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 amendment filed 05/14/2026 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 Claims 10-19 and are withdrawn from further consideration.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.
Claim Interpretation
Claims recite "and/or". The claims reciting "and/or" were interpreted as “or”.
Claim Objections
Claim 20, line 24 includes the typo “and; and”. Examiner interprets as “and" for examination purposes.
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The disclosure is inadequate to support the following limitations: "memory storing digitized transfer curve signals" in claim 1. As to memory storing signals, the disclosed memory stores data or information and not signals. The specification includes no description of any memory storing signals. The description reads (underline emphasis added):
'[0101]… storage nodes 104 may receive and store information from the sensor nodes 114, (e.g., 114a, 114b, 114c, 114d) such as output signals from the 2D FETs (e.g., gFETs), transfer curve information from 2D FETs, analysis results based on transfer curve…'
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.
Claim 20 is 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 20 recites the limitation "the transfer curve information" in line(s) 27-28. There is insufficient antecedent basis for this limitation in the claim. The anteceding limitation was amended.
Appropriate correction or clarification is required.
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… determines the one or more output characterization parameters as output data
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 digitized transfer curve signals for the 2D FETs" as performing generic computer functions routinely used in computer applications.
As to the limitations “models a selected form of transfer curve signals for an array of 2D field effect transistors (FETs) on a sensor integrated circuit (IC) for characterizing biochemical interactions occurring within a measurement distance of 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… of a machine learning model by applying the digitized transfer curve signals 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 digitized transfer curve signals for the 2D FETs", 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.
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 digitized transfer curve signals for the 2D FETs" at a high level of generality and as performing generic computer functions routinely used in computer applications (see 112(b) Rejection above). 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]… 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 invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. See MPEP 2106.05(f)(2). As to the limitations “a characterization parameter encoder… of a machine learning model by applying the digitized transfer curve signals 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 digitized transfer curve signals for the 2D FETs", the specification reads:
'[0121]… a characterization parameter encoder 130 that is operable to determine a set of output characterization parameters 132 for a form of an equation that models a reduced complexity form of a transfer curve, by applying a machine learning model 700 to a reduced complexity form of transfer curve information 128 from the measurement controller 122, where the machine learning model 700 is trained to associate the reduced complexity form of transfer curve information 128 with the set of output characterization parameters…
[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…
[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)'
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 a set of output characterization parameters for an equation that models a first derivative of a transfer curve
The claim is substantially drawn to mathematical concepts: relationships, formulas or equations, calculations; but for the recitation of generic computer components. "[A]n 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)'
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 operable to" 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 fluid; 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 fluid over gate areas of the array… determine digitized transfer curve signals for the 2D FETs of the array by applying bias conditions including a drain-to-source voltage, and a gate-to-source voltage; while varying the gate-to-source voltage, and producing output signals representative of the digitized transfer curve signals; and; and a characterization parameter encoder operable to… 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", 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.
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 operable to" 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 to the limitations "a measurement controller operable to", (see Applicant's arguments filed 05/14/2026 'In the context of the present claims and Specification, "controller" denotes a well-understood class of hardware structures (e.g., control circuitry and/or one or more processors executing control instructions) for coordinating measurement operations' in page 14, last paragraph to page 15, 2nd paragraph).
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. See MPEP 2106.05(f)(2). As to the limitations “determine digitized transfer curve signals for the 2D FETs of the array by applying bias conditions including a drain-to-source voltage, and a gate-to-source voltage; while varying the gate-to-source voltage, and producing output signals representative of the digitized transfer curve signals; and; and a characterization parameter encoder operable to… 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", (see Independent claim 1, Step 2B above). 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".
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 mental concepts of their independent claim. (See Independent claim 1, Step 2A, Prong One above). 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 "2… wherein the digitized transfer curve signals comprise a set of data points that associate a set of channel output currents of the 2D FETs 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", "4… wherein the digitized transfer curve signals comprise 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, and/or a 2D channel input bias voltage varied at a predetermined characteristic resonance frequencies; 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 fluid", "5… a complexity reduction module that produces a reduced complexity form of the digitized transfer curve signals by applying one or more operations to the digitized transfer curve signals in response to determining that applying the one or more operations continues to satisfy a predetermined goodness of fit requirement", "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", "7… wherein the one or more operations applied by the complexity reduction module are selected from: normalizing the digitized transfer curve signals 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 curve signals to align lowest points of the transfer curve signals at a VG=O point along an x-axis; normalizing the digitized transfer curve signals 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 digitized transfer curve signals, subtracting the minimum value from each instance of channel output current in the set of digitized transfer curve signals, dividing each instance of channel output current in the set of digitized transfer curve signals by the maximum value minus the minimum value; a first derivative of a 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", "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 Feta third output characterization parameter 'A' output by the machine learning model which corresponds to a vertical scaling numerator of a logistic function term of a first derivative of the digitized transfer curve signals with respect to gate voltage and varies based on ionic strength of the fluid 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", "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 first derivative of a resistance-adjusted change in current signals"; they represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
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 a training set representing digitized transfer curve signals that model a form of the digitized transfer curve signals for the 2D FETs", they 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 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 invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. (See Independent claim 1, Step 2B above).
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).
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 curve signals (see "transfer curves" as "I-V characteristics", "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) digitized transfer curve signals (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 digitized transfer curve signals 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 digitized transfer curve signals for the 2D FETs (see page 1481, 2nd-4th paragraphs:
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Cheng does not disclose, but Goldsmith discloses for an array of 2D field effect transistors (FETs) on a sensor integrated circuit (IC) (see page 2, Fig. 1) for characterizing biochemical interactions occurring within a measurement distance of the 2D FETs (see "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 digitized transfer curve signals comprise a set of data points that associate a set of channel output currents of the 2D FETs (see "transfer curve" as "I-V characteristics", "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 "Agile R100 system from Nanomed (a Cardea owned brand) was used for all measurements… 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).
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, (see supra).
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 a training set representing digitized transfer curve signals that model a form of the digitized transfer curve signals for the 2D FETs (see page 1480, Fig. 2).
As to claim 4, Goldsmith discloses wherein the digitized transfer curve signals comprise 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 "Agile R100 system from Nanomed (a Cardea owned brand) was used for all measurements… 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 "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 fluid (see "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).
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, (see supra).
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 digitized transfer curve signals by applying one or more operations to the digitized transfer curve signals in response to determining that applying the one or more operations continues to satisfy a predetermined goodness of fit requirement (see "transfer curve" as "I-V characteristics", "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:
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).
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 "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 fluid; 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 fluid over gate areas of the array (see page 2, Fig. 1); a measurement controller operable to (see "Agile R100 system from Nanomed (a Cardea owned brand) was used for all measurements… 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 digitized transfer curve signals (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), 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 producing output signals representative of the digitized transfer curve signals; 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:
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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:
no reference 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: normalizing the digitized transfer curve signals 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 curve signals to align lowest points of the transfer curve signals at a VG=O point along an x-axis; normalizing the digitized transfer curve signals 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 digitized transfer curve signals, subtracting the minimum value from each instance of channel output current in the set of digitized transfer curve signals, dividing each instance of channel output current in the set of digitized transfer curve signals by the maximum value minus the minimum value; a first derivative of a 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
Examiner invites Applicant to use the Specification of record in the present Application and not any other publication. The MPEP reads "2106.05(a)… evaluating the specification… if the specification sets forth… described in the specification", and not the U.S. Pre–Grant publication or any other publication.
Regarding the Claim Interpretations, Applicant acknowledges and does intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: a characterization parameter encoder that determines in claim 1, a complexity reduction module that produces in claim 5, and a characterization parameter encoder operable to in claim 20.
Regarding the Claim Interpretations of a measurement controller operable to in claim 20, Applicant's arguments, (see page 14, last paragraph to page 15, 2nd paragraph), have been considered along with the amendment and the Claim Interpretations are withdrawn.
Regarding the claim objections, the amendment corrected all deficiencies, and the objections are withdrawn.
Regarding the claim rejections - 35 USC § 112, the amendment corrected all deficiencies, and those objections are withdrawn.
Regarding the rejections under 101, Applicant's arguments have been considered, but they are not persuasive. Applicant argues, (see page 16, 2nd paragraph to page 19, 1st paragraph):
‘… claim amendments recite signals-based terminology to address the § 101 rejection under In re SiRF Technology, Inc., 601 F.3d 1319 (Fed. Cir. 2010). Independent Claims 1, 17, and 20 have been amended to recite "digitized transfer curve signals" in place of "transfer curve information," and Claim 20 has been further amended to recite "output signals" (corresponding to element 240 in Figure 2) produced by the measurement controller and applied to the characterization parameter encoder. As amended, the claims are firmly grounded in the SiRF framework, and the § 101 rejection should be withdrawn for the reasons set forth below.
D.1 - Step 2A, Prong One: The Claims Do Not Recite "Mental Acts"
At Step 2A, Prong One, the Examiner characterizes the claims as reciting "mental processes." Applicant respectfully submits that, under any reasonable construction, the claimed operations cannot be practically performed in the human mind (or with pen and paper) because they require the acquisition, digitization, storage, and machine-learning-based analysis of physical signals generated by a 2D FET sensor array. Under MPEP § 2106.04(a)(2)(III)(B), the "mental processes" grouping is limited to concepts that can practically be performed in the human mind…
The Specification likewise confirms the computational scale of the claimed systems. Paragraph [0086] discloses that transfer-curve acquisition can generate on the order of 1.125 terabytes per day of transfer curve data. Paragraph [0135] expressly discloses that the measurement circuitry 202 includes an analog-to-digital converter for digitizing the measured signals. No human being can perform the analog-to-digital conversion of sensor signals mentally, nor process 1.125 terabytes per day of digitized transfer curve signals with pen and paper. In view of the express computational and digitization scale and the claimed use of machine learning models, the claims do not recite "mental acts" within the meaning of the 2019 PEG, and therefore are not properly characterized as reciting a "mental process" abstract idea at Step 2A, Prong One.
D.2 - Step 2A, Prong Two: The Claims Integrate Any Alleged Abstract Idea into a Practical Application…
… The court concluded that "the presence of the GPS receiver in the claims places a meaningful limit on the scope of the claims" because "the use of a GPS receiver is essential to the operation of the claimed methods." Id. at 1332-33. Critically for the present case, the SiRF court emphasized that the physical machine generating the signals being processed "is integral to each of the claims at issue and places a meaningful limit on the claims' scope." Id. at 1333.
The same analysis compels a finding of eligibility here. The amended claims recite:
(a) an array of 2D field effect transistors on a sensor integrated circuit that physically transduces biochemical interactions into analog channel current signals (Specification [0069]: "current through the 2D transistor channel(s) is modulated or affected by events, occurrences, or interactions within a liquid in contact with the channel(s)");
(b) a measurement controller comprising excitation circuitry 204 and measurement circuitry 202 ([0123]), wherein the measurement circuitry 202 expressly "may include... an analog-to-digital converter, a processor executing code to receive and process signals via input/output pins..." ([0135]), producing output signals 240 representative of the digitized transfer curve signals (Fig. 2); and
(c) a characterization parameter encoder that applies a trained machine learning model to the resulting digitized transfer curve signals to produce biochemical characterization parameters ([0119], [0194], [0221]).
Just as in SiRF, the claimed 2D FET sensor array and measurement controller (including the analog-to-digital converter) are essential to the operation of the claimed apparatus -the "digitized transfer curve signals" recited in the claims cannot exist without the physical transduction of biochemical interactions into analog current signals by the 2D FET sensor array, followed by digitization in the measurement circuitry. As in SiRF, "there is no evidence here that the calculations... can be performed entirely in the human mind." 601 F.3d at 1333. The claimed machine "plays a significant part in permitting the claimed method to be performed" it is not "an obvious mechanism for permitting a solution to be achieved more quickly." Id. The sensor array and digitization hardware "place[] a meaningful limit on the scope of the claims." Id.’
The MPEP reads (underline emphasis added):
‘2106.04… II… A… 2. Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application?… If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B… For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice… either at Prong Two or in Step 2B’
‘2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]… In addition to the abstract idea, the claims also recited the additional element of…’.
‘2106.07(a)… II… After identifying the judicial exception in the rejection, identify any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception and explain why they do not integrate the judicial exception into a practical application and do not add significantly more to the exception’
About "additional elements", BASCOM2, (BASCOM hereinafter) reads:
“the ‘elements of each claim both individually and ‘as an ordered combination’ to determine whether the additional elements [beyond those that recite the abstract idea”.
Examiner's response: Applicant's argument is not persuasive, because claims 1-9 lack the 'measurement controller comprising excitation circuitry 204 and measurement circuitry 202' and claim 20 lacks the 'characterization parameter encoder that applies a trained machine learning model to the resulting digitized transfer curve signals' (underline emphasis added – see 112(b) Rejection above), as argued. As to claim 20, contrary to Applicant's argument ('applies a trained machine learning model to… signals'), Examiner notes that the applying of a machine learning model is to information and that the machine learning model is trained to associate information with parameters.
Examiner agrees with Applicant's argument "the presence of the GPS receiver in the claims places a meaningful limit on the scope of the claims" because "the use of a GPS receiver is essential to the operation of the claimed methods."… the SiRF court emphasized that the physical machine generating the signals being processed "is integral to each of the claims at issue and places a meaningful limit on the claims' scope." The claims must stand on their own. Examiner is not allowed to bring limitations set forth in the description into the claims. Although a claim should be interpreted in light of the Specification disclosure, it is generally considered improper to read limitations contained in the Specification into the claims. See Synopsys3 at page 20, 2nd paragraph, citing Accenture: 'The § 101 inquiry must focus on the language of the Asserted Claims themselves. See Accenture4… (admonishing that “the important inquiry for a § 101 analysis is to look to the claim”); see also Content Extraction5'.
Applicant argues that the additional elements are not judicial exception(s) or abstract idea(s), but the additional elements were addressed in Examiner's rejection Step 2A, Prong Two and/or Step 2B. Applicant's arguments do not address these limitations as additional elements, as pointed out by the Examiner. Applicant’s arguments conflate judicial exception(s) or abstract idea(s) (Step 2A, Prong One) with additional elements (Step 2A, Prong Two or Step 2B). Throughout the prosecution of this application, in accordance with the guidance set forth in MPEP (supra) and in several decisions, BASCOM (supra) for example, the Examiner does not conflate judicial exception(s) or abstract idea(s) (Step 2A, Prong One) with additional elements (Step 2A, Prong Two or Step 2B).
Applicant further argues, (see page 19, 2nd to next to last paragraph):
‘D.2.b - Improvement to Technology Under MPEP § 2106.05(a)
The amended claims also integrate any alleged abstract idea into a practical application because they improve a technological process for operating and analyzing 2D FET sensor arrays. See MPEP § 2106.05(a). The Specification describes concrete, quantitative improvements, including:
(i) a four-order-of-magnitude improvement in gFET transfer curve system efficiency ( [0101]); and
(ii) data compression from approximately 168 kB to approximately 12 bytes per voltage sweep ( [0087]), enabling scalable sensor deployments.
The Specification further explains that the output characterization parameters correspond to specific physical biochemical phenomena: parameter 'k' varies based on total volume of biochemical material ( [0207]); parameter 'A' varies based on ionic strength ( [0208]); and parameter 'w' varies based on total charge of biochemical material ( [0209]). These are technological improvements in measurement and analysis of biochemical interactions using 2D FET transfer curve signals -not mere mathematical manipulation in the abstract.
In contrast, the Examiner's cited Goldsmith 2019 reference relies on comparatively simple analytical techniques (e.g., percent change in current and AI/AVG) and does not provide the claimed parameterized modeling framework with the disclosed quantitative efficiency gains or parameter-to-phenomenon mapping. (Goldsmith 2019 at p. 3.) The Examiner's cited Wang et al. (2011) reference confirms that GFET current-voltage characteristics depend on physical device parameters, including "the virtual-source injection velocity vvs, which is a physical parameter with great technological significance," (Wang 2011 at Abstract), reinforcing that the signals being processed by the claimed encoder are grounded in physical device phenomena, not abstractions’
Examiner's response: Applicant's argument is not persuasive, because Applicant's argument does not elaborate how the claimed invention’s additional elements/limitations considered both individually and in combination integrate a judicial exception into a practical application in Step 2A Prong Two or amount to significantly more in Step 2B. Instead, Applicant makes prior art conclusory statements. “Integration into a practical application” requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
Therefore, the rejections are maintained.
Regarding the arguments with respect to the rejection under 103, Applicant’s arguments have been fully considered, but they are not persuasive.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
In response to Applicant’s arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller6; In re Merck & Co.7. The combination of cited references teaches or suggests all the elements of the claimed invention.
Applicant argues, (see page 22, 2nd paragraph), that Wang teaches away from the Office Action's proposed combination, because '"nothing in the model appear to provide guidance" (¶[0154]); "none of the curve-fitting described in the article appear to involve testing of transfer curves in the presence of gFETs functionalized with a representative range of surface chemistries" ((¶ [0155]); and "the inventors of the present subject matter determined to take an entirely different approach" ((¶[0155])'. However, the specification cites another NPL and not Wang. The specification reads (underline emphasis added):
'[0154] Figure 4 is an illustration of a compact piecewise model for extracting device parameters for electrolytically gated graphene FETs. According to a journal article by "Mackin, C. and Palacios, T., 2016 titled "Large-scale sensor systems based on graphene electrolyte-gated field effect transistor" ANALYST, 141(9), pp.2704-2711'
Therefore, the rejections are maintained.
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.
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.
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/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016
2 BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, U.S. Court of Appeals for the Federal Circuit, No. 2015-1763 (June 27, 2016)
3 Synopsys, Inc. v. Mentor Graphics Corp. (Fed. Cir. October 17, 2016)
4 Accenture Global Servs., GmbH v. Guidewire Software, Inc., 728 F.3d 1336, 1345 (Fed. Cir. 2013)
5 Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1346 (Fed. Cir. 2014)
6 In re Keller, 642 F.2d 413, 426 (CCPA 1981)
7 In re Merck & Co. Inc., 800 F.2d 1091, 1097 (Fed. Cir. 1986)
Prosecution Insights