DETAILED ACTION
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 .
Status of Claims
Claims 1-20 are presented for examination in this application. The application filing date on 06/27/2024. Claims 1, 14 and 20 are independent.
Examiner notes
(A). Examiner interpreted term “candidate development resource reference unit embedding vector filtering model" as refers to a data entity that describes parameters, hyper-parameters, or defined operations of a rules-based or machine learning model that is configured to filter a plurality of development resource reference unit embedding vectors per paragraph 0135-0136 which have been recited in claims 1, 14, and 20.
(B). Drawings submitted on 06/27/2024 comply with the provisions of 37 CFR 1.121(d).
(C). Limitations have been provided with the Bold fonts in order to distinguish from the cited part of the reference (Italic).
(D). Examiner has cited particular columns, line numbers, references, or figures in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses to fully consider the reference in entirety, as potentially teaching all or part of the claimed invention. See MPEP § 2141.02 VI and 2123.
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
Tittel Objections
Length: Must not exceed 500 characters. See, 37 C.F.R. 1.72.
Claim Objections
Claim 3 is objected to because of the following informalities:
Line 7, “the development resource unit evaluation model” lacks proper antecedent basis.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Claims 14-19 are directed to computer implemented methods and fall within the statutory category of processes. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
Analysis of Step 2A Prong 1:
Claim 14: The claim limitations “identifying a technology operations incident event;” , “filtering a plurality of development resource reference unit embedding vectors based at least in part on a technology operations incident nature classification associated with the technology operations incident event to identify a candidate development resource reference unit embedding vector;”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, these functions can be reasonably performed in the human mind, with the aid of pen and paper, through observation, evaluation, judgement, opinion, thus recite a mental process.
Analysis of Step 2A Prong 2:
Claim 14,: The additional elements recited in the claim do not integrate the judicial exception into a practical application. In particular, the claim recites the following additional elements – “generating a candidate development resource reference unit structure based at least in part on the candidate development resource reference unit embedding vector, wherein the candidate development resource reference unit structure comprises a candidate development resource reference unit fragment and a candidate development resource reference unit link;” , “generating a candidate technology operations incident responsive direction by processing the candidate development resource reference unit fragment using a technology operations incident responsive direction generation model;” , “generating a technology operations incident event response interface component comprising the candidate development resource reference unit structure and the candidate technology operations incident responsive direction;” and “outputting the technology operations incident event response interface component for rendering to a technology operations incident event response interface.”, merely define instructions to implement an abstract idea on a generic computer, or merely uses a generic computer component as a tool to perform an abstract idea. See MPEP 2106.05(f). Thus, they do not integrate the judicial exception into a practical application.
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Analysis of Step 2B:
Claim 14: Those above identified additional elements do not amount to significantly more than the judicial exception since they merely define instructions to implement an abstract idea on a generic computer, or merely uses a generic computer component as a tool to perform an abstract idea, thus do not provide any inventive concept to the claimed invention. See MPEP 2106.05(f). Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, Claim 14 do not recite patent eligible subject matter under 35 U.S.C. § 101.
As to claim 1-13, Step 1: is directed to apparatus and fall within the statutory category of processes. Therefore, “Are the claims to a process, machine, manufacture or composition of matter?” Yes.
Analysis of Step 2A Prong 1:
Claim 1: The claim limitations “identifying a technology operations incident event;” , “filtering a plurality of development resource reference unit embedding vectors based at least in part on a technology operations incident nature classification associated with the technology operations incident event to identify a candidate development resource reference unit embedding vector;”, as drafted, is a process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, these functions can be reasonably performed in the human mind, with the aid of pen and paper, through observation, evaluation, judgement, opinion, thus recite a mental process.
Analysis of Step 2A Prong 2:
Claim 1,: The additional elements recited in the claim do not integrate the judicial exception into a practical application. In particular, the claim recites the following additional elements – “generating a candidate development resource reference unit structure based at least in part on the candidate development resource reference unit embedding vector, wherein the candidate development resource reference unit structure comprises a candidate development resource reference unit fragment and a candidate development resource reference unit link;” , “generating a candidate technology operations incident responsive direction by processing the candidate development resource reference unit fragment using a technology operations incident responsive direction generation model;” , “generating a technology operations incident event response interface component comprising the candidate development resource reference unit structure and the candidate technology operations incident responsive direction;” and “outputting the technology operations incident event response interface component for rendering to a technology operations incident event response interface.” merely define instructions to implement an abstract idea on a generic computer, or merely uses a generic computer component as a tool to perform an abstract idea. See MPEP 2106.05(f). Thus, they do not integrate the judicial exception into a practical application. Further, “at least one processor” and “at least one memory” are recited at a high level of generality.
Therefore, “Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application and they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Analysis of Step 2B:
Claim 1: Those above identified additional elements do not amount to significantly more than the judicial exception since they merely define instructions to implement an abstract idea on a generic computer, or merely uses a generic computer component as a tool to perform an abstract idea, thus do not provide any inventive concept to the claimed invention. See MPEP 2106.05(f). Therefore, “Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception.
Having concluded analysis within the provided framework, Claim 1 do not recite patent eligible subject matter under 35 U.S.C. § 101.
As to claim 20, is not patent eligible for the same reasons given for claim 1, wherein the “A computer program product for outputting a technology operations incident event response interface component to a technology operations incident event response interface, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising an executable portion configured to: identify a technology operations … generate a candidate technology operations incident responsive direction by processing the candidate development resource reference unit fragment using a technology operations incident responsive direction generation model; generate a technology operations incident event response interface component comprising the candidate development resource reference unit structure and the candidate technology operations incident responsive direction; and output the technology operations incident event response interface component for rendering to a technology operations incident event response interface.” are merely a generic computer component for applying the abstract idea, thus fails to integrate the judicial exception into a practical application, nor an inventive concept.
Dependent Claims 11-12 are not patent eligible for the same reasons given for claim 1, wherein the “… rank the candidate development resource reference unit embedding vector and one or more other candidate development resource reference unit embedding vector ,,, vector ranking mode … ” and “… resource reference unit embedding vector ranking model using one or more reinforcement learning operations” these functions can be reasonably performed in the human mind, with the aid of pen and paper, through observation, evaluation, judgement, opinion, thus recite a mental process.
Further Dependent Claims 2, 5-9, 13, and 15-19 is rejected for similar reasons as articulated for claim 14 as the additional limitations “generate … ; … vector repository … ; … reference unit link … ; … generating … index operation; … ” perform by a generic computing apparatus that does not allude to a practical application of the abstract idea or amount to significantly more.
Further dependent claims 3-4 and 10 expand on the abstract idea in its access, generate, store and/or expand on the type that is perform that does not integrate the invention into a practical application of the abstract idea or amount to significantly more.
Claim Rejections - 35 USC § 103
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 of this title, 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.
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 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.
Claims 1-2, 4-6, 9, 13-17 and 20 are rejected under 35 U.S.C. 103 as being obvious over Beck et al (US 2019/0260804 A1, hereinafter Beck) in view of Morawiec et al (US 2023/0248911 A1, hereinafter Morawiec) and Aloraini et al. (US 12561445 B2, hereinafter Aloraini).
As to claim 1, Beck discloses an apparatus for outputting a technology operations incident event response interface component to a technology operations incident event response interface, 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 technology operations incident event (par. 0030, A trigger module may detect [i.e. identify] time stamped data indicating one or more i) events and/or ii) alerts from I) unusual or II) suspicious behavior/activity are occurring and then triggers that something unusual is happening …);
filter a plurality of development resource reference unit embedding vectors based at least in part on a technology operations incident nature classification associated with the technology operations incident event to identify a candidate development resource reference unit embedding vector (par. 0031, The gather module may comprise of multiple automatic data gatherers that each look at different aspects of the data depending on the particular hypothesis formed for the analyzed event and/or alert. The data relevant to each type [i.e. incident nature classification] of possible hypothesis will be automatically pulled from additional external and internal sources. … While an email module is mentioned, a similar module may be applied to other communication systems, such as text messaging and other possible vectors for malicious activity. … The gatherers may then filter or condense the mass of data down into the important or salient features of data. In an embodiment, the ingestion module and the coordinator module may be portions of the cyber threat module. Further, par. 0129, … FIG. 7 illustrates a flowchart of an embodiment of a method for developing an inoculation record based on the detection of an anomalous event. …);
(par. 0161, … a database management component. The server engine can perform basic processing and operating-system level tasks. The web page management component can handle creation and display or routing of web pages or screens associated with receiving and providing digital content and digital advertisements. Users (e.g., cloud users) can access one or more of the server computing systems by means of a Uniform Resource Locator (“URL”) [i.e. reference unit link]associated therewith. The content management component can handle most of the functions in the embodiments described herein. The database management component can include storage and retrieval tasks [i.e. candidate development resource reference unit structure] with respect to the database, queries to the database, and storage of data. Note: Applicant spec. par.0020 recites generating the candidate development resource reference unit structure includes performing one or more retrieval operations );
Beck does not explicitly disclose the following limitation but,
Morawiec discloses generate a candidate technology operations incident responsive direction by processing the candidate development resource reference unit fragment using a technology operations incident responsive direction generation model (par. 0007, … involve identifying a plurality of event patterns within a plurality of monitoring periods based on measurement values of a physiological condition. The techniques may involve causing display of a snapshot graphical user interface display, wherein the snapshot graphical user interface display comprises a graph overlay region and an event detection region, the graph overlay region comprises a graphical representation of the measurement values with respect to a time of day, and the event detection region comprises a pattern guidance [i.e. direction generation model] display for at least a subset of the plurality of event patterns, wherein the pattern guidance display for at least one event pattern of the subset of the plurality of event patterns includes a graphical representation of a recommended therapeutic remedial action that comprises therapy parameters, to be used by a medical device during a subsequent time period, configured to mitigate occurrence of the at least one event pattern during the subsequent time period);
generate a technology operations incident event response interface component comprising the candidate development resource reference unit structure and the candidate technology operations incident responsive direction (abstract, … graphical user interface display, wherein the snapshot graphical user interface display comprises a graph overlay region and an event detection region, the graph overlay region comprises a graphical representation [i.e. resource reference] of the measurement values, and the event detection region comprises a pattern guidance [i.e. direction] display for at least a subset of the plurality of event patterns, wherein the pattern guidance display for at least one event pattern includes a graphical representation of a recommended therapeutic remedial action that comprises therapy parameters, to be used by a medical device during a subsequent time period, configured to mitigate occurrence of the at least one event pattern); and
output the technology operations incident event response interface component for rendering to a technology operations incident event response interface (abstract, further, par. 0068, Referring to FIGS. 1-4, by virtue of the subject matter described above, a user may quickly ascertain or otherwise identify the most significant event patterns detected within a particular time period being evaluated on the snapshot GUI display 100, while also being able to quickly ascertain the relative impact or significance of the constituent events with respect to the patient's glycemic control, the temporal characteristics or significance of those events, and the potential causes or remedial actions for those events at the times of day during which they were detected. … ).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include candidate technology operations incident responsive direction by processing the candidate development resource reference unit fragment using a technology operations incident responsive direction generation model and generate a technology operations incident event response interface component comprising the candidate development resource reference unit structure and the candidate technology operations incident responsive direction and output the technology operations incident event response interface component for rendering to a technology operations incident event response interface, as disclosed by Morawiec, for the purpose of analyzing information associated with the patient to identify a remedial action that could potentially resolve, mitigate, correct or otherwise address the event pattern and provide corresponding graphical indicia of the remedial action in connection with the displayed event pattern. (see paragraph 0029 of Morawiec).
Aloraini discloses generate a candidate development resource reference unit structure based at least in part on the candidate development resource reference unit embedding vector, (col. 18, ll. 16-24, Language model 506 is generated in various forms. For instance, language model 506 is generated by applying a suitable supervised and/or unsupervised machine-learning algorithm. For example, language model 506 is generated by implementing a vector space learning algorithm to generate the embedding model as a vector space model. As a vector space model, language model 506 represents individual words or sequences of words in a continuous vector space),
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include generate a candidate development resource reference unit structure based at least in part on the candidate development resource reference unit embedding vector, as disclosed by Aloraini, for the purpose of evaluator determines a semantic similarity value based on a level of similarity of tokens in code changeset and tokens based on their respective vector representations. (see paragraph 0029 of Beck).
As to claim 2, Beck discloses the apparatus wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
generate the technology operations incident nature classification by processing the technology operations incident event using a technology operations incident event classification model (par. 0065, … The one or more machine-learning models may use at least unsupervised learning algorithms to establish what is the normal pattern of life for the system. The machine-learning models can train on both i) the historical normal distribution of alerts and events for that system … . Further, par. 0072, … These similarities of events or alerts in the chain may be, for example, alerts or events are coming from same device, same user credentials, same group, same source identifiers, same destination Internet Protocol addresses, same types of data transfers, same type of unusual activity, same type [i.e. class] of alerts, … ).
As to claim 4, Beck discloses the apparatus wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
access development resource reference unit data representative of a development resource reference unit (par. 0161, … a database management component [reference unit]. The server engine can perform basic processing and operating-system level tasks. The web page management component can handle creation and display or routing of web pages or screens associated with receiving and providing digital content and digital advertisements. Users (e.g., cloud users) can access one or more of the server computing systems by means of a Uniform Resource Locator (“URL”) associated therewith. The content management component can handle most of the functions in the embodiments described herein. The database management component can include storage and retrieval tasks with respect to the database, queries to the database, and storage of data);
store the development resource reference unit embedding vector (par. 0029, … a data store, iv) an ingestion module, v) a coordinator module, vi) a comparison module, vii) a cyber threat module, viii) a research module, ix) a user interface module, x) an incident module, xi) a logging module, xii) an autonomous response module, xiii) a communication module, xiv) at least one input or output (I/O) port to securely connect to other ports as required, xv) one or more machine-learning models such as a first Artificial Intelligence model trained on characteristics of vectors for malicious activity and related data).
Aloraini discloses generate a development resource reference unit embedding vector based at least in part on the development resource reference unit data, the development resource reference unit embedding vector corresponding to the development resource reference unit (col. 18, ll. 16-24, Language model 506 is generated in various forms. For instance, language model 506 is generated by applying a suitable supervised and/or unsupervised machine-learning algorithm. For example, language model 506 is generated by implementing a vector space learning algorithm to generate the embedding model as a vector space model. As a vector space model, language model 506 represents individual words or sequences of words in a continuous vector space),
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include a generate a development resource reference unit embedding vector based at least in part on the development resource reference unit data, the development resource reference unit embedding vector corresponding to the development resource reference unit as disclosed by Aloraini, for the purpose of evaluator determines a semantic similarity value based on a level of similarity of tokens in code changeset and tokens based on their respective vector representations. (see paragraph col. 18, ll. 35-39 of Aloraini).
As to claim 5, Beck discloses the apparatus wherein the development resource reference unit embedding vector is stored in a development resource reference unit embedding vector repository based at least in part on a development resource reference unit nature (par. 0029, … a data store (i.e. vector), iv) an ingestion module, v) a coordinator module, vi) a comparison module, vii) a cyber threat module, viii) a research module, ix) a user interface module, x) an incident module, xi) a logging module, xii) an autonomous response module, xiii) a communication module, xiv) at least one input or output (I/O) port to securely connect to other ports as required, xv) one or more machine-learning models such as a first Artificial Intelligence model trained on characteristics of vectors for malicious activity and related data, …).
As to claim 6, Beck discloses the apparatus wherein the development resource reference unit embedding vector is indicative of a development resource reference unit identifier (par. 0031, The gather module may comprise of multiple automatic data gatherers that each look at different aspects of the data depending on the particular hypothesis formed for the analyzed event and/or alert. The data relevant to each type of possible hypothesis will be automatically pulled from additional external and internal sources. Some data is pulled or retrieved by the gather module for each possible hypothesis. A feedback loop of cooperation occurs between the gather module, the ingestion module monitoring network and email activity, the comparison module to apply one or more models trained on different aspects of this process, and the cyber threat module to identify cyber threats based on comparisons by the comparison module. While an email module is mentioned, a similar module may be applied to other communication systems, such as text messaging and other possible vectors for malicious activity … ), a development resource reference unit link (par. 0161, … a database management component. The server engine can perform basic processing and operating-system level tasks. The web page management component can handle creation and display or routing of web pages or screens associated with receiving and providing digital content and digital advertisements. Users (e.g., cloud users) can access one or more of the server computing systems by means of a Uniform Resource Locator (“URL”) [i.e. reference unit link]associated therewith. The content management component can handle most of the functions in the embodiments described herein. The database management component can include storage and retrieval tasks with respect to the database, queries to the database, and storage of data, a development resource reference unit nature), and development resource reference unit fragment (par. 0029, … a data store (i.e. vector), iv) an ingestion module, v) a coordinator module, vi) a comparison module, vii) a cyber threat module, viii) a research module, ix) a user interface module, x) an incident module, xi) a logging module, xii) an autonomous response module, xiii) a communication module, xiv) at least one input or output (I/O) port to securely connect to other ports as required, xv) one or more machine-learning models such as a first Artificial Intelligence model trained on characteristics of vectors for malicious activity and related data, … ).
As to claim 9, Aloraini discloses the apparatus wherein generating the development resource reference unit embedding vector comprises processing the development resource reference unit data using a nature extraction model to generate a development resource reference unit nature (col. 21, ll. 42-49, the extracted feature comprises one or more additional features 808, such as a degree indicating how tested the computer code 226 or other code stored in the repository is, how complex such code is (e.g., based on a cyclomatic complexity technique or other type of complexity measure), or various other information. These examples are only illustrative, and other types of features).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include a the apparatus wherein generating the development resource reference unit embedding vector comprises processing the development resource reference unit data using a nature extraction model to generate a development resource reference unit nature as disclosed by Aloraini, for the purpose of implementing a vector space learning algorithm to generate the embedding model as a vector space model. (see paragraph col. 18, ll. 20-22 of Aloraini).
As to claim 13, Beck discloses the apparatus wherein generating the candidate development resource reference unit structure comprises performing one or more retrieval operations (par. 0161, … The server engine can perform basic processing and operating-system level tasks. The web page management component can handle creation and display or routing of web pages or screens associated with receiving and providing digital content and digital advertisements. Users (e.g., cloud users) can access one or more of the server computing systems by means of a Uniform Resource Locator (“URL”) associated therewith. The content management component can handle most of the functions in the embodiments described herein. The database management component can include storage and retrieval tasks with respect to the database).
As to claim 14, (a method claim) recites substantially similar limitations to claim 1 (a system claim) and is therefore rejected using the same art and rationale set forth above.
As to claim 15, (the method claim) recites substantially similar limitations to claim 2 (the system claim) and is therefore rejected using the same art and rationale set forth above.
As to claim 16, (the method claim) recites substantially similar limitations to claim 4 (the system claim) and is therefore rejected using the same art and rationale set forth above.
As to claim 17, (the method claim) recites substantially similar limitations to claim 6 (the system claim) and is therefore rejected using the same art and rationale set forth above.
As to claim 20, (a medium claim) recites substantially similar limitations to claim 1 (a system claim) and is therefore rejected using the same art and rationale set forth above.
Claim 3 is rejected under 35 U.S.C. 103 obvious over Beck et al. , Morawiec et al. and Aloraini et al. as applied to the claim 1 in above and further in view of Assuncao et al. (US 20240036829 A1, hereinafter Assuncao).
As to claim 3, Beck discloses the apparatus wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
access one or more development resource reference unit training vectors (par. 0041, system 100 may use [i.e. access] multiple machine learning models [i.e. development resource reference unit]. Each machine learning model may be trained on specific aspects of the normal pattern of life for the system such as devices, users, network traffic flow, outputs from one or more cyber security analysis tools analyzing the system, and others. One or more machine learning models may also be trained on characteristics and aspects of all manner of types of cyber threats. One or more machine learning models may also be trained by observing vectors for malicious activity, such as network activity or emails. One or more machine learning models may be trained by observing the activities of external hosts);
train a development resource reference unit assessment model using the one or more development resource reference unit training vectors (par. 0041, system 100 may use multiple machine learning models. Each machine learning model may be trained on specific aspects of the normal pattern of life for the system such as devices, users, network traffic flow, outputs from one or more cyber security analysis tools analyzing [i.e. assessment] the system, and others. One or more machine learning models may also be trained on characteristics and aspects of all manner of types of cyber threats. One or more machine learning models may also be trained by observing vectors for malicious activity, such as network activity or emails. One or more machine learning models may be trained by observing the activities of external hosts); and
Beck as modified by Morawiec and Aloraini does not disclose the following limitations but,
Assuncao discloses generate a development resource reference unit impact value by applying resource reference unit data to the development resource reference unit evaluation model (abstract, … a program development tool utilized to develop an application are tracked. Input features associated with the tracked user interactions and the states are provided to a trained machine learning model to determine a prediction result associated with whether a user is likely unable to proceed in the development of the application … . Further, pars.0073-0077, anonymized data include: [0074] Source code for cloud customers' software, which indicates how software is created [0075] Requirements, user stories, support tickets, or the like, which specify what the user wants [0076] Error logs, which can be analyzed to determine what went wrong with the developed software [0077] Performance analytics [evaluation] data, which indicate factors that impact processing speed/latency what is fast/slow [i.e. value]).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include generate a development resource reference unit impact value by applying resource reference unit data to the development resource reference unit evaluation model, as disclosed by Assuncao, for the purpose of developing a computer program, and then determines and suggests specific resolutions. (see paragraph 0013 of Assuncao).
Claims 7-8, 10-12 and 18-19 are rejected under 35 U.S.C. 103 as being obvious over Beck et al., Morawiec et al. and Aloraini as applied to claims 1 and 16 above, and further in view of Wang et al. (US 2020/0302187 A1, hereinafter Wang).
As to claim 7, Beck as modified by Morawiec and Aloraini does not disclose the following limitations but,
Wang discloses the apparatus wherein generating the development resource reference unit embedding vector comprises processing the development resource reference unit data using a fragment extraction model to generate a development resource reference unit fragment (par. 0365, … learning, machine learning, detection, estimation, feature extraction, learning network, feature extraction, denoising, signal enhancement, coding, encryption, mapping, remapping, vector quantization … ).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include the apparatus wherein generating the development resource reference unit embedding vector comprises processing the development resource reference unit data using a fragment extraction model to generate a development resource reference unit fragment as disclosed by Wang, for the purpose of preprocessing, processing, and/or post-processing applied jointly on multiple time series or functions.. (see paragraph 0029 of Wang).
As to claim 8, Wang discloses the apparatus wherein generating the development resource reference unit embedding vector comprises processing the development resource reference unit fragment using one or more embedding and indexing operations (par. 0124, … Each component may be associated with a component index. Each component may be a real, imaginary, or complex quantity, magnitude, phase, flag, and/or set. Each CI may comprise a vector or matrix of complex numbers, a set of mixed quantities, and/or a multi-dimensional collection of at least one complex numbers).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include the apparatus wherein generating the development resource reference unit embedding vector comprises processing the development resource reference unit fragment using one or more embedding and indexing operations as disclosed by Wang, for the purpose of collecting complex numbers. (see paragraph 0124 of Wang).
As to claim 10, Wang discloses the apparatus wherein processing the candidate development resource reference unit fragment using the technology operations incident responsive direction generation model comprises the program code being further configured to, with the at least one processor, cause the apparatus to:
perform one or more retrieval augment generation operations (par. 0488, … machine learning, machine learning, supervised learning, unsupervised learning, semi-supervised learning, clustering, feature extraction [i.e. retrieval], featuring training, principal component analysis, eigen-decomposition, frequency decomposition, time decomposition, time-frequency decomposition, functional decomposition, other decomposition, training, discriminative training, supervised training, unsupervised training, semi-supervised training, neural network, augmented reality … ).
As to claim 11, Wang disclose the apparatus wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
rank the candidate development resource reference unit embedding vector and one or more other candidate development resource reference unit embedding vector using a candidate development resource reference unit embedding vector ranking model (par. 0151, … computing a single link [i.e. reference] (e.g. path, communication path, link between a transmitting antenna and a receiving antenna) quantity, computing a quantity comprising multiple links, computing a function of the operands, filtering, linear filtering, nonlinear filtering, folding, grouping, energy computation, lowpass filtering, bandpass filtering, highpass filtering, median filtering, rank … first derivative, second order derivative, high order derivative, convolution, multiplication, division, addition, subtraction, integration, maximization, minimization, least mean square error, recursive least square, … vector addition, vector subtraction, vector multiplication, vector division, inverse, norm, distance, and/or another operation. The operation may be the preprocessing, processing, and/or post-processing. Operations may be applied jointly on multiple time series or functions).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include rank the candidate development resource reference unit embedding vector and one or more other candidate development resource reference unit embedding vector using a candidate development resource reference unit embedding vector ranking model as disclosed by Wang, for the purpose of analyzing based on filtering and ranking. (see paragraph 0361 of Wang).
As to claim 12, Wang discloses the apparatus wherein the program code is further configured to, with the at least one processor, cause the apparatus to:
train the candidate development resource reference unit embedding vector ranking model using one or more reinforcement learning operations (par. 0152, … , machine learning, detection, estimation, feature extraction, learning network, feature extraction, denoising, signal enhancement, coding, encryption, mapping, remapping, vector quantization, lowpass filtering, highpass filtering, bandpass filtering, matched filtering, Kalman filtering, preprocessing, postprocessing, particle filter, FIR filtering, IIR filtering, autoregressive (AR) filtering, adaptive filtering, first order [i.e. ranking] derivative, high order derivative … Poisson function, etc. Machine learning, training, discriminative training, deep learning, neural network, continuous time processing, … ).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to modify the system disclosed by Beck to include train the candidate development resource reference unit embedding vector ranking model using one or more reinforcement learning operations as disclosed by Wang, for the purpose of analyzing based on filtering and ranking. (see paragraph 0361 of Wang).
As to claim 18, (the method claim) recites substantially similar limitations to claim 7 (the system claim) and is therefore rejected using the same art and rationale set forth above.
As to claim 19, (the method claim) recites substantially similar limitations to claim 8 (the system claim) and is therefore rejected using the same art and rationale set forth above.
Conclusion
Prior arts made of record are considered pertinent to applicant's disclosure. See MPEP § 707.05 (C) For Examples:
I. Mishra et al. (US 20250004936 A1) discloses: “An apparatus comprises a processing device configured to determine specifications for an information technology asset to be developed, and to identify, utilizing at least one machine learning model, whether at least one of the specifications for the information technology asset is defect-prone, wherein a given specification is identified as defect-prone responsive to at least one output of the at least one machine learning model indicating that the given specification has at least a threshold likelihood of resulting in one or more defects during development of the information technology asset. The processing device is also configured to establish a mapping between the one or more identified defect-prone specifications for the information technology asset and one or more components of the information technology asset, and to modify one or more development processes for the information technology asset based at least in part on the established mapping.” (please see Abstract).
II. Austin et al. (US 20110275364 A1) discloses: “stem(s), device(s), method(s), and user interfaces are provided to enable a subscriber device to report network operation conditions, such as network performance incidents, to receive feedback from the network related to the incident and available or possibly available solutions, and to produce network intelligence suitable for network planning and network performance enhancement. Reporting of network performance incidents can be characterized by location and time of occurrence, wherein these intelligence is provided by the user device. Feedback provided by the network is based on the network performance data received as part of reporting an incident. The reporting described herein enables a network operator to generate network planning intelligence based on actual network performance as experienced at the subscriber level.” (please see Abstract).
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/Mohammad Kabir/
Examiner, Art Unit 2192
/S. Sough/SPE, Art Unit 2192
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