DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending.
This communication is in response to the communication filed June 9, 2025.
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 claims recite systems, methods, or apparatuses for classifying treatment data by collecting data, analyzing it, and outputting the results, which are statutory categories of inventions.
Specifically, the independent claims, taking claim 1 as exemplary, recite: providing…input data representing a description of the entity to be classified, wherein the machine learning model has been trained using training data to determine a class of multiple candidate entity classes, wherein the multiple candidate entity classes include (1) a responsive class of responding to the treatment that includes a platinum therapy and (2) a non-responsive class of not responding to the treatment that includes the platinum therapy; and processing…the input data to generate output data that represents an initial entity class of the multiple candidate entity classes for the entity; providing…the output data obtained for each of the plurality of machine learning models, wherein the output data includes data representing the initial entity class determined by each of the plurality of machine learning models; and determining…based on the output data for each of the plurality of machine learning models, an actual entity class for the entity, the actual entity class being the responsive class or the non-responsive class.
The limitations are interpreted as providing input data, processing input data to generate output data, providing output data, and determining entity class data. The limitations directed to training models or algorithms are interpreted as mathematical processes based on the foundational principles of computer science and the updated 2024 PEG. The limitations may be grouped within the “certain methods of organizing human activity” grouping of abstract ideas because, the claims involve collecting data, analyzing it, and outputting the results of the collection and analysis. See MPEP 2106.04. The claims are interpreted as reciting concepts relating to tracking or organizing health and treatment information. Accordingly, the claims recite an abstract idea.
The dependent claims further recite elements of the above recited abstract idea. The dependent claims specifically recite, applying rules, determining occurrences, selecting entity classes having highest number of occurrences, accessing confidence score, adjusting output data, explaining types of machine learning models that may be used, and explaining what the data may entail. The claims are similarity interpreted as reciting concepts relating to tracking or organizing health and treatment information. Accordingly, the claims recite an abstract idea. The claims further include limitations that are not interpreted as part of the abstract idea, but are interpreted as additional elements.
This judicial exception is not integrated into a practical application. Integration into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Here, the additional elements of the claims use a machine learning model, computer, storage media, performing nucleic acid sequencing to obtain input data, providing platinum therapy to the entity, and non-transitory computer readable medium.
The claims merely use the additional elements as tools to perform abstract ideas and generally link the use of a judicial exception to a particular technological environment. The use of the additional elements as tools to apply the abstract idea and generally to link the use of the abstract idea to a particular technological environment does not render the claim patent eligible, because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. Specifically, the non-transitory computer-readable storage medium, processor, and storage media may be part of a computer performing the steps or functions of storing, receiving, outputting, and processing data (specification par. 54). The machine learning model and voting unit may be machine learning models and include a random forest classification algorithm, support vector machine, logistic regression, k-nearest neighbor model, artificial neural network, naive Bayes model, quadratic discriminant analysis, or Gaussian processes model functioning to processing data (dependent claims). A conventional machine learning model training system may be used to train the machine learning model (specification p. 118) and an adjustment of a machine learning model may include manual tuning of the machine learning parameters. Here, a conventional machine learning system training a non-specified machine learning model where manual adjustments may be interpreted as generally linking the technology to the abstract idea and being extra solution activity. The providing of platinum therapy and performing nucleic acid sequencing are recited at a high level of generality. The sequencing is only to obtain input data and the therapy is provided to an entity regardless of the entity being responsive or non-responsive to the treatment, which are interpreted as extra-solution activity.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. See Apple v. Ameranth, 842 F.3d 1229, 1240 (Fed. Cir. 2016). The additional elements do not use the exception to affect a particular treatment or prophylaxis for a disease, do not apply the exception using particular machines, and do not effect a transformation or reduction of a particular article to a different state or thing, rather the computer elements are generally stated as to their structure and function and are only used to make determinations of classification instead of directly providing specific treatment or prophylaxis. Therefore, the additional elements do not impose any meaningful limits on practicing the abstract idea and the additional limitations are not indicative of materializing into a practical application. Accordingly, the claim is directed to an abstract idea.
Generic computer elements recited as performing generic computer functions that are well-understood, routine, or conventional activities amount to no more than implementing the abstract idea with a computerized system (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network and performing repetitive calculations); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); See MPEP 2106.05(d) and July 2015 Update: Section IV). Here, the claim limitations of providing input data, processing input data to generate output data, providing output data, and determining entity class data are similar to those of a computer receives and sends information over a network and performing repetitive calculations.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer receives and sends information over a network and performing repetitive calculations to perform the steps of providing input data, processing input data to generate output data, providing output data, and determining entity class data amount to no more than using computer related devices to automate or implement the abstract idea for classifying treatment data by collecting data, analyzing it, and outputting the results.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible.
In conclusion, the claims are directed to the abstract idea for classifying treatment data by collecting data, analyzing it, and outputting the results. The claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
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(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Davison et al. US20190316203 in view of Szallasi et al. US20160122827.
As per claim 1, Davison teaches
a method for classification of an entity for responding to a treatment, the method comprising: for each machine learning model of a plurality of machine learning models: (Davison par. 14, 204, 350-351 teaches various agents cancer treatment; par. 327 teaches a plurality of gene classification models; par. 392-394 and associated figures teach various cell models)
performing nucleic acid sequencing of a biological sample from the entity to obtain input data that represents the entity, wherein the input data includes data from at least one biomarker selected from the following: MYC, EP300, U2AF1, ASXL1, MAML2, CNTRL, WRN, CDX2, BCL9, PBX1, PRRX1, INHBA, YWHAE, GNAS, LHFPL6, CASP8, FH, CRKL, MLF1, TRRAP, AKT3, ACKR3, MSI2, PCM1, MNX1, AURKA, GAS7, MN1, SOX10, TCLlA, LMO1, BRD3, SMARCA4, PER1, PAX7, SBDS, SEPT5, PDGFB, AKT2, TERT, KEAP1, ETV6, TOP1, TLX3, COX6C, NFIB, ARFRP1, ARID1lA, MAP2K4, NFKBIA, WWTR1, ZNF217, IL2, NSD3, BRIP1, SDC4, EWSR1, FLT3, FLT1, FAS, CCNEl, RUNX1T1, EZR, FCRL4, BIRC3,and HOXAl1; (Davison par. 182 teaches biomarkers that may be used including BIRC3)
providing, to the machine learning model that has been trained to determine a classification, input data representing a description of the entity to be classified, wherein the machine learning model has been trained using training data to determine a class of multiple candidate entity classes, (Davison par. 173, 205, 327)
wherein the multiple candidate entity classes include (1) a responsive class of responding to the treatment…and (2) a non-responsive class of not responding to the treatment…; and (Davison par. 173, 198, 202)
processing, by the machine learning model, the input data to generate output data that represents an initial entity class of the multiple candidate entity classes for the entity; (Davison par. 197-198, 328)
providing the output data obtained for each of the plurality of machine learning models, wherein the output data includes data representing the initial entity class determined by each of the plurality of machine learning models; and (Davison par. 198, 202, 299)
determining based on the output data for each of the plurality of machine learning models, an actual entity class for the entity, the actual entity class being the responsive class or the non-responsive class (Davison par. 202 teaches the true positive rate is determined by counting the number of cases above the value for that feature and then dividing by the total number of case. Here, the voting unit tallies the number of occurrences of predictions, based on p. 37 of the specification).
Davison teaches platinum related treatment, but may not specifically teach the following limitations met by Szallasi, classification of treatments that includes a platinum therapy a responsive class of responding to the treatment that includes a platinum therapy and a non-responsive class of not responding to the treatment that includes the platinum therapy (Szallasi claim 1, par. 10, 23-24, 33)
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Davison to provide data of responsive and non-responsive classes regarding platinum therapy as taught by Szallasi with the motivation of needing identify the best chemotherapy for each patient and to eliminate agents that are inert and result in toxicity without benefit. The methods allow one to personalize the treatment of a cancer patient based on the cancer cells’ specific protein/gene expression profile (par. 5-10).
As per claim 2, Davison and Szallasi teach all the limitations of claim 1 and further teach wherein the actual entity class for the entity is determined by applying a majority rule to the output data for each of the plurality of machine learning models (Davison par. 329, 382).
As per claim 3, Davison and Szallasi teach all the limitations of claim 1 and further teach wherein determining based on the output data, the actual entity class for the entity comprises: determining a number of occurrences of each initial entity class of the multiple candidate entity classes; and selecting the initial entity class of the multiple candidate entity classes having a highest number of occurrences (Szallasi par. 5, 23, 33, 41).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Davison to determine a number of occurrences of each entity class and selecting the class with highest number of occurrences as taught by Szallasi with the motivation of needing identify the best chemotherapy for each patient and to eliminate agents that are inert and result in toxicity without benefit. The methods allow one to personalize the treatment of a cancer patient based on the cancer cells’ specific protein/gene expression profile (par. 5-10).
As per claim 4, Davison and Szallasi teach all the limitations of claim 1 and further teach accessing a confidence score for each of the plurality of machine learning models; and adjusting the output data generated by each machine learning model based on the confidence score that corresponds to each respective machine learning model (Davison fig. 3 and associated paragraphs, par. 22-26, 342)
As per claim 5, Davison and Szallasi teach all the limitations of claim 4 and further teach wherein the confidence score for each of the plurality of machine learning models is indicative of a historical accuracy of each of the plurality of machine learning models (Davison par. 202, 331).
As per claim 6, Davison and Szallasi teach all the limitations of claim 1 and further teach wherein each machine learning model of the plurality of machine learning models comprises a random forest classification algorithm, support vector machine, logistic regression, k-nearest neighbor model, artificial neural network, naive Bayes model, quadratic discriminant analysis, or Gaussian processes model (Davison par. 204, 206, 209, 210, 321).
As per claim 7, Davison and Szallasi teach all the limitations of claim 1 and further teach wherein each machine learning model of the plurality of machine learning models comprises a random forest classification algorithm (Davison par. 209).
As per claim 8, Davison and Szallasi teach all the limitations of claim 1 and further teach wherein the plurality of machine learning models includes multiple representations of a same type of classification algorithm (Davison par. 198-213)
As per claim 9, Davison and Szallasi teach all the limitations of claim 1 and further teach wherein at least two machine learning models of the plurality of machine learning models comprise a different type of machine learning model (Davison par. 352-356).
As per claim 10, Davison and Szallasi teach all the limitations of claim 1 and further teach wherein the input data represents a description of (i) entity attributes including the at least on biomarker and (ii) the platinum therapy for a disease or disorder (Davison par. 352-356).
As per claim 11, Davison and Szallasi teach all the limitations of claim 10 and further teach wherein the disease or disorder is a cancer (Davison abstract, par. 173).
As per claim 12, Davison and Szallasi teach all the limitations of claim 11 and further teach wherein the at least one biomarker includes a panel of genes that is less than all known genes of the entity (Davison par. 198-200).
As per claim 13, Davison and Szallasi teach all the limitations of claim 11 and further teach wherein the at least one biomarkers include a panel of genes that comprises all known genes for the entity (Davison par. 198-200).
As per claim 14, Davison and Szallasi teach all the limitations of claim 11 and further teach wherein the at least one biomarker include one or more biomarkers listed in any one of Tables 2-8 (Davison Table 1A, 2A-2D).
As per claim 15, Davison and Szallasi teach all the limitations of claim 11 and further teach wherein the input data further includes data representing a description of the disease or disorder (Davison par. 296, 357).
As per claim 16, Davison and Szallasi teach all the limitations of claim 11 and further teach providing the treatment including the platinum therapy to the entity (Davison par. 264-266).
As per claim 17-20, Davison and Szallasi teach all the limitations similarly to claims 1-16 above. Szallasi par. 68, 71, 316, 328 teach computer related limitations.
Response to Arguments and Amendments
Applicant’s arguments and amendments filed on June 9, 2025 have been fully considered and are addressed below.
Applicant’s arguments have been fully considered but are not persuasive.
Applicant argues that the claims do not recite an abstract idea directed to either a mathematical concept or organizing human activity (Remarks par. 8-9). Examiner respectfully disagrees.
Under 35 U.S.C. § 101, "[w]hoever 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." 35 U.S.C. § 101. The Supreme Court, however, has long interpreted§ 101 to include an implicit exception: "[l]aws of nature, natural phenomena, and abstract ideas" are not patentable. Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 216 (2014).
The first step, as set forth in the 2019 Revised Guidance (i.e., Step 2A), is, thus, a two-prong test. In Step 2A, Prong One, we look to whether the claim recites a judicial exception, e.g., one of the following three groupings of abstract ideas: (1) mathematical concepts; (2) certain methods of organizing human activity, e.g., fundamental economic principles or practices, commercial or legal interactions; and (3) mental processes. 2019 Revised Guidance, 84 Fed. Reg. at 54. If so, we next determine, in Step 2A, Prong Two, whether the claim as a whole integrates the recited judicial exception into a practical application, i.e., whether the additional elements recited in the claim beyond the judicial exception, apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. Id. at 54-55. Only if the claim (1) recites a judicial exception and (2) does not integrate that exception into a practical application do we conclude that the claim is "directed to" the judicial exception, e.g., an abstract idea. Id.
If the claim is determined to be directed to a judicial exception under revised Step 2A, we next evaluate the additional elements, individually and in combination, in Step 2B, to determine whether they provide an inventive concept, i.e., whether the additional elements or combination of elements amounts to significantly more than the judicial exception itself; only then, is the claim patent eligible. 2019 Revised Guidance, 84 Fed. Reg. at 56.
Here, in rejecting the pending claims under 35 U.S.C. § 101, the independent claims recite inventions for platinum therapy classification, which is an abstract idea grouped within the “certain methods of organizing human activity” grouping of abstract ideas because, the claims involve a series of steps for collecting data, analyzing it, and outputting the results of the collection and analysis. See MPEP 2106.04.
The Federal Circuit has explained that "the 'directed to' inquiry applies a stage-one filter to claims, considered in light of the specification, based on whether 'their character as a whole is directed to excluded subject matter."' Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F .3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a specific improvement in relevant technology or on a process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool. See id. at 1335-36. Here, it is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on an improvement to technology and/or a technical field, because the claims are directed to processing data and determining an actual entity class for the entity based on the outputs of the processing. Specification par. 9-11 states that an embodiment is to make predictions for treatment effectiveness, which are abstract ideas in a field of human interaction, healthcare.
Claim 1 recites that the claimed steps are performed using a machine learning model, computer, storage media, performing nucleic acid sequencing to obtain input data, providing platinum therapy to the entity, and non-transitory computer readable medium. Yet, apart from the use of generic computer components and extra solution activity, nothing in claim 1 precludes the limitations from being performed by a human either mentally or manually, e.g., using pen and paper, without the use of a computer or any other machine. As such, claim 1 recites a mental process, and, therefore, an abstract idea. See 2019 Revised Guidance, 84 Fed. Reg. at 52. See also, e.g., Univ. of Fla. Research Found., Inc. v. General Elec. Co., 916 F.3d 1363, 1367 (Fed. Cir. 2019) (claims "directed to the abstract idea of 'collecting, analyzing, manipulating, and displaying data"'); Voter Verified, Inc. v. Election Sys. & Software LLC, 887 F.3d 1376, 1385 (Fed. Cir. 2018) (determining that a self-verifying voting system's claimed steps, which were "human cognitive actions," were abstract ideas, even though they were performed on a computer); Fair Warning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016) (determining "that the 'realm of abstract ideas' includes 'collecting information, including when limited to particular content"' as well as analyzing and presenting information); Electric Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes); Cyber Source Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011) (holding that method steps that can be performed in the human mind, or by a human using a pen and paper, are unpatentable mental processes).
Applicant does not disclose any particular machine learning algorithms used in defining any weighted aggregation function or otherwise provide any implementation details. The machine learning techniques, as claimed, merely replicate the mental processes used in making predictions of treatment effectiveness, similar to claims that the Federal Circuit has held abstract. See, e.g., In re Meyer, 688 F.2d 789, 795 (Fed. Cir. 1982) (holding that claims to a computerized method of performing a neurological examination by using an algorithm to represent mental processes that a neurologist should follow to replace the thinking processes of a neurologist with a computer were not patent eligible); SmartGene, Inc. v. Advanced Biological Labs., SA, 555 F. App'x 950, 955 (Fed. Cir. 2014) (holding a claimed computer device replicated thought processes of a doctor where it contained, like a doctor's mind, "a set of 'expert rules for evaluating and selecting' from a stored 'plurality of different therapeutic treatment regimens"' and "advisory information useful for the treatment of a patient with different constituents of said different therapeutic treatment regimens").
Step 2B determines the inventive concept and if the claims recite additional elements that amount to significantly more than the abstract idea. Here, the claims recited additional elements, but were found not to be significantly more because the additional elements were generic computer hardware and software, extra-solution activities, and used only to apply steps of the abstract ideas (Step 2B: No). The USPTO memorandum published in light of Berkheimer v. HP stated on pages 3-4 that examiners may cite to an applicant’s specification, court decisions, publications demonstrating the well-understood, routine, conventional nature of elements, and can take official notice. The previous Office Action included citations to the Applicant’s specification to determine the general functionality of the limitations and cited case law to analogize the technologies and functions.
Examiner respectfully submits that the burden of establishing that the claimed subject matter falls under a judicial exception to patent eligibility has been met, along with a showing of how the additional elements are not significantly more than the judicial exception, therefore the claims remain rejected under 101.
Applicant argues the withdrawal of the obviousness rejections. Applicant states that the prior art references do not teach or suggest the following limitations: using a plurality of learning models and performing nucleic acid sequencing of a biological sample from the entity to obtain input data that represents the entity where the input data includes data from at least on biomarker. Examiner respectfully disagrees. Davison par. 182 teaches biomarkers for BIRC3. Davison par. 327 teaches a classification pipeline developed in accordance with commonly accepted good practice [MAQC Consortium, Nat Biotechnol 2010]. The process will, in parallel: 1) derive gene classification models from empirical data; and 2) assess the classification performance of the models, both under cross-validation. The performance and success of the classifier generation depends on a number of parameters that can be varied, for instance the choice of classification method or probe set filtering.
As such claims 1-20 are unpatentable as stated above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
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/JAY M. PATEL/Primary Examiner, Art Unit 3686