DETAILED ACTION
Case Status
This office action is in response to 6 February 2026. Claims 1-8 and 11-22 have been examined.
Claim Objections
Claims 1 and 14 are objected to because of an extraneous and that appears after the selecting limitation. Appropriate correction is required.
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 1-8 and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-8 and 11-22 are directed to one of the eligible categories of subject matter.
With respect to independent claim 1, 14 and 18, the analyzing and selecting recite an abstract idea, including a mental process of evaluating information and making a selection/recommendation, and a mathematical concept because the machine learning decision tree and majority vote limitations recite algorithmic classification operations for making the selection. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. The receiving limitation is directed to insignificant extra solution activity because it merely obtains input information. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components (interfacing, ML algorithms, device, apparatus, article, etc.). Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claim 7, the analyzing cover performance of the limitations manually and/or in the mind (mental processes abstract idea). The neural network, ML algorithms are recited at a high level of generality and do not add meaningful limitations to the abstract idea; these limitations are directed to insignificant extra solution activities. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
With respect to dependent claims 3, 4, 5, 6, 8, 11, 15, 16, 17, 19, 20, 21, 22 the identify, verifying, determining, generating, training, selection, invoke cover performance of the limitations manually and/or in the mind (mental processes abstract idea). No additional elements are recited and so the claims do not provide a practical application and are not considered to be significantly more. The claims are not eligible.
With respect to dependent claims 2, 12, 13 comprises metadata, collecting runtime metrics are recited at a high level of generality and do not add meaningful limitations to the abstract idea. The claims as a whole merely describe how to generally “apply” the exception in a computer environment using generic computer functions or components. Even when viewed in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
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.
Claims 1, 2, 3, 4, 5, 8, 11, 12, 13, 14, 16, 17, 18, 20, 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Makhija et al., Pub. No.: US 20230168874 A1, hereinafter Makhija in view of Rezaei, Pub. No: US 20210241048 A1, hereinafter Rezaei.
As per claim 1, Makhija discloses A method comprising:
receiving a request for cloud deployment of at least one function, wherein the request includes one or more features of the at least one function (at least pars. 38, 47, 68 disclose that a user interface is used to provide a user input/request concerning managing and deploying applications in a multi-cloud environment, the applications executing complex operations, the input/request including features and attributes);
analyzing, by one or more machine learning algorithms, the one or more features (see mapping including at least pars. 73, 83, 85), wherein the one or more machine learning algorithms are trained with historical feature data of a plurality of functions (see above cited pars including 47, 51, 62, 86);
selecting, based at least in part on the analyzing, a cloud platform of a plurality of cloud platforms to deploy the at least one function (see mapping above including at least par. 42 which discloses that machine learning techniques auto-scale the platforms to optimize costs and recommend deployment options for entity such as switching to other cloud vendors; additionally, and/or alternatively, pars. 47, 49, 53, 70-74, 83, 89 disclose that once the AI engine identifies the appropriate deployment configuration (DSC object) from the input features, the corresponding target cloud environment is determined for deployment); and
interfacing with the cloud platform to enable deployment of the at least one function (see mapping above including at least pars. 42, 68, 93);
Makhija does not expressly disclose however Makhija in view of Rezaei discloses wherein the one or more machine learning algorithms comprise a plurality of decision trees, the plurality of decision trees are respectively trained with different portions of the historical feature data, each of the plurality of decision trees yields one cloud platform of the plurality of cloud platforms to deploy the at least one function, and the selection of the cloud platform to deploy the at least one function corresponds to a result produced by a majority of the plurality of decision trees (Rezaei pars. 88-95 discloses random forest classifiers including a multiple decision tree ensemble and bagging wherein a different sample of rows and different subset of features are used to train each decision tree, each decision tree outputs a class (i.e. yields a result) and “The output of the random forest is the aggregation of outputs of decision trees in the random forest with a majority vote selected as the output of the random forest” (i.e. and the selection of a classification result corresponds to a result produced by a majority of the plurality of decision trees); see Makhija as cited above for the historical feature data and the class example of a cloud platform).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Rezaei would have allowed Makhija to implement its machine learning based cloud deployment selection using a known and predictable random forest technique in which multiple decision trees independently evaluate feature data and collectively determine a final prediction by majority vote. Makhija already teaches using data relating to AI/ML and historical deployment to recommend or select cloud deployment configurations in a multi cloud environment. Rezaei teaches that random forests improve prediction reliability by using multiple decision trees trained on different subsets of training data and aggregating the decision-tree outputs by majority vote. Applying Rezaei’s random forest structure to Makhija’s cloud platform selection would have been a predictable use of a known ML classifier to improve the reliability of Makhija’s deployment recommendation and selection process.
and wherein the steps of the method are executed by a processing device operatively coupled to a memory (Makhija pars. 46, 53-54, 113).
As per claim 2, Makhija as modified discloses The method of claim 1 wherein at least a portion of the one or more features comprises metadata (pars. 38, 47, 68 wherein input/request including features and attributes are metadata; see also, pars. 64-65, 103, 108).
As per claim 3, Makhija as modified discloses The method of claim 1 wherein the one or more features identify at least one of a size of code for the at least one function, a language of the code for the at least one function, a complexity tier of the at least one function, an interactivity determination of the at least one function, a cold start time of the at least one function, an execution time of the at least one function, a memory consumption of the at least one function and a cost of the at least one function (see at least pars. 38, 42, 47, 50).
As per claim 4, Makhija as modified discloses The method of claim 1 further comprising verifying a deployment history of the at least one function (abstract, pars. 38, 47, 50, 53, 86-88).
As per claim 5, Makhija as modified discloses The method of claim 4 wherein the verifying comprises: determining whether the at least one function was previously deployed on the cloud platform; and if the at least one function was previously deployed on the cloud platform, determining whether code for the at least one function is unchanged from the previous deployment (see rejection of claim 4 as well as pars. 10, 50, 51, 62, 64, 86, 98-108).
As per claim 8, Makhija as modified discloses the method of claim 1 further comprising wherein the historical feature data specifies respective ones of the plurality of functions associated with at least one of: (i) a code size; (ii) a code language; (iii) a complexity tier; (iv) an interactivity determination; (v) a cold start time; (vi) an execution time; (vii) a memory consumption; and (viii) a cost (see rejection of claim 1 including at least pars. 38, 42, 47, 50, 53, 85-86).
As per claim 11, Makhija as modified discloses The method of claim 1 wherein the interfacing comprises: generating one or more application programming interfaces based at least in part on code of the at least one function and metadata corresponding to the cloud platform; and invoking the one or more application programming interfaces to communicate the request for cloud deployment of the at least one function to the cloud platform (pars. 50, 51, 59, 64-67, 97-99, 101-108).
As per claim 12, Makhija as modified discloses The method of claim 1 further comprising collecting one or more runtime metrics corresponding to the deployment of the at least one function from the cloud platform (pars. 38, 47, 50, 53, 86 wherein historical data is collected runtime metrics over all deployments of all functions / operations).
As per claim 13, Makhija as modified discloses The method of claim 12 wherein the one or more runtime metrics are used for training the one or more machine learning algorithms (pars. 47, 50, 53, 86).
As per claims 14, 16, 17, 18, 20, 21, 22 they are analogous to claims above and therefore likewise rejected.
Claim 6, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Makhija as modified and further in view of Baimetov et al., Patent No.: US 8656386 B1, hereinafter Baimetov.
As per claim 6, Makhija as modified discloses The method of claim 1. Makhija as modified does not expressly disclose however Baimetov discloses further comprising generating a unique identifier for the deployment of the at least one function, wherein generating the unique identifier comprises using a hash function to generate a hash digest of one or more files corresponding to the deployment of the at least one function (Baimetov, col. 11, line 55 to col 12, line 50; see also, Makhija, par. 55).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Baimetov would have allowed Makhija as modified to implement for application deployments the concept of message digests as Baimetov explains in the above-cited portion: “The "message digest," as used in this context, is a concept known in cryptography. The message digest is usually some small identifier related to the file, such as a hash function value, an electronic signature, a checksum, etc. The important point is that the message digest is unique for each file with unique contents, and, files with different contents cannot have the same message digest.”
As per claims 15 and 19, they are analogous to claim 6 and therefore likewise rejected.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Makhija as modified and further in view of Ng et al., Pub. No.: US 20200285419 A1, hereinafter Ng.
As per claim 7, Makhija as modified discloses The method of claim 1. Makhija as modified does not expressly disclose however Ng discloses wherein:
the one or more machine learning algorithms comprise a neural network including at least two hidden layers utilizing a rectified linear unit activation function (Ng, pars. 73, 76);
the analyzing comprises inputting the one or more features to the neural network which predicts the cloud platform to deploy the at least one function (see Makhija as cited in the rejection of claim 1 including at least par. 42 which discloses that machine learning techniques auto-scale the platforms to optimize costs and recommend deployment options for entity such as switching to other cloud vendors; additionally, and/or alternatively, pars. 46, 47, 49, 53, 70-74, 83, 89 disclose that once the AI engine identifies the appropriate deployment configuration (DSC object) from the input features, the corresponding target cloud environment is determined/predicted for deployment; see Ng as cited above for the neural network); and
the neural network comprises a plurality of nodes connected with each other, respective ones of the connections comprising a weight factor and respective ones of the plurality of nodes comprising a bias factor (Ng, pars. 73, 76).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the cited references because Ng would have allowed Makhija as modified to implement the well known concept of a neural network model that has a configuration of multi-layer perceptron (MLP) including one hidden layer. The neural network model may be a fully connected MLP or a sparsely connected MLP. An activation function for the hidden layer is, for example, a rectified-linear-units (ReLU) function, and an activation function for the output layer is, for example, a linear function (Ng, par. 73).
Response to Arguments
Applicant's arguments filed 6 February 2026 have been fully considered.
With respect to the prior art rejection, Rezaei Pub. No: US 20210241048 A1, has been applied in response to claim amendments.
Regarding the 35 USC 101 rejection, page 7 of the remarks presents:
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Examiner respectfully disagrees. The claim does not require a specific technical change to the cloud system or the computer itself. It recites receiving feature information, using ML decision tree logic to analyze that information, selecting a cloud platform, and enabling deployment. The claim’s cloud platform selection limitations identify the desired result of selecting a deployment target, but do not recite a concrete technical mechanism that improves how the underlying computer system or cloud deployment infrastructure operates.
Page 8 of the remarks presents:
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Examiner respectfully disagrees. The claim does not require improvements to a ML model. The claim uses ML decision trees to arrive at a result, it does not create a better ML algorithm or model. Also, the claim does not recite an improvement to the training process itself; it merely states that training is done with a certain type of data.
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Examiner respectfully disagrees. The claim uses generic cloud platforms, a processor, memory, and ML algorithms to carry out the selection. The alleged practical application is simply the use of the selected cloud practical application for deployment. The claim does not recite how the cloud deployment system is technically improved in a different way.
Page 9 of the remarks includes:
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Examiner respectfully disagrees. A claim does not have to explicitly recite formulas or equations to involve mathematical concepts or algorithmic analysis. The amended claim includes steps of an algorithm because they recite ML decision trees trained on historical feature data, individual tree outputs, a majority vote result, etc.
Page 10 of the remarks starts with:
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Examiner respectfully disagrees. The claim does not improve machine learning itself. It does not change or improve how decision trees are built, trained, stored, or executed. It only applies ML to choose a cloud platform. Mere use of ML in this manner does not amount to a technological improvement.
Page 10 of the remarks includes:
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It should be noted that the “close calls” guidance does not require withdrawing a 101 rejection due to Applicant’s disagreement with the rejection. Examiner has evaluated the claim under the eligibility framework. The amended ML decision tree language narrows how the cloud platform is selected, but it does not recite an improvement to the computer or cloud system itself; if there is any improvement at all, it is to the abstract idea itself. Accordingly, the Examiner has met the required burden.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/SYED H HASAN/Primary Examiner, Art Unit 2154
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