Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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.
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 recites the mathematical process steps of preprocessing values, decomposing values, and to calculate a final value using discrete wavelet decomposition. Thus, the claim recites an abstract idea of calibrating data from a sensor using mathematical steps.
The claim does not include any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
a microcontroller and a recorder (interpreted as memory for storing information), both recited as generic computer equipment, and the implementation of an abstract idea on generic computer equipment cannot integrate the abstract idea into a practical application (see MPEP 2106.05(f)(2))
measuring an electrode current for the working electrode is insignificant extra-solution activity of data gathering, see MPEP 2106.05(g)
storing a plurality of values in said recorder and retrieving the plurality of values from said recorder are insignificant extra-solution activity of data gathering, see MPEP 2106.05(g)
using at least one machine learning model to calculate by the microcontroller again is merely using a computer or other machinery as a tool to perform the abstract idea, which by MPEP 2106.05(f)(2) cannot integrate the abstract idea into a practical application.
Thus, the claim is directed to the abstract idea of using mathematical steps to calibrate data from a sensor.
Further, the additional elements cannot provide significantly more than the abstract idea itself:
a microcontroller and a recorder (interpreted as memory for storing information), both recited as generic computer equipment, and the implementation of an abstract idea on generic computer equipment is not significantly more than the abstract idea itself (see MPEP 2106.05(f)(2))
measuring the electrode current for the working electrode is routine, conventional, and well-understood (see Telson, US PG Pub 2011/0040163, [0030], “Conventional in vivo CGM sensors may be configured [as] … ‘a sensor that produces an electrical current that is proportional to the blood or subcutaneous tissue level’ … the current may then be read to indicate glucose level”)
storing a plurality of values in said recorder and retrieving the plurality of values from said recorder are well-understood, routine, and conventional by MPEP 2106.05(d), “storing and retrieving data in memory”
using at least one machine learning model to calculate by the microcontroller again is merely using a computer or other machinery as a tool to perform the abstract idea, which by MPEP 2106.05(f)(2) cannot provide an inventive concept.
Thus, the additional elements of Claim 1, taken individually and in combination, do not represent significantly more than the abstract idea, and the claim is ineligible.
Claims 2-20, dependent upon Claim 1, recite additional details regarding the mental and mathematical processes, but no additional elements which integrate the abstract idea into a practical application nor are arguably an inventive concept nor significantly more than the abstract idea itself (Claims 2-5 specify the machine learning model used, which by MPEP 2106.05(h) merely specifies the field of use; Claim 6 recites fused SG is calculated based on an EIS parameter, which merely specifies the data used in the mental process; Claims 7 and 8 specify what data is to be manipulated in the mental process; Claims 9, 12-16, 18, and 19 each recite smoothing or blanking data, which are additional mathematical steps of data manipulation; Claims 10, 11, and 17 recite repeating the process over time; and Claim 20 recites calculating signal noise, which is an additional mathematical step of manipulating information) or additional steps which are insignificant extra-solution activity and routine, conventional, and well-understood (Claim 6 recites performing an electrochemical impedance spectroscopy procedure, see Telson, [0036], “Since the 1970’s, EIS has been used as a tool to analyze difficult and complicated systems”). Thus, dependent Claims 2-20 remain directed to the abstract idea of calibrating data from a sensor, without significantly more.
Claim Rejections - 35 USC § 103
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1, 3 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, US Patent 6,424,847, in view of Shao, “Multivariate calibration methods in near infrared spectroscopic analysis” (as cited by the applicant in the information disclosure statement dated 10/16/2023).
Regarding Claim 1, Mastrototaro teaches a method for retrospective calibration of a glucose sensor for measuring the level of glucose in a body of a user (title, “Glucose Monitor Calibration Methods”), said sensor including physical sensor electronics (column 3, lines 50-52, “The glucose sensor is electrically coupled to the glucose monitor to supply the glucose monitor data”), a microcontroller (column 3, lines 55-56, “the processor includes software to calculate calibration characteristics” & column 7, lines 30-31, “to control the infusion pump based on glucose sensor measurements”), a recorder (column 2, lines 23-24, “obtaining glucose monitor data at a predetermined memory storage rate” denotes the data is stored in a recorder), and a working electrode (column 5, lines 57-58, “electrodes of the glucose sensor terminating in the user’s subcutaneous tissue”), the method comprising: measuring, by said physical sensor electronics, [an] electrode current for the working electrode (column 8, lines 38-40, “the glucose monitor measures a continuous electrical current signal (ISIG) generated by the glucose sensor”); storing a plurality of said Isig values for said working electrode in said recorder; retrieving the plurality of Isig values from said recorder (column 8, lines 51-58, “at a glucose monitor memory storage rate … 3 interval values are averaged and stored in a glucose monitory memory as memory values. The memory values are retained in memory and may be downloaded to the data processor”), preprocessing said retrieved Isig values by said microcontroller (column 9, lines 7-12, referring to measured Isig values, “Clipping limits may be used to limit the signal magnitude variation from one value to the next thereby reducing the effects of extraneous data … in preferred embodiments, clipping limits are applied to the interval values. For instance, interval values that are above a maximum clipping limit or below a minimum clipping limit are replaced with the nearest clipping limit value”) … and using at least one machine learning model to calculate, by said microcontroller, a final sensor glucose (SG) value based on said Isig values (column 10, lines 56-57, “valid ISIG values are used to calculate blood glucose levels by the glucose monitor or post-processor” & Abstract, “the calculation of the calibration is obtained by regression … [or] alternatively … non-linear regression” where “regression” and “non-linear regression” are various machine learning models).
Mastrototaro is silent regarding decomposing said preprocessed Isig values using discrete wavelet decomposition and to calculate a final sensor glucose value based on … said discrete wavelet decomposition. However, Shao teaches a method for measurement calibration (Shao, title, “Multivariate calibration methods”) using a discrete wavelet decomposition (Shao, pg. 1664, 1st column, 3rd paragraph, “wavelet transform (WT) was introduced into the local regression” & 2nd column, “signal decomposition such as wavelet transform” indicates that they use a discrete wavelet decomposition to perform their regression model for calibration). It would have been obvious to incorporate such a transform into the regression model for calibration (and thus to calculate a final SG value) of Mastrototaro (Mastrototaro, Abstract, “the calculation of the calibration characteristics is obtained using linear [or alternatively, non-linear] regression”) because both inventions are about the calibration of sensor-measured data. The motivation to do so is that an “approach to avoid non-linearity is local regression” which can be performed with the discrete wavelet decomposition transform (Shao, pg. 1664, 1st column, 3rd paragraph).
Regarding Claim 3, the Mastrototaro/Shao combination of Claim 1 teaches the method of Claim 1. Mastrototaro teaches learning the calibration with linear or non-linear regression (Mastrototaro, Abstract) but also teaches that that “other alternative embodiments may utilize singular and multiple, non-linear regression techniques” (Mastrototaro, column 16, lines 28-29). Shao teaches calibration using multiple non-linear regression techniques (Shao, Abstract, “non-linear approaches and ensemble techniques” where “ensemble” means the combination of multiple models to make a prediction), including wherein said at least one machine learning model is a neural network (Shao, pg. 1662, “artificial neural network … have been used for multivariate calibration”). It would have been obvious to incorporate features, such as a neural network model, from Shao into the regression model for calibration of Mastrototaro (Mastrototaro, Abstract, “the calculation of the calibration characteristics is obtained using linear [or alternatively, non-linear] regression”) because both inventions are about the calibration of sensor-measured data. The motivation to do so is that “ANN … can represent any non-linear function with sufficient accuracy” (Shao, pg. 1663, 1st column, 3rd paragraph).
Regarding Claim 18, the Mastrototaro/Shao combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Mastrototaro further teaches smoothing of the preprocessed Isig values (Mastrototaro, column 10, lines 6-8, “Fig. 8 shows interval value R’, which is calculated by averaging sampled values N through Q”, see Fig. 8, sampled values are clipped and averaged, which results in smoothing).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao, and further in view of Salas-Boni, US PG Pub 2016/0029966, with a filing date of July 29th, 2015.
Regarding Claim 2, the Mastrototaro/Shao combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Mastrototaro teaches learning the calibration with linear or non-linear regression (Mastrototaro, Abstract) but also teaches that that “other alternative embodiments may utilize singular and multiple, non-linear regression techniques” (Mastrototaro, column 16, lines 28-29). Salas-Boni teaches calibration of an analyte/glucose measurement (Salas-Boni, Fig. 6 for analyte/glucose equivalence & Abstract, “performing a calibration operation on values of the analyte parameter”) using non-linear regression (Salas-Boni, [0058], “Block S130 can, however, additionally or alternatively include any other suitable data-processing method that facilitates calibration. For instance …non-linear regressions (decision tree, random forest, CART methods, etc.)” where “CART” is an acronym for Classification And Regression Trees), thus teaching wherein said at least one machine learning model is one of … regression decision tree. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaches of Salas-Boni into the invention of Mastrototaro because both inventions are about the calibration of glucose sensors. The motivation to do so is that “any other suitable data-processing method that facilitates calibration” can be used (Salas-Boni, [0058]) – that is, Mastrototaro uses a generic a non-linear regression to perform calibration, and Salas-Boni teaches that a regression tree is a suitable non-linear regression to perform calibration.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao, and further in view of Dinney, US PG Pub 2013/0058925.
Regarding Claim 4, the Mastrototaro/Shao combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Mastrototaro teaches learning the calibration with linear or non-linear regression (Mastrototaro, Abstract) but also teaches that that “other alternative embodiments may utilize singular and multiple, non-linear regression techniques” (Mastrototaro, column 16, lines 28-29). Shao teaches an ensemble method of creating a non-linear regression (Shao, pg. 1662, Abstract, “calibration methods … including non-linear approaches and ensemble techniques”) thus teaching wherein a first … sensor value is calculated and wherein a second … sensor value is calculated (Shao, pg. 1664, 2nd column, 1st paragraph, “Ensemble modeling is a statistical technique that combines the results of multiple individual models to produce a single prediction”). It would have been obvious to incorporate features, such as ensemble prediction, from Shao into the regression model for glucose prediction calibration of Mastrototaro (Mastrototaro, Abstract, “the calculation of the calibration characteristics is obtained using linear [or alternatively, non-linear] regression”) because both inventions are about the calibration of sensor-measured data. The motivation to do so is that “in ensemble modeling … multiple models will effectively identify and encode more aspects of the relationship between independent and dependent variables than a single model” (Shao, pg. 1663, 1st column, 3rd paragraph).
Neither Mastrototaro nor Shao use the specific machine learning models of Claim 1 (i.e. genetic programming or regression decision tree), but Dinney teaches both of these methods (Dinney, [0011], “using … classification and regression trees (CART), or genetic programming”) to perform prediction. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use some of the non-linear predictive machine learning models of Dinney in the invention of Mastrototaro/Shao because each of the inventions concern physiological predictions from data. The motivation to do so is that the Mastrototaro/Shao combination teaches prediction with an ensemble of predictive models, and Dinney teaches that these predictive models can be used “to improve accuracy” (Dinney, [0011]).
Regarding Claim 5, the Mastrototaro/Shao/Dinney combination of Claim 4 teaches the method of Claim 4 (and thus the rejection of Claim 4 is incorporated). The combination has already been shown to teach, through Shao (for the ensemble/fused SG) and Mastrototaro (for application to glucose), to teach fusing said first and second sensor glucose values to obtain a fused SG, wherein said final sensor glucose value is determined based on said fused SG (Shao, pg. 1664, 2nd column, 1st paragraph, “Ensemble modeling is a statistical technique that combines the results of multiple individual models to produce a single prediction”).
Claims 6-12 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao and Dinney, and further in view of Telson, US PG Pub 2011/0040163.
Regarding Claim 6, the Mastrototaro/Shao/Dinney combination of Claim 5 teaches the method of Claim 5 (and thus the rejection of Claim 5 is incorporated). Mastrototaro does not teach, but Telson teaches, an electrochemical impedance spectroscopy (EIS) procedure for said working electrode (Telson, Abstract, “electrical impedance spectroscopy to adjust calibration settings in … an in vivo continuous glucose monitoring system”) to obtain a plurality of values of an EIS-based parameter for said electrode, wherein … SG is further calculated based on said values of the impedance-based parameter (Telson, [0013], “obtaining a references parameter value … performing EIS on the in vivo sensor to obtain an in vivo parameter value, and comparing the in vivo parameter value and the reference parameter value to identify the particular characteristics of the in vivo sensor” which are used in calibration, thus used, by Mastrototaro and Shao, to calculated said fused SG). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate EIS for glucose sensing, as does Telson, in the invention of Mastrototaro, because both inventions uses electrode sensor current values to calculate and calibrate glucose sensors. The motivation to do so is that “the adjustments can compensate for the condition of the sensor membrane in vivo” (Telson, Abstract).
Regarding Claim 7, the Mastrototaro/Shao/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The EIS procedure of Telson uses the EIS-based parameter of capacitance (Telson, [0014], “the reference parameter value and the in vivo parameter value may be capacitance values”) which is imaginary impedance (impedance is a complex value with imaginary and real parts of capacitance and resistance, respectively, which are both used in, for example, [0046-00047]).
Regarding Claim 8, the Mastrototaro/Shao/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The EIS procedure of Telson uses the EIS-based parameter of resistance (Telson, [0043], “there are two parameters that may be extracted from the EIS measurement, R and C”) which is imaginary impedance (impedance is a complex value with imaginary and real parts of capacitance and resistance, respectively, which are both used in, for example, [0046-00047]).
Regarding Claim 9, the Mastrototaro/Shao/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). Mastrototaro further teaches smoothing said values of any of the measurements that are taken (Mastrototaro, column 10, lines 21-25, “In further alternatives, clipping may be applied to the sampled values, interval values, memory values, calculated glucose values, estimated values of a measured characteristic, or any combination of the values” where “clipping” is an example of smoothing) thus smoothing said values of the EIS-based parameter prior to calculating said fused SG would be obvious in light of the cited references because any measured or estimated values might be noisy. The motivation to do so is “to limit the signal magnitude variation from one value to the next thereby reducing the effects of extraneous data, outlying data points, or transients” (Mastrototaro, column 9, lines 7-9).
Regarding Claim 10, the Mastrototaro/Shao/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The combination, through Mastrototaro and Shao (for fused) further teaches calculation of the fused SG is repeated periodically to generate a plurality of fused SG values over time (Mastrototaro, column 6, lines 44-47, “The glucose monitor might contain the necessary software to calibrate glucose sensor signals, display a real-time blood glucose value, show blood glucose trends, activate alarms and the like” where “trends” denotes to generate a plurality of … SG values over time).
Regarding Claim 11, the Mastrototaro/Shao/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The combination, through Mastrototaro and Shao (for fused) further teaches wherein calculation of said fused SG is repeated continuously to generate a stream of fused SG values over time (Mastrototaro, column 4, lines 62-52, “a glucose monitor that is coupled to a sensor set to provide continuous data recording of readings of glucose levels from a sensor for a period of time” & column 6, lines 44-47, “The glucose monitor might … show blood glucose trends” where “trends” denotes a stream).
Regarding Claim 12, the Mastrototaro/Shao/Dinney/Telson combination of Claim 11 teaches the method of Claim 11 (and thus the rejection of Claim 11 is incorporated). The combination, through Mastrototaro and Shao (for fused) further teaches including smoothing one or more segments of said stream of fused SG values (Mastrototaro, column 10, lines 21-25, “In further alternatives, clipping may be applied to the sampled values, interval values, memory values, calculated glucose values” where “clipping” is an example of smoothing; also see column 17, lines 60-61, “readings are processed (filtered, smoothed, clipped, averaged, and the like)”).
Regarding Claim 14, the Mastrototaro/Shao/Dinney/Telson combination of Claim 11 teaches the method of Claim 11 (and thus the rejection of Claim 11 is incorporated). Mastrototaro (in light of Shao for fused) further teaches blanking one or more portions of said stream of fused SG values (Mastrototaro, column 9, lines 13-15, “interval values that are outside of the clipping limits are ignored” with column 10, lines 21-24, “clipping may be applied to the … calculated glucose values” shows that blanking/ignoring predicted SG values is envisioned by Mastrototaro, in particular values outside of the clipping limits can either be smoothed, i.e. replaces with the clipping limit value, or blanked, i.e. ignored and not used).
Regarding Claim 15, the Mastrototaro/Shao/Dinney/Telson combination of Claim 14 teaches the method of Claim 14 (and thus the rejection of Claim 14 is incorporated). Mastrototaro (in light of Shao for fused) further teaches wherein said blanking is based on a level of noise in said stream of fused SG values (Mastrototaro, column 9, lines 13-15, “interval values that are outside of the clipping limits are ignored” with column 9, lines 23-26, “the level that the clipping limits are set to is dependent on an acceptable amount of change … which is affected by the sensor sensitivity, signal noise, signal drift, and the like”).
Regarding Claim 16, the Mastrototaro/Shao/Dinney/Telson combination of Claim 14 teaches the method of Claim 14 (and thus the rejection of Claim 14 is incorporated). Mastrototaro (in light of Shao for fused) further teaches wherein said blanking is based on representative values of one or more of Isig, a counter electrode voltage (Vcntr), and said EIS-based parameter (Mastrototaro, column 10, lines 50-61, “each memory storage value is considered valid (Valid ISIG value) unless one of the following calibration cancellation events occurs … once a calibration cancellation event occurs, the successive memory storage values are not valid, and therefore not used to calculate blood glucose” that is, the fused SG values are blanked based on Isig values being invalid).
Regarding Claim 17, the Mastrototaro/Shao/Dinney/Telson combination of Claim 14 teaches the method of Claim 14 (and thus the rejection of Claim 14 is incorporated). Mastrototaro (in light of Shao for fused) further teaches wherein fused SG and said final SG are calculated in real time (Mastrototaro, column 6, line 28, “The glucose monitor takes raw glucose sensor data from the glucose sensor and assesses it during real-time”).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao, Dinney, and Telson, and further in view of Abeyratne, US PG Pub 2011/0301487.
Regarding Claim 13, the Mastrototaro/Shao/Dinney/Telson combination of Claim 12 teaches the method of Claim 12 (and thus the rejection of Claim 12 is incorporated). Mastrototaro teaches smoothing and filtering the data results (column 17, lines 60-61, “readings are processed (filtered, smoothed, clipped, averaged, and the like)”) but is silent regarding smoothing with a low-pass filter. Abeyratne teaches smoothing, i.e. removing noise, with a low-pass filter ([0174], “The digital signal … is passed to a band-pass digital filter to remove out-of-band noise” where “band-pass filter” is a low-pass filter in combination with a high-pass filter to remove both high and low frequency noise). It would have been obvious to one or ordinary skill in the art before the effective filing date to use a low-pass filter, like Abeyratne, to smooth the data of Mastrototaro, because both inventions need to smooth the data in their calculations. The motivation to do so is “to remove out-of-band noise” (Abeyratne, [0174]).
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao, and further in view of Abeyratne.
Regarding Claim 19, the Mastrototaro/Shao combination of Claim 18 teaches the method of Claim 18 (and thus the rejection of Claim 18 is incorporated). Mastrototaro teaches wherein the preprocessed Isig values are smoothed (column 10, lines 6-8, “Fig. 8 shows interval value R’, which is calculated by averaging sampled values N through Q”, see Fig. 8, sampled values are clipped and averaged, which results in smoothing) but does not do so using a polynomial model for local regression with weighted linear least squares. However, Abeyratne teaches this limitation (Abeyratne, [0137], “to reveal the slow changes and remove outliers [the] Loess Smoothening Method is used, which is based on a local regression using weighted linear least squares and a 2nd degree polynomial model”). It would have been obvious to one of ordinary skill in the art to incorporate this feature from Abeyratne into the invention of Mastrototaro because both inventions desire to smooth sensor data, i.e. “to reveal slow changes and remove outliers.” The motivation to do so is “in order to improve the performance … and to remove outliers” (Abeyratne, [0137]).
Regarding Claim 20, the Mastrototaro/Shao/Abeyratne combination of Claim 19 teaches the method of Claim 19 (and thus the rejection of Claim 19 is incorporated). Mastrototaro further teaches calculating signal noise for said smoothed Isig values (Mastrototaro, column 10, lines 39-49, “unstable signal alarm limits are set to detect when memory storage values” i.e. smoothed Isig values “change too much … in essence, the glucose monitor has detected too much noise in the ISIG from the glucose sensor” denotes that signal noise for said smoothed Isig values has been calculated to determine if the noise is so great that the sensor needs to be re-calibrated or replaced).
Conclusion
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122