DETAILED 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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/7/2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-10, 12-20 are rejected under 35 U.S.C. 101
because the claimed invention is directed to an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-2, 4-8, 9-10, 12-16, 17-20 are directed to a method, medium and system of a ML model. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 9 and 17 these claims recite
training a multitask machine learning (MTML) deep convolutional neural network (DCN) model to map an input formula/structure of a material onto a complex wave function state vector; enforcing a mathematical structure to machine learning of multiple properties of the materials during the training to provide the trained MTML DCN model;
inferring, by a trained, MTML DCN model, observable property matrices for each observable property of the material; converting the observable property matrices into complex Hermitian operators for each observable property of the material; and predicting, by the trained MTML DCN model, target properties of the material according to the complex Hermitian operators of each observable property of the material and the complex wave function state vector for each observable property of the material; predicting target properties of the material according to the complex Hermitian operators and the complex wave function state vector by performing a loss function to reduce a difference between the predicted target properties (Ypred) and the target properties (Y) of the material to a threshold value; and identifying the material as a new material discovery when a unique combination of the predicted target properties (Ypred) is observed in the material according to the loss function.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that a human user performing these functions, simply using a computer as a tool as disclosed at Fig. 1, [0026]-[0027], etc. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 9 and 17 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f)) which is not indicative of integration into a practical application. MPEP 2106.05(f).)
MPEP § 2106.05(f): Mere Instructions to Apply an Exception. Do the additional element(s) amount to merely the words “apply it” (or an equivalent)
or are mere instructions to implement an abstract idea or other exception on a computer? (Yes)
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 9 and 17: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options). the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “training…” “enforcing…” “inferring…”, converting…” “predicting…” “identifying…These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions or generic ML method, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 10
Claim 2, 10 merely recite other additional elements that calculating parameters for predicting properties which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 12
Claim 4, 12 merely recite other additional elements that performing parameter formatting and normalization for predicting properties which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5, 13
Claim 5, 13 merely recite other additional elements that applying quantum theory to ML which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 6, 14, 18
Claim 6, 14, 18 merely recite other additional elements that define property matrices which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 15, 19
Claim 7, 15, 19 merely recite other additional elements that define property matrices which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 8, 16, 20
Claim 8, 16, 20 merely recite other additional elements that calculating target properties which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible
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.
Claims 1, 4-9, 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hagawa et al. (Hagawa) US 2021/0133635 in view of LI et al. (Li) “Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation” Published 6/23/2022 at Nature Computational Science2, 367-377 (2022) https://doi.org/10.1038/s43588-022-00265-6 and Morgan et al. (Morgan) US 2022/0076076
In regard to claim 1, Hagawa disclose A method for multitask learning ([0040]-[0044] [0091]-[0107] [0174][0175] multilevel ML learning method) , comprising:
training a multitask machine learning (MTML) deep convolutional neural network (DCN) model to map an input formula/structure of a material onto a vector; (Fig. 1, ([0041]-[0044] [0091]-[0107] [0138]-[0139] [0152]-[0156] [0174][0175] [0237] [0242]-[0258] Fig. 1, training a model (deep learning multilayer neural network) to calculate the material descriptors of the material (vector) representing the material corresponding to the inputted material formula/structure of the material)
inferring, by the trained, MTML DCN model, observable property matrices for each observable property of the material; ([0041]-[0044] [0091]-[0107] [0138]-[0139] [0152]-[0156] [0174][0175] [0237] [0242]-[0258] derived, by the model, the material descriptor vector for the properties of the material, such as structure, test env., composition formula, etc. Note: types of NN used are design choices and but not an invent, please further define observable property matrices, etc. to help move forward the prosecution)
converting the observable property matrices into a sequence for each observable property of the material; ([0041]-[0047] [0082] [0091]-[0107] [0138]-[0139] [0152]-[0156] [0174][0175] [0237] [0242]-[0258] convert the property vector into a sequence for the properties of the material, which is a format.)
predicting, by the trained MTM DCN model, target properties of the material according to the vector. ([0040]-[0050] [0071]-[0078] [0091]-[0107] predicting the property of a material through the ML based on the material descripts) by performing a loss function to reduce a difference between the predicted target properties (Ypred) and the target properties (Y) of the material; ([0041]-[0056] [0242]- [0256] a loss function is calculated between the input properties of the material and the predicted properties of the target material to minimize the differences (error) of the input properties of the material and the predicted properties of the target material to decrease the value of the loss function according to a gradient descent algorithm)
identifying the material property value when a combination of the predicted target properties (Ypred) is observed in the material according to the loss function. ([0041]-[0056][0242]-[0261] identify the material property value whey a combination of the properties observed based on the loss function)
But Hagawa fail to explicitly disclose “the method based on Hermitian operators; a complex wave function state vector; enforcing a mathematical structure to machine learning of multiple properties of the materials during the training to provide a trained MTML DCN model; converting the observable property matrices into complex Hermitian operators for each observable property of the material; and predicting, by the trained MTM DCN model, according to the complex Hermitian operators of each observable property of the material and the complex wave function state vector.”
Li disclose the method for multitask learning based on Hermitian operators, (abstract, Results, p 367-376 “H” “Hamiltonian is a specific type of Hermitian operator”, deep-learning with Hermitian operators)
a complex wave function state vector; (Results, p 368-376, with wave function related state vector)
enforcing a mathematical structure to machine learning of multiple properties of the materials during the training to provide a trained MTML DCN model; (page 367-375 using physical properties and quantum theory to improve a structure on ML of the properties of materials to generate a trained ML model)
converting the observable property matrices into complex Hermitian operators for each observable property of the material; (Results, p 368-376, Fig. 1a, the material properties are constructing into Hamiltonian operators)
and predicting the target properties of the material according to the complex Hermitian operators of each observable property of the material and the complex wave function state vector. (Results, p 368-376, predicting the properties of the material based on the Hermitian operators and wave function related state vector, the material properties are constructing into Hamiltonian operators)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Li‘s method of deep learning into Hagawa’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Li‘s method of deep learning model using Hermitian operators and wave function related state vector would help to provide more training method into Hagawa’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using Hermitian operators and wave function related state vector to train and predict the model would help to improve accuracy of prediction and training efficiency.
But Hagawa and Li fail to explicitly disclose “by performing the loss function to a threshold value; identifying the material as a new material discovery when a unique combination of the predicted target properties (Ypred) is observed in the material according to the loss function.”
Morgan disclose by performing the loss function to a threshold value; ([0005] [0037]-[0041] [0055]-[0064] performing the loss (error) function to a threshold value)
identifying the material as a new material discovery when a unique combination of the predicted target properties (Ypred) is observed in the material according to the loss function. ([0005] [0037]-[0041] [0055]-[0064] discover the novel material based on the properties of interest of the material with key chemical or physical relationships based on the error function)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Morgan‘s method of discovery new material into Li and Hagawa’s invention as they are related to the same field endeavor of material identification. The motivation to combine these arts, as proposed above, at least because Morgan‘s discovery new material based on the chemical and physical properties according to loss function would help to provide more new material identification method into Li and Hagawa’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more new material identification based on the loss function would help to improve accuracy of the new material discovery.
In regard to claim 4, Hagawa and Li, Morgan disclose The method of claim 1, the rejection is incorporated herein.
Hagawa disclose in which training comprises:
learning parameters of the multitask model for mapping the input formula/structure of the material to the complex wave function state vector; ([0041]-[0044] [0091]-[0107] [0138]-[0139] [0152]-[0156] [0174][0175] [0237] [0242]-[0258] Fig. 1, training the model to calculate the material descriptors of the material (vector) representing the material corresponding to the inputted material formula/structure of the material)
dividing the learned parameters into rows and columns according to a predetermined format; (Fig. 4-6, [0048]-[0068][0081]-[0082] [0158]- [0160] diving each of the coefficients of the formulas expressing the dopants in the list according to the format)
But Hagawa and Morgan fail to explicitly disclose “and normalizing the predetermined format of the learned parameters to form the complex wave function state vector.”
Li disclose and normalizing the predetermined format of the learned parameters to form the complex wave function state vector. (p 369-375, normalization is employed of the parameters to form the wave function related state vector)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Li‘s method of deep learning into Morgan and Hagawa’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Li‘s method of deep learning model using Hermitian operators and wave function related state vector would help to provide more training method into Morgan and Hagawa’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using Hermitian operators and wave function related state vector to train and predict the model would help to improve accuracy of prediction and training efficiency.
In regard to claim 5, Hagawa and Li, Morgan disclose The method of claim 1, the rejection is incorporated herein.
But Hagawa and Morgan fail to explicitly disclose “in which enforcing further comprises applying physical observables and quantum mechanics theory to enforce the mathematical structure on machine learning of multiple properties of materials.”
Li disclose in which training further comprises applying physical observables and quantum mechanics theory to enforce a mathematical structure on machine learning of multiple properties of materials. (page 367-375 using physical properties and quantum theory to improve a structure on ML of the properties of materials)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Li‘s method of deep learning into Morgan and Hagawa’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Li‘s method of deep learning model using quantum theory would help to provide more training method into Morgan and Hagawa’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using quantum theory to train and predict the model would help to improve accuracy of prediction and training efficiency.
In regard to claim 6, Hagawa and Li, Morgan disclose The method of claim 1, the rejection is incorporated herein.
Hagawa disclose in which the observable property matrices comprise square matrices. ([0215]-[0220] root-mean-square error of the property values)
In regard to claim 7, Hagawa and Li, Morgan disclose The method of claim 1, the rejection is incorporated herein.
Hagawa disclose in which the target properties of the material comprise a bandgap, a lattice constant, an elastic property, and/or a formation energy. ([0003]-[0008] [0148] band gap, formation energy, etc.)
In regard to claim 8, Hagawa and Li, Morgan disclose The method of claim 1, the rejection is incorporated herein.
But Hagawa and Takeda fail to explicitly disclose “in which target properties are calculated as expectation values of respective operators of Hermitian matrices given the complex wave function state vector.”
Li disclose in which target properties are calculated as expectation values of respective operators of Hermitian matrices given the complex wave function state vector. (abstract, Results, p 367-376 “H” “Hamiltonian is a specific type of Hermitian operator”, respective Hermitian operators using Hermitian matrices corresponding to wave function related state vector.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Li‘s method of deep learning into Morgan and Hagawa’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Li‘s method of deep learning model using Hermitian operators and wave function related state vector would help to provide more training method into Morgan and Hagawa’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using Hermitian operators and wave function related state vector to train and predict the model would help to improve accuracy of prediction and training efficiency.
In regard to claims 9, 12-16, claims 9, 12-16 are medium claims corresponding to the method claims 1, 4-8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1, 4-8.
In regard to claims 17-20, claims 17-20 are system claims corresponding to the method claims 1, 6-8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1, 6-8.
Claims 2, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Hagawa et al. (Hagawa) US 2021/0133635 and LI et al. (Li) “Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation” Published 6/23/2022 at Nature Computational Science2, 367-377 (2022) https://doi.org/10.1038/s43588-022-00265-6, and Morgan et al. (Morgan) US 2022/0076076 as applied to claim 1, further n view of Almog et al. (Almog) US 2022/0128611
In regard to claim 2, Hagawa and Li, Morgan disclose The method of claim 1, the rejection is incorporated herein.
But Hagawa and Morgan fail to explicitly disclose “in which the predicting target properties comprises: calculating a product of the complex Hermitian operators and the complex wave function state vector;”
Li disclose in which the predicting target properties comprises: calculating a product of the complex Hermitian operators and the complex wave function state vector;(page 375-376 methods, the overlap matrix is obtained by the inner product of the basis and H, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Li‘s method of deep learning into Morgan and Hagawa’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Li‘s method of deep learning model using Hermitian operators and wave function related state vector would help to provide more training method into Morgan and Hagawa’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using Hermitian operators and wave function related state vector to train and predict the model would help to improve accuracy of prediction and training efficiency.
But Hagawa and Li, Morgan fail to explicitly disclose “and predicting the target properties of the material as expectation values of a complex conjugate operation between the calculated product and the complex wave function state vector.”
Almog disclose and predicting the target properties of the material as expectation values of a complex conjugate operation between the calculated product and the complex wave function state vector. ([0008]-[0014] [0152]-[0174] predicting the properties of the object using calculated parameters form the conjugate operation between product and vectors)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Almog‘s method of property estimation into Morgan, Li and Hagawa’s invention as they are related to the same field endeavor of material property identification. The motivation to combine these arts, as proposed above, at least because Almog‘s method of property estimation using conjugate operation would help to provide more property identification method into Li and Morgan, Hagawa’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using conjugate operation to estimate property of the object would help to improve accuracy of the estimation.
In regard to claim 10, claim 10 is a medium claim corresponding to the method claim 2 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 2.
Response to Arguments
Applicant’s arguments with respect to claims 1-2, 4-10, 12-20 filed on 11/7/2025 have been considered but are moot because the arguments do not apply to the current rejection.
With respect to 35 USC § 101, please see the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
PATENT PUB. # PUB. DATE INVENTOR(S) TITLE
US 20230193720 A1 2023-06-22 Amini et al.
Cementing Lab Data Validation Based On Machine Learning
Amini et al. disclose Techniques of the present disclosure relate to validating data for a composition design. A method comprises applying a machine learning model to at least two inputs comprising parameters of a cement composition and experimental conditions such that the machine learning model outputs at least one predicted property of the cement composition; performing a laboratory experiment to determine at least one experimental property of the cement composition; calculating an error between the at least one predicted property and the at least one experimental property; and recording the experimental data in a cement property database if the error is within an error range or repeating the performing the laboratory experiment if the error is outside the error range… see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm.
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, Jennifer Welch can be reached at 571-272-7212. 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.
XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143