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 .
Status of Claims
This Final Office Action is in response to the Amendment and Remarks filed 08/29/2025, in which claims 1, 7 and 13 are amended and claims 1-18 are currently pending and considered herein.
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 1-18 are rejected under 35 U.S.C. §101 because they recite an abstract idea without significantly more.
Claim 1 recites, wherein the abstract idea is not emboldened:
One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising: receiving, via an electronic interface, Electronic Health Record (EHR) data; encoding the received EHR data as a fixed-length matrix including a plurality of fixed-length vectors, wherein each vector represents a particular visit and an amount of time between each particular visit is variable; providing the matrix to a machine learning model as input to produce a plurality of visit history representations; for one or more particular visit histories, of the plurality of visit history representations, appending code information associated with the particular visit history to create a visit-wise record of code information; providing the one or more appended visit histories to one or more masked linear layers to produce a probability matrix, the probability matrix comprising probabilities for each code for each visit; and producing one or more synthetic EHRs based on repeated sequential generation and sampling from the probability matrix.
Independent claims 7 and 13 recite substantially similar limitations. The claims recite subject matter within a statutory category as a computer-implemented process, method, and system, which broadly recite the steps of receiving a plurality of patient data, analyzing the data with predefined machine learning models and producing specified results based on the analyses.
These steps of “receiving EHR data; [and vectors] wherein each vector represents a particular visit and an amount of time between each particular visit is variable; and to produce a plurality of visit history representations; and for one or more particular visit histories, of the plurality of visit history representations, applying code information associated with the particular visit history to create a visit-wise record of code information; providing the one or more appended visit histories to one or more masked linear layers to produce a probability matrix, the probability matrix comprising probabilities for each code for each visit; and producing one or more synthetic EHRs based on repeated sequential generation and sampling from the probability matrix,” as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, “one or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, causes performance of operations,” an “electronic interface,” and “encoding the received EHR data as a fixed-length matrix including a plurality of fixed-length vectors; providing the matrix to a machine learning model as input,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “one or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, causes performance of operations,” an “electronic interface,” and “encoding the received EHR data as a fixed-length matrix including a plurality of fixed-length vectors; providing the matrix to a machine learning model as input,” in the context of this claim encompasses a mental process of the user comparing measured or observed patient health records to a pre-defined or expected value or event. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The claims as drafted also encompass a system/process that, under its broadest reasonable interpretation, is an abstract idea that covers performance of the limitation as certain methods of organizing human activity. For example, but for the generic computer system language including “one or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, causes performance of operations,” an “electronic interface,” and “encoding the received EHR data as a fixed-length matrix including a plurality of fixed-length vectors; providing the matrix to a machine learning model as input,” in the context of this claim, is an abstract idea that covers performance of the limitation as organizing human activity including following rules or instructions. These recited limitations fall within certain methods of organizing human activity grouping of abstract ideas because the limitations allowing users to access patient data, analyze the data, and determine results based on the analyses. This is a method of managing interactions between people. Under its broadest reasonable interpretation, the limitations are categorized as methods of organizing human activity, specifically associated with managing patients or interactions between people including a patient and physician (e.g. patient/user data compared to other modeled data, analyzing the data and determining pertinent results). Therefore, the limitation falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a). The mere nominal recitation of a generic computer system and machine learning techniques including probability matrixes does not remove the claims from the method of organizing human interactions grouping. In addition, the EHR analysis and machine learning models and matrices amount to mathematic abstraction (See Examples 47-49 of 2024 PEG) and also cannot provide an inventive concept. Thus, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which amount to mere instructions to apply an exception (such as recitation of computer implementation and machine learning models and matrices amounts to invoking computers as a tool to perform the abstract idea. See MPEP 2106.05(f). The devices and machine learning model in these steps are recited at a high-level of generality (i.e., as a generic processor/server/storage/display performing a generic computer function of receiving inputs, analyzing the inputs, and displaying pertinent information) such that it amounts 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. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The limitations appear to monopolize the abstract idea of patient data analysis and general diagnostic techniques between a clinician and her patient. Furthermore, there is no clear improvement to the underlying computer technology in the claim. The claim is thus directed to an abstract idea.
The dependent claims do not remedy the deficiencies of the independent claims with respect to patent eligible subject matter. The dependent claims further limit the abstract idea. Claims 2, 8 and 14 further define HER data formatted as a matrix and limit the abstract idea. Claims 3, 9 and 15 detail and input module and transformer decoders, which are recited at a high level of generality such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the input module and transformer decoders do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Claims 4, 10 and 16 further detail the representations of patient visit history and data and limit the abstract idea. Claims 5, 11 and 17 describe masked layers of a machine learning model and are recited are recited at a high level of generality such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the claimed layers do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Claims 6, 12 and 18 detail the normalization of data and generation of synthetic EHRs, which are recited at a high level of generality such that they amount to no more than mere instructions to apply the judicial exception using a generic computer component and cannot provide an inventive concept. Even in combination, the synthetic EHRs do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Therefore, the 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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2020/0303072 A1 to Drokin et al., hereinafter “Drokin,” in view of U.S. 2022/0036981 A1 to Min et al., hereinafter “Min” and further in view of U.S. 2023/0368879 A1 to Kim et al., hereinafter “Kim.”
Regarding claim 1, Drokin discloses One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors (See Drokin at least at Paras. [0018], [0294]-[0295]; Fig. 8), causes performance of operations comprising: receiving, via an electronic interface, Electronic Health Record (EHR) data (See id. at Abstract; Paras. [0044]-[0045]; [0287]; Figs. 1, 2, 8); encoding the received EHR data as a fixed-length matrix including a plurality of fixed-length vectors (See id. at least at Paras. [0186] (“Weight values of the last hidden layer further will be used by the server as primary vector representations, which are modality mapping into the vector of fixed sized defined by the model.), [0271]-[0291] (“Pretraining of the Weight Matrix […] The scheme of obtaining the medical concept vectors for a patient is shown in FIG. 6 […] [E]vents rearranged in random manner within one appointment (and one time mark) are considered, fixed amount of the latest events is recorded into the event vector.”); Figs. 2, 6, 7, 10); providing the matrix to a machine learning model as input to produce a plurality of visit history representations (See id. at least at Paras. [0041], [0052]-[0057], [0102], [0204]-[0217], [0225], [0234], [0239]-[0246], [0271]-[0291] (Pretraining the Weight Matrix); Figs. 2, 6, 7, 10); for one or more particular visit histories, of the plurality of visit history representations, appending code information associated with the particular visit history to create a visit-wise record of code information (See id. at least at Paras. [0239]-[0246] (patient histories and stored medical data), [0266]-[0287] (“For the purpose of obtaining the contracted representation at its simplest the so-called embedding-matrix is used, by which the sparse vector of the health record is multiplied. Several matrices have been considered […] This weight matrix was learned from the health record comprising codes of diagnoses, symptoms, prescribed procedures and prescribed medicines to extract more information about relations between diagnoses.”); Figs. 2, 3, 6, 7, 9, 10); and providing the one or more appended visit histories to one or more masked linear layers to produce a probability matrix, the probability matrix comprising probabilities for each code for each visit (See id. at least at Paras. [0017], [0186], [0231]-[0235], [0243], [0254], [0271]-[0291]; Claim 1; Figs. 2, 6, 7, 9, 10).
Drokin may not specifically describe but Min teaches producing one or more synthetic EHRs based on repeated sequential generation and sampling from the probability matrix (See Min at least at Abstract (“Synthetic EHR dataset X′ is reconstructed from the latent space Z after being applied with the stochastic process prior.”); Paras. [0006], [0049], [0053], [0066]-[0072], [0079]-[0083]; Claim 1).
Drokin as modified by Min may not specifically describe but Kim teaches wherein each vector represents a particular visit and an amount of time between each particular visit is variable (See Kim at least at Paras. [0033]-[0038] (“The visualization system may identify and assign each event in the history to a relevant input category and display the event at a corresponding time on the timeline for that input category. For example, the first timeline indicates an ER visit event with the visual indicator (element “6” in FIG. 3A) that corresponds to a time the subject made an ER visit. The horizontal axis may correspond to an amount of time (e.g., year of time, five years of time), and the visual indicators may be presented at a corresponding time point on the timeline. Similarly, the remaining timelines indicate a corresponding type of event with visual indicators shaped as circles for the second timeline, a pentagon for the third timeline, an inverse triangle for the fourth timeline, a diamond for the fifth timeline, and a triangle for the sixth timeline.”), [0063]-[0064]; Figs. 2-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Drokin and incorporate the teachings of Min and Kim and provide synthetic EHR generation and vectors representing a visit and time. Min is directed to electronic health record data synthesization. Kim relates to health and medical history visualization using machine learning. Incorporating the EHR synthesization as in Min with the health and medical history visualization of Kim and the system for supporting medical decision making using mathematical models of patients as in Drokin would thereby improve the applicability, efficacy, and accuracy of the claimed platform for synthesizing high-dimensional longitudinal electronic health records using a deep learning language model.
Regarding claims 7 and 13, independent claims 7 and 13 recite substantially the same limitations as included in claim 1. Thus, claims 7 and 13 are rejected under the same grounds of rejection and for the same reasoning applied to claim 1, above.
Claims 2, 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Drokin, in view of Min, in view of Kim and further in view of U.S. 11,887,736 B1 to Norgeot et al., hereinafter “Norgeot.”
Regarding claim 2, Drokin as modified by Min and Kim discloses all the limitations of claim 1. The references may not specifically describe but Norgeot teaches wherein the received EHR data is formatted as a matrix, such that each column of the matrix represents a unique visit and each row of the matrix represents a unique code (See Norgeot at least at Col. 11, ln. 31-63; Col. 17, ln. 30-65).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Drokin, Min and Kim and incorporate the teachings of Norgeot and provide EHR data matrix formatting. Norgoet is directed to evaluating clinical efficacy using health data and AI. Incorporating the data formatting techniques of Norgeot with the EHR synthesization as in Min and the system for supporting medical decision making using mathematical models of patients as in Drokin would thereby improve the applicability, efficacy, and accuracy of the claimed platform for synthesizing high-dimensional longitudinal electronic health records using a deep learning language model.
Regarding claims 8 and 14, claims 8 and 14 recite substantially the same limitations as included in claim 2. Thus, claims 8 and 14 are rejected under the same grounds of rejection and for the same reasoning applied to claim 2, above.
Claims 3, 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Drokin, in view of Min, in view of Kim, in view of U.S. 2018/0211010 A1 to Malhotra et al., hereinafter “Malhotra” and further in view of U.S. 2023/0377748 A1 to Yang et al., hereinafter “Yang.”
Regarding claim 3, Drokin as modified by Min and Kim discloses all the limitations of claim 1 and Drokin further discloses a positional embedding matrix that captures the relative position of each visit in the sequence (See Drokin at least at Paras. [0267]-[0279]; Figs. 2, 7-10).
The references may not specifically describe but Malhotra teaches wherein the machine learning model comprises an input module, wherein the input module comprises: a code embedding matrix that maps each visit code to a dense vector representation (See Malhotra at least at Paras. [0006], [0059]-[0064], [0068], [0074], [0109]-[0111]).
The references may not specifically describe but Yang teaches a plurality of Transformer decoders (See Yang at least at Paras. [0048]-[0050], [0059], [0061]-[0064], [0093], [0096]; Figs. 2, 4); wherein the input module transforms the input EHR data encoded as the fixed-length matrix to a plurality of initial embeddings, which are provided to the plurality of Transformer decoders (See id at least at Paras. [0090]-[0101], [0121]; Figs. 2, 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Drokin, Min and Kim and incorporate the teachings of Malhotra and Yang and provide a machine learning model that maps inputs, a code embedding matrix and transformer decoders. Malhotra is directed to a method of building a machine learning pipeline using electronic health records. Yang relates to automated clinical assessment generation. Incorporating the machine learning pipeline using EHR as in Malhotra with the clinical assessment and data transformation as in Yang, the EHR synthesization as in Min and the system for supporting medical decision making using mathematical models of patients as in Drokin would thereby improve the applicability, efficacy, and accuracy of the claimed platform for synthesizing high-dimensional longitudinal electronic health records using a deep learning language model.
Regarding claims 9 and 15, claims 9 and 15 recite substantially the same limitations as included in claim 3. Thus, claims 9 and 15 are rejected under the same grounds of rejection and for the same reasoning applied to claim 3, above.
Claims 4, 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Drokin, in view of Min, in view of Kim and further in view of Malhotra.
Regarding claim 4, Drokin as modified by Min and Kim discloses all the limitations of claim 1 and Drokin further discloses wherein each particular visit history representation, of the plurality of visit history representations comprises collective information from all visits prior to the particular visit (See Drokin at least at Paras. [0265]-[0269]); wherein the appending comprises appending to one or more particular visit history representations, code information associated with the particular visit such that each of the one or more appended visit history representations comprises: code information associated with the particular visit (See id. at least at Paras. [0265]-[0283]).
The references may not specifically describe but Malhotra teaches collective information from all visits prior to the particular visit (See Malhotra at least at Paras. [0006], [0059]-[0064], [0068], [0074], [0109]-[0111]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Drokin, Min and Kim and incorporate the teachings of Malhotra and provide inputting information from all visits prior to a patient visit. Malhotra is directed to a method of building a machine learning pipeline using electronic health records. Incorporating the machine learning pipeline using EHR as in Malhotra with the EHR synthesization as in Min and the system for supporting medical decision making using mathematical models of patients as in Drokin would thereby improve the applicability, efficacy, and accuracy of the claimed platform for synthesizing high-dimensional longitudinal electronic health records using a deep learning language model.
Regarding claims 10 and 16, claims 10 and 16 recite substantially the same limitations as included in claim 4. Thus, claims 10 and 16 are rejected under the same grounds of rejection and for the same reasoning applied to claim 4, above.
Claims 5, 11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Drokin, in view of Min, in view of Kim, in view of U.S. 2022/0164626 A1 to Bird et al., hereinafter “Bird” and further in view of U.S. 2022/0093265 A1 to Lei et al., hereinafter “Lei.”
Regarding claim 5, Drokin as modified by Min and Kim discloses all the limitations of claim 1. The references may not specifically describe but Bird teaches wherein the one or more masked linear layers comprises a plurality of masked linear layers including: a linear layer maintaining the same dimensionality of visit history embedding size and initial fixed-length vector size; wherein the plurality of masked linear layer preserves the autoregressive property of the probabilities of the probability matrix (See Bird at least at Paras. [0123]-[0133]).
The references may not specifically describe but Lei teaches an upper triangular mask matrix of ones multiplied with the linear layer to create the masked layer (See Lei at least at Paras. [0092]-[0099], [0110]-[0113], [0122], [0127]; Figs. 16-20).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Drokin, Min and Kim and incorporate the teachings of Bird and Lei and provide probability matrices. Bird is directed to a neural encoder transformer model that is trained with machine learning. Lei relates to registering feature vectors including a plurality of elements. Incorporating the transformers as in Bird with the feature vectors and matrices as in Lei, the EHR synthesization as in Min and the system for supporting medical decision making using mathematical models of patients as in Drokin would thereby improve the applicability, efficacy, and accuracy of the claimed platform for synthesizing high-dimensional longitudinal electronic health records using a deep learning language model.
Regarding claims 11 and 17, claims 11 and 17 recite substantially the same limitations as included in claim 5. Thus, claims 11 and 17 are rejected under the same grounds of rejection and for the same reasoning applied to claim 5, above.
Claims 6, 12 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Drokin, in view of Min, in view of Kim and further in view of Yang.
Regarding claim 6, Drokin as modified by Min and Kim discloses all the limitations of claim 1. The references may not specifically describe but Yang teaches normalizing the probability matrix using one or more normalization functions, such that the one or more synthetic EHRs are generated based on the repeated generation of and sampling from the normalized probability matrix (See Yang at least at Paras. [0006], [0059]-[0064], [0068], [0074], [0090]-[0101], [0109]-[0111], [0121]; Figs. 2, 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the disclosure of Drokin, Min and Kim and incorporate the teachings of Yang and provide probability matrices and synthetic EHRs. Yang relates to automated clinical assessment generation. Incorporating the clinical assessment and data transformation as in Yang, the EHR synthesization as in Min and the system for supporting medical decision making using mathematical models of patients as in Drokin would thereby improve the applicability, efficacy, and accuracy of the claimed platform for synthesizing high-dimensional longitudinal electronic health records using a deep learning language model.
Regarding claims 12 and 18, claims 12 and 18 recite substantially the same limitations as included in claim 6. Thus, claims 12 and 18 are rejected under the same grounds of rejection and for the same reasoning applied to claim 6, above.
Response to Arguments
Applicant’s amendments and remarks filed August 29, 2025 have been fully considered, but they are not persuasive. The following explains why:
Applicant’s arguments pertaining to prior art rejections are not persuasive. The claims have been addressed with regard to the 35 U.S.C. §103 rejection discussed above. The arguments pertaining to prior art references of the Applicant’s Remarks at Pages 27-29 are not persuasive. The arguments are moot in light of new reference Kim, discussed above. As such, it is submitted that the cited prior art, including those identified by Applicant, in the same field of endeavor, i.e., health/medical records and using generic machine learning models teaches and/or suggests all of the limitations of the pending claims under a broad and reasonable interpretation thereof.
Applicant’s arguments pertaining to subject matter eligibility are not persuasive. The basis for the previous rejection under 35 U.S.C. §101 is still operative and the claims have been addressed with regard to the updated 35 U.S.C. §101 rejection discussed above, and considered under the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) and Updated PEG. The largely boilerplate arguments at pages 11-26 of Applicant’s Response are not persuasive. The Examiner disagrees there is not an abstract idea; there is a judicial exception of at least organizing human activity and also mental processes, as discussed above. The Examiner disagrees that there is a technological improvement presented in the claims. Conclusory statements asserting otherwise are not persuasive. The examiner disagrees there is a practical application that is integrated in the claims. It appears the computer technology (machine learning, neural networks, etc.) is/are leveraged as mere instructions to apply the judicial exception abstract idea. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. For at least these reasons and those stated above, the claims are not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM T. MONTICELLO whose telephone number is (313)446-4871. The examiner can normally be reached M-Th; 08:30-18:30 EST.
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, MARC Q. JIMENEZ can be reached at (571) 272-4530. 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.
/WILLIAM T. MONTICELLO/Examiner, Art Unit 3681
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681