DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 09/25/2025.
Claims 1 and 10 have been amended.
Claims 1-3, 10-11 and 18-30 are currently pending and have been examined.
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 .
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 1-3, 10-11 and 18-30 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-3, 10-11 and 18-30 are directed to a method (i.e., a process). Accordingly, claims 1-3, 10-11 and18-30 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 8 includes limitations that recite an abstract idea. Note that independent claims 1 and 10 cover method claims.
Specifically, independent claim 1 recites:
A method of generating variable entries in a clinical statement for a patient health record comprising:
preparing a customized user interface, the customized user interface tailored for a clinician role and a patient encounter type;
based on the clinician role and the patient encounter type, conducting a first series of randomization algorithms for randomizing a sentence structure of the clinical statement to increase variability and compliance with regulatory requirements and generating a randomized sentence structure;
conducting a second series of randomization algorithms for randomizing a structure of clinical finding value to increase variability and compliance with regulatory requirements and generating a randomized clinical finding value structure;
conducting a third series of randomization algorithms for randomizing a structure of a patient name to increase variability and compliance with regulatory requirements and generating a randomized patient name structure,
wherein the first, second, and third series of randomization algorithms are conducted by a system comprising one or more processors in communication with the customized user interface, the one or more processors having at least one memory storage device capable of storing protocols executable by the one or more processors, the protocols causing the one or more processors to conduct the first, second, and third series of randomization algorithms;
receiving, via the customized user interface, a first input corresponding to a clinical finding value, the clinical finding value input via the customized user interface;
receiving, via the customized user interface, a second input corresponding to a patient name;
generating the clinical statement based on the received first input and the received second input, the clinical statement including at least one variable entry generated using/based on the randomized sentence structure, randomized clinical finding value structure, and randomized patient name structure;
receiving, via the customized user interface, a third input corresponding to an accuracy of the generated clinical statement; and
generating a final clinical statement based on the received third input, the final clinical statement generated in a format compliant with regulatory requirements prohibiting duplication of clinical notes, thereby improving the accuracy and integrity of electronic health records.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because generating variable entries in a clinical statement and generating a final clinical statement based on the received input are a part of a medical workflow, which is managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “mathematical concepts” because generating the clinical statement based on the received first input and the received second input, the clinical statement including at least one variable entry that corresponds to the randomized sentence structure, randomized clinical finding value structure, and randomized patient name structure is using math. The foregoing underlined limitations also relate to claim 1 (similarly to claim 10).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2-3, 11 and 18-30, these claims merely recite determining steps such as: claim 2 - generating the clinical statement comprises concatenating results from the first series of randomization algorithms, claim 3 - conducting a fourth series of randomization algorithms for randomizing a structure of a clinician name and generating a randomized clinician name structure, claim 11 – generating an encounter statement comprises generating more than one encounter statement, each generated encounter statement based on the received first input, claim 18 - a clinical finding value comprises receiving, through the customized user interface, a risk factor impacting a care plan for a patient, claim 19 - a clinical finding value comprises receiving, through the customized user interface, a level of engagement for a patient, claim 20 - a clinical finding value comprises receiving, through the customized user interface, a drug profile for a patient, claim 21 - the set of predictor parameters includes daily progress notes per day, long notes per day, orders per day, a length of stay, and an attending parameter, claim 22 – outputting a first variable entry corresponding to the clinical finding value received as the first input and the randomized clinical finding value structure, outputting a second variable entry corresponding to the patient name received as the second input and the randomized patient name structure and aggregating the first variable entry and the second variable entry into the clinical statemen, claim 23 – outputting a third variable entry corresponding to a clinical finding value received as a fourth input, the fourth input different from the first input, each of the first, second, and third variable entries unique from each other; and aggregating the third variable entry into the clinical statement, claim 24 - generating a second final encounter statement based on the received first input and second input, the second final encounter statement containing one or more variable narratives unique from the one or more variable narratives of the final encounter statement, claim 25 - corresponding to one or more clinical finding values, the third input different from the first input, claim 26 - displaying the final encounter statement on a customized user interface, claim 27 – receiving an additional variable narrative to the generated encounter statement a modification to the at least one variable narrative receiving, a deletion of a variable narrative and receiving, approval of the generated encounter statement, claim 28 - variable narratives contained in the final encounter statement are unique, claim 29 - a risk factor implementing care planning for a patient, a level of engagement for the patient, and a drug profile for the patient and claim 30 - variable narrative in the final encounter statement includes a unique variable sentence structure generated by a randomized sentence structure algorithm.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1 and 10, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations mathematically but for the recitation of generic computer components. That is, other than reciting a customized user interface to perform the limitations, nothing in the claim elements precludes the steps from practically being performed mathematically. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment performed using math but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the customized user interface is recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “conducting a first series of randomization algorithms for randomizing a sentence structure of the clinical statement and generating a randomized sentence structure,” “conducting a second series of randomization algorithms for randomizing a structure of clinical finding value and generating a randomized clinical finding value structure” and “conducting a third series of randomization algorithms for randomizing a structure of a patient name and generating a randomized patient name structure” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “receiving a first input corresponding to a clinical finding value, the clinical finding value input …,” and “receiving a second input corresponding to a patient name” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
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 the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 1, regarding the additional limitations of the customized user interface, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 8 and analogous independent claims 1 and 10 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-3, 10-11 and 18-30 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 10-11 and 18-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lucas (US 2021/0210184 A1).
Claim 1:
Lucas discloses A method of generating variable entries in a clinical statement for a patient health record (See generating data objectives in P0036, P0040-P0042, P0227, Fig. 4 regarding medical records mentioned in Abstract.) comprising:
preparing a customized user interface, the customized user interface tailored for a clinician role and a patient encounter type (See P0027, P0045 where user input, queries and commands by a physician, nurse, or other medical professional or representative construe tailored for a clinician. See diagnoses, therapies, outcomes, genetic markers, procedure, medication, treatment, or other data in P0024, P0033, P0043 and genetic profiling in P0061.);
based on the clinician role and the patient encounter type, conducting a first series of randomization algorithms for randomizing a sentence structure of the clinical statement to increase variability and compliance with regulatory requirements and generating a randomized sentence structure (See P0003 where compliance with regulatory requirements are established. See entity recognition (NER) model using random fields in P0155-P0158 representing top-level sentence, verb phrase, noun phrase, and prepositional phrase and P0176.);
conducting a second series of randomization algorithms for randomizing a structure of clinical finding value to increase variability and compliance with regulatory requirements and generating a randomized clinical finding value structure (See Fig. 4 and entity recognition (NER) model using random fields in P0155-P0158 processing progress note, pathology report, or other EHR/EMR documents.);
conducting a third series of randomization algorithms for randomizing a structure of a patient name to increase variability and compliance with regulatory requirements and generating a randomized patient name structure (See P0047 where structured, curated, or electronic medical or health records include patient’s name and entity recognition (NER) model using random fields in P0104, P0155-P0158.);
wherein the first, second, and third series of randomization algorithms are conducted by a system comprising one or more processors in communication with the customized user interface, the one or more processors having at least one memory storage device capable of storing protocols executable by the one or more processors, the protocols causing the one or more processors to conduct the first, second, and third series of randomization algorithms (See P0064 where the random forest model would allow randomization algorithms to be performed.);
receiving a via the customized user interface, first input corresponding to a clinical finding value, the clinical finding value input via the customized user interface (See [P0105] the word “cancer” may be given a starting score of 0.1, a following score of 0.7, and a standalone score of 0.2. A sentence analysis for the exemplary NER may find that because “stage” has a high probability for being a starting word and “iv” has a high probability for being a following word, that “______” may have a higher probability for being a following word that matches “stage iv ______” or “stage iv ______ cancer” in a phrase.);
receiving via the customized user interface, a second input corresponding to a patient name (See P0047 where structured, curated, or electronic medical or health records include patient’s name and entity recognition (NER) model using random fields in P0104, P0155-P0158.);
generating the clinical statement based on the received first input and the received second input, the clinical statement including at least one variable entry that corresponds to the randomized sentence structure, randomized clinical finding value structure, and randomized patient name structure (Exemplary in P0105 shown in Fig. 4 and entity recognition (NER) model using random fields in P0155-P0158 processing progress note pathology report, or other EHR/EMR documents.);
receiving a third input corresponding to an accuracy of the generated clinical statement (See [P0107] a patient onboarding form may have a header which lists patient information such as name, address, symptoms, medications; a progress note may have a table which allows the physician to catalog treatment options recommended to the patient, treatment options which were pursued from a previous visit, and any updates to the status of the patient relating to the treatment options pursued.); and
generating a final clinical statement based on the received third input (See summarizing conclusion made from the sequencing results in P0108. Also, see Field with corresponding Value in Fig. 2 [P0239] structured entities relating to diagnosis may be summarized with the final normalized entity, information from the entity structuring, and any confidence values generated during the classification and/or ranking/filtering.), the final clinical statement generated in a format compliant with regulatory requirements prohibiting duplication of clinical notes, thereby improving the accuracy and integrity of electronic health records (See P0078, P0092, P0099 and P0249 where a viewable PDF copy would prohibit duplication of clinical notes.).
Regarding claim 2, Lucas discloses the method of claim 1, wherein generating the clinical statement comprises concatenating results from the first series of randomization algorithms (See P0047 where structured, curated, or electronic medical or health records include patient’s name and entity recognition (NER) model using random fields in P0104, P0155-P0158. Also, see machine learning series of layers in P0111-P0112.).
Regarding claim 3, Lucas discloses the method of claim 1, further comprising conducting a fourth series of randomization algorithms for randomizing a structure of a clinician name and generating a randomized clinician name structure (See [P0041] The process of enumerating the known drugs into a list may include identifying clinical drugs prescribed by healthcare providers, pharmaceutical companies, and research institutions.).
Claim 10:
Lucas discloses A method of providing narrative variations in a clinical statement such that the clinical statement is in compliance with regulatory requirements prohibiting copy-paste and cloning in electronic health records (EHRs) (See generating data objectives in P0036, P0040-P0042, P0227 and Fig. 4. Also, see P0078, P0092, P0099 and P0249 where a viewable PDF copy would prohibit copy-paste and cloning of clinical notes.), the method comprising:
conducting a first randomization algorithm to prepare a clinical statement template (Besides random forest models in P0064, see neural networks (NN) including conventional random fields in P0157-P0158.);
receiving a first input corresponding to one or more clinical finding values (See exemplary document classifier in P0157-P0158 extracted from documents such as progress note, pathology report, or other EHR/EMR documents.);
conducting a second randomization algorithm to generate one or more random clinical finding value structures (See [P0105] the word “cancer” may be given a starting score of 0.1, a following score of 0.7, and a standalone score of 0.2. A sentence analysis for the exemplary NER may find that because “stage” has a high probability for being a starting word and “iv” has a high probability for being a following word, that “______” may have a higher probability for being a following word that matches “stage iv ______” or “stage iv ______ cancer” in a phrase.);
aggregating outputs from the first randomization algorithm and the second randomization algorithm to generate (See P0064 where the random forest model would allow randomization algorithms to be performed.);
an encounter statement based on the clinical statement template, the received first input and the generated one or more random clinical finding value structures, the encounter statement including at least one variable narrative (Exemplary in P0105 shown in Fig. 4 and entity recognition (NER) model using random fields in P0155-P0158 processing progress note pathology report, or other EHR/EMR documents.);
presenting the generated encounter statement to a clinician for review and modification (See physician provide updates in P0097 and [P0107] a patient onboarding form may have a header which lists patient information such as name, address, symptoms, medications; a progress note may have a table which allows the physician to catalog treatment options recommended to the patient, treatment options which were pursued from a previous visit, and any updates to the status of the patient relating to the treatment options pursued.); and
generating a final encounter statement based on clinician review and modification, the final encounter statement including one or more variable narratives (See summarizing conclusion made from the sequencing results in P0108. Also, see Field with corresponding Value in Fig. 2 [P0239] structured entities relating to diagnosis may be summarized with the final normalized entity, information from the entity structuring, and any confidence values generated during the classification and/or ranking/filtering. (See physician provide updates in P0097 and P0107 as clinician review and modification.).
Regarding claim 11, Lucas discloses the method of claim 10, wherein generating an encounter statement comprises generating more than one encounter statement, each generated encounter statement based on the received first input (Shown in Fig. 2 the Field with corresponding Value mentioned in P0040, P0105.).
Regarding claim 18, Lucas discloses the method of claim 1, wherein receiving a first input corresponding to a clinical finding value comprises receiving, through the customized user interface, a risk factor impacting a care plan for a patient (See P0049-P0050 where target therapies remedy the growth of cancer cells as a risk. Also, see [P0061] the individual's genotype can be compared with the published literature to determine likelihood of trait expression and disease risk to enhance personalized medicine suggestions.).
Regarding claim 19, Lucas discloses the method of claim 1, wherein receiving a first input corresponding to a clinical finding value comprises receiving, through the customized user interface, a level of engagement for a patient (See symptoms that the patient brought to their physicians attention during a routine checkup in P0061 and P00246-P0247 where corresponding question and feedback serve as a level of engagement for a patient.).
Regarding claim 20, Lucas discloses the method of claim 1, wherein receiving a first input corresponding to a clinical finding value comprises receiving, through the customized user interface, a drug profile for a patient (See sources of patient history, treatments, medications, therapies, hospice, responses to treatments, laboratory and testing results in P0056 would allow the user to customize a drug profile for a patient.).
Regarding claim 21, Lucas discloses the method of claim 1, further comprising generating, via the customized user interface, a patient assessment form based on one or more of: the clinician role, the patient encounter type, and a type of assessment to be conducted (See Fig. 4, P0162 where the role of the clinician is to give the patient 50 mg of Tylenol and brachytherapy.).
Regarding claim 22, Lucas discloses the method of claim 1, wherein generating the clinical statement based on the received first input and the received second input comprises: outputting a first variable entry corresponding to the clinical finding value received as the first input and the randomized clinical finding value structure (See summarizing conclusion made from the sequencing results in P0108. Also, see Field with corresponding Value in Fig. 2 [P0239] structured entities relating to diagnosis may be summarized with the final normalized entity, information from the entity structuring, and any confidence values generated during the classification and/or ranking/filtering.);
outputting a second variable entry corresponding to the patient name received as the second input and the randomized patient name structure; and aggregating the first variable entry and the second variable entry into the clinical statement (See summarizing conclusion made from the sequencing results in P0108. Also, see Field with corresponding Value in Fig. 2 [P0239] structured entities relating to diagnosis may be summarized with the final normalized entity, information from the entity structuring, and any confidence values generated during the classification and/or ranking/filtering.).
Regarding claim 23, Lucas discloses the method of claim 22, further comprising: outputting a third variable entry corresponding to a clinical finding value received as a fourth input, the fourth input different from the first input, each of the first, second, and third variable entries unique from each other; and aggregating the third variable entry into the clinical statement (See [P0027] the system may detect when a patient record has been received, either partially or in full, and begin processing the patient record in aggregate or as a whole to determine relevant medical concepts for entry into the EMR. Also, see P0258 aggregating textual context data regarding a patient with cancer.).
Regarding claim 24, Lucas discloses the method of claim 10, further comprising generating a second final encounter statement based on the received first input and second input, the second final encounter statement containing one or more variable narratives unique from the one or more variable narratives of the final encounter statement (Shown in Fig. 2 the Field with corresponding Value mentioned in P0040, P0105.).
Regarding claim 25, Lucas discloses the method of claim 10, further comprising receiving a third input corresponding to one or more clinical finding values, the third input different from the first input (Besides performance scores, lab tests, pathology results and prognostic indicators in P0043, see Fig. 4 sample scores 95%, 97% and 85% mentioned in P0161-P00162.).
Regarding claim 26, Lucas discloses the method of claim 10, further comprising displaying the final encounter statement on a customized user interface (Taught as summarizing conclusion made from the sequencing results in P0108. Also, see Field with corresponding Value in Fig. 2 [P0239] structured entities relating to diagnosis may be summarized with the final normalized entity, information from the entity structuring, and any confidence values generated during the classification and/or ranking/filtering.).
Regarding claim 27, Lucas discloses the method of claim 10, wherein receiving a second input corresponding to an accuracy of the generated encounter statement comprises one or more of: receiving, via a customized user interface, an additional variable narrative to the generated encounter statement; receiving, via the customized user interface, a modification to the at least one variable narrative (See [P0160-P0161] if a document has been determined to have a high incidence of accuracy because a table on page 3 of a document may always return the correct gender for the patient, then the algorithm may identify that high accuracy has been provided for the document based on the one sentence of that document and stop processing a gender classification at the sentence level vector for that patient.); receiving, via the customized user interface, a deletion of a variable narrative; and receiving, via the customized user interface, approval of the generated encounter statement (See [P0171-P0172] for each mismatch in character, operations may be performed to elicit a match. For example, a mismatching character may be deleted, and the next character considered for a hit, which would account for having an extraneous character in a word, a character may be inserted at the mismatching character.).
Regarding claim 28, Lucas discloses the method of claim 10, wherein each of the one or more variable narratives contained in the final encounter statement are unique (See P0093-P0094, [P0173] Fuzzy matching is structured around the text concepts included in the above enumerated list or the UMLS, including metadata fields CUI (the UMLS unique ID) and AUI (dictionary-specific unique ID), so that an exhaustive search may be performed for all medical concepts.).
Regarding claim 29, Lucas discloses the method of claim 10, wherein receiving a first input corresponding to one or more clinical finding values comprises receiving at least one of: a risk factor implementing care planning for a patient, a level of engagement for the patient, and a drug profile for the patient (See P0049-P0050 where target therapies remedy the growth of cancer cells as a risk. Also, see [P0061] the individual's genotype can be compared with the published literature to determine likelihood of trait expression and disease risk to enhance personalized medicine suggestions.).
Regarding claim 30, Lucas discloses the method of claim 10, wherein each variable narrative in the final encounter statement includes a unique variable sentence structure generated by a randomized sentence structure algorithm (See [P0160-P0161] if a document has been determined to have a high incidence of accuracy because a table on page 3 of a document may always return the correct gender for the patient, then the algorithm may identify that high accuracy has been provided for the document based on the one sentence of that document and stop processing a gender classification at the sentence level vector for that patient.).
Response to Arguments
Applicant’s remarks, see page 10, filed 09/25/2025, with respect to the drawing objectives have been fully considered and are persuasive. The objection of FIG 1-5 has been withdrawn.
Applicant argues that the amendments to claims 1 and 10recite specific structural and functional limitations that overcome the present patentability rejection by reciting concrete technological implementations and practical applications beyond a mere abstract idea, e.g. see pgs. 12-13 & 15 of Remarks – Examiner disagrees.
Besides no technological implementations or improvements being claimed, the instant case is not solving a problem in assisting regulatory requirements in the electronic health records (EHR) field. Implementing randomization algorithms, prohibiting duplication, copy-paste and cloning are basic data processing tasks that a generic computer would be expected to do, especially using basic PDF file formatting. Also, no technological implementations or improvements to the functioning of the computer itself have been genuinely set forth and are nonetheless directed towards improving the abstract idea and not the computer itself – that is, the recited invention may improve variability and compliance with regulatory requirements (i.e. the abstract idea), but there is no evidence to show that it improves the structural or functional properties of the computer itself, outside of improving the computer specifically for implementing the abstract idea.
Applicant's arguments filed 09/25/2025 have been fully considered but they are not persuasive. The revised amendments do not sufficiently overcome the art rejection. See Lucas’ Abstract, P0003, P0064, P0078, P0092, P0099 and P0249.
Conclusion
THIS ACTION IS MADE FINAL. 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 TERESA S WILLIAMS whose telephone number is (571)270-5509. The examiner can normally be reached Mon-Fri, 8:30 am -6:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/T.S.W./Examiner, Art Unit 3687 01/02/2026
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687