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. This Office Action is in response to the application filed on 03/28/2024. Claims 1-20 are pending in this application. Claims 1, 8 and 15 are independent claims.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 8 and 15 are corresponding to one of four statutory categories including method, system, and method respectively under step 1.
The claim 1 recites “an apparatus for identifying potential incident-causing deployments by ranking candidate code deployment data objects, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least: identify a plurality of candidate code deployment data objects for an incident comprising an affected service data object; for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object; generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; and generate a temporal score for the candidate code deployment data object based on a current timestamp; and rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object”.
The claim 8 recites “a computer-implemented method for identifying potential incident-causing deployments by ranking candidate code deployment data objects, the computer-implemented method comprising: for each candidate code deployment data object of a plurality of candidate code deployment data objects for an incident comprising an affected service data object: generating a semantic similarity score for the candidate code deployment data object using a machine learning model and based on the affected service data object; generating a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; generating a temporal score for the candidate code deployment data object based on a current timestamp; ranking the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object; and selecting one or more candidate code deployment data objects based on the ranking”.
The claim 15 recites “at least one non-transitory computer-readable storage medium for identifying potential incident-causing deployments by ranking candidate code deployment data objects, the at least one non-transitory computer-readable storage medium having computer coded instructions configured to, when executed by at least one processor: for each candidate code deployment data object of a plurality of candidate code deployment data objects for an incident comprising an affected service data object: generate a semantic similarity score for the candidate code deployment data object using a machine learning model and based on the affected service data object; generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; and generate a temporal score for the candidate code deployment data object based on a current timestamp; and rank the plurality of candidate code deployment data objects to generate using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object”.
The limitation of the claim 1 of “identify a plurality of candidate code deployment data objects for an incident comprising an affected service data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “identifying” in the context of this claim encompasses the user may identify a plurality of candidate code deployment data objects [in a human-readable format] for an incident comprising an affected service data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “generate a temporal score for the candidate code deployment data object based on a current timestamp”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate a temporal score for the candidate code deployment data object based on a current timestamp with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 1, 8 and 15 of “rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “ranking [ordering]” in the context of this claim encompasses the user may rank the plurality of candidate code deployment data objects [human-readable format] using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claim 8 of “selecting one or more candidate code deployment data objects based on the ranking”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “selecting” in the context of this claim encompasses the user may select one or more candidate code deployment data objects [in a human readable format] based on the ranking with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
This judicial exception is not integrated into a practical application. In particular, the dependent claims 2, 9 and 16 recites additional elements such as “wherein the ranking model comprise a learning-to-rank model”
Examiner would like to point out that with the broad reasonable interpretation, this element amount to an insignificant extra-solution activity such as “field of use”, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under Prong II step 2A and 2B.
This judicial exception is not integrated into a practical application. In particular, the claims 3, 10 and 17 recite additional elements such as “receive training input dataset comprising user feedback with respect to historical incident mitigating predictions”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data gathering under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
The limitation of the claims 3, 10 and 17 of “finetune the learning-to-rank model based on the training input dataset”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “finetuning [adjusting or changing parameters]” in the context of this claim encompasses the user may finetune the learning-to-rank model based on the training input dataset with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 4, 11 and 18 of “extracting, using a feature extraction model, a code deployment description feature associated with the candidate deployment data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “extracting [selecting]” in the context of this claim encompasses the user may extract, using a feature extraction model, a code deployment description feature associated with the candidate deployment data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 4, 11 and 18 of “extracting, using the feature extraction model, an incident description feature associated with an incident data object corresponding to the incident”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “extracting [selecting]” in the context of this claim encompasses the user may extract, using the feature extraction model, an incident description feature associated with an incident data object corresponding to the incident with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 4, 11 and 18 of “generating the semantic similarity score for a pair comprising the code deployment description feature and the incident description feature”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mathematical operation but for the recitation of generic computer components. For example, but for the “generating a score [calculating]” in the context of this claim encompasses the user may generate the semantic similarity score for a pair comprising the code deployment description feature and the incident description feature with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mathematical Operations” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
This judicial exception is not integrated into a practical application. In particular, the claims 5, 12 and 19 recite additional elements such as “wherein the machine learning model comprises a large language model”.
Examiner would like to point out that with the broad reasonable interpretation, this element especially amounts to insignificant additional elements such as “field of use” as in MPEP § 2106.05(h): Field of Use and Technological Environment, which does not impose any meaningful limits on practicing the mental process. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea under step 2B.
The limitation of the claims 6, 13 and 20 of “traversing the topological graph structure”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “traversing” in the context of this claim encompasses the user may traverse the topological graph structure with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
The limitation of the claims 6, 13 and 20 of “determining a distance between a first node associated with the candidate code deployment data object and second node associated with the affected service data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components. For example, but for the “determining” in the context of this claim encompasses the user may determine a distance between a first node associated with the candidate code deployment data object and second node associated with the affected service data object with a pen and paper or in a human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea under step 2A Prong 1.
This judicial exception is not integrated into a practical application. In particular, the claims 7 and 14 recite additional elements such as “cause rendering of a user interface comprising at least a portion of the ranked candidate code deployment data objects”.
Examiner would like to point out that with the broad reasonable interpretation, this element amounts to mere data displaying under MPEP § 2106.05(g): Insignificant Extra-Solution Activity, which does not impose any meaningful limits on practicing the mental process (insignificant additional element). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to insignificant additional elements under Step 2A Prong 2 and Step 2B.
Dependent claims 2-7, 9-14 and 16-20 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-7, 9-14 and 16-20 are also rejected for incorporating the deficiency of their independent claims 1, 8 and 15 respectively.
Reasons for Allowance
4. The following is an examiner’s statement of reasons for allowance: the prior-art, the prior-art, Agarwal (US PGPub 20200153925), in view of Anderson (US Patent 8135775), in view of HajiahmadiFoomani (US PGPub 20240296310), in view of Kang (US PGPub 20130254209), and further in view of Li (US Patent 11093819) failed to disclose: an apparatus for identifying potential incident-causing deployments by ranking candidate code deployment data objects, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least: identify a plurality of candidate code deployment data objects for an incident comprising an affected service data object; for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object; generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; and generate a temporal score for the candidate code deployment data object based on a current timestamp; and rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object, as recited by the independent claim 1.
Regarding Claim 1, the closest prior-art found, Agarwal, Anderson, HajiahmadiFoomani, Kang and Li
discloses of an apparatus for identifying potential incident-causing deployments by ranking candidate code deployment data objects, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least: identify a plurality of candidate code deployment data objects for an incident comprising an affected service data object; for each candidate code deployment data object of the plurality of candidate code deployment data objects: generate a semantic similarity score for the candidate code deployment data object by applying a machine learning model to the affected service data object; generate a topological distance score for the candidate code deployment data object using a topological graph structure associated with the affected service data object and the candidate code deployment data object; and generate a temporal score for the candidate code deployment data object based on a current timestamp.
Individually, Agarwal teaches that the processor rolls back the deployment of the first software patch. The determination is made to determine that a second solution (150B) has the next highest ranking after a first solution (150A). The processor generates a second software patch that implements the second solution in response to the determination that the second solution has the next highest ranking after the first solution and deploys the second software patch. determine that the first software exception occurred previously in the plurality of desktops by querying a database using keywords from the first software exception; determine that the second software exception did not occur previously in the plurality of desktops by querying the database using keywords from the second software exception; determine a first solution and a second solution for resolving the first software exception; determine that the first solution has a higher ranking than the second solution; generate a first software patch that implements the first solution in response to the determination that the first solution has a higher ranking than the second solution; deploy the first software patch;
Anderson teaches of assigning at least one node in a network to a first deployment group and at least one other node in the network to a second deployment group; assigning a stage value to each of the deployment groups, wherein the stage value controls the order in which software is deployed to each deployment group; receiving a set of distribution criteria that specifies a triggering event which must occur before a software package is deployed to the second deployment group, wherein the distribution criteria is configurable to specify different triggering events based upon the severity level of the software package; distributing the software package to at least one node within the first deployment group; and deferring distribution of the software package to at least one node within the second deployment group until after the triggering event has occurred.
HajiahmadiFoomani teaches that in the testing/deployment phase 782, ML manager 416 operates or implements neural network 410 to generate deployment anomaly scores 714 for deployed printheads 104, and operates or implements recurrent neural network 412 to scale the deployment anomaly scores 714 generated by neural network 410. For example, ML manager 416 inputs or enters input samples 766 (i.e., first input samples) of deployment printhead data 704 for printheads 104 (i.e., deployed printheads) into neural network 410 to generate deployment anomaly scores 714 for printheads 104. One technical benefit of generating deployment anomaly scores 714 is that the magnitude of a deployment anomaly score 714 output by neural network 410 may be used to indicate a failure condition in an individual printhead. However, the ranges of anomaly scores may vary per printhead 104. For example, the range of anomaly scores for one printhead 104 may be “0-500”, while the range of anomaly scores for another printhead 104 may be “0-2000”. Thus, it is difficult to identify a threshold score indicative of a failure condition. Printhead maintenance supervisor 400 is configured to scale the anomaly scores nonlinearly via recurrent neural network 412 to determine whether to recommend replacement of certain printheads 104. One technical benefit of scaling is that scaled anomaly scores from different printheads 104 may be readily compared to each other or to a common threshold. ML manager 416 formats input samples 768 (i.e., second input samples) for recurrent neural network 412 based on the deployment anomaly scores 714 generated by neural network 410 for the printheads 104. ML manager 416 then inputs or enters the input samples 768 into recurrent neural network 412 to generate scaled anomaly scores 716 for printheads 104. One technical benefit is that the scaled anomaly scores 716 may be used to recommend replacement for certain printheads 104. A further operation of printhead maintenance supervisor 400 is described below.
Kang teaches upon the completion of the above process, the semantic search module 143 assigns relation scores to the extracted objects according to the number of the semantic segments for the each extracted object. The semantic search module 143 in accordance with an illustrative embodiment of the present inventive concept may calculate the relation score for each extracted object based on a topological distance method.
Li teaches that at sub-stage 304b, the pre-processing subsystem determines whether the target object to be classified at the current time step is the same or different from the target object that was classified at a preceding time step. For example, the pre-processing subsystem may use an object detector to process one or more frames of sensor data at a current time step and, based on the processing, to generate a signature for the target object at the current time step. The signature for a target object at the current time step can be compared to the signatures of target objects that were generated at one or more preceding time steps. If the compared signatures are sufficiently similar (e.g., a score that indicates a measure of similarity of the signatures meets a threshold similarity score), then the target object at the current time step is deemed to be the same as the target object from the one or more preceding time steps. If the compared signatures are dissimilar (e.g., a score that indicates a measure of similarity of the signatures does not meet a threshold similarity score), then the target object at the current time step is deemed to be different from the target object from the one or more preceding time steps.
However, the prior art, Agarwal, Anderson, HajiahmadiFoomani, Kang and Li failed to disclose the following subject matter such as “rank the plurality of candidate code deployment data objects using a ranking model and based on (i) the semantic similarity score, (ii) the topological distance score, and (iii) the temporal score for each candidate code deployment data object”.
Claim 8 is the method claim, similar to the claim 1, and claim 15 is the product claim, similar to the claim 1. Therefore, claims 1-20 contain allowable subject matter.
5. Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
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/JAE U JEON/Primary Examiner, Art Unit 2193
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