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 .
This action is responsive to the Application filed on 04/30/2024. Claims 1-20 are pending in the case. Claims 1, 10, and 19 are independent claims.
Claim Rejections - 35 U.S.C. § 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 an abstract idea without significantly more.
Step 1: Claims 1-9 are directed towards a process. Claims 10-18 are directed towards a system. Claims 19 and 20 are directed towards a process. Therefore, claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 1:
2A Prong 1:
"cleansing the data stream by identifying and rectifying one or more of an error, inconsistency, or missing value, producing a cleansed data stream" This constitutes a mental process because cleansing data by identifying and rectifying errors can be done with pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“enriching the cleansed data stream … producing a transformed data stream;” This constitutes a mental process because one could perform enrichment using evaluation. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“detecting a trigger event;” This constitutes a mental process because one could perform detecting a trigger event using observation. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“and initiating fine-tuning of a target machine learning model with the transformed data in response to the trigger event.” This constitutes a mental process because one could perform initiating fine-tuning of a target machine learning model with the transformed data in response to the trigger event mentally or by using a pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
"receiving a data stream associated with application deployment, wherein the data stream is a continuous sequence of data produced over time" This constitutes an insignificant extra-solution activity in the form of mere data gathering, which does not integrate the abstract idea into a practical application, see MPEP 2106.05(g).
“saving the transformed data stream to a repository as transformed data;” This constitutes an insignificant extra-solution activity in the form of mere data outputting, which does not integrate the abstract idea into a practical application, see MPEP 2106.05(g).
“…with one or more machine learning models…” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Re-evaluation of Insignificant Extra-Solution Activities:
"receiving a data stream associated with application deployment, wherein the data stream is a continuous sequence of data produced over time" “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well-understood, routine, conventional activity when claimed in a merely generic manner (as it is in the present claim), as discussed in MPEP § 2106.05(d)(II).
“saving the transformed data stream to a repository as transformed data;” “Storing and retrieving information in memory” is a well-understood, routine, conventional activity when claimed in a merely generic manner (as it is in the present claim), as discussed in MPEP § 2106.05(d)(II).
Additional elements:
“…with one or more machine learning models…” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
With respect to claim 2:
2A Prong 1:
" The method of claim 1, wherein enriching the cleansed data stream … comprises adding one or more pseudo labels to the cleansed data stream.” This constitutes a mental process because enrichment and adding labels can be done with pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“…with one or more machine learning models…” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“…with one or more machine learning models…” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
With respect to claim 3:
2A Prong 1:
" The method of claim 1, wherein cleansing and enriching the data stream is performed in real time as the data stream is received.” This constitutes a mental process cleansing and enriching the data stream is performed in real time can be done with evaluation or pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4:
2A Prong 1:
“and adding the supplemental machine learning model to the one or more machine learning models. This constitutes a mental process and adding the supplemental machine learning model to the one or more machine learning models can be done with pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“receiving a supplemental machine learning model through a side input; “This constitutes an insignificant extra-solution activity in the form of mere data gathering, which does not integrate the abstract idea into a practical application, see MPEP 2106.05(g).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Re-evaluation of Insignificant Extra-Solution Activities:
“receiving a supplemental machine learning model through a side input;” “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well-understood, routine, conventional activity when claimed in a merely generic manner (as it is in the present claim), as discussed in MPEP § 2106.05(d)(II).
With respect to claim 5:
2A Prong 1:
"The method of claim 1, further comprising: assigning data in the data stream to a class;” This constitutes a mental process because assigning data in the data stream to a class can be done with pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“and forwarding the data in the data stream to at least one of the one or more machine learning models associated with the class.” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“and forwarding the data in the data stream to at least one of the one or more machine learning models associated with the class.” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
With respect to claim 6:
2A Prong 1:
"The method of claim 1, wherein detecting the trigger event further comprises: “This constitutes a mental process because detecting the trigger event can be done with an evaluation or pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“and determining that negative user feedback satisfies a threshold.” This constitutes a mental process because determining that negative user feedback satisfies a threshold can be done with an evaluation or pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“receiving user feedback associated with an output of a target machine learning model;.” This constitutes an insignificant extra-solution activity in the form of mere data outputting, which does not integrate the abstract idea into a practical application, see MPEP 2106.05(g).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Re-evaluation of Insignificant Extra-Solution Activities:
“receiving user feedback associated with an output of a target machine learning model;.” “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well-understood, routine, conventional activity when claimed in a merely generic manner (as it is in the present claim), as discussed in MPEP § 2106.05(d)(II).
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception. Inherits the abstract ideas from claim 1, via dependency.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“receiving user feedback associated with an output of a target machine learning model;” This constitutes an insignificant extra-solution activity in the form of mere data outputting, which does not integrate the abstract idea into a practical application, see MPEP 2106.05(g).
“and initiating the fine-tuning with the output and the user feedback as a label.” (High level machine learning. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Re-evaluation of Insignificant Extra-Solution Activities:
“receiving user feedback associated with an output of a target machine learning model;.” “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is a well-understood, routine, conventional activity when claimed in a merely generic manner (as it is in the present claim), as discussed in MPEP § 2106.05(d)(II).
Additional elements:
“and initiating the fine-tuning with the output and the user feedback as a label.” (High level machine learning. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
With respect to claim 8:
2A Prong 1:
"The method of claim 1, … configured to output a natural language summary of an operational event and a potential root cause”. This constitutes a mental process because output a natural language summary of an operational event and a potential root cause can be done with an evaluation or pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“…wherein the target machine learning model is a large language model…”. Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“…wherein the target machine learning model is a large language model…”. Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
With respect to claim 9:
2A Prong 1: This claim is directed to a judicial exception. Inherits the abstract ideas from claim 1, 8, via dependency.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“The method of claim 8, wherein the operational event is a rollback of the application deployment.” (Applying the method wherein the operational event is a rollback of the application deployment merely indicates the technological environment in which to apply a judicial exception, as discussed in MPEP § 2106.05(h))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“The method of claim 8, wherein the operational event is a rollback of the application deployment.” (Applying the method wherein the operational event is a rollback of the application deployment merely indicates the technological environment in which to apply a judicial exception, as discussed in MPEP § 2106.05(h))
With respect to claim 10:
See the rejection for claim 1 above. Note that the primary difference between claim 1 and claim 10 is that claim 1 is directed towards a method whereas claim 10 is directed towards a system that performs that method. The minute differences in phrasing and wording used do not change the analysis of judicial exceptions provided above.
The only additional element in claim 10 is analyzed as follows:
“A system, comprising: at least one processor; and at least one memory coupled to the at least one processor that stores instructions, that when executed by the at least one processor, cause the system to:” Adding generic computer components to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
With respect to claims 11-18:
See the rejections above for claims 2-9 respectively. Note that the primary difference between claims 2-9 and claims 11-18 is that claims 2-9 are directed towards a method whereas claims 11-18 are directed towards a system that performs that method. The minute differences in phrasing and wording used do not change the analysis of judicial exceptions provided above. Any of claims 11-18 that recite the additional limitation of “A system, comprising: at least one processor; and at least one memory coupled to the at least one processor that stores instructions, that when executed by the at least one processor, cause the system to:..” are directed to the analysis of the generic computer components provided in the rejection for claim 10 above.
With respect to claim 19:
2A Prong 1:
"cleansing the data stream by identifying and rectifying one or more of an error, inconsistency, or missing value, producing a cleansed data stream" This constitutes a mental process because cleansing data by identifying and rectifying errors can be done with pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“enriching the cleansed data stream … producing a transformed data stream;” This constitutes a mental process because one could perform enrichment using evaluation. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“detecting a trigger event;” This constitutes a mental process because one could perform detecting a trigger event using observation. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“…wherein the large language model is configured to output a natural language summary of an operational event and a potential root cause.” This constitutes a mental process because output a natural language summary of an operational event and a potential root cause can be done with an evaluation or pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
“and initiating fine-tuning of a target machine learning model with the transformed data in response to the trigger event.” This constitutes a mental process because one could perform initiating fine-tuning of a target machine learning model with the transformed data in response to the trigger event mentally or by using a pen and paper. See MPEP 2106.04(a)(2). In general, examples of mental processes include observations, evaluations, judgments, and opinions. Furthermore, the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
"receiving a data stream associated with application deployment, wherein the data stream is a continuous sequence of data produced over time" This constitutes an insignificant extra-solution activity in the form of mere data gathering, which does not integrate the abstract idea into a practical application, see MPEP 2106.05(g).
“saving the transformed data stream to a repository as transformed data;” This constitutes an insignificant extra-solution activity in the form of mere data outputting, which does not integrate the abstract idea into a practical application, see MPEP 2106.05(g).
“…with one or more machine learning models…” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Re-evaluation of Insignificant Extra-Solution Activities:
"receiving a data stream associated with application deployment, wherein the data stream is a continuous sequence of data produced over time" “Receiving or transmitting data over a network, e.g., using the Internet to gather data“ is a well-understood, routine, conventional activity when claimed in a merely generic manner (as it is in the present claim), as discussed in MPEP § 2106.05(d)(II).
“saving the transformed data stream to a repository as transformed data;” “Storing and retrieving information in memory” is a well-understood, routine, conventional activity when claimed in a merely generic manner (as it is in the present claim), as discussed in MPEP § 2106.05(d)(II).
Additional elements:
“…with one or more machine learning models…” Adding generic one or more machine learning models to perform the method is not sufficient. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f))
With respect to claim 20:
See the rejection for claims 6 and 19 above.
Claim Rejections - 35 U.S.C. § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention absent any evidence to the contrary. Applicant are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-6, 10-15 are rejected under 35 U.S.C. § 103 as being unpatentable over Ramamurthy et al. (U.S. Pat. Pub. No. 2022/0405619 A1, hereinafter Ramamurthy) in view of Chen et al. (U.S. Pat. Pub. No. US2025/0077792 A1, hereinafter Chen).
As to independent claim 1, Ramamurthy discloses
receiving a data stream associated with application deployment, wherein the data stream is a continuous sequence of data produced over time (Paragraph 24, " Some embodiments of the present disclosure are directed to receiving a large pool of stream data from various data sources…" Paragraph 70, "The raw training data 204 may include any type of raw data that may be used by the training component 209 to generate the trained model 210. The raw training data 204 may be collected, generated, and/or obtained from any number of sources, such as files, databases, data stores, sensors, and/or other sources of structured, semi-structured, or non-structured data."; The examiner interrupts this as encompassing application deployment)
cleansing the data stream by identifying and rectifying one or more of an error, inconsistency, or missing value, producing a cleansed data stream (Paragraph 72, "During preprocessing, the raw training data 204 may undergo various processes, such as data cleaning, data integration, data scaling, normalization, data transformation, data reduction, and/or the like. For example, when the raw training data 204 includes inaccurate or missing data, the rows with missing data may be ignored (or deleted) when generating the training data 206 (in examples where the number of missing values is not too large), the missing data may be manually filled (in examples where the number of missing values is not too large to create an unreasonable task), and/or the missing values may be filled using computed values.");
saving the transformed data stream to a repository as transformed data (Paragraph 4, " This stream data (along with machine learning model predictions) can be stored to a computer object that stores time-based streaming events (e.g., a journal).");
detecting a trigger event (Paragraph 134, "As described above, in some embodiments, timestamp data of the journal data structure 600 of FIG. 6 is used to generate a machine learning model estimate or prediction based on a triggering event or other policy/rule.);
and initiating fine-tuning of a target machine learning model with the transformed data in response to the trigger event (Paragraph 23, " At least partially in response to receiving a triggering event (e.g., an HTTP request), an estimate is generated via the trained machine learning model using the set of stream data as input. In some embodiments, the estimate itself is stored to a second computer object (e.g., another journal) that records estimates of the trained machine learning model. The first computer object and/or the second computer object can be scanned in order to detect an anomaly (e.g., data drift or model drift) with the set of stream data and/or the estimate. Based at least in part on the detection of the anomaly, some embodiments feed the set of stream data and/or the estimate to the trained machine learning model to update the trained machine learning model.").
Ramamurthy does not appear to expressly teach enriching the cleansed data stream with one or more machine learning models, producing a transformed data stream.
Chen teaches enriching the cleansed data stream with one or more machine learning models, producing a transformed data stream. (Paragraph 40, " Training the training data generator 156 to generate outputs associated with the content items 160 (e.g., pseudo label the content items) is described in more detail with reference to FIG. 2. The training data generator 156 labels the content items 160, generating input-output pairs. In some embodiments, the training data generator 156 trained by the training component 152 is an LLM.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the fine-tuning large language models for domain-specific environments of Chen such that the training time associated with training the machine learning model to learn domain-specific tasks is reduced (See Chen Paragraph 21).
As to dependent claim 2,
Ramamurthy does not appear to expressly teach the method of claim 1, wherein enriching the cleansed data stream with one or more machine learning models comprises adding one or more pseudo labels to the cleansed data stream.
Chen teaches enriching the cleansed data stream with one or more machine learning models comprises adding one or more pseudo labels to the cleansed data stream (Paragraph 40, " Training the training data generator 156 to generate outputs associated with the content items 160 (e.g., pseudo label the content items) is described in more detail with reference to FIG. 2. The training data generator 156 labels the content items 160, generating input-output pairs. In some embodiments, the training data generator 156 trained by the training component 152 is an LLM.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the fine-tuning large language models for domain-specific environments of Chen such that the training time associated with training the machine learning model to learn domain-specific tasks is reduced (See Chen Paragraph 21).
As to dependent claim 3, Ramamurthy as modified discloses “The method of claim 1, wherein cleansing and enriching the data stream is performed in real time as the data stream is received.” (Paragraph 101, " In some embodiments, each of the data sources 301 produce, to the stream filtering component 302, its data in real time or near real-time. The term “real time” includes any time frame of sufficiently short duration as to provide reasonable response time for information processing as described. Additionally, the term “real time” includes what is commonly termed “near real-time,” generally any time frame of sufficiently short duration as to provide reasonable response time for on-demand information processing as described (e.g., within a portion of a second or within a few seconds).
As to dependent claim 4, Ramamurthy as modified discloses
The method of claim 1, further comprising:
receiving a supplemental machine learning model through a side input (Claim 8, "...and mapping a second set of stream data of the larger pool of streaming data to a second machine learning model");
and adding the supplemental machine learning model to the one or more machine learning models. (Claim 8, "... the second machine learning model being a copy of the trained machine learning model, and wherein a second estimation of the second machine learning model is based at least in part on the mapping of the second set of stream data")
As to dependent claim 5, Ramamurthy as modified discloses
The method of claim 1, further comprising:
assigning data in the data stream to a class (Paragraph 73, "… similar data may be grouped together (e.g., into the same cluster or class)...”)
and forwarding the data in the data stream to at least one of the one or more machine learning models associated with the class. (Paragraph 73, "When the raw training data 204 includes noisy data, the data preprocessing may include data binning, clustering, employing a machine learning model, and/or manual removal. For example, substantially continuous data from the raw training data 204 can be grouped together into a smaller number of “bins” (e.g., if the raw training data 204 includes every age from 0-100, the ages may be “binned” into groups of ages at five year intervals). As another example, similar data may be grouped together (e.g., into the same cluster or class), and/or a machine learning model (such as a regression algorithm) may be used to smooth the raw training data 204.")
As to dependent claim 6, Ramamurthy as modified discloses
The method of claim 1, wherein detecting the trigger event further comprises: receiving user feedback associated with an output of a target machine learning model (Paragraph 76, "The trained model 210 may be generated by the training component 209 using the raw training data 204 and/or the training data 206. Once it is determined that the trained model 210 has acceptable accuracy (e.g., the loss or distance is acceptable based on a loss function), the trained model 210 may be deployed (e.g., as the deployed model 224). The determination that a trained model 210 has acceptable accuracy or confidence may include a threshold accuracy, such as, for example and without limitation, 80%, 90%, 98%, etc. The threshold accuracy may be predefined by the model training system 202, or may be user defined.");
and determining that negative user feedback satisfies a threshold. (Paragraph 76, "The trained model 210 may be generated by the training component 209 using the raw training data 204 and/or the training data 206. Once it is determined that the trained model 210 has acceptable accuracy (e.g., the loss or distance is acceptable based on a loss function), the trained model 210 may be deployed (e.g., as the deployed model 224). The determination that a trained model 210 has acceptable accuracy or confidence may include a threshold accuracy, such as, for example and without limitation, 80%, 90%, 98%, etc. The threshold accuracy may be predefined by the model training system 202, or may be user defined.")
As to independent claim 10, Ramamurthy as modified discloses
A system, comprising: at least one processor (Paragraph 29, " For instance, various functions may be carried out by a processor executing instructions stored in memory.");
and at least one memory coupled to the at least one processor that stores instructions, that when executed by the at least one processor, cause the system to (Claim 10, " A system comprising: one or more processors; and one or more computer storage media storing computer-useable instructions that, when used by the one or more processors, cause the one or more processors to perform a method, the method comprising: "):
receive a data stream associated with application deployment, wherein the data stream is a continuous sequence of data produced over time (Paragraph 24, " Some embodiments of the present disclosure are directed to receiving a large pool of stream data from various data sources…" Paragraph 70, "The raw training data 204 may include any type of raw data that may be used by the training component 209 to generate the trained model 210. The raw training data 204 may be collected, generated, and/or obtained from any number of sources, such as files, databases, data stores, sensors, and/or other sources of structured, semi-structured, or non-structured data.");
cleanse the data stream by identifying and rectifying one or more of an error, inconsistency, or missing value, producing a cleansed data stream (Paragraph 72, "During preprocessing, the raw training data 204 may undergo various processes, such as data cleaning, data integration, data scaling, normalization, data transformation, data reduction, and/or the like. For example, when the raw training data 204 includes inaccurate or missing data, the rows with missing data may be ignored (or deleted) when generating the training data 206 (in examples where the number of missing values is not too large), the missing data may be manually filled (in examples where the number of missing values is not too large to create an unreasonable task), and/or the missing values may be filled using computed values.");
enrich the cleansed data stream with one or more machine learning models, producing a transformed data stream (“Paragraph 75, "The training component 209 may use the raw training data 204 and/or the training data 206 to generate the trained model 210." Paragraph 67, " Reference to output data may include data output by a trained machine learning model 210.");
save the transformed data stream to a repository as transformed data (Paragraph 4, " This stream data (along with machine learning model predictions) can be stored to a computer object that stores time-based streaming events (e.g., a journal).");
detect a trigger event (Paragraph 134, "As described above, in some embodiments, timestamp data of the journal data structure 600 of FIG. 6 is used to generate a machine learning model estimate or prediction based on a triggering event or other policy/rule.);
and initiate fine-tuning of a target machine learning model with the transformed data in response to the trigger event. (Paragraph 23, " At least partially in response to receiving a triggering event (e.g., an HTTP request), an estimate is generated via the trained machine learning model using the set of stream data as input. In some embodiments, the estimate itself is stored to a second computer object (e.g., another journal) that records estimates of the trained machine learning model. The first computer object and/or the second computer object can be scanned in order to detect an anomaly (e.g., data drift or model drift) with the set of stream data and/or the estimate. Based at least in part on the detection of the anomaly, some embodiments feed the set of stream data and/or the estimate to the trained machine learning model to update the trained machine learning model.")
As to dependent claim 11,
Ramamurthy does not appear to expressly teach the system of claim 10, wherein enrich the cleansed data stream with one or more machine learning models comprises addition of one or more pseudo labels to the cleansed data stream.
Chen teaches to enrich the cleansed data stream with one or more machine learning models comprises addition of one or more pseudo labels to the cleansed data stream (Paragraph 40, " Training the training data generator 156 to generate outputs associated with the content items 160 (e.g., pseudo label the content items) is described in more detail with reference to FIG. 2. The training data generator 156 labels the content items 160, generating input-output pairs. In some embodiments, the training data generator 156 trained by the training component 152 is an LLM ").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the fine-tuning large language models for domain-specific environments of Chen such that the training time associated with training the machine learning model to learn domain-specific tasks is reduced (See Chen Paragraph 21).
As to dependent claim 12, Ramamurthy as modified discloses “The method of claim 1, wherein cleanse the data stream and enrich the cleansed data stream is performed in real time as the data stream is received.” (Paragraph 101, " In some embodiments, each of the data sources 301 produce, to the stream filtering component 302, its data in real time or near real-time. The term “real time” includes any time frame of sufficiently short duration as to provide reasonable response time for information processing as described. Additionally, the term “real time” includes what is commonly termed “near real-time,” generally any time frame of sufficiently short duration as to provide reasonable response time for on-demand information processing as described (e.g., within a portion of a second or within a few seconds).").
As to dependent claim 13, Ramamurthy as modified discloses
The system of claim 10, wherein the instructions further cause the system to: receive a supplemental machine learning model through a side input (Claim 8, "...and mapping a second set of stream data of the larger pool of streaming data to a second machine learning model");
and add the supplemental machine learning model to the one or more machine learning models. (Claim 8, "... the second machine learning model being a copy of the trained machine learning model, and wherein a second estimation of the second machine learning model is based at least in part on the mapping of the second set of stream data")
As to dependent claim 14, Ramamurthy as modified discloses
The system of claim 10, wherein the instructions further cause the system to: assign data in the data stream to a class (Paragraph 73, "… similar data may be grouped together (e.g., into the same cluster or class)...”);
and forward the data in the data stream to at least one of the one or more machine learning models associated with the class. (Paragraph 73, "When the raw training data 204 includes noisy data, the data preprocessing may include data binning, clustering, employing a machine learning model, and/or manual removal. For example, substantially continuous data from the raw training data 204 can be grouped together into a smaller number of “bins” (e.g., if the raw training data 204 includes every age from 0-100, the ages may be “binned” into groups of ages at five year intervals). As another example, similar data may be grouped together (e.g., into the same cluster or class), and/or a machine learning model (such as a regression algorithm) may be used to smooth the raw training data 204.")
As to dependent claim 15, Ramamurthy as modified discloses
The system of claim 10, wherein detect the trigger event further comprises (Paragraph 134, "As described above, in some embodiments, timestamp data of the journal data structure 600 of FIG. 6 is used to generate a machine learning model estimate or prediction based on a triggering event or other policy/rule.):
receive user feedback associated with an output of the target machine learning model (Paragraph 76, "The trained model 210 may be generated by the training component 209 using the raw training data 204 and/or the training data 206. Once it is determined that the trained model 210 has acceptable accuracy (e.g., the loss or distance is acceptable based on a loss function), the trained model 210 may be deployed (e.g., as the deployed model 224). The determination that a trained model 210 has acceptable accuracy or confidence may include a threshold accuracy, such as, for example and without limitation, 80%, 90%, 98%, etc. The threshold accuracy may be predefined by the model training system 202, or may be user defined.");
and determine that negative user feedback satisfies a threshold. (Paragraph 76, "The trained model 210 may be generated by the training component 209 using the raw training data 204 and/or the training data 206. Once it is determined that the trained model 210 has acceptable accuracy (e.g., the loss or distance is acceptable based on a loss function), the trained model 210 may be deployed (e.g., as the deployed model 224). The determination that a trained model 210 has acceptable accuracy or confidence may include a threshold accuracy, such as, for example and without limitation, 80%, 90%, 98%, etc. The threshold accuracy may be predefined by the model training system 202, or may be user defined.")
Claim 7 is rejected under 35 U.S.C. § 103 as being unpatentable over Ramamurthy et al. (U.S. Pat. Pub. No. 2022/0405619 A1, hereinafter Ramamurthy) in view of Chen et al. (U.S. Pat. Pub. No. US2025/0077792 A1, hereinafter Chen, further in view of Asirvatham et al. (WIPO Pat. Pub. No. 2024/112915 A2, hereinafter Asirvatham).
Ramamurthy as modified teaches the method of claim 1, further comprising: receiving user feedback associated with an output of the target machine learning model; (Paragraph 76, "The trained model 210 may be generated by the training component 209 using the raw training data 204 and/or the training data 206. Once it is determined that the trained model 210 has acceptable accuracy (e.g., the loss or distance is acceptable based on a loss function), the trained model 210 may be deployed (e.g., as the deployed model 224). The determination that a trained model 210 has acceptable accuracy or confidence may include a threshold accuracy, such as, for example and without limitation, 80%, 90%, 98%, etc. The threshold accuracy may be predefined by the model training system 202, or may be user defined.").
Ramamurthy does not appear to expressly teach receiving user feedback associated with an output of the target machine learning model.
Asirvatham teaches teach receiving user feedback associated with an output of the target machine learning model. (“Paragraph 34, "Continuing to refer to FIG.1, Processor 112 may use user feedback to train the models described above. For example, generative model 166 may be trained using past inputs and outputs of generative model. In some embodiments, if user feedback indicates that an output of the generative model was “bad,” then that output and the corresponding input may be removed from training data used to train generative model, and/or may be replaced with a value entered by, e.g., another user that represents an ideal output given the input the classifier originally received, permitting use in retraining, and adding to training data; in either case, classifier may be retrained with modified training data as described in further detail below. In some embodiments, training data of generative model 166 may include user feedback. " Paragraph 141, " For example, some embodiments can process users' requests to train the model using new stream data, update the model using new predictions, completely disregard a trained model and generate a new model based on a set of historical input data, and the like.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the system and apparatus for generating imaging information of Asirvatham such that the target model is optimized toward user preferences derived from the user feedback (See Asirvatham Paragraph 35).
Claims 8, 17, 19, 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Ramamurthy et al. (U.S. Pat. Pub. No. 2022/0405619 A1, hereinafter Ramamurthy) in view of Chen et al. (U.S. Pat. Pub. No. US2025/0077792 A1, hereinafter Chen), further in view of Kasap et al. (U.S. Pat. Pub. No. US 2024/0414048 A1, hereinafter Kasap).
As to dependent claim 8,
Ramamurthy teaches the method of claim 1, wherein the target machine learning model is a large language model configured to output a natural language summary of an operational event and a potential root cause.
Ramamurthy does not expressively teach that the target machine learning model is a large language model configured to output a natural language summary of an operational event and a potential root cause.
Kasap teaches teach that the target machine learning model is a large language model configured to output a natural language summary of an operational event and a potential root cause (Paragraph 5, " In particular, the disclosed technology is directed to using a pre-trained generative model to generate a summary of at least a part of the program instructions and to recommend missing function codes with fingerprint data for inserting into a code of the program instructions. Given the natural language summary and functions with fingerprint data referenced from a system log, the disclosed technology determines a root cause of a problem in the 5G, 6G, and other telecommunication networks. A result of analyzing a root cause includes references to a part of a code of program instructions.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the automated root cause determination using a pre-trained generative model of Kasap such that the generative model is enabled to determine, with efficiency and with accuracy, a root cause of a problem in the complex systems (See Kasap Paragraph 29).
As to dependent claim 17,
Ramamurthy does not expressively teach the system of claim 10, wherein the target machine learning model is a large language model that outputs a natural language summary of an operational event and a potential root cause.
Kasap teaches the target machine learning model is a large language model that outputs a natural language summary of an operational event and a potential root cause (Paragraph 5, " In particular, the disclosed technology is directed to using a pre-trained generative model to generate a summary of at least a part of the program instructions and to recommend missing function codes with fingerprint data for inserting into a code of the program instructions. Given the natural language summary and functions with fingerprint data referenced from a system log, the disclosed technology determines a root cause of a problem in the 5G, 6G, and other telecommunication networks. A result of analyzing a root cause includes references to a part of a code of program instructions.").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the automated root cause determination using a pre-trained generative model of Kasap such that the generative model is enabled to determine, with efficiency and with accuracy, a root cause of a problem in the complex systems (See Kasap Paragraph 29).
As to independent claim 19, Ramamurthy teaches,
A method, comprising: receiving a data stream associated with application deployment, wherein the data stream is a continuous sequence of operational data produced over time (Paragraph 24, " Some embodiments of the present disclosure are directed to receiving a large pool of stream data from various data sources…" Paragraph 70, "The raw training data 204 may include any type of raw data that may be used by the training component 209 to generate the trained model 210. The raw training data 204 may be collected, generated, and/or obtained from any number of sources, such as files, databases, data stores, sensors, and/or other sources of structured, semi-structured, or non-structured data.");
cleansing the data stream by identifying and rectifying one or more of an error, inconsistency, or missing value, producing a cleansed data stream in real time (Paragraph 72, "During preprocessing, the raw training data 204 may undergo various processes, such as data cleaning, data integration, data scaling, normalization, data transformation, data reduction, and/or the like. For example, when the raw training data 204 includes inaccurate or missing data, the rows with missing data may be ignored (or deleted) when generating the training data 206 (in examples where the number of missing values is not too large), the missing data may be manually filled (in examples where the number of missing values is not too large to create an unreasonable task), and/or the missing values may be filled using computed values.");
enriching the cleansed data stream with one or more machine learning models, producing a transformed data stream in real time (Paragraph 75, "The training component 209 may use the raw training data 204 and/or the training data 206 to generate the trained model 210." Paragraph 67, " Reference to output data may include data output by a trained machine learning model 210.");
saving the transformed data stream to a repository as transformed data (Paragraph 4, " This stream data (along with machine learning model predictions) can be stored to a computer object that stores time-based streaming events (e.g., a journal).");
detecting a trigger event (Paragraph 134, "As described above, in some embodiments, timestamp data of the journal data structure 600 of FIG. 6 is used to generate a machine learning model estimate or prediction based on a triggering event or other policy/rule.);
Ramamurthy does not expressively teach initiating fine-tuning of a large language model with the transformed data in response to the trigger event, wherein the large language model is configured to output a natural language summary of an operational event and a potential root cause.
Kasap teaches initiating fine-tuning of a large language model with the transformed data in response to the trigger event, wherein the large language model is configured to output a natural language summary of an operational event and a potential root cause (Paragraph 5, " In particular, the disclosed technology is directed to using a pre-trained generative model to generate a summary of at least a part of the program instructions and to recommend missing function codes with fingerprint data for inserting into a code of the program instructions. Given the natural language summary and functions with fingerprint data referenced from a system log, the disclosed technology determines a root cause of a problem in the 5G, 6G, and other telecommunication networks. A result of analyzing a root cause includes references to a part of a code of program instructions.")
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the automated root cause determination using a pre-trained generative model of Kasap such that the generative model is enabled to determine, with efficiency and with accuracy, a root cause of a problem in the complex systems (See Kasap Paragraph 29).
As to dependent claim 20, Ramamurthy as modified further teaches
The method of claim 19, wherein detecting the trigger event further comprises (Paragraph 134, "As described above, in some embodiments, timestamp data of the journal data structure 600 of FIG. 6 is used to generate a machine learning model estimate or prediction based on a triggering event or other policy/rule.):
receiving user feedback associated with the output of the large language model (Paragraph 76, "The trained model 210 may be generated by the training component 209 using the raw training data 204 and/or the training data 206. Once it is determined that the trained model 210 has acceptable accuracy (e.g., the loss or distance is acceptable based on a loss function), the trained model 210 may be deployed (e.g., as the deployed model 224). The determination that a trained model 210 has acceptable accuracy or confidence may include a threshold accuracy, such as, for example and without limitation, 80%, 90%, 98%, etc. The threshold accuracy may be predefined by the model training system 202, or may be user defined.");
and determining that negative user feedback satisfies a threshold.( Paragraph 76, "The trained model 210 may be generated by the training component 209 using the raw training data 204 and/or the training data 206. Once it is determined that the trained model 210 has acceptable accuracy (e.g., the loss or distance is acceptable based on a loss function), the trained model 210 may be deployed (e.g., as the deployed model 224). The determination that a trained model 210 has acceptable accuracy or confidence may include a threshold accuracy, such as, for example and without limitation, 80%, 90%, 98%, etc. The threshold accuracy may be predefined by the model training system 202, or may be user defined.")
Claims 9, 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Ramamurthy et al. (U.S. Pat. Pub. No. 2022/0405619 A1, hereinafter Ramamurthy), in view of Chen et al. (U.S. Pat. Pub. No. US2025/0077792 A1, hereinafter Chen), in view of in view of Kasap et al. (U.S. Pat. Pub. No. US 2024/0414048 A1, hereinafter Kasap, further in view of Agrawal et al. (U.S. Pat. Pub. No. 2023/0359449 A1, hereinafter Agrawal).
As to dependent claim 9,
Ramamurthy and Kasap do not expressively teach the method of claim 8, wherein the operational event is a rollback of the application deployment.
Agrawal teaches that the operational event is a rollback of the application deployment (Paragraph 33, "Furthermore, some embodiments may detect that application code in a production environment deployment tool had experienced a rollback operation, where program code in the production environment had been rolled back to an earlier version. Some embodiments may determine whether an execution of the rolled-back program code had failed one or more rules. In response to a determination that the rolled-back program code had failed one or more rules, some embodiments may update a machine learning model to increase the weights associated with the failed rule(s).").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the automated root cause determination using a pre-trained generative model of Kasap in addition to the learning-augmented application deployment pipeline of Agrawal such to reduce the risk of unready applications being deployed to a production environment (See Agrawal Paragraph 1).
As to dependent claim 18,
Ramamurthy and Kasap do not expressively teach the system of claim 17, wherein the operational event is a rollback of the application deployment.
Agrawal teaches that the operational event is a rollback of the application deployment (Paragraph 33, "Furthermore, some embodiments may detect that application code in a production environment deployment tool had experienced a rollback operation, where program code in the production environment had been rolled back to an earlier version. Some embodiments may determine whether an execution of the rolled-back program code had failed one or more rules. In response to a determination that the rolled-back program code had failed one or more rules, some embodiments may update a machine learning model to increase the weights associated with the failed rule(s).").
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the intelligent updating and data processing for deployed machine learning models of Ramamurthy to include the automated root cause determination using a pre-trained generative model of Kasap in addition to the learning-augmented application deployment pipeline of Agrawal such to reduce the risk of unready applications being deployed to a production environment (See Agrawal Paragraph 1).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action. Stewart et al. (US 11704387 B2) teaches a method to cleanse and sort incoming data streams as they arrive, rather than waiting for a batch process. The system turns raw text from different sources into a standard format by removing spacing issues, stray characters, and capitalization differences. Chaturvedi (US 20240054298 A1) teaches using embeddings and machine learning to classify user preferences into parent and child intent classes and to flag novel intents when a message deviates from class standards. Yang et al. (US 20230153307 A1) teaches receiving a labeled source-domain training dataset and a streaming target query in addition to training multiple independent prediction models on different source-target combinations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Misha D. Zatezalo whose telephone number is 571-272-0591. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.D.Z./
Examiner, Art Unit 2123
/ALEXEY SHMATOV/ Supervisory Patent Examiner, Art Unit 2123
