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 .
DETAILED ACTION
This action is response to the application filed on 02/21/2025.
Claim 1 stands rejected, and are pending in this Office Action. Claim 1 is an independent claim.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over both:
(i) Claims 1-16 of U.S. Patent No. 11308101. Although the claims at issue are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations. Especially, the U.S. Patent No. 11308101 discloses more details in logic assets with the application scenario. Therefore, it would have been obvious to one of ordinary skill in the art to realize that claims 1-16 of the instant application is fully disclosed by the U.S. Patent No. 11308101; and
(ii) Claims 1-3 of U.S. Patent No. 12013863. Although the claims at issue are not identical, they are not patentably distinct from each other because they are substantially similar in scope and they use the same limitations. Especially, the U.S. Patent No. 12013863 discloses more details in logic assets with the application scenario. Therefore, it would have been obvious to one of ordinary skill in the art to realize that claims 1-3 of the instant application is fully disclosed by the U.S. Patent No. 12013863.
The following table shows the claims in Instant Application that are rejected by corresponding claim(s) in U.S. Patent No. 11308101 and 12013863, respectively.
Instant Application
11308101
1. A method to identify driver mutations across a genome, comprising:
receiving an input dataset that is a non-stationary discrete stochastic process,
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions;
one-time training a model using the input dataset to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions;
receiving a query associated with any arbitrary set of indexed positions within the plurality of regions and,
in response, scaling existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions;
based on the response, identifying genomic elements that constitute one or more driver mutations;
using the one or more driver mutations to identify one or more potentially druggable targets for therapeutic development.
1. A method to identify driver mutations across a genome, comprising:
receiving an input dataset that is a non-stationary discrete stochastic process,
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions;
one-time training a model using the input dataset to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions;
receiving a query associated with any arbitrary set of indexed positions within the plurality of regions and,
in response, scaling existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions;
based on the response, identifying genomic elements that constitute one or more driver mutations;
using the one or more driver mutations to identify one or more potentially druggable targets for therapeutic development.
1. A method to identify driver mutations across a genome, comprising:
receiving an input dataset that is a non-stationary discrete stochastic process,
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions;
one-time training a model using the input dataset to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions;
receiving a query associated with any arbitrary set of indexed positions within the plurality of regions and,
in response, scaling existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions;
based on the response, identifying genomic elements that constitute one or more driver mutations;
using the one or more driver mutations to identify one or more potentially druggable targets for therapeutic development.
1. A method of information search and retrieval associated with a non-stationary discrete stochastic process representing a pattern of mutations across a genome of a cancer of interest,
wherein events of interest are cancer-specific mutations that occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions, wherein at least one region is a non-coding region of the genome, comprising:
responsive to a set of input data having a first dimensionality, training a model for the cancer of interest a single time to predict rate parameters and their associated estimation uncertainty for each region of the plurality of regions, the model being trained by:
processing the input data through a first process that maps patterns in the input data to a reduced data set having a second dimensionality that is at least an order of magnitude less than the first dimensionality, and
processing the reduced set through a second process to output a distribution of the events of interest and their associated estimation uncertainty for each region of the plurality of regions;
receiving a query associated with any arbitrary set of indexed positions within the plurality of regions;
responsive to receipt of the query, and by scaling existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions, obtaining a response to the query, the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions; and
providing the response for use in a target application.
2. The method as described in claim 1 wherein the query is processed using a function computed in constant time, wherein the function represents a closed form solution to a marginalization of a multi-variate probability distribution for the set of indexed positions.
3. The method as described in claim 2 wherein the closed form solution to the marginalization of the multi-variate probability distribution is a variant of a Poisson-Gamma (PG) distribution.
4. The method as described in claim 1 wherein the first process is a Convolutional Neural Network (CNN), and the second process is a Gaussian Process (GP).
5. The method as described in claim 1 wherein the first process is one of: a Convolutional Neural Network (CNN), an Auto-Encoder (AE), Principal Component Analysis (PCA), and Uniform Manifold Approximation and Projection (UMAP) learning.
6. The method as described in claim 1 wherein the second process is one of: a Gaussian Process, a Random Forest (RF) regression, a Negative Binomial Regression (NBR); and a Binomial Regression (BR).
7. The method as described in claim 1 wherein the input data is a set of genomic feature data.
8. The method as described in claim 7 wherein patterns in the input data comprise epigenetic tracks.
9. The method as described in claim 8 wherein an epigenetic track comprises a chromatin track that measures an abundance of a particular chromatin mark genome-wide.
10. The method as described in claim 4 wherein the CNN generates the reduced data set by performing a non-linear feature space reduction over information in the local epigenetic patterns.
11. The method as described in claim 1 wherein estimating the distribution of events includes formulating a closed-form generative model for a number of events of interest at any position within a particular region.
12. The method as described in claim 11 wherein the closed-form generative model is formulated using predicted regional rate parameters and their associated estimation uncertainty, together with local context information.
13. The method as described in claim 12 wherein the local context information is an observed genetic context of the particular region.
14. An information search and retrieval apparatus for use with an input dataset that is a non-stationary discrete stochastic process representing a pattern of mutations across a genome of a cancer of interest,
wherein events of interest are cancer-specific mutations that occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions,
wherein at least one region is a non-coding region of the genome, comprising: a hardware processor; and computer memory holding computer program code executed by the hardware processor, the computer program code comprising:
a neural network that uses the input dataset to one-time train a model for the cancer of interest to predict rate parameters and their associated estimation uncertainty for each region of the plurality of regions; and
a query function that receives a query associated with any arbitrary set of indexed positions within the plurality of regions and,
in response, scales existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions; and an output function that applies data derived from the response to a target application.
15. A computer program product in a non-transitory computer readable medium for use in a data processing system, the computer program product holding computer program instructions which, when executed by the data processing system, are operative to perform information search and retrieval with respect to a non-stationary discrete stochastic process representing a pattern of mutations across a genome for a cancer of interest,
wherein events of interest are cancer-specific mutations that occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprises a plurality of regions, wherein at least one region is a non-coding region of the genome, the computer program instructions comprising program code configured to:
train a model for the cancer of interest a single time to predict rate parameters and their associated estimation uncertainty for each region of the plurality of regions;
receiving a set of one or more queries, wherein a query is associated with any arbitrary set of indexed positions within the plurality of regions;
responsive to receipt of each query, scale existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions; and
provide the response for use in a target application.
16. The computer program product as described in claim 15 wherein the program code configured to train the model comprises a neural network and a Gaussian process.
Instant Application
12013863
1. A method to identify driver mutations across a genome, comprising:
receiving an input dataset that is a non-stationary discrete stochastic process,
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions;
one-time training a model using the input dataset to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions;
receiving a query associated with any arbitrary set of indexed positions within the plurality of regions and,
in response, scaling existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions;
based on the response, identifying genomic elements that constitute one or more driver mutations;
using the one or more driver mutations to identify one or more potentially druggable targets for therapeutic development.
1. A method to identify driver mutations across a genome, comprising:
receiving an input dataset that is a non-stationary discrete stochastic process,
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions;
one-time training a model using the input dataset to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions;
receiving a query associated with any arbitrary set of indexed positions within the plurality of regions and,
in response, scaling existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions;
based on the response, identifying genomic elements that constitute one or more driver mutations;
using the one or more driver mutations to identify one or more potentially druggable targets for therapeutic development.
1. An information search and retrieval apparatus for use with an input dataset that is a non-stationary discrete stochastic process,
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprising a plurality of regions, comprising:
a hardware processor; and
computer memory holding computer program code executed by the hardware processor, the computer program code comprising:
a neural network and a Gaussian process that uses the input dataset to one-time train a model to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions; and
a query function that receives a query associated with any arbitrary set of indexed positions within the plurality of regions and,
in response, scales existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions; and
an output function that applies data derived from the response to a target application;
wherein the target application identifies anomalies in one of:
a genome for a cancer of interest, a computer network, and a population.
2. A computer program product in a non-transitory computer readable medium for use in a data processing system, the computer program product holding computer program instructions which, when executed by the data processing system, are operative to perform information search and retrieval with respect to a non-stationary discrete stochastic process,
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate λ.sub.R that is approximately constant across R,
wherein λ.sub.R has an associated estimation uncertainty defined by a set of parameters that include expectation μ.sub.R and variance σ.sub.R.sup.2, and
wherein the non-stationary discrete stochastic process comprises a plurality of regions, the computer program instructions comprising program code configured to:
train a model a single time to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions;
receiving a set of one or more queries, wherein a query is associated with any arbitrary set of indexed positions within the plurality of regions;
responsive to receipt of each query, scale existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query,
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions; and
provide the response for use in a target application,
wherein the wherein the target application identifies anomalies in one of: a genome for a cancer of interest, a computer network, and a population.
3. The computer program product as described in claim 2 wherein the program code configured to train the model comprises a neural network and a Gaussian process.
“Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U.S. Court of Customs and Patent Appeals.
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.
Claim1 is 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.
The term “approximately” in claim 1 is a relative term which renders the claim indefinite. The term “approximately” 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. Accordingly, the limitation “wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate
PNG
media_image1.png
18
19
media_image1.png
Greyscale
that is approximately constant across R” render claim 1 indefinite.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness reject
ions 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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over
Tran; Bao: “MESH NETWORK STROKE MONITORING APPLIANCE” (United States Patent Application Publication 20070276270 A1, DATE PUBLISHED 2007-11-29; and DATE FILED 2006-05-24, hereafter “Tran”), in view of
Kates et al.: “METHOD FOR TRAINING A LEARNING-CAPABLE SYSTEM” (United States Patent Application Publication 20060248031 A1, DATE PUBLISHED 2006-11-02; and DATE FILED 2003-07-03, hereafter “Kates”).
As per claim 1, Tran teaches a method to identify driver mutations across a genome, comprising:
receiving an input dataset that is a non-stationary discrete stochastic process (See [0464], analyzing surface myoelectric signals recorded during dynamic contractions, which can be modeled as realizations of nonstationary stochastic process, Here the surface myoelectric signals recorded reads on the input dataset),
wherein events of interest occur within the process approximately independently at units i within a region R, and with an unknown rate
PNG
media_image1.png
18
19
media_image1.png
Greyscale
that is approximately constant across R (See [0271] and [0400], the BI sensors detecting the rate of change to the heart. The BI sensors can be supplemented by the EKG sensors and normal, healthy, heart beats at a regular rate. Heart sounds are automatically segmented into a segment of a single heart beat cycle. Here the heart beats teaches the events of interest and heart beats rate reads on the rate being constant across the cycle, the range and the segment is interpreted the unit within the range).
Tran does not explicitly teach the heart beats rate is associated with estimation uncertainty defined by a set of parameters that include expectation
PNG
media_image2.png
16
21
media_image2.png
Greyscale
and variance
PNG
media_image3.png
20
23
media_image3.png
Greyscale
.
On the other hand, Kates teaches wherein
PNG
media_image1.png
18
19
media_image1.png
Greyscale
has an associated estimation uncertainty defined by a set of parameters that include expectation
PNG
media_image2.png
16
21
media_image2.png
Greyscale
and variance
PNG
media_image3.png
20
23
media_image3.png
Greyscale
(See [0048], using an expectation method to pre-process the data used to train a learning-capable system lacks any mechanism for providing an indication of that part of the uncertainty of outcome estimation associated with uncertainty in the imputed values.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kates with the teachings of Tran because Tran is dedicated to monitoring a person specifically for application related to strokes and Kates is dedicated to training a learning-capable system, and the combined teaching would have enabled Tran’s stroke monitoring system learning capable for early detection by using the augmented input data set and/or the augmented outcome data set.
Tran in view of Kates further teaches the following:
wherein the non-stationary discrete stochastic process comprising a plurality of regions (See Tran: [0271] and [0400], the BI sensors detecting the rate of change to the heart. The BI sensors can be supplemented by the EKG sensors and normal, healthy, heart beats at a regular rate. Heart sounds are automatically segmented into a segment of a single heart beat cycle. Here the segments of heart beat cycle reads on the regions.);
one-time training a model using the input dataset to predict rate parameters and their associated estimation uncertainty for each region R of the plurality of regions (See Kates: [0049], the learning-capable system is intended for application in a decision support framework and an underestimate of the uncertainty of an outcome prediction could lead to an underestimate of the risk of unusual outcome events; and Tran: [0271] and [0400], the BI sensors detecting the rate of change to the heart. The BI sensors can be supplemented by the EKG sensors and normal, healthy, heart beats at a regular rate. Heart sounds are automatically segmented into a segment of a single heart beat cycle. Here the segments of heart beat cycle reads on the regions);
receiving a query associated with any arbitrary set of indexed positions within the plurality of regions (See Tran: [0209], the base station server 20 broadcasts a query to all nodes in the mesh network to retrieve identification information for the appliance such as manufacturer information, appliance model information, appliance serial number and optionally a hub number (available on hub packaging). The user may register more than one appliance at this point), and
in response, scaling existing predictions of the rate parameters and their associated estimation uncertainties from the trained model for any region of the plurality of regions to obtain a response to the query (See Tran: [0210]-[0211], the base station 20 frequently collects and synchronizes data from the appliances, and users may set up alerts or reminders that are triggered when one or more reading meet a certain set of conditions, depending on parameters defined by the user. The user chooses the condition that they would like to be alerted to and by providing the parameters (e.g. threshold value for the reading) for alert generation. Each alert may have an interval which may be either the number of data points or a time duration in units such as hours, days, weeks or months),
the response being a distribution of the events of interest and their associated estimation uncertainties for the set of indexed positions (See Tran: [0210]-[0211], the base station 20 frequently collects and synchronizes data from the appliances, and users may set up alerts or reminders that are triggered when one or more reading meet a certain set of conditions, depending on parameters defined by the user. The user chooses the condition that they would like to be alerted to and by providing the parameters (e.g. threshold value for the reading) for alert generation. Each alert may have an interval which may be either the number of data points or a time duration in units such as hours, days, weeks or months);
based on the response, identifying genomic elements that constitute one or more driver mutations (See Tran: [0487], the system can also use genomics to define patterns of genes associated with ADRs.);
using the one or more driver mutations to identify one or more potentially druggable targets for therapeutic development (See Tran: [0488], drug efficacy and toxicity vary substantially across individuals. Because drugs and doses are typically adjusted by trial and error if needed, clinical consequences may include a prolonged time to optimal therapy. In some cases, serious adverse events may result).
Related Prior Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited.
Conclusion
Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours.
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, Mr. Ajay Bhatia can be reached on 5712723906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
KUEN S LU /Kuen S Lu/
Art Unit 2156
Primary Patent Examiner
December 5, 2025