DETAILED ACTION
This Action is responsive to Claims filed 08/01/2023.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/01/2026 and 02/20/2026 were filed before the mailing date of the first action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
Receipt of Drawings filed 08/01/2023 is acknowledged. These Drawings are acceptable.
Status of the Claims
Claims 1-14 have been preliminarily amended. Claim 15 has been preliminarily canceled. Claims 1-14 and 16-17 are currently pending.
Claim Objections
Claims 1, 13-14, and 16-17 objected to because of the following informalities:
The “k-means clustering…” limitations of Claim 1 could be reworded for clarity (“of the data records the at least one…” is not grammatically correct).
The statutory category of Claims 13 and 16 (a device) is different than that of Claims 1 and 12 (a method) on which they depend. Consistency between statutory categories would improve clarity.
The statutory category of Claims 14 and 17 (a non-transitory computer readable medium) is different than that of Claims 1 and 12 (a method) on which they depend. Consistency between statutory categories would improve clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 and 16-17 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1 (all claims):
Claims 1-11 and 13 recite a method, which falls under the statutory category of a process. Claims 12 and 16 recite a method, which falls under the statutory category of a process. Claim 14 recites a non-transitory computer readable medium, which falls under the statutory category of a manufacture. Claim 17 recites a non-transitory computer readable medium, which falls under the statutory category of a manufacture.
Claim 1:
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations “generating a balanced dataset…”, “representing the data records…”, “k-means clustering…”, “selecting…”, “aggregating…”, and “providing…” under the broadest reasonable interpretation, cover 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. These limitations therefore fall within the mental process group.
Generating a generic dataset is practically performed within the human mind or with the aid of pen and paper. Generically representing data records using generic content-based representation(s) is practically performed within the human mind or with the aid of pen and paper. Generically performing k-means clustering of the data records is practically performed within the human mind or with the aid of pen and paper. Selecting a data record closest to the centroid of a cluster is practically performed within the human mind or with the aid of pen and paper. Aggregating the generic data records is practically performed within the human mind or with the aid of pen and paper. Generically providing aggregated data is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “A method” and “dataset” which are recognized as generic computer components recited at a high level of generality (the Specification does not indicate these elements are different from a typical processing unit). Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitations “…a balanced training dataset for a Machine Learning (ML) model…” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The limitation “receiving…” is found to be pre- or post-extra-solution activity or data gathering steps (See MPEP 2106.05(g)).
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “A method” and “dataset” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements recited in the limitations “…a balanced training dataset for a Machine Learning (ML) model…” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The limitation “receiving…” are found to well-understood, routine, or conventional activity (See MPEP 2106.05(d)(II)(i)(first list)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim 12:
Step 2A – Prong 1:
Claim 12 recites an abstract idea, law of nature, or natural phenomenon. The limitations “selecting representative data records…”, “representing the data records…”, “k-means clustering…”, “selecting…”, and “providing…” under the broadest reasonable interpretation, cover 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. These limitations therefore fall within the mental process group.
Selecting generic representatives of data is practically performed within the human mind or with the aid of pen and paper. Generically representing data records using generic content-based representation(s) is practically performed within the human mind or with the aid of pen and paper. Generically performing k-means clustering of the data records is practically performed within the human mind or with the aid of pen and paper. Selecting a data record closest to the centroid of a cluster is practically performed within the human mind or with the aid of pen and paper. Generically providing selected data is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “A method” and “dataset” which are recognized as generic computer components recited at a high level of generality (the Specification does not indicate these elements are different from a typical processing unit). Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The limitation “receiving…” is found to be pre- or post-extra-solution activity or data gathering steps (See MPEP 2106.05(g)).
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “A method” and “dataset” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The limitation “receiving…” are found to well-understood, routine, or conventional activity (See MPEP 2106.05(d)(II)(i)(first list)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Dependent Claims:
Claim 2 recites an abstract idea mathematical concept or relationship refining the k-means clustering.
Claim 3 recites instructions to apply the abstract ideas of Claim 1 by training the ML model with the data of Claim 1. See MPEP 2106.05(f).
Claim 4 recites an abstract idea mental process step (the steps of Claim 1 are performed periodically).
Claim 5 recites refinements to the k-means clustering.
Claim 6 recites an abstract idea mental process step (the steps of Claim 1 are performed periodically), and additional elements (ensemble classifier) recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
Claim 7 recites additional elements found to be pre- or post-extra-solution activity or data gathering steps (See MPEP 2106.05(g)) and well-understood, routine, or conventional activity (See MPEP 2106.05(d)(II)(i)(first list)).
Claim 8 recites refinements to the data types.
Claim 9 recites refinements to the representation of the data.
Claim 10 recites refinements to the data types.
Claim 11 recites refinements to the representation of the data.
Claim 13 (Claim 16) recites generic computer components at a high level of generality (the Specification does not indicate these elements are different from a typical processing unit). Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
Claim 14 (Claim 17) recites generic computer components at a high level of generality (the Specification does not indicate these elements are different from a typical processing unit). Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 7, 12-14, and 16-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin et al. (Clustering-based undersampling in class-imbalanced data, 2017), hereinafter Lin.
In regards to Claim 1: The present invention claims: “A method for generating a balanced training dataset for training a Machine Learning model, the method comprising:” Lin, in at least Fig. 4 (Page 21), teaches a method of transforming an imbalanced dataset into a balanced training dataset. See also Lin Abstract and Section for further discussion of balancing datasets.
“receiving a multi-class dataset containing data records of at least one majority class and at least one minority class;” Lin, in at least Fig. 4 (Page 21), teaches the training dataset received is comprised of at least one majority and minority classes (M and N). See also “Given a (two-class) imbalanced data set D composed of a majority class and a minority class, the majority and minority classes contain M and N data points, respectively. The first step is to divide this imbalanced data set into training and testing sets based on the k -fold cross validation method [21] . The second step is to divide the training set into a majority class subset and a minority class subset.” (Page 20).
“representing the data records of the at least one majority class using at least one content-based representation of the data records;” Examiner’s Note: Based on Claims 8, 9, and 11, the Examiner interprets this limitation broadly to mean the data records are in some feature/embedding space or format in order for the Machine Learning model to operate on the data records. Given that Lin teaches forming a training data set, and references features spaces in “The aim of clustering analysis is to group similar objects (i.e. data samples) into the same clusters; the objects in different clusters are different in terms of their feature representations [16] . Therefore, using clustering analysis to undersample the majority class generates a number of clusters, with each cluster containing similar data. Specifically, each cluster centroid (or center), which is based on the mean of similar data in the same group calculated by the k-means algorithm [14] , can be used to represent the data in the whole group.” (Page 18), the Examiner submits Lin broadly reads on the data records existing in some form of feature/embedding space or format.
“k-means clustering of the data records the at least one majority class based on the at least one content-based representation, wherein the number k of clusters is set based on a number and/or size of the at least one minority class;” Lin, in at least Fig. 4 (Page 21), teaches performing clustering on the Majority class before the balanced training dataset is formed. See also “In this paper, two strategies employing a clustering algorithm to undersample the majority class data set are discussed. Note that although numerous clustering algorithms are mentioned in the literature, we consider only the k -means algorithm in this paper because it is widely used and can thus be regarded as a baseline clustering method [16] . The two strategies are described as follows. In the first strategy, the number of clusters (i.e. k ) is set to be equal to the number of data samples in the minority class (i.e. k = N ).” (Page 21), which reads on the number of clusters being based on the number or size of the minority class.
“selecting the data record closest to the centroid of each cluster as representative of each respective cluster,” Lin teaches “In the second strategy, because each cluster center is the mean of the data samples in a cluster, it is a new additional data sample for the majority class. The nearest neighbor of each cluster center, which is a real data sample of M , is selected to replace the k cluster centers used in the first strategy” (Page 21).
“aggregating the selected data records of the at least one majority class and the data records of the at least one minority class;” Lin, in at least Fig. 4 (Page 21), teaches the clustered Majority class and Minority class being aggregated into the balanced training dataset. See also “The reduced majority class subset is then combined with the minority class subset, resulting in a balanced training set.” (Page 20).
“and providing the aggregated data records as the generated training dataset.” Lin, in at least Fig. 4 (Page 21), teaches the balanced training dataset being supplied to a classifier for training. See also “Finally, the classifier is trained and tested by the balanced training and testing sets, respectively.” (Page 20).
In regards to Claim 3: The present invention claims: “wherein at least one Machine Learning model is trained using the generated training dataset.” Lin, in at least Fig. 4 (Page 21), teaches the balanced training dataset being supplied to a classifier for training. See also “Finally, the classifier is trained and tested by the balanced training and testing sets, respectively.” (Page 20).
In regards to Claim 7: The present application claims: “loading the at least one trained Machine Learning model into the memory of at least one control device or processing device for application.” Lin teaches “To examine the classification performance by clustering-based undersampling, five different classifiers were constructed, namely C4.5, k-nearest neighbor (k-NN), support vector machine (SVM), naïve Bayes (NB), and multilayer perceptron (MLP). In addition, the AdaBoost algorithm was employed to develop classifier ensembles of C4.5, k-NN, SVM, and NB for further comparison. Note that these classifiers were constructed based on the default parameters used in the Weka software package.” (Page 22), which the Examiner submits reads broadly on the method of Lin being implemented in hardware/software for execution.
In regards to Claim 12: Although the scope of the method of Claim 12 is slightly different than that of Claim 1, the limitations of Claim 12 are practically identical to Claim 1, and are all taught broadly by Lin Fig. 4 (Page 21), at least; therefore, both claims are similarly rejected.
In regards to Claim 13: The present invention claims: “A data processing device comprising at least one processor configured to perform the method of claim 1.” Lin teaches “To examine the classification performance by clustering-based undersampling, five different classifiers were constructed, namely C4.5, k-nearest neighbor (k-NN), support vector machine (SVM), naïve Bayes (NB), and multilayer perceptron (MLP). In addition, the AdaBoost algorithm was employed to develop classifier ensembles of C4.5, k-NN, SVM, and NB for further comparison. Note that these classifiers were constructed based on the default parameters used in the Weka software package.” (Page 22), which the Examiner submits reads broadly on the method of Lin being implemented in hardware/software for execution.
In regards to Claim 14: The present invention claims: “A non-transitory computer readable medium including a computer program product comprising instructions which, when the program is executed by a computer or data processing device, cause the computer or the data processing device to carry out the method of claim 1.” Lin teaches “To examine the classification performance by clustering-based undersampling, five different classifiers were constructed, namely C4.5, k-nearest neighbor (k-NN), support vector machine (SVM), naïve Bayes (NB), and multilayer perceptron (MLP). In addition, the AdaBoost algorithm was employed to develop classifier ensembles of C4.5, k-NN, SVM, and NB for further comparison. Note that these classifiers were constructed based on the default parameters used in the Weka software package.” (Page 22), which the Examiner submits reads broadly on the method of Lin being implemented in hardware/software for execution.
In regards to Claims 16-17: Claims 16-17 recite similar limitations to claims 13-14, with the exception of the method of claim 12; therefore, both sets of claims are similarly rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 4-6 and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin as applied to claims 1 above, in further view of Caron et al. (Deep Clustering for Unsupervised Learning of Visual Features, 2018), hereinafter Caron.
In regards to Claim 4: While Lin teaches the use of k-means clustering (Which the Examiner notes is typically initialized with random seeds (see citation below); therefore, unless stated otherwise, is assumed in a typical implementation of k-means clustering) Lin fails to explicitly teach all of the limitations of “wherein a new training dataset is generated for each epoch of the training of the at least one Machine Learning model using different random seeds for the k-means clustering of the data records.” However, Caron, in a similar field of endeavor of clustering data for classifier training teaches alternating between k-means clustering and training a classifier on the assignments made during the clustering (Section 3.2, Page 5) (Examiner’s Note: As mentioned above, no reference to the seeding is made in Caron, and given the broad use of k-means Caron recites in Section 3.2, it is assumed the clusters are randomly seeded on each round of clustering). Caron also teaches “At each epoch, we reassign the images to a new set of clusters, with no guarantee of stability.” (Page 8), which would necessarily randomly change the clustering seed.
Caron teaches “In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.” (Abstract) It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to combine the iteratively clustering and training of a machine learning model taught by Caron in a system such as Lin’s in order to leverage the performance benefits of Caron’s method.
In regards to Claim 5: The present invention claims: “setting a minimum number of iterations for the k-means clustering.” Caron teaches “We train the models for 500 epochs, which takes 12 days on a Pascal P100 GPU for AlexNet.” (Page 7) (Examiner’s Note: Given the broadness of this claim and the lack of connectivity to any other claim language, the Examiner maps this limitation broadly to Caron merely selecting a number of training/clustering epochs).
In regards to Claim 6: The present invention claims: “wherein the at least one Machine Learning model comprises an ensemble classifier, wherein a different training dataset is generated for each instance of the ensemble classifier using different random seeds for the k-means clustering of the data records.” Lin teaches “A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes.” (Abstract) and “Classifier ensembles are typically trained with several different balanced data sets for later classification.” (Page 24). See above how a combination of Lin and Caron would read alternating the training of a classifier and the clustering of the training data. In Lin’s ensemble case, the same reasoning applies of training each classifier, and re-clustering training data as performed in Caron.
In regards to Claim 10: The present invention claims: “wherein the data records within the multi-class dataset are of at least one of the group comprising image data, video data and/or audio data.” While Lin does not explicitly teach a data type to be operated-on, Caron is expressly for image-related data (Fig. 1 and Page 2)
In regards to Claim 11: The present invention claims: “wherein the data records are of an image data category, wherein the content-based representation is using uses at least one of the group comprising a color distribution of the images, high-level objects in the images, Speeded-Up Robust Features and/or scale-invariant feature transform.” As previously noted, the Examiner interprets this limitation broadly to indicate various forms of feature/embedding space for image data. A combination of Lin and Caron reads on the operating-on of image data, and Caron teaches “Unsupervised methods often do not work directly on color and different strategies have been considered as alternatives [13, 42]. We apply a fixed linear transformation based on Sobel filters to remove color and increase local contrast [5, 47].” (Page 6) and “As shown in the left panel of Fig. 3, most filters capture only color information that typically plays a little role for object classification [61]. Filters obtained with Sobel preprocessing act like edge detectors.” (Page 9), which the Examiner submits broadly reads on “color” distribution” and/or “high-level objects.”
Claim(s) 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin as applied to claim 1 above, and further in view of Khan et al. (Extractive based Text Summarization Using K-Means and TF-IDF, 2019), hereinafter Khan.
In regards to Claim 8: While Lin teaches the use of k-means clustering, Lin fails to explicitly teach all of the limitations of “wherein the data records within the multi-class dataset are text documents, and wherein tf-idf is used as content-based representation of the data records.” Khan, in a similar field of endeavor of data clustering, teaches the use of tf-idf in the process of formulating a representation for text-document(s) before clustering. (Section V, A and B, Page 35).
Khan teaches “The paper also reflects the idea of true K and using that value of K divides the sentences of the input document to present the final summary. Furth more, we have combined the K-means, TF-IDF with the issue of K value and predict the resulting system summary which shows comparatively best results.” (Abstract) and “Preprocessing is the primary step required to prepare the data in a readable format for the text mining process. It is useful for noise reduction in data and makes the data clean. The actual goal is to convert the original data into a machine-understandable form. The process of preprocessing includes tokenization, filtering, stemming or lemmatization and stop-word removal.” Page 35). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to leverage known methods like those demonstrated in Khan in a system such as Lin’s when operating on text-based documents to preprocessing the data for clustering.
In regards to Claim 9: The present invention claims: “wherein tf-idf of n-grams of the text documents is used as content-based representation of the data records.” Khan teaches “Tokenization: It is a process of dividing a long sentence into small pieces word by word using space division or we can also use a regular expression to do this task and it is one of the initial steps before converting text into numbers.” (Page 35), which the Examiner submits sufficiently reads on splitting a text document into n-grams (See also Khan tables 2-4 for a evaluation over several n-grams).
Allowable Subject Matter
Claim 2 is allowable over the prior art, the combination of the aforementioned references do not teach determining a cluster count k with the formula recited in “wherein the number k of clusters is defined as k = (1+δ) x |minor class(es)|”, although the Examiner notes that Lin, on Page 21, sets k to the number of minority class examples (functionally “|minor class(es)|”) and the difference between that value and 1.2 x |minor class(es)| (for example, as per the Instant Specification [00020]) is fairly negligible. Pertinent Prior Art is cited below.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Low, Jia Shun, et al. "Seeding on samples for accelerating k-means clustering." Proceedings of the 3rd international conference on big data and internet of things. 2019. Pertinent to k-means typically involving random seeds (Introduction)
Feng, Shou, Chunhui Zhao, and Ping Fu. "A cluster-based hybrid sampling approach for imbalanced data classification." Review of Scientific Instruments 91.5 (2020). Utilizes similar measurements (imbalance ratio, size, etc.), but not in the formulation of k (Section III.C).
Gupta, Subodhini, and Anjali Jivani. "A cluster-based under-sampling solution for handling imbalanced data." International Journal on Emerging Technologies 10.4 (2019): 160-170. Utilizes similar measurements (imbalance ratio, size, etc.), but not in the formulation of k ().
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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