DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the election and amendment filed on 6/8/26. Claims 14-33 are pending. Claims 15-33 are new.
Election/Restrictions
Applicant’s election without traverse of Invention I (claims 14-20 ( original); and 15-33 new) in the reply filed on 6/8/26 is acknowledged.
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 14-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e, a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
35 USC 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter.
Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Applicant’s claims fall within at least one of the four categories of patent eligible subject matter because claims 14-23, are drawn to a method, and claims 24-33 are drawn to a system.
Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101 (i.e., process, machine, manufacture, or composition of matter) in Step 1 does not complete the eligibility analysis. Claims drawn only to an abstract idea, a natural phenomenon, and laws of nature are not eligible for patent protection. As described in MPEP 2106, subsection III, Step 2A of the Office’s eligibility analysis is the first part of the Alice/Mayo test, i.e., the Supreme Court’s “framework for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of those concepts.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l,134 S. Ct. 2347, 2355, 110 USPQ2d 1976, 1981 (2014) (citing Mayo, 566 U.S. at 77-78, 101 USPQ2d at 1967-68).
In 2019, the United States Patent and Trademark Office (USPTO) prepared revised guidance (2019 Revised Patent Subject Matter Eligibility Guidance) for use by USPTO personnel in evaluating subject matter eligibility. The framework for this revised guidance, which sets forth the procedures for determining whether a patent claim or patent application claim is directed to a judicial exception (laws of nature, natural phenomena, and abstract ideas), is described in MPEP sections 2106.03 and 2106.04.
As explained in MPEP 2106.04(a)(2), the 2019 Revised Patent Subject Matter Eligibility Guidance explains that abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes. Moreover, this guidance explains that a patent claim or patent application claim that recites a judicial exception is not ‘‘directed to’’ the judicial exception if the judicial exception is integrated into a practical application of the judicial exception. A claim that recites a judicial exception, but is not integrated into a practical application, is directed to the judicial exception under Step 2A and must then be evaluated under Step 2B (inventive concept) to determine the subject matter eligibility of the claim.
Step 2A asks: Does the claim recite a law of nature, a natural phenomenon (product of nature) or an abstract idea? (Prong One) If so, is the judicial exception integrated into a practical application of the judicial exception? (Prong Two) A claim recites a judicial exception when a law of nature, a natural phenomenon, or an abstract idea is set forth or described in the claim. A claim that integrates a judicial exception into a practical application will 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.
In the instant case, claims 14-33 recite(s) a method and system for certain methods of organizing human activities, which is subject matter that falls within the enumerated groupings of abstract ideas described in MPEP 2106.04 (2019 Revised Patent Subject Matter Eligibility Guidance) Certain methods of organizing human activities includes fundamental economic practices, like insurance; commercial interactions (i.e. legal obligations, marketing or sales activities or behaviors, and business relations). Organizing human activity also encompasses managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions.) The recited method and system are drawn to determining whether a prescription event is anomalous/fraudulent based a scoring assessment. (i.e. managing personal behavior or relationships or interactions between people
In particular, claims 14 and 24 recite a method and system for:
generating one or more vectors describing the prescription event, the clinician, the pharmacy, and the medication;
generating an event score for the prescription event;
determining whether the event score meets an alert threshold; and
in response to determining that the event score meets the alert threshold, providing an alert to the pharmacy.
The judicial exception is not integrated into a practical application because the claim language does not recite any improvements to the functioning of a computer, or to any other technology or technical field (See MPEP 2106.04(d)(1); see also MPEP 2106.05(a)(I-II)). Moreover, the claims do not integrate the judicial exception into a practical application because the claimed invention does not: apply the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)); effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)); or apply or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment see MPEP 2106.05(e). (Considerations for integration into a practical application in Step 2A, prong two and for recitation of significantly more than the judicial exception in Step 2B)
While abstract ideas, natural phenomena, and laws of nature are not eligible for patenting by themselves, claims that integrate these exceptions into an inventive concept are thereby transformed into patent-eligible inventions. Step 2B asks: Does the claim recite additional elements that amount to significantly more than the judicial exception? The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 14 and 24 additionally recite: receiving a prescription event, the prescription event identifying a clinician, a pharmacy, and a medication. The additional step amounts to insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). In particular, the additional steps amount to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering)
Claim 24 recite(s) additional limitation(s), including: at least one processor; and memory storing instructions. The additional components is/are generic components that perform conventional activities which amount to no more than implementing the abstract idea with a computerized system.
The generic nature of the computer system used to carryout steps of the recited method is underscored by the system description in the instant application, which discloses: “The scoring server 110 may include a computing device, such as a computer, a server or a number of communicatively connected distributed servers, a mainframe, etc., that has one or more processors 112 (e.g., a processor formed in a substrate) configured to execute instructions stored in memory, such as main memory, RAM, or disk.” (par. 19 of the PG-Pub)
The application explains: “The clinician computing device 150 may be a personal computing system, a terminal, a laptop, a tablet, a mobile device such as a smartphone, or a wearable computing device (e.g., a smart watch or smart glasses). The clinician computing device 150 may include one or more processors 156 (e.g., a processor formed in a substrate) configured to execute instructions stored in memory such as main memory, RAM, flash, cache, or disk. The clinician computing device 150 may also include input devices, such as a microphone, a keyboard (virtual or physical), a touch screen, a mouse, a camera, a voice recorder, etc. The clinician computing device 150 also includes a display or other output device, such as a speaker, lights, or haptic feedback device.” (see par. 21)
The disclosure further states: “In addition to the configurations described above, an apparatus can include one or more apparatuses in computer network communication with each other or other devices. In addition, a computer processor can refer to one or more computer processors in one or more apparatuses or any combinations of one or more computer processors and/or apparatuses. An aspect of an embodiment relates to causing and/or configuring one or more apparatuses and/or computer processors to execute the described operations. The results produced can be output to an output device, for example, displayed on the display. An apparatus or device refers to a physical machine that performs operations, for example, a computer (physical computing hardware or machinery) that implement or execute instructions, for example, execute instructions by way of software, which is code executed by computing hardware including a programmable chip (chipset, computer processor, electronic component), and/or implement instructions by way of computing hardware (e.g., in circuitry, electronic components in integrated circuits, etc.)…, to achieve the functions or operations being described. The functions of embodiments described can be implemented in any type of apparatus that can execute instructions or code.”
Such language underscores that the applicant's perceived invention/ novelty focuses on the computerized implementation of the abstract idea, not the underlying structure of generic system components.
Furthermore, the courts have recognized certain computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05 (d) (II)). Among these are the following features, which are recited in the claims:
- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
- Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); 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.");
- Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Claims 14 and 24 further recite: a machine learning model and “by providing the one or more vectors describing the prescription event, the clinician, the pharmacy, and the medication to a machine-learning model, the machine-learning model using the one or more vectors to provide the event score as output.” As currently drafted, the recitation of a machine learning models in the claims fail to recite significantly more, and amounts to adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984. (See MPEP 2106.05(A))
The recitation of and “the machine-learning model using the one or more vectors to provide the event score as output” recites only the idea of a solution or outcome, but the claims fail to recite details of how a solution to a problem is accomplished. Moreover, as drafted, the claims invoke the use of the “machine learning model” merely as a tool to perform an existing process. (i.e. evaluating factors to determine outlier or anomalous events relating to prescription generation for potential fraud or abuse)
Claims 15-23 are dependent from Claim 14 and include(s) all the limitations of claim(s) 14. However, the additional limitations of the claims 15-23 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types, or the additional steps amount to insignificant extra solution activities. Therefore, claim(s) 15-23 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 25-33 are dependent from Claim 24 and include(s) all the limitations of claim(s) 24. However, the additional limitations of the claims 25-33 fail to recite significantly more than the abstract idea. More specifically, the additional limitations further define the abstract idea with additional steps or details regarding data types; or the additional steps amount to insignificant extra solution activities. Therefore, claim(s) 25-33 are also rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Because Applicant’s claimed invention recites a judicial exception that is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself, the claimed invention is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 14-20; 22; 24-30; and 32 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bostic et al ( US 20210202105 A1).
Claims 14, 24 Bostic teaches a system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor,(par. 266-267; par. 341) cause the system to perform the method:
receiving a prescription event, the prescription event identifying a clinician, a pharmacy, and a medication; (par. 193- a claim record may include and/or may be related to a claim ID that identifies the insurance-related event, a patient ID of the patient involved in the event, a clinic ID of the clinic that the patient visited, a physician ID of the provider who treated the patient, diagnosis data that indicates the provider's medical diagnosis, prescription data that indicates any prescriptions that were prescribed in relation to the event, including dosages, amounts, etc., lab test data that indicates any tests that were ordered in relation to the event; par. 198);
generating one or more vectors describing the prescription event, the clinician, the pharmacy, and the medication; (par. 214: generate a set of features (e.g., a feature vector) based on the proposed prescription (e.g., a medication identifier, a dosage amount, a number of pills, etc.) for the patient and the patient information; par. 231; par. 174-pharmacy information: A respective prescription record may store a prescription ID that uniquely identifies a prescription of a patient, a patient ID identifying the patient to whom the prescription corresponds, a physician ID that identifies the physician that wrote the prescription, a medication ID that identifies the medication that was prescribed, a quantity of the medication that is prescribed, a dosage of the medication that is prescribed, a date on which the medication was prescribed, a date on which the prescription expires; par. 198- The insurer-related information may include events related to a healthcare organization, clinic, and a physician; par. 203- the data structuring module 204 may include prescription-related data obtained by the interception module to track the prescriptions of controlled medications (e.g., opiates, benzodiazepines, amphetamines) written for the patient in the patient profile. This information may include the medication that was prescribed, the physician who wrote each prescription, when the prescription was written, the pharmacy that filled the prescription, and other relevant prescription-related data items )
generating an event score for the prescription event by providing the one or more vectors describing the prescription event, the clinician, the pharmacy, and the medication to a machine-learning model, the machine-learning model using the one or more vectors to provide the event score as output; (par. 161- A machine learning model or other forms of artificial intelligence are utilized to generate a potential misuse score or similar measurement of the likelihood that a patient is or has the potential for misusing a controlled substance; par. 184-prescription misuse score)
determining whether the event score meets an alert threshold; and ( par. 184-the classification may include a confidence score, whereby a higher confidence score indicates a higher degree of confidence in the classification. In some of these embodiments, the machine learned model(s) may be trained to identify the type of abuse of a medication (e.g., overuse/addiction, use with other controlled substances, and the like; par. 231- the classification may include a confidence score in the classification, whereby the patient monitoring module 402 may select a classification based on the confidence score thereof (e.g., when the confidence score exceeds a threshold). In the event the patient monitoring module 402 determines that the classification indicates misuse, the patient monitoring module 402 may output a notification indicating the potential misuse (which may also include the type of misuse).
in response to determining that the event score meets the alert threshold, providing an alert to the pharmacy. (par. 73- generating an alert based at least in part on an indicator of aberrant prescription activity among the determined one or more relationships within the enriched data set; par. 161- a notification or report of the patient's potential for misusing a controlled substance can be provided in order to assist with the treatment of the patient.)
Claims 15, 25 Bostic discloses the system of claim 24, wherein the one or more vectors include an outlier score for the clinician, the outlier score representing a probability that a prescription history for the clinician represents an outlier. (par. 210- if a clinic is found to have a statistical anomaly, the physician records corresponding to the individual physicians of the clinic may be analyzed individually to ensure that the anomaly is not attributable to a single or small set of physicians.)
Claims 16, 26 Bostic teaches, wherein the operations further include, in response to determining that the event score meets the alert threshold: determining whether the outlier score meets a trustworthiness threshold; and in response to determining that the outlier score meets the trustworthiness threshold, providing a confirmation notification to the clinician. (par. 97- a recommendation module that provides the physician with an indicated likelihood that the patient is abusing and a risk report for the physician to work with the patient; par. 184-the classification may include a confidence score, whereby a higher confidence score indicates a higher degree of confidence in the classification. In some of these embodiments, the machine learned model(s) may be trained to identify the type of abuse of a medication (e.g., overuse/addiction, use with other controlled substances, and the like; par. 231- the classification may include a confidence score in the classification, whereby the patient monitoring module 402 may select a classification based on the confidence score thereof (e.g., when the confidence score exceeds a threshold). In the event the patient monitoring module 402 determines that the classification indicates misuse, the patient monitoring module 402 may output a notification indicating the potential misuse (which may also include the type of misuse).
Claims 17, 27 Bostic teaches wherein the outlier score is generated periodically by an outlier detection model. (par. 201-202-updating profile data clinicians and patients as new information/prescriptions are generated) Claims 18, 28 Bostic discloses wherein the outlier score is generated as part of generating the event score for the prescription event. (par. 97- a recommendation module that provides the physician with an indicated likelihood that the patient is abusing and a risk report for the physician to work with the patient; par. 184-the classification may include a confidence score, whereby a higher confidence score indicates a higher degree of confidence in the classification. In some of these embodiments, the machine learned model(s) may be trained to identify the type of abuse of a medication (e.g., overuse/addiction, use with other controlled substances, and the like; par. 231- the classification may include a confidence score in the classification, whereby the patient monitoring module 402 may select a classification based on the confidence score thereof (e.g., when the confidence score exceeds a threshold). In the event the patient monitoring module 402 determines that the classification indicates misuse, the patient monitoring module 402 may output a notification indicating the potential misuse (which may also include the type of misuse).
Claims 19, 29 Bostic teaches wherein the prescription event further identifies a patient, and the one or more vectors further describes the patient, and wherein the one or more vectors describing the prescription event, the clinician, the pharmacy, the medication, and the patient are provided to the machine-learning model. (par. 214: generate a set of features (e.g., a feature vector) based on the proposed prescription (e.g., a medication identifier, a dosage amount, a number of pills, etc.) for the patient and the patient information; par. 231; par. 174-pharmacy information: A respective prescription record may store a prescription ID that uniquely identifies a prescription of a patient, a patient ID identifying the patient to whom the prescription corresponds, a physician ID that identifies the physician that wrote the prescription, a medication ID that identifies the medication that was prescribed, a quantity of the medication that is prescribed, a dosage of the medication that is prescribed, a date on which the medication was prescribed, a date on which the prescription expires; par. 198- The insurer-related information may include events related to a healthcare organization, clinic, and a physician; par. 203- the data structuring module 204 may include prescription-related data obtained by the interception module to track the prescriptions of controlled medications (e.g., opiates, benzodiazepines, amphetamines) written for the patient in the patient profile. This information may include the medication that was prescribed, the physician who wrote each prescription, when the prescription was written, the pharmacy that filled the prescription, and other relevant prescription-related data items )
Claims 20, 30 Bostic teaches wherein the prescription event further identifies a sponsor, and the one or more vectors further describes the sponsor, and wherein the one or more vectors describing the prescription event, the clinician, the pharmacy, the medication, the patient, and the sponsor are provided to the machine-learning model. (par. 174-175: insurance organization information included; par. 214: generate a set of features (e.g., a feature vector) based on the proposed prescription (e.g., a medication identifier, a dosage amount, a number of pills, etc.) for the patient and the patient information; par. 231; par. 174-pharmacy information: A respective prescription record may store a prescription ID that uniquely identifies a prescription of a patient, a patient ID identifying the patient to whom the prescription corresponds, a physician ID that identifies the physician that wrote the prescription, a medication ID that identifies the medication that was prescribed, a quantity of the medication that is prescribed, a dosage of the medication that is prescribed, a date on which the medication was prescribed, a date on which the prescription expires; par. 198- The insurer-related information may include events related to a healthcare organization, clinic, and a physician; par. 203- the data structuring module 204 may include prescription-related data obtained by the interception module to track the prescriptions of controlled medications (e.g., opiates, benzodiazepines, amphetamines) written for the patient in the patient profile. This information may include the medication that was prescribed, the physician who wrote each prescription, when the prescription was written, the pharmacy that filled the prescription, and other relevant prescription-related data items )
Claims 22, 32. Bostic discloses the system of claim 24, the operations further including: using reinforcement learning to update the machine-learning model based on clinicians positively identified as having outlier prescribing histories. (par. 218: outcome data may be used to reinforce the models that are used to make the recommendation)
Claim Rejections - 35 USC § 103
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.
Claim(s) 21 and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bostic et al ( US 20210202105 A1) in view of Valuck et al (US 20210043292 A1)
Claims 21, 31 Bostic does not disclose, but Valuck teaches wherein the medication is a first medication and the operations further include: identifying second medications as having illicit market value; (par. 10-The processor may analyze, using one or more trained machine learning algorithms, a plurality of data records to determine a score for drug efficacy, side effect levels, and addiction risk for each of a plurality of alternative drugs. The processor may provide a therapeutic success rating for the current drug and each of the plurality of alternative drugs based on the weight and the scores for the respective drug; par. 28; par. 39-40: the drug analysis module 160 may include a machine learning component 170 that manages and executes a plurality of machine learning algorithms to generate score and a weighting component 180 that applies weights based on patient preferences to determine the therapeutic success rating; par. 49- identifying other drugs in an opioid class; par. 53) and using reinforcement learning to update the machine-learning model based on the second medications being identified as having illicit market value. (use of reinforcement learning in model refinement- par. 47-48- the machine learning component 170 and/or the shared algorithms 172 may include algorithms for determining a score for drug efficacy, side effect levels, and addiction risk for a current drug each of a plurality of alternative drugs…, the algorithms may receive the collected data records associated with a patient and a particular drug. The algorithms may each output a respective score for the patient and one of the drugs for drug efficacy, side effect levels, and addiction risk; par. 50- A machine-learning algorithm may include a model trained on sample patients labeled as likely OUD cases and unlikely OUD cases based on continued observation. The model may be trained to predict whether a particular patient is likely to currently experience OUD or develop OUD based on the risk metrics. The model may also be developed using reinforcement learning.)
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify the system and method of Bostic with the teaching of Valuck to identify additional/secondary medications which may be abused and to use reinforcement learning to update the model. One would have been motivated to include these features to improve and minimize possible therapeutic treatment protocols involving potentially addictive drugs.
Claim(s) 23 and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bostic et al ( US 20210202105 A1) in view of Benoit et al (US 11037666 B1)
Claims 23, 33 Bostic does not disclose, but Benoit teaches wherein the alert comprises a message to block completion of the prescription event. (col. 7, lines 32-55) At the time of the effective filing date it would have been obvious to one of ordinary skill in the art to modify the system of Bostic with the teaching of Benoit with the motivation of preventing potentially fraudulent activity/transactions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Rachel L Porter whose telephone number is (571)272-6775. The examiner can normally be reached M-F, 10-6:30.
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RACHEL L. PORTER
Primary Examiner
Art Unit 3684
/Rachel L. Porter/ Primary Examiner, Art Unit 3684
Prosecution Insights