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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 23-30, 33-42, 45, and 46 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang (US 2021/0005321 -cited by applicant) in view of Farley et al. (US 2020/0243199 -cited by applicant).
Regarding system claim 23 and 34 and corresponding method claims 35 and 46, Hwang teaches an apparatus/a method (“system and method for predicting patient risk outcomes” [0002]), comprising: a processor (processor 810 in Fig. 13); a display (display 840 in Fig. 13) and a non-transitory memory storing instructions (memory 820 in Fig. 13) which, when executed by the processor (“the processor may be any type of logic implemented in hardware, software, or both, for executing programs and instructions stored in the memory 820” ([0080])), cause the processor to: receive pre-operative patient specific data for an arthroplasty surgery to be performed on a joint of a patient; wherein the pre-operative patient specific data comprises: (i) a medical history of the patient, (ii) a measured range of movement for at least one type of joint movement of the joint, and (iii) at least one pain metric associated with the joint: “Referring to Fig. 1, the system includes a predictive modeling engine 100 that computes one or more predictive risk outcomes based on a number of inputs. The inputs may include medical history information 10, test results 20, social factors 30, medical insurance information 40, and healthcare resources 50” ([0022]). Further, Fig. 3 illustrates an example “performing a left knee arthroplasty” ([0037]). Additionally, a screen illustrated in Fig. 6 includes a “functional data section 540” which “may include information indicating current or last-reported pain levels of the patient, KOOS JR. information, and range of motion” ([0053]) related to the left knee arthroplasty.
Further, Hwang teaches inputting the pre-operative patient specific data to at least one first machine learning model to determine a first predicted post-operative joint performance data output: First, Fig. 1 illustrates the various inputs providing patient information to the predictive modeling engine 100, which “may use various algorithms to generate the risk outcomes” that “include machine-learning algorithms” ([0031]). Further, “the algorithms may be used individually or may be considered in combination when computing final risk outcomes,” which suggests the presence of multiple machine learning models. The multiple risk outcomes illustrated in Fig. 1 also correspond to the multiple post-operative joint performance data output. Hwang further teaches wherein the first predicted post-operative joint performance data output comprises at least one first predicted post-operative outcome metric of the joint; display the first predicted post-operative joint performance data output on a display to a user as a displayed first post-operative joint performance data output in Fig. 3 and accompanying paragraph [0038]: “the first section 301 may indicate all of a portion of the risk outcomes generated by the predictive modeling engine 100, with an indication of their risk scores,” wherein “in this example, there is 0.1% chance that patient Brynlee Rees will have to undergo a re-operation revision if the arthroplasty procedure is performed.” In other words, the arthroplasty “re-operation revision” corresponds to the first predicted post-operative joint outcome metric that is displayed on the screen of “a graphical user interface” ([0036]). Further, the data is output at a plurality of first post-operative timepoints after surgery and wherein the at least one first machine learning model is configured to output a plurality of first values for the at least one first predicted post-operative outcome metric of the joint at the plurality of first post-operative timepoints after surgery, each first value is at each particular first timepoint of the plurality of first post-operative timepoints after surgery. First, in the screen generated by graphical user interface of Fig. 3 (modified reproduction below), “area 324 shows seven possible risk outcomes under consideration, namely extended length of stay, readmission, infections, re-operation revision, cardiac complications, renal complications, and respiratory complications… The patient optimization section 320 may also include a menu to allow a user to select other parameters for the risk outcomes. In the example shown, one-year post-operative is selected as the parameters for the risk outcomes displayed in section 324” ([0040]).
PNG
media_image1.png
608
744
media_image1.png
Greyscale
Additionally, “[i]n one embodiment, the predictive modeling engine 100 may calculate the data in the screen of Fig. 4 for a predetermined number of time points, e.g., a first timepoint at 90 days and a second timepoint of 1 year. Data for different and/or additional timepoints (e.g., a 30-day timepoint) may calculated and displayed in other embodiments” ([0046]). These timepoints “post OP” correspond to the plurality of first post-operative timepoints after surgery.
Further, the previous mention of “various algorithms to generate the risk outcomes” and the plurality of risk outcomes in Fig. 1, as well as disclosure that the “predictive modeling engine 100 may use one or more algorithms to compute risk outcomes for each patient under consideration” ([0028]) teach at least one second machine learning model to determine a second predicted post-operative joint performance data output.
Hwang also teaches inputting the preoperative patient specific data…into at least one second machine learning model as conveyed above in by paragraphs [0031] (input data) and paragraph [0028] (one or more algorithms to compute risk outcome) and displaying the second predicted post-operative joint performance data output on the display to the user as a displayed second predicted post-operative joint performance data output such that “the risk outcomes may be output in one or more predetermined formats and/or screens of an interactive display 80 using a display controller 70” ([0031]). In other words, a plurality of risk outcomes are displayed, wherein the “risk outcomes” correspond to the predicted post-operative performance data output.
However, Hwang does not explicitly teach causing the processor to receive at least one medical image of the joint obtained from at least one medical imaging procedure performed on the patient; generate a reconstruction plan of the joint of the patient based at least in part on the at least one medical image of the joint, and at least one arthroplasty surgical parameter obtained from the user in response to the displayed first predicted post-operative joint performance data output; wherein the reconstruction plan comprises the at least one arthroplasty surgical parameter that is selected from: (i) at least one implant, (ii) at least one implant size, (iii) at least one arthroplasty surgical procedure, (iv) at least one position for implanting the at least one implant in the joint, or (v) any combination thereof, input the pre-operative patient specific data, the at least one arthroplasty surgical parameter, or any combination thereof into at least one second machine learning model to determine a second predicted post-operative joint performance data output at a plurality of second post-operative timepoints after surgery; wherein the second predicted post-operative joint performance data output comprises at least one second predicted post-operative outcome metric of the joint; wherein the at least one second machine learning model is configured to output a plurality of second values for the at least one second predicted post-operative outcome metric of the joint at the plurality of second post-operative timepoints after surgery, each second value is at a particular second timepoint of the plurality of second post-operative timepoints after surgery; and update the displayed second predicted post-operative joint performance data output to comprise at least one arthroplasty surgery recommendation, in response to the user varying any of the at least one arthroplasty surgical parameter, before the arthroplasty surgery, during the arthroplasty surgery, or both. Farley is relied upon instead, which discloses analogous methods, systems, and apparatuses to the instant application for determining a care plan for shoulder, hip, and knee arthroplasties.
Specifically, Farley teaches that “data acquired during the pre-operative phase generally includes all information collected or generated prior to the surgery” such as “patient demographics, diagnoses, medical histories, progress notes, vital signs, medical history information, allergies, and lab results” as well as “images related to the anatomical area of interest” ([0078]). This reads on receiving at least one medical image of the joint obtained from at least one medical imaging procedure performed on the patient as well as encompassing pre-operative patient specific data that is analogous to the various input data of Hwang.
Farley also teaches generating a reconstruction plan of the joint of the patient based at least in part on the at least one medical image of the joint, and at least one arthroplasty surgical parameter obtained from the user in response to the displayed first predicted post-operative joint performance data output: First, “the machine learning model is trained to predict one or more values based on the input data” where “it is assumed that the machine learning model is trained to generate predictor equations…optimized to determine the optimal size, position, and orientation of the implants to achieve the best outcome or satisfaction level” ([0127]). Further, “once the procedure is complete, all patient data and available outcome data, including the implant size, position and orientation determined by the CASS 100” (computer-assisted surgical system), “are collected and stored in the historical database” ([0128]). Here, the “predictor equations” correspond to the reconstruction plan. Further, the arthroplasty surgical parameters of the “implants” (e.g., “optimal size, position, and orientation”) are necessarily selected by the user given some other information with regard to the patient. Additionally, the “implant size, position and orientation” ([0128]) teaches wherein the reconstruction plan comprises the at least one arthroplasty surgical parameter that is selected from: (i) at least one implant, (ii) at least one implant size, (iii) at least one arthroplasty surgical procedure, (iv) at least one position for implanting the at least one implant in the joint, or (v) any combination thereof.
It is further noted, that the Hwang teaches “a number of modifiable factors that may allow a user to set or change values corresponding to one or more inputs used to generate the risk outcomes” ([0039]). In other words, the user may make a determination on which arthroplasty surgical parameter(s) to choose after viewing the output results of the first machine learning model and its given inputs of Hwang, and thereby “modify” the input to include the chosen arthroplasty surgical parameter as an input for the second machine learning model of Farley.
Further, “the machine learning model” and “predictor equations” of paragraph [0127] teach to the ‘second’ machine learning model including the at least one arthroplasty surgical parameter. Further, the optimization of the “size, position and orientation of the implants to achieve the best outcome or satisfaction level” is broad enough to read on a second predicted post-operative joint performance data output comprising at least one second predicted post-operative outcome metric of the joint. Additionally, Hwang teaches “example outcomes involving readmission,” which “may include “arthrofibrosis, aseptic loosening, patellofemoral dislocation” (Hwang [0048]). These readmission outcomes correspond to a second predicted post-operative outcome metric of the joint which would necessarily be affect by an characteristics related to an arthroplasty surgical parameter. Since a higher score of readmission would likely result in a lower satisfaction level, one of ordinary skill in the art would appreciate an association between the “best outcome of satisfaction level” of Farley and the readmission outcomes of Hwang.
Finally, Farley teaches updating the displayed second predicted post-operative joint performance data output to comprise at least one arthroplasty surgery recommendation, in response to the user varying any of the at least one arthroplasty surgical parameter, before the arthroplasty surgery, during the arthroplasty surgery, or both in paragraph [0130]: “if the surgeon determines that the size, position and/or orientation of the femoral implant in a TKA needs to be updated or modified intraoperatively, the femoral implant position is locked relative to the anatomy, and the new optimal position of the tibia will be calculated (via global optimization) considering the surgeon's changes to the femoral implant size, position and/or orientation.” Here, calculation of “the new optimal position of the tibia” corresponds to the arthroplasty surgery recommendation.
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 system and method of Hwang with the machine learning model of Farley utilizing image data and implant parameters in order to take into consideration the wider set of parameters of Farley relative to Hwang in order to “achieve the best outcome or satisfaction level” for the patient (Farley, [0127]).
Further, since Hwang discloses determining outcomes at different timepoints “post OP”, in the modification of Hwang the outcomes which include the second post-operative outcomes from the second machine learning model of Farley would necessarily be provided at those different timepoints. Additionally, as there is no limitation that the first post-operative timepoints and second post-operative timepoints are different sets of timepoints, it is not precluded that they are the same timepoints (e.g., 30 days, 90 days, 1 year).
Regarding system claim 24 and associated method claim 36, Hwang further teaches wherein the processor is configured to receive the pre-operative patient specific data by receiving the pre-operative patient specific data over a communication network from at least one electronic medical resource: “The information stored in the databases may be accessed, for example, through one or more networks coupled to a processing system which includes the predictive modeling engine 100” ([0022]).
With regard to system claim 25 and associated method claim 37, Hwang does not teach wherein the at least one medical image comprises at least one of: (a) an X-ray image, (b) a computerized tomography image, (c) a magnetic resonance image, (d) a three-dimensional (3D) image, (e) a 3D medical image generated from multiple X-ray images, (f) a frame of a video, or any combination thereof. Instead, Farley discloses that “the pre-operative data may also include images related to the anatomical area of interest. These images may be captured, for example, using Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, ultrasound, or any other modality known in the art” ([0078]).
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 system of Hwang to collect pre-operative image data from imaging modalities such as MRI, CT and X-ray as widely acquired type of data obtained for patients requiring arthroplasty procedures.
Regarding system claim 26 and associated method claim 38, the modification of Hwang further teaches wherein the at least one first predicted post-operative outcome metric and at least one second predicted post-operative outcome metric are predicted for at least one of: (a) a number of days, (b) a number of months, and (c) a number of years in paragraph [0046]: “In one embodiment, the predictive modeling engine 100 may calculate the data in the screen of Fig. 4 for a predetermined number of time points, e.g., a first timepoint at 90 days and a second timepoint of 1 year. Data for different and/or additional timepoints (e.g., a 30-day timepoint) may calculated and displayed in other embodiments.” As established in claim 1, Hwang teaches multiple risk factors that are displayed as a result of multiple algorithms and multiple inputs (Fig. 1), while Farley teaches the elements of the second predicted post-operative outcome metric as previously convey in paragraph [0127].
Therefore, 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 Hwang with Farley for the same reasons as previously provided for claim 1.
With regard to system claim 27 and associated method claim 39, the modification of Hwang teaches wherein the processor is configured to display the second predicted post-operative joint performance data output with recommendations for the at least one arthroplasty surgical parameter as previously conveyed for claim 1.
With regard to system claim 28 and associated method claim 40, the modification of Hwang teaches wherein the joint is selected from the group consisting of a hip joint, a knee joint, a shoulder joint, an elbow joint, and an ankle joint. First, Hwang teaches an example of Fig. 3 consisting of “a left knee arthroplasty”. Farley further teaches that “the disclosed techniques may be applied to, for example, shoulder, hip, and knee arthroplasties” ([0002]).
With regard to system claim 29 and associated method claim 41, the modification of Hwang teaches wherein the joint is a shoulder joint: Farley is relied on to teach that “the disclosed techniques may be applied to, for example, shoulder, hip, and knee arthroplasties” ([0002]).
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 system of Hwang with a risk outcome prediction of a knee arthroplasty by assessing the shoulder arthroplasty of Farley as an obvious variant of a joint that is subject to similar treatment in the field of arthroscopy.
With regard to system claim 30 and associated method claim 42, Hwang further teaches wherein the pre-operative patient specific data comprises: (a) patient demographics, (b) a patient diagnosis, (c) a patient comorbidity, (d) a patient medical history, (e) a shoulder active range of motion measure, (f) a patient self-reported measure of pain, function, or both, (g) a patient score based on American Shoulder and Elbow Surgeons Shoulder Score (ASES), (h) a patient score based on Constant Shoulder Score (CSS), or any combination thereof: “The inputs may include medical history information 10, test results 20, social factors 30, medical insurance information 40, and healthcare resources 50” ([0022]), wherein “the social factors 30 may include information indicating whether the patient is a smoker or drug user, whether and how much the patient drinks, lifestyle information, marital status, country of origin, ethnicity, race, house hold income, education level, distance from health care system, recent travel information, and other information” ([0025]), which encompass patient demographics. Further, “the medical history information 10 is different for each patient and may indicate, for example, previous diagnoses, procedures, treatments, and healthcare usage” ([0023]), which reads on a patient medical history. Additionally, “the pair and functional data section 540 may include information indicating current or last-reported pain levels of the patient, KOOS JR. information, and range of motion” ([0053]) as previously conveyed in claim 1. Further, “various comorbidity indexes (e.g., Elixhauser, Charlson, Functional, etc.) may be specially designed from diagnosis codes and used to train the predictive modeling engine” ([0034]).
Claims 31 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang and Farley as applied to parent claims 23 and 35, respectively, and further in view of Kohli et al. (US 2016/0350506 -cited by applicant).
Regarding the system of claim 31 and the method of claim 43, the modification of Hwang teaches the system/method of claim 29/41, but does not teach wherein the at least one arthroplasty surgical procedure is selected from the group consisting of an anatomic total shoulder arthroplasty, a reverse total shoulder arthroplasty, deltopectoral technique, and a superior-lateral technique.
Kohli discloses methods and systems for determining the probability of an outcome of a therapeutic treatment of a musculoskeletal joint disorder, which shares a technical field with the instant application. Specifically, Kohli teaches that “‘treatment datapoints’ refers to datapoints associated with the treatment the subject has received to address their musculoskeletal joint disorder” ([0030]), wherein “surgical procedures datapoints may be considered a subset of the treatment datapoints,” with “non-limiting examples” including “Total Shoulder Arthroplasty” ([0031]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Hwang to include predicting the risk outcome of a total shoulder arthroplasty as suggested by Kohli as a common arthroscopic surgical procedure.
Claims 32 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Hwang and Farley as applied to claims 23 and 35, respectively, and further in view Levy et al. (“Factors predicting postoperative range of motion for anatomic total shoulder arthroplasty” -cited by applicant).
Regarding the system of claim 32 and the method of claim 44, the modification of Hwang teaches the system/method of claim 29/41, but does not teach wherein the at least one first predicted post-operative outcome metric and the at least one second predicted post-operative outcome metric is selected from the group consisting of an American Shoulder and Elbow (ASES) score, a University of California, Los Angeles (UCLA) patient reported outcome measures score, a constant score, a global shoulder function score, a Visual Analogue Scale (VAS) Pain score, a smart shoulder arthroplasty score, an internal rotation (IR) score, an abduction measurement, a forward elevation measurement, and an external rotation measurement.
Levy discloses a study for examining factors that influence ultimate postoperative motion after total should arthroplasty (TSA), which shares a technical field with the instant application. Specifically, Levy teaches “a retrospective query of prospective collected data of all patients treated with TSA” (1st paragraph of Materials and methods, pg. 56), where “data analyzed from the repository included measured preoperative and most recent postoperative motion, perceived preoperative and most recent postoperative motion, age at the time of surgery, body mass index (BMI), individual comorbidities (smoking, diabetes, osteoporosis, hypercholesterolemia, inflammatory arthritis, and thyroid disease), and total number of comorbidities. The focus of the data analysis was on the correlations of each variable with measured postoperative motion in each direction” (4th paragraph of Materials and methods, pg. 56). Further, “to determine the relationship between the variables analyzed in this study and postoperative ROM, linear regression analyses, Pearson correlations, Spearman correlations, and point-biserial correlations were used where appropriate” (5th paragraph of Materials and methods, pg. 56), where range of motion (ROM) measurements include “forward flexion, abduction, and external rotation” as well as “internal rotation…based on the highest midline segment of the back that can be reached” (3rd paragraph of Materials and methods, pg. 56). In other words, Levy attempts to identify pre-operative patient specific data that display a correlation with post-operative joint performance, in order for one of ordinary skill in the art to predict post-operative joint performance data from pre-operative patient specific data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the multiple “risk outcomes” of Hwang with the various ROMs (e.g., abduction, forward flexion, external and internal rotation) as suggested by Levy as the first and second post-operative joint performance data output, since “gaining a better understanding of which factors truly influence postoperative motion after TSA is vital in defining realistic patient expectations and ultimately producing satisfactory patient outcomes” (Introduction, pg. 56).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 23-46 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-22 of U.S. Patent No. 11,490,966. Although the claims at issue are not identical, they are not patentably distinct from each other because ‘966 features an apparatus with software instructions and corresponding method to: receive pre-operative patient specific data for an arthroplasty surgery, receive a medical image of the joint, receive a surgical parameter, generate a reconstruction plan of the joint, input the pre-operative patient specific data and reconstruction plan in a machine learning model, display the plan and predicted performance data, and update the data output to a user. Furthermore, ‘966 features receiving data over a communication network, determining and outputting recommendations for the surgical parameter, and inputting the patient specific data into another machine learning model prior to generating the reconstruction plan. While the claim of ‘966 feature additional limitations including that the output of the model comprises a predicted range of movement and a predicted pain metric, it would have been obvious to the skilled artisan to conclude that the instant claims are an obvious variant.
Conclusion
This is a CON of applicant's earlier Application No. 17/233,152. All claims are identical to, patentably indistinct from, or have unity of invention with the invention claimed in the earlier application (that is, restriction (including lack of unity) would not be proper) and could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the earlier application. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action in this case. See MPEP § 706.07(b). 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 MICHAEL T ROZANSKI whose telephone number is (571)272-1648. The examiner can normally be reached Mon - Fri 8:00-4:00.
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, Christopher Koharski can be reached at 571-272-7230. 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.
/MICHAEL T ROZANSKI/Primary Examiner, Art Unit 3797