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 .
DETAILED ACTION
This is the first action on the merits. Claims 1-20 are currently pending.
Priority
This application claims priority from Provisional Application Nos. 63635873 dated 04/18/2024 and from Provisional Application Nos. 63559525 dated 02/29/2024.
Drawings
The drawings are further objected to as failing to comply with 37 CFR 1.84(I) because the following figure(s) is/are unreadable and/or are unsatisfactory for reproduction:
Fig. 10A, 10B
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and a system for medication review reconciliation, which are within a statutory category.
Regarding claims 1, 8 and 15, the limitation of (claim 1 being representative) retrieving a plurality of data attributes associated with a visit for a patient; collecting medication data and care provider information associated with the patient; synthesizing one or more care optimizations based on the collected medication data and the care provider information as input; generating text output that includes the synthesized one or more care optimizations based on the collected medication data and the care provider information; presenting including the generated text output for display; receiving a selection of a care optimization; and causing the care optimization to be performed and regarding claim 15- the limitation of identifying a care provider at a facility based on an indication of arrival of the patient at the facility as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e. managing personal behavior including following rules or instructions) but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to retrieve a plurality of data attributes, collect medication data and care provider information, identify care provider, generate user interface, synthesize one or more care optimizations, generate text output, present the generated text output for display, receive a selection of a care optimization, and cause the care optimization to be performed in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “retrieving… collecting… identifying, generating… synthesizing… generating… presenting… receiving and causing care optimization” as indicated supra. Other than reciting generic computer components i.e. the one or more processors and memory in claim 8, the claimed invention amounts to managing personal behavior or interaction between people (i.e., rules or instructions). The Examiner notes that Claims 1 and 15 are not tied to any particular technological environment. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People (e.g. social activities, teaching, following rules or instructions)” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claim further recites “one or more machine learning models.” When given the broadest reasonable interpretation in light of the disclosure, using the one or more machine learning models to synthesizing one or more care optimizations as described in the specification represent the creation of mathematical interrelationships between data, see Spec. Para. [0120]-[0123]. Thus given the broadest reasonable interpretation, the Examiner interprets the one or more machine learning models to be implemented using existing, known mathematical techniques and interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
This judicial exception is not integrated into a practical application. Claims 1 and 15 are not tied to any particular technological environment. Claim 8 recites the additional elements of one or more processors and memory. These additional elements are not exclusively defined by the applicant and are recited at a high-level of generality (i.e., a generic computer components for enabling access to medical information or for performing generic computer functions, see Spec. at para. [0069]-[0071]) such that they amounts to no more than mere instructions to apply the exception using a generic computer component. As set forth in MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 1, 8 and 15 further recite the additional element of a user interface and a client device. These additional element are recited at a high level of generality (i.e. a general means to generate/present/display/receive data) and amount to extra solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Claims 1 and 8 also recites the additional element of one or more machine learning models to synthesizing one or more care optimizations. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the one or more machine learning models to synthesizing one or more care optimizations merely confines the use of the abstract idea (i.e., the machine learning model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of the one or more processors and memory to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Moreover, using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Also as discussed with respect to integration of the abstract idea into a practical application, the additional elements of a user interface and a client device were considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving and/or transmitting data over a network has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). Well-understood, routine and conventional activity cannot provide an inventive concept (“significantly more”). As such the claim is not patent eligible.
Also as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the one or more machine learning models to synthesizing one or more care optimizations was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the machine learning model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
The examiner notes that: A well-known, general-purpose computer has been determined by the courts to be a well-understood, routine and conventional element (see, e.g., Alice Corp. v. CLS Bank; see also MPEP 2106.05(d)); and Performing repetitive calculations is/are also well-understood, routine and conventional computer functions when they are claimed in a merely generic manner (see, e.g., Parker v. Flook; MPEP 2016.05(d)).
Claims 2-7, 9-14 and 16-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim(s) 2 and 9 further merely describe(s) determining clinical data, selecting a machine learning model, determining a portion of the generated text output and presenting the user interface, which further define the abstract idea. Claim(s) 3 and 10 further merely describe(s) generating and sending a notification and receiving an acceptance. Claim(s) 4 and 11 further merely describe(s) detecting arrival, generating a notification and presenting a medication reconciliation interface. Claim(s) 4 and 11 also include the additional element of “a medication reconciliation interface” which is interpreted the same as the user interface and does not provide a practical application nor significantly more for the same reasons. Claim(s) 5 and 12 further merely describe(s) analyzing the collected medication data, generating additional text output and presenting the additional text output. Claim(s) 6 and 13 further merely describe(s) the one or more care optimizations. Claim(s) 7, 14 and 17-20 further merely describe(s) the care optimization to be performed. Claim(s) 16 further merely describe(s) generating and prioritizing the one or more care optimizations. Claims 2-7, 9-14 and 16-20 further define the abstract idea and are rejected for the same reason presented above with respect to claims 1, 8 and 15.
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, 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 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 Owen (US 2019/0051381), in view of Moturu US (2017/0235912) and in further view of De Vries (US 2023/0170065).
REGARDING CLAIM 1
Owen discloses a method comprising: retrieving a plurality of data attributes associated with a visit for a patient ([0071] teaches collection and processing of clinical encounter information (interpreted by examiner as the plurality of data attributes associated with a visit for a patient) associated with the patient encounter (e.g., the visit to the doctor's office));
Owen does not explicitly disclose, however Moturu discloses:
receiving a selection of a care optimization from the client device; and causing the care optimization to be performed ([0058] teaches selecting an initial list of potential therapeutic interventions, narrowing the initial list based on patient digital behavior information, symptoms, drug information, and/or other suitable information, and presenting the narrowed list of potential therapies to a care provider for providing decision support. Additionally or alternatively, a prioritized list of therapeutic interventions (e.g., prioritized based on strength of model recommendation) can be presented to the care provider (interpreted by examiner as receiving a selection of a care optimization from the client device). User response to the therapeutic interventions can be monitored (e.g., through data collected etc.) and used in updating therapeutic interventions, which can include one or more of: modifying dosage (e.g., increasing or decreasing dosage, changing frequency, etc.), augmenting the medication regimen (e.g., adding medication types, removing medication types, etc.), switching a medication type, and/or any other suitable modifications (interpreted by examiner as causing the care optimization to be performed)).
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the patient visits of Owen to incorporate the care optimization as taught by Moturu, with the motivation of improving system processing ability for supporting care providers. (Moturu at [0017]).
Owen and Moturu do not explicitly disclose, however De Vries discloses:
collecting medication data and care provider information associated with the patient (De Vries at [0021] teaches other provider reported data can include medications prescribed for specific patient types and diagnoses and [0023] teaches the received data can include historical information on the patient’s medications (interpreted by examiner as the medication data) [0030] teaches a subset of the received healthcare information data is provider data that characterizes provider actions (e.g., prescribing behaviors, quality metrics, intervention types, and intervention frequencies) (interpreted by examiner as the care provider information)); using one or more machine learning models, synthesizing one or more care optimizations based on the collected medication data and the care provider information as input to the one or more machine learning models (De Vries at [0049] teaches using a recommendation rules engine (interpreted by examiner as the one or more machine learning models) to generate the treatment recommendation (interpreted by examiner as the one or more care optimizations) and that the recommendation rules engine can analyze inputs (interpreted by examiner to be the collected medication data and the care provider information) and execute the rules on the inputs to determine a treatment recommendation. [0044] teaches optimizing health outcome, [0050] teaches optimizing recommendation, [0061] teaches optimizing treatment intervention and [0070] teaches optimizing the dose of a patient’s medications. [0060]-[0061] teaches the use of machine learning models.); generating text output at a user interface that includes the synthesized one or more care optimizations based on the collected medication data and the care provider information; presenting the user interface including the generated text output for display on a client device associated with the patient (De Vries at [0032] teaches that characterize patients health. [0007] teaches presentation recommendation (interpreted by examiner as the generated text output that includes the synthesized one or more care optimizations based on the collected medication data and the care provider information) on a graphical user interface of a client device (interpreted by examiner as presenting the user interface including the generated text output for display on a client device associated with the patient));
It would have been obvious for one of the ordinary skill in the art before the effective filling date of the claimed invention to have modified the patient visits of Owen and the care optimization of Moturu to incorporate collecting medication data and care provider information, using one or more machine learning models, synthesizing one or more care optimizations, generating text output at a user interface and presenting the user interface for display on a client device as taught by De Vries, with the motivation of providing patients access to highly needed treatment education and counseling over the course of their care, thereby improving a sustained rate of medication errors. (De Vries at [0004]).
REGARDING CLAIM 2
Claim 2 is analogous to Claim 1 Claim 2 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
De Vries further discloses determining clinical data associated with the patient, the clinical data including one or more diseases associated with the patient, one or more treatments for the one or more diseases, one or more symptoms associated with the one or more diseases and one or more drug protocol regimens for treatment of the one or more diseases associated with the patient (De Vries [0007] teaches a clinical patient profile can be determined for the patient based on the received healthcare information data and the determined health outcome evaluation. [0028] teaches determining disease profile (e.g., diagnoses, duration, utilization by disease) and [0034] teaches patient reported symptoms. [0060] teaches that if a patient has uncontrolled type 2 diabetes and concurrent cardiovascular disease, the initial recommendation is to recommend a glucagon-like peptide-1 agonist or sodium-glucose co-transporter-2 inhibitors (interpreted by examiner as one or more treatments for the one or more diseases and one or more drug protocol regimens for treatment)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen and Moturu are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 3
Owen, Moturu and De Vries disclose the limitation of claim 1.
Owen and Moturu do not explicitly disclose, however De Vries further discloses:
The method of claim 1, further comprising: generating a notification regarding a suggested action based on priority; sending the notification to the client device associated with the patient; and receiving acceptance of the suggested action from the client device (De Vries at [0018] teaches generate prioritized treatment recommendations for patients. [0057] teaches the suggested treatment recommendations can drive the creation of a task associated with the patient. These tasks can in turn be prioritized in a list format such that platform users can view suggested actions (interpreted by examiner as generating notifications regarding the suggested action based on priority and sending to the client device) aimed at reducing the patient risk. A task can be created to follow-up with the provider if they did not respond to the treatment recommendation as evidenced by a lack of medication change in the health outcome evaluation data and [0058] teaches the implementation of the suggested treatment recommendations by a patient and/or a provider can be continuously measured and recorded (interpreted by examiner as means receiving acceptance of the suggested action from the client device)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen and Moturu are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 4
Owen, Moturu and De Vries disclose the limitation of claim 1.
Owen further discloses:
The method of claim 1, further comprising: detecting arrival of the patient at a facility ([0085] teaches upon arriving for the patient encounter (e.g., encounter participant 228 visiting the doctor's office), the patient (e.g., encounter participant 228) may be directed to a “check-in” area within the monitored space (e.g., monitored space 130) of the clinical environment (interpreted by examiner as detecting arrival)); generating a notification at a client device associated with a care provider assigned to the patient at the facility, the notification including the medication data ([0109] teaches notification to medical professional concerning potential medical situation and [0106] teaches medical situation such as potential medication issue for example (interpreted by examiner as generating a notification including medication data)); responsive to selection of the notification, presenting a medication reconciliation interface for display on a client device associated with the care provider assigned to the patient at the facility, the medication reconciliation interface including the selected care optimization by the patient ([0109] teaches if a potential medical situation is identified, automated clinical documentation process may initiate an inquiry concerning the potential medical situation and provide a notification to the medical professional concerning the potential medical situation. Example of such an inquiry may be asking one or more questions, such as “Does this patient have diabetes?”, “Should we arrange home healthcare for this patient?” of “Would you like to substitute Medication Y for Medication X?”. [0056] teaches a display rendering system 108 may include one or more of each of a tablet computer, a computer monitor, and a smart television and [0110] teaches a private display device (interpreted by examiner as presenting the medication reconciliation interface for display on a client device including the selected care optimization by the patient)).
REGARDING CLAIM 5
Claim 5 is analogous to Claim 1 thus Claim 5 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
REGARDING CLAIM 6
Owen, Moturu and De Vries disclose the limitation of claim 1.
Owen and De Vries do not explicitly disclose, however Moturu further discloses:
The method of claim 5, wherein the one or more care optimizations are prioritized by the analyzed medication data (Moturu at [0058] teaches a prioritized list of therapeutic interventions (e.g., prioritized based on strength of model recommendation) can be presented to the care provider. User response to the therapeutic interventions can be monitored (e.g., through data collected) and used in updating therapeutic interventions, which can include one or more of: modifying dosage (e.g., increasing or decreasing dosage, changing frequency, etc.), augmenting the medication regimen (e.g., adding medication types, removing medication types, etc.), switching a medication type, and/or any other suitable modifications (interpreted by examiner as the one or more care optimizations are prioritized by the analyzed medication data)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen and De Vries are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 7
Owen, Moturu and De Vries disclose the limitation of claim 1.
Owen and De Vries do not explicitly disclose, however Moturu further discloses:
The method of claim 1, wherein the care optimization to be performed is prompting a care provider assigned to the patient to inform the patient of one or more alternative medications during the visit ([0058] teaches a therapy recommendation modification of switching from medication “A” to medication “B” can be generated and presented to the care provider (interpreted by examiner as prompting a care provider assigned to the patient to inform the patient of one or more alternative medications)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen and De Vries are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIMS 8-14 and 16
Claims 8-14 and 16 are analogous to Claims 1-7 thus Claims 8-14 and 16 are similarly analyzed and rejected in a manner consistent with the rejection of Claims 1-7.
REGARDING CLAIM 15
Claim 15 is analogous to Claims 1 and 8 thus Claim 15 is similarly analyzed and rejected in a manner consistent with the rejection of Claims 1 and 8.
Owen discloses identifying a care provider at a facility based on an indication of arrival of the patient at the facility ([0085] teaches upon arriving for the patient encounter (e.g., encounter participant 228 visiting the doctor's office), the patient (e.g., encounter participant) may be directed to a “check-in” area within the monitored space (of the clinical environment. An example of this “check-in” area may include a booth into which the patient enters. Upon entering this “check-in” area, the pre-visit portion (e.g., the patient intake portion) of the patient encounter may begin and [0142] teaches assign a first role to the first encounter participant, a role for encounter participant may be assigned if that identity defined is associated with a role (e.g., the identity defined for encounter participant is Doctor Susan Jones) (interpreted by examiner as identifying a care provider at a facility based on an indication of arrival of the patient at the facility));
REGARDING CLAIM 17
Owen, Moturu and De Vries disclose the limitation of claim 15.
Owen and De Vries do not explicitly disclose, however Moturu further discloses:
The method of claim 15, wherein the care optimization to be performed is confirming the medication data with the patient after the visit (Moturu at [0040] teaches therapeutic interventions prescribed to patients (e.g., prescribed and/or verified as efficacious for patients sharing the condition, patients with shared medical parameters; etc.) (interpreted by examiner as the care optimization to be performed is confirming the medication data with the patient after the visit)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen and De Vries are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 18
Owen, Moturu and De Vries disclose the limitation of claim 15.
Owen and De Vries do not explicitly disclose, however Moturu further discloses:
The method of claim 15, wherein the care optimization to be performed is updating the medication data with the patient during the visit ([0056] teaches diagnostic analysis can guide further diagnostic steps that can be taken by the care provider (e.g., a series of questions to ask the patient). The care provider can input patient data (e.g., patient answers to care provider questions) into a computing device (e.g., an application executing on the smartphone of the care provider), where generating medical status analysis can include generating an updated diagnostic analysis based on the care provider-inputted data and [0058] teaches user response to the therapeutic interventions can be monitored (e.g., through data collected in Blocks S110-S125, etc.) and used in updating therapeutic interventions, which can include one or more of: modifying dosage (e.g., increasing or decreasing dosage, changing frequency, etc.), augmenting the medication regimen (e.g., adding medication types, removing medication types, etc.), switching a medication type, and/or any other suitable modifications (interpreted by examiner as the care optimization to be performed is updating the medication data with the patient during the visit)).
The obviousness of combining the teachings of Moturu within the systems taught by Owen and De Vries are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 19
Owen, Moturu and De Vries disclose the limitation of claim 15.
Owen and Moturu do not explicitly disclose, however De Vries further discloses:
The method of claim 15, wherein the care optimization to be performed is generating a new machine learning model based on the received medication data (De Vries at [0061] teaches the machine learning model can identify the predictors of diabetes control via measurement of hemoglobin A1c as an indicator of treatment success. Using the identified predictors of success, the system can generate additional logic (e.g., modifications to a rule, a new rule, a deletion of a rule, and the like) that can be inserted into the recommendation rules engine (interpreted by examiner as means for generating a new machine learning model based on the received medication data)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen and Moturu are discussed in the rejection of claim 1, and incorporated herein.
REGARDING CLAIM 20
Owen, Moturu and De Vries disclose the limitation of claim 1.
Owen and Moturu do not explicitly disclose, however De Vries further discloses:
The method of claim 15, wherein the care optimization to be performed is generating a determination of sentiment of user feedback received (De Vries at [0007] teaches the treatment recommendation rules engine can be modified by a predictive model that identifies a predictor variable characterizing a likelihood of success of an intervention characterized by the treatment recommendation, the identifying based on received feedback data that indicates a level of success of the intervention, determines a modification to the recommendation rule based on the identified predictor variable, and modifies the recommendation rule based on the determined modification and the behavioral risk parameter can be dynamically updated based on received feedback data characterizing the patient (interpreted by examiner as the care optimization to be performed is generating a determination of sentiment of user feedback received)).
The obviousness of combining the teachings of De Vries within the systems taught by Owen and Moturu are discussed in the rejection of claim 1, and incorporated herein.
Conclusion
The prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
DeFrank (US 2020/0118164) teaches integrated mobile device management system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIZA TONY KANAAN whose telephone number is (571)272-4664. The examiner can normally be reached on Mon-Thu 9:00am-6:00pm ET.
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/L.T.K./Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683