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
Status of Claims
This office action is in response to the amendment and remarks filed on 11/10/2025. In making the below rejections, the examiner has considered and addressed each of the applicants arguments. Claims 1-79 and 88 have been canceled, and Claims 80-109 are currently pending and being examined.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 80, 85, 86, 102, and 104-108 are rejected under 35 U.S.C. 103 as being unpatentable over Gough (USPAP 2021/0059602) in view of Hraha (USAP 2022/0260585).
In reference to independent claim 80, Gough teaches a method for estimating a true analyte concentration (glucose) in a subcutaneous space of a subject (para 0096 discloses “FIG. 7B shows a flow diagram of an example method, labeled 740, for estimating blood glucose concentration from signals of glucose sensors.”),
comprising:
(a) delivering a composition into the subcutaneous space (para 0091 discloses “the medicament delivery device 725 includes an insulin pump”);
(b) measuring a first concentration of an analyte in interstitial fluid in the subcutaneous space using a sensor at a first time (741, “obtain a set of time-series values that includes tissue glucose sensor values”);
(c) predicting a second concentration of the analyte in the interstitial fluid at the first time using a forecasting model (743, “creating a matched blood glucose model to provide the matched blood glucose reference values”);
wherein the predicting is performed real-time (para 0006 discloses “for determining optimal dynamic parameters of the estimator, and for continuous, discrete-time, and/or real-time use of the estimator”)
(d) combining the first concentration of the analyte and the second concentration of the analyte using a signal processing algorithm (fig 7B-7D) to estimate a true analyte concentration of the analyte in the interstitial fluid in the subcutaneous space (747, fig 7b discloses “Produce estimated blood glucose values for true blood glucose of the subject”); and
(e) repeating (b) to (d) (para 0058 discloses “The disclosed estimator 100 can be implemented continuously, in which the estimator operates on uninterrupted sensor signals and simulations of uninterrupted signals, in discrete-time, as a discrete-time simulator and simulations of discrete-time where time-series sensor signals are available at regular intervals, and in real-time where continuous and discrete sensor signals are generated in actual time.”)
to mitigate a dilution artifact of the measuring responsive to a proximity of the delivered composition to the sensor (para 0128 discloses “For example, the disclosed systems, devices and methods provide an estimator that can process the obtained glucose signals from a glucose sensor (such as a tissue glucose sensor) with blood glucose reference values and determine an estimation of the composite error between the signals of glucose sensor and the reference blood glucose values, in which the composite error contains measurement error due to variations in tissue oxygen and microcirculatory perfusion, glucose diffusion lag error, random error (e.g., when significant), and residual error. The estimator can also produce an estimated blood glucose based on the difference of the tissue glucose signal and the determined composite error.” The cite discloses correcting for various types of error which would necessarily include the dilution artifact due to proximity of the sensor).
Gough is silent to wherein the forecasting model compensates for a dilution artifact of the first concentration of the analyte measured during the delivering of (a),
Hraha, a similar system for handling analyte measurements, teaches wherein the forecasting model compensates for a dilution artifact of the first concentration of the analyte measured during the delivering of a analyte (para 0146-0147 discloses “a predicted relative dilution value based on a corresponding composite dilution model generated for the respective selected analyte; and [0147] determining the predicted relative dilution of the biological sample based on the determined predicted relative dilution values for the selected analytes.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the disclosed invention to use the predictive dilution modeling of Hraha in the method of Gough to “provide for more consistent and meaningful datasets for experimental and clinical applications” para 0003, Hraha.
In reference to dependent claim 85, Gough in view of Hraha teaches the method of claim 80, Gough further discloses the method further comprising delivering the composition using by a tubed pump or a patch pump (725, fig 7a shows a patch pump) using a delivery system.
In reference to dependent claim 86, Gough in view of Hraha teaches the method of claim 85, Gough further discloses the method further comprising delivering the composition using a continuous infusion pump (725, fig 7a).
In reference to dependent claim 102, Gough in view of Hraha teaches the method of claim 80, Gough further discloses the method wherein the disease or disorder comprises an insulin resistance, Type 1 diabetes mellitus, or Type 2 diabetes mellitus (para 0003 discloses “Diabetes is wide-spread condition, affecting hundreds of millions of people, and is among the leading causes of death globally. Diabetes has been categorized into three categories or types: type 1, type 2, and gestational diabetes. Type 1 diabetes is associated with the body's failure to produce sufficient levels of insulin for cells to uptake glucose. Type 2 diabetes is associated with insulin resistance, in which cells fail to use insulin properly.” The paragraph makes it clear when taken in context that the point of the Gough is to treat insulin resistance and diabetes).
In reference to dependent claim 104, Gough in view of Hraha teaches the method of claim 80, Gough further discloses the method wherein the analyte comprises a carbohydrate (para 0004 discloses “Disclosed are systems, devices and methods for estimating blood glucose parameters, including blood glucose concentration” glucose is a carbohydrate).
In reference to dependent claim 105, Gough in view of Hraha teaches the method of claim 104, Gough further discloses the method wherein the carbohydrate comprises glucose (para 0004 discloses “Disclosed are systems, devices and methods for estimating blood glucose parameters, including blood glucose concentration”).
In reference to dependent claim 106, Gough in view of Hraha teaches the method of claim 80, Gough further discloses the method wherein the composition comprises a hormone (para 0091 discloses “the medicament delivery device 725 includes an insulin pump” insulin is a hormone).
In reference to dependent claim 107, Gough in view of Hraha teaches the method of claim 106, Gough further discloses the method wherein the hormone comprises insulin (para 0091 discloses “the medicament delivery device 725 includes an insulin pump”), glucagon, pramlintide, or any combination thereof.
In reference to dependent claim 108, Gough in view of Hraha teaches the method of claim 107, Gough further discloses the method wherein the hormone comprises insulin (para 0091 discloses “the medicament delivery device 725 includes an insulin pump”).
Claim 81 is rejected under 35 U.S.C. 103 as being unpatentable over Gough (USPAP 2021/0059602) in view of Hraha (USAP 2022/0260585) further in view of Ward (USPAP 2006/0263839).
In reference to dependent claim 81, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to delivering the composition within about 15 millimeters (mm) from the sensor.
Ward, a similar insulin and glucose delivery and monitoring system, teaches delivering the composition within about 15 millimeters (mm) from the sensor (claims 27-29 disclose “said hollow structure exits said on-skin structure at a first exit point and said analyte sensor exits said on-skin structure at a second exit point, said first exit point being separated from said second exit point.
28. The device of claim 27, wherein said first exit point is separated from said second exit point by about 6 mm or more.
29. The device of claim 27, wherein said first exit point is separated from said second exit point by more than about 15 mm.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the sensor to infusion cite as taught in Ward in the method of Gough in view of Hraha to provide a system “in which very high rates of insulin are being delivered, a larger separation distance may be beneficial, such as more than 12 mm, or more than 15 mm” para 006, Ward. Ward’s claimed size ranges encompass applicant’s.
The MPEP specifically states “PRIOR ART WHICH TEACHES A RANGE OVERLAPPING, APPROACHING, OR TOUCHING THE CLAIMED RANGE ANTICIPATES IF THE PRIOR ART RANGE DISCLOSES THE CLAIMED RANGE WITH "SUFFICIENT SPECIFICITY"” MPEP 2131.03, II
"[W]hen, as by a recitation of ranges or otherwise, a claim covers several compositions, the claim is ‘anticipated’ if one of them is in the prior art." Titanium Metals Corp. v. Banner, 778 F.2d 775, 227 USPQ 773 (Fed. Cir. 1985).
Claims 82-84, 89-92, and 96 are rejected under 35 U.S.C. 103 as being unpatentable over Gough (USPAP 2021/0059602) in view of Hraha (USAP 2022/0260585) further in view of Dalal (USPAP 2020/0245913).
In reference to dependent claim 82, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to the forecasting model is based at least in part on, previous analyte concentrations measured by the sensor, the delivering of the composition, or any combination thereof.
Dalal, a similar insulin and glucose modeling system, teaches the forecasting model (418, fig 4a) is based at least in part on previous analyte concentrations measured by the sensor (442 subject historical data), the delivering of the composition, or any combination thereof.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control method as taught in Dalal in the method of Gough in view of Hraha “can provide various health and quality-of-life benefits. Such an understanding can enable an individual to make better choices to improve their health” para 0003, Dalal.
In reference to dependent claim 83, Gough in view of Hraha teaches the method of claim 80, Gough discloses the signal processing algorithm (fig 7B-7D), however
Gough and Hraha are silent to wherein (d) further comprises using the signal processing algorithm determines a weighted sum of the first concentration of the analyte and the second concentration of the analyte.
Dalal, a similar insulin and glucose modeling system, teaches determining a weighted sum (para 0107 discloses “transformation operations may be referred to as neurons. The output of a particular neuron can be a weighted sum of the inputs to the neuron, adjusted with a bias and multiplied by an activation function, e.g., a rectified linear unit (ReLU) or a sigmoid function.”) of the first concentration of the analyte and the second concentration of the analyte (examiner takes the position that once combined the neural network method can be used with the first concentration and the second concentration of Gough).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control method as taught in Dalal in the method of Gough in view of Hraha “can provide various health and quality-of-life benefits. Such an understanding can enable an individual to make better choices to improve their health” para 0003, Dalal. To be clear the control method of Dalal is added into the algorithm of Gough.
In reference to dependent claim 84, Gough in view of Hraha and Dalal teaches the method of claim 83, Dalal further teaches the wherein the weighted sum (para 0107 discloses “transformation operations may be referred to as neurons. The output of a particular neuron can be a weighted sum of the inputs to the neuron, adjusted with a bias and multiplied by an activation function, e.g., a rectified linear unit (ReLU) or a sigmoid function.”) is based at least in part on a covariance of the first concentration of the analyte and the second concentration of the analyte (examiner takes the position that once combined the neural network method can be used with the first concentration and the second concentration of Gough, as a result if the first concentration varies the second concentration should vary with it and thus be captured by the neural network as a “covariance”).
In reference to dependent claim 89, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to the forecasting model comprises a machine learning model, an ordinary differential equation (ODE)-based model, or a combination thereof.
Dalal, a similar insulin and glucose modeling system, teaches forecasting model comprises a machine learning model (para 0014 discloses “In another aspect, the present disclosure provides a method that can comprise: creating a biophysical model with at least one machine learning architecture to predict a first biophysical response”), an ordinary differential equation (ODE)-based model, or a combination thereof.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control method as taught in Dalal in the method of Gough in view of Hraha “can provide various health and quality-of-life benefits. Such an understanding can enable an individual to make better choices to improve their health” para 0003, Dalal.
In reference to dependent claim 90, Gough in view of Hraha and Dalal teaches the method of claim 89, Dalal further teaches the method wherein the machine learning model comprises a linear regression model, a support vector regression model, a multivariable adaptive regressive spline model, a neural network model (para 0015 discloses “the glucose regulation model includes at least one statistical model selected form the group consisting of: a long short-term memory neural network and recurrent neural network”), a ridge regression model, a Lasso regression model, or an ElasticNet regression model.
In reference to dependent claim 91, Gough in view of Hraha and Dalal teaches the method of claim 90, Dalal further teaches the method wherein the neural network model comprises a convolutional neural network, a recurrent neural network (para 0015 discloses “the glucose regulation model includes at least one statistical model selected form the group consisting of: a long short-term memory neural network and recurrent neural network”), or a combination thereof.
In reference to dependent claim 92, Gough in view of Hraha and Dalal teaches the method of claim 91, Dalal further teaches the method wherein the recurrent neural network comprises a long- short term memory neural network (para 0015 discloses “the glucose regulation model includes at least one statistical model selected form the group consisting of: a long short-term memory neural network and recurrent neural network”).
In reference to dependent claim 96, Gough in view of Hraha and Dalal teaches the method of claim 89, Dalal further teaches the method wherein the machine learning model is trained using training data comprising analyte sensor measurements from a population of subjects with a disease or disorder (para 0015 discloses “In some embodiments, the glucose regulation model includes at least one neural network trained with data of a predetermined population.” The predetermined population would naturally have diabetes as training with a population that didn’t would render the model unreliable at best and misleading at worst).
Claims 87, 89, 93-95, 97-101, and 103 rejected under 35 U.S.C. 103 as being unpatentable over Gough (USPAP 2021/0059602) in view of Hraha (USAP 2022/0260585) further in view of Mastrototaro (WO2014/035672).
In reference to dependent claim 87, Gough in view of Hraha teaches the method of claim 85, however
Gough and Hraha are silent to further comprising delivering the composition using an open loop delivery system, a closed loop delivery system, or a hybrid closed loop delivery system.
Mastrototaro, a similar insulin infusion device control system, teaches delivering the composition using a closed loop delivery system (para 0031 discloses “FIG. 1 is a block diagram of a closed loop glucose control system in accordance with an embodiment of the present invention.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control method as taught in Mastrototaro in the method of Gough in view of Hraha “to provide an improved desired insulin value based on the subcutaneous insulin concentration (with the other calculations following).” para 0238, Mastrototaro.
In reference to dependent claim 89, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to the forecasting model comprises a machine learning model, an ordinary differential equation (ODE)-based model, or a combination thereof.
Mastrototaro, a similar insulin infusion device control system, teaches forecasting model comprises a machine learning model, an ordinary differential equation (ODE)-based model (para 00577 discloses “the sensor glucose prediction model is expressed as a fourth order ordinary differential equation that, when solved given the initial conditions, provides model-predicted sensor glucose values”), or a combination thereof.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control method as taught in Mastrototaro in the method of Gough in view of Hraha “to provide an improved desired insulin value based on the subcutaneous insulin concentration (with the other calculations following).” para 0238, Mastrototaro.
In reference to dependent claim 93, Gough in view of Hraha and Mastrototaro teaches the method of claim 89, Mastrototaro further teaches a method wherein the ODE-based model comprises an ODE solver to solve a system of ODEs for a time of interest (para 00582 discloses “As mentioned previously, the exemplary sensor glucose prediction model utilized here is expressed as a fourth order ordinary differential equation. In accordance with conventional mathematics, the model-predicted sensor glucose values (G) in time are calculated as a function of the two model prediction initial conditions Go and clGg. Here, Go is the estimated sensor glucose value for the begin-training sampling period 1124 (the start of LTH .sup.' FIG. 54), and clGo is the derivative of Go.” Para 00598 goes onto disclose “the model supervisor module 914 estimates the user's glucose concentration in real-time based on the insulin delivered, the sensor Isig values, and sensor calibration factors”), wherein the system of ODEs comprises kinetics or dynamics of the analyte (in this case it would be the dynamics of the glucose as it perfuses thru the tissue), the composition, or a combination thereof.
In reference to dependent claim 94, Gough in view of Hraha and Mastrototaro teaches the method of claim 89, Mastrototaro further teaches a method wherein the ODE-based model comprises a metabolism regulatory model (para 00130 discloses “As shown in the drawings for purposes of illustration, the invention is embodied in a closed loop infusion system for regulating the rate of fluid infusion into a body of a user based on feedback from an analyte concentration measurement taken from the body. In particular embodiments, the invention is embodied in a control system for regulating the rate of insulin infusion into the body of a user based on a glucose concentration measurement taken from the body.” The invention is a metabolism regulatory model, per applicant’s specification.).
In reference to dependent claim 95, Gough in view of Hraha and Mastrototaro teaches the method of claim 94, Mastrototaro further teaches a method wherein the metabolism regulatory model comprises a glucoregulatory model (para 00130 discloses “As shown in the drawings for purposes of illustration, the invention is embodied in a closed loop infusion system for regulating the rate of fluid infusion into a body of a user based on feedback from an analyte concentration measurement taken from the body. In particular embodiments, the invention is embodied in a control system for regulating the rate of insulin infusion into the body of a user based on a glucose concentration measurement taken from the body.” The invention is a metabolism regulatory model, per applicant’s specification.).
In reference to dependent claim 97, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to wherein (d) further comprises signal processing the first concentration of the analyte using a filter to remove noise.
Mastrototaro, a similar insulin infusion device control system, teaches signal processing the first concentration of the analyte using a filter to remove noise (para 00288 discloses “Generally the body's blood glucose level 18 changes relatively slowly compared to a rate at which digital sensor values Dsig are collected. Therefore, high frequency signal components are typically noise, and a low pass filter may be used to improve the signal to noise ratio.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control method as taught in Mastrototaro in the method of Gough in view of Hraha “to improve the signal to noise ratio” para 0288, Mastrototaro. To be clear it would have been obvious to add the control method to the step of (d).
In reference to dependent claim 98, Gough in view of Hraha and Mastrototaro teaches the method of claim 97, Mastrototaro further teaches the method wherein the filter comprises a low-pass filter (para 00288 discloses “a low pass filter may be used to improve the signal to noise ratio”), a bandpass filter, a high pass filter, or a combination thereof.
In reference to dependent claim 99, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to wherein (d) further comprises signal processing the first concentration of the analyte and the second concentration of the analyte using a filter.
Mastrototaro, a similar insulin infusion device control system, teaches signal processing the first concentration of the analyte and the second concentration of the analyte using a filter (para 00288 discloses “high frequency signal components are typically noise, and a low pass filter may be used to improve the signal to noise ratio.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the control method as taught in Mastrototaro in the method of Gough in view of Hraha “to improve the signal to noise ratio” para 0288, Mastrototaro. To be clear the filter can be applied to each specifically or be added to the combined signal either/both are obvious in view of the art
In reference to dependent claim 100, Gough in view of Hraha and Mastrototaro teaches the method of claim 99, Mastrototaro further teaches the method wherein the filter comprises a Kalman filter (para 00214 discloses “the controller employs a Kalman filter”), an extended Kalman filter, or sigma point Kalman filter.
In reference to dependent claim 101, Gough in view of Hraha and Mastrototaro teaches the method of claim 99, Mastrototaro further teaches the method wherein the first concentration of the analyte is weighted (para 00289 discloses “filter weighting coefficients may be applied to digital sensor values Dsig collected at time intervals shorter or longer than one minute depending on the desired sensor sample rate based on the body's physiology”) based at least in part on the variance of the measuring (para 00290 goes onto disclose “pre-filter the digital sensor values Dsig such as rate-of-change thresholds, rate-of-change squared thresholds, noise thresholds about a least squares fit line rather than about the average of a subset of a group's values, higher or lower noise threshold lines, or the like” examiner takes the position that filter weighting is effectively similar to weighting and para 00290 discloses taking into effect the variance of the measuring as “rate of change” and “thresholds” denotes values that vary drastically from the norm).
In reference to dependent claim 103, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to the sensor comprises a continuous amperometric glucose sensor.
Mastrototaro, a similar insulin infusion device control system, teaches the sensor comprises a continuous amperometric glucose sensor (para 00141 discloses “The system in FIG. 40 uses a peristaltic pump 420 to withdraw blood across an amperometric sensor 410 (the same technology as used in sensor 26)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the sensor taught in Mastrototaro in the system of Gough in view of Hraha to “provide a more accurate indication of sensor resistance” para 00337, Mastrototaro.
Claim 109 is rejected under 35 U.S.C. 103 as being unpatentable over Gough (USPAP 2021/0059602) in view of Hraha (USAP 2022/0260585) further in view of Joseph (USPAP 2016/0375104).
In reference to dependent claim 109, Gough in view of Hraha teaches the method of claim 80, however
Gough and Hraha are silent to the composition further comprises a pharmaceutical acceptable excipient comprising phenol, cresol, a salt, a stabilizing agent, or any combination thereof.
Joseph, an insulin like is disclosed in Gough, teaches the composition (insulin) further comprises a pharmaceutical acceptable excipient comprising phenol, cresol, a salt, a stabilizing agent, or any combination thereof (para 0075 discloses “In certain embodiments, the pharmaceutical composition may include one or more stabilizing agents for stabilizing the insulin formulations. Exemplary stabilizing agents include, but are not limited to, zinc (e.g., at a molar ratio less than 0.05 to the insulin in the formulation), phenol, m-cresol, benzoate salts, TRIS, non-reducing carbohydrates (e.g., mannitol or dextran), surfactants (e.g., polysorbates such as TWEEN, bile salts, salts of fatty acids, or phospholipids”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the insulin formulations taught in Joseph in the method of Gough in view of Hraha “insulin analogue that is stable and provided in a substantially zinc-free formulation that avoids hexamer assembly” para 0014; Joseph.
Response to Arguments
Applicant's arguments filed on 11/10/2025 have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES W NICHOLS whose telephone number is (571)272-6492. The examiner can normally be reached Monday-Friday 8am-5pm EST.
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, Michael Tsai can be reached at (571) 270-5246. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.W.N/Examiner, Art Unit 3783
/WESLEY G HARRIS/Examiner, Art Unit 3783