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 .
DETAILED ACTION
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 6, 2026 has been entered.
2. Claims 1-6 and 11-20 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-6 and 11-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independent claim 11, which is analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a system for predicting porting work effort. Thus, the claim is to a machine, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites a system for predicting a total porting work effort. The claim recites the steps of: predict the total porting work effort to port the software project from a source platform to a disparate target platform using the first porting work effort, the second porting work effort, and the third porting work effort, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “a communication fabric”, “a set of computer-readable storage media connected to the communication fabric”, “a set of processors connected to the communication fabric”, “retrieve a set of historical porting software projects data”, “extract feature data associated with software project porting phases”, “port the software project from the source platform to the disparate target platform”, and “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort.” The claim also recites that the steps of “retrieve a set of historical porting software projects data; extract feature data associated with software project porting phases; train the machine learning models; predict a total porting work effort to port a software project from a source platform to a disparate target platform …; and port the software project from the source platform to the disparate target platform” are performed by a set of processors.
The additional elements “a communication fabric”, “a set of computer-readable storage media connected to the communication fabric”, “a set of processors connected to the communication fabric”, “retrieve a set of historical porting software projects data”, “extract feature data associated with software project porting phases” and “port the software project from the source platform to the disparate target platform” are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, transmitting and outputting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the communication fabric, they are just merely used as general means for collecting and transmitting data.
The additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” are used to generally apply the abstract idea without placing any limits on how the machine learning models function. Rather, these limitations only recite the outcome of “predicting a number of new testcases, predicting a number of bugs detected, predicting a first/second/third porting work, and predicting a total porting work effort” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” limit the identified judicial exceptions “predicting a number of new testcases, predicting a number of bugs detected, predicting a first/second/third porting work, and predicting a total porting work effort”, this type of limitations merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Further, the steps of “retrieve a set of historical porting software projects data; extract feature data associated with software project porting phases; predict a total porting work effort to port a software project from a source platform to a disparate target platform …; and port the software project from the source platform to the disparate target platform”, are recited as being performed by the processors. The processors are recited at a high level of generality. In the limitations “retrieve a set of historical porting software projects data; extract feature data associated with software project porting phases; and port the software project from the source platform to the disparate target platform”, the processors are used as a tool to perform the generic computer function of gathering and transmitting data. See MPEP 2106.05(f). In limitations “predict the total porting work effort to port a software project from a source platform to a disparate target platform…”, the processors are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the set of processors, a set of computer-readable storage media, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, improvements to machine learning, or other technology. They just merely used as general means for performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, the additional elements of “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements “a communication fabric”, “a set of computer-readable storage media connected to the communication fabric”, “a set of processors connected to the communication fabric”, “retrieve a set of historical porting software projects data”, “extract feature data associated with software project porting phases” and “port the software project from the source platform to the disparate target platform”, were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and transmitting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the additional elements of “a communication fabric”, “a set of computer-readable storage media connected to the communication fabric”, “a set of processors connected to the communication fabric”, “retrieve a set of historical porting software projects data”, “extract feature data associated with software project porting phases” and “port the software project from the source platform to the disparate target platform”, are recited at a high level of generality. This element amounts to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition). Moreover, porting software is a well-known process in the software development field, involving in the adaptation of software to run on different platforms or operating systems.
As discussed in Step 2A, Prong Two above, the recitation of the set of processors to perform limitations “retrieve a set of historical porting software projects data; extract feature data associated with software project porting phases; predict the total porting work effort to port a software project from a source platform to a disparate target platform…; and port the software project from the source platform to the disparate target platform…”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 1 and 16, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 1 directed to a method, independent claim 16 directed to a medium, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 11.
Regarding dependent claims 2-6, 12-15 and 17-20, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claims 2, 12 and 17, the claims recite the additional element predicting, by the computer, utilizing the first machine learning model, the first porting work effort…, which is used to generally apply the abstract idea without placing any limits on how the machine learning model functions. Rather, this limitation only recites the outcome of “predicting the first porting work effort” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f). Moreover, this additional element also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “utilizing the first machine learning model” limits the identified judicial exceptions “predicting the first porting work effort”, this type of limitations merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). (See claim 11 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 3, 13 and 18, the claims recite the additional elements predicting, by the computer, utilizing the second machine learning model, the number of new testcases…, and predicting, by the computer, utilizing the third machine learning model, the second porting work effort…, which are used to generally apply the abstract idea without placing any limits on how the machine learning models function. Rather, these limitations only recite the outcome of “predicting the number of new testcases and predicting the second porting work effort” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). Moreover, these additional elements also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “utilizing the second machine learning model and utilizing the third machine learning model” limit the identified judicial exceptions “predicting a number of testcases and predicting the second porting work effort”, this type of limitations merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). (See claim 11 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 4, 14 and 19, the claims recite the additional elements predicting, by the computer, utilizing the fourth machine learning model, a number of bugs detected…, and predicting, by the computer, utilizing the fifth machine learning model, the third porting work effort…, which are used to generally apply the abstract idea without placing any limits on how the machine learning models function. Rather, these limitations only recite the outcome of “predicting a number of buds detected and predicting the third porting work” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). Moreover, these additional elements also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “utilizing the fourth machine learning model and utilizing the fifth machine learning model” limit the identified judicial exceptions “predicting a number of bugs detected and predicting the third porting work effort”, this type of limitations merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). (See claim 11 above). Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 5, 15 and 20, the claims simply refine the abstract idea by further reciting wherein the first porting work effort is a base porting work effort, the second porting work effort is a testing effort, and the third porting work effort is a bug fixing effort, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 11. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 6, the claim simply refines the abstract idea by further reciting performing, by the computer, an analysis of the set of historical data…; and extracting, by the computer, feature data for each respective phase of a plurality of phases…, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 11. Moreover, the claim recites the additional elements receiving, by the computer, a request to port the software project from the source platform to the disparate target platform; retrieving, by the computer, a set of historical data…, which are mere data gathering and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) These additional elements amount to gathering and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. (See claim 11 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-6 and 11-20 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Novelty and Non-Obviousness
5. No prior arts were applied to the claims because the Examiner is unaware of any prior arts, alone or in combination, which disclose at least the limitations of “predicting, by a computer, utilizing the sixth machine learning model, the total porting work effort to port a software project from a source platform to a disparate target platform using the first porting work effort predicted by the first machine learning model, the second porting work effort predicted by the third machine learning model, and the third porting work effort predicted by the fifth machine learning model; and porting, by the computer, the software project from the source platform to the disparate target platform based on the total porting work effort being less than a defined maximum porting work effort threshold level” recited in the independent claims 1, 11, and 16.
Response to Arguments/Amendment
6. Applicant's arguments with respect to claims 1-6 and 11-20 have been fully considered but are not persuasive.
Claim Rejections - 35 USC § 101
Claims 1-6 and 11-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more (see more details above).
Step 2A-Prong One: In response to the Applicant’s arguments the claims do not recite a Mental Process, the Examiner respectfully disagrees and submits that the claims recite a system and method for predicting a total porting work effort comprising the steps of: predict a total porting work effort to port a software project from a source platform to a disparate target platform using a first porting work effort, a second porting work effort, and a third porting work effort, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claims recite a “Mental Processes” grouping of abstract ideas.
Moreover, the Applicant argues “training machine learning models does not include observations, evaluations, judgment, or opinions”, the Examiner submits that the recited “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” are additional elements and are analyzing under Step 2A-Prong Two.
Step 2A-Prong Two:
The additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” are used to generally apply the abstract idea without placing any limits on how the machine learning models function. Rather, these limitations only recite the outcome of “predicting a number of new testcases, predicting a number of bugs detected, predicting a first/second/third porting work, and predicting a total porting work effort” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort”, limit the identified judicial exceptions “predicting a number of new testcases, predicting a number of bugs detected, predicting a first/second/third porting work, and predicting a total porting work effort”, this type of limitations merely confines the use of the abstract idea to a particular technological environment (machine learning model) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, improvements to machine learning, or other technology. They just merely used as general means for performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
In response to the Applicant’s arguments regarding to the Example 39, the Examiner submit that the Example 39 recites: A computer-implemented method of training a neural network for facial detection comprising: collecting a set of digital facial images from a database; applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and training the neural network in a second stage using the second training set, found by the Court eligible because the claim does not recite a mental process. In contrast, the pending claims are totally different than the Example 39, claims recite a system and method for predicting a total porting work effort comprising the steps of: predict a total porting work effort to port a software project from a source platform to a disparate target platform using a first porting work effort, a second porting work effort, and a third porting work effort, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. The claimed “train a first machine learning model to predict a first porting work effort using the feature data….”, “train a second machine learning model to predict a number of new testcases using the feature data…”, “train a third machine learning model to predict a second porting work effort using the number of new testcases…”, “train a fourth machine learning model to predict a number of bugs detected…”, “train a fifth machine learning model to predict a third porting work effort using the number of bugs detected…”, and “train a sixth machine learning model to predict a total porting work effort using the first porting work effort, the second porting work effort, and the third porting work effort” are used to generally apply the abstract idea without placing any limits on how the machine learning models function. Rather, these limitations only recite the outcome of “predicting a number of new testcases, predicting a number of bugs detected, predicting a first/second/third porting work, and predicting a total porting work effort” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (see above).
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept. Therefore, the claims are not patent eligible.
Accordingly, the 101 rejection is maintained.
Conclusion
7. Claims 1-6 and 11-20 are rejected.
8. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Greco (US 2012/0265493) disclose method for technology porting of CAD designs.
Gabel et al. (US 2024/0184564) disclose a system that trains a machine learning model to assist with performing software engineering tasks.
Viswanathan et al. (US 2021/0056007) disclose a computing platform retrieves, based on a machine learning model, a code fix for the code segment and a test case associated with the code modification.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 March 21, 2026