DETAILED ACTION
Notice of 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 .
Response to Arguments
Applicant’s amendments and arguments filed 02/05/2026, with respect to claim(s) 1-20 have been fully considered. Applicant amended claims 1, 5, 6, 8, 12, 15 and 18.
Applicant’s arguments filed 02/05/2026, with respect to claim(s) 1-20, under 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant argued that plain-language indicators in Pai are not used to configure the pipeline or elements thereof, but merely indicate stages of processing in the pipeline. Applicant further argued Pai in view of Eckart, further in view of Ben Shahar does not disclose, teach, or suggest the amended claimed features, at least "a configuration instruction... comprising... at least one plain-language indicator of at least one of: an option for configuring the at least one NLP operation, and an instruction of the at least one NLP operation" and "configuring, by the at least one processor, the ML pipeline using the configuration instruction, the configuring comprising... configuring the at least one ML processing element according to the at least one of: the option for configuring the at least one NLP operation, and the instruction of the at least one NLP operation." Examiner respectfully disagrees. Pai, in Column 18, lines 4-18, describes the stage “preprocess” in the configuration file by the plain language indicator “preprocess” and in lines 14 and 17 indicate “tokenization” and “stemming”, which are the steps of preprocessing and by stating them in the configuration file is giving the user the option or instruction of the NLP operation by the Preprocess components which is a collection of Model- Data Preprocessing components ( corresponding to preprocess components 181 of FIG. 1) that are used to convert the raw data into a clean data set. Examiner believes previously cited prior art does teach the amended limitations. Therefore, the 35 U.S.C. rejection by the previously cited prior art of Pai in view of Eckart, further in view of Ben Shahar is maintained. Please see the rejections below.
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 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 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.
Claims 1-3, 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Pai et al. ( US 12,242,543 B1), hereinafter referenced as Pai, in view of Eckart et al. (A broad-coverage collection of portable NLP components for building shareable analysis pipelines, Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks, August 23rd, 2014), hereinafter referenced as Eckart.
Regarding Claim 1, Pai teaches a method comprising:
obtaining, by at least one processor, a configuration instruction configured to direct operations of a natural language processing (NLP) machine learning (ML) pipeline ( Pai: Column 10, lines 20-27, 43-52, Fig. 2, processor 213. Framework module 230 may be configured to read configuration files 240, to manage execution of pipeline 290. Column 16, lines 46-47, it could be ML pipeline. Column 17, lines1-4, data processing techniques could be NLP techniques),
the configuration instruction comprising at least one plain-language indicator of at least one NLP operation to be performed by the ML pipeline ( Pai: Column 18, lines 2-10, configuration instruction shows in plain language, such as “preprocess”) ;
and at least one plain-language indicator of at least one of: an option for configuring the at least one NLP operation, and an instruction of the at least one NLP operation ( Pai: Column 17, lines 50-62, column 18, lines 4-18, Fig.1, the stage “preprocess” in the configuration file states “tokenization” and “stemming”, which are the steps of preprocessing and by stating them in the configuration file is giving the user the instruction of the NLP operation by the Preprocess components which is a collection of Model- Data Preprocessing components ( corresponding to preprocess components 181) that are used to convert the raw data into a clean data set );
[[and]] loading the at least one ML processing element into the ML pipeline ( Pai: Column 17, lines 55-59, Figs. 1, 3A illustrates pipeline config file, which shows “preprocess” by preprocessing element 181. Column 12, lines 51-59, Fig. 2, pipeline configuration file 242, preconfigured in such a way that it causes execution of a specified set of stages (e.g., "data transform," "preprocess," "predict," and "persist")),
And configuring the at least one ML processing element according to the at least one of: the option for configuring the at least one NLP operation, and the instruction of the at least one NLP operation ( Pai: Column 17, lines 50-62, column 18, lines 4-18, Fig.1, the stage “preprocess” in the configuration file states “tokenization” and “stemming”, which are the steps of preprocessing and by stating them in the configuration file is giving the user the instruction of the NLP operation by the Preprocess components which is a collection of Model- Data Preprocessing components ( component 181) that are used to convert the raw data into a clean data set );
and performing, by the at least one processor, NLP on text data using the configured ML pipeline, the NLP including processing by the at least one ML processing element ( Pai: Column 5, lines 42-50, column 17, column 12, lines 51-59, Figs. 1, 2, preprocess components 181 may convert data into a clean data set. Pipeline configuration file 242, preconfigured in such a way that it causes execution of a specified set of stages (e.g., "data transform," "preprocess," "predict," and "persist"). Column 17, lines1-4, data processing techniques could be NLP techniques).
Pai while teaching the method of claim 1, fails to explicitly teach the claimed, configuring, by the at least one processor, the ML pipeline using the configuration instruction, the configuring comprising: determining at least one ML processing element configured to perform the at least one NLP operation.
However, Eckart does teach the claimed configuring, by the at least one processor, the ML pipeline using the configuration instruction, the configuring comprising: determining at least one ML processing element configured to perform the at least one NLP operation ( Eckart: Section 3.2, paragraph 4, listing 1 illustrates codes for executable NLP pipeline, where lines 1-10 identify the components used in the pipeline by name and version. Lines 12-19 are necessary boilerplate code making the components accessible within the script . Lines 21-29 to assemble and run a pipeline consisting of components ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Eckart’s teaching of combining natural language processing (NLP) tools and resources into processing pipelines, into the system and method of configuration-based data flow pipelines, taught by Pai, because, by implementing NLP pipelines supported by a broad-coverage collection of interoperable NLP components, would allow components to select suitable resources at runtime and to obtain them automatically from the repository (Eckart [Section 5]).
Regarding Claim 2, Pai in view of Eckart teach the method of claim 1. Pai further teaches, wherein the determining comprises identifying the at least one plain-language indicator within an NLP configuration schema ( Pai: Column 18, lines 2-5, “preprocess” is a plain language indicator).
Regarding Claim 3, Pai in view of Eckart teach the method of claim 1. Pai further teaches, wherein the loading and the NLP are facilitated by communicating, by the at least one processor, with the at least one ML processing element through at least one application programming interface (API) ( Pai: Column 11, lines 56-67, user can develop an application programming interface ( API) associated with framework module 230 to communicate between the components over network).
Regarding Claim 5, Pai in view of Eckart teach the method of claim 1. Pai further teaches, wherein the at least one option for configuring the at least one NLP operation ( Pai: Column 18, lines 2-18, “stemming”, “tokenization” are plain language indicator ( NLP parameter) which define the option of NLP operation for pre-processing operation).
Regarding Claim 7, Pai in view of Eckart teach the method of claim 5. Pai further teaches, wherein the NLP comprises processing the text data according to the at least one NLP parameter ( Pai: Column 18, lines 2-18, “stemming”, “tokenization” are plain language indicator which define NLP parameters for pre-processing operation. Column 5, lines 41-46, processing text data into tokens, indicated by the parameter tokenization ).
Claims 4, 6 and 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pai et al. ( US 12,242,543 B1), hereinafter referenced as Pai, in view of Eckart et al. (A broad-coverage collection of portable NLP components for building shareable analysis pipelines, Proceedings of the Workshop on Open Infrastructures and Analysis Frameworks, August 23rd, 2014), hereinafter referenced as Eckart, further in view of Ben Shahar et al. (US 20230385541 A1), hereinafter referenced as Ben Shahar.
Regarding Claim 4, Pai in view of Eckart teach the method of claim 3. Pai in view of Eckart fail to explicitly teach the claimed, wherein the loading comprises configuring code for communication through the at least one API according to the at least one NLP operation indicated by the at least one plain-language indicator.
However, Ben Shahar does teach the claimed, wherein the loading comprises configuring code for communication through the at least one API according to the at least one NLP operation indicated by the at least one plain-language indicator ( Ben Shahar: Para.[0021], [0204],[0205], the configuration code shows that API is used to select NLP tasks and execute).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ben Shahar’s teaching of a system and method for processing a text using a variety of machine learning (ML) and/or natural language processing (NLP) models, into the system and method of configuration-based data flow pipelines, taught by Pai in view of Eckart, because, this would provide a standardized/shared protocol or format that enables flexible organization and execution of various NLP tasks. (Ben Shahar,[ Para.[0021]).
Claim 11 is a method claim performing the steps in system claim 4 above and as such, claim 11 is similar in scope and content to claim 4 and therefore, claim 11 is rejected under similar rationale as presented against claim 4 above.
Regarding Claim 6, Pai in view of Eckart teach the method of claim [[5]] 1. Pai in view of Eckart fail to explicitly teach the claimed, wherein the configuring the at least one ML processing element translating the at least one plain-language indicator into code configured to perform the at least one NLP operation.
However, Ben Shahar does teach the claimed, wherein the configuring the at least one ML processing element translating the at least one plain-language indicator into code configured to perform the at least one NLP operation ( Ben Shahar: Para.[0202]-[0205], a user may insert a text, in this case a link to an online article. The text may be converted into code using any one of JSON, phyton code, to create a first NLP object suitable for use as an input for the computer implemented method for enabling streamlined text language processing utilizing a plurality of ML and/or NLP models for an HTML extracted article type document. The user may then select NLP tasks that he would like to be executed on the text as well as a pipeline of the tasks).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ben Shahar’s teaching of a system and method for processing a text using a variety of machine learning (ML) and/or natural language processing (NLP) models, into the system and method of configuration-based data flow pipelines, taught by Pai in view of Eckart, because, this would provide a standardized/shared protocol or format that enables flexible organization and execution of various NLP tasks. (Ben Shahar,[ Para.[0021]).
Regarding Claim 8, Pai teaches a method comprising:
obtaining, by at least one processor, a configuration instruction configured to direct operations of a natural language processing (NLP) machine learning (ML) pipeline ( Pai: Column 10, lines 20-27, 43-52, Fig. 2, processor 213. Framework module 230 may be configured to read configuration files 240, to manage execution of pipeline 290. Column 16, lines 46-47, it could be ML pipeline. Column 17, lines1-4, data processing techniques could be NLP techniques),
the configuration instruction comprising at least one plain-language indicator of at least one NLP operation to be performed by the ML pipeline( Pai: Column 18, lines 2-10, configuration instruction shows in plain language, such as “preprocess”) ,
and at least one plain-language indicator of at least one of: an option for configuring the at least one NLP operation, and an instruction of the at least one NLP operation ( Pai: Column 17, lines 50-62, column 18, lines 4-18, Fig.1, the stage “preprocess” in the configuration file states “tokenization” and “stemming”, which are the steps of preprocessing and by stating them in the configuration file is giving the user the instruction of the NLP operation by the Preprocess components which is a collection of Model- Data Preprocessing components ( corresponding to preprocess components 181) that are used to convert the raw data into a clean data set );
and performing, by the at least one processor, NLP on text data using the configured ML pipeline, the NLP including executing the code ( Pai: Column 5, lines 42-50, column 17, column 12, lines 51-59, Figs. 1, 2, preprocess components 181 may convert data into a clean data set. Pipeline configuration file 242, preconfigured in such a way that it causes execution of a specified set of stages (e.g., "data transform," "preprocess," "predict," and "persist"). Column 17, lines1-4, data processing techniques could be NLP techniques).
Pai while teaching the method of claim 8, fails to explicitly teach the claimed, configuring, by at least one processor, the ML pipeline using the configuration instruction, the configuring comprising translating the at least one plain language indicator into code configured to perform the at least one NLP operation according to the at least one of the option for configuring the at least one NLP operation and the instruction of the at least one NLP operation.
However, Eckart does teach the claimed configuring, by at least one processor, the ML pipeline using the configuration instruction, the configuring comprising [translating the at least one plain language indicator into code] configured to perform the at least one NLP operation ( Eckart: Section 3.2, paragraph 4, listing 1 illustrates codes for executable NLP pipeline, where lines 1-10 identify the components used in the pipeline by name and version. Lines 12-19 are necessary boilerplate code making the components accessible within the script . Lines 21-29 to assemble and run a pipeline consisting of components ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Eckart’s teaching of combining natural language processing (NLP) tools and resources into processing pipelines, into the system and method of configuration-based data flow pipelines, taught by Pai, because, by implementing NLP pipelines supported by a broad-coverage collection of interoperable NLP components, would allow components to select suitable resources at runtime and to obtain them automatically from the repository (Eckart [Section 5]).
Pai in view of Eckart while teaching the method of claim 8, fails to explicitly teach the claimed, configuring, by the at least one processor, the ML pipeline using the configuration instruction, the configuring comprising translating the at least one plain-language indicator into code configured to perform the at least one NLP operation according to the at least one of the option for configuring the at least one NLP operation and the instruction of the at least one NLP operation.
However, Ben Shahar does teach the claimed, configuring, by the at least one processor, the ML pipeline using the configuration instruction, the configuring comprising translating the at least one plain-language indicator into code configured to perform the at least one NLP operation according to the at least one of the option for configuring the at least one NLP operation and the instruction of the at least one NLP operation ( Ben Shahar: Para.[0068], Figs. 3A, 3B illustrates pipeline of the disclosed computer implemented method for enabling streamlined text language processing utilizing a plurality of ML and/or NLP models. Para.[0184]- [0186], the text ( plain language indicator) can be covered into code using any one of JSON, phyton, CURL to create a first NLP object suitable for use as an input for the computer implemented method. The user can select NLP tasks (from multiple tasks ) to be executed on the text as well as a pipeline (hierarchy) of the tasks);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ben Shahar’s teaching of a system and method for processing a text using a variety of machine learning (ML) and/or natural language processing (NLP) models, into the system and method of configuration-based data flow pipelines, taught by Pai in view of Eckart, because, this would provide a standardized/shared protocol or format that enables flexible organization and execution of various NLP tasks. (Ben Shahar,[ Para.[0021]).
Claim 9 is a method claim performing the steps in system claim 2 above and as such, claim 9 is similar in scope and content to claim 2 and therefore, claim 9 is rejected under similar rationale as presented against claim 2 above.
Claim 10 is a method claim performing the steps in system claim 3 above and as such, claim 10 is similar in scope and content to claim 3 and therefore, claim 10 is rejected under similar rationale as presented against claim 3 above.
Claim 12 is a method claim performing the steps in system claim 5 above and as such, claim 12 is similar in scope and content to claim 5 and therefore, claim 12 is rejected under similar rationale as presented against claim 5 above.
Claim 13 is a method claim performing the steps in system claim 6 above and as such, claim 13 is similar in scope and content to claim 6 and therefore, claim 13 is rejected under similar rationale as presented against claim 6 above.
Claim 14 is a method claim performing the steps in system claim 7 above and as such, claim 14 is similar in scope and content to claim 7 and therefore, claim 14 is rejected under similar rationale as presented against claim 7 above.
Regarding Claim 15, Pai teaches a system comprising:
at least one processor ( Pai: Column 10, lines 20-27, Fig. 2, processor 213);
and at least one non-transitory computer-readable memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform processing comprising ( Pai: Column 25: lines 24-33, non-transitory computer-readable memory storing instructions and executed by one or more processors) :
obtaining a configuration instruction configured to direct operations of a natural language processing (NLP) machine learning (ML) pipeline ( Pai: Column 10, lines 20-27, 43-52, Fig. 2, processor 213. Framework module 230 may be configured to read configuration files 240, to manage execution of pipeline 290. Column 16, lines 46-47, it could be ML pipeline. Column 17, lines1-4, data processing techniques could be NLP techniques),
the configuration instruction comprising at least one plain-language indicator of at least one NLP operation to be performed by the ML pipeline( Pai: Column 18, lines 2-10, configuration instruction shows in plain language, such as “preprocess”),
and at least one plain-language indicator of at least one of: an option for configuring the at least one NLP operation, and an instruction of the at least one NLP operation ( Pai: Column 17, lines 50-62, column 18, lines 4-18, Fig.1, the stage “preprocess” in the configuration file states “tokenization” and “stemming”, which are the steps of preprocessing and by stating them in the configuration file is giving the user the instruction of the NLP operation by the Preprocess components which is a collection of Model- Data Preprocessing components ( corresponding to preprocess components 181) that are used to convert the raw data into a clean data set );
[[and]] loading the at least one ML processing element into the ML pipeline ( Pai: Column 17, lines 55-59, Fig. 3A illustrates pipeline config file, which shows “preprocess”. Column 12, lines 51-59, Fig. 2, pipeline configuration file 242, preconfigured in such a way that it causes execution of a specified set of stages (e.g., "data transform," "preprocess," "predict," and "persist")),
and configuring the at least one ML processing element according to the at least one of the option for configuring the at least one NLP operation and the instruction of the at least one NLP operation ( Pai: Column 17, lines 50-62, column 18, lines 4-18, Fig.1, the stage “preprocess” in the configuration file states “tokenization” and “stemming”, which are the steps of preprocessing and by stating them in the configuration file is giving the user the instruction of the NLP operation by the Preprocess components which is a collection of Model- Data Preprocessing components ( corresponding to preprocess components 181) that are used to convert the raw data into a clean data set ),
and performing NLP on text data using the configured ML pipeline, the NLP including at least one of processing by the at least one ML processing element and executing the code ( Pai: Column 5, lines 42-50, column 17, column 12, lines 51-59, Figs. 1, 2, preprocess components 181 may convert data into a clean data set. Pipeline configuration file 242, preconfigured in such a way that it causes execution of a specified set of stages (e.g., "data transform," "preprocess," "predict," and "persist"). Column 17, lines1-4, data processing techniques could be NLP techniques).
Pai while teaching the system of claim 15, fails to explicitly teach the claimed, configuring the ML pipeline using the configuration instruction, the configuring comprising at least one of: determining at least one ML processing element configured to perform the at least one NLP operation.
However, Eckart does teach the claimed, configuring the ML pipeline using the configuration instruction, the configuring comprising at least one of: determining at least one ML processing element configured to perform the at least one NLP operation ( Eckart: Section 3.2, paragraph 4, listing 1 illustrates codes for executable NLP pipeline, where lines 1-10 identify the components used in the pipeline by name and version. Lines 12-19 are necessary boilerplate code making the components accessible within the script . Lines 21-29 to assemble and run a pipeline consisting of components ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Eckart’s teaching of combining natural language processing (NLP) tools and resources into processing pipelines, into the system and method of configuration-based data flow pipelines, taught by Pai, because, by implementing NLP pipelines supported by a broad-coverage collection of interoperable NLP components, would allow components to select suitable resources at runtime and to obtain them automatically from the repository (Eckart [Section 5]).
Pai in view of Eckart while teaching the system of claim 15, fails to explicitly teach the claimed, and translating the at least one plain-language indicator into code configured to perform the at least one NLP operation according to the at least one of the option for configuring the at least one NLP operation and the instruction of the at least one NLP operation
However, Ben Shahar does teach the claimed, and translating the at least one plain-language indicator into code configured to perform the at least one NLP operation according to the at least one of the option for configuring the at least one NLP operation and the instruction of the at least one NLP operation ( Ben Shahar: Para.[0068], Figs. 3A, 3B illustrates pipeline of the disclosed computer implemented method for enabling streamlined text language processing utilizing a plurality of ML and/or NLP models. Para.[0184]- [0186], the text ( plain language indicator) can be covered into code using any one of JSON, phyton, CURL to create a first NLP object suitable for use as an input for the computer implemented method. The user can select NLP tasks (from multiple tasks ) to be executed on the text as well as a pipeline (hierarchy) of the tasks);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Ben Shahar’s teaching of a system and method for processing a text using a variety of machine learning (ML) and/or natural language processing (NLP) models, into the system and method of configuration-based data flow pipelines, taught by Pai in view of Eckart, because, this would provide a standardized/shared protocol or format that enables flexible organization and execution of various NLP tasks. (Ben Shahar,[ Para.[0021]).
Claim 16 is a system claim performing the steps in system claim 2 above and as such, claim 16 is similar in scope and content to claim 2 and therefore, claim 16 is rejected under similar rationale as presented against claim 2 above.
Claim 17 is a system claim performing the steps in system claim 3 above and as such, claim 17 is similar in scope and content to claim 3 and therefore, claim 17 is rejected under similar rationale as presented against claim 3 above.
Claim 18 is a system claim performing the steps in system claim 5 above and as such, claim 18 is similar in scope and content to claim 5 and therefore, claim 18 is rejected under similar rationale as presented against claim 5 above.
Claim 19 is a system claim performing the steps in system claim 6 above and as such, claim 19 is similar in scope and content to claim 6 and therefore, claim 19 is rejected under similar rationale as presented against claim 6 above.
Claim 20 is a system claim performing the steps in system claim 7 above and as such, claim 20 is similar in scope and content to claim 7 and therefore, claim 20 is rejected under similar rationale as presented against claim 7 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADIRA SULTANA whose telephone number is (571)272-4048. The examiner can normally be reached M-F,7:30 am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D. Shah can be reached on (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NADIRA SULTANA/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
04/21/2026
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