DETAILED ACTION
This action is in response to the claims filed 2/21/2025. Claims 1-15 are pending. Independent claims 1 and 14-15, and corresponding dependent claims are directed towards a device, method and program for information processing.
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Objections
Claims 1, 9-10 and 14-15 are objected to because of the following informalities, shown with suggested amendments: Claim 1 l. 15 “the plurality of clusters” for proper antecedent basis; Claim 9 l. 4, ll. 5-6 and l. 6 “the plurality of clusters” for proper antecedent basis; Claim 9 ll. 7-8 “than [[the]] other clusters of the plurality of clusters” for proper antecedent basis; Claim 10 l. 6-7 “[[the]] a result of the comparison” as this is the first recitation; Claim 14 l. 15 “the plurality of clusters” for proper antecedent basis; and Claim 15 l. 14 “the plurality of clusters” for proper antecedent basis. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a contribution calculation unit that calculates”, “a clustering execution unit that executes”, and “a cluster comparison unit that compares” in claim 1; “a training execution unit that generates” in claim 3; “a model application unit that outputs” in claim 4; and “a data correction unit that corrects” in claim 5.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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.
Claims 9-13 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 9 ll. 8-9 recite the limitation “identifies that he counterfeit sample belongs to the cluster” of which the term “the cluster” lacks proper antecedent basis as it is unclear which “cluster” is being referenced (Claim 1 l. 16 or Claim 9 l. 6 “one of the clusters”). For purposes of applying prior art the limitation has been construed as referencing the “cluster” of Claim 1 l. 16.
Claim 9 l. 7 recites “significantly smaller in the feature variability” of which “significantly” is a relative term that renders the claim indefinite. The term “significantly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As such, the “smaller in the feature variability” is rendered indefinite. For purposes of applying prior art the limitation has been construed as “smaller in the feature variability”.
Claims 10-13 incorporate the deficiencies of claim 9, through dependency, and are therefore also rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4, 9-11 and 14-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 1, the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) an “information processing device” that “calculates a contribution”, “executes clustering” and “compares feature variability” to “identify a cluster” the entirety of which falls under the abstract idea enumerated grouping of mathematical concepts (see MPEP 2106.04(a) and 2106.04(a)(2)). This judicial exception is not integrated into a practical application because the claims fail to recite additional elements that integrate the judicial exception into a practical application. The entirety of the claim is directed towards mathematical calculations resulting in the identification of a “cluster” having a “counterfeit sample”. The identified “cluster” is not being practically applied in any way (e.g. correcting the counterfeit sample, taking remediation action with regards to fraud). With regards to the “contribution calculation unit”, “clustering execution unit”, and “cluster comparison unit”, although not labelled as such these elements can be considered as software/processor/memory components of a general-purpose computer as the functionality to calculate a contribution, execute clustering and compare feature variability (i.e. the mathematical concepts) can be performed by a general-purpose computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because software/processor/memory components, can be considered to be well-understood, routine, or conventional elements of a general-purpose computer.
Claims 2-4, 9-11 further fail to recite any meaningful limitations that integrate the judicial exception into a practical application to overcome the 35 U.S.C. §101 issues of claim 1, discussed above, and are also rejected.
Claims 14 and 15 have the same deficiencies as claim 1, in that they lack a practical application and are rejected for the same reasoning.
Note: Claims 5-8 and 12-13 recite varying forms of taking action to “correct” the “counterfeit sample” or “training model” and is a remediation action that is considered a practical application.
Regarding claim 15, the claimed invention is drawn to a “program” which is solely considered software. Thus, it is clear that the invention does not contain tangibly-embodied structural features. As such, the invention does not fall within at least one of the four categories of patent eligible subject matter recited in 35 U.S.C § 101 (process, machine, manufacture or composition of matter). Examiner recommends amending the limitation to claim a form of “non-transitory computer-readable medium” or similar “medium” that includes the “program” as this would result in a tangible-embodiment of the program.
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 of this title, 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, 3-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Schulth et al., “Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering”, published Apr. 27, 2022, in view of Aamir et al., “Clustering based semi-supervised machine learning for DDoS attack classification”, published Feb. 5, 2019.
As to claim 1, Schulth substantially discloses an information processing device (Schulth §5.6¶1 Inception-v3 network architecture with modules) comprising: a contribution calculation unit (Schulth §5.6¶1 module) that calculates a contribution indicating how much each feature in a training dataset contributes to a predicted label output from a trained model (Schulth §3 predicted output label of trained model; §5.4 Layer-wise Relevance Propagation (LRP) - relevance score for output layer propagated to the input layer), the training dataset including both a legitimate sample including only legitimate data that does not contain trigger information (Schulth §3¶3 datapoints with no trigger) and a counterfeit sample at least partially including counterfeit data that contains trigger information (Schulth §1¶2 poisoned dataset with trigger for poisoning model; §3¶3 datapoints with trigger); a clustering execution unit (Schulth §5.6¶1 module) that executes clustering to classify each sample of the training dataset into a plurality of clusters using unsupervised learning with the contribution as input (Schulth §4¶2 clustering original and manipulated datapoints using unsupervised clustering algorithm; §4 Heatmap Clustering – using LRP as image representation/input); and a cluster comparison unit (Schulth §5.6¶1 module) that identifies a cluster to which the counterfeit sample included in the training dataset belongs (Schulth §4¶2 cluster subset containing manipulated data points). Schulth fails to disclose comparing feature variability between the clusters in a result of the clustering. Aamir describes clustering based semi-supervised attack classification. With this in mind, Aamir discloses comparing feature variability between the clusters in a result of the clustering (Aamir pg. 442 ¶prior to equation (3) – calculate entropy of each feature within cluster, compare clusters to find cluster with highest entropy to identify as attack). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the cluster labeling technique of Aamir with the clustering results of Schulth, such that manipulated clusters can be determined based on feature variability, as it would advantageously reduce the need for manual inspection of cluster data.
As to claim 3, Schulth and Aamir disclose the invention as claimed as described in claim 1, including further comprising: a training execution unit that generates the trained model by executing training using the training dataset as input to a machine learning algorithm (Schulth §3¶1 network trained on dataset).
As to claim 4, Schulth and Aamir disclose the invention as claimed as described in claim 3, including further comprising: a model application unit (Schulth §5.6¶1 module) that outputs the predicted label by applying the training dataset to the trained model generated by the training execution unit and provides the predicted label to the contribution calculation unit (Schulth §3¶1 & Equation 1 – showing input and predicted label output; §4 clustering of data points; §5.4¶1-2 LRP processing of output).
As to claim 5, Schulth and Aamir disclose the invention as claimed as described in claim 3, including further comprising: a data correction unit that corrects the training dataset for the counterfeit sample belonging to the cluster identified by the cluster comparison unit (Schulth §4¶1 remove manipulated data samples from dataset).
As to claim 6, Schulth and Aamir disclose the invention as claimed as described in claim 5, including wherein the data correction unit removes the counterfeit sample from the training dataset (Schulth §4¶1 remove manipulated data samples from dataset).
As to claim 14, Schulth discloses an information processing method (Schulth [Abstract] method called Heatmap Clustering) comprising: causing an information processing device to calculate a contribution indicating how much each feature in a training dataset contributes to a predicted label output from a trained model (Schulth §3 predicted output label of trained model; §5.4 Layer-wise Relevance Propagation (LRP) - relevance score for output layer propagated to the input layer), the training dataset including both a legitimate sample including only legitimate data that does not contain trigger information (Schulth §3¶3 datapoints with no trigger) and a counterfeit sample at least partially including counterfeit data that contains trigger information (Schulth §1¶2 poisoned dataset with trigger for poisoning model; §3¶3 datapoints with trigger); causing the information processing device to execute clustering to classify each sample of the training dataset into a plurality of clusters using unsupervised learning with the contribution as input (Schulth §4¶2 clustering original and manipulated datapoints using unsupervised clustering algorithm; §4 Heatmap Clustering – using LRP as image representation/input); and causing the information processing device to identify a cluster to which the counterfeit sample included in the training dataset belongs (Schulth §4¶2 cluster subset containing manipulated data points). Schulth fails to disclose comparing feature variability between the clusters in a result of the clustering. Aamir discloses comparing feature variability between the clusters in a result of the clustering (Aamir pg. 442 ¶prior to equation (3) – calculate entropy of each feature within cluster, compare clusters to find cluster with highest entropy to identify as attack). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the cluster labeling technique of Aamir with the clustering results of Schulth, such that manipulated clusters can be determined based on feature variability, as it would advantageously reduce the need for manual inspection of cluster data.
As to claim 15, Schulth discloses a program causing a computer of an information processing device to execute information processing (Schulth §5.6¶1 Inception-v3 network architecture with modules), the information processing comprising: calculating a contribution indicating how much each feature in a training dataset contributes to a predicted label output from a trained model (Schulth §3 predicted output label of trained model; §5.4 Layer-wise Relevance Propagation (LRP) - relevance score for output layer propagated to the input layer), the training dataset including both a legitimate sample including only legitimate data that does not contain trigger information (Schulth §3¶3 datapoints with no trigger) and a counterfeit sample at least partially including counterfeit data that contains trigger information (Schulth §1¶2 poisoned dataset with trigger for poisoning model; §3¶3 datapoints with trigger); executing clustering to classify each sample of the training dataset into a plurality of clusters using unsupervised learning with the contribution as input (Schulth §4¶2 clustering original and manipulated datapoints using unsupervised clustering algorithm; §4 Heatmap Clustering – using LRP as image representation/input); and identifying a cluster to which the counterfeit sample included in the training dataset belongs (Schulth §4¶2 cluster subset containing manipulated data points). Schulth fails to disclose comparing feature variability between the clusters in a result of the clustering. Aamir discloses comparing feature variability between the clusters in a result of the clustering (Aamir pg. 442 ¶prior to equation (3) – calculate entropy of each feature within cluster, compare clusters to find cluster with highest entropy to identify as attack). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the cluster labeling technique of Aamir with the clustering results of Schulth, such that manipulated clusters can be determined based on feature variability, as it would advantageously reduce the need for manual inspection of cluster data.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Schulth et al., “Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering”, published Apr. 27, 2022, in view of Aamir et al., “Clustering based semi-supervised machine learning for DDoS attack classification”, published Feb. 5, 2019, in view of Anunciacao et al. (US 2020/0286095 A1), published Sep. 10, 2020.
As to claim 2, Schulth and Aamir substantially disclose the invention as claimed as described in claim 1, failing, however, to explicitly disclose wherein the trained model is used as a fraud detection model that detects fraud. Anunciacao describes a machine-learning system for classifying a transaction as fraudulent or genuine. With this in mind, Anunciacao discloses wherein the trained model is used as a fraud detection model that detects fraud (Anunciacao [0008]). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to use the system of Schulth and Aamir with the fraud detection of Anunciacao, such that the relevance score clustering is used to detect fraudulent samples, as it would advantageously assist in protecting organizations from fraudulent activities (Anunciacao [0003]).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Schulth et al., “Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering”, published Apr. 27, 2022, in view of Aamir et al., “Clustering based semi-supervised machine learning for DDoS attack classification”, published Feb. 5, 2019, in view of Chan et al., "Poison as a Cure: Detecting & Neutralizing Variable-Sized Backdoor Attacks in Deep Neural Networks" published Nov. 19, 2019.
As to claim 7, Schulth and Aamir substantially disclose the invention as claimed as described in claim 5, failing, however, to explicitly disclose wherein the data correction unit modifies the counterfeit data included in the counterfeit sample. Chan describes a method for countering poisoning of deep learning models. With this in mind, Chan discloses wherein the data correction unit modifies the counterfeit data included in the counterfeit sample (Chan §1¶4 relabel poisoned samples to base class; §2¶2 poisoning modifies samples base class to target class). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the poison sample retraining of Chan with the poisoning attack detection of Schulth and Chan, such that modified poisoned samples are used to retrain the model, as it would advantageously train the model to neutralize the attack (Chan [Abstract]).
As to claim 8, Schulth and Aamir substantially disclose the invention as claimed as described in claim 5, failing, however, to explicitly disclose wherein the training execution unit updates the trained model by executing training using the training dataset corrected by the data correction unit as input to the machine learning algorithm. Chan discloses updating a trained model by executing training using the training dataset corrected by the data correction unit as input to the machine learning algorithm (Chan §1¶4 augment training data using detected poisoned sample for relabeling to neutralize backdoor through training; §7 retrain model to unlearn the poison pattern). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the poison sample retraining of Chan with the poisoning attack detection of Schulth and Chan, such that modified poisoned samples are used to retrain the model, as it would advantageously train the model to neutralize the attack (Chan [Abstract]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Schulth et al., “Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering”, published Apr. 27, 2022, in view of Aamir et al., “Clustering based semi-supervised machine learning for DDoS attack classification”, published Feb. 5, 2019, in view of Feng et al., “Ensuring Honest Data Collection Against Collusive CSDF Attack With Binary-Minmaxs Clustering Analysis in Mobile Crowd Sensing”, published Sept. 2, 2019.
As to claim 9, Schulth and Aamir substantially disclose the invention as claimed as described in claim 3, including wherein the cluster comparison unit calculates the feature variability of the training dataset for each of the clusters, compares the feature variability between the clusters (Aamir pg. 442 ¶prior to equation (3) – calculate entropy of each feature within cluster, compare clusters to find cluster with highest entropy to identify as attack), and, identifies that the counterfeit sample belongs to the cluster (Schulth §4¶2 cluster subset containing manipulated data points). Schulth and Aamir fail to explicitly disclose determining that the feature variability between the clusters is significantly smaller than the other clusters. Feng describes determining a collusive attack using binary-minmax clustering analysis. With this in mind, Feng discloses determining that the feature variability between the clusters is significantly smaller than the other clusters (Feng §IV(C) – detect collusive attackers based on low variance (compared to high) in clustering). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the low-variance determination of Feng with the feature variance comparison of Schulth and Aamir, such that clusters with low-variance (i.e. significantly smaller variance) are considered as manipulated data, as it would advantageously assist in identifying a group of malicious data points (Feng §IV(C)¶3).
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Schulth et al., “Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering”, published Apr. 27, 2022, in view of Aamir et al., “Clustering based semi-supervised machine learning for DDoS attack classification”, published Feb. 5, 2019, in view of Feng et al., “Ensuring Honest Data Collection Against Collusive CSDF Attack With Binary-Minmaxs Clustering Analysis in Mobile Crowd Sensing”, published Sept. 2, 2019, in view of Webber et al. (US 2022/0004565 A1), published Jan. 6, 2022.
As to claim 10, Schulth, Aamir and Feng substantially disclose the invention as claimed as described in claim 9, including identifying a counterfeit sample (Schulth §4¶2 cluster subset containing manipulated data points). Schulth, Aamir and Feng fail to explicitly disclose further comprising: a display unit that displays a user interface screen, wherein the user interface screen is provided with a cluster comparison result display section where the result of the comparison between the clusters by the cluster comparison unit is displayed, and in the cluster comparison result display section, the cluster identified as containing the counterfeit sample is displayed in an emphasized manner. Webber describes review and curation of record clustering changes at large scale. With this in mind, Webber discloses a display unit that displays a user interface screen (Webber Fig. 7; [0043] user interface of review application; [claim 10] user interface to review/approve/reject clustering records), wherein the user interface screen is provided with a cluster comparison result display section where the result of the comparison between the clusters by the cluster comparison unit is displayed (Webber [0043]; claim 10 – individual records displayed), and in the cluster comparison result display section (Webber [0043]; claim 10 – display panes), a cluster is displayed in an emphasized manner (Webber [0043] user display features such as coloration or highlights may identify changes to values). It would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains to combine the clustering display/review method of Webber with the clustering used for poison data set detection of Schulth, Aamir and Feng, such that the processed clusters are displayed with the identified malicious data points highlighted to bring attention, as it would advantageously enable identification of specific clusters in a large dataset (Webber [0004]).
As to claim 11, Schulth, Aamir, Feng and Webber disclose the invention as claimed as described in claim 10, including wherein the user interface screen is provided with a selected data display section where data of the counterfeit sample belonging to the cluster selected using the cluster comparison result display section is displayed (Webber Fig. 7 item 710; [0043] display individual records in display pane for selected cluster).
As to claim 12, Schulth, Aamir, Feng and Webber disclose the invention as claimed as described in claim 11, including wherein the user interface screen is provided with a remove button that is operated to remove the counterfeit sample displayed in the selected data display section (Webber [0043] selection tools including buttons; [claim 10] undo change or move data record to a different cluster; Schulth §4¶1 remove manipulated data samples from dataset).
As to claim 13, Schulth, Aamir, Feng and Webber disclose the invention as claimed as described in claim 11, including wherein the user interface screen is provided with a modify button that is operated to modify the counterfeit sample displayed in the selected data display section, and a selected data modify section where the counterfeit sample to be corrected is modified in response to operation of the modify button (Webber [0043] selection tools including buttons; [claim 10] edit record details).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. “Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering” is related to clustering to detect backdoor attacks.
Karame et al. (US 2021/0051169 A1) is related to thwarting model poisoning in federated learning.
Chen et al. (US 2020/0050945 A1) is related to detecting poisoning attacks on neural networks by activation clustering.
Liebman (US 10,706,323 B1) is related to weighted feature importance estimation.
Watson (US 11,868,852 B1) is related to introspection of machine learning estimations.
Hazard et al. (US 2020/0134484 A1) is related to feature prediction contribution for a cluster.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC W SHEPPERD whose telephone number is (571)270-5654. The examiner can normally be reached Monday - Thursday, Alt. Friday, 7:30AM - 5:00PM, EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rupal Dharia can be reached at (571)272-3880. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Eric W Shepperd/Primary Examiner, Art Unit 2492
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