DETAILED ACTION
This Office Action is in response to the RCE filed on 04/20/2026.
Claims 1, 9, and 17 are currently amended.
Claims 1-6, 8-14, and 16-20 are currently pending in this application and have been examined.
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 .
Response to Arguments
In reference to Applicant’s arguments on page(s) 9-11 regarding rejections made under 35 U.S.C. 101:
The Office Action rejects claims 1-6, 8-14, and 16-20 under 35 U.S.C. § 101 as allegedly directed to an abstract idea without significantly more. Applicant respectfully traverses these rejections.
The Examiner has acknowledged that "the human mind cannot train a quantum machine learning model based on NISQ devices." Office Action, page 10. The claims as amended now explicitly recite that the first subset includes fewer data points than the dataset "such that the first subset is loadable into the qubits within limited qubit resources of the one or more NISQ devices." This amendment ties the data reduction steps directly to the physical constraints of NISQ hardware.
The data preprocessing steps recited in the claims are not generic mental processes. Rather, they are specifically structured around subject-oriented weight vectors and weighted centroids- where each subject "indicates a parameter associated with a topic related to the quantum machine learning model" to produce a representative subset that fits within the qubit limitations of NISQ devices. The claims as a whole cannot practically be performed in the human mind because they require loading data into physical qubits and training a quantum machine learning model on NISQ devices.
Even assuming, arguendo, that the preprocessing steps are considered abstract, the claims integrate the judicial exception into a practical application. MPEP § 2106.04(d)(1) provides that a claim integrates a judicial exception into a practical application when it "improves the functioning of a computer or improves another technology or technical field."
The claims provide a specific technical solution to a specific technical problem: NISQ devices have insufficient qubits to train quantum machine learning models on large datasets. As explained in the specification, "[p]erforming computations for training a quantum machine learning model using a NISQ device may be impractical because NISQ devices may not include sufficient qubits for performing the operations necessary to train the quantum machine learning model." As-Filed Specification, paragraph [0015]. The claimed method addresses this by using subject-oriented weight vectors and weighted centroids to intelligently reduce the dataset while maintaining representativeness, such that the reduced subset is loadable into the limited qubits of NISQ devices.
This is not merely "less data to process" as the Examiner has characterized it. The data reduction is specifically structured to preserve the semantic content of the dataset relative to the subjects of the machine learning task, enabling quantum machine learning training that would otherwise be impossible on NISQ devices. The specification confirms that "training of a quantum machine learning model implemented on one or more NISQ devices may be facilitated and/or improved by representing a large dataset using a subset of data points representative of the larger dataset according to the present disclosure." As-Filed Specification, paragraph [0015].
The present claims are analogous to USPTO Example 47, Claim 3, which was found eligible because it integrated abstract mathematical calculations into a practical application of improving network security. Similarly here, the preprocessing steps are integrated into the practical application of enabling quantum machine learning training on resource-constrained NISQ devices. The claims do not merely recite an abstract idea with instructions to "apply it" on a computer; rather, they recite a specific technical solution that enables a technological capability (quantum machine learning training) that would otherwise be impossible due to hardware constraints.
Additionally, the Examiner's characterization of NISQ devices as "generic computer components" is not accurate. NISQ devices are specialized quantum hardware with specific physical constraints, including limited qubits and noise characteristics. They are not generic computers. Loading data into qubits involves encoding classical data into quantum states, which is a physical transformation fundamentally different from classical data processing. The claims are specifically designed to address the unique constraints of this specialized hardware.
Even if the claims were directed to an abstract idea, they amount to significantly more than the judicial exception. The specific combination of subject-oriented weight vectors, weighted centroids, partition weights, and partition removal based on relationships between centroids and partition weights-all specifically structured to enable training on qubit-limited NISQ devices is not well-understood, routine, or conventional activity. The unconventional ordered combination of these elements provides significantly more than the judicial exception.
For at least the foregoing reasons, Applicant respectfully requests withdrawal of the rejections of claims 1-6, 8-14, and 16-20 under 35 U.S.C. § 101.
Examiner’s response: Applicant’s arguments have been fully considered but are found to be not persuasive.
Applicant argues that the data preprocessing steps are not generic mental processes because they are structured around qubits and NISQ devices. Examiner disagrees. The actions of preprocessing the data are not required to be performed by the quantum machine learning model, and therefore are akin to preprocessing any type of data for any generic machine learning model. Stating that the limitations of data preprocessing cannot be performed in the human mind simply because the data is then to be loaded into qubits of a quantum model is mischaracterizing the actions taken in the claims. The actions of loading the data into qubits and training the quantum model on said loaded data are additional elements that are directed to applying the exception using generic computer components.
Applicant argues that the additional elements integrate the judicial exception into a practical application. Examiner disagrees. Applicant states that the improvement lies in the fact that NISQ devices are not capable of training a model with large datasets due to hardware constraints, and the datasets are reduced in order to utilize the NISQ devices for training the model. Applicant also states that “performing computations for training a quantum machine learning model using a NISQ device may be impractical because NISQ devices may not include sufficient qubits for performing the operations necessary” in [0015]. Not only are NISQ devices impractical for training a quantum model with large amounts of data, the data preprocessing steps of the instant application simply reduce the amount of data, while maintaining the same representativeness of the whole dataset, to be able to utilize the NISQ devices, therein sacrificing some amount of accuracy that would otherwise be present when training a model with the entire dataset. This results in using an admittedly impractical hardware device to process a reduced amount of data, thereby sacrificing data integrity and accuracy in order to use the impractical device. The actions stated in the claims do not serve to improve the training of the quantum model, but simply representatively reduce a dataset so that it may be processed by an impractical hardware device.
Applicant argues that the instant application is similar to Claim 3 of Subject Matter Eligibility Example 47. Examiner disagrees. Claim 3 of Example 47 is not similar to the instant application because the instant application does not recite any abstract ideas related to mathematical concepts, wherein Claim 3 of Example 47 was found eligible because the claim improves the functioning of a computer or technical field. As presented in Applicant’s arguments, it would be impractical to use a NISQ device for processing large amounts of data in a quantum model. The actions stated in the claims do not serve to improve the training of the quantum model, but simply representatively reduce a dataset so that it may be processed by an impractical hardware device.
Applicant argues that NISQ devices are not simply “generic computer components”. Examiner disagrees. In the context of the instant application, no specifics are recited in the claims as to what makes the NISQ devices unique compared to other quantum computing devices, other than that they have limited qubit resources. In order for a NISQ device to be considered as a non-generic computing component within the technological field of quantum computing, some amount of detail as to what makes the NISQ devices of the instant application unique over any generic NISQ devices needs to be present.
Applicant argues that the action of loading the dataset into the qubits of the NISQ devices is not something that can be done by the human mind. Examiner disagrees. Since loading data into a qubit of a quantum device involves encoding classical data into quantum states, which is a physical transformation fundamentally different from classical data processing, as Applicant has argued above, it is the recommendation of the Examiner to include a step or steps of this physical transformation of the data so that the devices can be seen as non-generic and so that the action of loading the data can be seen as impossible in the human mind, or with the aid of pencil and paper.
Applicant argues that the claim limitations amount to significantly more than the judicial exception(s). Examiner disagrees. The methods presented in the claims are akin to data preprocessing steps seen in many machine learning algorithms, namely K-nearest neighbors, wherein the centroids of each cluster are weighted, the partitions of each cluster can be weighted, and outliers are removed from the data set. The methods presented are well-known in the art and are just being applied to a dataset that is being used by a quantum learning model.
In light of the amendments made on the claims, the rejections made under 35 U.S.C 101 are maintained and updated below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 9, and 17 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 the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "wherein each subject of the target number of subjects indicates a parameter associated with a topic related to" in limitation 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "wherein each subject of the target number of subjects indicates a parameter associated with a topic related to the quantum machine learning model" in limitation 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitation "wherein each subject of the target number of subjects indicates a parameter associated with a topic related to the quantum machine learning model" in limitation 2. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-6, 8-14, and 16-20 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a method, therefore falling into the statutory category of process. Independent Claim 9 recites, in part, one or more non-transitory computer-readable storage media configured to store instructions, therefore falling into the statutory category of manufacture. Independent Claim 17 recites, in part, a system comprising one or more processors and one or more non-transitory computer-readable storage media, therefore falling into the statutory category of machine.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“separating the dataset into a plurality of partitions based on a target number of subjects and a dimensionality of the data points included in the dataset, each of the partitions including one or more data points of the plurality of data points, wherein each subject of the target number of subjects indicates a parameter associated with a topic related to the quantum machine learning model”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses separating data according to a number of separations and a dimensionality.
“obtaining a plurality of weight vectors, each respective weight vector corresponding to a respective subject of the target number of subjects and including a value indicative of relative contribution of each data point to the respective subject”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses creating weight vectors that correspond to the number of data separations.
“determining a plurality of first weighted centroids of the dataset each respective first weighted centroid corresponding to a respective subject of the target number of subjects and being determined based on the plurality of data points and a respective weight vector associated with the respective subject that corresponds to the respective first weighted centroid”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses clustering a dataset based on the number of separations of the dataset.
“determining a plurality of first partition weights, each of the first partition weights being determined based on the respective data points included in a respective partition and one or more elements of a respective weight vector associated with the respective data points”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses determining parameter values based on data that has previously been augmented.
“selecting a first partition of the plurality of partitions to remove from the dataset based on respective relationships between the respective first weighted centroid and each of the first partition weights”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses selecting data to be removed from a dataset.
“obtaining a first subset of the dataset by removing the data points associated with the first partition from the dataset, wherein the first subset includes fewer data points than the dataset such that the first subset is loadable into the qubits within limited qubit resources of the one or more NISQ devices”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses removing data from a dataset.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“obtaining a dataset including a plurality of data points”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g): i.e., pre-solution activity of gathering data for use in the claimed process.
“loading each data point of the first subset into qubits of one or more noisy intermediate-scale quantum (NISQ) devices”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (NISQ device) (See MPEP 2106.05(f)).
“training a quantum machine learning model based on the one or more NISQ devices with the qubits representing the data points of the first subset of the dataset”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (NISQ device) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “obtaining a dataset including a plurality of data points ” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As discussed above, the additional element(s) of “loading each data point of the first subset into qubits of one or more noisy intermediate-scale quantum (NISQ) devices” and “training a quantum machine learning model based on the one or more NISQ devices with the qubits representing the data points of the first subset of the dataset” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 1 analysis:
Claim 2 recites in part:
“determining one or more second weighted centroid of the dataset each corresponding to a respective subject of the target number of subjects, each of the second weighted centroids being determined based on the data points included in the first subset and the respective weight vector associated with the respective subject”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses clustering a dataset based on the number of separations of the dataset.
“determining one or more second partition weights included in the first subset, each of the second partition weights being determined based on one or more elements of a weight vector associated with a respective subject of the target number of subjects”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses determining parameter values based on data that has previously been augmented.
“identifying a second partition of the partitions included in the first subset having a least influence on the determining the one or more second weighted centroid based on the second partition weights”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses determining a second separation of the dataset.
“obtaining a second subset by removing the data points associated with the second partition from the first subset”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses removing data from a dataset.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“training the quantum machine learning model based on the second subset of the data set”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (quantum computer) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “training the quantum machine learning model based on the second subset of the data set” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“determining an iteration condition”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses determining a condition to iterate on.
“determining whether the iteration condition is satisfied”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses determining that a condition is satisfied.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“wherein the dataset is separated into 2k(d + 1) partitions, wherein "k" represents a target number of points and "d" represents the dimensionality of the data points”. As drafted and under its broadest reasonable interpretation, this limitation covers a mathematical relationship.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 5:
Step 2A: Prong 1 analysis:
Claim 5 recites in part:
“wherein selecting the first partition of the plurality of partitions to remove from the dataset comprises identifying the partition as having a least influence on the determining the first weighted centroid of the dataset by comparing the first partition weights to the first weighted centroid to determine which partitions corresponding to the first partition weights contributes the least to representation of the first weighted centroid”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses comparing weights and data to determine what data to remove.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The claim does not recite any additional elements that integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
Regarding Claim 6:
Step 2A: Prong 1 analysis:
Claim 6 recites in part:
“determining one or more machine-learning parameters based on quantum data points”. Under the broadest reasonable interpretation, this limitation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. See MPEP 2106.04(a)(2)(III). As drafted, this encompasses determining parameters of a machine learning model.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“loading each data point included in the first subset into a quantum state”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (quantum computing) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “loading each data point included in the first subset into a quantum state” is/are directed to particular field(s) of use (quantum computing) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the plurality of data points included in the dataset include financial or economic data”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (financial and economic data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“the quantum machine learning model is trained to perform analysis of financial data or economic data”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (quantum computing) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “the plurality of data points included in the dataset include financial or economic data” and “the quantum machine learning model is trained to perform analysis of financial data or economic data” is/are directed to particular field(s) of use (financial and economic data and quantum computing) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Due to claim language similar to that of Claim 1, Claim 9 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the following limitation.
Step 2A: Prong 2 analysis
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed by one or more processors, cause a system to perform operations”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (storage media and processor) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed by one or more processors, cause a system to perform operations” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Due to claim language similar to that of Claim 2, Claim 10 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 11:
Due to claim language similar to that of Claim 3, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 12:
Due to claim language similar to that of Claim 4, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 13:
Due to claim language similar to that of Claim 5, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 14:
Due to claim language similar to that of Claim 6, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 16:
Due to claim language similar to that of Claim 8, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 17:
Due to claim language similar to that of Claims 1 and 9, Claim 17 is rejected for the same reasons as presented above in the rejection of Claims 1 and 9.
Regarding Claim 18:
Due to claim language similar to that of Claims 2 and 10, Claim 18 is rejected for the same reasons as presented above in the rejection of Claims 2 and 10.
Regarding Claim 19:
Due to claim language similar to that of Claims 3 and 11, Claim 19 is rejected for the same reasons as presented above in the rejection of Claims 3 and 11.
Regarding Claim 20:
Due to claim language similar to that of Claims 6 and 14, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 6 and 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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