DETAILED ACTION
This action is in response to the claims filed 03/13/2026 for Application number 17/848,301. Claims 1, 2, 4, 5, 7, 8, 12-14, 16, 17, 19, 20, 24, 25, 27, 30, 31, and 36 have been amended. Thus claims 1-36 are currently pending. A new ground of rejection has been made in this office action that were not necessitated by amendment therefore this action is NON-FINAL.
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 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “strongly correlated” in claims 6 and 18 is a relative term which renders the claim indefinite. The term “strongly correlated” 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. The specification fails to define what is considered to be “strongly correlated”. Since strongly correlated is a subjective definition which differs from person to person, the metes and bounds of the claim is not made clear and one of ordinary skill in the art would not be able to properly avoid infringing upon a claim when no definition of “strongly correlated” has been made.
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 1-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1 Analysis: Claim 1 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part, The limitations of:
“generating a model M that, when applied to an algorithm A, predicts a performance y of the algorithm A on a problem instance p without running the algorithm A, wherein generating the model M… can be considered to be an evaluation in the human mind;
defining a set of training problem feature vectors {right arrow over}vi for a set of training problem instances pn; can be considered to be an evaluation in the human mind
encoding a set of training algorithms Am in a set of training algorithm features vectors {right arrow over}um, wherein the set of training algorithms Am does not include the algorithm A; can be considered to be an evaluation in the human mind
generating a set of training data (ym,n, {right arrow over}um, {right arrow over}vn) by computing a set of performance metrics ym,n, wherein the set of performance metrics ym,n depends on data generated from the set of training algorithms Am solving problem instances pn”; can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “the computer system comprising a classical processor, a non-transitory computer-readable medium, and computer instructions stored in the non-transitory computer-readable medium” and “using supervised learning to train the model M based on the set of training data such that ym,n≈M({right arrow over}um, {right arrow over}vn”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B Analysis: The claims do not include additional elements 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 of utilizing a classical processor, a non-transitory computer readable medium, and supervised learning to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 2, the rejection of claim 1 is further incorporated, and further, the claim recites: defining a problem feature vector {right arrow over}v for a problem instance p;
encoding the algorithm A in an algorithm feature vector {right arrow over}u
computing the performance y of algorithm A according to y = M({right arrow over (u)}, {right arrow over (v)}), without running the algorithm A
This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 3, the rejection of claim 1 is further incorporated, and further, the claim recites: using the model M to predict the performance of an algorithm B, other than the algorithm A. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 4, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the set of training algorithm feature vectors {right arrow over (um)} includes hyper-parameters of the set of training algorithms Am. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 5, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the set of training algorithm feature vectors {right arrow over (um)} includes an indicator of performance of the set of training algorithms Am on representative problem instances. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 6, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein a 1D Fermi-Hubbard model is used as an application benchmark for gauging an ability of the algorithm A to handle strongly correlated fermionic problems. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 7, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the set of training algorithm feature vectors {right arrow over (um)} includes properties of outputs of the set of training algorithms Am. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein a benchmarking testbed supplies information for the set of training algorithm feature vectors {right arrow over (um)}. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 9, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the computer system further comprises a quantum computer, the quantum computer including a quantum component, having a plurality of qubits, which accepts a sequence of instructions to evolve a quantum state based on a series of quantum gates; wherein the algorithm A comprises a quantum algorithm. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 10, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the quantum algorithm A produces a quantum state |ᴪ as its output. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 11, the rejection of claim 1 is further incorporated, and further, the claim recites: wherein the quantum state |ᴪ) overlaps with another quantum state |ᴪ). This limitation amounts to more specifics of the judicial exception identified in the rejection of claim 1.
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 12, the rejection of claim 1 is further incorporated, and further, the claim recites: defining the set of training problem feature vectors {right arrow over}vn comprises using domain knowledge provided by domain specialists about application based on the set of training problem instances pn. This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 1, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding Claims 13-24, they recite features similar to claims 1-12 and are rejected for at least the same reasons therein.
Regarding claim 25,
Step 1 Analysis: Claim 25 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 25 recites, in part, The limitations of:
defining a set of training problem features for the set of training problem instances can be considered to be an evaluation in the human mind
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “the computer system comprising a classical processor, a non-transitory computer-readable medium, and computer instructions stored in the non-transitory computer-readable medium” and “using machine learning to train an empirical hardness model using the set of training problem features, wherein the empirical hardness model is adapted to generate a performance measurement representing performance of an input algorithm without running the input algorithm, wherein the input algorithm differs from the given algorithm, thereby generating the performance estimator model.”. Thus, these elements in the claim are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim further recites: receiving a set of training problem instances for a given algorithm. This limitation is a mere data gathering step and thus is an insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea.
Step 2B Analysis: The claims do not include additional elements 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 of utilizing a classical processor, a non-transitory computer readable medium, and machine learning to train an empirical hardness model to perform the steps of the claimed process amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Furthermore, the limitation of receiving a set of training problem instances for a given algorithm is well-understood, routine, and conventional, as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. These limitations therefore remain insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, these additional elements amount to mere instructions to apply the exception using generic computer components and insignificant extra-solution activity, which cannot provide an inventive concept. The claim is not patent eligible.
Regarding claim 26, the rejection of claim 25 is further incorporated, and further, the claim recites: wherein the performance measurement is for algorithm runtime. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 27, the rejection of claim 25 is further incorporated, and further, the claim recites: using the performance estimator model to estimate a performance of an algorithm other than the given algorithm. This limitation amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 28, the rejection of claim 27 is further incorporated, and further, the claim recites: wherein the algorithm other than the given algorithm comprises a quantum algorithm. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 29, the rejection of claim 25 is further incorporated, and further, the claim recites: wherein the given algorithm comprises a quantum algorithm. This limitation amounts to generally linking the judicial exception to a field of use or technological environment. Please see MPEP 2106.05(h).
The claim does not include any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 30, the rejection of claim 25 is further incorporated, and further, the claim recites: receiving an algorithm and a set of problem instances as input to the empirical hardness model; This limitation is considered to be mere data gathering and thus is an insignificant extra-solution activity.
and generating, by the empirical hardness model, the performance estimator model amounts to mere instructions to apply the judicial exception using a generic computer component.
The claim does not include any additional elements that amount to significantly more than the judicial exception. The limitation of “receiving an algorithm and a set of problem instances as input to the empirical hardness model” is just a nominal or tangential addition to the claim, and is also well-understood, routine and conventional as evidenced by MPEP §2106.05(d)(II)(I), “receiving or transmitting data over a network”. This limitation therefore remains insignificant extra-solution activity even upon reconsideration, and does not amount to significantly more. Even when considered in combination, this additional element represents an insignificant extra-solution activity which cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claims 31-36, they recite features similar to claims 25-30 and are rejected for at least the same reasons therein.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 25-28, 30-34 and 36 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Barbosa et al. ("Using machine learning for quantum annealing accuracy prediction", hereinafter "Barbosa").
Regarding claim 25, Barbosa teaches A method, performed on a computer system, for generating a performance estimator model, the computer system comprising a classical processor, a non- transitory computer-readable medium, and computer instructions stored in the non-transitory computer-readable medium, wherein the computer instructions, when executed by the classical processor, perform the method (“which is used to embed the QUBO problem into the quantum processor.” [pg. 3, top para]), the method comprising:
receiving a set of training problem instances for a given algorithm (“In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems.” [Abstract]);
defining a set of training problem features for the set of training problem instances (“Third, an assortment of graph-related features has to be selected to serve as inputs to the machine learning model. Those features are selected to cover a wide variety of potential metrics impacting solvability, however the list below is neither exhaustive nor rigorously proven.” [pg. 5, top para]); and
using machine learning to train an empirical hardness model using the set of training problem features (“Using those features of the training MC instances and their maximum clique result computed by the D-Wave 2000Q annealer, we train a machine learning classifier implemented in the sklearn.tree.DecisionTreeClassifier() class provided in scikit-learn” [pg. 5, bottom para]), wherein the empirical hardness model is adapted to generate a performance measurement representing performance of an input algorithm without running the input algorithm (“In this article, we aim to identify those types of QUBOs that are solvable on D-Wave 2000Q with given hardware parameters, without actually using the annealer. This could help decide on what resources (annealing time, number of reads) should be allocated depending on the problem hardness.” [pg. 2, ¶2]), wherein the input algorithm differs from the given algorithm, thereby generating the performance estimator model. (“As in the previous section, several regression models are suitable for this task. We chose to predict the clique size returned by D-Wave 2000Q with gradient boosting, a popular machine learning regression model. (performance estimator model)” [pg. 6, 2.2, ¶2])
Regarding claim 26, Barbosa teaches The method of claim 25, wherein the performance measurement is for algorithm runtime. (“This could help decide on what resources (annealing time, number of reads) should be allocated depending on the problem hardness.” [pg. 2, ¶2])
Regarding claim 27, Barbosa teaches The method of claim 25, further comprising using the performance estimator model to estimate a performance of an algorithm other than the given algorithm. (“The decision tree highlights in what order/importance the features contribute to solvability on the D-Wave 2000Q, and by tuning the decision tree for simplicitly, it allows one to (manually) determine with high probability in advance if an instance is likely solvable.” [pg. 4, bottom para – pg. 5, top para])
Regarding claim 28, Barbosa teaches The method of claim 27, wherein the algorithm other than the given algorithm comprises a quantum algorithm. (“In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems” [Abstract])
Regarding claim 30, Barbosa teaches The method of claim 25, further comprising, at the performance estimator model:
receiving an algorithm and a set of problem instances as input to the empirical hardness model (“Using those features of the training MC instances and their maximum clique result computed by the D-Wave 2000Q annealer, we train a machine learning classifier implemented in the sklearn.tree.DecisionTreeClassifier() class provided in scikit-learn” [pg. 5, bottom para]); and
generating, by the empirical hardness model, the performance estimator model (“As in the previous section, several regression models are suitable for this task. We chose to predict the clique size returned by D-Wave 2000Q with gradient boosting, a popular machine learning regression model. (performance estimator model)” [pg. 6, 2.2, ¶2])
Regarding claims 31-34 and 36, they are substantially similar to claims 25-28 and 30 respectively, and are rejected in the same manner, the same art, and reasoning applying.
Allowable Subject Matter
Claims 1-24, 29 and 35 are objected to as being allowable over prior art if all outstanding rejections were withdrawn. None of the prior art, either alone or in combination, fairly discloses limitations of claims 1, 13, 29 and 35 in particular:
Regarding claims 1 and 13:
“generating a model M that, when applied to an algorithm A, predicts a performance y of the algorithm A on a problem instance p without running the algorithm A, wherein generating the model M…
defining a set of training problem feature vectors {right arrow over}vi for a set of training problem instances pn;
encoding a set of training algorithms Am in a set of training algorithm features vectors {right arrow over}um, wherein the set of training algorithms Am does not include the algorithm A;
generating a set of training data (ym,n, {right arrow over}um, {right arrow over}vn) by computing a set of performance metrics ym,n, wherein the set of performance metrics ym,n depends on data generated from the set of training algorithms Am solving problem instances pn”;
using supervised learning to train the model M based on the set of training data such that ym,n≈M({right arrow over}um, {right arrow over}vn
No prior art was uncovered which fairly discloses all of the limitations of claims 1 and 13.
Regarding claims 29 and 35:
… wherein the given algorithm comprises a quantum algorithm
No prior art was uncovered which fairly discloses the particular algorithms and models disclosed in claims 29 and 35 (including all of the limitations in their respective independent claims)
Response to Arguments
Applicant’s arguments, see pgs. 9-21, filed 03/13/2026, with respect to the rejection(s) of claim(s) 1-5, 7-9, 12-17, 19-21, and 24-36 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Barbosa et al. ("Using machine learning for quantum annealing accuracy.
Regarding the 35 U.S.C. §112(d) Rejection:
Applicant’s arguments, see pgs. 9-10, filed 03/13/2026, with respect to Claims 2-3, 14-15 and 27 have been fully considered and are persuasive. The rejection has been withdrawn.
Regarding the 35 U.S.C. §101 Rejection:
A new grounds of rejection has been made in this office action in regards to the previous 101 rejection.
Regarding applicant’s assertion that the previous examiner has entirely ignored the requirement of “using supervised learning to train the model M” has been considered but is not persuasive. The previous examiner did properly examine this limitation under Step 2A Prong Two analysis and considered the additional element to be mere instructions to apply the judicial exception using a generic computer component and not a mental process as argued by applicant. As noted in the updated 101 rejection above, this limitation is still analyzed as an additional element and further the examiner maintains that it amounts to mere instructions to apply the judicial exception using a generic computer component. Please see MPEP 2106.05(f).
Applicant further asserts the claims solve a technical problem via a non-abstract implementation by focusing on the computational architecture for training the Model M. Examiner respectfully disagrees. The claims as currently recited are directed towards a mental process. The steps of generating a model that predicts…, defining a set of training problem feature vectors…, encoding a set of training algorithms, generating a set of training data all amount to steps which can be practically performed in the human mind. The additional element of using supervised learning to train is recited in a generic and broad manner that it amounts to mere instructions to apply the judicial exception using a generic computer component. There are no details in the claims to reflect any improvement in the training of a machine learning model. Therefore, applicant’s arguments are not persuasive.
Applicant further asserts the claims provide a technological improvement to algorithm performance estimation. Examiner respectfully disagrees. The claims as currently recited appear to be directed towards an improvement in an abstract idea. (i.e. improving performance estimation). Applicant further argues the present claims synthesize a predictive model that allows for resource estimation and benchmarking of algorithms however examiner respectfully disagrees. The claims as currently recited do not appear to reflect the “resource estimation and benchmarking of algorithms” rather the claims are merely focused on estimating the performance of an algorithm. Therefore, the examiner asserts that the technological improvement asserted by the applicant is not clearly reflect in the present claims.
Applicant further asserts that the supervised learning limitation is a specific implementation and not a generic instruction. Examiner respectfully disagrees. The claim merely recites using supervised learning to train the model without any further details on the actual training process of the model itself. The claims do not go into specifics how supervised learning is used to train the model thus fail to reflect any improvement over generic training of machine learning models.
Applicant further argues the ordered combination of elements is unconventional. Examiner notes that the updated 101 rejection does not analyze any of limitations to be well-understood, routine, and conventional under Step 2B of the 101 analysis thus Berkheimer analysis is not required. The Step 2B analysis in the updated rejection above, reemphasizes that the additional elements analyzed in Step 2A Prong 2 are mere instructions to apply the judicial exception using a generic computer component. Please see the updated 101 rejection above.
Regarding the Prior art rejection:
Applicant’s arguments regarding the prior art rejection in the last office action has been considered and are persuasive. Therefore, the previous prior art rejection has been withdrawn. A new grounds of rejection has been made in this office action in view of the newly applied prior art of Barbosa et al. Please see the updated prior art rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL H HOANG whose telephone number is (571)272-8491. The examiner can normally be reached Mon-Fri 8:30AM-4:30PM.
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, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122
Prosecution Insights