DETAILED ACTION
The present office action represents a final action on the merits.
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 .
Priority
This application claims the priority date of July 29, 2024.
Status of Claims
Claims 1, 6, 8-9, 12, and 16-17 are amended, claim 5 is cancelled, claim 21 is added, and claims 1-4 and 6-21 are pending.
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 1-4 and 6-21 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 1-4, 6-7, and 21 are drawn to a method implemented in a communication network, which is within the four statutory categories (i.e., process). Claims 8-15 are drawn to a method implemented in a communication network, which is within the four statutory categories (i.e., process). Claims 16-20 are drawn to a healthcare facility system, which is within the four statutory categories (i.e., machine).
Claims 1-4, 6-7, and 21 recite a method implemented in a communication network including to provide artificial intelligence enhanced communications between healthcare workers for patient care and documentation, wherein the method comprises:
transmitting, by one or more first devices in the communication network, medical data associated with a patient to a healthcare facility system in the communication network, wherein the medical data comprises a recording of a conversation between a first healthcare worker and the patient and at least one of biometric data of the patient or current symptoms experienced by the patient, and wherein the one or more first devices are operated by the first healthcare worker;
identifying, by a routing application of the healthcare facility system, using a neural network artificial intelligence model, a second device operated by a second healthcare worker based on, wherein the second device is positioned at least a predefined distance away from the one or more first devices and wherein the neural network artificial intelligence model is trained with healthcare worker expertise data describing medical specialties and experience levels of a plurality of healthcare workers, healthcare worker capacity data describing availability of the plurality of healthcare workers, and historical patient interaction data correlating patient medical conditions with optimal healthcare worker assignments;
converting, by a record application of the healthcare facility system, using a speech- to-text algorithm, the recording of the conversation between the first healthcare worker and the patient into text of the conversation, wherein the text of the conversation includes a transcript of the conversation between the first healthcare worker and the patient;
verifying, by the record application, using the neural network artificial intelligence model, accuracy of the text by comparing the text with medical notes received from the one or more first devices to obtain verified converted medical data, wherein the neural network artificial intelligence model is trained with medical terminology data;
transmitting, by the record application, the verified converted medical data to the second device operated by the second healthcare worker;
receiving, by the record application from the second device operated by the second healthcare worker, records comprising data describing at least one of the patient being at least one of admitted into a healthcare facility, tested for one or more conditions, treated for the one or more conditions, medicated with one or more medications, or discharged from the healthcare facility;
determining, by a medical application of the healthcare facility system, using the neural network artificial intelligence model, one or more medical recommendations based on a pattern identified in the medical data and historical medical data associated with a plurality of prior patients, wherein the neural network artificial intelligence model is trained with the historical medical data comprising symptoms data, diagnosis data, and treatment data from the prior patients to identify correlations between specific combinations of patient symptoms, biometric data, and successful treatment outcomes;
transmitting, by the medical application, the one or more medical recommendations to the second device operated by the second healthcare worker, wherein each of the one or more medical recommendations indicates a task to be performed by the first healthcare worker with regard to the patient;
receiving, by the medical application, from the second device operated by the second healthcare worker, a confirmation of at least one of the medical recommendations; and
transmitting, by the medical application, the at least one of the medical recommendations to the one or more first devices operated by the first healthcare worker.
Claims 8-15 recite a method implemented in a communication network including to provide artificial intelligence enhanced communications between healthcare workers for patient care and documentation, wherein the method comprises:
receiving, by a healthcare facility system in the communication network, medical data associated with a patient from one or more first devices associated with a first healthcare worker, wherein the medical data comprises a recording of a conversation between the first healthcare worker and the patient and at least one of biometric data of the patient or current symptoms experienced by the patient;
converting, by a record application of the healthcare facility system using a speech- to-text algorithm, the recording of the conversation between the first healthcare worker and the patient into text of the conversation, to obtain converted medical data, wherein the text
verifying, by the record application, using a neural network artificial intelligence model, accuracy of the text by comparing the text with medical notes received from the one or more first devices to obtain verified converted medical data, wherein the neural network artificial intelligence model is trained with medical terminology data;
transmitting, by the record application, the verified converted medical data to a second device operated by a second healthcare worker;
receiving, by the record application from the second device operated by the second healthcare worker, a patient record comprising data describing the patient being at least one of admitted into a healthcare facility, tested for one or more conditions, treated for the one or more conditions, medicated with one or more medications, or discharged from the healthcare facility;
determining, by a medical application of the healthcare facility system, using the neural network artificial intelligence model, one or more medical recommendations based on a pattern identified in the medical data and historical medical data associated with a plurality of prior patients, wherein the neural network artificial intelligence model is trained with the historical medical data comprising symptoms data, diagnosis data, and treatment data from the prior patients to identify correlations between specific combinations of patient symptoms, biometric data, and successful treatment outcomes;
transmitting, by the medical application to the second device operated by the second healthcare worker, the one or more medical recommendations, wherein each of the one or more medical recommendations represents a task to be performed by the first healthcare worker with regard to the patient;
receiving, by the medical application, from the second device operated by the second healthcare worker, a confirmation of at least one of the medical recommendations; and
transmitting, by the medical application, the at least one of the medical recommendations to the one or more first devices operated by the first healthcare worker.
Claims 16-20 recite a healthcare facility system, comprising:
a non-transitory memory;
a processor coupled to the non-transitory memory;
a record application stored at the non-transitory memory, which when executed by the processor, causes the processor to be configured to:
receive medical data associated with a patient from one or more first devices associated with a first healthcare worker, wherein the medical data comprises a recording of a conversation between the first healthcare worker and the patient and at least one of biometric data of the patient or current symptoms experienced by the patient;
convert, using a speech-to-text algorithm, the recording of the conversation between the first healthcare worker and the patient into text of the conversation to obtain converted medical data, wherein the text of a conversation includes a transcript of the conversation between the first healthcare worker and the patient;
verify, using a neural network artificial intelligence model, accuracy of the text by comparing the text with medical notes received from the one or more first devices to obtain verified converted medical data, wherein the neural network artificial intelligence model is trained with medical terminology data:
transmit the verified converted medical data to a second device operated by a second healthcare worker; and
receive, from the second device operated by the second healthcare worker, a patient record comprising data describing the patient being at least one of admitted into a healthcare facility, tested for one or more conditions, treated for the one or more conditions, medicated with one or more medications, or discharged from the healthcare facility;
a medical application stored at the non-transitory memory, which when executed by the processor, causes the processor to be configured to:
determine, using the neural network artificial intelligence model, one or more medical recommendations based on a pattern identified in the medical data and historical medical data associated with a plurality of prior patients, wherein the neural network artificial intelligence model is trained with the historical medical data comprising symptoms data, diagnosis data, and treatment data from the prior patients to identify correlations between specific combinations of patient symptoms, biometric data, and successful treatment outcomes;
transmit the one or more medical recommendations to the second device operated by the second healthcare worker, wherein each of the one or more medical recommendations represents a task to be performed by the first healthcare worker with respect to the patient; and
automatically perform at least one of the one or more medical recommendations in response to a confirmation received from the second device to perform the at least one of the one or more medical recommendations.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity or mathematical concepts, but for the recitation of generic computer components (e.g., gathering patient information; managing patient information, in this case providing artificial intelligence enhanced communications between healthcare workers for patient care and documentation). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity or math concepts) and are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2-4, 6-7, 9-15, and 17-21 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. These limitations only serve to further limit the abstract idea (or contain the same additional elements found in the independent claim), and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 8, and 16.
The additional elements from claims 1, 8, and 16 include:
one or more first devices (apply it, MPEP 2106.05(f)).
a second device (apply it, MPEP 2106.05(f)).
a speech-to-text algorithm (apply it, MPEP 2106.05(f)).
The additional elements from claims 1 and 8 include:
the communication network (apply it, MPEP 2106.05(f)).
a healthcare facility system in the communication network (apply it, MPEP 2106.05(f)).
a record application of the healthcare facility system (apply it, MPEP 2106.05(f)).
a medical application of the healthcare facility system (apply it, MPEP 2106.05(f)).
The additional elements from claim 1
a routing application of the healthcare facility system (apply it, MPEP 2106.05(f)).
The additional elements from claim 16 include:
a healthcare facility system (apply it, MPEP 2106.05(f)).
a non-transitory memory (apply it, MPEP 2106.05(f)).
a processor coupled to the non-transitory memory (apply it, MPEP 2106.05(f)).
a record application stored at the non-transitory memory, which when executed by the processor, causes the processor to be configured to (apply it, MPEP 2106.05(f)).
a medical application stored at the non-transitory memory, which when executed by the processor, causes the processor to be configured to (apply it, MPEP 2106.05(f)).
The dependent claims contain additional elements other than those recited in the independent claims, including:
at least one of a portable handheld device or a wearable device (apply it, MPEP 2106.05(f)).
the one or more first devices comprise a radio transceiver (apply it, MPEP 2106.05(f)).
one or more medical devices (apply it, MPEP 2106.05(f)).
via a user interface of the one or more first devices (apply it, MPEP 2106.05(f)).
a voice biometrics application (apply it, MPEP 2106.05(f)).
a routing application stored at the non-transitory memory, which when executed by the processor, causes the processor to be configured to (apply it, MPEP 2106.05(f)).
These additional elements, in the independent claims are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of one or more first devices, a speech-to-text algorithm, a second device, the communication network, a healthcare facility system, a record application, a medical application, a routing application, a non-transitory memory, a processor, at least one of a portable handheld device, a wearable device, a radio transceiver, one or more medical devices, a user interface, voice biometrics application, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g., see Specification paragraphs [0021]-[0025], [0029]-[0032], [0092], and [0106] (See MPEP 2106.05(f)).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., the Specification paragraphs [0021]-[0025], [0029]-[0032], [0092], and [0106] disclose that the additional elements (i.e., one or more first devices, a second device, a speech-to-text algorithm, the communication network, a healthcare facility system, a record application, a medical application, a routing application, a non-transitory memory, a processor, at least one of a portable handheld device, a wearable device, a radio transceiver, one or more medical devices, a user interface, voice biometrics application) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e., receiving and transmitting data) that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare, provide artificial intelligence enhanced communications between healthcare workers for patient care and documentation);
Relevant court decisions: The following example of court decision demonstrating well understood, routine and conventional activities, e.g., see MPEP 2106.05(d)(II): Receiving medical data, e.g., see Intellectual Ventures v. Symantec – similarly, the current invention receives medical data
Dependent claims 2-7, 9-15, and 17-21 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than receiving or transmitting data over a system (e.g., receive data claims 1, 4, 8, and 15-16.).
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves any other technology, and their collective functions merely provide conventional computer implementation.
The application, is an attempt to organize human activity or mathematical concepts. The inventive concept is methods and systems to provide artificial intelligence enhanced communications between healthcare workers for patient care and documentation, which is not patentable. Therefore, whether taken individually or as an ordered combination, claims 1-4 and 6-21 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free from Prior Art
Examiner acknowledges the limitations in claims 1, 8, and 16:
A method implemented in a communication network including to provide artificial intelligence enhanced communications between healthcare workers for patient care and documentation, wherein the method comprises:
transmitting, by one or more first devices in the communication network, medical data associated with a patient to a healthcare facility system in the communication network, wherein the medical data comprises a recording of a conversation between a first healthcare worker and the patient and at least one of biometric data of the patient or current symptoms experienced by the patient, and wherein the one or more first devices are operated by the first healthcare worker;
identifying, by a routing application of the healthcare facility system, using a neural network artificial intelligence model, a second device operated by a second healthcare worker based on the medical data associated with the patient, wherein the second device is positioned at least a predefined distance away from the one or more first devices, and wherein the neural network artificial intelligence model is trained with healthcare worker expertise data describing medical specialties and experience levels of a plurality of healthcare workers, healthcare worker capacity data describing availability of the plurality of healthcare workers, and historical patient interaction data correlating patient medical conditions with optimal healthcare worker assignments;
converting, by a record application of the healthcare facility system using a speech- to-text algorithm, the recording of the conversation between the first healthcare worker and the patient into text of the conversation, wherein the text of the conversation includes a transcript of the conversation between the first healthcare worker and the patient;
verifying, by the record application, using the neural network artificial intelligence model, accuracy of the text by comparing the text with medical notes received from the one or more first devices to obtain verified converted medical data, wherein the neural network artificial intelligence model is trained with medical terminology data;
transmitting, by the record application, the verified converted medical data to the second device operated by the second healthcare worker;
receiving, by the record application from the second device operated by the second healthcare worker, records comprising data describing at least one of the patient being at least one of admitted into a healthcare facility, tested for one or more conditions, treated for the one or more conditions, medicated with one or more medications, or discharged from the healthcare facility;
determining, by a medical application of the healthcare facility system, using the neural network artificial intelligence model, one or more medical recommendations based on a pattern identified in the medical data and historical medical data associated with a plurality of prior patients, wherein the neural network artificial intelligence model is trained with the historical medical data comprising symptoms data, diagnosis data, and treatment data from the prior patients to identify correlations between specific combinations of patient symptoms, biometric data, and successful treatment outcomes;
transmitting, by the medical application, the one or more medical recommendations to the second device operated by the second healthcare worker, wherein each of the one or more medical recommendations indicates a task to be performed by the first healthcare worker with regard to the patient;
receiving, by the medical application, from the second device operated by the second healthcare worker, a confirmation of at least one of the medical recommendations; and
transmitting, by the medical application, the at least one of the medical recommendations to the one or more first devices operated by the first healthcare worker.
are free from prior art when considered in combination with the other limitations, and are not subject to any prior art rejections under 103. The closest prior art is:
Subramanian (U.S. Pub. No. 2022/0199267 A1) (Paragraphs [0005], [0010], [0116], and FIG. 9 discuss allow remote intensivists to perform video assessment of patients, view bedside devices, interfaced vital signs, laboratory data and/or exchange clinical information through secure messaging and audio-video communications to allow efficient critical care support whenever and wherever it is needed and transmitting an electronic signal, by the one or more computer processors, to one or more network entities, the communication request including the request description; and a network entity of a third party is a communication device of a remote healthcare provider; the device includes a live, delayed, or recorded video, or image of patient and provider.);
Subramanian (U.S. Pub. No. 2022/0199267 A1) (Paragraphs [0005], [0013], [0028], [0072], [0102], [0224], discuss a platform and application that aggregate historical data for retrospective analysis, and a mechanism to utilize retrospective analysis to build a model which may i) provide better matching between healthcare provider and patient in need of medical assistance, using bedside devices; patients require monitoring, telemonitoring can be provided by using the patient's own personal health device, if they are wearing one, or with wireless body sensors that can be rapidly brought to the location of the event. An integrated clinical environment platform may be used to integrate multiple sensors, actuators, and medical devices from different vendors and to ensure the documentation of frequent spot checks or continuous stream of physiological data used by the local care team to augment local decision making, and/or by remote care experts, who can remotely monitor and/or silence alarms, change device settings, and/or directly care for patients while the local care team assists other patient; the machine learning algorithm may include one or more of neural networks and train a prediction model.);
Subramanian (U.S. Pub. No. 2022/0199267 A1) (Paragraphs [0006], [0013], and FIG. 29D discuss a platform and application gather data from environmental sensors and other nonmedical sensors in order to provide additional context to the scenario, extracts raw data from one or more of the disparate devices and sensors and maps the data to a uniform template; the data mapped to a uniform template shown to a remote healthcare provider via a mobile app dashboard). This can provide standardized data to facilitate a healthcare provider’s evaluation of the patient status; The collected and aggregated standardized data can be readily available for real time analysis and dashboards.);
Subramanian (U.S. Pub. No. 2022/0199267 A1) (Paragraphs [0013], [0060], and FIG. 29E discuss a platform and application and a dashboard with an eConsults feature for initiating an electronic consult by inputting relevant patient information for a consult request to be sent to a remote provider's queue, a patient census feature for the patient (which allows initiation of the consultation by selecting a particular patient), a provider directory allowing access to the list of remote healthcare providers available as on-call (e.g., indicated by a status indicator such as a green icon), and eRequests which allows routine requests such as PRN medications, labs, and other such requests to be sent from bedside clinicians in a different category to enhance the remote provider's workflow and streamline such requests for bedside clinicians.);
Subramanian (U.S. Pub. No. 2022/0199267 A1) (Paragraphs [0013] and [0124] discuss a platform and application and once the provider has completed their assessment of the patient, they can write an Order (e.g., medications, lab tests) for that patient (FIG. 7) or a Note (FIG. 7) that documents the provider's recommendations or observations. Orders and notes may be transmitted through secure back-end system to the patient's permanent health records and after the remote healthcare provider supports and advises the bedside user or clinician as needed, and both parties are satisfied that patient issues have been resolved, the remote healthcare provider may complete a summary note with an assessment and plan for the consultation.); and
Subramanian (U.S. Pub. No. 2022/0199267 A1 (Paragraphs [0013] and [0124] discuss a platform and application and once the provider has completed their assessment of the patient, they can write an Order (e.g., medications, lab tests) for that patient (FIG. 7) or a Note (FIG. 7) that documents the provider's recommendations or observations. Orders and notes may be transmitted through secure back-end system to the patient's permanent health records and after the remote healthcare provider supports and advises the bedside user or clinician as needed, and both parties are satisfied that patient issues have been resolved, the remote healthcare provider may complete a summary note with an assessment and plan for the consultation.).
It would not be obvious to combine all of the references, accordingly, the 103 rejection is withdrawn.
Response to Arguments
Applicant’s arguments filed 1/14/2026 have been fully considered.
Rejections under 35 U.S.C. 101:
With respect to claim 1 and the Prong 1 35 U.S.C. 101 rejection, Applicant’s amendment fails to overcome the previous rejection. Claim 1 as amended recites an abstract idea, a method of organizing human activity. See MPEP 2106.04(a)(2)(II)(C) Managing Personal Behavior or Relationships or Interactions Between People. Applicant argues, “the claims are directed to a specific technological solution: a neural network artificial intelligence model trained with multiple integrated healthcare-specific datasets to perform three distinct technical functions that improve healthcare system operations. Specifically, the claimed embodiments cannot be directed to a method for organizing human activity because the independent claims explicitly recite the training and use of a neural network artificial intelligence model as a technological tool to: (1) identify optimal healthcare worker assignments by processing patient medical data against healthcare worker expertise, experience levels, and real-time availability data; (2) verify transcription accuracy by comparing speech-to-text output against medical notes using medical terminology training; and (3) determine medical recommendations by identifying patterns across historical patient symptoms, diagnoses, biometric data, and treatment outcomes.” (Remarks, pages 24-25). Examiner respectfully disagrees. Here, Applicant’s claims are managing personal behavior or relationships or interactions between people - the claims are directed to communications between healthcare workers for patient care and documentation. Using a standard algorithm where the algorithm is not improved is not a technical problem rooted in the technology.
Practical application is a way to overcome the Prong 2 35 U.S.C 101 rejection, however, here, as written, the claims do not result in a practical application. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicate that a practical application may be present where the claimed invention provides a technical solution to a technical problem. Applicant states, “The claims focus on the practical application of a trained neural network model to solve specific healthcare communication and decision-making problems, not on mathematical concepts themselves. Even if the features were directed to a judicial exception, which Applicant does not concede, each of the independent claims, as a whole, integrates the alleged mental processes into a practical application given the combination of technical pieces now recited in the amended claim. The claimed embodiments provide a technical solution to the problem of efficient healthcare communication and medical decision-making.” (Remarks, page 25). Examiner respectfully disagrees. The limitations in the Application, are part of the abstract idea and the abstract idea cannot be used to integrate itself into a practical application and therefore is not an additional element. Here, the additional elements, including one or more first devices, a second device, the communication network, a healthcare facility system, a record application, a medical application, a routing application, a non-transitory memory, a processor, at least one of a portable handheld device, a wearable device, a radio transceiver, one or more medical devices, a user interface, voice biometrics application, do not result in a practical application or technical improvement, as they are recited at an apply it level, as stated above. Efficient healthcare communication and medical decision-making is not a technical problem. The Application is an improvement to the abstract idea and does not improve any computer element or any other technology. The Recentive Analytics, Inc. v. Fox Corp. decision is directed to an ineligibility analysis rather than an eligibility test. Recentive held that non-specifically claimed training of an [AI/ML] algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better.” Recentive at 12. The decision further instructed that “[i]terative training using selected training material…are incident to the very nature of machine learning” and thus does not provide for an improvement. Recentive at 12. 2023-2437 (CAFC, April 18, 2025),
All components in the claims are being used for their intended purpose and as written do not result in a practical application or significantly more than the abstract idea. Applicant states, “the claimed training of a neural network artificial intelligence model with integrated healthcare-specific datasets-including healthcare worker expertise data, capacity data, historical patient interaction data, medical terminology data, and historical medical data comprising symptoms, diagnosis, and treatment correlation data-enables the system to perform three interconnected functions that collectively improve the model itself while also improving healthcare delivery accuracy and speed. That is, the claimed embodiments use an optimally trained neural network artificial intelligence model to intelligently route medical data to optimal healthcare workers based on expertise, availability, and patient history, verify transcription accuracy to reduce documentation errors, and determine medical recommendations by recognizing patterns between patient conditions and successful historical treatments. See id. at [0023]-[0024], [0025], [0029], [0050], [0060].” (Remarks, page 26). Examiner respectfully disagrees. Here, the claims are directed to an abstract idea and as written, the inventive concept is using a standard algorithm and the algorithm is not improved. Accordingly, individually and in combination, the additional elements do not provide significantly more than the abstract idea. The claims recite features that are "well-understood, routine, conventional activities”. There is no technological improvement to any additional element. For the reasons stated above, claims 8 and 16 similarly fails to overcome the 35 U.S.C. 101 rejection.
Rejections under 35 U.S.C. 103:
Applicant’s arguments with regard to 103 are moot because the 103 rejection has been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAWN TRINAH HAYNES whose telephone number is (571)270-5994. The examiner can normally be reached M-F 7:30-5:15PM.
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, Jason Dunham can be reached on (571)272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAWN T. HAYNES/
Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686
Prosecution Insights