Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application filed on September 10, 2024.
2. Claim(s) 1-19 are currently pending and have been examined.
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 1-19 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 1, 10 and 19 are directed to an abstract idea consisting of a method for supporting self-management of people with at least one chronic condition, and the assessment of their psychical and mental conditions.
Independent Claim 1 recites “a) receiving, a plurality of first health parameters of a user, wherein said first health parameters denote the user’s objective data, and wherein at least one of the first health parameters represents data measured; b) receiving, a plurality of second health parameters of a user, wherein said second health parameters comprise at least one subjective measurement, and wherein said at least one subjective measurement denotes a perception of the user’s subjective health; c) receiving, additional data describing the interactions of the user with previous data generated by the health recommender; d) receiving, content data, said content data comprising information relevant to the self-management of chronic conditions and characteristics of the recommendable content; e) processing, the first and second health parameters to generate a first user model, wherein the first user model describes the user characteristics, and wherein the at least one processor has further access to additional user models previously generated for people with at least one chronic condition; f) generating, at least one health recommendation to the user comprising content data, said recommendation based on the first user model, the content data and, the other user models; g) selecting at least one recommendation for the user; and h) providing the selected at least one recommendation to the user.”
Independent Claim 10 recites “receive and store a plurality of first health parameters of a user, wherein said first health parameters denote the user’s objective data and wherein at least one of the first health parameters represents data measured; receive a plurality of second health parameters of a user, wherein said second health parameters comprise at least one subjective measurement; wherein said at least one subjective measurement denotes a perception of the user’s subjective health; receive additional data describing the interactions of the user with previous data generated by the health recommender; and receive content data comprising information relevant to the self-management of chronic conditions and characteristics of the recommendable content; and store and retrieve the plurality of first health parameters, the plurality of second health parameters of a patient, the content data, and the additional data; perform functions related to the first health parameters, the second health parameters, the content data and the additional data, said functions comprising: a) receiving, a plurality of first health parameters of a user, wherein said first health parameters denote the user’s objective data, and wherein at least one of the first health parameters represents data measured; b) receiving, a plurality of second health parameters of a user, wherein said second health parameters comprise at least one subjective measurement, and wherein said at least one subjective measurement denotes a perception of the user’s subjective health; c) receiving, additional data describing the interactions of the user with previous data generated by the health recommender; d) receiving, content data, said content data comprising information relevant to the self-management of chronic conditions and characteristics of the recommendable content; e) processing, the first and second health parameters to generate a first user model, wherein the first user model describes the user characteristics, and wherein the at least one processor has further access to additional user models previously generated for people with at least one chronic condition; f) generating, at least one health recommendation to the user comprising content data, said recommendation based on the first user model, the content data and, the other user models; g) selecting at least one recommendation for the user; and h) providing the selected at least one recommendation to the user.”
Independent Claim 19 recites “a) receiving, a plurality of first health parameters of a user, wherein said first health parameters denote the user’s objective data, and wherein at least one of the first health parameters represents data measured; b) receiving, a plurality of second health parameters of a user, wherein said second health parameters comprise at least one subjective measurement, and wherein said at least one subjective measurement denotes a perception of the user’s subjective health; c) receiving, additional data describing the interactions of the user with previous data generated by the health recommender; d) receiving, content data, said content data comprising information relevant to the self-management of chronic conditions and characteristics of the recommendable content; e) processing, the first and second health parameters to generate a first user model, wherein the first user model describes the user characteristics, and wherein the at least one processor has further access to additional user models previously generated for people with at least one chronic condition; f) generating, at least one health recommendation to the user comprising content data, said recommendation based on the first user model, the content data and, the other user models; g) selecting at least one recommendation for the user; and h) providing the selected at least one recommendation to the user.”
The limitations of Claims 1, 10 and 19, as drafted, under its broadest reasonable interpretation, covers the performance of a Certain Methods of Organizing Human Activity which are concepts performed by managing personal behavior, relationships or interactions between people (including fundamental economic principles, commercial or legal interactions, social activities, teaching, and following rules or instructions). That is, other than reciting, “processor, external device, communication channel, storage device, memory, health recommender, NTCRM” nothing in the claim element precludes the step from practically managing personal interactions between people by following rules or instructions. For example, but for the “at least one processor” language, “receiving” in the context of this claim encompasses the user manually retrieving health parameters and data. Similarly, the processing, of the first and second health parameters as well as the data to generate a user model, under its broadest reasonable interpretation, covers the performance of managing personal interactions between people by following rules or instructions, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of managing personal interactions between people by following rules or instructions, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of using a “processor, external device, communication channel, storage device, memory, health recommender, NTCRM” to perform all of the “obtaining, transforming, parsing, determining, transforming, selecting and storing” steps. The “processor, external device, communication channel, storage device, memory, health recommender, NTCRM” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does 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.
Claim 1 has the following additional elements (i.e., processor, external device, health recommender). Claim 10 has the following additional elements (i.e., processor, external device, communication channel, storage device, health recommender). Claim 19 has the following additional elements (i.e., processor, external device, communication channel, storage device, memory, health recommender, NTCRM). Looking to the specification, these components are described at a high level of generality (¶ 113; Any or all functionalities of the present disclosure shown and described herein, such as but not limited to operations within flowcharts, may be performed by anyone or more of: at least one conventional personal computer processor, workstation, or other programmable device or computer or electronic computing device or processor, either general-purpose or specifically constructed, used for processing; a computer display screen and/or printer and/or speaker for displaying; machine-readable memory such as optical disks, CDROMs, DVDs, Blu-ray, magnetic-optical discs or other discs; RAMs, ROMs, EPROMs, EEPROMs, Flash memories, magnetic or optical or other cards, for storing, and keyboard or mouse for accepting). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, although the claims add “[storage]” steps, it is only considered as insignificant extrasolution activity. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-9 and 11-18). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claims 1-19 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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 –
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
Claims 1-19 are rejected under 35 U.S.C. 102(b) as being anticipated by Pub. No.: US 20220028541 A1 to Paull et al.
As per Claim 1, Paull et al. teaches a method for supporting self-management of people with at least one chronic condition, and supporting the assessment of their psychical and mental conditions, wherein said method comprises a health recommender, the method carried out by at least one processor, said method comprising:
a) receiving, by the at least one processor, a plurality of first health parameters of a user, wherein said first health parameters denote the user’s objective data, and wherein at least one of the first health parameters represents data measured by an external device (see Paull et al. paragraphs 107-108; As indicated, in some embodiments, the external system(s) 130 and/or client devices 120 can further include sensing components configured to generate data from which patient biometrics and/or behaviors can be extracted. In relation to biometric data, the external system(s) 130 and/or client devices 120 can include sensing components associated with one or more of: activity of a patient (e.g., through accelerometers, gyroscopes, motion coprocessing devices, etc.); facial expressions of the patient (e.g., through eye tracking, through image/video processing) for determination of cognitive states (e.g., associated with depression, anxiety, emotions, etc.) and/or performance of activities and/or interacting with content provided through the intervention regimen; physiological and/or psychological stress of a patient (e.g., in relation to respiration parameters, in relation to cardiovascular parameters, in relation to galvanic skin response, in relation to neurological activity, in relation to other stress biometrics, etc.); sleep behavior of a patient (e.g., with a sleep-monitoring device); digestive health of a patient (e.g., in relation to microbiome composition, in relation to stool-based assays, in relation to urine-based assays, in relation to smart-pill devices, in relation to smart toilet devices); and any other suitable sensors or devices from which biometric signals can be acquired for assessment of patient health.);
b) receiving, by the at least one processor, a plurality of second health parameters of a user, wherein said second health parameters comprise at least one subjective measurement, and wherein said at least one subjective measurement denotes a perception of the user’s subjective health (see Paull et al. paragraphs 104 and 161; In relation to performing the pre-assessment and/or onboarding process, in various embodiments, the online system and/or other system components can implement surveying tools (e.g., to obtain self-report data from the patient) and/or non-survey-based tools for acquisition of data. Survey tools can be delivered through an application associated with the PDT system executing on the client device of the patient and/or through another suitable method, where the survey tools can implement architecture for assessing the patient in relation to mental health, pain, GI and/or inflammatory health symptom severity or disease activity, types of GI and/or inflammatory health condition symptoms, and/or other statuses.);
c) receiving, by the at least one processor, additional data describing the interactions of the user with previous data generated by the health recommender (see Paull et al. paragraph 104; In some embodiments, the client device(s) 120 can be configured to store and/or execute an application (e.g., mobile application, web application) that allows a user of the client device 120 to interact with the online system 110 by way of the network 140, in order to receive digital content associated with one or more therapeutic interventions and/or provide data associated with survey responses, sensor-derived data associated with interactions with such interventions, and/or any other suitable data. In relation to providing treatments, in some embodiments, the client device(s) 120 can include operation modes for administering treatments to the user (e.g., in relation to providing prescription digital therapeutics upon diagnosis of the GI and/or inflammatory health condition of the user, in relation to providing medications, in relation to providing pain management therapies, etc.);
d) receiving, by the at least one processor, content data, said content data comprising information relevant to the self-management of chronic conditions and characteristics of the recommendable content (see Paull et al. paragraph 130; In one embodiment, once patient profile and pre-assessment data 158 and patient illness condition data 162 have been generated, personalized regimen generation system 168 utilizes patient profile and pre-assessment data 158 and patient condition data 162 to generate a personalized intervention regimen for the patient, which is represented in FIG. 1B by patient personalized regimen data 164. In various embodiments, patient personalized regimen data 164 includes data representing regimen details such as, but not limited to, which of the available remaining therapy modules to administer to the patient, in what order to administer the therapy modules, a time schedule for when/how often to administer the therapy modules, what content to include in each of the therapy modules, and how to present the therapy module content to the patient.);
e) processing, by the at least one processor, the first and second health parameters to generate a first user model, wherein the first user model describes the user characteristics, and wherein the at least one processor has further access to additional user models previously generated for people with at least one chronic condition (see Paull et al. paragraph 179; In some embodiments, such recommendations are generated via use of one or more machine learning modules that receive, as input, data corresponding to patient symptom and/or habits, as tracked by various modules such as those described herein. In some embodiments, this information is used as feedback, to refine and dynamically update parameters of machine learning modules. In some embodiments, dosage recommendations provided via the approaches described herein are restricted to fall within a pre-defined range, for example as specified by a physician. In some embodiments, dosage recommendations provided by technologies (e.g., systems and methods) described herein comprise an identification of one or more specific symptoms, so as to provide recommendation to discuss particular symptoms with a medical professional, such as a physician and/or therapist (e.g., to discuss particular changes to medication regimens and/or types of medication, so as to best manage particular symptoms);
f) generating, by the at least one processor, at least one health recommendation to the user comprising content data, said recommendation based on the first user model, the content data and, the other user models (see Paull et al. paragraph 179; In some embodiments, such recommendations are generated via use of one or more machine learning modules that receive, as input, data corresponding to patient symptom and/or habits, as tracked by various modules such as those described herein. In some embodiments, this information is used as feedback, to refine and dynamically update parameters of machine learning modules. In some embodiments, dosage recommendations provided via the approaches described herein are restricted to fall within a pre-defined range, for example as specified by a physician. In some embodiments, dosage recommendations provided by technologies (e.g., systems and methods) described herein comprise an identification of one or more specific symptoms, so as to provide recommendation to discuss particular symptoms with a medical professional, such as a physician and/or therapist (e.g., to discuss particular changes to medication regimens and/or types of medication, so as to best manage particular symptoms);
g) selecting at least one recommendation for the user (see Paull et al. paragraph 132; In some embodiments, content selection system 170 may provide one or more options, notifications, alerts, and/or recommendations to patient 142 through PDT user interface 150, wherein the one or more options, notifications, alerts, and/or recommendations relate to current or potential complementary (non-behavioral) therapies to be administered in combination with the behavioral therapy modules/components.); and
h) providing the selected at least one recommendation to the user (see Paull et al. paragraph 178; In some embodiments, the prescription digital therapeutics (PDT) systems and methods disclosed herein collect patient medication information, for example in the pre-assessment operation discussed above, via interactive lesson modules such as a pre-assessment module, via modules specifically related to medication, etc., and/or via user entry into a user profile. In some embodiments, the prescription digital therapeutics (PDT) systems and methods described herein may collect and offer recommendations regarding an amount and/or timing of dosage of particular medications. Such recommendations regarding amount and/or timing may be absolute and/or relative to other events and/or activities. For example, recommendations may comprise a particular schedule of dosage, e.g., a particular rate, timing (e.g., in a morning, afternoon, or evening), etc. Recommendations regarding timing may including timings and/or amounts relative to other activities, such as meal consumption, physical exercises, seasons, social gatherings, travel, work, etc. Such recommendations may be based on data provided by the patient, for example in daily symptom diary entries, via assessment modules, personal model creation modules, etc., such as those described in more detail below).
As per Claim 2, Paull et al. teaches the method of claim 1 wherein said at least one processor is configured to communicate with at least one external device and wherein said external device is configured to exchange data with the at least one processor (see Paull et al. paragraphs 107-108; As indicated, in some embodiments, the external system(s) 130 and/or client devices 120 can further include sensing components configured to generate data from which patient biometrics and/or behaviors can be extracted. In relation to biometric data, the external system(s) 130 and/or client devices 120 can include sensing components associated with one or more of: activity of a patient (e.g., through accelerometers, gyroscopes, motion coprocessing devices, etc.); facial expressions of the patient (e.g., through eye tracking, through image/video processing) for determination of cognitive states (e.g., associated with depression, anxiety, emotions, etc.) and/or performance of activities and/or interacting with content provided through the intervention regimen; physiological and/or psychological stress of a patient (e.g., in relation to respiration parameters, in relation to cardiovascular parameters, in relation to galvanic skin response, in relation to neurological activity, in relation to other stress biometrics, etc.); sleep behavior of a patient (e.g., with a sleep-monitoring device); digestive health of a patient (e.g., in relation to microbiome composition, in relation to stool-based assays, in relation to urine-based assays, in relation to smart-pill devices, in relation to smart toilet devices); and any other suitable sensors or devices from which biometric signals can be acquired for assessment of patient health.).
As per Claim 3, Paull et al. teaches the method of claim 1 wherein said second health parameters comprise at least one subjective measurement, and wherein said subjective measurement comprises at least one of: psychometric parameters, questionnaire results, and the like (see Paull et al. paragraphs 162 and 317; In relation to performing the pre-assessment and/or onboarding process, in various embodiments, the online system and/or other system components can implement surveying tools (e.g., for self-report of data from the patient) and/or non-survey-based tools for acquisition of data. Survey tools can be delivered through an application associated with the PDT system executing on the client device of the patient and/or through another suitable method, where the survey tools can implement architecture for assessing the patient in relation to mental health, pain, GI and/or inflammatory health symptom severity or disease activity (e.g. IBS-symptom severity scale), types of GI and/or inflammatory health condition symptoms, and/or other statuses. In examples the surveying tools can be derived from one or more tools such as, but not limited to: a patient health questionnaire (e.g., PHQ-9), an anxiety disorder questionnaire (e.g., GAD-7, PC-PTSD, SCARED), a work and social adjustment scale (WSAS)-derived tool, a pain assessment questionnaire (e.g., numerical rating scale, Wong-Baker faces scale, FLACC scale, CRIES scale, COMFORT scale, McGill scale, Color Analog scale, etc.), a clinical disease activity measurement (e.g., CDAI, PUCAI, Mayo Score) and any other tool or instrument.).
As per Claim 4, Paull et al. teaches the method of claim 1 wherein said processor has further access to previous recommendations provided by the health recommender and wherein said generating further comprises the interaction of the patient with at least one previous recommendation of the health recommender (see Paull et al. paragraphs 179-180; In some embodiments, such recommendations are generated via use of one or more machine learning modules that receive, as input, data corresponding to patient symptom and/or habits, as tracked by various modules such as those described herein. In some embodiments, this information is used as feedback, to refine and dynamically update parameters of machine learning modules. In some embodiments, dosage recommendations provided via the approaches described herein are restricted to fall within a pre-defined range, for example as specified by a physician. In some embodiments, dosage recommendations provided by technologies (e.g., systems and methods) described herein comprise an identification of one or more specific symptoms, so as to provide recommendation to discuss particular symptoms with a medical professional, such as a physician and/or therapist (e.g., to discuss particular changes to medication regimens and/or types of medication, so as to best manage particular symptoms). In one embodiment, the non-behavioral therapy components are related to therapies that have previously been administered to the patient. In one embodiment, the non-behavioral therapy components are related to therapies that are currently being administered to the patient. In one embodiment, the non-behavioral therapy components are related to therapies that will be and/or are recommended to be administered to the patient.).
As per Claim 5, Paull et al. teaches the method of claim 1 wherein said health recommender further comprises reducing the health data dimension, wherein said reducing comprises consideration of the unique individual health context of the user and health context of other users with similar characteristics, and wherein said reducing is performed before said generating (see Paull et al. paragraph 367; Furthermore, any algorithm(s) can implement any one or more of: … a dimensionality reduction method (e.g., principal component analysis, partial least squares regression, etc.).
As per Claim 6, Paull et al. teaches the method of claim 1 wherein at least one function performed by the at least one processor further comprises the use of machine learning and artificial intelligence (see Paull et al. paragraphs 179-180; In some embodiments, such recommendations are generated via use of one or more machine learning modules…).
As per Claim 7, Paull et al. teaches the method of claim 1, wherein said providing further comprises tailoring the selected recommendation to include personal characteristics of the user (see Paull et al. paragraph 372; Turning to FIG. 9D, a GUI may also comprise a stored profile of the user. A user profile may be populated via various lesson modules that solicit input from the user, for example regarding personal characteristics, thoughts and feelings, symptom logging, identification of stressors, stress level tracking, and completion of diagnostic assessments aimed at characterizing their condition. In some embodiments, various patient reported outcome instruments, which, for example, measure condition symptom severity, quality of life, etc., can be used.).
As per Claim 8, Paull et al. teaches the method of claim 7 wherein said tailoring further comprises adapting the selected at least one recommendation to the user, by combining artificial intelligence models about symptoms predicted trends and their contributing factors (see Paull et al. paragraphs 179-180 and 372).
As per Claim 9, Paull et al. teaches the method of claim 1, wherein said first and second health parameters further comprise weighing factors and wherein said processing considers said weighing factors to generate the first user model (see Paull et al. paragraph 89; As used herein, the term “personal model” and/or “personal disease model” may include a construction built based on patient input, which enables the patient to identify stressors, counter-productive behaviors, unhelpful thoughts, and negative emotions as associated with the patient's disease, disorder, and/or condition. In some embodiments, a personal model is constructed as a graphical representation, which comprises text corresponding to patient-selected counter-productive behavior(s), unhelpful thought(s), and negative emotion(s), superimposed on a flow diagram illustrating links between the patient's behaviors, thoughts, and emotions. In some embodiments, a personal model graphical representation comprises text corresponding to causes and/or stressors of symptoms. A personal model may be utilized to help a patient identify links between their behaviors, thoughts, and emotions, and to help a patient weigh possible changes in their behavior that could be implemented to address their symptoms.).
As per Claims 10-18, Claims 10-18 are directed to a health recommender system to support the self-management of people with at least one chronic condition and to support the assessment of their psychical and mental conditions. Claims 10-18 recite the same or substantially similar limitations as those addressed above for Claims 1-9 as taught by Paull et al. Claims 10-18 are therefore rejected for the same reasons as set forth above for Claims 1-9 respectively.
As per Claim 19, Claim 19 is directed to a non-transitory computer readable storage medium in a system comprising at least one processor with at least one communication channel configured to communicate with external devices, a memory and, access to a storage device to store and retrieve data, wherein said computer readable storage medium stores at least one readable program, wherein said at least one program supports the self-management of people with at least one chronic condition and supports the assessment of their psychical and mental conditions, and when said at least one program is executed by the at least one processor. Claim 19 recites the same or substantially similar limitations as those addressed above for Claim 1 as taught by Paull et al. Claim 19 is therefore rejected for the same reasons as set forth above for Claim 1 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220384002 A1; A method for generating treatment regimen for one or more health conditions includes retrieving a stored healthcare treatment model that has been trained to identify, for each of a plurality of health conditions, one or more respective treatment programs. Each of the treatment programs includes a respective treatment user interface to modify a respective behavior associated with one or more neurohumoral factors that are associated with the respective health condition. In response to receiving input that specifies a first health condition of the one or more health conditions, the method uses the healthcare treatment model to select one or more treatment programs corresponding to the first health condition and provides the treatment user interfaces for the one or more treatment programs.
US 11244762 B2; A system for evaluating mental health of patients includes a memory and a control system. The memory contains executable code storing instructions for performing a method. The control system is coupled to the memory and includes one or more processors. The control system is configured to execute the machine executable code to cause the control system to perform the method: A selection of answers associated with a patient is received. The selection of answers corresponds to each question in a series of questions from mental health questionnaires. Unprocessed MRI data are received. The unprocessed MRI data correspond to a set of MRI images of a biological structure associated with the patient. The unprocessed MRI data is processed to output a set of MRI features. Using a machine learning model, the selection of answers and the set of MRI features are processed to output a mental health indication of the patient.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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, Robert Morgan can be reached at (571) 272-6773. 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.
/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683