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 .
This communication is in response to the amendment received on 06/24/2026. Claims 1-2, 5-6, 8-12, 15-16, 18-22 remain pending in this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 5-6, 8-12, 15-16, 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-2, 5-6, 8-10 and 21 are drawn to a system which is within the four statutory categories (i.e. machine). Claims 11-12, 15-16, 18-20 and 22 are drawn to a method which is within the four statutory categories (i.e. process).
Step 2A, Prong 1:
Claim 1 has been amended to recite:
“generate an image of the patient’s anatomy before a surgery;
generate a surgical plan for performing the surgery, the surgical plan including a digital model of a patient-specific device for use during the surgery and a generated digital model of a planned surgical result including the patient-specific device located on the patient’s anatomy in a planned position;
output the digital model of the patient-specific device to cause a fabrication machine to fabricate a physical patient specific device based on the digital model;
generate an image of the patient's anatomy after surgery using the patient-specific device, the image comprising data representing the achieved surgical result;
digitally compare the digital model of the planned surgical result including the patient-specific device located on the patient’s anatomy in a planned position with the data representing the achieved surgical result using the patient-specific device;
generate a quantification of the surgical result using neural network that is trained to quantify surgical results, the training being based on first images of treatment planning images of past surgeries and second images of corresponding surgical results, the images of treatment planning images of past surgeries and second images of corresponding surgical results annotated for expected and actual results and the digital comparison;
compare the quantification of the surgical result using the patient-specific device to a threshold of deviation of the surgical result from the surgical plan; and
output an indication of a successful surgery using the patient-specific device if the quantification of the surgical result using the patient-specific device is less the threshold.”
The limitations of “generating a surgical plan for performing the surgery…, comparing the digital model of the planned surgical result with the data representing the achieved surgical result…, comparing the quantification of the surgical result to a threshold of deviation of surgical result from the surgical plan…, and output an indication of a successful surgery…if the quantification of the surgical result…is less the threshold” correspond to an abstract idea of certain methods of organizing human activity based on managing personal behavior and interactions between people regarding generating a surgical plan for performing the surgery, and based on comparing the planned surgical result with the achieved surgical result and generating a quantification the surgical result. This is a method of managing interactions between people (such as user following rules and instructions) (MPEP 2106.04(a)(2) II C: The sub-grouping "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions.).
The mere nominal recitation of a generic processor and generic memory devices does not take the claim out of the methods of organizing human interactions grouping. The current specification recites: “…The system 310 comprises a computing system 312 coupled to an imaging system 314...An input device 318 receives input from a computer or an operator (such as a surgeon) and transmits inputted information to the computing system 312 for processing. Such input devices 318 are well known in the art and will not be described in greater detail. The imaging system 314 may include a bone imaging machine for forming three-dimensional image data from a bone structure of a patient. The computing system 312 may include a patient-specific device generator for processing and generating images, and a patient-specific device converter for generating design control data… [0029] …The computing system 312 typically includes at least one processor that communicates with one or more peripheral devices via bus subsystem. These peripheral devices typically include a storage subsystem, including a memory subsystem and file storage subsystem, a set of user interface input and output devices, and an interface to outside networks. This interface may be coupled to corresponding interface devices in other data processing systems via a communication network interface. Data The computing system 312 can include, for example, one or more computers, such as a personal computer, workstation, mainframe, laptop, and the like.” In [0028]-[0030]. Therefore, the processor and the memory recited in the claims are generic computing devices.
The limitations of “compare the quantification of the surgical result using the patient-specific device to a threshold of deviation of the surgical result from the surgical plan; and output an indication of a successful surgery using the patient-specific device if the quantification of the surgical result using the patient-specific device is less the threshold” also correspond to an abstract idea of a mental process, since a medical professional can make a comparison of the quantification of the surgical result in mind (or using pen and paper) and generate (provide) a feedback indicating a successful surgery result. These limitations cover the performances of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processor,” nothing in the claim element precludes the step from practically being performed in the mind.
The limitation of “generate a quantification of the surgical result using neural network that is trained to quantify surgical results, the training being based on first images of treatment planning images of past surgeries and second images of corresponding surgical results, the images of treatment planning images of past surgeries and second images of corresponding surgical results annotated for expected and actual results and the digital comparison” corresponds to performing mathematical calculations, therefore the limitation falls within the “mathematical concept” grouping of abstract ideas.
Accordingly, claim 1 recites an abstract idea.
Claim 11 recites similar limitations and these limitations also rejected for the same reason given for claim 1.
Dependent claims 8, 18 also correspond to an abstract idea of certain methods of organizing human activity, such as, claim 8 recites “compare the quantification of the surgical result to a threshold, and generate feedback indicating a successful surgery based on the quantification of the surgical result.”, claim 18 recites “comparing the quantification of the surgical result to a threshold; and generating feedback indicating a successful surgery based on the quantification of the surgical result”.
Claims 2, 5-6, 8-10, 12, 15-16, 18-22 are ultimately dependent from Claims 1, 11 and include all the limitations of Claims 1 and 11. Therefore, claims 2, 5-6, 8-10, 12, 15-16, 18-22 recite the same abstract idea. Claims 2, 5-6, 8-10, 12, 15-16, 18-22 describe further limitations determining whether the surgical result is less the threshold and indicating a successful surgery. These are all just further describing the abstract idea recited in claims 1 and 11, without adding significantly more.
After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Step 2A, Prong 2:
This judicial exception is not integrated into a practical application. In particular, claims recite the additional elements of “a processor”, “a memory”, “using processor to generate a surgical plan…digitally compare the digital model of the planned surgical result with the data representing the achieved surgical result and generate a quantification, generating a quantification of the surgical result using a neural network that is trained on…, comparing the quantification of the surgical result to a threshold”. These are hardware or software elements, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these elements are merely invoked as a tool to apply instructions of the abstract idea in a particular technological environment.
Claims 1 and 11 recite “generating an image of a patient’s anatomy before the surgery” and “generating an image of the patient’s anatomy after the surgery…” and these features correspond to extra solution activity, i.e. necessary data gathering (see MPEP 2106.05(g)). Also, “generating an image of a patient’s anatomy” is a well-understood, routine and conventional activity used in the medical field (for instance, Casey teaches this feature in col. 4, lines 7-35). Casey also teaches “Optionally, other I/O devices 240 can also be coupled to the processor(s) 210,… can also include input ports for information from directly connected medical equipment such as imaging apparatuses, including MRI machines, X-Ray machines, CT machines, etc. Other I/O devices 240 can further include input ports for receiving data from these types of machine from other sources, such as across a network or from previously captured data, for example, stored in a database.” in col. 13, lines 39-50.
Claims 21 and 22 recite “…wherein digitally comparing the digital model of the planned surgical result using the patient-specific device with the data representing the achieved surgical result using the patient-specific device includes using a trained AI algorithm to perform the digital comparison”. The use of artificial intelligence in this application is described in the current specification as “At block 970 the surgical results are evaluated. Postoperative imaging of the patient may be performed. Postoperative imaging may include a variety of diagnostic images of the patient, such as CT, MRI, and other scans and imaging techniques, such as x-rays, with or without the use of artificial intelligence algorithms, such as neural networks trained based on imaging of past surgeries, such as annotated images of past surgeries and their planning images with annotations for expected and actual results for the parameters and/or scoring….” in [0075]. Therefore, the artificial intelligence algorithm (neural networks) is used as a tool to apply instructions of the abstract idea.
Additionally, the current specification describes the “digitally comparing the planned surgical outcome with the achieved surgical result” as:
“…At block 120, the preoperative planned position and orientation of the implants and/or prosthetics and/or the shape of the patient's anatomy, such as a bone of the patient is compared to the postoperative achieved position and orientation of the implants and/or prosthetics and/or the shape of the patient's anatomy, such as a bone of the patient. The deviation of the achieved result from the planned result may be compared both quantitively and qualitatively. For example, the deviation of the position and orientation of an implant or prosthetic may be determined in one or more degrees of freedom, such as one or more rotational degrees of freedom and one or more translational degrees of freedom….” In [0042], and “…In some embodiments, the achieved result may be qualitatively compared to the preoperative plan, such as through a ranking and grading system based on the quantitative results. For example, the ranking or grading may be based on the achieved result matching the preoperative plan within one or more thresholds. For example, a scale of 1-5 may be used for ranking or grading the procedure….” in [0044].
Therefore, using the generic processor to digitally compare the images (the planned outcome and the result) is merely invoked as a tool to apply the instructions of the abstract idea in a particular technological environment and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (MPEP 2106.05(f) & (h)).
Claims also recite other additional limitations beyond abstract idea, including functions such as receiving image data from/to a memory, generating data/notification, which are insignificant extra-solution activities (see MPEP 2106.05 (g)), which do not provide a practical application for the abstract idea.
The limitation of “output the digital model of the patient-specific device to cause a fabrication machine to fabricate a physical patient specific device based on the digital model” corresponds to insignificant application (see MPEP 2106.05(g)).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform digitally comparing images steps amounts to no more than mere instructions to apply the exception using a generic computer component. The feature of “digitally comparing images” is a well-understood, routine and conventional activity in the industry, as indicated above.
The feature of “digitally comparing images” is a well-understood, routine and conventional activity in the industry, as evidenced by the prior art reference that has been applied (see the art rejection below), and MacDonald reference teaches “…The current patient images and data are sent to a medical device and implant manufacturer having an extensive database of prior Successful Surgical outcomes. Based on highly similar to identical axis and morphology, data image recognition computer systems correlate current patient data with prior patient outcomes within a highly Successful Surgical database…” in [0011].
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-6, 8-12, 15-16 and 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Casey et al. (hereinafter Casey) (US 10,902,944 B1), Wollowick et al. (hereinafter Wollowick) (US 11,642,174 B2), Roh et al. (hereinafter Roh) (US 2019/0029757 A1) and further in view of Plessers et al. (hereinafter Plessers) (JP2022531795A).
Claim 1 has been amended now to recite a surgical planning and evaluation system comprising:
a processor (Casey; col. 3, lines 57-64); and
memory (Casey; col. 3, lines 57-64) comprising instructions that when executed by the processors cause the system to:
generate an image of the patient's anatomy before a surgery (Casey teaches “The client computing device 102 is configured to receive a patient data set 108 associated with a patient to be treated…the patient data set 108 can include… image data (e.g., camera images, Magnetic Resonance Imaging (MRI) images, ultrasound images, Computerized Aided Tomography (CAT) scan images, Positron Emission Tomography (PET) images, X-Ray images),…” in col. 4, lines 7-35);
generate a surgical plan for performing the surgery, the surgical plan including a digital model of a patient-specific device for use during the surgery and a generated digital model of a planned surgical result including the patient-specific device located on the patient’s anatomy in a planned position (Casey teaches “…the treatment planning module 118 is configured to generate the treatment plan based on previous treatment data from reference patients. For example, the treatment planning module 118 can receive a selected subset of reference patient data sets and/or similar patient data sets from the data analysis module 116, and determine or identify treatment data from the selected subset. The treatment data can include, for example, treatment procedure data (e.g., surgical procedure or intervention data) and/or medical device design data (e.g. implant design data) that are associated with favorable or desired treatment outcomes for the corresponding patient. The treatment planning module 118 can analyze the treatment procedure data and/or medical device design data to determine an optimal treatment protocol for the patient to be treated.” in col. 8, lines 12-35, col. 11, lines 13-37);
output the digital model of the patient-specific device to cause a fabrication machine to fabricate a physical patient specific device based on the digital model (Casey teaches “…the medical device design(s) generated by the treatment planning module 118 can be transmitted from the client computing device 102 and/or server 106 to a manufacturing system 124 for manufacturing a corresponding medical device. .…” in col. 11, lines 38-50);
Casey fails to expressly teach the following limitations, however these features are well known in the art, as evidenced by Wollowick.
In particular, Wollowick discloses:
generate an image of the patient's anatomy after surgery using the patient-specific device, the image comprising data representing the achieved surgical result (“…a system and method according to the parent application analyzes images to provide guidance to optimize the restoration of orthopaedic functionality at a surgical site within a patient, including capturing, selecting or receiving: (i) at least a first, reference image along at least a first viewing angle including one of a preoperative image of the surgical site and a contralateral image on an opposite side of the patient from the surgical site; and (ii) at least a second, results image of the site, preferably also along the first viewing angle, after a surgical procedure has been performed at the site.,…” in col. 14, lines 17-27);
digitally compare the digital model of the planned surgical result including the patient-specific device located on the patient’s anatomy in a planned position with the data representing the achieved surgical result using the patient-specific device (“…The system and method according to the parent application further include generating on each of the first and second images at least two points to establish a stationary base on a stable portion of the surgical site and identifying at least one landmark on another portion of the surgical site spaced from the stationary base, and providing at least one of (a) an overlay of the first and second images to enable comparison of at least one of bone and implant alignment within the images, (b) matching of at least one digital template to at least one feature in each of the first and second images, and (b) a numerical analysis of at least one difference between points of interest, such as an analysis of at least one of offset, length differential and orientation of at least one of a bone and an implant within the images.” in col. 14, lines 27-41, col. 18, lines 5-8, fig. 3);
generate a quantification of the surgical result … (“…comparing the relative location of the fixed point on the second digital implant representation with respect to the center of rotation of the second digital implant representation and comparing the relative location of the fixed point on the alternative articulating bone component with respect to a center of rotation of the alternative articulating bone component; and estimating changes in offset and length differential based on a comparison of the relative location of the fixed points with respect to the centers of rotation for each of the second digital implant representation and the alternative articulating bone component. …” in col. 7, line 67 to col. 8, line 11);
output an indication of a successful surgery using the patient-specific device if the quantification of the surgical result using the patient-specific device is less the threshold (“…the inventive Image Overlay technique can analyze how "similar" these images are to give the user feedback as to how accurate the results are, that is, to provide a confidence interval.” in col. 13, lines 55-58 and “…providing at least one of (a) an overlay of the first and second images to enable comparison of at least one of bone and implant alignment within the images, (b) matching of at least one digital template to at least one feature in each of the first and second images, and (b) a numerical analysis of at least one difference between points of interest, such as an analysis of at least one of offset, length differential and 40 orientation of at least one of a bone and an implant within the images.” in col. 14, lines 33-41)
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include the aforementioned limitation as disclosed by Wollowick with the motivation of to accurately and effectively analyze and/or perform calculations on images of anatomical features and/or implants such as prosthetic devices (Wollowick; col. 3, lines 15-18).
Casey and Wollowick fail to expressly teach “generate a quantification of the surgical result using neural network that is trained to quantify surgical results, the training being based on first images of treatment planning images of past surgeries and second images of corresponding surgical results, the images of treatment planning images of past surgeries and second images of corresponding surgical results annotated for expected and actual results and the digital comparison”. However this feature is well known in the art, as evidenced by Roh.
In particular, Roh discloses “The neural network can be trained with training items each comprising a set of images scans (e.g. camera, MRI , CT , X - ray , etc.) and patient information , an implant configuration used in the surgery , and / or a scored surgery outcome resulting from one or more of : surgeon feedback , patient recovery level , recovery time , results after a set number of years , etc…” in [0026], and “The training data input can be paired with results to create training items . The results can be, for example, human annotated medical imaging data (as a comparison for identifications such as boundaries and insertion points identified by a model) , human feedback to model outputs , surgeons ' post - operative suggestion feedback ( e . g . whether the surgeon accepted model provided recommendations completely, or made certain changes, or disregarded), surgeons post - operative operation outcome success score, post - operative images that can be analyzed to determine results, the existence of certain positive or negative patient results, such as cortical breaches or other complications that might have occurred in the procedure, overall level of recovery, or recovery time.” in [0099].
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include the aforementioned limitation as disclosed by Roh with the motivation of to building a virtual model (Roh; [0028]).
Casey, Wollowick and Roh fail to expressly teach “compare the quantification of the surgical result using the patient-specific device to a threshold of deviation of the surgical result from the surgical plan”. This feature is well known in the art, as evidenced by Plessers.
In particular, Plessers discloses “Collection of postoperative data: After treatment, postoperative medical images, virtual 3D model based on such images, postoperative measurement results,… can be collected and captured through the feedback loop….A special form of intraoperative or postoperative feedback loop collects intraoperative and postoperative information about complications and uses it to, for example, preoperative planning based on certain thresholds…” on pages 9-10.
It would have been obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to include the aforementioned limitation as disclosed by Plessers with the motivation of to improve the decision support system (Plessers; page 10, lines 7-9).
Claims 2, 5-6, 8-10, 12, 15-16, 18-22 have not been amended, and Applicant does not appear to argue the separate patentability of these claims. As such, claims 2, 5-6, 8-10, 12, 15-16, 18-22 are rejected for the same reasons given in the previous Office Action and incorporated herein.
The amendment to method claim 11 reflects the same changes made to system claim 1, and is therefore rejected for the same reasons given above for system claim 1 in addition to the reasons given in the prior Office Action.
Response to Arguments
Applicant's arguments filed 06/24/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appear.
Arguments about 35 USC 101 rejection:
Applicant argues that claims do not recite rules or instructions, such as rules for a game or styling hair, or hedging funds. Applicant argues that the pending claims provide steps for carrying out a method and are more analogous to the set of steps for combination particular ingredients.
In response, Examiner submits that claim limitations of “generating a surgical plan for performing the surgery…, comparing the digital model of the planned surgical result with the data representing the achieved surgical result…, comparing the quantification of the surgical result to a threshold of deviation of surgical result from the surgical plan…, and output an indication of a successful surgery…if the quantification of the surgical result…is less the threshold” correspond to an abstract idea of certain methods of organizing human activity based on managing personal behavior and interactions between people regarding generating a surgical plan for performing the surgery, and based on comparing the planned surgical result with the achieved surgical result and generating a quantification the surgical result. This is a method of managing interactions between people (such as user following rules and instructions) (MPEP 2106.04(a)(2) II C: The sub-grouping "managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions.). Claims limitation correspond to user following rules and instructions and using generic computing components to perform generating a surgical plan, comparing the digital models of the planned surgical result to the achieved surgical result, comparing the quantification of the surgical results, and outputting an indication of a successful surgery, that is directed to an abstract idea of "managing personal behavior or relationships or interactions between people", hence “certain methods of organizing human activity”.
Applicant argues that claim limitations do not recite a mental process, since the claim requires a neural network that is trained to quantify surgical results, which is not a human observation, judgement or opinion.
In response, Examiner submits that the limitation of “generating a quantification of the surgical result using a neural network…” is not part of the “mental process rejection. The limitations of “compare the quantification of the surgical result using the patient-specific device to a threshold of deviation of the surgical result from the surgical plan; and output an indication of a successful surgery using the patient-specific device if the quantification of the surgical result using the patient-specific device is less the threshold” also correspond to an abstract idea of a mental process, since a medical professional can make a comparison of the quantification of the surgical result in mind (or using pen and paper) and generate (provide) a feedback indicating a successful surgery result.
Applicant argues that the claim as a whole integrates any abstract idea into a practical application, since the claim recites a neural network that is trained to quantify surgical results, and these limitations provide a particular technical implementation in the field of computer-assisted surgical-result assessment.
In response, Examiner submits that the use of artificial intelligence described in the current specification as “At block 970 the surgical results are evaluated. Postoperative imaging of the patient may be performed. Postoperative imaging may include a variety of diagnostic images of the patient, such as CT, MRI, and other scans and imaging techniques, such as x-rays, with or without the use of artificial intelligence algorithms, such as neural networks trained based on imaging of past surgeries, such as annotated images of past surgeries and their planning images with annotations for expected and actual results for the parameters and/or scoring….” in [0075]. Therefore, the artificial intelligence algorithm (neural networks) is used as a tool to apply instructions of the abstract idea.
Applicant argues that the claim applies a specifically trained neural network to quantify surgical results where the training is based on images of treatment planning images of past surgeries and second images of corresponding surgical results annotated for expected and actual results and the digital comparison, thereby providing a particular machine-learning solution for surgical image analysis rather that merely automating a generic mental process.
In response, Examiner submits that using the generic processor to digitally compare the images (the planned outcome and the result) is merely invoked as a tool to apply the instructions of the abstract idea in a particular technological environment and mere instructions to apply/implement/automate an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular field or technological environment do not provide practical application for an abstract idea (see the rejection above).
Therefore, the arguments are not persuasive and claims are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
Arguments about 35 USC 103 rejection:
Applicant argues that Roh does not teach how to user past surgical result to aid in training a model, and the analysis occurs before surgery rather than after surgery.
In response, Examiner submits that Roh discloses “The neural network can be trained with training items each comprising a set of images scans (e.g. camera, MRI , CT , X - ray , etc.) and patient information , an implant configuration used in the surgery , and / or a scored surgery outcome resulting from one or more of : surgeon feedback , patient recovery level , recovery time , results after a set number of years , etc…” in [0026], and “The training data input can be paired with results to create training items . The results can be, for example, human annotated medical imaging data (as a comparison for identifications such as boundaries and insertion points identified by a model) , human feedback to model outputs , surgeons ' post - operative suggestion feedback ( e . g . whether the surgeon accepted model provided recommendations completely, or made certain changes, or disregarded), surgeons post - operative operation outcome success score, post - operative images that can be analyzed to determine results, the existence of certain positive or negative patient results, such as cortical breaches or other complications that might have occurred in the procedure, overall level of recovery, or recovery time.” in [0099].
Therefore, the arguments are not persuasive.
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 DILEK B COBANOGLU whose telephone number is (571)272-8295. The examiner can normally be reached 8:30-5:00 ET.
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, Obeid Mamon can be reached at (571) 270-1813. 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.
/DILEK B COBANOGLU/ Primary Examiner, Art Unit 3687
Prosecution Insights