Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/13/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
The present application is being examined under the claims filed on 3/13/2024.
Claims 1-20 are rejected.
Claims 1-20 are pending.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
Drawings
The drawings filed on 3/13/2024 are acceptable for examination purposes.
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 2, 4-11, 13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 2,
Step 1: Claim 2 is a method claim. Therefore, Claim 2 is directed to a process.
Step 2A Prong 1:
for each of the one or more samples, normalizing the peptide binding values according to a median binding value of peptides associated with the peptide array (mental process – for each of the one or more samples, normalizing the peptide binding values according to a median binding value of peptides associated with the peptide array may be performed manually by a user with the aid of pen and paper by observing/analyzing a median binding value of peptides associated with the peptide array and adjusting the peptide binding values. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
training a regressor using dense compact representations of the peptide sequence data and peptide binding values (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training a regressor using dense compact representations of the peptide sequence data and peptide binding values (Gao et al. (“Deep Learning in Protein Structural Modeling and Design“), hereinafter Gao, teaches in Section - Convolutional Neural Networks, “CNNs, especially ResNets, have been widely used in protein structure prediction. An example is AlphaFold, which used ResNets to predict protein inter-residue distance maps from amino acid sequences.” Gao has recognized training a regressor (i.e. ResNets) using dense compact representations of the peptide sequence data and peptide binding values (i.e. predicting protein inter-residue distance maps from amino acid sequences) as a well-understood, routine, and conventional activity previously known in the industry. See MPEP 2106.05(d).)
For the reasons above, Claim 2 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 4,
Step 1: Claim 4 is a method claim. Therefore, Claim 4 is directed to a process.
Step 2A Prong 1:
[providing an output of the regressor to a classifier], [wherein the classifier is configured] to determine whether a patient has one of the plurality of conditions based on the output of the regressor (mental process - to determine whether a patient has one of the plurality of conditions based on the output of the regressor may be performed manually by a user with the aid of pen and paper by observing/analyzing the output of the regressor. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
training a regressor using dense compact representations of the peptide sequence data and peptide binding values (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
providing an output of the regressor to a classifier (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the classifier is configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training a regressor using dense compact representations of the peptide sequence data and peptide binding values (Gao et al. (“Deep Learning in Protein Structural Modeling and Design“), hereinafter Gao, teaches in Section - Convolutional Neural Networks, “CNNs, especially ResNets, have been widely used in protein structure prediction. An example is AlphaFold, which used ResNets to predict protein inter-residue distance maps from amino acid sequences.” Gao has recognized training a regressor (i.e. ResNets) using dense compact representations of the peptide sequence data and peptide binding values (i.e. predicting protein inter-residue distance maps from amino acid sequences) as a well-understood, routine, and conventional activity previously known in the industry. See MPEP 2106.05(d).)
providing an output of the regressor to a classifier (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
wherein the classifier is configured (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 4 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 5-8. The additional limitations of the dependent claims are addressed below.
Regarding Claim 5,
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 5 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the classifier comprises a support vector machine (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the classifier comprises a support vector machine (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 6,
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 6 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the classifier comprises a neural network (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the classifier comprises a neural network (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 7,
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 7 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the output comprises an output layer of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the output comprises an output layer of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 8,
Step 2A Prong 1:
See the rejection of Claim 4 above, which Claim 8 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the output comprises predicted values of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the output comprises predicted values of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 9,
Step 1: Claim 9 is a method claim. Therefore, Claim 9 is directed to a process.
Step 2A Prong 1:
determining, [using the classifier], whether the patient has one of the plurality of conditions based on the output from the regressor (mental process - determining, using the classifier, whether the patient has one of the plurality of conditions based on the output from the regressor may be performed manually by a user with the aid of pen and paper by observing/analyzing the output from the regressor. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
training a regressor using dense compact representations of the peptide sequence data and peptide binding values (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
obtaining a sample from a patient (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
obtaining, using the peptide array, sample peptide sequence data and sample peptide binding values from the sample (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
providing the sample peptide sequence data and sample peptide binding values to the regressor (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
providing an output of the regressor to a classifier (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
using the classifier (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
training a regressor using dense compact representations of the peptide sequence data and peptide binding values (Gao et al. (“Deep Learning in Protein Structural Modeling and Design“), hereinafter Gao, teaches in Section - Convolutional Neural Networks, “CNNs, especially ResNets, have been widely used in protein structure prediction. An example is AlphaFold, which used ResNets to predict protein inter-residue distance maps from amino acid sequences.” Gao has recognized training a regressor (i.e. ResNets) using dense compact representations of the peptide sequence data and peptide binding values (i.e. predicting protein inter-residue distance maps from amino acid sequences) as a well-understood, routine, and conventional activity previously known in the industry. See MPEP 2106.05(d).)
obtaining a sample from a patient (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
obtaining, using the peptide array, sample peptide sequence data and sample peptide binding values from the sample (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
providing the sample peptide sequence data and sample peptide binding values to the regressor (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
providing an output of the regressor to a classifier (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
using the classifier (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 9 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 10-11. The additional limitations of the dependent claims are addressed below.
Regarding Claim 10,
Step 2A Prong 1:
See the rejection of Claim 9 above, which Claim 10 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the classifier is used in connection with a diagnostic test (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the classifier is used in connection with a diagnostic test (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 11,
Step 2A Prong 1:
See the rejection of Claim 9 above, which Claim 11 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the classifier is used in connection with a biosurveillance system (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the classifier is used in connection with a biosurveillance system (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 13,
Step 1: Claim 13 is a system claim. Therefore, Claim 13 is directed to a machine.
Step 2A Prong 1:
[wherein the instructions that, when executed by the processor, further cause the computer system to:] for each of the one or more samples, normalizing the peptide binding values according to a median binding value of peptides associated with the peptide array (mental process – for each of the one or more samples, normalizing the peptide binding values according to a median binding value of peptides associated with the peptide array may be performed manually by a user with the aid of pen and paper by observing/analyzing a median binding value of peptides associated with the peptide array and adjusting the peptide binding values. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receive the peptide sequence data and the peptide binding values corresponding to one or more samples (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
train a regressor using dense compact representations of the peptide sequence data and peptide binding values (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the instructions that, when executed by the processor, further cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receive the peptide sequence data and the peptide binding values corresponding to one or more samples (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
train a regressor using dense compact representations of the peptide sequence data and peptide binding values (Gao et al. (“Deep Learning in Protein Structural Modeling and Design“), hereinafter Gao, teaches in Section - Convolutional Neural Networks, “CNNs, especially ResNets, have been widely used in protein structure prediction. An example is AlphaFold, which used ResNets to predict protein inter-residue distance maps from amino acid sequences.” Gao has recognized training a regressor (i.e. ResNets) using dense compact representations of the peptide sequence data and peptide binding values (i.e. predicting protein inter-residue distance maps from amino acid sequences) as a well-understood, routine, and conventional activity previously known in the industry. See MPEP 2106.05(d).)
wherein the instructions that, when executed by the processor, further cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 13 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 15,
Step 1: Claim 15 is a system claim. Therefore, Claims 15-19 are directed to a machine.
Step 2A Prong 1:
[wherein the instructions that, when executed by the processor, further cause the computer system to:] [providing an output of the regressor to a classifier], [wherein the classifier is configured to] determine whether a patient has one of the plurality of conditions based on the output of the regressor (mental process – determining whether a patient has one of the plurality of conditions based on the output of the regressor may be performed manually by a user with the aid of pen and paper by observing/analyzing the output of the regressor. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receive the peptide sequence data and the peptide binding values corresponding to one or more samples (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
train a regressor using dense compact representations of the peptide sequence data and peptide binding values (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the instructions that, when executed by the processor, further cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
providing an output of the regressor to a classifier (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the classifier is configured to (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receive the peptide sequence data and the peptide binding values corresponding to one or more samples (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
train a regressor using dense compact representations of the peptide sequence data and peptide binding values (Gao et al. (“Deep Learning in Protein Structural Modeling and Design“), hereinafter Gao, teaches in Section - Convolutional Neural Networks, “CNNs, especially ResNets, have been widely used in protein structure prediction. An example is AlphaFold, which used ResNets to predict protein inter-residue distance maps from amino acid sequences.” Gao has recognized training a regressor (i.e. ResNets) using dense compact representations of the peptide sequence data and peptide binding values (i.e. predicting protein inter-residue distance maps from amino acid sequences) as a well-understood, routine, and conventional activity previously known in the industry. See MPEP 2106.05(d).)
wherein the instructions that, when executed by the processor, further cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
providing an output of the regressor to a classifier (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
wherein the classifier is configured to (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 15 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 16-19. The additional limitations of the dependent claims are addressed below.
Regarding Claim 16,
Step 2A Prong 1:
See the rejection of Claim 15 above, which Claim 16 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the classifier comprises a support vector machine (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the classifier comprises a support vector machine (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 17,
Step 2A Prong 1:
See the rejection of Claim 15 above, which Claim 17 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the classifier comprises a neural network (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the classifier comprises a neural network (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Regarding Claim 18,
Step 2A Prong 1:
See the rejection of Claim 15 above, which Claim 18 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the output comprises an output layer of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the output comprises an output layer of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 19,
Step 2A Prong 1:
See the rejection of Claim 15 above, which Claim 19 depends on.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
wherein the output comprises predicted values of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
wherein the output comprises predicted values of the regressor (merely reciting the words "apply it" (or an equivalent) with the judicial exception. See MPEP 2106.05(f).)
Regarding Claim 20,
Step 1: Claim 20 is a system claim. Therefore, Claim 20 is directed to a machine.
Step 2A Prong 1:
determining, [using the classifier], whether the patient has one of the plurality of conditions based on the output from the regressor (mental process - determining, using the classifier, whether the patient has one of the plurality of conditions based on the output from the regressor may be performed manually by a user with the aid of pen and paper by observing/analyzing the output from the regressor. See MPEP 2106.04(a)(2)(III)(C).)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application.
Additional Elements:
a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receive the peptide sequence data and the peptide binding values corresponding to one or more samples (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
train a regressor using dense compact representations of the peptide sequence data and peptide binding values (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
wherein the instructions that, when executed by the processor, further cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
providing sample peptide sequence data and sample peptide binding values obtained from a patient to the regressor (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
providing an output of the regressor to a classifier (Adding insignificant extra-solution activity to the judicial exception. See MPEP 2106.05(g).)
using the classifier (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional Elements:
a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
receive the peptide sequence data and the peptide binding values corresponding to one or more samples (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
train a regressor using dense compact representations of the peptide sequence data and peptide binding values (Gao et al. (“Deep Learning in Protein Structural Modeling and Design“), hereinafter Gao, teaches in Section - Convolutional Neural Networks, “CNNs, especially ResNets, have been widely used in protein structure prediction. An example is AlphaFold, which used ResNets to predict protein inter-residue distance maps from amino acid sequences.” Gao has recognized training a regressor (i.e. ResNets) using dense compact representations of the peptide sequence data and peptide binding values (i.e. predicting protein inter-residue distance maps from amino acid sequences) as a well-understood, routine, and conventional activity previously known in the industry. See MPEP 2106.05(d).)
wherein the instructions that, when executed by the processor, further cause the computer system to: (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
providing sample peptide sequence data and sample peptide binding values obtained from a patient to the regressor (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
providing an output of the regressor to a classifier (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
using the classifier (merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
For the reasons above, Claim 20 is rejected as being directed to an abstract idea without significantly more.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Johnston et al. (US 20140087963 A1) (hereinafter Johnston), in view of Woodbury et al. (WO 2020167872 A1) (hereinafter Woodbury).
Regarding Claim 1,
Johnston teaches:
“A method comprising:” (preamble)
“obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions” (Johnston, Paragraphs [0055] and [0056], “The invention provides arrays and methods for the association of a biological sample, such as a blood, a dry blood, a serum, a plasma, a saliva sample, a check swab, a biopsy, a tissue, a skin, a hair, a cerebrospinal fluid sample, a feces, or an urine sample to a state of health of a subject. In some embodiments, the biological sample is a blood sample that is contacted to a peptide array of non-natural peptide sequences […] A peptide array of the invention can be structured to detect with high sensitivity a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array. In some embodiments, the invention provides a method of detecting, processing, analyzing, and correlating the pattern of binding of the biological sample to the plurality of peptides with a condition.”; Johnston, Paragraphs [0074] and [0075], “Immunosignaturing queries all of the peptides on the array and produces binding values for each […] each with specific binding values that can robustly classify one state of disease from others.”; Examiner’s note: obtaining, using a peptide array, peptide sequence data (i.e. a peptide array of non-natural peptide sequences) and peptide binding values from one or more samples (i.e. a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array and immunosignaturing), wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions (i.e. detecting, processing, analyzing, and correlating the pattern of binding of the biological sample to the plurality of peptides with a condition) is taught.)
Johnston does not explicitly teach:
“training a regressor using dense compact representations of the peptide sequence data and peptide binding values”
Woodbury teaches:
“training a regressor using dense compact representations of the peptide sequence data and peptide binding values” (Woodbury, Paragraph [0049], “The process started by randomly selecting 1000 known molecules (e.g., peptides) with low functional property values (e.g., binding values) (902 in FIG. 9) from an array and using these known molecules (e.g., peptides) to train a neural network. The neural network is then used to iteratively predict the molecules with the top 100 functional property values (e.g., tightest binding peptides) from the array that have not yet been used to train the neural network (904 in FIG. 9). These predicted molecules are then added to the neural network’s training set […]”; Examiner’s note: training a regressor (i.e. training a neural network) using dense compact representations of the peptide sequence data and peptide binding values (i.e. iteratively predicting the molecules with the top 100 functional property values (e.g., tightest binding peptides)) is taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the immunosignaturing in Johnston, and the molecule design using machine learning mechanisms as taught in Woodbury. Johnston teaches obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions. Woodbury teaches training a regressor using dense compact representations of the peptide sequence data and peptide binding values. One of ordinary skill would have motivation to combine Johnston and Woodbury to “improve[] the model in the neural network each iteration and thus improve[] the prediction of which proposed molecules should be made next from which building block molecules” (Woodbury, Paragraph [0049]).
Regarding Claim 2,
The combination of Johnston and Woodbury teaches:
“The method of claim 1, further comprising:” (preamble)
“for each of the one or more samples, normalizing the peptide binding values according to a median binding value of peptides associated with the peptide array” (Johnston, Paragraphs [0165], [0176] and [0179], “Peptide array #1 comprises 10,420 peptides and was experimentally tested on Trial #1 […] TABLE 4 summarizes the results of a T-test statistical analysis of Trial #1 peptides. A T-test was used to compare the 20 training samples for each disease against 20 controls […] Data was median-normalized and log10 transformed for visualization of line graphs. Initial selection of peptides for classification was performed using ANOVA and T-tests were corrected for multiple-testing using Family Wise Error Rate (FWER) set to 5% […] each disease group (Disease) was compared to all other disease groups (cumulatively referred to as Non-Disease). Peptides with consistently high signal in Disease and consistently low signal in Non-Disease were chosen […]”; Examiner’s note: for each of the one or more samples (i.e. 20 training samples for each disease), normalizing the peptide binding values according to a median binding value (i.e. median-normalized data) of peptides associated with the peptide array (i.e. 10,420 peptides in Peptide array #1) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 3,
The combination of Johnston and Woodbury teaches:
“The method of claim 1,” (preamble)
“wherein the regressor comprises a neural network” (Woodbury, Paragraph [0009], “As illustrated, after process 100 begins at 102, the process, at 104, selects features (e.g., chemical properties) of building block molecules, and functional properties of to-be- proposed molecules to be made from the building block molecules to be considered by a machine learning mechanism, such as a neural network.”; Examiner’s note: wherein the regressor (i.e. machine learning mechanism) comprises a neural network (i.e. such as a neural network) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 4,
The combination of Johnston and Woodbury teaches:
“The method of claim 1, further comprising:” (preamble)
“providing an output of the regressor to a classifier” (Woodbury, Paragraph [0049], “The neural network is then used to iteratively predict the molecules with the top 100 functional property values (e.g., tightest binding peptides) from the array that have not yet been used to train the neural network (904 in FIG. 9). These predicted molecules are then added to the neural network’s training set, and then the neural network is retrained using the new training set.”; Examiner’s note: providing an output of the regressor (i.e. predicted molecules from the neural network) to a classifier (i.e. a neural network) is taught.)
“wherein the classifier is configured to determine whether a patient has one of the plurality of conditions based on the output of the regressor” (Johnston, Paragraphs [0177] and [0184], “When using only peptides from a T-test with FWER=5%, perfect binary classification into disease versus healthy was possible using Support Vector Machines (SVM) as the classifier […] Trial #2 tested if Immunosignatures could classify fourteen different diseases including three subtypes of breast cancer. 1536 samples were used to create a set of 255 discriminatory peptides.”; Examiner’s note: wherein the classifier (i.e. Support Vector Machines (SVM)) is configured to determine whether a patient has one of the plurality of conditions (i.e. classifying fourteen different diseases including three subtypes of breast cancer) based on the output of the regressor (i.e. binary classification) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 5,
The combination of Johnston and Woodbury teaches:
“The method of claim 4,” (preamble)
“wherein the classifier comprises a support vector machine” (Johnston, Paragraph [0177], “When using only peptides from a T-test with FWER=5%, perfect binary classification into disease versus healthy was possible using Support Vector Machines (SVM) as the classifier.”)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 6,
The combination of Johnston and Woodbury teaches:
“The method of claim 4,” (preamble)
“wherein the classifier comprises a neural network” (Woodbury, Paragraph [0049], “The neural network is then used to iteratively predict the molecules with the top 100 functional property values (e.g., tightest binding peptides) from the array that have not yet been used to train the neural network (904 in FIG. 9). These predicted molecules are then added to the neural network’s training set, and then the neural network is retrained using the new training set.”)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 7,
The combination of Johnston and Woodbury teaches:
“The method of claim 4,” (preamble)
“wherein the output comprises an output layer of the regressor” (Woodbury, Paragraph [0092], “The peptide real-valued space vector is then passed through a feedforward neural network with two hidden layers with 100 nodes each and a bias term to predict the binding value […] A final output layer transforms the hidden layer representations into the predicted binding value, and no activation function is applied to this output.”; Examiner’s note: wherein the output (i.e. the predicted binding value) comprises an output layer (i.e. a final output layer) of the regressor (i.e. a feedforward neural network) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 8,
The combination of Johnston and Woodbury teaches:
“The method of claim 4,” (preamble)
“wherein the output comprises predicted values of the regressor” (Woodbury, Paragraph [0092], “The peptide real-valued space vector is then passed through a feedforward neural network with two hidden layers with 100 nodes each and a bias term to predict the binding value […] A final output layer transforms the hidden layer representations into the predicted binding value, and no activation function is applied to this output.”; Examiner’s note: wherein the output comprises predicted values (i.e. the predicted binding value) of the regressor (i.e. a feedforward neural network) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 4 above and applicable herein.
Regarding Claim 9,
The combination of Johnston and Woodbury teaches:
“The method of claim 1, further comprising:” (preamble)
“obtaining a sample from a patient” (Johnston, Paragraph [0086], “These binding profiles correctly classify blinded sera samples obtained from patients […]”)
“obtaining, using the peptide array, sample peptide sequence data and sample peptide binding values from the sample” (Johnston, Paragraphs [0055] and [0056], “The invention provides arrays and methods for the association of a biological sample, such as a blood, a dry blood, a serum, a plasma, a saliva sample, a check swab, a biopsy, a tissue, a skin, a hair, a cerebrospinal fluid sample, a feces, or an urine sample to a state of health of a subject. In some embodiments, the biological sample is a blood sample that is contacted to a peptide array of non-natural peptide sequences […] A peptide array of the invention can be structured to detect with high sensitivity a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array. In some embodiments, the invention provides a method of detecting, processing, analyzing, and correlating the pattern of binding of the biological sample to the plurality of peptides with a condition.”; Johnston, Paragraphs [0074] and [0075], “Immunosignaturing queries all of the peptides on the array and produces binding values for each […] each with specific binding values that can robustly classify one state of disease from others.”; Examiner’s note: obtaining, using the peptide array, sample peptide sequence data (i.e. the biological sample contacted to a peptide array of non-natural peptide sequences) and sample peptide binding values from the sample (i.e. a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array and immunosignaturing) is taught.)
“providing the sample peptide sequence data and sample peptide binding values to the regressor” (Woodbury, Paragraph [0049], “The process started by randomly selecting 1000 known molecules (e.g., peptides) with low functional property values (e.g., binding values) (902 in FIG. 9) from an array and using these known molecules (e.g., peptides) to train a neural network. The neural network is then used to iteratively predict the molecules with the top 100 functional property values (e.g., tightest binding peptides) from the array that have not yet been used to train the neural network (904 in FIG. 9). These predicted molecules are then added to the neural network’s training set […]”; Examiner’s note: providing the sample peptide sequence data and sample peptide binding values (i.e. selecting 1000 known molecules (e.g., peptides) with low functional property values (e.g., binding values) from an array and using these known molecules (e.g., peptides) to train a neural network) to the regressor (i.e. a neural network) is taught.)
“providing an output of the regressor to a classifier” (Woodbury, Paragraph [0049], “The neural network is then used to iteratively predict the molecules with the top 100 functional property values (e.g., tightest binding peptides) from the array that have not yet been used to train the neural network (904 in FIG. 9). These predicted molecules are then added to the neural network’s training set, and then the neural network is retrained using the new training set.”; Examiner’s note: providing an output of the regressor (i.e. predicted molecules from the neural network) to a classifier (i.e. a neural network) is taught.)
“determining, using the classifier, whether the patient has one of the plurality of conditions based on the output from the regressor” (Johnston, Paragraphs [0177] and [0184], “When using only peptides from a T-test with FWER=5%, perfect binary classification into disease versus healthy was possible using Support Vector Machines (SVM) as the classifier […] Trial #2 tested if Immunosignatures could classify fourteen different diseases including three subtypes of breast cancer. 1536 samples were used to create a set of 255 discriminatory peptides.”; Examiner’s note: determining, using the classifier (i.e. Support Vector Machines (SVM)), whether the patient has one of the plurality of conditions (i.e. classifying fourteen different diseases including three subtypes of breast cancer) based on the output from the regressor (i.e. binary classification) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Regarding Claim 10,
The combination of Johnston and Woodbury teaches:
“The method of claim 9,” (preamble)
“wherein the classifier is used in connection with a diagnostic test” (Johnston, Paragraphs [0085] and [0177], “The disease determinations by immunosignaturing have correlated well with the results obtained using current diagnostic tests […] using Support Vector Machines (SVM) as the classifier […]”; Examiner’s note: wherein the classifier (i.e. Support Vector Machines (SVM)) is used in connection with a diagnostic test (i.e. diagnostic tests) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 9 above and applicable herein.
Regarding Claim 11,
The combination of Johnston and Woodbury teaches:
“The method of claim 9,” (preamble)
“wherein the classifier is used in connection with a biosurveillance system” (Johnston, Paragraphs [0148] and [0177], “The methods, devices, kits, arrays, and systems of the invention can be used to monitor a subject through the lifespan of the subject […] using Support Vector Machines (SVM) as the classifier […]”; Examiner’s note: wherein the classifier (i.e. Support Vector Machines (SVM)) is used in connection with a biosurveillance system (i.e. systems to monitor a subject through the lifespan of the subject) is taught.)
The reasons of obviousness have been noted in the rejection of Claim 9 above and applicable herein.
Regarding Claim 12,
Johnston teaches:
“the computer system comprising:” (preamble)
“A computer system for use with peptide sequence data and peptide binding values obtained using a peptide array” (Johnston, Paragraphs [0055], [0056] and [0199], “[…] the biological sample is a blood sample that is contacted to a peptide array of non-natural peptide sequences […] A peptide array of the invention can be structured to detect with high sensitivity a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array. In some embodiments, the invention provides a method of detecting, processing, analyzing, and correlating the pattern of binding of the biological sample to the plurality of peptides with a condition.”; Johnston, Paragraphs [0074] and [0075], “Immunosignaturing queries all of the peptides on the array and produces binding values for each […] each with specific binding values that can robustly classify one state of disease from others […] FIG. 11 is a block diagram illustrating a first example architecture of a computer system 1100 that can be used in connection with example embodiments of the present invention.”; Examiner’s note: A computer system (i.e. a computer system 1100) for use with peptide sequence data (i.e. the biological sample contacted to a peptide array of non-natural peptide sequences) and peptide binding values (i.e. a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array and immunosignaturing) obtained using a peptide array (i.e. a peptide array of the invention) is taught.)
“a processor; and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computer system to:” (Johnston, Paragraphs [0199] and [0200], “As depicted in FIG. 11, the example computer system can include a processor 1102 for processing instructions […] the processor 1102 to provide a high speed memory for instructions or data that have been recently, or are frequently, used by processor 1102. The processor 1102 is connected to a north bridge 1106 by a processor bus 1105. The north bridge 1106 is connected to random access memory (RAM) 1103 by a memory bus 1104 and manages access to the RAM 1103 by the processor 1102.”)
“receive the peptide sequence data and the peptide binding values corresponding to one or more samples” (Johnston, Paragraphs [0055] and [0056], “In some embodiments, the biological sample is a blood sample that is contacted to a peptide array of non-natural peptide sequences […] A peptide array of the invention can be structured to detect with high sensitivity a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array. In some embodiments, the invention provides a method of detecting, processing, analyzing, and correlating the pattern of binding of the biological sample to the plurality of peptides with a condition.”; Johnston, Paragraphs [0074] and [0075], “Immunosignaturing queries all of the peptides on the array and produces binding values for each […] each with specific binding values that can robustly classify one state of disease from others.”; Examiner’s note: receiving the peptide sequence data (i.e. a peptide array of non-natural peptide sequences) and the peptide binding values corresponding to one or more samples (i.e. a pattern of binding of a small quantity of a biological sample to a plurality of peptides in the array and immunosignaturing) is taught.)
Johnston does not explicitly teach:
“train a regressor using dense compact representations of the peptide sequence data and peptide binding values”
Woodbury teaches:
“train a regressor using dense compact representations of the peptide sequence data and peptide binding values” (Woodbury, Paragraph [0049], “The process started by randomly selecting 1000 known molecules (e.g., peptides) with low functional property values (e.g., binding values) (902 in FIG. 9) from an array and using these known molecules (e.g., peptides) to train a neural network. The neural network is then used to iteratively predict the molecules with the top 100 functional property values (e.g., tightest binding peptides) from the array that have not yet been used to train the neural network (904 in FIG. 9). These predicted molecules are then added to the neural network’s training set […]”; Examiner’s note: training a regressor (i.e. training a neural network) using dense compact representations of the peptide sequence data and peptide binding values (i.e. iteratively predicting the molecules with the top 100 functional property values (e.g., tightest binding peptides)) is taught.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the immunosignaturing in Johnston, and the molecule design using machine learning mechanisms as taught in Woodbury. Johnston teaches obtaining, using a peptide array, peptide sequence data and peptide binding values from one or more samples, wherein the peptide sequence data and the peptide binding values correspond to a plurality of conditions. Woodbury teaches training a regressor using dense compact representations of the peptide sequence data and peptide binding values. One of ordinary skill would have motivation to combine Johnston and Woodbury to “improve[] the model in the neural network each iteration and thus improve[] the prediction of which proposed molecules should be made next from which building block molecules” (Woodbury, Paragraph [0049]).
Regarding Claim 13,
Claim 13 recites substantially the same limitations as Claim 2, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 14,
Claim 14 recites substantially the same limitations as Claim 3, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 15,
Claim 15 recites substantially the same limitations as Claim 4, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 16,
Claim 16 recites substantially the same limitations as Claim 5, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 17,
Claim 17 recites substantially the same limitations as Claim 6, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 18,
Claim 18 recites substantially the same limitations as Claim 7, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 19,
Claim 19 recites substantially the same limitations as Claim 8, in the form of a system, therefore
it is rejected under the same rationale.
Regarding Claim 20,
The combination of Johnston and Woodbury teaches:
“The computer system of claim 12, wherein the instructions that, when executed by the processor, further cause the computer system to:” (preamble)
providing sample peptide sequence data and sample peptide binding values to the regressor (Woodbury, Paragraph [0049], “The process started by randomly selecting 1000 known molecules (e.g., peptides) with low functional property values (e.g., binding values) (902 in FIG. 9) from an array and using these known molecules (e.g., peptides) to train a neural network. The neural network is then used to iteratively predict the molecules with the top 100 functional property values (e.g., tightest binding peptides) from the array that have not yet been used to train the neural network (904 in FIG. 9). These predicted molecules are then added to the neural network’s training set […]”; Examiner’s note: providing sample peptide sequence data and sample peptide binding values (i.e. selecting 1000 known molecules (e.g., peptides) with low functional property values (e.g., binding values) from an array and using these known molecules (e.g., peptides) to train a neural network) obtained from a patient to the regressor (i.e. a neural network) is taught.)
providing sample peptide sequence data and sample peptide binding values obtained from a patient (Johnston, Paragraph [0015], “The values for each of the 120 peptides and 120 patient samples are plotted with blue indicating low binding and red indicating high binding.”; Examiner’s note: providing sample peptide sequence data (i.e. the values for each of the 120 peptides) and sample peptide binding values (i.e. peptides and patient samples plotted with low binding and high binding) obtained from a patient (i.e. 120 patient samples) is taught.)
“providing an output of the regressor to a classifier” (Woodbury – see supra claim 9)
“determining, using the classifier, whether the patient has one of the plurality of conditions based on the output from the regressor” (Johnston – see supra claim 9)
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Jumper et al. teaches highly accurate protein structure prediction.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YONG D RHO whose telephone number is (571)270-0194. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at 5712705871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YONG DOO RHO/Examiner, Art Unit 2147 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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