Notice of Pre-AIA or AIA Status
1. 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 § 102
2. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
3. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vijayaraghavan et al. Pub No. US-20220357929-A1 (hereafter Vijayaraghavan).
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);
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).”
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 ([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 ([0031] teaches computing system with a processor and a memory. [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 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).”
13. Regarding claim 5, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein processing at least a portion of the task execution workflow data using at least a second set of artificial intelligence techniques comprises processing the at least a portion of the task execution workflow data using at least one deep neural network having multiple parallel branches each acting as a regressor ([0063-0064] teaches an estimation engine and dynamic model selection engine selecting inputs for the estimation engine. Embodiments of the estimation model include multiple layer neural network regression models Also see Figures 4 & 5).”
15. Regarding claim 17, it is similar to that of claim 5, and is rejected with the same teachings.
16. Regarding claim 20, it is similar to that of claim 5, and is rejected with the same teachings.
17. Regarding claim 6, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein predicting one or more efforts associated with executing the task comprises predicting a number of resources needed to execute the task ([0039], [0042] teaches the estimation engine predicting target parameters (estimation output). Some examples include effort, cost, Full Time Equivalent, person hours, person days needed to complete the user input such that it teaches a number of resources needed).”
18. Regarding claim 7, Vijayaraghavan teaches “The computer-implemented method of claim 1, wherein predicting one or more efforts associated with executing the task comprises predicting an amount of time needed to execute the task ([0039], [0042] teaches person hours and/or person-days).”
19. 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).”
20. 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).”
21. 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).”
22. 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).”
23. 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).”
24. 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).”
Conclusion
25. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON A NGUYEN whose telephone number is (571)272-6074. The examiner can normally be reached Mon-Fri (10am-7pm).
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/BRANDON NGUYEN/Examiner, Art Unit 2195
/Aimee Li/Supervisory Patent Examiner, Art Unit 2195