DETAILED ACTION
Claims 1-20 are pending in this application and have been examined with the priority date of 11/30/2023 in accordance with applicant’s claim to the benefit of U.S. Provisional Patent Application No. 63/604,673.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2/21/2025 and 5/12/25 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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 limitations are:
Electronic processing device in claims 1, 19 and 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 1-3, 5 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Banville (US 20230306267 A1).
Regarding claim 1 Banville discloses; A method for providing classification parameters executed by an electronic processing device, the method comprising (Banville, [0018] the system has at least one processor to train a neural network, processor is shown in figure 20, as 2010):
PNG
media_image1.png
538
566
media_image1.png
Greyscale
(Banville, Figure 20)
receiving a test set comprising unlabeled data to classify (Banville, [0083] the system receives unlabeled bio signal data to feed to a classifier);
PNG
media_image2.png
110
336
media_image2.png
Greyscale
PNG
media_image3.png
334
336
media_image3.png
Greyscale
(Banville, [0083])
pooling the test set and a training set into a concatenated dataset comprising a plurality of parameters (Banville, [0059] a labeled training set and unlabeled data are sent to the training computing apparatus to be fed to a classifier model, [0114] combined training and test data are embedded using the models), wherein the training set comprises labeled data (Banville, [0059] training data is labeled);
PNG
media_image4.png
414
336
media_image4.png
Greyscale
PNG
media_image5.png
152
340
media_image5.png
Greyscale
(Banville, [0059])
PNG
media_image6.png
230
336
media_image6.png
Greyscale
(Banville, [0114])
normalizing the concatenated dataset (Banville, [0344] windows from both datasets/the combined dataset is normalized);
PNG
media_image7.png
428
340
media_image7.png
Greyscale
(Banville, [0342]-[0344])
non-linearly reducing a dimensionality of the normalized concatenated dataset to a reduced dimension space (Banville, [0114] the combined testing and training datasets are projected into two dimensions using a UMAP projection (non-linear reduction of dimensions));
computing classification parameters by classifying the unlabeled data from the reduced dimension space using the labeled data from the reduced dimension space (Banville, [0117]-[0118] binary labels for sleep apnea, pathologic information and gender were visualized using a heatmap to compute overlap in labels for the merged data, [0135] the labeled data was used to label the unlabeled data in the combined set);
PNG
media_image8.png
472
344
media_image8.png
Greyscale
(Banville, [0117]-[0118])
PNG
media_image9.png
336
342
media_image9.png
Greyscale
(Banville, [0135])
and providing the classification parameters for further processing (Banville, [0136] trainable parameters can be updated further after the label computation).
PNG
media_image10.png
310
340
media_image10.png
Greyscale
PNG
media_image11.png
386
348
media_image11.png
Greyscale
(Banville, [0136])
Regarding claim 2 Banville discloses; The method of claim 1, wherein the concatenated dataset is normalized by applying a z-score method (Banville, [0401] a z-score normalization method may be used on the combined dataset).
PNG
media_image12.png
178
338
media_image12.png
Greyscale
PNG
media_image13.png
50
336
media_image13.png
Greyscale
(Banville, [0401])
Regarding claim 3 Banville discloses; The method of claim 1, wherein the normalized concatenated dataset provides spatial location consistency between the labeled data and the unlabeled data in a same reduced dimension space (Banville, [0114] the combined testing and training datasets are projected into two dimensions using a UMAP, which preserves the structure of the data and keeps points that are similar close together).
Regarding claim 5 Banville discloses; The method of claim 1, wherein the classification parameters comprise a particle class, a confidence score, or coordinates in the reduced dimension space (Banville, [0118] the embedder classifications (classification parameters) include a measured probability that a label is true, which is functionally equivalent to a confidence score).
PNG
media_image14.png
236
340
media_image14.png
Greyscale
(Banville, [0118])
Regarding claim 20 Banville discloses; A method for computing classification parameters executed by an electronic processing device, the method comprising (Banville, [0018] the system has at least one processor to train a neural network, processor is shown in figure 20, as 2010):
receiving a test set comprising unlabeled data to classify (Banville, [0083] the system receives unlabeled bio signal data to feed to a classifier);
non-linearly reducing, to a reduced dimension space, a dimensionality of a normalized concatenated dataset comprising the test set and a training set comprising labeled data (Banville, [0114] the combined testing and training datasets are projected into two dimensions using a UMAP projection (non-linear reduction of dimensions));
and computing classification parameters by classifying the unlabeled data from the reduced dimension space using the labeled data from the reduced dimension space (Banville, [0117]-[0118] binary labels for sleep apnea, pathologic information and gender were visualized using a heatmap to compute overlap in labels for the merged data, [0135] the labeled data was used to label the unlabeled data in the combined set).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
2. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Banville (US 20230306267 A1) in view of Amit (US 20220392571 A1).
Regarding claim 4 Banville fails to teach; The method of claim 3, wherein the unlabeled data is classified using a K-nearest neighbors (k-NN) classifier.
However, Amit teaches; wherein the unlabeled data is classified using a K-nearest neighbors (k-NN) classifier (Amit, [0182] the grouping or clustering and classification of cells may use a K-nearest neighbors algorithm)
The combination of Banville and Amit would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the use of a k-NN classifier is widely known in the art and is a conventional analytical tool for grouping and classifying data using machine learning which allows for clustering and pairing of data points using common features of the nearest neighbors to a point. This would allow data points to be clustered based on common characteristics which is advantageous when working with data, such as cellular image data, where cell types/characteristics are being used to differentiate different tissue groups. (Amit, [0170-[0185])
Claims 6-19 are rejected under 35 U.S.C. 103 as being unpatentable over Banville (US 20230306267 A1) in view of Randolph (US 20210303818 A1).
Regarding claim 6 Banville fails to teach; The method of claim 1, further comprising: determining a plurality of image parameters by processing the test set through an image processing model;
and extracting singlets by applying image feature gates to the plurality of image parameters.
However, in the same field of endeavor, Randolph teaches; determining a plurality of image parameters by processing the test set through an image processing model (Randolph, [0026] a test dataset of cell images is sent to a machine learning system (image processing module) to extract multiple features (parameters));
and extracting singlets by applying image feature gates to the plurality of image parameters (Randolph, [0098] the data is analyzed by gating, [0100] single cells (singlets) can be gated from multiple cells in the image and gating is performed using at least one physical feature).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the addition of the cell feature gating analysis of Randolph to the machine learning system and model of Banville would allow for cell image data to be automatically sorted by features of the cells in order to obtain characteristics of a single cell more quickly. (Randolph, [0098]-[0102])
Regarding claim 7 the combination of Banville and Randolph teaches; The method of claim 6, wherein the image processing model includes a classifier model selected by a user (Randolph, [0161] the system is set up such that each component of the machine learning system may be optimized and fined tuned where a user may interface with the system components during this optimization,[0011] the machine learning system using an image processing network, such as ConvNets, which contain a classifier, [0058]-[0059] the classifier modules may have their parameters fined tuned and validated, [0067] the classifiers have thresholds which allow the feature classifications to be adjusted and selected, [0077]-[0078] where the classifier or model type may be selected from the listed options based upon the features of interest).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that allowing a user to optimize and fine tune a machine learning system based on specific features of interest as taught by Randolph may allow for more applications of the system based upon individual need. (Randolph, [0070]-[0082])
Regarding claim 8 the combination of Banville and Randolph teaches; The method of claim 6, wherein the plurality of image parameters includes flow parameters or fluorescence parameters (Randolph, [0097] the parameters used in the gating and cytogram generation may be based on flow or fluorescence parameters).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the use of flow or fluorescence parameters is advantageous for grouping cells or particles with similar characteristics for analysis purposes. (Randolph, [0095]-[0099])
Regarding claim 9 the combination of Banville and Randolph teaches; The method of claim 6, wherein the plurality of image parameters includes cell size, a number of particles, or pixel intensity-based parameters (Randolph, [0096] one of the parameters of the images may include cell size, volume, or morphology, as well as granularity).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the use of cell size parameters is advantageous for grouping cells or particles with similar characteristics for analysis purposes. (Randolph, [0095]-[0099])
Regarding claim 10 the combination of Banville and Randolph teaches; The method of claim 1, further comprising:
generating the training set by: generating a two-dimensional (2D) map by applying a dimensionality reduction technique to a normalized data set comprising flow parameters, image parameters, or flow parameters and image parameters (Randolph, [0113] a database of images and cell count parameters may be generated, where the data is sorted based upon image and cell parameters for analysis indicating it is a normalized dataset, [0015] the set of features (image parameters) may be converted to a lower dimensionality and have embedding maps (2D maps) generated for the features);
and generating a clustering map comprising a plurality of clusters by applying a clustering algorithm to the 2D map, wherein the labeled data comprises the plurality of clusters (Randolph, [0123]-[0124] the embeddings have either a Principal component analysis or a random forest model used to label inputs from the dataset based on learned features of interest (labelled training data) for each molecule type (clustering based on type of molecule)).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that adding the data clustering methods for cellular clustering as taught by Randolph to the machine learning model of Banville would allow the system to generate multiple sets of training data allowing the system to be trained for multiple cellular analysis applications. (Randolph, [0011]-[0016])
Regarding claim 11 the combination of Banville and Randolph teaches; The method of claim 10, wherein each of the plurality of clusters corresponds to a class determined according to user input (Randolph, [0123] the model takes as input features of interest, [0099] the user may input or output features of interest for gating cells of interest).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that adding the user selection capabilities of Randolph to the model of Banville allows for the user to control which features are used in training, which may be helpful in fine tuning the model. (Randolph, [0099], [0123], and [0113])
Regarding claim 12 the combination of Banville and Randolph teaches; The method of claim 10, wherein the dimensionality reduction technique comprises a nonlinear dimensionality reduction technique or a linear dimensionality reduction technique (Randolph, [0078] the model may perform dimension reduction on using a neural network,[0138] the dimensional reduction may be performed using principal component analysis (linear dimension reduction)).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the dimension reduction method of Randolph would allow the model of Banville to complete data processing tasks with very little preprocessing of the data, allowing for more efficient data processing. (Randolph, [0078])
Regarding claim 13 the combination of Banville and Randolph teaches; The method of claim 12, wherein the linear dimensionality reduction technique comprises random projection and Principal Component Analysis (PCA) (Randolph, [0078] the model may perform dimension reduction on using a neural network,[0138] the dimensional reduction may be performed using principal component analysis (linear dimension reduction)).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the dimension reduction method of Randolph would allow the model of Banville to complete data processing tasks with very little preprocessing of the data, allowing for more efficient data processing. (Randolph, [0078])
Regarding claim 14 the combination of Banville and Randolph teaches; The method of claim 12, wherein the nonlinear dimensionality reduction technique comprises kernel principal component analysis (KernelPCA), Isometric Mapping (Isomap) embedding, Uniform Manifold Approximation and Projection (UMAP), or t-distributed Stochastic Neighbor Embedding (t-SNE) (Banville, [0114] the combined testing and training datasets are projected into two dimensions using a UMAP projection (non-linear reduction of dimensions)).
Regarding claim 15 the combination of Banville and Randolph teaches; The method of claim 10, wherein the normalized concatenated dataset is reduced to the reduced dimension space using the dimensionality reduction technique or a different dimensionality reduction technique than the dimensionality reduction technique applied to the normalized data set (Banville, [0114] the combined testing and training datasets are projected into two dimensions using a UMAP projection (non-linear reduction of dimensions)).
Regarding claim 16 the combination of Banville and Randolph teaches; The method of claim 10, wherein the clustering algorithm comprises an agglomerative clustering algorithm, a k-means clustering algorithm, a spectral clustering algorithm, a mean-shift clustering algorithm, or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm (Randolph, [0108] the system generate cytograms, which involves clustering, using intensity and fluorescence parameters, indicating it is a spectral clustering/grouping method).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the use of clustering by spectral parameters allows for the better mapping and visualization of characteristics of interest for particles of interest. (Randolph, [0106]-[0108])
Regarding claim 17 the combination of Banville and Randolph teaches; The method of claim 1, further comprising: generating the training set by gating a plurality of flow parameters into a plurality of gates (Randolph, [0099] the data is gated using a computing platform, [0110] gating is performing using forward scatter and other light intensity properties, as well as based upon fluorescent markers and cell morphology markers, applicant notes in specification paragraph [0037] that fluorescence flow markers are used for the flow parameters during gating),
wherein each of the plurality of gates comprises a group of images (Randolph, [0113] imaging is used to capture images of the particles under a microscope, and thresholding is performed to create sets of images with the same parameters as each gate, thereby generating a database of gated images which may be used to train neural networks);
and wherein the training set comprises the plurality of gates (Randolph,[0055] training data may be generated for each method (including gating) from the microscopic images, where the feature of interest may be used as a means of grouping the images).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the training data generation method of Randolph would allow the model of Banville to be trained to identify and analyze particles and cells for a plurality of features of interest depending on the research and analysis goals. (Randolph, [0055], [0110]-[0113])
Regarding claim 18 the combination of Banville and Randolph teaches; The method of claim 1, further comprising: generating the training set by grouping a plurality of images according to a similarity metric respective to a plurality of defined images (Randolph, [0055] multiple training image sets are generated depending on application or feature of interest, [0017] a similarity of test distribution for the data may be determined for the embeddings of the image data to generate sets with different feature embeddings),
wherein each of the defined images is associated with a unique particle class (Randolph, [0055] multiple training image sets are generated depending on application or feature of interest, [0080] where the features of interest may be associated with a unique feature of interest for a cell, such as morphology, or other characteristics of particles in the liquid suspension),
wherein the similarity metric is expressed in percentage based on a Euclidean distance in image parameter space (Randolph, [0131]-[0133] the system may use cellular images to train the model, and in determining features of interest and grouping the images, the system may use a Euclidean distance transformation to determine local minima pixel values which can indicate artifacts and features of interest).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the method of grouping training data by feature of interest as taught in Randolph allows for feat generation of multiple training sets based on model application. (Randolph, [0017], [0055], [0080] and [0131]-[0133])
Regarding claim 19 the combination of Banville and Randolph teaches; A particle classification system, comprising: an electronic processing device configured to: receive, from an imaging flow cytometer instrument, a test set comprising unlabeled data to classify (Randolph, [0053] and [0055] the method modifies known machine learning and training methods to be used with flow cytometry data or other similar processes, [0085]-[0086] flow cytometry instrument channel data is recited to measure cell presence and other features of interest, [0092] samples may be processed later, indicating they are unlabeled or unprocessed after receipt);
pool the test set and a training set into a concatenated dataset comprising a plurality of parameters (Banville, [0059] a labeled training set and unlabeled data are sent to the training computing apparatus to be fed to a classifier model, [0114] combined training and test data are embedded using the models), wherein the training set comprises labeled data (Banville, [0059] training data is labeled);
normalize the concatenated dataset by bringing a variance to one for each of the plurality of parameters (Banville, [0344] windows from both datasets/the combined dataset is normalized);
non-linearly reduce a dimensionality of the normalized concatenated dataset to a reduced dimension space (Banville, [0114] the combined testing and training datasets are projected into two dimensions using a UMAP projection (non-linear reduction of dimensions));
compute classification parameters by classifying the unlabeled data from the reduced dimension space using the labeled data from the reduced dimension space (Banville, [0117]-[0118] binary labels for sleep apnea, pathologic information and gender were visualized using a heatmap to compute overlap in labels for the merged data, [0135] the labeled data was used to label the unlabeled data in the combined set);
and provide the classification parameters for further processing (Banville, [0136] trainable parameters can be updated further after the label computation).
The combination of Banville and Randolph would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that the method of training a machine learning model as taught by Banville would have been well known in the art as a method capable of use with images and other bioimages, therefore it would have been obvious to use the methods disclosed in Banville with flow cytometry image data as taught by Randolph (Banville, [0275]-[0280]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous prior art as determined by the examiner, please see the attached PTO-892 Notice of References Cited form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN M ELLIOTT whose telephone number is (703)756-5463. The examiner can normally be reached M-F 8AM-5PM ET.
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, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.M.E./Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
Prosecution Insights