DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The Response to Election/Restriction filed 12/01/2025 has been received and considered. Claims 1-20 are pending. Claims 10-19 and are withdrawn from further consideration. Claims 1-9 and 20 are elected with traverse and presented for examination.
The previous Examiner is no longer prosecuting this application. Examiner Juan Carlos Ochoa is taking over the prosecution of this application.
Claim Interpretation
Claims recite "and/or". The claims reciting "and/or" were interpreted as “or”.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a characterization parameter encoder that determines in claim 1, a complexity reduction module that produces in claim 5, a measurement controller operable to in claim 20, and a characterization parameter encoder operable to in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
As to the previously identified means for limitations, a corresponding structure in the specification reads:
"[0096]… a sensor node 114 implements the characterization parameter encoder 130 and complexity reduction module 126 using the processor 136, memory 138, display 140, and communication interface 134 of a smart phone, tablet, or other portable electronic device".
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claim 1 line 3 uses the acronym or variable “IC”, the first use of an acronym or variable in a claim should be defined to avoid any possible indefiniteness issues.
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 7-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 4 recites the limitation "the liquid" in the last line. There is insufficient antecedent basis for this limitation in the claim. There is no "liquid" anteceding this limitation in the claim.
Claim 7 recites the limitation "the transfer curve model" in the last line. There is insufficient antecedent basis for this limitation in the claim. There is no "transfer curve model" anteceding this limitation in the claim.
As to claim 8, line(s) 10, it is unclear what the cited "the first derivative" represents. While there are "a first derivative of the transfer curve model normalized along x and y axes" in claim 7 and other functions anteceding this limitation, what the "first derivative" applies to lacks elaboration. The recitation of “the first derivative” is unclear because it is uncertain "the first derivative" of which of the plurality functions was intended.
Dependent claims inherit the defect of the claim from which they depend.
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 1-9 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1, Step 1: an apparatus (machine = 2019 PEG Step 1 = yes).
Independent claim 1 Step 2A, Prong One: claim recites:
for determining one or more output characterization parameters of a fit function that models a selected form of transfer curves for an array of 2D field effect transistors (FETs) on a sensor IC for characterizing biochemical interactions occurring within a measurement distance of the 2D FETs…
determines the one or more output characterization parameters as output data of a machine learning model
The claim is substantially drawn to mental concepts: observation, evaluation, judgment, opinion. Information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power Group1 (Electric Power hereinafter).
As to the limitations determining output characterization parameters, as drafted and under a broadest reasonable interpretation, they are mental in nature. As to these limitations, solving mathematical equations are activities that can be performed in the human mind or by a human using a pen and paper and predictions are mental in nature. The specification reads (underline emphasis added):
'[0148]… As used herein, the term “output characterization parameters” refers to parameters that relate to a form of a transfer curve equation or model the models or predicts the response of a device such as a liquid gated 2D FET for a range of respected 2D FETs'.
If a claim limitation, under its broadest reasonable interpretation, covers abstract ideas, then it falls within groupings of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes).
Independent claim 1 Step 2A, Prong Two: claim recites the additional elements "a memory storing transfer curve information for the 2D FETs obtained by applying bias conditions including a drain-to-source voltage and a gate-to-source voltage to the 2D FETs, and measuring channel currents for the 2D FETs while varying the gate-to-source voltage of the 2D FETs; and a characterization parameter encoder" as performing generic computer functions routinely used in computer applications.
As to the limitations "by applying the transfer curve information for the 2D FETs as input data to the machine learning model, wherein the machine learning model has been trained to produce as outputs the one or more output characterization parameters of the fit function that models the selected form of the transfer curve information for the 2D FETs", these limitations represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e. these claim limitations fail to recite details of how a solution to a problem is accomplished.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 1 Step 2B: As discussed with respect to Step 2A, the claim recites "a memory storing transfer curve information for the 2D FETs obtained by applying bias conditions including a drain-to-source voltage and a gate-to-source voltage to the 2D FETs, and measuring channel currents for the 2D FETs while varying the gate-to-source voltage of the 2D FETs; and a characterization parameter encoder" at a high level of generality and as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of a computer to implement the abstract idea of a mathematical or mental algorithm has not been held by the courts to be enough to qualify as “significantly more”. The implementation on a computing system is described in the specification (underline emphasis added):
"[0096]… a sensor node 114 implements the characterization parameter encoder 130 and complexity reduction module 126 using the processor 136, memory 138, display 140, and communication interface 134 of a smart phone, tablet, or other portable electronic device".
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because these claim limitations fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Independent claim 20, Step 1: a system (machine = 2019 PEG Step 1 = yes).
Independent claim 20 Step 2A, Prong One: claim recites:
determine transfer curve information for the 2D FETs of the array by applying bias conditions including a drain-to-source voltage and a gate-to-source voltage, and measuring drain currents for the 2D FETs while varying the gate-to-source voltage; (mental concepts)
determine a set of output characterization parameters for an equation that models a first derivative of a transfer curve (mathematical concepts)
The claim is substantially drawn to abstract ideas – mental concepts: observation, evaluation, judgment, opinion and mathematical concepts: relationships, formulas or equations, calculations; but for the recitation of generic computer components.
As to the limitations "determine transfer curve information for the 2D FETs of the array by applying bias conditions including a drain-to-source voltage and a gate-to-source voltage, and measuring drain currents for the 2D FETs while varying the gate-to-source voltage", these limitations are not elaborated but merely repeated in the Application description. These limitations, as drafted and under a broadest reasonable interpretation, can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining (judgments, opinions), i.e., processing curves information and/or data, that can be performed in the human mind or by a human using a pen and paper.
As to the limitations “determine a set of output characterization parameters for an equation that models a first derivative of a transfer curve”, under its broadest reasonable interpretation, "an equation that models" is a mathematical model. See for example in the Specification (underline emphasis added):
'[0187] Figure 6A is an illustration of a chart 610 that models results of transfer curves in a reduced complexity form that is a normalized current version of a first derivative of the I-VG curves for the normalized I-VG curves depicted in Figure 5C;
[0188]
d
I
d
V
G
=
k
V
G
+
B
+
A
1
+
e
-
w
V
G
(601)'
[AltContent: connector]
If a claim limitation, under its broadest reasonable interpretation, covers abstract ideas, then it falls within groupings of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes).
Independent claim 20 Step 2A, Prong Two: claim recites the additional elements "a data repository… a measurement controller… and a characterization parameter encoder" as performing generic computer functions routinely used in computer applications.
As to the limitations “a plurality of distributed sensor nodes, each sensor node comprising: an integrated circuit (“IC”) comprising; a sensor array of two-dimensional field effect transistors (“2D FETs”), each 2D FET in the array comprising: a 2D transistor channel formed in a layer of 2D material disposed on a substrate; a gate area for receiving a volume of liquid; a conductive source electrically coupled to a first end of the 2D transistor channel; a conductive drain electrically coupled to a second end of the 2D transistor channel; and an insulating layer disposed over the conductive source and the conductive drain; one or more integrated gate biasing electrodes disposed on the substrate for biasing and/or measuring electrical characteristics of the liquid over gate areas of the array", they represent no more than just “apply it” limitations, because the limitations invoke computers or other machinery merely as a tool to perform an existing process.
As to the limitations "by applying a machine learning model to the transfer curve information from the measurement controller, wherein the machine learning model is trained to associate transfer curve information with parameters", these limitations represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e. these claim limitations fail to recite details of how a solution to a problem is accomplished.
This judicial exception is not integrated into a practical application (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Independent claim 20 Step 2B: As discussed with respect to Step 2A, the claim recites "a data repository… a measurement controller… and a characterization parameter encoder" at a high level of generality and as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. As to the limitations "data repository", these limitations are not elaborated but merely repeated in the Application description. (See Independent claim 1 Step 2B above).
As discussed with respect to Step 2A, Prong two, the limitations "a plurality of distributed sensor nodes, each sensor node comprising: an integrated circuit (“IC”) comprising; a sensor array of two-dimensional field effect transistors (“2D FETs”), each 2D FET in the array comprising: a 2D transistor channel formed in a layer of 2D material disposed on a substrate; a gate area for receiving a volume of liquid; a conductive source electrically coupled to a first end of the 2D transistor channel; a conductive drain electrically coupled to a second end of the 2D transistor channel; and an insulating layer disposed over the conductive source and the conductive drain; one or more integrated gate biasing electrodes disposed on the substrate for biasing and/or measuring electrical characteristics of the liquid over gate areas of the array" appear to be just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)(2). As to the limitations "sensor nodes", they are recited at a high level of generality and as performing generic sensor functions routinely used in sensor applications. The specification reads:
"[0099] The sensor nodes 114a-d may include varying numbers of sensor ICs 116 with different sized arrays of 2D FETs 118, such as gFETs, for detecting target substances 226a, 226b, 226c, 226d (a few of which are described below with respect to Figure 2), interactions, or the like in a liquid. The depicted sensor IC 116 is referred to as a four-plex BPUTM and includes four simultaneously accessible 2D FETs that may be heterogeneously functionalized".
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because these claim limitations fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Dependent claims Step 2A, Prong One: Dependent claims limitations further the mathematical or mental concepts of their independent claims. (See Independent claim 1, Step 2A, Prong One above).
As to the limitations "5… determining that applying the one or more operations continues to satisfy a predetermined goodness of fit requirement" and “6… wherein the predetermined goodness of fit requirement is satisfied in response to values output from the machine learning model fitting actual values with a coefficient of determination of 0.98 or greater", these limitations, as drafted and under a broadest reasonable interpretation, can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining (judgments, opinions), i.e., processing information and/or data, that can be performed in the human mind or by a human using a pen and paper.
As to the limitations “7… wherein the one or more operations applied by the complexity reduction module are selected from: normalized transfer curve information along an x-axis representing a gate voltage VG by subtracting a charge neutrality point voltage from a measured value VRef of a gate voltage for the transfer curves to align lowest points of the transfer curves at a VG =0 point along an x-axis; normalized transfer curve information along a y-axis representing channel output current to be within a range of from 0 to 1 by determining a minimum value and a maximum value for each instance of channel output current in a set of transfer curve information, subtracting the minimum value from each instance of channel output current in the set of transfer curve information, dividing each instance of channel output current in the set of transfer curve information by the maximum value minus the minimum value; a first derivative of the transfer curve model normalized along x and y axes and comprising a slope intercept form of a line plus a logistic function with a sigmoid curve and a vertical scaling numerator; a resistance corrected version thereof; and combinations thereof“, subtractions, floor, ceiling, derivatives, and divisions are mathematical concepts. As to the limitations “resistance corrected version", as drafted and under a broadest reasonable interpretation, they are mathematical in nature. The specification reads: "[0185]… performing a resistance correction by dividing by the resistance".
If a claim limitation, under its broadest reasonable interpretation, covers abstract ideas, then it falls within groupings of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes).
Dependent claims Step 2A Prong two:
As to the limitations "3… wherein the machine learning model comprises a feed forward neural network encoder that has been trained to determine a fit function comprising four or less output characterization parameters curve based on training set data comprising the transfer curve information that model a form of the transfer curves for the 2D FETs", these limitations represent no more than just “apply it” limitations, because they recite only the idea of a solution or outcome, i.e. these claim limitations fail to recite details of how a solution to a problem is accomplished.
As to the limitations "5… a complexity reduction module that produces a reduced complexity form of the transfer curve information by applying one or more operations to the transfer curve information", because transformation of information and/or data is not statutory, these limitations are just “apply it” limitations. The claim invokes computers merely as a tool to perform an existing process.
As to the limitations "8… wherein the characterization parameter encoder indicates a biochemical interaction occurring within a measurement distance of the 2D FET based on one or more of: a first output characterization parameter ‘k’ output by the machine learning model which corresponds to one or more slopes of p-type and n-type plateau regions of the sigmoid curve and varies based on total volume of biochemical material interacting with the channel of the 2D FET; a third output characterization parameter ‘A’ output by the machine learning model which corresponds to a vertical scaling numerator of a logistic function term of the first derivative and varies based on ionic strength of a liquid containing the biochemical material; and a fourth output characterization parameter ‘w’ output by the machine learning model which corresponds to the slope of logistic function exponential growth region and varies based on a total charge of the biochemical material interacting with the channel of the 2D FETs" and 9… "wherein the characterization parameter encoder indicates a potential manufacturing anomaly in the 2D FET based on a second output characterization parameter ‘B’ output by the machine learning model which corresponds to a vertical offset in the derivative of a resistance adjusted change in currents", they are insignificant extra-solution activity – data outputting.
This judicial exception is not integrated into a practical application of the exception (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial exception/Abstract idea into a practical application? = NO).
Dependent claims, Step 2B:
As discussed with respect to Step 2A, Prong two, limitations reciting only the idea of a solution or outcome are just “apply it” limitations, because these claim limitations fail to recite details of how a solution to a problem is accomplished. See MPEP 2106.05(f)(1).
As discussed with respect to Step 2A, the limitations identified as just “apply it” merely invoke computers as a tool to perform an existing process. Transformation of information or data is not statutory. Information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power. As to the limitations “produces a reduced complexity form”, the limitations are not elaborated but merely repeated in the Application description.
As discussed with respect to Step 2A, Prong two, the limitations identified as data outputting are recited at a high level of generality; and therefore, remain insignificant extra-solution activity even upon reconsideration. See MPEP 2106.05(g)(3). As to the limitations “the characterization parameter encoder indicates”, they are not elaborated but merely repeated in the Application description.
Therefore, the claims do not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
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(a) 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.
Examiner would like to point out that any reference to specific figures, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1-4 are rejected under 35 U.S.C. 103(a) as being unpatentable over Guojian Cheng et al., (Cheng hereinafter), "Graphene field-effect transistor modeling based on artificial neural network", taken in view of Brett R. Goldsmith et al., (Goldsmith hereinafter), "Digital biosensing by foundry-fabricated graphene sensors".
As to claim 1, Cheng discloses an apparatus for determining one or more output characterization parameters of a fit function that models a selected form of transfer curves (see "model for top-gate graphene FET is put forward based on the artificial neural network" in page 1479, next to last paragraph; "The drain current ID of a graphene FET is determined by the drain-source voltage VDS, the gate-source voltage VGS, gate oxide thickness tox and the channel width WCH. The I-V characteristics of the graphene FET are simulated with BP neural network, in which the drain current ID is the single output and above four parameters determining the drain current are inputs" in page 1481, col. 1, next to last paragraph)… the apparatus comprising: a memory storing (see "CPU time consumptions for simulations on the graphene inverter with above methods are listed in TABLE III, which are realized on a computer with an Intel I3 530 CPU and 8GB memory" in page 1481, last paragraph) transfer curve information (see "Data for training and optimizing the neural network are obtained from a traditional analytical model" in page 1482, next to last paragraph; "Figure 3. I-V characteristics of an n-channel graphene FET, (a) and (b) are family of iD versus vDS curves and transferring properties" in page 1482; "To train the neural network, about 7700 data for simulation on the I-V characteristics of the graphene FET are obtained using the HSPICE model" in page 1481, col. 1, last paragraph) for the 2D FETs obtained by applying bias conditions including a drain-to-source voltage and a gate-to-source voltage to the 2D FETs (see "the graphene FET is a voltage controlled current source and can be modeled by the circuit in Fig .1 (b)… IDS indicates the current follows through the channel…" in page 1480, 1st paragraph; Fig .1; "The drain current ID of a graphene FET is determined by the drain-source voltage VDS, the gate-source voltage VGS…" in page 1481, col. 1, next to last paragraph), and measuring channel currents for the 2D FETs while varying the gate-to-source voltage of the 2D FETs (see "the graphene FET is a voltage controlled current source and can be modeled by the circuit in Fig .1 (b). In this model, VCH is the potential of the channel, IDS indicates the current follows through the channel" in page 1480, 1st paragraph and col. 2, 1st paragraph); and a characterization parameter encoder that determines the one or more output characterization parameters as output data of a machine learning model by applying the transfer curve information for the 2D FETs as input data to the machine learning model (see "model for top-gate graphene FET is put forward based on the artificial neural network" in page 1479, next to last paragraph; "The drain current ID of a graphene FET is determined by the drain-source voltage VDS, the gate-source voltage VGS, gate oxide thickness tox and the channel width WCH. The I-V characteristics of the graphene FET are simulated with BP neural network, in which the drain current ID is the single output and above four parameters determining the drain current are inputs" in page 1481, col. 1, next to last paragraph), wherein the machine learning model has been trained (see "To train the neural network, about 7700 data for simulation on the I-V characteristics of the graphene FET are obtained using the HSPICE model" in page 1481, col. 1, last paragraph) to produce as outputs the one or more output characterization parameters of the fit function that models the selected form of the transfer curve information for the 2D FETs (see page 1481, 2nd-4th paragraphs:
PNG
media_image1.png
638
441
media_image1.png
Greyscale
).
Cheng does not disclose, but Goldsmith discloses for an array of 2D field effect transistors (FETs) on a sensor IC (see page 2, Figure 1) for characterizing biochemical interactions occurring within a measurement distance of the 2D FETs (see "Our process for manufacturing digital biosensors… creates the routing for the source-drain voltage on 15 graphene transistors per die, as well as the platinum reference and counter electrodes… Encapsulation here refers to deposition of a dielectric barrier layer across nearly the entire chip that is intended to prevent mechanical and chemical damage during the chip packaging and printed circuit board (PCB) assembly processes… After packaging, the chips are annealed" in page 6, 6th paragraph; "Agile COOH biosensor chips (Nanomed) were used for IL6 measurements. COOH chips were prepared via incubation of clean graphene chips with 3 mM pyrene-carboxylic acid (TCI # P1687) in methanol for two hours" in page 8, 6th paragraph).
Cheng and Goldsmith are analogous art because they are related to gFETs.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Goldsmith with Cheng, because Goldsmith points out that "[t]here is a need for information-dense single assays that break the mold of expensive labs running colorimetric and PCR based assays4. Label-free measurement tools based on field-effect sensors should remove the need for most liquid reagents, decrease power requirements, and shrink the size of handheld testing devices5. These tools will be capable of performing a wide variety of chemical and biochemical assays built on top of a single sensor manufacturing chain, leading to lower overall cost for biological measurements. To demonstrate and validate this approach, we have commercially produced and sold a digital biosensor based on graphene-enabled Field Effect Biosensing (FEB). These sensors can be described as a biologically specialized Ion Sensitive Field Effect Transistor (ISFET) " (see page 1, 2nd-3rd paragraphs), and as a result, Goldsmith reports that "[t]he demonstrated biological sensing capability, low power requirements, and compact size of graphene-based biosensors will enable development of the next generation of biochemical applications. With the most difficult piece of the puzzle – cost-effective large-scale manufacturing – solved, low-power, portable digital biosensors can significantly impact healthcare industries with innovative new products that enable cutting-edge life science research, drug discovery applications, and diagnostic and health monitoring platforms" (see page 8, 2nd paragraph).
As to claim 2, Cheng discloses wherein the transfer curve information comprises a set of data points that associate a set of channel output currents of the 2D FETs (see "The I-V characteristics of the graphene FET are simulated with BP neural network, in which the drain current ID is the single output and above four parameters determining the drain current are inputs" in page 1481, col. 1, next to last paragraph) and Goldsmith discloses measured in response to one or more excitation conditions comprising a voltage sweep of liquid gate bias voltage applied to a fluid covering the 2D FETs (see "The Agile R100 system from Nanomed (a Cardea owned brand) was used for all measurements… The gate voltage was swept between ±100 mV in a triangle wave at a slow speed of 0.3 Hz, while Vsd was held at 10 mV. An example of the raw data measured this way is shown in Supplemental Fig. 2… Agile Plus software was used to run the hardware… Agile COOH biosensor chips (Nanomed) were used for IL6 measurements. COOH chips were prepared via incubation of clean graphene chips with 3 mM pyrene-carboxylic acid (TCI # P1687) in methanol for two hours" in page 8, 3rd-6th paragraphs).
As to claim 3, Cheng discloses wherein the machine learning model comprises a feed forward neural network encoder that has been trained to determine a fit function comprising four or less output characterization parameters curve based on training set data comprising the transfer curve information that model a form of the transfer curves for the 2D FETs (see page 1480, Fig. 2).
As to claim 4, Goldsmith discloses wherein the transfer curve information comprises one or more vectors comprising elements corresponding to 2D FET excitation conditions varied in accordance with a predetermined incrementally varying voltage sweep of a liquid gate bias voltage (see "The Agile R100 system from Nanomed (a Cardea owned brand) was used for all measurements… The gate voltage was swept between ±100 mV in a triangle wave at a slow speed of 0.3 Hz, while Vsd was held at 10 mV… These voltage ranges were selected to minimize the electric fields on the proteins" in page 8, 3rd paragraph), and/or a 2D channel input bias voltage varied at a predetermined characteristic resonance frequencies (see "transfer curve information" out of equation 1, "The voltage in the bulk liquid is controlled by conventional electrochemical means. From an electrical perspective, the system can be understood with the bulk liquid as the gate of a transistor, and the combined Donnan region and Debye length as the dielectric between the graphene channel and the gate. From a biological perspective, the system can be understood as a voltage sensitive membrane incorporating proteins with driven voltages, like action potentials, in the bulk liquid. The model below explains how sensing is accomplished for binding interactions… (1) Equation 1 shows a modification of previously developed compact models for graphene FETs when combined with ISFET models… source-drain voltage applied directly to the graphene (Vsd) and the gate voltage (Vg)" in page 2, last paragraph to page 3, 2nd paragraph); and further comprising output elements corresponding to 2D FET output signals generated in response to the 2D FET excitation conditions and to biochemical interactions occurring in the liquid (see "the active region of the biosensor is shown in Fig. 1(b). During measurement, a liquid drop is placed onto the circular region defined by the black epoxy shown here. The platinum counter and reference electrodes built into the sensor surface control and monitor a voltage in the bulk liquid" in page 2, 2nd paragraph).
Claims 5 and 6 are rejected under 35 U.S.C. 103(a) as being unpatentable over Cheng taken in view of Goldsmith as applied to claim 1 above, and further in view of Wang et al. , (Wang hereinafter), "Compact virtual-source current voltage model for top- and back-gated graphene field-effect transistors" (see IDS dated 05/25/2023).
As to claim 5, Cheng and Goldsmith do not disclose, but in a NPL cited by Goldsmith, Wang discloses a complexity reduction module that produces a reduced complexity form of the transfer curve information by applying one or more operations to the transfer curve information in response to determining that applying the one or more operations continues to satisfy a predetermined goodness of fit requirement (see "a new class of semiempirical physics-based compact models strictly based on carrier charge and transport has been proposed for short-channel Si MOSFETs… we extend this virtual-source model to GFETs, with the goal of providing a simple and intuitive understanding of the underlying carrier transport in graphene transistors as well as providing the basis for a numerically efficient compact model. The model shows very good agreement with experimental data with only a small set of fitting parameters and is valid for predicting the I–V characteristics of GFETs, accounting for the combined effects of the drain–source voltage VDS, the top-gate voltage VTGS, and the back-gate voltage VBGS" in page 1524, 1st paragraph).
Cheng, Goldsmith, and Wang are analogous art because they are related to gFETs.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Wang with Cheng and Goldsmith, because Wang points out that his "paper has presented a compact virtual-source model for the current–voltage characteristics of GFETs", and as a result, Wang reports that "[t]he derived I–V characteristics account for the combined effects of the drain–source voltage VDS, the top-gate voltage VTGS, and the back-gate voltage VBGS and is valid for both saturation and nonsaturation regions. With only a small set of mostly physical fitting parameters, the model agrees well with the experimental data for GFETs fabricated in our laboratory using CVD graphene and, also, the experimental data reported in the literature using epitaxial graphene. The simplicity and flexibility of the model promise attractive potential applications for circuit-level modeling of GFETs" (see page 1531, last paragraph).
As to claim 6, Cheng discloses wherein the predetermined goodness of fit requirement is satisfied in response to values output from the machine learning model fitting actual values with a coefficient of determination of 0.98 or greater (see in page 1481, col. 2, 1st paragraph:
PNG
media_image2.png
422
465
media_image2.png
Greyscale
).
Claim 20 is rejected under 35 U.S.C. 103(a) as being unpatentable over Goldsmith taken in view of Cheng, and further in view of Ada Shuk Yan Poon, (Poon hereinafter), U.S. Pre–Grant publication 20250193559.
As to claim 20, Goldsmith discloses a system comprising… a plurality of distributed sensor nodes (see "Our process for manufacturing digital biosensors… creates the routing for the source-drain voltage on 15 graphene transistors per die, as well as the platinum reference and counter electrodes… Encapsulation here refers to deposition of a dielectric barrier layer across nearly the entire chip that is intended to prevent mechanical and chemical damage during the chip packaging and printed circuit board (PCB) assembly processes… After packaging, the chips are annealed" in page 6, 6th paragraph), each sensor node comprising: an integrated circuit (“IC”) comprising (see "Agile COOH biosensor chips (Nanomed) were used for IL6 measurements. COOH chips were prepared via incubation of clean graphene chips with 3 mM pyrene-carboxylic acid (TCI # P1687) in methanol for two hours" in page 8, 6th paragraph); a sensor array of two-dimensional field effect transistors (“2D FETs”), each 2D FET in the array comprising: a 2D transistor channel formed in a layer of 2D material disposed on a substrate; a gate area for receiving a volume of liquid; a conductive source electrically coupled to a first end of the 2D transistor channel; a conductive drain electrically coupled to a second end of the 2D transistor channel; and an insulating layer disposed over the conductive source and the conductive drain; one or more integrated gate biasing electrodes disposed on the substrate for biasing and/or measuring electrical characteristics of the liquid over gate areas of the array (see page 2, Figure 1); a measurement controller operable to (see "The Agile R100 system from Nanomed (a Cardea owned brand) was used for all measurements… The gate voltage was swept between ±100 mV in a triangle wave at a slow speed of 0.3 Hz, while Vsd was held at 10 mV. An example of the raw data measured this way is shown in Supplemental Fig. 2… Agile Plus software was used to run the hardware… Agile COOH biosensor chips (Nanomed) were used for IL6 measurements. COOH chips were prepared via incubation of clean graphene chips with 3 mM pyrene-carboxylic acid (TCI # P1687) in methanol for two hours" in page 8, 3rd-6th paragraphs)…
Goldsmith does not disclose, but Cheng discloses a data repository (see "CPU time consumptions for simulations on the graphene inverter with above methods are listed in TABLE III, which are realized on a computer with an Intel I3 530 CPU and 8GB memory" in page 1481, last paragraph)… determine transfer curve information (see "Data for training and optimizing the neural network are obtained from a traditional analytical model" in page 1482, next to last paragraph; "Figure 3. I-V characteristics of an n-channel graphene FET, (a) and (b) are family of iD versus vDS curves and transferring properties" in page 1482; "To train the neural network, about 7700 data for simulation on the I-V characteristics of the graphene FET are obtained using the HSPICE model" in page 1481, col. 1, last paragraph) for the 2D FETs of the array by applying bias conditions including a drain-to-source voltage and a gate-to-source voltage (see "the graphene FET is a voltage controlled current source and can be modeled by the circuit in Fig .1 (b)… IDS indicates the current follows through the channel…" in page 1480, 1st paragraph; Fig .1; "The drain current ID of a graphene FET is determined by the drain-source voltage VDS, the gate-source voltage VGS…" in page 1481, col. 1, next to last paragraph), and measuring drain currents for the 2D FETs while varying the gate-to-source voltage (see "the graphene FET is a voltage controlled current source and can be modeled by the circuit in Fig .1 (b). In this model, VCH is the potential of the channel, IDS indicates the current follows through the channel" in page 1480, 1st paragraph and col. 2, 1st paragraph); and a characterization parameter encoder operable to determine a set of output characterization parameters… of a transfer curve (see "model for top-gate graphene FET is put forward based on the artificial neural network" in page 1479, next to last paragraph; "The drain current ID of a graphene FET is determined by the drain-source voltage VDS, the gate-source voltage VGS, gate oxide thickness tox and the channel width WCH. The I-V characteristics of the graphene FET are simulated with BP neural network, in which the drain current ID is the single output and above four parameters determining the drain current are inputs" in page 1481, col. 1, next to last paragraph), by applying a machine learning model to the transfer curve information from the measurement controller, wherein the machine learning model is trained (see "To train the neural network, about 7700 data for simulation on the I-V characteristics of the graphene FET are obtained using the HSPICE model" in page 1481, col. 1, last paragraph) to associate transfer curve information with parameters (see page 1481, 2nd-4th paragraphs:
PNG
media_image1.png
638
441
media_image1.png
Greyscale
).
Goldsmith and Cheng are analogous art because they are related to gFETs.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Cheng with Goldsmith, because Cheng points out that in his paper, a "model for top-gate graphene FET is put forward based on the artificial neural network. The proposed model has a high accuracy and the advantage of low time consuming. Finally, the model for the graphene FET is realized in HSPICE package as a subcircuit. These explorations may promote studies on the graphene integrated circuits" (see page 1479, next to last paragraph), and as a result, Cheng reports that "[c]ompared with the analytical model, the proposed neural network model has a higher efficiency, which saves about 34% time of the traditional analytical model. This advantage may be more significant in large scale integrated circuit simulations. More importantly, the inputs of our proposed model can be extended to include other parameters of the graphene FET, such as temperature and the number of graphene" (see page 1482, 1st paragraph).
Goldsmith and Cheng do not disclose, but Poon discloses for an equation that models a first derivative (see “[0232] The g1(·) in Eq. 1a is described by the nonlinear i-v relationship of the negative resistance… [0235]… g1p(·), is the voltage (or, equivalently, charge) derivative of the i-v relationship…”).
Goldsmith, Cheng, and Poon are analogous art because they are related to gFETs.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Poon with Goldsmith and Cheng, because Poon discloses "[0006]… a sensing method which enables real-time, distance/orientation immune, and robust measurement applied to resistive sensing… [0010] Another application for this technology is in health-care consumer devices. Passive sensors offer low-cost and manufacturing simplicity as well as versatility in sensing parameter. For example, passive biosensors have emerged using Graphene-based field-effect transistors (GFETs) which can be used as an effective point-of-care tool for the rapid detection of the Coronavirus Disease COVID-19", and as a result, Poon reports that "[0102]… a nonlinear gain allows for automatic gain/loss balance and self-oscillation, obviating the need for gain sweeping and forced excitation… The continual reliance on sweeping prohibits real-time wireless sensing as each sweep point requires a finite transient settling time; a single-point sensing method is therefore desirable as it simplifies readout and achieves real-time operation".
Allowable Subject Matter
Claims 7-9 are allowable over prior art of record. They will be allowed once all outstanding rejections/objections are traversed.
The following is a statement of reasons for the indication of allowable subject matter:
While Cheng discloses "To train the neural network, about 7700 data for simulation on the I-V characteristics of the graphene FET are obtained using the HSPICE model" in page 1481, col. 1, last paragraph,
Goldsmith discloses "From a biological perspective, the system can be understood as a voltage sensitive membrane incorporating proteins with driven voltages, like action potentials, in the bulk liquid. The model below explains how sensing is accomplished for binding interactions… (1) Equation 1 shows a modification of previously developed compact models for graphene FETs when combined with ISFET models… source-drain voltage applied directly to the graphene (Vsd) and the gate voltage (Vg)" in page 2, last paragraph to page 3, 2nd paragraph,
Wang discloses "a new class of semiempirical physics-based compact models strictly based on carrier charge and transport has been proposed for short-channel Si MOSFETs… we extend this virtual-source model to GFETs, with the goal of providing a simple and intuitive understanding of the underlying carrier transport in graphene transistors as well as providing the basis for a numerically efficient compact model. The model shows very good agreement with experimental data with only a small set of fitting parameters and is valid for predicting the I–V characteristics of GFETs, accounting for the combined effects of the drain–source voltage VDS, the top-gate voltage VTGS, and the back-gate voltage VBGS" in page 1524, 1st paragraph).
and Poon discloses "[0006]… a sensing method which enables real-time, distance/orientation immune, and robust measurement applied to resistive sensing… [0010] Another application for this technology is in health-care consumer devices. Passive sensors offer low-cost and manufacturing simplicity as well as versatility in sensing parameter. For example, passive biosensors have emerged using Graphene-based field-effect transistors (GFETs)",
none of the references cited taken either alone or in combination and with the prior art of record disclose
claim 7, " wherein the one or more operations applied by the complexity reduction module are selected from: normalized transfer curve information along an x-axis representing a gate voltage VG by subtracting a charge neutrality point voltage from a measured value VRef of a gate voltage for the transfer curves to align lowest points of the transfer curves at a VG =0 point along an x-axis ; normalized transfer curve information along a y-axis representing channel output current to be within a range of from 0 to 1 by determining a minimum value and a maximum value for each instance of channel output current in a set of transfer curve information, subtracting the minimum value from each instance of channel output current in the set of transfer curve information, dividing each instance of channel output current in the set of transfer curve information by the maximum value minus the minimum value ; a first derivative of the transfer curve model normalized along x and y axes and comprising a slope intercept form of a line plus a logistic function with a sigmoid curve and a vertical scaling numerator ; a resistance corrected version thereof; and combinations thereof",
in combination with the remaining steps, elements, and features of the claimed invention. Also, there is no motivation to combine none of these references to meet these limitations. It is for these reasons that Applicant's invention defines over the prior art of record.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Response to Arguments
Regarding the Election/Restrictions, Applicant argues, (see page 2, 1st paragraph to page 4, last paragraph):
‘… Under 35 U.S.C. § 121 and MPEP §§ 802.01 and 808.02, a restriction requirement should be based not only on the identification of plural inventions, but also on a showing that examining all claims together would cause a serious search or examination burden.
… the Office has not adequately explained such a serious burden. In particular:
1. Common technical field and disclosure.
All claim groups arise from a single specification directed to scalable apparatuses, methods, and systems for determining analytically efficient transfer-curve characterization parameters for sensor integrated circuits…
2. Overlapping prior art.
A prior-art search addressing 2D-FET-based sensor ICs that (i) acquire transfer-curve information under swept gate-bias conditions, (ii) normalize or otherwise process those curves to derive a reduced-complexity fit function that still satisfies a high coefficient-of-determination requirement, and (iii) apply a machine-learning model (e.g., a feed-forward neural-network encoder) to output a small number of transfer-curve characterization parameters, would be expected to uncover art also relevant to both the apparatus and method claims. In particular, the same references in the GOIN 27/4148 and related GOIN 27/4145 subclasses that disclose sensor IC structures, transfer-curve acquisition schemes, normalization/complexity-reduction operations, or training and use of machine-learning models for parameter extraction would be directly pertinent to:
Group I… and
Group II…
Thus, the same body of prior art and the same search fields and query strategies (e.g., 2D FET biosensors, transfer-curve fitting, neural-network-based parameter extraction, normalized derivative/logistic models, high R2 fit requirements) would be expected to be applied to both groups, rather than requiring materially different fields of search...
3. Substantially similar examination issues.
The anticipated questions of patentability-e.g., whether the prior art teaches or suggests:
obtaining transfer-curve information for 2D FET-based sensor arrays by applying drain-to-source and gate-to-source bias conditions and sweeping the gate voltage while measuring channel currents;
applying normalization and/or complexity-reduction operations to that transfer-curve information…
training and/or using a machine-learning model…
interpreting those output characterization parameters (e.g., k, A, B, and w) to distinguish among biochemical interaction properties…
are expected to be substantially similar across apparatus and method claims. The same claim limitations concerning the form of the normalized transfer-curve data, the structure and behavior of the reduced-complexity fit function, the nature and training of the machine-learning model, the required reconstruction or fit quality, and the technical meaning of the output parameters will need to be evaluated for novelty and non-obviousness in both Groups I and II. Accordingly, any rejection of either group under 35 U.S.C. §§ 102 or 103 would be expected to rely on substantially the same factual showings and lines of reasoning.
Given the shared disclosure and overlapping subject matter, Applicant respectfully submits that the record does not clearly establish that examination of the full claim set would result in a serious additional search or examination burden, as distinct from the ordinary burden associated with considering related apparatus, method, and system claims from a single specification…’
As pointed out by Applicant, the MPEP reads (underline emphasis added):
'802.01 Meaning of "Independent" and "Distinct" [R-08.2012]
35 U.S.C. 121… states that the Director may require restriction if two or more "independent and distinct" inventions are claimed in one application. In 37 CFR 1.141, the statement is made that two or more "independent and distinct inventions" may not be claimed in one application'
Examiner's response: Applicant's argument is not persuasive, because the claimed inventions are distinct and the searches required for each group are independent (underline emphasis added).
Invention I lacks at least:
"10… A method for determining a machine learning model with a minimized number of output characterization parameters useful for characterizing differences in biochemical interactions occurring in a fluid within a measurement distance of an array of 2D FETs on a sensor IC, the method comprising:
determining a preliminary fit function that satisfies a predetermined goodness of fit requirement for a set of transfer curve information…
determining a reduced complexity fit function that has fewer linear terms or constants terms than the preliminary fit function…
training a machine learning model to reconstruct transfer curve information that corresponds to expected outputs from the reduced complexity fit function for the transfer curve information within a predetermined reconstruction coefficient of determination" and
"17… determining transfer curve information for the 2D FETs of the array…
determining one or more output characterization parameters as output data of a machine learning model by applying a reduced complexity form of a fit function that models transfer curves of the 2D FETs to transfer curve information used as input data to the machine learning model, wherein the machine learning model has been trained to output the one or more output characterization parameters of the reduced complexity form of the fit function in response to receive the 2D FET transfer curve information as input data".
Invention II lacks at least:
"1. An apparatus for determining one or more output characterization parameters of a fit function that models a selected form of transfer curves for an array of 2D field effect transistors (FETs) on a sensor IC for characterizing biochemical interactions occurring within a measurement distance of the 2D FETs, the apparatus comprising:
a memory storing transfer curve information for the 2D FETs obtained by applying bias conditions…
a characterization parameter encoder that determines the one or more output characterization parameters as output data of a machine learning model by applying the transfer curve information for the 2D FETs as input data to the machine learning model, wherein the machine learning model has been trained to produce as outputs the one or more output characterization parameters of the fit function that models the selected form of the transfer curve information for the 2D FETs".
Contrary to Applicant's argument "claims from a single specification" or "shared disclosure", the MPEP reads distinct claimed inventions. As pointed out by the previous Examiner in the previous communication, "the inventions require a different field of search (e.g., searching different classes/subclasses or electronic resources, or employing different search strategies or search queries)]". The requirement is still deemed proper and is therefore made FINAL.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM - 7:00 PM.
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, Renee Chavez can be reached at 571-270-1104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016