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 .
Formal Matters
Applicant's response, filed 25 February 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-8 are currently pending and have been examined.
Claims 1-4 and 7-8 have been amended.
Claims 1-8 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for parent Application No. PT117610, filed on 28 May 2024.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 29 November 2021 claiming benefit to PT117610.
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 1-8 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1-8 are drawn to a method, which is a statutory category of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for orthopedic surgery planning in part performing the steps of receiving a user selection of a medical procedure to which to apply the imported orthopedic medical image; performing a pre-operative diagnosis, the pre-operative diagnosis comprising at least automatic bone segmentation and classification; automatic landmark detection on the digital representation; automatic classification of bone density, and automatic osteophytes detection; generating a bone model and a landmark position from a polygonal mesh extracted from the imported orthopedic medical image, wherein the generation of the bone model and the landmark position is based on information from the pre-operative diagnosis, receiving a user adjustment to the landmark position generated from the imported orthopedic medical image; automatically creating a pre-operative planning proposal; and receiving user approval of the proposed pre-operative planning proposal.
Dependent claim 2 recites, in part, allowing the user to perform at least one of… adjusting the landmark position to refine the landmark position.
Dependent claim 3 recites, in part, wherein the pre-operative planning proposal comprises a proposal for bone alignment based on clinical angles, a proposal for bone resections, a proposal for template dimensioning and placement procedures, and is based on user preferences.
Dependent claim 4 recites, in part, wherein the creation of the pre-operative planning proposal comprises at least one of a landmark and/or template repositioning; measurement of distances and/or angles; intersecting template 3D models and anatomical structure 3D models; anatomical structure 3D models resecting; template 3D models dimensioning or replacement; or zooming, in a 3D environment, allowing the manual adjustment and refinement of the automatic pre-operative planning proposal.
Dependent claim 5 recites, in part, wherein the user preferences comprise a pre-operative planning user adjustment; a user preference setting; a manual review of user preferences for integration within the training workflow; an AI training module, and a user preferences statistical model.
Dependent claim 6 recites, in part, wherein the user preferences comprise a manual review of user preferences to train the AI models based on annotated datasets.
Dependent claim 7 recites, in part, wherein creating the pre-operative planning user adjustment comprises validating of the automatic pre-operative planning proposal.
Dependent claim 8 recites, in part, wherein the orthopedic surgery planning data file comprises at least one of 3D template information comprising implant brand, implant size, anatomical position, amount of bone resection.
These steps of receiving and planning an orthopedic surgical procedure utilizing past outcome to predict a current patient’s plan amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites importing at least one orthopedic medical image of a patient’s anatomy into a software application. The limitations are only recited as a tool which only serves to input data for use by the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to mere data gathering to obtain input) and is therefore not a practical application of the recited judicial exception.
Claim 1 recites a trained artificial intelligence model for osteophytes detection. The specification does not provide any specific details regarding the artificial intelligence model specially tailored for osteophyte detection (see the instant specification on p. 42 ¶ 2 and p. 44 ¶ 3). The use of a trained artificial intelligence model, in this case to detect osteophytes, only recites the trained artificial intelligence model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 1 recites exporting an orthopedic surgery planning data file. Claim 8 recites exporting, downloading, or saving in a document format, printing, sending to a PACS, exporting 3D bone models, exporting 3D template information. The limitations are only recited as a tool which only serves as output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant post-solution activity that amounts to post-solution output on a well-known device) and is therefore not a practical application of the recited judicial exception.
Claim 2 recite allowing a user to perform at least one of visualizing the generated bone model, rotating the generated bone model, zooming in on the generated bone model, interacting with the generated bone model. The specification does not provide any software requirements for performing any of the listed operations (see the instant specification on p. 28-29 and p. 34). The use of visualizing, rotating, zooming, interacting by a user, in this case to interact with the software program, only recites interactions as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). Furthermore, the software implementation that allows for the visualizing, rotating, zooming, interacting by a user on the 3d image and surgical plan would amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, and does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it" (MPEP 2106.05(f)(I) see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015)).
Claim 8 recites integrating with external devices or software for the purposes of surgical execution. The specification teaches generally on the integration of the plan with external surgical systems but provides no details regarding the execution or use of said data files by the external devices (see the instant specification on p. 9 and p. 35-36). The use of the integrating step therefore amounts to a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself (MPEP § 2106.05(h) similar to example vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) wherein the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 1 recites a trained artificial intelligence model for osteophytes detection. Claim 2 recite allowing a user to perform at least one of visualizing the generated bone model, rotating the generated bone model, zooming in on the generated bone model, interacting with the generated bone model. Claim 8 recites integrating with external devices or software for the purposes of surgical execution.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claim 1 recites importing at least one orthopedic medical image of a patient’s anatomy into a software application. Claim 1 recites exporting an orthopedic surgery planning data file. Claim 1 recites exporting an orthopedic surgery planning data file. Claim 8 recites exporting, downloading, or saving in a document format, printing, sending to a PACS, exporting 3D bone models, exporting 3D template information. The courts have decided that receiving or transmitting data over a network as well-understood, routine, conventional activity when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II) other types of activities example i. receiving or transmitting data over a network, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Furthermore, the courts have decided that storing and retrieving information in memory as well-understood, routine, conventional activity as a computer function when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (MPEP § 2106.05(d)(II)).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-8 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
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 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Poltaretskyi (US Patent Application No. 2019/0380792)[hereinafter Poltaretskyi] in view of Bregman-Amitai (US Patent Application No. 2019/0336097)[hereinafter Bregman-Amitai] in further view of Harris (US Patent Application No. 20230005232)[hereinafter Harris].
As per claim 1, Poltaretskyi teaches on the following limitations of the claim:
importing at least one orthopedic medical image of a patient into a software application is taught in the Detailed Description in ¶ 0194 and in the Figures at fig. 4 reference character 406 (teaching on acquiring an image of the patient from a imaging device)
receiving a user selection of a medical procedure to which to apply the imported orthopedic medical image is taught in the Detailed Description in ¶ 0195, ¶ 0203, ¶ 0223-224, ¶ 0821, in the Figures at fig. 4 reference characters 408, 410, and 412, and fig. 8 (teaching on automatic processing and virtual planning of a specific preliminary orthopedic surgery plan for the patient and corresponding diagnosis from the acquired image for display on a mixed reality 3D display wherein the surgical plan is developed utilizing a plurality of machine learning/AI models)
performing a pre-operative diagnosis, the pre-operative diagnosis comprising at least is taught in the Detailed Description in ¶ 0223-224, ¶ 0279-280, ¶ 0345, and ¶ 0232 (teaching on a per-op patient diagnosis including a 3D virtual bone model of the surgical site and corresponding anatomical landmark positioning )
automatic bone segmentation and classification is taught in the Detailed Description in ¶ 0821, ¶ 0831-833, and ¶ 0865-866 (teaching on the preliminary orthopedic surgery plan including an AI generated bone segmentation and classification algorithms)
automatic landmark detection is taught in the Detailed Description in ¶ 0345, ¶ 348, and ¶ 0915 (teaching on a automatic landmark tracking algorithm)
...bone density, and is taught in the Detailed Description in ¶ 0798 and ¶ 0890 (teaching on an input to the AI generated preliminary orthopedic surgery plan including a bone density estimation score)
automatic osteophytes detection is taught in the Detailed Description in ¶ 0256 (teaching on an osteophytes assessment for automatically identifying osteophytes)
generating a bone model and a landmark position from a polygonal mesh extracted from the imported orthopedic medical image, wherein the generation of the bone model and the landmark position is based on information from the pre-operative diagnosis is taught in the Detailed Description in ¶ 0223-224, ¶ 0279-280, ¶ 0345, and ¶ 0232 (teaching on the preliminary orthopedic surgery plan based on a patient diagnosis including a 3D virtual bone model of the surgical site and corresponding anatomical landmark positioning)
receiving a user adjustment to the landmark position generated from the imported orthopedic medical image is taught in the Detailed Description in ¶ 0209, ¶ 0280, and ¶ 0915 (teaching on the user adjusting the landmark (Examiner notes that the prior art also referred to landmarks as virtual markers) to an alternate position on the patient anatomy)
automatically creating a pre-operative planning proposal is taught in the Detailed Description in ¶ 0184, ¶ 0209, ¶ 0240, and ¶ 0915 (teaching on updating the surgical plan according the adjustments)
receiving user approval of the proposed pre-operative planning proposal; and is taught in the Detailed Description in ¶ 0240 (teaching on the surgeon confirming the final preoperative surgical plan)
exporting an orthopedic surgery planning data file is taught in the Detailed Description in ¶ 0200 and in the Figures at fig. 4 reference character 418 (teaching on sending the surgical procedure plan to a surgical operation device for surgical guidance)
Poltaretskyi fails to teach the following limitation of claim 1. Bregman-Amitai, however, does teach the following:
automatic classification of bone density, and is taught in the Summary in ¶ 0007-8, ¶ 0015-17, and in the Detailed Description in ¶ 0102 (teaching on a machine learning model for automatic classification of a bone grade to represent a bone density estimate from a patient image)
One of ordinary skill in the art before the effective filing date would combine the orthopedic preoperative surgical planning system of Poltaretskyi with the automatic classification of bone density from a patient image of Bregman-Amitai with the motivation of providing “[b]etter care may be provided by prevention and/or early intervention and/or treatment when risk of fracture is detected earlier” (Bregman-Amitai in the Detailed Description in ¶ 0344).
The combination of Poltaretskyi and Bregman-Amitai fails to teach the following limitation of claim 1. Harris, however, does teach the following:
wherein the automatic osteophyte detection is performed using a trained artificial intelligence model for osteophytes detection is taught in the Detailed Description in ¶ 0046 and ¶ 0121 (teaching on a machine learning model for osteophytes detection)
One of ordinary skill in the art before the effective filing date would combine the orthopedic preoperative surgical planning system with the automatic classification of bone density of Poltaretskyi and Bregman-Amitai with the machine learning model for osteophyte detection of Harris with the motivation of “aid[ing] surgeons and technicians in planning and executing orthopedic surgeries” (Harris in the § 1. Technical Field).
As per claim 2, the combination of Poltaretskyi, Bregman-Amitai, and Harris discloses all of the limitations of claim 1. Poltaretskyi also discloses the following:
computer-implemented method according to claim 1, further comprising allowing a user to perform at least one of visualizing the generated bone model, rotating the generated bone model, zooming in on the generated bone model, interacting with the generated bone model, and adjusting the landmark position to refine the landmark position is taught in the Detailed Description in ¶ 0236, ¶ 0209, ¶ 0280, and ¶ 0915 (teaching on presenting the AI generated preliminary orthopedic surgery plan in a mixed reality display where the user/surgeon may zoon, rotate, reorient, and adjust the planning and surgical parameters preoperatively including an surgical landmark)
As per claim 3, the combination of Poltaretskyi, Bregman-Amitai, and Harris discloses all of the limitations of claim 1. Poltaretskyi also discloses the following:
computer-implemented method according to claim 1, wherein the pre-operative planning proposal comprises a proposal for bone alignment based on clinical angles is taught in the Detailed Description in ¶ 0245-246 (teaching on the preliminary orthopedic surgery plan including a 3D virtual bone model and 3D virtual implant model wherein the points of intersect (treated as synonymous to bone alignment) are analyzed and modeled based on clinical angles)
a proposal for bone resections is taught in the Detailed Description in ¶ 0359 and ¶ 0390-391 (teaching on the preliminary orthopedic surgery plan including a bone resection technique guide)
a proposal for template dimensioning and placement procedures, and is taught in the Detailed Description in ¶ 0245-246 (teaching on the preliminary orthopedic surgery plan including a 3D virtual bone model and 3D virtual implant model wherein the points of intersect (treated as synonymous to bone alignment) are analyzed and modeled based on clinical angles for implant placement optimization (treated as synonymous to dimensioning and placement procedures))
is based on user preferences is taught in the Detailed Description in ¶ 0230 (teaching on the preliminary orthopedic surgery plan including a user preference widget)
As per claim 4, the combination of Poltaretskyi, Bregman-Amitai, and Harris discloses all of the limitations of claim 1. Poltaretskyi also discloses the following:
computer implemented method according to claim 1, wherein the creation of the pre-operative planning proposal comprises at least one of a landmark and/or template repositioning; measurement of distances and/or angles; intersecting template 3D models and anatomical structure 3D models; anatomical structure 3D models resecting; template 3D models dimensioning or replacement; or zooming, in a 3D environment, allowing the manual adjustment and refinement of the automatic pre-operative planning proposal is taught in the Detailed Description in ¶ 0236, ¶ 0209, ¶ 0280, and ¶ 0915 (teaching on presenting the AI generated preliminary orthopedic surgery plan in a mixed reality display where the user/surgeon may zoon, rotate, reorient, and adjust the planning and surgical parameters preoperatively including an surgical landmark)
As per claim 5, the combination of Poltaretskyi, Bregman-Amitai, and Harris discloses all of the limitations of claim 3. Poltaretskyi also discloses the following:
computer-implemented method according to claim 3, wherein the user preferences comprise a pre-operative planning user adjustment, a user preference setting is taught in the Detailed Description in ¶ 0230 and ¶ 0845-846 (teaching on the preliminary orthopedic surgery plan including a user preference widget and training of the AI generated models according to user preferences)
a manual review of user preferences for integration within the training workflow is taught in the Detailed Description in ¶ 0230 and ¶ 0844-846 (teaching on the preliminary orthopedic surgery plan including a user preference widget and training of the AI generated models according to user preferences including preferences of a plurality of users (a manual review of user preferences) wherein said confidence levels are used to tune (i.e. train) the AI neural network models)
an AI training module, and is taught in the Detailed Description in ¶ 0195, ¶ 0203, ¶ 0223-224, ¶ 0821, in the Figures at fig. 4 reference characters 408, 410, and 412, and fig. 8 (teaching on automatic processing and virtual planning of a specific preliminary orthopedic surgery plan for the patient developed utilizing a plurality of machine learning/AI models)
a user preferences statistical model is taught in the Detailed Description in ¶ 0230 and ¶ 0845-846 (teaching on the preliminary orthopedic surgery plan including a user preference widget and training of the AI generated models according to user preferences including confidence interval tolerances (treated as synonymous to statistical models))
As per claim 6, the combination of Poltaretskyi, Bregman-Amitai, and Harris discloses all of the limitations of claim 3. Poltaretskyi also discloses the following:
computer-implemented method according to claim 3, wherein the user preferences comprise a manual review of user preferences to train the AI models based on annotated datasets is taught in the Detailed Description in ¶ 0230, ¶ 0844-846, and ¶ 0891 (teaching on the preliminary orthopedic surgery plan including a user preference widget and training of the AI generated models according to user preferences including preferences of a plurality of users (a manual review of user preferences) and historical changes/decision/procedures datasets (treated as synonymous to annotated/labeled datasets) wherein said confidence levels and historical training sets are used to tune (i.e. train) the AI neural network models - Examiner notes that ¶ 0617 also teaches on an interoperative machine learning algorithm wherein one of ordinary skill in the art would recognize that the outcome from said ML change would affect the historical dataset utilized to train the pre-operative set accordingly)
As per claim 7, the combination of Poltaretskyi, Bregman-Amitai, and Harris discloses all of the limitations of claim 5. Poltaretskyi also discloses the following:
computer-implemented method according to claim 5, wherein creating the pre-operative planning user adjustment comprises validating the automatic pre-operative planning proposal is taught in the Detailed Description in ¶ 0240 (teaching on the surgeon confirming the final preoperative surgical plan)
As per claim 8, the combination of Poltaretskyi, Bregman-Amitai, and Harris discloses all of the limitations of claim 1. Poltaretskyi also discloses the following:
computer-implemented method according to claim 1, wherein exporting the orthopedic surgery planning data file comprises at least one of: exporting, downloading, or saving in a document format, printing, sending to a PACS, exporting 3D bone models, exporting 3D template information comprising implant brand, implant size, anatomical position, amount of bone resection, and integrating with external devices or software for the purposes of surgical execution is taught in the Detailed Description in ¶ 0200 and in the Figures at fig. 4 reference character 418 (teaching on sending the surgical procedure plan to a surgical operation device for surgical guidance (treated as synonymous to integrating with external devices or software for the purposes of surgical execution))
Response to Arguments
Applicant's arguments filed for claims 1-8 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant asserts that as the claim does not recite any relationship or transaction between people, social activities, and human behavior, the claims do not recite a method or organizing human activity. Applicant further asserts that as the claim recites a computer performing the process, the claim cannot be considered analogous to In Re Meyer. Examiner disagrees. The use of electronic means for performing the abstract idea is not enough to overcome Step 2A Prong 1 (2019 Revised Patent Subject Matter Eligibility Guidance, 84 FED. REG. 4 (January 7, 2019) at p. 8 footnote 54 further citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316-18 (Fed. Cir. 2016) where the electronic implementation of human activity was not adequate to overcome Step 2A Prong 1).
Applicant’s arguments filed for claims 1-8 with respect to 35 USC § 103 have been considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Harris, as per the rejection above.
Conclusion
THIS ACTION IS MADE FINAL. 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 JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857