DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 2/2/2023 for application number 18/104,946.
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. For independent claims 1, 10, and 15, claim 1 (representative of independent claims 10 and 15) recites:
A method comprising: receiving, by a processing device, code data describing information associated with a set of new code defining new functionality for an application, the set of new code to be incorporated into a set of existing code defining existing functionality of the application; processing, by the processing device, the code data using a machine learning model trained on training data to generate classifications of incorporation metrics for sets of new code defining new functionalities to be incorporated into sets of existing code defining existing functionalities; outputting, by the processing device, a classification of an incorporation metric for the set of new code using the machine learning model based on processing the code data; and generating, by the processing device, an indication of the classification of the incorporation metric for the set of new code for display in a user interface.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically a mental process. A human can mentally make a judgement about a likelihood that a portion of code will be incorporated into an existing codebase.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional limitations of (1) generic computing elements like a processor, memory, etc., (2) receiving new code, (3) processing using a ML model and outputting the classification; and (4) displaying the classification. Additional elements (1) and (3) are mere instruction to apply the exception because they invoke generic computer components to perform the abstract idea, and they only recite the outcome of the ML model classifying incorporation metrics of a new code without explaining how the solution works. Additional elements (2) and (4) are insignificant extra-solution activity because they amount to mere necessary data gathering and outputting for the abstract idea. Even when all of the additional elements are considered together with the abstract idea, they do not integrate the abstract idea into a practical application because they merely add mere instructions to apply and insignificant extra-solution activity 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. As explained above, additional elements (1) and (3) are mere instruction to apply the exception. Also, additional elements (2) and (4) are well-understood, routine, and conventional activity, 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) (for element 2) and presenting offers and gathering statistics, see MPEP 2106.05(d) citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (for element 4). Even when all of the additional elements are considered together with the abstract idea, they do not amount to significantly more than the abstract idea itself because they merely add mere instructions to apply and insignificant extra-solution activity that is well-understood, routine, and conventional to the abstract idea.
Dependent claims 2, 10, and 19 recite the machine learning model includes a histogram-based gradient boosting classifier. This adds a mathematical calculation to additional element (3): using a histogram-based gradient boosting classifier as the ML model to produce the classification explicitly requires using particular mathematical calculations (see, e.g. Ke et al., LightGBM: A Highly Efficient Gradient Boosting Decision Tree, attached NPL, which outlines the calculations required to implement a histogram-based gradient boosting classifier).
Dependent claims 3 and 13 specify the classification is binary. This adds to the mental process: a human can make a mental judgment of a binary classification.
Dependent claims 4, 12, and 20 specify the metric is one of: a metric specifying that the set of new code is not incorporated into the set of existing code within a first threshold number of days, a metric specifying that the set of new code does not receive a first approval within a second threshold number of days, a metric specifying that the set of new code is not incorporated into the set of existing code, or a metric specifying that the set of new code receives more than a threshold number of comments in a review. This adds to the mental process: a human can make a mental judgment of the likelihood of rejecting the new code, how many comments the new code will need, how long the code will take to review, etc.
Dependent claims 5-7, 11, and 16-18 recite the information associated with the new code is particular types of data. These limitations add particular data types to additional element (2). However, limitation (2) still amounts to insignificant extra-solution activity as it is mere data gathering for the abstract idea, and 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).
Dependent claims 8-9 recite the generated indication for display includes an actionable change to modify the classification based on a derivative of the classification. This is a further mental step: a human can make a mental judgment about what changes to new code would make it more likely to be accepted based on their judgment about the classification of the new code.
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) 1-5, 8-10, 12-15, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Islam et al., Early Prediction for Merged vs Abandoned Code Changes in Modern Code Reviews, see attached NPL in view of Ivankovic et al. (US 2021/0132915 A1).
In reference to claim 1, Islam teaches a method comprising: receiving, by a processing device, code data describing information associated with a set of new code defining new functionality for an application, the set of new code to be incorporated into a set of existing code defining existing functionality of the application (developer submits code for merging into larger project, pages 2-3); processing, by the processing device, the code data using a machine learning model trained on training data to generate classifications of incorporation metrics for sets of new code defining new functionalities to be incorporated into sets of existing code defining existing functionalities (classification model is trained to generate a likelihood of either merged or abandoned states, page 4, page 26); outputting, by the processing device, a classification of an incorporation metric for the set of new code using the machine learning model based on processing the code data (model outputs prediction of merged or abandoned, page 4, 26, 46).
However, Islam does not explicitly teach generating, by the processing device, an indication of the classification of the incorporation metric for the set of new code for display in a user interface.
Ivankovic teaches generating, by the processing device, an indication of the classification of the incorporation metric for the set of new code for display in a user interface (predictions of code review can be displayed in user interface, see figs. 4C and 4D, para. 0049-50).
It would have been obvious to one of ordinary skill in art, having the teachings of Islam and Ivankovic before the earliest effective filing date, to modify the prediction as disclosed by Islam to include the UI display as taught by Ivankovic.
One of ordinary skill in the art would have been motivated to modify the prediction of Islam to include the UI display of Ivankovic because it would allow the code developer to view the predicted feedback (Ivankovic, para. 0049-50).
In reference to claim 2, Islam teaches the method as described in claim 1, wherein the machine learning model includes a histogram-based gradient boosting classifier (LightGBM may be used as the classifier, page 21-22, page 5 at top; LightGBM is a gradient boosting decision tree for binary classification that builds feature histograms, see Ke et al., LightGBM: A Highly Efficient Gradient Boosting Decision Tree, attached NPL at pages 1-6).
In reference to claim 3, Islam teaches the method as described in claim 1, wherein the classification is a binary classification (binary classification of either of merged or abandoned, page 4, 26, 46).
In reference to claim 4, Islam teaches the method as described in claim 1, wherein the incorporation metric is at least one of a metric specifying that the set of new code is not incorporated into the set of existing code within a first threshold number of days, a metric specifying that the set of new code does not receive a first approval within a second threshold number of days, a metric specifying that the set of new code is not incorporated into the set of existing code, or a metric specifying that the set of new code receives more than a threshold number of comments in a review (classification of either of merged or abandoned, page 4, 26, 46, which is a metric if the new code is not incorporated into the existing set).
In reference to claim 5, Islam teaches the method as described in claim 1, wherein the information associated with the set of new code includes at least one of code coverage for the set of new code, a number of lines to be added to the set of existing code, or a number of lines to be removed from the set of existing code (lines added or removed, table 4 on page 15).
In reference to claim 8, Islam does not explicitly teach the method as described in claim 1, wherein the indication of the classification includes an indication of an actionable change to modify the classification.
Ivankovic teaches the method as described in claim 1, wherein the indication of the classification includes an indication of an actionable change to modify the classification (ML can predict how the code can be improved, fig. 4C, para. 0049).
It would have been obvious to one of ordinary skill in art, having the teachings of Islam and Ivankovic before the earliest effective filing date, to modify the prediction as disclosed by Islam to include the actionable change as taught by Ivankovic.
One of ordinary skill in the art would have been motivated to modify the prediction of Islam to include the actionable change of Ivankovic because it can help the developer gain more insights into possible changes the code needs (Ivankovic, para. 022-26).
In reference to claim 9, Ivankovic further teaches the method as described in claim 8, wherein the actionable change is determined based on a derivative of the classification with respect to the code data (predicted classification from ML model is used to generate message, para. 0049).
In reference to claim 10, 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 12, this claim is directed to a system associated with the method claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 13, this claim is directed to a system associated with the method claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 14, 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 15, this claim is directed to a non-transitory computer-readable medium associated with the method claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a non-transitory computer-readable medium associated with the method claimed in claim 5 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a non-transitory computer-readable medium 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 non-transitory computer-readable medium associated with the method claimed in claim 4 and is therefore rejected under a similar rationale.
Claim(s) 6, 11, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Islam et al., Early Prediction for Merged vs Abandoned Code Changes in Modern Code Reviews, see attached NPL in view of Ivankovic et al. (US 2021/0132915 A1) as applied to claims 1, 10, and 15 above, and in further view of Chopra et al. (US 10,324,822 B1).
In reference to claim 6, Islam and Ivankovic do not explicitly teach the method as described in claim 1, wherein the information associated with the set of new code includes at least one of a number of non- comment lines, a number of comment lines, or a timestamp of a request to incorporate the set of new code into the set of existing code.
Chopra teaches the method as described in claim 1, wherein the information associated with the set of new code includes at least one of a number of non- comment lines, a number of comment lines, or a timestamp of a request to incorporate the set of new code into the set of existing code (number of comment lines, col. 5, lines 55-58).
It would have been obvious to one of ordinary skill in art, having the teachings of Islam, Ivankovic, and Chopra before the earliest effective filing date, to modify the features as disclosed by Islam to include the lines of comments as taught by Chopra.
One of ordinary skill in the art would have been motivated to modify the features of Islam to include the lines of comments of Chopra because those features can help predict code quality, and therefore the feature would be relevant to predicting if new code will be accepted into the existing codebase (Chopra, col. 1, lines 13-50).
In reference to claim 11, this claim is directed to a system associated with the method claimed in claim 6 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a non-transitory computer-readable medium associated with the method claimed in claim 6 and is therefore rejected under a similar rationale.
Claim(s) 7 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Islam et al., Early Prediction for Merged vs Abandoned Code Changes in Modern Code Reviews, see attached NPL in view of Ivankovic et al. (US 2021/0132915 A1) as applied to claims 1 and 15 above, and in further view of Gousios et al., An Exploratory Study of the Pull-Based Software Development Model, see attached NPL.
In reference to claim 7, Islam and Ivankovic do not explicitly teach the method as described in claim 1, wherein the information associated with the set of new code includes at least one of code coverage for the set of existing code or a number of lines included in the set of existing code.
Gousios teaches the method as described in claim 1, wherein the information associated with the set of new code includes at least one of code coverage for the set of existing code or a number of lines included in the set of existing code (total executable lines of code in project and test coverage, see table 1, page 350).
It would have been obvious to one of ordinary skill in art, having the teachings of Islam, Ivankovic, and Gousios before the earliest effective filing date, to modify the features as disclosed by Islam to include the lines / code coverage as taught by Gousios.
One of ordinary skill in the art would have been motivated to modify the features of Islam to include the lines / code coverage of Gousios because those features can help predict if code merge requests will be accepted (Gousios, page 349-50).
In reference to claim 18, this claim is directed to a non-transitory computer-readable medium associated with the method claimed in claim 7 and is therefore rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. References C, D, and X (see Notice of References Cited) all teach general information on automatic code review.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144