Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the amendment filed 27 April 2026:
Claim 17 is cancelled
Claim 22 is new
Claims 1,16 are amended
Claims 1-2,4-10,12-16,18-22 are pending
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 27 March 2026 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
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 1-2,4-10,12-16,18-22 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, 16, and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claims recite methods and system, which are within a statutory category.
Step 2A1
The limitations of:
Claims 1, 16 and 22 (Claim 22 being representative)
generate physiological baseline screening data while an individual is at rest and physiological activity screening data while the individual performs a predetermined sequence of activities, wherein the predetermined sequence of activities includes at least one exertion configured to induce a physiological or mechanical change in the individual that is predictive of a presence of a disorder;
process raw sensor information of the physiological baseline screening data to derive a plurality of physiological baseline signals and raw sensor information of the physiological activity screening data to derive a plurality of physiological activity signals from each activity of the predetermined sequence of activities, the plurality of physiological baseline signals and the plurality of physiological activity signals collectively predictive for detecting the presence of the disorder;
extract a plurality of feature values from the plurality of physiological baseline signals and the plurality of physiological activity signals, the plurality of feature values including one or more feature values representing differences between the plurality of physiological baseline signals and the plurality of physiological activity signals and defining baseline and activity-associated physiological response patterns of the individual;
aggregate at least a subset of the plurality of feature values to define an aggregated feature set;
and compute an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to distinguish the disorder from one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns,
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to rapidly screen of signs and symptoms associated with disorders in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “generating, accessing, conducting, extracting, aggregating and computing” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of a processor that implements the identified abstract idea. The sensor system and processor are not described by the applicant and is recited at a high-level of generality (i.e., a generic computer performing generic computer functions) 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 claims are directed to an abstract idea.
The claim further recites the additional element of using a trained machine learning model to rapidly screen of signs and symptoms associated with disorders. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). Alternatively, or in addition, the implementation of the trained machine learning model to rapidly screen of signs and symptoms associated with disorders merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use and thus fails to add an inventive concept to the claims.
The claim further recites the additional element of a sensor system. The sensor system merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained machine learning model to rapidly screen of signs and symptoms associated with disorders was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of a sensor system was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. As such the claims are not patent eligible.
Claims 2,4-10,12-15,18-21 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.
Claim(s) 2 merely describe(s) configuring the machine learning model, which further defines the abstract idea.
Claim 2 further recites “training a machine learning model.” When given its broadest reasonable interpretation in light of the disclosure, the training of a machine learning model to screen patients for a disorder represents the creation of mathematical interrelationships between data. As such, the training of the machine learning model represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes
Claim(s) 4 merely describe(s) aggregation of data, which further defines the abstract idea.
Claim(s) 5-6 merely describe(s) the plurality of features used, which further defines the abstract idea.
Claim(s) 7 merely describe(s) how the physiological signals during a physiological screening are detected, which further defines the abstract idea.
Claim(s) 8 merely describe(s) the machine learning model as a probabilistic model, which further defines the abstract idea.
Claim(s) 9 merely describe(s) data used in the machine learning model, which further defines the abstract idea.
Claim(s) 10 merely describe(s) the type of physiological signals, which further defines the abstract idea.
Claim(s) 12-14 merely describe(s) the sensors used in the sensor system, which further defines the abstract idea.
Claim(s) 15 merely describe(s) the disorders detected and how its detected, which further defines the abstract idea.
Claim(s) 18 merely describe(s) selecting activities, which further defines the abstract idea.
Claim(s) 19 merely describe(s) recommending a treatment plan, which further defines the abstract idea.
Claim(s) 20 merely describe(s) administering a treatment, which further defines the abstract idea.
Claim(s) 21 merely describe(s) assessing treatment efficacy, which further defines the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1,9,12-14,19-21 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman et al (US Publication No. 20140074179) in view of ROGERS et al (Foreign Publication WO-2020092786-A1) in view of Rao et al (US Publication No. 20190110754).
Regarding Claim 1
Heldman teaches a non-invasive method of predicting the presence of a disorder, comprising:
generating physiological baseline screening data by a sensor system worn on a body of an individual of a plurality of individuals for screening the individual for a disorder while the individual is at rest [Heldman at Para. 0123 teaches to better understand the diurnal fluctuations of the subject's symptoms, the physician may program a display unit to intermittently alarm over a certain duration of time and to instruct the subject to, for example, wear the sensor on the subject's right hand while performing hand grasping exercises, finger tapping exercises and to simply wear the sensor for a period of time while resting to examine the severity of a subject's rest tremor (interpreted as baseline)];
generating physiological activity screening data by the sensor system worn on the body of the individual while the individual performs a predetermined sequence of activities [Heldman at Para. 0023 teaches movement may be continuously measured over long time spans, or may be measured only over a short time span, for example, as during the period of one or more tests taken from or based on the UPDRS motor exam. A measurement time period comprises two separate time periods: (i) a sensing time during which the movement disorder diagnostic device and its included sensors are used to sense and measure the subject's external physical motion; and (ii) a processing or calculation time wherein the measured motion data is used to calculate objective scores and/or other kinematic data that quantify the severity of the subject's movement disorder symptoms and side effects, and wherein the scores; Heldman at Para. 0115 teaches data may also be collected in an on-board memory contained within the command module 3. Such onboard or internal memory may be used for temporary storage so that the data may be saved and then downloaded to the tablet computer 6 later, advantageously allowing the subject to wear the movement disorder diagnostic device comprising sensor unit 2 and command module 3 for more prolonged symptom monitoring], wherein the predetermined sequence of activities includes at least one exertion configured to induce a physiological or mechanical change in the individual that is predictive of a presence of the disorder [Heldman at Para. 0013 teaches the current standard in evaluating the severity of movement disorder symptoms in Parkinson's disease is the manually human scored Unified Parkinson's Disease Rating Scale (UPDRS) used to score motor tests, many of which involve repetitive movement tasks such as touching the nose and drawing the hand away repeatedly, or rapidly tapping the fingers together. A battery of exercises, typically a subset of the upper extremity motor section of the UPDRS, is normally completed during DBS lead placement surgery and subsequent programming sessions to evaluate performance while a clinician qualitatively assesses symptoms. Each test is evaluated by a clinician based solely on visual observation and graded on a scale that ranges from 0 (minor) to 4 (severe); Heldman at Para. 0023 teaches movement may be continuously measured over long time spans, or may be measured only over a short time span, for example, as during the period of one or more tests taken from or based on the UPDRS motor exam. A measurement time period comprises two separate time periods: (i) a sensing time during which the movement disorder diagnostic device and its included sensors are used to sense and measure the subject's external physical motion; and (ii) a processing or calculation time wherein the measured motion data is used to calculate objective scores and/or other kinematic data that quantify the severity of the subject's movement disorder symptoms and side effects, and wherein the scores];
accessing by a processor of a plurality of processing elements the physiological baseline screening data and the physiological activity screening data [Heldman at Para. 0026 teaches following measurement of symptomatic movement, the next step in objective quantification of a subject's movement disorder symptoms is the extraction of statistical kinematic features from the acquired movement data via processing. This processing may take place during or following data acquisition and may occur within a movement data acquisition device or within a different processing device, such as a personal computer, PDA, smart phone, tablet computer, touch screen interface, or the like, with which the acquisition device interfaces, either through a cable connection or by wireless transmission];
conducting by the processor, signal processing of the physiological baseline screening data to derive a plurality of physiological baseline signals from the individual at rest and the physiological activity screening data to derive a plurality of physiological activity signals from each activity of the predetermined sequence of activities, the plurality of physiological baseline signals and the plurality of physiological activity signals collectively predictive for detecting-the presence of the disorder [Heldman at Para. 0022 teaches the movement disorder diagnostic device used in the present invention may incorporate one or more of any of the above sensors or systems. Currently used movement data acquisition and diagnostic systems, such as the one described in U.S. Pat. No. 8,187,209, herein incorporated by reference, may similarly be used. In the present disclosure, “movement data” is construed as including, but not being limited to, any signal or set of signals, analog or digital, corresponding to movement of any part of the body or multiple parts of the body, independently or in conjunction with each other. This includes physiological signals from which movement data or symptoms can be derived. Preferably, this movement data is generated with a movement sensor such as for example a gyroscope and/or an accelerometer, and additionally or optionally a video sensor; Heldman at Para. 0114 teaches the command module 3 may perform rudimentary signal processing, such as filtering and analog-to-digital conversion, on the movement signals received from the sensor unit 2 before transmitting the movement signals to a receiver unit 5.], and wherein the plurality of physiological activity signals include at least … [ … ] …, motion, … [ … ] … [Heldman at Para. 0075 teaches the sensor unit preferably not only comprises at least an accelerometer and a gyroscope, but also allows for integration of other sensors external to the sensor unit; Heldman at Para. 0113 teaches subject 1 wears a movement disorder diagnostic device comprising a sensor unit 2 and a command module 3. The sensor unit 2 comprises at least one sensor(s), preferably a physiological or movement sensor(s), such as accelerometers and/or gyroscopes (both not shown), or other similar sensors, as well as a transmission system (not shown)];
extracting, by the processor, a plurality of feature values from the plurality of physiological baseline signals and the plurality of activity signals, the plurality of feature values quantifying physiological and statistical properties in the time and frequency domains for baseline and each activity of the predetermined sequence of activities [Heldman at Para. 0026 teaches useful kinematic features that may be extracted from accelerometer data may include, for example, peak power acceleration, peak power velocity, peak power position, RMS acceleration, RMS velocity, RMS position, frequency, maximum amplitude, maximum peak-to-peak amplitude, mean acceleration, and wavelet parameters, as well as the covariance or standard deviation over time of any of these metrics. In a movement data acquisition system, or movement disorder diagnostic measuring apparatus, that combines a three-axis accelerometer and a three-axis gyroscope to produce 6 channels of movement data, one or any combination of the above kinematic features can be extracted from any of the 6 kinematic channels to be used as inputs to a trained scoring algorithm in the next step. The listed kinematic features for the sensors above are intended to be exemplary, and not limiting; other types of sensors will produce different data from which different sets of features may be extracted.];
aggregating, by the processor, the plurality of features to define an aggregated feature set characterizing baseline and activity-associated physiological response patterns of the individual [Heldman at Para. 0026]:
Heldman does not teach and computing, by the processor, an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to a machine learning model configured to distinguish the disorder from the one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns.
ROGERS teaches [ … ] … physiological, … [ … ] …, and mechano-acoustic signals [ROGERS at Page 31-32 Lines 31-32,1 teaches the chest EES is mounted on the chest to record electrocardiograms (ECGs), mechano-acoustic signals, and skin temperature (interpreted the ECG as physiological signals, the skin temperature and mechano-acoustic signals in combination with the motion sensor data of Heldman)];
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Heldman with teaching of Heldman since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). It can be seen that each element claimed is present in either Heldman or ROGERS. The ECG and mechano-acoustic signals (as taught by ROGERS) does not change or affect the sensor unit of Heldman. The sensor unit would be performed the same way even with the addition of ECG and mechano-acoustic signals. Since the functionalities of the elements in Heldman and ROGERS do not interfere with each other, the results of the combination would be predictable.
Heldman/ROGERS do not teach and computing, by the processor, an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to a machine learning model configured to distinguish the disorder from the one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns.
Rao teaches and computing, by the processor, an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to a machine learning model configured to distinguish the disorder from the one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns [Rao at Para. 0074 teaches “Thus, in certain embodiments, the purpose of the machine learning system is to take as input the temporal or static data recorded from the sensors and produce as output a probability score for each of a collection of diagnoses” (interpreted to correspond to the physiological data of Heldman)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS with the features of Rao with the motivation to improve symptom detection associated with Parkinson's Disease or stroke [Rao at Para. 0029].
Regarding Claim 9
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao further teach further comprising: applying to the machine learning model additional data derived from medical history information associated with the individual being screened or like individuals, diseases specific domain knowledge, or sensor features [Rao at Para. 0078 teaches in certain embodiments, the machine learning system as a whole will take the data acquired during these tests and use them to produce the desired output. In other embodiments, the system may also integrate background information about a patient including but not limited to age, sex, prior medical history, family history, and results from any additional or alternate medical tests].
Regarding Claim 12
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao further teach wherein the sensor system includes a first sensor worn on a chest of the individual to monitor movement and gait patterns, respiratory dynamics, and heart dynamics of the individual, and a second sensor worn on a finger of the individual including a PPG sensing device [ROGERS at Page 31 Lines 7-10 teaches FIG. 6A schematically shows a functional block diagram of core components of an apparatus including two time-synchronized EES including analog-front-end for ECG processing, 3-axis accelerometer, thermometer IC, and the BLE SoC for the Chest EES and pulse oximeter IC, thermometer, and the BLE SoC for the Limb EES. Specifically, the apparatus as shown in FIG; ROGERS at Page 17 Lines 24-27 teaches as shown in FIG. 2A, the sensor member 163 of the extremity sensor system 150 includes a PPG sensor located within a sensor footprint, which has an optical source having an infrared (IR) light emitting diode (LED) 161 and a red LED 162, and an optical detector (PD) electrically coupled to the IR LED 161 and the red LED 162.].
Regarding Claim 13
Heldman/ROGERS/Rao teach the method of claim 12,
Heldman/ROGERS/Rao further teach wherein the first sensor measures acceleration, ECG, and a first temperature, and the second sensor measures blood- oxygen and a second temperature [ROGERS at Page 33 Lines 27-30 teaches the Chest EES measures ECGs, the chest movement through the accelerometer, and skin temperature each sampled at 504, 100, and 5 Hz, respectively. The Limb EES measure PPGs and skin temperature sampled at 100 and 5 Hz, respectively].
Regarding Claim 14
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao further teach wherein the sensor system includes a motion sensor defining an accelerometer and a photopletysmography (PPG) sensor, such that the plurality of physiological signals includes mechano-acoustic signals recorded by the accelerometer and blood oxygen levels recorded by the PPG sensor [ROGERS at Page 31 Lines 12-21 teaches The Chest EES includes an ECG sensing unit, a motion sensing unit through a 3-axial accelerometer (BMI160, Bosch Sensortec), and a clinical-grade thermometer (MAX30205, Maxim Integrated). The ECG sensing unit includes two gold plated electrodes, an instrumentation amplifier, analog filters, and amplifiers, and a BLE SoC (nRF52832, Nordic Semiconductor). Remained for black PDMS. Data acquisition of the motion sensing by the accelerometer is controlled by BLE SoC through Serial Peripheral Interface (SPI) communication protocol, while the temperature data by the thermometer is acquired through the Inter-integrated Circuit (I2C) communication protocol. The Limb EES includes an integrated pulse oximetry module (MAX30101, Maxim Integrated) for measuring blood oxygenation (Sp02) and the thermometer (MAX30205, Maxim Integrated)].
Regarding Claim 18
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao further teach further comprising selecting a predetermined sequence of activities that are predictive of the presence of the disorder [Heldman at Para. 0122 teaches as noted above, however, in certain other embodiments, the display unit may not be programmed to alert a subject, but instead may simply be left available for a subject to input data regarding his or her symptoms or to select movement disorder assessment tasks to perform from among various options according to the subject's personal preferences and schedule as well as the subject's own subjective view of the severity of his or her symptoms].
Regarding Claim 19
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao further teach further comprising recommending, by the clinician, a treatment plan based on the computed output, wherein the individual is a patient [Heldman at Para. 0031 teaches many embodiments of the present invention include optimization or tuning algorithm(s) which are used to determine or recommend optimum therapy settings or parameters.].
Regarding Claim 20
Heldman/ROGERS/Rao teach the method of claim 19,
Heldman/ROGERS/Rao further teach further comprising administering a treatment according to the recommended treatment plan to the patient [Heldman at Para. 0107 teaches the present invention further optionally allows the clinician, physician or technician the ability to review recommended second level therapy parameters before or after those therapy parameters or settings are entered into the therapy device and to change those recommended settings].
Regarding Claim 21
Heldman/ROGERS/Rao teach the method of claim 20,
Heldman/ROGERS/Rao further teach further comprising assessing, by the clinician, a treatment efficacy based on the computed output [Heldman at Para. 0127 teaches thus, by allowing monitoring over a longer period of time, a physician or other clinician or even researcher could use the movement disorder monitoring device of the present invention to collect objective data regarding a subject's disease progression and, hence, the efficacy of a given treatment at stopping or slowing a subject's disease progression.].
Claims 2,4, 10 rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman/ROGERS/Rao as applied to claim 1 above, and further in view of Howard et al (US Publication No. 20170251985) in view of JHA et al (Foreign Publication WO-2018128927-A1).
Regarding Claim 2
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao further teach further comprising:
configuring the machine learning model, by [Rao at Para. 0074 (see Claim 1 for explanation)]:
Heldman/ROGERS/Rao do not teach accessing by at least one of the plurality of processing elements one or more training datasets, each of the one or more training datasets generated from an implementation of the sensor system worn on a body of a sample individual of the plurality of individuals as the sample individual performs the predetermined sequence of activities;
and conducting signal processing by the processor for each of the one or more training datasets to derive a plurality of sample signals from one or more activities of the predetermined sequence of activities, wherein the machine learning model is trained and configured based on the plurality of sample signals.
Howard teaches accessing by at least one of the plurality of processing elements one or more training datasets, each of the one or more training datasets generated from an implementation of the sensor system worn on a body of a sample individual of the plurality of individuals as the sample individual performs the predetermined sequence of activities [Howard at Para. 0536 teaches the first generation of the project is referred to as the BCCS which will collect multiple data streams using noninvasive body sensors, and capture image and audio. It is intended for use at home and to be convenient and user friendly for the user. The second generation is the LEAPS device. It consists of the BCCS redefined to specifically target PTSD patients and contain a specific app that is an interactive tool to collect data. The LEAPS analysis uses the BCCS codes and algorithms to train classifiers. Unlike current evaluations that are being used, this app removes the need to face-to-face clinician involvement. Beyond the app, the LEAPS device will consist of upper limb sensors, lower limb sensors, EKG sensor, EEG electrodes, headphones, mic, and video recording. The games/tasks the patient is asked to complete will collect consistent data for each ToDM. The data collected will be related to specific biomarkers already established. The microphone and audio capture will collect language and speech data which will be reflected by the natural language processing analysis. Facial feature characterization will be classified using a face recognition software that measures a variety of values related to facial expression. This data will then be sent and stored in the cloud platform and stored anonymously];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS, Rao with the training data of Heldman with the motivation to improve detection of detection of disease conditions and comorbidities [Howard at Para. 0005].
Heldman/ROGERS/Rao/Howard do not teach and conducting signal processing by the processor for each of the one or more training datasets to derive a plurality of sample signals from one or more activities of the predetermined sequence of activities, wherein the machine learning model is trained and configured based on the plurality of sample signals.
JHA teaches and conducting signal processing by the processor for each of the one or more training datasets to derive a plurality of sample signals from one or more activities of the predetermined sequence of activities, wherein the machine learning model is trained and configured based on the plurality of sample signals [JHA at Para. 0068 teaches This tier uses WMS data to detect/track multiple diseases. As shown in Figure 3 the diagnostic decision flow of PHDS 14 is shown using six sequential stages: (1) selection of target physiological signals 32, (2) matching of these signals with their WMSs 34, (3) preprocessing of the collected signals for machine learning models (MLMs) pre-trained using machine learning systems 36, (4) decision making through MLMs 38, (5) obtaining disease signatures 40, and (6) responding according to the decisions 42. Diagnosis of disease i is done through its own tier-wise disease module 44. Using this structure, PHDS 14 can monitor any number n diseases in parallel; JHA at Para. 0087 teaches A decision maker 104, which stores the MLM, makes diagnostic predictions based on the latest domain knowledge extractable from an up-to-date training dataset on disease i, and thus acts as the core of a DDM].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS, Rao, Howard with the signal processing of JHA with the motivation to improve the quality of clinical practice in healthcare.
Regarding Claim 4
Heldman/ROGERS/Rao/Howard/JHA teach the method of claim 2,
Heldman/ROGERS/Rao/Howard/JHA further teach further comprising:
aggregating by the processor the plurality of features across a portion of the plurality of sample signals for a portion of the predetermined sequence of activities, and applying all of the plurality of features as inputs to the machine learning model [Rao at Para. 0074 teaches Thus, in certain embodiments, the purpose of the machine learning system is to take as input the temporal or static data recorded from the sensors and produce as output a probability score for each of a collection of diagnoses].
Regarding Claim 10
Heldman/ROGERS/Rao/Howard/JHA teach the method of claim 2,
Heldman/ROGERS/Rao/Howard/JHA further teach further wherein the plurality of physiological signals and the plurality of sample signals include physiological, motion, and mechano-acoustic signals associated with a symptom of the disorder [ROGERS at Page 31-32 Lines 31-32,1 (see Claim 1 for explanation)].
Claim 5 rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman/ROGERS/Rao as applied to claim 1 above, and further in view of HAO et al (Foreign Publication CN-111407262-A) in view of Zia et al (US Publication No. 20140180153).
Regarding Claim 5
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao do not teach wherein the plurality of features relate to averages, standard deviations, ranges, minimums, maximums, root-mean squared, quantiles, moments, entropy metrics, skewness, kurtosis, and linear and non- linear metrics.
HAO teaches averages, … [ … ] …, ranges, minimums, maximums, root-mean squared, quantiles, … [ … ] …, entropy metrics, skewness, kurtosis, and linear and non-linear metrics [HAO at Page 12 Para 12 teaches time domain characteristics: 10 common features for Heart Rate Variability (HRV) analysis were extracted, as well as 34 common statistical features on RR intervals, such as mean, quantile, range, etc. We also extracted 5 non-linear features including sample entropy, zero-crossing analysis. However, the abrupt changes in RR intervals are not well captured using these features alone. To solve this problem, we have devised three new features, as follows; HAO at Page 13 Para 12 teaches feature 5, RMSSD: root mean square of adjacent RR interval differences; HAO at Page 14 Para 19 teaches feature 35, rr _ range: RR interval maximum minus RR interval minimum; HAO at Page 15 Para 16 teaches similar to extracting features at RR intervals, we extracted 25 statistical features from the respiratory signal. For example, where the time domain features include mean and standard deviation of a sequence of respiratory peaks, kurtosis, skewness, etc., the frequency domain features include highest peak, energy values, etc].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS, Rao with the metrics of Rao with the motivation to improve the classification of physiological signals.
Heldman/Howard /HAO do not teach [ … ] … standard deviations, …. [ … ] …., moments, … [ … ].
Zia teaches [ … ] … standard deviations, …. [ … ] …., moments, … [ … ] [Zia at Para. 0039 teaches the global detection algorithms 108 allow for the capturing and analysis of data set characteristics relating to inter-segment variability, segment non-Gaussianity, and general structure features of the edited signal. Variability parameters are based on the segment-to-segment variance of other feature parameters. Measures of non-Gaussianity include kurtosis and other higher-order moments, and are useful since signals associated with turbulence are generally non-Gaussian.].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS, Rao, HAO with the features of Zia with the motivation to improve detection of disease.
Claim 6 rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman/ROGERS/Rao as applied to claim 1 above, and further in view of Chiang et al ("Temporal and Spectral Characteristics of Dynamic Functional Connectivity between Resting-State Networks Reveal Information beyond Static Connectivity").
Regarding Claim 6
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao do not teach wherein the plurality of features relate to frequency domain features including power spectral density features, peak frequency, power skewness, kurtosis, entropy, center, and spread.
Chiang teaches wherein the plurality of features relate to frequency domain features including power spectral density features, peak frequency, power skewness, kurtosis, entropy, center, and spread [Chiang teaches at Page 10 Para 2 teaches to capture the distributional properties of the power spectra, we examined the first four spectral central moments of dFC, including the (a) spectral centroid (SCO), which measures the center of mass of the dFC spectrum, with higher values indicating greater energy concentrated at higher frequencies; (b) spectral spread (SPR), which measures the bandwidth of the dFC spectrum; (c) spectral skewness (SKW), which measures the symmetry of the power spectral density, with positive (negative) values indicating positive (negative) skewness; and (d) spectral kurtosis (KURT), which measures the distribution of frequencies around the spectral centroid, with higher values indicating dFC frequencies more highly clustered around the spectral centroid (centroid interpreted as center); Chiang teaches at Page 11 Para 1 teaches Seven other features in the frequency domain were considered: (a) dominant frequency (PEAK), which is the dominant frequency of dFC oscillations, calculated as the peak with the largest average power in its bin [39]; (b) spectral crest (CREST), which measures the peakiness of the power spectral density].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS, Rao with the frequency domain features of Chiang with the motivation to increase sensitivity for disease detection.
Claim 7 rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman/ROGERS/Rao as applied to claim 1 above, and further in view of Rolley et al (US Publication No. 20160158600).
Regarding Claim 7
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao do not teach further comprising detecting by the processor changes to the plurality of physiological signals of the physiological screening data during a pre-exertion activity, during an exertion activity including the at least one exertion, and during a post-exertion activity of the predetermined sequence of activities.
Rolley teaches further comprising detecting by the processor changes to the plurality of physioloqical signals of the physioloqical screening data during a pre-exertion activity, during an exertion activity including the at least one exertion, and during a post-exertion activity of the predetermined sequence of activities [Rolley at Para. 0063 teaches for example, in one embodiment, the system 10 may include wearable sensors that can be attached to the body or is contained within a particular material worn as clothing or an attachment to the attire of the patron. In addition, the system 10 may include a portable EEG system that attaches and provides ongoing data of the frequency of the brain waves prior to/during and after training sessions; Rolley at Para. 0075 teaches in the illustrated embodiment, the exercise sequence module 64 receives data indicative of a fitness activity being performed by the user and generates a current exercise sequence as a function of the received data. In addition, the exercise sequence module 64 may compare the current exercise sequence with a planned exercise sequence associated with the patron and determine a condition and/or quality of the patron's fitness activity based on the current and planned exercise sequences].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS, Rao with the activities of Rolley with the motivation to improve an individual's fitness training efforts and better manage the risk of potential injury.
Claim 8 rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman/ROGERS/Rao as applied to claim 1 above, and further in view of Spurlock et al (US Publication No. 20190108915).
Regarding Claim 8
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao do not teach wherein the machine learning model is a probabilistic model such that the output defines a number between 0 and 1, wherein 0 predicts a minimal probability of a positive diagnosis of the disorder by the individual being screened.
Spurlock teaches wherein the machine learning model is a probabilistic model such that the output defines a number between 0 and 1, wherein 0 predicts a minimal probability of a positive diagnosis of the disorder by the individual being screened [Spurlock at Para. 0080 teaches FIG. 12 gives probability calls from machine learning experiments using mRNA or annotated lncRNA datasets. Cross-sectional expression data from patients at the time of diagnosis but before treatment (MS-NAÏVE) and established MS patients (MS-EST) sub-divided into those receiving glatiramer acetate and those receiving natalizumab. Machine learning scores are determined for MS and reported on a scale from 0 to 1].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine machine learning of Heldman, ROGERS, Rao with the probability of Spurlock with the motivation to improve disease outcomes.
Claim 15 rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman/ROGERS/Rao as applied to claim 1 above, and further in view of Antonelli et al (“Comparison between the Airgo™ Device and a Metabolic Cart during Rest and Exercise”).
Regarding Claim 15
Heldman/ROGERS/Rao teach the method of claim 1,
Heldman/ROGERS/Rao do not teach wherein the disorder is a COVID-19 infection, and the plurality of physiological signals includes a heart signal, and a change in heart signal between activities in the predetermined sequence of activities is extracted by the processor as a feature for the machine learning model, and the plurality of physiological signals further includes an acceleration signal indicative of a respiration rate of the individual.
Antonelli teaches wherein the disorder is a COVID-19 infection, and the plurality of physiological signals includes a heart signal, and a change in heart signal between activities in the predetermined sequence of activities is extracted by the processor as a feature for the machine learning model, and the plurality of physiological signals further includes an acceleration signal indicative of a respiration rate of the individual individual [Antonelli at Page 3 Para 3 teaches Test under physical exercise: execution of a cardiopulmonary exercise test on the Ergoline cycle ergometer 800S, followed by a recovery period. A symptom-limited incremental exercise test was performed and designed to achieve a maximum load in 10 ± 2 min in each subject wearing the Airgo™ device; Antonelli at Page 16 Para 5 teaches Applications of this device can be found in chronic respiratory diseases as well as acute pathologies, such as the novel COVID-19, where the respiratory rate is predictive of the worsening of the disease.; Antonelli at Page 4 Para 3 teaches volunteers able to perform a routine respiratory function test (spirometry and cardiopulmonary exercise testing)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Heldman, ROGERS, Rao with the infection detection of Antonelli with the motivation to improve the quality of life of the patients, reduce mortality, and limit the costs sustained by healthcare systems.
Claims 16, 22 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Heldman et al (US Publication No. 20140074179) in view of Rao et al (US Publication No. 20190110754).
Regarding Claim 16
Heldman teaches a non-invasive method of predicting the presence of a disorder, comprising:
prompting an individual of a plurality of individuals to perform a predetermined sequence of activities, the predetermined sequence of activities including at least one exertion configured to induce a physiological or mechanical change in the individual that is predictive of a presence of a disorder [Heldman at Para. 0045 teaches a method of tuning a movement disorder therapy system comprising steps of providing a movement disorder diagnostic device to a subject having a deep brain stimulation (DBS) device with a first level of DBS parameters, the movement disorder diagnostic device comprising at least one physiological or movement sensor having a signal, and a processor comprising an algorithm, displaying on a programming device a list of activities, actions or tasks for the subject to select from, having the subject elect at least one activity, action or task from the list on the programming device, selecting with the movement disorder diagnostic device a predetermined set of DBS parameters corresponding to the elected at least one activity, action or task, and entering with the programming device the group of selected DBS parameters corresponding to the at least one elected activity, action or task into the subject's DBS device such that the subject's DBS device operates under the selected group of DBS parameters while the subject performs the at least one elected activity, action, or task];
generating physiological baseline screening data by a sensor system worn on a body of the individual while the individual is at rest [Heldman at Para. 0123 (see Claim 1 for explanation)];
generating physiological activity screening data by the sensor system worn on the body of the individual while the individual performs the predetermined sequence of activities [Heldman at Para. 0023, 0115 (see Claim 1 for explanation)];
accessing, by a processor of a plurality of processing elements, the physiological baseline screening data and the physiological screening data [Heldman at Para. 0026 (see Claim 1 for explanation)];
processing, by the processor, raw sensor information of the physiological baseline screening data to derive a plurality of physiological baseline signals and raw sensor information of the physiological activity screening data to derive a plurality of physiological activity signals from each activity of the predetermined sequence of activities, the plurality of physiological baseline signals and the plurality of physiological activity signals collectively predictive for detecting the presence of the disorder [Heldman at Para. 0022 (see Claim 1 for explanation)];
extracting, by the processor, a plurality of feature values from the plurality of physiological baseline signals and the plurality of physiological activity signals, the plurality of feature values including one or more feature values representing differences between the plurality of physiological baseline signals and the plurality of physiological activity signals and defining baseline and activity-associated physiological response patterns of the individual [Heldman at Para. 0026 (see Claim 1 for explanation); Heldman at Para. 0125 teaches by examining a subject's symptoms before beginning treatment, a physician or other clinician can establish a “baseline” against which to monitor changes in the severity of a subject's symptoms as treatment methods are changed and/or as time passes and movement disorder symptoms worsen or improve (interpreted as representing differences plurality of physiological baseline signals and the plurality of physiological activity signals)];
aggregating, by the processor, at least a subset of the plurality of feature values to define an aggregated feature set [Heldman at Para. 0026 (see Claim 1 for explanation)];
Heldman does not teach and computing, by the processor, an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to a machine learning model configured to distinguish the disorder from one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns.
Rao teaches and computing, by the processor, an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to a machine learning model configured to distinguish the disorder from one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns [Rao at Para. 0074 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine data of Heldman with the features of Rao with the motivation to improve symptoms associated with Parkinson's Disease or stroke [Rao at Para. 0029].
Regarding Claim 22
Heldman teaches a system, comprising:
a sensor system configured to generate physiological baseline screening data while an individual is at rest [Heldman at Para. 0123 (see Claim 1 for explanation)]and physiological activity screening data while the individual performs a predetermined sequence of activities, wherein the predetermined sequence of activities includes at least one exertion configured to induce a physiological or mechanical change in the individual that is predictive of a presence of a disorder [Heldman at Para. 0023 (see Claim 1 for explanation)];
and a processor in operable communication with the sensor system, the processor configured to [Heldman at Para. 0086 teaches more preferably, the heart of the digital section of the sensor board is a micro-controller or processor]:
process raw sensor information of the physiological baseline screening data to derive a plurality of physiological baseline signals [Heldman at Para. 0123 (see Claim 1 for explanation)] and raw sensor information of the physiological activity screening data to derive a plurality of physiological activity signals from each activity of the predetermined sequence of activities, the plurality of physiological baseline signals and the plurality of physiological activity signals collectively predictive for detecting the presence of the disorder [Heldman at Para. 0023, 0115 (see Claim 1 for explanation)];
extract a plurality of feature values from the plurality of physiological baseline signals and the plurality of physiological activity signals, the plurality of feature values including one or more feature values representing differences between the plurality of physiological baseline signals and the plurality of physiological activity signals and defining baseline and activity-associated physiological response patterns of the individual [Heldman at Para. 0026 (see Claim 1 for explanation); Heldman at Para. 0125 (see Claim 16 for explanation];
aggregate at least a subset of the plurality of feature values to define an aggregated feature set [Heldman at Para. 0026 (see Claim 1 for explanation)];
Heldman does not teach and compute an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to a machine learning model configured to distinguish the disorder from one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns.
Rao teaches and compute an output defining a probability measure of risk of a positive diagnosis of the disorder attributable to the individual by inputting the aggregated feature set to a machine learning model configured to distinguish the disorder from one or more other disorders or conditions based on the baseline and activity-associated physiological response patterns [Rao at Para. 0074 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine data of Heldman with the features of Rao with the motivation to improve symptoms associated with Parkinson's Disease or stroke [Rao at Para. 0029].
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-2,4-10,12-16,18-22, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
Those recitations are directed to a concrete signal-processing and classification workflow operating on physiological sensor data, not to a human organizational practice or other abstract method of organizing human activity. Nor do the amended claims recite a mental process. The amended claims inherently involve computational derivation of physiological signals from baseline and activity sensor data, extraction of time-domain and frequency-domain feature values, aggregation of those feature values across baseline and activity conditions, and model- based distinction among disorders or conditions based on the resulting response patterns. The claimed operations are thus directed to machine-implemented analysis of structured physiological sensor information and are not practically performable in the human mind.
Regarding (a), the Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions for a person or persons, with or without the aid of a computer, to follow to rapidly screen of signs and symptoms associated with disorders. Furthermore, the Examiner submits that healthcare itself is inherently represents the organization of human activity. Applicant has not pointed to anything in the claims that fall outside of this characterization. Because the claim elements fall under a series of rules or instructions for a person or persons to follow to rapidly screen of signs and symptoms associated with disorders, the claimed invention is directed to an abstract idea.
The Office Action previously characterized the claims as involving mere data gathering and output of a result by a machine learning model. As amended, however, the claims are not directed merely to collecting information and displaying a result. They now recite a particular application of computing technology to structured physiological sensor data gathered under different physiological conditions, namely baseline/rest and activity/exertion. The processor is not simply receiving data and labeling it. Rather, the processor derives baseline and activity physiological signals, extracts feature values that quantify physiological and statistical properties in the time and frequency domains for baseline and each activity, aggregates those feature values into a feature set that characterizes baseline and activity-associated physiological response patterns, and uses that aggregated feature set as the model input for distinguishing among disorder states. That is a specific practical implementation of physiological signal analysis, not an attempt to monopolize the idea of diagnosing a disorder.
The subject application likewise supports that practical application. The specification describes a scripted or predetermined sequence of activities, collection of physiological and related sensor data during rest and activity, derivation of physiological signals from the raw data, extraction of features in both time and frequency domains, and aggregation of features across signals and activities for machine-learning classification.
Thus, the amended claims are tied to a specific technical implementation for processing physiological sensor data in a way that yields a disorder-risk output based on baseline and activity-associated physiological response patterns. Under Step 2A, Prong Two, the amended claims integrate any alleged judicial exception into a practical application.
Regarding (b), the Examiner respectfully disagrees. The functions performed by the processor are merely functions that processors normally perform, which is collecting and analyzing data. The specific data used and technical implementation is irrelevant when the processor is performing its normal functions.
Especially as amended, the claims also recite significantly more than any alleged judicial exception as the claims now recite additional specific limitations that materially clarify and add technical specificity to the claim scope, including: the generation and separate access of physiological baseline screening data and physiological activity screening data; the derivation of plural physiological baseline signals and plural physiological activity signals from those different data sets; extraction of feature values quantifying physiological and statistical properties in the time and frequency domains for baseline and each activity; aggregation of those feature values into an aggregated feature set characterizing baseline and activity-associated physiological response patterns; and use of that aggregated feature set as input to a machine learning model configured to distinguish the disorder from one or more other disorders or conditions based on those response patterns. These are not token post-solution steps or insignificant extra-solution activity. They define the technical mechanism by which the claimed method operates and by which the claimed disorder-risk output is generated. In combination, they amount to significantly more than any alleged abstract concept and confine the claims to a specific machine-implemented physiological signal-processing framework.
Regarding (c), the Examiner respectfully disagrees. The functions described are part of the abstraction and cannot provide an improvement. Furthermore, this does not amount to significantly more because the computer nor any of the other additional elements are performing tasks outside of their normal function. These are the equivalent of generic computer components and a generic machine learning model. Therefore, Applicant’s argument is unpersuasive.
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claims 1-2,4-10,12-16,18-22, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Applicant argues:
Amended claims 1 and 16 now recite separate baseline and activity screening data, derivation of baseline physiological signals and activity physiological signals from those distinct data sets, extraction of feature values that quantify physiological and statistical properties for baseline and each activity, aggregation of those feature values into an aggregated feature set characterizing baseline and activity-associated physiological response patterns, and machine-learning distinction of the disorder from one or more other disorders or conditions based on those response patterns. Neither Heldman nor Howard teaches or suggests that claimed architecture.
Regarding (a), the Examiner respectfully disagrees. The prior art of Heldman in combination with the prior art of ROGERS, and Rao teach the amended limitations in the independent claims. The Examiner respectfully points to the updated rejection for full explanation.
Heldman is fundamentally directed to movement-disorder assessment and therapy tuning, especially in the context of DBS programming and symptom evaluation during selected tasks. The Office Action itself relies on Heldman's disclosure of a movement disorder diagnostic device, selected activities or tasks, and collection of physiological or movement sensor data while a subject performs those tasks. Heldman's focus is therefore on assessing motor symptoms and tuning or recommending therapy settings, not on generating baseline screening data at rest, comparing that baseline data against multi-activity physiological screening data, aggregating baseline and activity feature values, and using those aggregated feature values to distinguish among different disorders or conditions.
Regarding (b), the Examiner respectfully disagrees. The “focus” of the application is not a requirement to teach the art, so long as it teaches the features being claimed.
Regarding the other arguments, the Examiner has considered the Applicant' s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Miller et al (US Publication No. 10368744) discloses a system for baselining user profiles from portable device information.
Torres et al (US Publication No. 20170340261) discloses a system and method for measuring physiologically relevant motion
LANGSTON et al (Foreign Publication WO-2017106363-A1) discloses methods for defining a disease or condition with a wide range of etiologies
THIS ACTION IS MADE FINAL, necessitated by amendment. 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 extension fee 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 JONATHAN C EDOUARD whose telephone number is (571)270-0107. The examiner can normally be reached M-F 730 - 430.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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