DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 8/11/2023. Claims 21-40 are pending in this Office Action.
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 .
Claim Objections
Claim 21 and 31 are objected to because of the following informalities: claims 21 and 31 recite the phrase “Conditional Boltzmann Machine (CRBM)”. It appears that applicant intends to recite “Conditional Restricted Boltzmann Machine (CRBM)” or “Conditional Boltzmann Machine (CBM)”.Appropriate correction is required.
Claims 23 and 33 recite “backpropogation” which appears to be a typo error of “backpropagation”.
Double Patenting
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 21,22,23,31,32,33 are rejected on the ground of nonstatutory obviousness type double patenting as being unpatentable over claims 1,3,5,11,13,15 of the co-pending application 18/448,843. Although the claims at issue are not identical, they are not patentably distinct from each other because all the elements of the instant application claims 21,22,23,31,32,33 are to be found in the claims 1,3,5,11,13,15 of the co-pending application 18/448,843.
Instant Application (18/352,960)
Co-Pending application (18/448,843)
21. (New) A method for training a conditional generative model, the method comprising: defining a joint distribution, wherein: the joint distribution corresponds to a combination of a Conditional Boltzmann Machine (CRBM) and a point prediction model, and the point prediction model is configured to obtain a measurement of regression accuracy;
deriving an energy function for the joint distribution; obtaining, from the energy function for the joint distribution, an approximation for a conditional distribution,
wherein an output of the point prediction model is a parameter of the approximation;
determining, from a loss function, at least one training parameter;
training the CRBM based on the at least one training parameter to operate as a conditional generative model, wherein the trained CRBM follows the conditional distribution; and applying the trained CRBM to a dataset corresponding to a randomized trial.
22. (New) The method of claim 21, wherein applying the trained CRBM to a dataset corresponding to a randomized trial comprises using the CRBM to generate a set of samples of a target population.
23. (New) The method of claim 21, wherein the combination is trained by using gradient descent through gradients obtained through backpropagation.
Claims 31,32,33
1. (Currently Amended) A method for training a conditional generative model, the method comprising: defining a joint distribution, wherein: the joint distribution corresponds to a combination model comprising a probabilistic model and a point prediction model; and the point prediction model is configured to obtain a measurement of regression accuracy;
deriving an energy function for the joint distribution; obtaining, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is configured to derive a conditional mean parameter for the approximation;
determining, from a loss function corresponding to the approximation for the conditional distribution, at least one training parameter;
training the combination model to operate as a conditional generative model that follows the conditional distribution, wherein training the combination model comprises: defining a plurality of schema, according to a plurality of data types associated with a training dataset; configuring, using a schema processor and based on the plurality of schema, at least one visible layer of the probabilistic model to be a composite layer, wherein each constituent layer of the composite layer corresponds to at least one individual data type of the plurality of data types; inputting, into the at least one visible layer, the training dataset; and implementing a gradient descent using: at least one derivative of the at least one training parameter; and observed sample values output by the at least one visible layer of the probabilistic model; inputting a trial dataset corresponding to a randomized trial into the trained combination model to output a multivariate output vector comprising an estimated outcome, wherein the estimated outcome is based on a plurality of pre-trial covariates, included in the trial dataset and comprises: a point estimate for at least one characteristic in the estimated outcome; and an outcome distribution, representing variability in the point estimate.
3. (Previously Presented) The method of claim 2, wherein applying the trained probabilistic model to the trial dataset corresponding to the randomized trial comprises using the CRBM to generate a set of samples of a target population.
5. (Previously Presented) The method of claim 1, wherein a gradient that is used in the gradient descent is obtained from at least one of: backpropagation; or automatic differentiation.
Claims 11,13 and 15
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 21:
Step 1: Statutory Category ?: Yes. claim 1 recites a method (i.e., a “process”) which is statutory category.
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 21 recites limitations that are mathematical calculations and falls within the mathematical concepts grouping of abstract idea:
defining a joint distribution, wherein: the joint distribution corresponds to a combination of a Conditional Boltzmann Machine (CRBM) and a point prediction model, and the point prediction model is configured to obtain a measurement of regression accuracy; deriving an energy function for the joint distribution; obtaining, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is a parameter of the approximation; determining, from a loss function, at least one training parameter.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 21 recites additional elements “training the CRBM based on the at least one training parameter to operate as a conditional generative model, wherein the trained CRBM follows the conditional distribution; and applying the trained CRBM to a dataset corresponding to a randomized trial” . The training and applying CRBM which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model .
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 21 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional elements of training and applying the CRBM are at best equivalent of merely adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 21 therefore is ineligible.
Claim 22 recites additional element of “wherein applying the trained CRBM to a dataset corresponding to a randomized trial comprises using the CRBM to generate a set of samples of a target population” . The additional elements is at best equivalent of merely adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 22 therefore is ineligible.
Claim 23 recites additional element of “wherein the combination is trained by using gradient descent through gradients obtained through backpropagation” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 23 is not patent eligible.
Claim 24 recites additional element of “wherein the CRBM is based on at least one of a weight matrix and a precision matrix” which is mathematical relationships that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 24 is not patent eligible.
Claim 25 recites additional element of “wherein at least one of the weight matrix and the precision matrix is: a function of conditioning data (x) of the conditional distribution (y|x); and parameterized by matrix parameters learned by the CRBM” which is mathematical formulas or equations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 25 is not patent eligible.
Claim 26 recites additional element of “the precision matrix is diagonal and positive definite; the precision matrix is defined as: P=diag(eb); and P represents the precision matrix and b represents a learned parameter” which is mathematical formulas or equations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 26 is not patent eligible.
Claim 27 recites additional element of “the approximation is a Laplace approximation represented as: y|x∼N(fθ(x),(P-WW')-1); x represents feature units of the CRBM; y|x represents the approximation; P represents the precision matrix; W represents the weight matrix; θ represents a model parameter used to parameterize the point prediction model; and fθ(·) represents the point prediction model” which is mathematical formulas or equations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 27 is not patent eligible.
Claim 28 recites additional element of “wherein the mode of the conditional distribution is identified by the point prediction model” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 28 is not patent eligible.
Claim 29 recites additional element of “wherein the approximation is used to produce at least one selected of the group consisting of time-series estimates and clinical trial result estimates” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 29 is not patent eligible.
Claim 30 recites additional element of “wherein samples from the dataset corresponding to the randomized trial are obtained based on at least one selected from the group consisting of Monte Carlo sampling, Gibbs sampling, Persistent Contrastive Divergence sampling, and Gibbs-Langevin sampling” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 30 is not patent eligible.
Claim 31:
Step 1: Statutory Category ?: Yes. claim 31 recites a non-transitory computer readable medium (i.e., i.e., an article of manufacture) which is statutory category.
Step 2A-Prong 1: Judicial Exception Recited ?: Yes.
Claim 31 recites limitations that are mathematical calculations and falls within the mathematical concepts grouping of abstract idea:
defining a joint distribution, wherein: the joint distribution corresponds to a combination of a Conditional Boltzmann Machine (CRBM) and a point prediction model, and the point prediction model is configured to obtain a measurement of regression accuracy; deriving an energy function for the joint distribution; obtaining, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is a parameter of the approximation; determining, from a loss function, at least one training parameter.
Step 2A-Prong 2: Integrated into a practical application? No.
Claim 31 recites additional elements “a processor” and “training the CRBM based on the at least one training parameter to operate as a conditional generative model, wherein the trained CRBM follows the conditional distribution; and applying the trained CRBM to a dataset corresponding to a randomized trial” . The additional element of “processor” amounts to mere instructions to implement an abstract idea on a computer, or merely uses a computer a computer as a tool to perform an abstract idea (See MPEP 210605(f)). The training and applying CRBM which is recited at the very high level of generality such that it amounts no more than mere instructions to apply the exception using generic machine learning model .
Step 2B: Recites additional elements that amount to significantly more than the judicial exception? No.
Claim 31 does not include additional elements that are sufficient to amount to significantly more than judicial exception. As indicates above, the additional elements of “processor” , training and applying the CRBM are at best equivalent of merely adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 31 therefore is ineligible.
Claim 32 recites additional element of “wherein applying the trained CRBM to a dataset corresponding to a randomized trial comprises using the CRBM to generate a set of samples of a target population” The additional element is at best equivalent of merely adding the words “apply it” to the exception. Even when considered in combination, the additional elements do not provide an inventive concept, claim 32 therefore is ineligible.
Claim 33 recites additional element of “wherein the combination is trained by using gradient descent through gradients obtained through backpropogation” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 33 is not patent eligible.
Claim 34 recites additional element of “wherein the CRBM is based on at least one of a weight matrix and a precision matrix” which is mathematical relationships that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 34 is not patent eligible.
Claim 35 recites additional element of “wherein at least one of the weight matrix and the precision matrix is: a function of conditioning data (x) of the conditional distribution (y|x); and parameterized by matrix parameters learned by the CRBM” which is mathematical formulas or equations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 35 is not patent eligible.
Claim 36 recites additional element of “wherein: the precision matrix is diagonal and positive definite; the precision matrix is defined as: P=diag(eb); and P represents the precision matrix and b represents a learned parameter” which is mathematical formulas or equations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 36 is not patent eligible.
Claim 37 recites additional element of “wherein: the approximation is a Laplace approximation represented as: y|x∼N(fθ(x),(P-WW')-1); x represents feature units of the CRBM; y|x represents the approximation; P represents the precision matrix; W represents the weight matrix; θ represents a model parameter used to parameterize the point prediction model; and fθ(·) represents the point prediction model” which is mathematical formulas or equations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 37 is not patent eligible.
Claim 38 recites additional element of “wherein the mode of the conditional distribution is identified by the point prediction model” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 38 is not patent eligible.
Claim 39 recites additional element of “wherein the approximation is used to produce at least one selected of the group consisting of time-series estimates and clinical trial result estimates” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 39 is not patent eligible.
Claim 40 recites additional element of “wherein samples from the dataset corresponding to the randomized trial are obtained based on at least one selected from the group consisting of Monte Carlo sampling, Gibbs sampling, Persistent Contrastive Divergence sampling, and Gibbs-Langevin sampling” which is mathematical calculations that falls within the mathematical concepts grouping of abstract idea. The claim does not include any additional element that integrates the abstract idea into practical application in step 2A-Prong 2 and amounts to significantly more than the judicial exception in step 2B. Claim 40 is not patent eligible.
Allowable Subject Matter
Claims 27 and 37 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
3. Claims 21-23.28-29,31-33 and 38-39 are rejected under 35 U.S.C. 103 as being unpatentable over Mnih et al., "Conditional Restricted Boltzmann Machines for Structured Output Prediction", (2012) (Cited on applicant IDS ), hereinafter “Mnih”, and further in view of Morgan et al.(US Patent Application Publication 2020/0134642 A1, hereinafter “Morgan”)
As to claim 21, Mnih teaches method for training a conditional generative model, the method comprising: defining a joint distribution, wherein: the joint distribution corresponds to a combination of a Conditional Boltzmann Machine (CRBM) (Mnih section 2.1 teaches defining a probability distribution) and [a point prediction model, and the point prediction model is configured to obtain a measurement of regression accuracy];
deriving an energy function for the joint distribution; (Mnih section 3 teaches energy equation (9) and (10))
obtaining, from the energy function for the joint distribution, an approximation for a conditional distribution, [wherein an output of the point prediction model is a parameter of the approximation]; (Mnih section 3 teaches computing energy or free energy for energy-based model)
determining, from a loss function, at least one training parameter (Mnih section 6.2 teaches the loss function Lp can then be approximately optimized efficiently for the same class of energy functions for which the free energy can be computed efficiently);
training the CRBM based on the at least one training parameter to operate as a conditional generative model, (Mnih section 6.2 teaches Mnih section 6.2 teaches the loss function Lp can then be approximately optimized efficiently for the same class of energy functions for which the free energy can be computed efficiently……. Intuitively, training a conditional RBM in this manner has the appealing quality that the gradient should be large when the model makes a bad prediction);
wherein the trained CRBM follows the conditional distribution; and applying the trained CRBM to a dataset corresponding to a randomized trial. (Mnih section 6.2.1 teaches for the second dataset, the vector u is obtained by setting a random 8 by 8 patch of image v to 0)
Mnih fails to expressly teach a point prediction model, and the point prediction model is configured to obtain a measurement of regression accuracy; wherein an output of the point prediction model is a parameter of the approximation;.
However, Morgan teaches a point prediction model (Morgan par [0062] an ensemble model is constructed around a linear regression of actual demand on demand forecasts ), and the point prediction model is configured to obtain a measurement of regression accuracy (Morgan par [0026] teaches forecast models are validated) ; wherein an output of the point prediction model is a parameter of the approximation;.(Morgan par [0082] teaches run the regression to yield estimates for coefficients )
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mnih and Morgan to achieve the claimed invention. One would have been motivated to make such combination to increase accuracy as more data is analyzed.(Morgan par [0064])
As to claim 22, Mnih and Morgan teach the method of claim 21, wherein applying the trained CRBM to a dataset corresponding to a randomized trial comprises using the CRBM to generate a set of samples of a target population. (Mnih section 6.2.1 teaches for the second dataset, the vector u is obtained by setting a random 8 by 8 patch of image v to 0)
As to claim 23, Mnih and Morgan teach the method of claim 21, wherein the combination is trained by using gradient descent through gradients obtained through backpropogation (Mnih section 3 teaches gradient descent)
As to claim 28, Mnih and Morgan teach the method of claim 21, wherein the mode of the conditional distribution is identified by the point prediction model.(Morgan par [0069] teaches the use of a combination of spline and GGM decomposition methods)
As to claim 29, Mnih and Morgan teach the method of claim 21, wherein the approximation is used to produce at least one selected of the group consisting of time-series estimates and clinical trial result estimates. (Morgan par [0068] teaches time series data)
Claims 31-33 and 38-39 merely recite a non-transitory computer readable medium storing instructions when executed by processor, perform the method of claims 21-23 and 28-29 respectively. Accordingly, Mnih and Morgan teach every limitation of claims 31-33 and 38-39 as indicates in the above rejection of claims 21-23 and 28-29 respectively.
3. Claims 24-26,30,34-36 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Mnih, Morgan and further in view of Andreou et al.(US Patent Application Publication 2020/0160046 A1, hereinafter “Andreou”)
As to claim 24, Mnih and Morgan teach the method of claim 21 but fail to teach wherein the CRBM is based on at least one of a weight matrix and a precision matrix.
However, Andreou teaches wherein the CRBM is based on at least one of a weight matrix and a precision matrix.(Andreou par [0141] teaches the conditional restricted Boltmann machine (CRBM). Andreou par [0143] teaches The parameters of a CRBM are θ=(W, a, b, A, B), where the autoregressive connection weights are actually sets of weights, one matrix for each time step in the buffer)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mnih, Morgan and Andreou to achieve the claimed invention. One would have been motivated to make such combination to efficiently implement learning .(Andreou par [0136])
As to claim 25, Mnih , Morgan and Andreou teach the method of claim 24, wherein at least one of the weight matrix and the precision matrix is: a function of conditioning data (x) of the conditional distribution (y|x); and parameterized by matrix parameters learned by the CRBM. (Andreou par [0141] teaches the conditional restricted Boltmann machine (CRBM). Andreou par [0143] teaches The parameters of a CRBM are θ=(W, a, b, A, B), where the autoregressive connection weights are actually sets of weights, one matrix for each time step in the buffer.)
As to claim 26, Mnih , Morgan and Andreou teach method of claim 24, wherein: the precision matrix is diagonal and positive definite; the precision matrix is defined as: P=diag(eb); and P represents the precision matrix and b represents a learned parameter. (Andreou par [0143] teaches The parameters of a CRBM are θ=(W, a, b, A, B), where the autoregressive connection weights are actually sets of weights, one matrix for each time step in the buffer. The examiner interprets precision matrix is optional and not required by claim 24 which claim 26 depends on because of “one of” language recited in claim 24)
As to claim 30, Mnih and Morgan teach the method of claim 21 but fail to teach wherein samples from the dataset corresponding to the randomized trial are obtained based on at least one selected from the group consisting of Monte Carlo sampling, Gibbs sampling, Persistent Contrastive Divergence sampling, and Gibbs-Langevin sampling.
However, Andreou teaches wherein samples from the dataset corresponding to the randomized trial are obtained based on at least one selected from the group consisting of Monte Carlo sampling, Gibbs sampling, Persistent Contrastive Divergence sampling, and Gibbs-Langevin sampling.( Andreou par [0136] teaches Markov chain Monte Carlo sampling methods)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mnih, Morgan and Andreou to achieve the claimed invention. One would have been motivated to make such combination to efficiently implement learning .(Andreou par [0136])
As to claims 34-36 and 40 see the above rejection of claims 24-26 and 30.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HIEN DUONG whose telephone number is (571)270-7335. The examiner can normally be reached Monday-Friday 8:00AM-5:00PM.
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, Viker Lamardo can be reached at 571-270-5871. 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.
/HIEN L DUONG/Primary Examiner, Art Unit 2147
Prosecution Insights