branches having an input coupled to an output of the input layer, each of the multiple parallel network branches acting as an independent regressor, and each of the multiple parallel network branches having at least one respective hidden layer and a respective output layer … ([0038] teaches of multiple neural network layers, wherein a first branch has a fully connected layer and an output of the layer separate of the second branch with its own fully connected layer and outputs)”.
It would have been obvious to a person of ordinary skill in the art before the effective
filing date to combine the teachings of Oreshkin to the invention of Vijayaraghavan in order to modify the neural network to use a parallel multi-branch architecture. A person having ordinary skill in the art would have been motivated to make this combination in order to simultaneously produce multiple outputs from a shared common input.
The combination may not explicitly teach of the branches being independent regressors.
Zhang teaches a multi-output regression network such that it teaches the limitation “wherein a first network branch of the multiple parallel network branches predicts an amount … , and wherein a second network branch of the multiple parallel network branches predicts an amount … ([Zhang’s abstract] teaches of a multi-output regression network with a plurality of input nodes and plurality of output nodes)”.
It would have been obvious to a person of ordinary skill in the art before the effective
filing date to combine the teachings of Zhang to the combination of Vijayaraghavan and Oreshkin in order to modify the parallel multi-branch architecture to include a multiple-output regression architecture. A person having ordinary skill in the art would have been motivated to make this combination in order to allow separate regression outputs for the respective prediction targets.
5. Regarding claim 14, it is similar to that of claim 1, and is rejected with the same teachings. Claim 14 is directed towards “A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device (Vijayaraghavan [0015]).”
6. Regarding claim 18, it is similar to that of claim 1, and is rejected with the same teachings. Claim 18 is directed towards “An apparatus comprising: at least one processing device comprising a processor coupled to a memory (Vijayaraghavan [0031] teaches computing system with a processor and a memory. Vijayaraghavan [0075] has more specifics on types of processors/memory).”
7. Regarding claim 2, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein determining intent information associated with the at least a portion of the task comprises classifying intent information associated with the at least a portion of the task by processing data related to the task using one or more neural networks ([0009] teaches the complexity embedding engine transforms text into neural vectors CENV and WENV. They are then processed by a complexity classification engine. [0057] teaches that the complexity classification engine includes a neural network classifier which is a type of neural network).”
8. Regarding claim 15, it is similar to that of claim 2, and is rejected with the same teachings.
9. Regarding claim 19, it is similar to that of claim 2, and is rejected with the same teachings.
10. Regarding claim 3, Vijayaraghavan teaches “The computer-implemented method of claim 2, wherein processing data related to the task using one or more neural networks comprises processing at least a portion of the data related to the task using at least one bi-directional recurrent neural network ([0051] teaches a bi-directional Long Short-Term Memory (LSTM) which is a bi-directional recurrent neural network. Also see Fig. 3).”
11. Regarding claim 16, it is similar to that of claim 3, and is rejected with the same teachings.
12. Regarding claim 21, it is similar to that of claim 3 and is rejected with the same teachings.
13. Regarding claim 4, Vijayaraghavan teaches “The computer-implemented method of claim 3, wherein processing at least a portion of the data related to the task using at least one bi-directional recurrent neural network comprises using at least one bi-directional recurrent neural network with at least one long short- term memory (LSTM) network, in conjunction with one or more natural language understanding techniques ([0051] teaches a LSTM network in conjunction with token-ID mapping which is a NLU technique. Also see Figures 3-5).”
14. Regarding claim 22, it is similar to that of claim 4 and is rejected with the same teachings.
15. Regarding claim 8, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the at least a first set of artificial intelligence techniques using feedback related to the at least one of the one or more predicted efforts ([0064] teaches the estimation model includes continuous self-learning based on feedback from output like effort. Also see fig. 5).”
16. Regarding claim 23, it is similar to that of claim 8 and is rejected with the same teachings.
17. Regarding claim 9, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the at least a second set of artificial intelligence techniques using feedback related to the at least one of the one or more predicted efforts ([0066-0072] teaches the what-if analysis engine being used to simulate different scenarios, also using feedback training to reimplement back into the estimation model to run the simulation for the new scenario and efforts calculated).”
18. Regarding claim 24, it is similar to that of claim 9 and is rejected with the same teachings.
19. Regarding claim 10, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically modifying one or more task execution parameters with respect to at least one of technology, scope, deployment platform, and enterprise objective ([0068] teaches real time simulation of different scenarios, where the what-if analysis engine will modify the input into scores based on change in user input or output parameters, and the estimation engine will then determine the updated output parameters. [0073] and Fig. 8 teach a method including automatically processing input project parameters by a dynamic selection engine to determine hyperparameters. [0060] teaches hyperparameters being selected dynamically based on API call after parsing technology and industry domain parameters such that it teaches automatically modifying a parameter based on technology and domain parsed).”
20. Regarding claim 25, it is similar to that of claim 10 and is rejected with the same teachings.
21. Regarding claim 11, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein determining intent information associated with the at least a portion of the task comprises processing data related to the task using one or more natural language processing techniques ([0009-0015] introduces NLP techniques used to determine intent; generating CENV and WENV to create a vector average and then forwarded to a complexity classifier engine for processing. [0045-0054] go into more specific details. Also see Fig. 2).”
22. Regarding claim 12, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein determining intent information associated with the at least a portion of the task comprises processing, using the at least a first set of artificial intelligence techniques, data pertaining to one or more of at least one enterprise domain related to the task, task type, one or more technological parameters related to the task, one or more task-related application programming interfaces, and one or more task-related hosting platforms ([0008-0018] teaches data pertaining to technology and industry domain as project parameters).”
23. Regarding claim 13, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein predicting one or more efforts associated with executing the task comprises processing, using the at least a second set of artificial intelligence techniques and in conjunction with the at least a portion of the task execution workflow data, feature data associated with the task comprises one or more of temporal information, the intent information associated with at least a portion of the task, task type, enterprise domain associated with the task, technologies used in connection with the task, and deployment information related to the task ([0031]-[0068] teaches of estimation engine and what-if analysis engines using in conjunction all the previous portions of information).”
Additional references not cited but are pertinent to the art are as follows:
US 20220122025 A1
teaches
Software Development Task Effort Estimation
US 12032957 B2
teaches
Systems And Methods For Providing Software Development Performance Predictions
US 20240152393 A1
teaches
TASK EXECUTION METHOD AND APPARATUS - determining a plurality of deep learning tasks to be concurrently executed and an model for implementing each deep learning task;
Conclusion
24. 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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRANDON NGUYEN/Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195
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