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 . Claims 1-20 are presented in the case.
Information Disclosure Statement
The information disclosure statement submitted on 02/26/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”)
Claims 1, 13 and 20 have the following abstract idea analysis.
Step 1: The claim is directed to “a method, apparatus and CRM”. The claims are directed to the statutory categories accordingly.
Step 2A Prong 1: claims recite the abstract idea limitations of "evaluating the plurality of machine learning models against the one or more objective functions to establish a comparative assessment of each of the plurality of machine learning models for the one or more objective functions; and providing the comparative assessment". These limitations include mental concepts (act of evaluating. Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2)). "comparing collected information to a predefined threshold, which is an act of evaluating information that can be practically performed in the human mind. The specification also provides example operations performed such as creating a score, ranking or rating. See USPGPUB ¶58. Other sections of the claims such as "a device", "accessing", "learning models" and "objective functions" are advanced processes, too generic or high level to be listed as a mental process judicial exception given the available descriptions and MPEP comparisons.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking ""a device", "learning models" and "objective functions" do not yield eligibility. Claims are still in line with mental concepts such as claim 1, 13 and 20 are not specific to a practical application. The additional elements as such are processors and instructions which do not include specialized hardware. See MPEP § 2106.05(f).
Claims 1, 13 and 20 do not include a particular field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying an model to a field or data without an advancement in the new field or new hardware is ineligible. MPEP § 2106.05(h).
Step 2B: The claims do not contain significantly more than their judicial exceptions. Processors, memory and other hardware are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claims lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations.
Regarding claims 2-12 and 14-19, they merely narrow the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claims 2-12 and 14-19 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 2-12 and 14-19.
With respect to step 2B These claims disclose similar limitations described for the independent claims above and do not provide anything significantly more than mathematical or mental concepts. Claims 2-12 and 14-19recites the additional elements of "creating one or more checkpoints during development of a specific machine learning model; and establishing one or more selectable machine learning model versions of the specific machine learning model that each correspond to a respective configuration of the specific machine learning model at a respective checkpoint of the one or more checkpoints, wherein the plurality of machine learning models comprise the one or more selectable machine learning model versions. tracking a lineage of each of the one or more selectable machine learning model versions. computing a respective metrics vector for the one or more selectable machine learning model versions at their respective checkpoint; and saving the respective metrics vector with the respective configuration for each of the one or more selectable machine learning model versions. wherein creating the one or more checkpoints is in response to a manual user selection. invoking each of the one or more objective functions against metrics vectors for configurations of each of the plurality of machine learning models to determine suitability of each of the plurality of machine learning models for each of the one or more objective functions. wherein the model selection process comprises one of either a user interface displaying the comparative assessment of each of the plurality of machine learning models for user selection or an auto-selection process based on a best comparative assessment according to a selected objective function of the one or more objective functions. wherein the plurality of machine learning models comprise either a plurality of disparate machine learning models, a plurality of different versions of a same machine learning model, or a combination of both. wherein the one or more objective functions are each associated with a respective specific utility. wherein each respective specific utility is selected from a group consisting of: accuracy; fairness; accuracy with fairness constraints; and performance. creating a catalog of different objective functions from which to select as the one or more objective functions against which the plurality of machine learning models are evaluated. wherein the comparative assessment of each of the plurality of machine learning models is selected from a group consisting of: a score; a ranking; a grade; and a tiered rating system." These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-12 and 14-19 also recites abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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, 6-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Achin et al. (US 20150339572 A1) hereinafter Achin in view of Steinberg (US 20080168011 A1)
As to independent claim 1, Achin teaches a method, comprising:
accessing, by a device, a plurality of machine learning models; [Accesses a library of modeling techniques ¶64-65 "exploration engine 110 may use the library 130 of modeling techniques to evaluate potential modeling solutions in the search space"]
determining, by the device, one or more objective functions for the plurality of machine learning models; [determines scores from objective functions for the models ¶127, ¶102 "a predictive model's expected performance on a prediction problem includes one or more expected scores (e.g., expected values of one or more objective functions)"]
evaluating, by the device, the plurality of machine learning models against the one or more objective functions to establish a comparative assessment of each of the plurality of machine learning models for the one or more objective functions; and [evaluates models for suitability assessment against each other (compares) using ranks ¶220, ¶116 "exploration engine 110 may select the fraction of the modeling procedures having the highest suitability ranks (e.g., in cases where the suitability scores for the modeling procedures are not available, but the ordering (ranking) of the modeling procedures' suitabilities is available)."]
selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions. [selects models with the highest score or threshold score ¶128 " space search engine 110 may select the model with the highest score, or any model having a score that exceeds a threshold score, or any model having a score within a specified range of the highest score."]
Wang does not specifically teach providing, by the device, the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions.
However, Steinberg teaches providing, by the device, the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions. [Fig. 7 illustrates a comparative table with assessments (test) and models for choosing highest ¶50 "chart that displays selected rows from a table 710 summarizing the test results for a set of pruned decision trees. Column 720 identifies each model with a number. Column 730 contains the size of the model measured by the number of terminal nodes in the tree, and column 740 lists the model performance."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model assessment disclosed by Achin by incorporating the providing, by the device, the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions disclosed by Steinberg because both techniques address the same field of machine learning and by incorporating Steinberg into Achin provides more optimal model (tree) selection according to metrics enabling improved marketing decisions [Steinberg ¶20-21]
As to dependent claim 6, the rejection of claim 1 is incorporated, Achin and Steinberg further teach invoking each of the one or more objective functions against metrics vectors for configurations of each of the plurality of machine learning models to determine suitability of each of the plurality of machine learning models for each of the one or more objective functions. [Steinberg each model is tested with different metrics (scores, accuracy, misclassification) to determine highest performance Fig. 7 740, ¶50 "model obtaining the highest performance is singled out."]
As to dependent claim 7, the rejection of claim 1 is incorporated, Achin and Steinberg further teach wherein the model selection process comprises one of either a user interface displaying the comparative assessment of each of the plurality of machine learning models for user selection or an auto-selection process based on a best comparative assessment according to a selected objective function of the one or more objective functions. [Steinberg each model is tested with different metrics (scores, accuracy, misclassification) to determine highest performance Fig. 7 740, ¶50 "model obtaining the highest performance is singled out."]
As to dependent claim 8, the rejection of claim 1 is incorporated, Achin and Steinberg further teach wherein the plurality of machine learning models comprise either a plurality of disparate machine learning models, a plurality of different versions of a same machine learning model, or a combination of both. [Steinberg different models Fig. 7 720 ¶50 "Column 720 identifies each model with a number"]
As to dependent claim 9, the rejection of claim 1 is incorporated, Achin and Steinberg further teach wherein the one or more objective functions are each associated with a respective specific utility. [Steinberg cross-entropy, ROC, lift, accuracy (utility). ¶50-51 "performance might be measured as R-squared, and for classification trees, performance might be measured as classification accuracy,"]
As to dependent claim 10, the rejection of claim 1 is incorporated, Achin and Steinberg further teach wherein each respective specific utility is selected from a group consisting of: accuracy; fairness; accuracy with fairness constraints; and performance. [Steinberg accuracy/performance ¶50-51]
As to dependent claim 11, the rejection of claim 1 is incorporated, Achin and Steinberg further teach creating a catalog of different objective functions from which to select as the one or more objective functions against which the plurality of machine learning models are evaluated. [Achin options for scoring metrics (functions) to select ¶127 " select a standard scoring metric (e.g., goodness-of-fit, R-square, etc.) from a set of options presented via user interface 120, or specific a custom scoring metric (e.g., a custom objective function) via user interface 120"]
As to dependent claim 12, the rejection of claim 1 is incorporated, Achin and Steinberg further teach wherein the comparative assessment of each of the plurality of machine learning models is selected from a group consisting of: a score; a ranking; a grade; and a tiered rating system. [Achin scoring ¶127, ranks ¶116]
As to independent claim 13, Achin teaches an apparatus, comprising: [modeling system ¶239]
one or more network interfaces to communicate with a network; [network connections ¶240]
a processor coupled to the one or more network interfaces and configured to execute one or more processes; and [processing nodes ¶240]
a memory configured to store a process that is executable by the processor, the process, when executed, configured to: [memory, instructions for processor ¶52]
access a plurality of machine learning models; [Accesses a library of modeling techniques ¶64-65 "exploration engine 110 may use the library 130 of modeling techniques to evaluate potential modeling solutions in the search space"]
determine one or more objective functions for the plurality of machine learning models; [determines scores from objective functions for the models ¶127, ¶102 "a predictive model's expected performance on a prediction problem includes one or more expected scores (e.g., expected values of one or more objective functions)"]
evaluate the plurality of machine learning models against the one or more objective functions to establish a comparative assessment of each of the plurality of machine learning models for the one or more objective functions; and [evaluates models for suitability assessment against each other (compares) using ranks ¶220, ¶116 "exploration engine 110 may select the fraction of the modeling procedures having the highest suitability ranks (e.g., in cases where the suitability scores for the modeling procedures are not available, but the ordering (ranking) of the modeling procedures' suitabilities is available)."]
selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions. [selects models with the highest score or threshold score ¶128 " space search engine 110 may select the model with the highest score, or any model having a score that exceeds a threshold score, or any model having a score within a specified range of the highest score."]
Wang does not specifically teach provide the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions.
However, Steinberg teaches provide the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions. [Fig. 7 illustrates a comparative table with assessments (test) and models for choosing highest ¶50 "chart that displays selected rows from a table 710 summarizing the test results for a set of pruned decision trees. Column 720 identifies each model with a number. Column 730 contains the size of the model measured by the number of terminal nodes in the tree, and column 740 lists the model performance."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model assessment disclosed by Achin by incorporating the provide the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions disclosed by Steinberg because both techniques address the same field of machine learning and by incorporating Steinberg into Achin provides more optimal model (tree) selection according to metrics enabling improved marketing decisions [Steinberg ¶20-21]
As to dependent claim 16, the rejection of claim 13 is incorporated, Achin and Steinberg further teach invoke each of the one or more objective functions against metrics vectors for configurations of each of the plurality of machine learning models to determine suitability of each of the plurality of machine learning models for each of the one or more objective functions. [Steinberg each model is tested with different metrics (scores, accuracy, misclassification) to determine highest performance Fig. 7 740, ¶50 "model obtaining the highest performance is singled out."]
As to dependent claim 17, the rejection of claim 13 is incorporated, Achin and Steinberg further teach wherein the model selection process comprises one of either a user interface displaying the comparative assessment of each of the plurality of machine learning models for user selection or an auto-selection process based on a best comparative assessment according to a selected objective function of the one or more objective functions. [Steinberg each model is tested with different metrics (scores, accuracy, misclassification) to determine highest performance Fig. 7 740, ¶50 "model obtaining the highest performance is singled out."]
As to dependent claim 18, the rejection of claim 13 is incorporated, Achin and Steinberg further teach wherein the plurality of machine learning models comprise either a plurality of disparate machine learning models, a plurality of different versions of a same machine learning model, or a combination of both. [Steinberg different models Fig. 7 720 ¶50 "Column 720 identifies each model with a number"]
As to dependent claim 19, the rejection of claim 13 is incorporated, Achin and Steinberg further teach wherein the one or more objective functions are each associated with a respective specific utility. [Steinberg cross-entropy, ROC, lift, accuracy (utility). ¶50-51 "performance might be measured as R-squared, and for classification trees, performance might be measured as classification accuracy,"]
As to independent claim 20, Achin teaches a tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising: [memory, instructions for processor ¶52]
accessing a plurality of machine learning models; [Accesses a library of modeling techniques ¶64-65 "exploration engine 110 may use the library 130 of modeling techniques to evaluate potential modeling solutions in the search space"]
determining one or more objective functions for the plurality of machine learning models; [determines scores from objective functions for the models ¶127, ¶102 "a predictive model's expected performance on a prediction problem includes one or more expected scores (e.g., expected values of one or more objective functions)"]
evaluating the plurality of machine learning models against the one or more objective functions to establish a comparative assessment of each of the plurality of machine learning models for the one or more objective functions; and [evaluates models for suitability assessment against each other (compares) using ranks ¶220, ¶116 "exploration engine 110 may select the fraction of the modeling procedures having the highest suitability ranks (e.g., in cases where the suitability scores for the modeling procedures are not available, but the ordering (ranking) of the modeling procedures' suitabilities is available)."]
selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions. [selects models with the highest score or threshold score ¶128 " space search engine 110 may select the model with the highest score, or any model having a score that exceeds a threshold score, or any model having a score within a specified range of the highest score."]
Wang does not specifically teach providing the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions.
However, Steinberg teaches providing the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions. [Fig. 7 illustrates a comparative table with assessments (test) and models for choosing highest ¶50 "chart that displays selected rows from a table 710 summarizing the test results for a set of pruned decision trees. Column 720 identifies each model with a number. Column 730 contains the size of the model measured by the number of terminal nodes in the tree, and column 740 lists the model performance."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model assessment disclosed by Achin by incorporating the providing the comparative assessment of each of the plurality of machine learning models to a model selection process for selection of a specific machine learning model from the plurality of machine learning models for a given objective function of the one or more objective functions disclosed by Steinberg because both techniques address the same field of machine learning and by incorporating Steinberg into Achin provides more optimal model (tree) selection according to metrics enabling improved marketing decisions [Steinberg ¶20-21]
Claims 2-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Achin in view of Steinberg, as applied to the rejection of claim 1 above, and further in view of Hornick et al. (US 7117391 B1) hereinafter Hornick.
As to dependent claim 2, the combination of Achin and Steinberg teach all the limitations of claim 1 that is incorporated.
Achin and Steinberg do not specifically teach creating one or more checkpoints during development of a specific machine learning model; and establishing one or more selectable machine learning model versions of the specific machine learning model that each correspond to a respective configuration of the specific machine learning model at a respective checkpoint of the one or more checkpoints, wherein the plurality of machine learning models comprise the one or more selectable machine learning model versions.
However, Hornick teaches creating one or more checkpoints during development of a specific machine learning model; and [initiates checkpoints during building (development) Col. 1 ln. 50-57]
establishing one or more selectable machine learning model versions of the specific machine learning model that each correspond to a respective configuration of the specific machine learning model at a respective checkpoint of the one or more checkpoints, wherein the plurality of machine learning models comprise the one or more selectable machine learning model versions.[stores intermediate representations (versions) Col. 2 ln. 13-15, selectable (re-loadable) based on checkpointed models Col. 4 ln. 20-35 "initiate the re-loading of checkpoints and instruct the one or more analysis algorithms 312a 312n to continue execution from the checkpoint state "]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model development by Achin and Steinberg by incorporating the creating one or more checkpoints during development of a specific machine learning model; and establishing one or more selectable machine learning model versions of the specific machine learning model that each correspond to a respective configuration of the specific machine learning model at a respective checkpoint of the one or more checkpoints, wherein the plurality of machine learning models comprise the one or more selectable machine learning model versions disclosed by Hornick because all techniques address the same field of machine learning and by incorporating Hornick into Achin and Steinberg preserve time and avoid waste of computing effort [Hornick Col. 1 ln. 13-41]
As to dependent claim 3, the rejection of claim 2 is incorporated, Achin, Steinberg and Hornick further teach tracking a lineage of each of the one or more selectable machine learning model versions. [Hornick Fig. 6 illustrates tracked threads with state information Col. 5 ln. 48-67 "checkpoint information 600 obtained and stored at a checkpoint state may include, for example in a neural network, the number of computational iterations performed on a data set 602, the set of accumulated error 604, the number of records in the data set that have been completed for the current iteration 606, as well as the set of weights and topology 608 used by the algorithm. Information of a similar nature may also be generated for each specific thread being implemented by an analysis algorithm 312a 312n"]
As to dependent claim 4, the rejection of claim 2 is incorporated, Achin, Steinberg and Hornick further teach wherein establishing the one or more selectable machine learning model versions of the specific machine learning model comprises: computing a respective metrics vector for the one or more selectable machine learning model versions at their respective checkpoint; and [Hornick metrics like iteration, error and etc. Col. 5 ln. 48-67 ]
saving the respective metrics vector with the respective configuration for each of the one or more selectable machine learning model versions. [Hornick Fig. 6 illustrates stores state information Col. 5 ln. 48-67 "checkpoint information 600 obtained and stored"]
As to dependent claim 5, the rejection of claim 2 is incorporated, Achin, Steinberg and Hornick further teach wherein creating the one or more checkpoints is in response to a manual user selection. [Hornick manual checkpointing Col. 1 ln. 34-41 " checkpointing to occur through manual, dynamic, and automated initiation"]
As to dependent claim 14, the combination of Achin and Steinberg teach all the limitations of claim 13 that is incorporated.
Achin and Steinberg do not specifically teach create one or more checkpoints during development of a specific machine learning model; and establishing one or more selectable machine learning model versions of the specific machine learning model that each correspond to a respective configuration of the specific machine learning model at a respective checkpoint of the one or more checkpoints, wherein the plurality of machine learning models comprise the one or more selectable machine learning model versions.
However, Hornick teaches create one or more checkpoints during development of a specific machine learning model; and [initiates checkpoints during building (development) Col. 1 ln. 50-57]
establish one or more selectable machine learning model versions of the specific machine learning model that each correspond to a respective configuration of the specific machine learning model at a respective checkpoint of the one or more checkpoints, wherein the plurality of machine learning models comprise the one or more selectable machine learning model versions.[stores intermediate representations (versions) Col. 2 ln. 13-15, selectable (re-loadable) based on checkpointed models Col. 4 ln. 20-35 "initiate the re-loading of checkpoints and instruct the one or more analysis algorithms 312a 312n to continue execution from the checkpoint state "]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the model development by Achin and Steinberg by incorporating the create one or more checkpoints during development of a specific machine learning model; and establish one or more selectable machine learning model versions of the specific machine learning model that each correspond to a respective configuration of the specific machine learning model at a respective checkpoint of the one or more checkpoints, wherein the plurality of machine learning models comprise the one or more selectable machine learning model versions disclosed by Hornick because all techniques address the same field of machine learning and by incorporating Hornick into Achin and Steinberg preserve time and avoid waste of computing effort [Hornick Col. 1 ln. 13-41]
As to dependent claim 15, the rejection of claim 14 is incorporated, Achin, Steinberg and Hornick further teach track a lineage of each of the one or more selectable machine learning model versions. [Hornick Fig. 6 illustrates tracked threads with state information Col. 5 ln. 48-67 "checkpoint information 600 obtained and stored at a checkpoint state may include, for example in a neural network, the number of computational iterations performed on a data set 602, the set of accumulated error 604, the number of records in the data set that have been completed for the current iteration 606, as well as the set of weights and topology 608 used by the algorithm. Information of a similar nature may also be generated for each specific thread being implemented by an analysis algorithm 312a 312n"]
Conclusion
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.
MARUO et al. (US 20240111823 A1) teaches AutoML with comparison of different models and functions (see Fig. 8, Fig. 11, ¶105)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388.
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/BEAU D SPRATT/ Primary Examiner, Art Unit 2143
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