DETAILED ACTION
Claims 1-19 are pending in the current application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-8 and 17-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 7 and 17 recite at line 6 “detecting at least one based on outputs the machine learning model..” it is unclear from this language what is being detected as appears to be missing word(s), however, based on context it is viewed and being interpreted for claim analysis as should recite “detecting at least one anomaly based on outputs the machine learning model..” but appropriate clarification is required.
As to claims 8 and 18 they depend from claims 7 and 17 respectively above and do not overcome the issue and thus rejected under the same reasoning.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Claims 1-19 are claims that are directed to a process, machine, manufacture or composition of matter.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1, 10 and 11: The limitation of “generating fingerprinting code based on a knowledge base, wherein the knowledge base includes a plurality of nodes representing respective software components of a plurality of software components, wherein the fingerprinting code includes instructions that, when executed by a processing circuitry, configure the processing circuitry to perform a text search in order to identify patterns in at least one code repository defined with respect to the knowledge base and to generate statistical data about the identified patterns” and “…scan the at least one code repository” as drafted, are functions thus under its broadest reasonable interpretation recite the abstract idea of a mental process. The limitations encompasses a human mind carrying out the function of generate code based on analysis of knowledge base of information to perform a functionality when executed and the ability to scan/analyze code repository to identify code fingerprints/pattern and determine statistical data of the code in the repository through observation, evaluation, judgment and/or opinion or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Process” grouping of abstract ideas under Prong 1.
The claims have been identified to recite an abstract idea, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2:
Claims 1, 10 and 11: The abstract idea is not integrated into a practical application. In particular the claims recite the following additional element “a processing circuitry,” “A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising” and “A system for code fingerprinting, comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components. Further, the claims recite the additional elements of converting “causing the fingerprinting code to run on the at least one code repository, wherein causing the fingerprinting code to run further comprises executing the instructions of the fingerprinting code in order to scan the at least one code repository” fails to meaningfully limit the claim because it does not require any particular application of the recited “causing the fingerprint code to run” and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
After having evaluating the inquires set forth in Steps 2A Prong 1 and 2, it has been concluded that claims, 1, 10 and 11 not only recite an abstract idea but that the claims are directed to the abstract idea as the abstract idea has not been integrated into practical application.
Step 2B:
Claims 1, 10 and 11: The claims do not include additional elements, alone or in combination that are sufficient to amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of ““a processing circuitry,” “A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising” and “A system for code fingerprinting, comprising: a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to” amount to no more than mere instructions, or generic computer/computer components to carry out the exception. Further, the additional element of converting “causing the fingerprinting code to run on the at least one code repository, wherein causing the fingerprinting code to run further comprises executing the instructions of the fingerprinting code in order to scan the at least one code repository” does not require any particular application of the recited run execution of code and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Mere instructions to apply an exception cannot provide an inventive concept. The recitation of generic computer instruction and computer components to apply the judicial exception, and mere instructions to apply an exception, do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101.
Having concluded analysis within the provided framework, claims 1, 10 and 11 do not recite patent eligible subject matter under 35 USC 101
As to claims 2 and 12 the limitation of “wherein the patterns are patterns in text, wherein the instructions for scanning code repositories include instructions that, when executed by a processing circuitry, configure the processing circuitry to perform at least one text search” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claim 2 and 12 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 2 and 12 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 2 and 12 does not recite patent eligible subject matter under 35 USC 101.
As to claims 3 and 13 the limitation of “wherein the statistical data includes a plurality of statistics vectors, wherein each statistics vector includes a plurality of values representing statistics for respective aspects of the plurality of software components represented in the knowledge base” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claim 3 and 13 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 3 and 13 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 3 and 13 does not recite patent eligible subject matter under 35 USC 101.
As to claims 4 and 14 the limitation of “wherein the plurality of statistics vectors include a plurality of counts vectors, wherein each of the plurality of values of each counts vector is a count of instances for a respective aspect of the plurality of software components represented in the knowledge base” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claim 4 and 14 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 4 and 14 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 4 and 14 does not recite patent eligible subject matter under 35 USC 101.
As to claims 5 and 15 the limitation of “wherein generating the fingerprinting code further comprises: querying the knowledge base in order to obtain query results, wherein the fingerprinting code is generated based on the query results” is an additional mental process element under prong 1. Moreover, claims 5 and 15 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 5 and 15 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 5 and 15 do not recite patent eligible subject matter under 35 USC 101.
As to claims 6 and 16 the limitation of “wherein the knowledge base is queried for at least one string of text of the plurality of nodes of the plurality of software components represented in the knowledge base, wherein the identified patterns are patterns defined with respect to the at least one string of text” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 6 and 16 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 6 and 16 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 6 and 16 does not recite patent eligible subject matter under 35 USC 101.
With regard to claims 7 and 17 they recite the limitation of “detecting at least one based on outputs the machine learning model when the machine learning model is applied to the features extracted from the statistical data” is an additional mental process element under prong 1. Further the claims recites additional elements of “applying a machine learning model to features extracted from the statistical data, wherein the machine learning model is trained using training statistical data for the knowledge base, wherein the machine learning model is trained to output anomalies when applied to the features extracted from the statistical data” which merely describe in generic terms the applying of a trained machine learning model to output data anomalies by providing input into a trained machine learning model because it does not require any particular application of the recited “applying a machine learning model” and “training” of the machine learning model and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Moreover, claims 7 and 17 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 7 and 17 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 7 and 17 does not recite patent eligible subject matter under 35 USC 101.
As to claims 8 and 18 the limitation of “further comprising: training the machine learning model based on a historical state of the knowledge base” which is merely a field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claim 8 and 18 does not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 8 and 18 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 8 and 18 does not recite patent eligible subject matter under 35 USC 101.
With regard to claims 9 and 19 they recites additional elements of “further comprising: performing at least one remedial action based on results of the fingerprinting code being run on the at least one code repository” which merely describe in generic terms the performing of a remedial action because it does not require any particular application of the recited “performing” of the remedial action and is at best the equivalent of merely adding the words “apply it” to the judicial exception. Moreover, claims 9 and 19 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claim 9 and 19 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 9 and 19 does not recite patent eligible subject matter under 35 USC 101.
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.
Claims 1-2, 9-12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hines et al. (Pub. No. US 2004/0015816 A1) in view of Carback III et al. (Pub. No. US 2015/0363294 A1) and further in view of Kirat et al. (Pub. No. US 2025/0190575 A1).
As to claims 1 and 10, Hines discloses a method for code fingerprinting, comprising: generating fingerprinting code based on a knowledge base, wherein the knowledge base includes a plurality of nodes representing respective software components of a plurality of software components (Hines [0056] lines 4-23, [0092] lines 1-6 and claim 8; which shows being able to generate code based on the coordination graph, viewed as a type of knowledge base/graph composed of a plurality of node elements that represent software functional components that when executed will perform the associated functionality where the functionality being generated in not specifically limited and viewed as including fingerprinting type functionality of code seen specifically disclosed in the teachings of Kirat below showing the specifics of the software for performing fingerprinting).
Hines does not specifically disclose wherein the fingerprinting code includes instructions that, when executed by a processing circuitry, configure the processing circuitry to perform a text search in order to identify patterns in at least one code repository defined with respect to the knowledge base and to generate statistical data about the identified patterns.
However Carback discloses wherein the fingerprinting code includes instructions that, when executed by a processing circuitry, configure the processing circuitry to perform a text search in order to identify patterns in at least one code repository defined with respect to the knowledge base and to generate statistical data about the identified patterns (Carback [0073] lines 1-9, [0075] lines 7-15, [0082] lines 1-12, [0091] lines 1-10 and [0120] lines 8-22; which shows the specific software program, viewed as type of fingerprinting code, that is executable to perform software analysis on code where the analysis of the code information includes identifying/recognizing patterns in the analyzed code where the patterns can be labeled/identified based on information extracted from associated metadata where the metadata information can include information such as program context including information for loop/invariants counters viewed as type of statistical value associated the associated pattern).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carback showing the analyzing a body of knowledge to identify information about code including patterns into the analysis of information about code used to generate code of Hines for the purpose of increasing the adaptability of code analysis to determine more detailed information related to code including associated patterns, as taught by Carback [0120] lines 8-22.
Hines as modified by Carback do not specifically disclose causing the fingerprinting code to run on the at least one code repository, wherein causing the fingerprinting code to run further comprises executing the instructions of the fingerprinting code in order to scan the at least one code repository.
However, Kirat discloses causing the fingerprinting code to run on the at least one code repository, wherein causing the fingerprinting code to run further comprises executing the instructions of the fingerprinting code in order to scan the at least one code repository (Kirat [0005] lines 1-13, [0020] lines 8-13[0031] lines 1-7, [0032] lines 1-10, [0033] lines 1-6, [0036] lines 8-13, [0043] lines 1-6 and claim 11; which shows the code executed by a processor of computer environment that is able to perform assessment and fingerprinting from a knowledge graph generated from code repository thus viewed as running the code associated with fingerprinting to scan and extract and determine associated fingerprint of the software code components of the software packages in the repository).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kirat showing the specifics of an application that is able to perform specific code analysis of code fingerprinting from code repository into the code analysis of Hines as modified by Carback for the purpose of improving application code through improved analysis to identify deficiencies associated with the application and perform correcting actions, as taught by Kirat [0030] lines 1-14.
As to claims 2 and 12, Hines does not specifically disclose, however, Carback discloses wherein the patterns are patterns in text, wherein the instructions for scanning code repositories include instructions that, when executed by a processing circuitry, configure the processing circuitry to perform at least one text search (Carback [0082] lines 1-12, [0091] lines 1-10 and [0120] lines 8-22; which shows that the pattens can include patterns in text that can be found identified as part of text/key word search that are viewed as part of the software instruction for an analytics module to analyze/search for the design patterns that can include the key word searching).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carback showing the analyzing a body of knowledge to identify information about code including patterns into the analysis of information about code used to generate code of Hines for the purpose of increasing the adaptability of code analysis to determine more detailed information related to code including associated patterns, as taught by Carback [0120] lines 8-22.
As to claims 9 and 19, Hines as modified by Carback do not specifically disclose, however, Kirat discloses further comprising: performing at least one remedial action based on results of the fingerprinting code being run on the at least one code repository (Kirat [0021] lines 1-13, [0022] lines 1-14, [0030] lines 1-14 and Fig. 2; which shows is response to running fingerprinting on code repository/knowledge graph being able to determine a responsive/remedial action as action to correct the determined particular needs of the application/code).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Kirat showing the specifics of an application that is able to perform specific code analysis of code fingerprinting from code repository into the code analysis of Hines as modified by Carback for the purpose of improving application code through improved analysis to identify deficiencies associated with the application and perform correcting actions, as taught by Kirat [0030] lines 1-14.
As to claim 11, Hines discloses A system for code fingerprinting, comprising: a processing circuitry (Hines [0003] lines 1-9 and [0070] lines 4-9); and
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to (Hines [0003] lines 1-9 and [0015] lines 5-12)
The remaining limitations of the claim are comparable to claim 1 above and rejected under the same reasoning.
Claims 3-4, 7-8, 13-14 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hines, Carback, and Kirat as applied to claims 1 and 11 above, and further in view of Alshawabkeh et al. (Pub. No. US 2024/0281362 A1).
As to claims 3 and 13 Hines as modified by Carback and Kirat do not specifically disclose wherein the statistical data includes a plurality of statistics vectors, wherein each statistics vector includes a plurality of values representing statistics for respective aspects of the plurality of software components represented in the knowledge base.
However, Alshawabkeh discloses wherein the statistical data includes a plurality of statistics vectors, wherein each statistics vector includes a plurality of values representing statistics for respective aspects of the plurality of software components represented in the knowledge base (Alshawabkeh [0005] lines 1-12; which shows being able analyze code and determine software telemetry counter result over a time period, viewed as a type of statistical value where that input is used to create a specific input vector, viewed as a type of statistic vector representing aspects of the software/software component, that in light of the teachings of Hines above shows the specifics of the software/software components being part of a knowledge base).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Alshawabkeh showing the specifics of code analysis to generate vector representing count/statistical value associated with the code, into the statistical analysis of code of Hines as modified by Carback and Kirat for the purpose of being able further adapt the statistical data of the software into different representations so that it can be used for additional types of analysis, as taught by Alshawabkeh [0004] lines 1-6 and [0005] lines 1-12.
As to claims 4 and 14 Hines as modified by Carback and Kirat do not specifically disclose, however, Alshawabkeh discloses wherein the plurality of statistics vectors include a plurality of counts vectors, wherein each of the plurality of values of each counts vector is a count of instances for a respective aspect of the plurality of software components represented in the knowledge base (Alshawabkeh [0004] lines 1-6 and [0005] lines 1-12; which shows being able analyze code and determine software telemetry counter result over a time period, viewed as a type of statistical value count of an aspect of the software program where that input is used to create a specific input vector, viewed as a type of statistic vector representing aspects of the software/software component that can be determined and generated for a plurality of software components/versions, that in light of the teachings of Hines above shows the specifics of the software/software components being part of a knowledge base).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Alshawabkeh showing the specifics of code analysis to generate vector representing count/statistical value associated with the code, into the statistical analysis of code of Hines as modified by Carback and Kirat for the purpose of being able further adapt the statistical data of the software into different representations so that it can be used for additional types of analysis, as taught by Alshawabkeh [0004] lines 1-6 and [0005] lines 1-12.
As to claims 7 and 17 Hines as modified by Carback and Kirat do not specifically disclose, however, Alshawabkeh discloses applying a machine learning model to features extracted from the statistical data, wherein the machine learning model is trained using training statistical data for the knowledge base, wherein the machine learning model is trained to output anomalies when applied to the features extracted from the statistical data; and detecting at least one based on outputs the machine learning model when the machine learning model is applied to the features extracted from the statistical data (Alshawabkeh [0005] lines 1-12, [0055] lines 1-23, [0068] lines 12-19, [0069] lines 1-12 and [0074] lines 1-11; which shows being able to train the machine learning self organizing maps/machine learning model of the software based on values of associated software telemetry counters associated with the software are collected/extracted viewed as type of statistical data used in training the machine learning model thus viewed as trained based on the knowledge/information of the software program where the trained machine learning model is able to determine and output the most discriminated/different telemetry counter viewed as an anomalies, that is seen as associated with software portion, and detecting/determine the portion of code associated with the telemetry counter that is most discriminated against as a type of anomaly).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Alshawabkeh showing the specifics of code analysis to generate vector representing count/statistical value associated with the code, into the statistical analysis of code of Hines as modified by Carback and Kirat for the purpose of being able further adapt the statistical data of the software into different representations so that it can be used for additional types of analysis, as taught by Alshawabkeh [0004] lines 1-6 and [0005] lines 1-12.
As to claims 8 and 18, Hines does not specifically disclose, however, Carback discloses further comprising: training the machine learning model based on a historical state of the knowledge base (Carback [0090] lines 1-10, [0098] lines 9-21 and [0099] lines 1-10; which shows the deep learning/machine learning model is trained on the corpus/knowledge base of information that can include data on the software before and after revisions and the software over time, viewed as type of historic state information of the knowledge base).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carback showing the analyzing a body of knowledge to identify information about code including patterns into the analysis of information about code used to generate code of Hines for the purpose of increasing the adaptability of code analysis to determine more detailed information related to code including associated patterns, as taught by Carback [0120] lines 8-22.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Hines, Carback, Kirat and Alshawabkeh as applied to claims 3 and 13 above, and further in view of Schloegel et al. (Pub. No. US 2004/0044990 A1)
As to claims 5 and 15 Hines as modified by Carback, Kirat and Alshawabkeh do not specifically disclose wherein generating the fingerprinting code further comprises: querying the knowledge base in order to obtain query results, wherein the fingerprinting code is generated based on the query results.
However, Schloegel discloses wherein generating the fingerprinting code further comprises: querying the knowledge base in order to obtain query results, wherein the fingerprinting code is generated based on the query results (Schloegel [0032] lines 20-35; which shows for generating code/functionality is done querying the graphical model/knowledge base for specific code generator routine/results where the code is generated based on the results, where the specifics of generating fingerprinting code functionality is seen specifically disclosed above in the teachings of Hines and Kirat and together can be viewed as disclosing the specifics of querying the knowledge base in order to obtain query results, wherein the fingerprinting code is generated based on the query results).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Scholegel showing generating specific code for functionality based on searching for specific functionality, into the generating code functionality of Hines as modified by Carback, Kirat and Alshawabkeh for the purpose of increasing the ease of generating code with specific functionality, as taught by Schloegel [0020] lines 1-8 and [0032] lines 20-35.
As to claims 6 and 16, Hines does not specifically disclose, however, Carback discloses wherein the knowledge base is queried for at least one string of text of the plurality of nodes of the plurality of software components represented in the knowledge base, wherein the identified patterns are patterns defined with respect to the at least one string of text (Carback [0006] lines 1-12, [0073] lines 1-9, [0075] lines 7-15, [0082] lines 1-12, [0091] lines 1-10 and [0120] lines 8-22; which shows the specific software program functionality can include an analytics module to determine artifacts and design pattern from the corpus/knowledge base of information where the patterns can be identified base on key word search/string of text search/query thus in light of the teachings of Schloegel and Kirat above showing the specifics of generating code functionality by querying a knowledge based for specific code functionality and using that information to generate associated code that together in light of Carback showing the specifics of functionality for querying a corpus/knowledge base for specific key word/text string where the key word used to identify specific patterns can be viewed together as showing the specifics of wherein the knowledge base is queried for at least one string of text of the plurality of nodes of the plurality of software components represented in the knowledge base, wherein the identified patterns are patterns defined with respect to the at least one string of text).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Carback showing the analyzing a body of knowledge to identify information about code including patterns into the analysis of information about code used to generate code of Hines for the purpose of increasing the adaptability of code analysis to determine more detailed information related to code including associated patterns, as taught by Carback [0120] lines 8-22.
Conclusion
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/BRADFORD F WHEATON/Examiner, Art Unit 2193