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.
Claim(s) 1-3, 5-14, 16-20, 34, and 38-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over SUNDET (PGPUB: 20190224357 A1) in view of COLLINS (AU 2006254689 B2 COLLINS), in view of Bias-y (JP 2016509709 A).
Regarding claim 9. SUNDET teaches a medical device inspection system comprising:
an inspection scope including a camera, wherein the inspection scope performs an inspection of the medical device to capture inspection data (see Fig. 1, paragraph 29, artificial intelligence may be used, for example, to allow the processor to identify irregularities inside the lumen of medical device 30 in images of the lumen captured by the fiber scope); and
a computing device comprising an inspection analyzer, wherein the inspection analyzer analyzes the inspection data to generate analysis data including possible abnormalities of the medical device (see Fig. 1, paragraph 29, the processor may be able to identify contaminants, gouges, kinks, cracks, moisture and/or the like inside medical device 30. This identification may be enhanced via artificial intelligence, where the processor has been “taught” to detect irregularities by learning images of similar irregularities in other medical devices; see paragraph 41, determining a distance from the opening in the lumen to a defect in the lumen, using a processor of the medical device inspection system. The method may also involve using artificial intelligence in the medical device inspection system to determine that the lumen contains the defect).
However, SUNDET does not expressly teach wherein the computing device presents a user interface, the user interface configured to allow a user to accept, reject, or modify the analysis data prior to storage of the analysis data.
COLLINS teaches that a segmentation module for processing each of said plurality of medical images and delineating a boundary outline enclosing a region within said each medical image corresponding to a suspected lesion and for providing a plurality of alternative outlines of the region for user review and modification, said graphical user interface being configured to display for user selection the boundary outline and the plurality of alternative boundary outlines and being responsive to a user indication for selecting one of the boundary outline and the alternative boundary lines; an image processor for processing each of said plurality of medical images and identifying an initial set of features within said each medical image relevant to diagnosing the abnormalities, a decision engine for computing an initial diagnosis from said plurality of said initial sets of identified features; and an annotation and modification tool for a user to modify the initial set of identified features to obtain a modified set of identified features, wherein the decision engine re-computes a computed diagnosis for user validation from said modified set of identified features (see paragraph 12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination by an annotation and modification tool for a user to modify the initial set of identified features to obtain a modified set of identified features, wherein the decision engine re-computes a computed diagnosis for user validation from said modified set of identified features to obtain COLLINS, in order to provide wherein the computing device presents a user interface, the user interface configured to allow a user to accept, reject, or modify the analysis data. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
However, the data prior to storage of the analysis data.
Bias-y teaches that the user makes a change in the DERS editor based on the accepted update request. In some embodiments, the user can make changes by manually updating the records by following steps similar to steps 718 and 720 shown in FIG. Preferably, the user can accept or decline the update request by clicking on a virtual button on the DERS editor user interface (see page 66, lines 4-8); When the user finishes updating the various aspects of the DERS editor, at step 339, the DERS editor service can save the changes made to the database. This database is the database 113 of FIG. 4 or in some embodiments a user database (see page 42, lines 14-17).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination by Bias-y to obtain when the user finishes updating the various aspects of the DERS editor, at step 339, the DERS editor service can save the changes made to the database. This database is the database 113 of FIG. 4 or in some embodiments a user database, in order to provide data prior to storage of the analysis data. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Regarding claim 10. SUNDET teaches the medical device inspection system of claim 9 further comprising:
a position tracker for determining a relative position of the inspection scope with respect to the medical device, wherein the inspection data includes data from the position tracker (see paragraph 12, positioning a flexible fiber scope around a portion of a first roller of a medical device inspection system, such that a handle of the flexible fiber scope is positioned on one side of the first roller and a feeder of the medical device inspection system is on an opposite side of the roller; see Fig. 6, paragraph 39, a method 250 for inspecting an inside of a medical device may first involve setting up 252 or positioning the fiber scope on or in the medical device inspection system. This step of setting up 252 may include, for example, positioning a flexible fiber scope around a portion of a first roller of a medical device inspection system, such that a handle of the flexible fiber scope is positioned on one side of the first roller and a feeder of the medical device inspection system is on an opposite side of the roller).
Regarding claim 11. SUNDET teaches the medical device inspection system of claim 10, wherein at least some of the data from the position tracker is collected manually (see, paragraph 14, with the medical device inspection system, multiple distances into the lumen of the medical device at which images are captured by the flexible fiber scope).
Regarding claim 12. SUNDET teaches the medical device inspection system of claim 10, wherein the position tracker operates automatically in cooperation with an advancement system (see paragraph 8, the controller may be further in communication with the fiber scope, and the processor may be configured to instruct the fiber scope to capture images at multiple positions within the lumen of the medical device).
Regarding claim 13. SUNDET teaches the medical device inspection system of claim 9, wherein the inspection analyzer further comprises an abnormality detector that automatically identifies possible abnormalities of the medical device (see Fig. 5, paragraph 37, the medical device inspection system may include a computer processor with artificial intelligence capabilities and/or instructions for running an algorithm, either or both of which may allow the system to identify abnormalities within a medical device and even label the abnormalities according to types).
Regarding claim 14. SUNDET teaches the medical device inspection system of claim 13, wherein the abnormality detector automatically detects abnormalities in the medical device by processing the inspection data (see paragraph 38, the system may identify the abnormality in the lumen based on learned shapes of images of medical device lumens stored in the system's processor).
Regarding claim 16. The combination teaches the medical device inspection system of claim 14, wherein the user input overrides an automatic abnormality detection by the abnormality detector (see NESTERENKO, Fig. 2, paragraph 67, when the technician provides input that the cleaning was performed, processor 202 may perform a series of internal checks to confirm, based on the cleaning information received via user input or through its sensors, that all cleaning steps were executed according to a predetermined cleaning procedure).
Regarding claim 17. The combination teaches the medical device inspection system of claim 9, wherein the user manually identifies an abnormality in the medical device that was not detected by the abnormality detector (see SUNDET, paragraph 43, the artificial intelligence may also be used to record an image, a location, a description, a date, a time, a name of a person operating the system, and/or a recommended course of corrective action pertaining to an identified defect in the lumen of the medical device).
Regarding claim 18. SUNDET teaches the medical device inspection system of claim 9, wherein the inspection analyzer comprises one or more machine learning neural networks (see paragraph 8, the processor may use artificial intelligence to determine that the lumen contains the defect).
Regarding claim 19. SUNDET does not expressly teach the medical device inspection system of claim 18, wherein at least one of the one or more machine learning neural networks is trained with training data including positive and negative training examples including images of medical devices with and without abnormalities.
The examiner is taking "Official Notice" that training machine learning neural networks with training data including positive and negative training examples including images of medical devices with and without abnormalities is well known in the art.
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was made to have modified SUNDET so that the limitation of “ wherein at least one of the one or more machine learning neural networks is trained with training data including positive and negative training examples including images of medical devices with and without abnormalities” would be available to one skill in art.
Regarding claim 20. SUNDET teaches the medical device inspection system of claim 9, wherein the computing device presents a user interface (see Fig. 1, paragraph 27, system 10 may include a display and control module 13 (or multiple modules). Module 13 may include a display portion, such as a video monitor with or without touch screen capabilities. Module 13 may also include one or more controls for controlling feeder 20, controlling the fiber scope and the like. The display portion of module 13 may show images taken with the fiber scope, indicator light(s) signifying a contaminated or damaged area in the lumen of medical device 30, information about a contamination or damaged area in medical device 30, identifying information identifying medical device 30 and/or any other suitable information).
Regarding claim 1 and 34. SUNDET teaches a method of inspecting a medical device performed using the system of claim 9, the method comprising:
identifying a medical device (see Fig. 6, paragraph 39, a method 250 for inspecting an inside of a medical device may first involve setting up 252 or positioning the fiber scope on or in the medical device inspection system);
inspecting the medical device with the inspection scope to capture the inspection data (see Fig. 1, paragraph 29, artificial intelligence may be used, for example, to allow the processor to identify irregularities inside the lumen of medical device 30 in images of the lumen captured by the fiber scope);
using the inspection analyzer, analyzing the inspection data using a machine learning model (see paragraph 8, the processor may use artificial intelligence to determine that the lumen contains the defect) included in the inspection of the medical device (see Fig. 6, paragraph 43, using artificial intelligence in the medical device inspection system to determine that the lumen contains the defect. Some examples of the method involve using the artificial intelligence to distinguish differently labeled shapes within the lumen of the medical device, where the differently labeled shapes may include normal, gouged, oval, wet and debris-containing);
generating analysis data based on the analysis of the inspection data (see paragraph 8, the processor is further configured to determine, from an image captured by the fiber scope, that the lumen contains a defect, and instruct the feeder and/or the fiber scope to record a location of the defect in the lumen); and
generating one or more outputs based on the analysis data, the one or more outputs including the possible abnormalities of the medical device (see paragraph 38, The feeder may advance the camera through the medical device lumen in stepwise fashion or continuously until the system identifies an abnormality in the lumen, at which point the system may automatically stop advancing the camera and capture a video or still image of the area with the abnormality. (2) The system may identify the abnormality in the lumen based on learned shapes of images of medical device lumens stored in the system's processor. (3) The system may display the irregularity on the system display with some kind of label, such as a word description and/or an indicator light. (4) The system may provide other information about the irregularity, such as its location in the lumen (a distance from one end of the medical device, for example). (5) The system may automatically emit a UV light to disinfect an identified contamination in the lumen).
Regarding claim 2. SUNDET teaches the method of claim 1, wherein the inspection data includes any one or more of:
(a) image data (see Fig. 6, paragraph 40, capturing at least one image 258 of the lumen of the medical device with the flexible fiber scope);
(b) video data;
(c) inspection metadata;
(d) operational data documenting the operation of the inspection system; or
(e) any combination of (a), (b), (c) and (d).
Regarding claim 3. SUNDET teaches the method of claim 1, wherein the analysis data includes a prediction of whether the medical device may have an abnormality (see paragraph 38, The feeder may advance the camera through the medical device lumen in stepwise fashion or continuously until the system identifies an abnormality in the lumen, at which point the system may automatically stop advancing the camera and capture a video or still image of the area with the abnormality).
Regarding claim 5. SUNDET teaches the method of claim 1, further comprising generating a user interface, wherein the user interface includes any one or more of:
(a) an area of the device that should be inspected;
(b) known hotspots for the type of device;
(c) history of the device;
(d) reference images;
(e) historical test data associated with the device;(f) instructions for using (IFU) the device;
(g) analysis data;
(g) one or more images from within the medical device taken during the inspection (see Fig. 6, paragraph 40, capturing at least one image 258 of the lumen of the medical device with the flexible fiber scope . This image capturing step 258 may be done automatically in some examples, where the processor identifies the abnormality and sends a signal to the camera to capture the image 258); or
(h) any combination of (a), (b), (c), (d), (e), (f), and (g).
Regarding claim 6. SUNDET teaches the method of claim 5, wherein the analysis data includes any one or more of:
(i) a prediction that the medical device may have an abnormality;
(j) a prediction that the medical device may not have an abnormality;
(k) an indication that no abnormalities were detected;
(l) an indication that possible abnormalities were detected (see paragraph 43, using the artificial intelligence to distinguish differently labeled shapes within the lumen of the medical device, where the differently labeled shapes may include normal, gouged, oval, wet and debris-containing); or
(m) any combination of (i)-(m).
Regarding claim 7. SUNDET teaches the method of claim 1, wherein the one or more outputs include one or more suggested actions (see paragraph 8, the processor may use artificial intelligence to distinguish differently labeled shapes within the lumen of the medical device, such as normal, gouged, oval, wet and debris-containing. In various embodiments, the processor may use artificial intelligence to record an image, a location, a description, a date, a time, a name of a person operating the system, and/or a recommended course of corrective action pertaining to an identified defect in the lumen of the medical device).
Regarding claim 8. SUNDET teaches the method of claim 7, wherein the one or more suggested actions include any one or more of:
(i) no further action is required (see Fig. 6, paragraph 40, include capturing at least one image 258 of the lumen of the medical device with the flexible fiber scope . This image capturing step 258 may be done automatically in some examples, where the processor identifies the abnormality and sends a signal to the camera to capture the image 258);
(ii) re-clean;
(iii) ask the device manufacturer;
(iv) send out for repair;
(v) replace device;
(vi) ready for use on patient;
(vii) do not use on patient;
(viii) quarantine until further notice;
(ix) medical device has reached its end of life; or
(x) any combination of (i)-(ix).
Regarding claim 38. SUNDET teaches the method of claim 34, wherein the one or more outputs include at least a picture of the medical device taken during the inspection and at least one representative picture of the medical device or another related medical device from the retrieved medical data, for comparison (see paragraph 38, The feeder may advance the camera through the medical device lumen in stepwise fashion or continuously until the system identifies an abnormality in the lumen, at which point the system may automatically stop advancing the camera and capture a video or still image of the area with the abnormality; the system may identify the abnormality in the lumen based on learned shapes of images of medical device lumens stored in the system's processor).
Regarding claim 39. SUNDET teaches the method of claim 38, wherein the at least one representative picture is at least one historical picture of the medical device taken during a previous inspection (see paragraph 38, the feeder may advance the camera through the medical device lumen in stepwise fashion or continuously until the system identifies an abnormality in the lumen, at which point the system may automatically stop advancing the camera and capture a video or still image of the area with the abnormality. (2) The system may identify the abnormality in the lumen based on learned shapes of images of medical device lumens stored in the system's processor).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over SUNDET (PGPUB: 20190224357 A1), in view of COLLINS (AU 2006254689 B2 COLLINS), in view of Bias-y (JP 2016509709 A), and further in view of Miller (PGPUB: 20210186428 A1).
Regarding claim 21. SUNDET teaches the medical device inspection system of claim 20, wherein the user interface includes a reference image of the medical device (see paragraph 27, the display portion of module 13 may show images taken with the fiber scope, indicator light(s) signifying a contaminated or damaged area in the lumen of medical device 30, information about a contamination or damaged area in medical device 30, identifying information identifying medical device 30 and/or any other suitable information).
SUNDET does not expressly teach the medical device is without abnormalities and an inspection image rendered from the inspection data.
Miller teaches that by constructing a medical device, such as a Foley catheter, with a relatively thin layer of a substantially non-permeable material to a substance of interest, such as nylon, PET or PTFE, disposed on a body material (e.g., silicone rubber), degradation of the substance of interest during transit through the medical device may be mitigated or introduction of contaminants into the fluid in the medical device may be mitigated without substantially affecting the flexibility of the medical device (see paragraph 26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify SUNDET by Miller to obtain that the medical device may be mitigated or introduction of contaminants into the fluid in the medical device may be mitigated without substantially affecting the flexibility of the medical device, in order to provide the medical device is without abnormalities and an inspection image rendered from the inspection data. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Claim(s) 4, 37, and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over SUNDET (PGPUB: 20190224357 A1) in view of COLLINS (AU 2006254689 B2 COLLINS), in view of Bias-y (JP 2016509709 A), and further in view of Hameed (US-PAT-NO: 11577170 B1).
Regarding claim 4. SUNDET does not expressly teach the method of claim 1, wherein the analysis data includes a confidence score associated with a probability that the medical device may have an abnormality.
Hameed teaches that the AI/ML techniques may be trained to recognize visual characteristics of different contaminants, defects, and/or other issues on a variety of medical instruments, may analyze the real-time video feed of the inspection alongside the technician, may detect issues separate from the technician, and may score the performance of the technician by comparing the issues that are identified and/or tagged by the technician using the graphical targeting element against the issues automatically identified by the AI/ML techniques (see Col. 2, Lines 26-34); IGS 100 may use the AI/ML techniques to analyze the video feed images alongside the user, and to automatically detect and/or classify various issues appearing in the video feed images based on a modeled set of visual characteristics for different issues that the AI/ML techniques are trained to recognize. IGS 100 may compare the user's finding and tagging of issues to those detected using the AI/ML techniques, and may generate scores that quantify the accuracy, performance, and effectiveness of the user in correctly detecting the issues based on the comparisons (see Col. 7, lines 53-62).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify SUNDET by Hameed to obtain the AI/ML techniques may be trained to recognize visual characteristics of different contaminants, defects and IGS 100 may compare the user's finding and tagging of issues to those detected using the AI/ML techniques, and may generate scores that quantify the accuracy, performance, and effectiveness of the user in correctly detecting the issues based on the comparisons in order to provide wherein the analysis data includes a confidence score associated with a probability that the medical device may have an abnormality. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Regarding claim 37. SUNDET does not expressly teach the method of claim 34, wherein the one or more outputs include at least some of the retrieved medical device data.
Hameed teaches that controlling (at 628) the distribution of the medical instrument may include entering the medical instrument into available inventory, adding the medical instrument to an instrument set for an upcoming procedure, submitting the medical instrument to be sanitized, submitting the medical instrument for repair, maintenance, or other remediation, and/or removing the medical instrument from service based on the issues that were detected during the inspection and/or the severity of those issues (see Col. 11 and 12, lines 62-67 and 1-3).
Regarding claim 40. SUNDET does not expressly teach the method of claim 34, wherein the retrieved medical device data is inspection assistance information.
Hameed teaches that the medical instrument may be entered in the available inventory or added to the instrument set when no issues are detected or the detected issues are minor or harmless, may submit the medical instrument for sanitizing when harmful biological contaminants are detected, may submit the medical instrument for repair when defects with a severity that impacts safety and/or performance are detected, and may remove the medical instrument from service when the number of issues exceed a certain threshold or when the severity of the issues are beyond remediation (see Col. 12, lines 3-13).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify SUNDET by Hameed to obtain the medical instrument may be entered in the available inventory or added to the instrument set when no issues are detected or the detected issues are minor or harmless, may submit the medical instrument for sanitizing when harmful biological contaminants are detected, may submit the medical instrument for repair when defects with a severity that impacts safety and/or performance are detected, in order to provide wherein the retrieved medical device data is inspection assistance information. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Claim(s) 35, 36, and 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over SUNDET (PGPUB: 20190224357 A1) in view of COLLINS (AU 2006254689 B2 COLLINS), in view of Bias-y (JP 2016509709 A), and further in view of JACOBS (PGPUB: 20210193310 A1).
Regarding claim 35. SUNDET teaches the method of claim 34, wherein analyzing the inspection data comprises the inspection data with the medical device data (see paragraph 8, the processor may use artificial intelligence to distinguish differently labeled shapes within the lumen of the medical device, such as normal, gouged, oval, wet and debris-containing. In various embodiments, the processor may use artificial intelligence to record an image, a location, a description, a date, a time, a name of a person operating the system, and/or a recommended course of corrective action pertaining to an identified defect in the lumen of the medical device).
SUNDET does not expressly teach comparing the inspection data with the medical device data.
JACOBS teaches that the computer system can compare the one or more characteristics of a given medical device to the rule associated with the given medical device. Based on the comparison of the one or more characteristics of the medical device to the rule set corresponding to the medical device, the medical device computer system can identify an anomaly such as a flaw, defect, damage, or contamination associated with the medical device (see paragraph 62).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify SUNDET by JACOBS to obtain the computer system can compare the one or more characteristics of a given medical device to the rule associated with the given medical device. Based on the comparison of the one or more characteristics of the medical device to the rule set corresponding to the medical device, the medical device computer system can identify an anomaly such as a flaw, defect, damage, or contamination associated with the medical device, in order to provide comparing the inspection data with the medical device data. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Regarding claim 36. SUNDET teaches the method of claim 35, further comprising determining that the medical device may have an abnormality based at least in part on the comparison of the inspection data with the medical device data (see SUNDET, paragraph 29, the processor may be used to detect an irregularity in the lumen during advancement, instruct the fiber scope to capture an image of the irregularity, determine a location of the irregularity in the form of a distance of the irregularity from a distal opening of the lumen, and store identifying information about the type and location of the irregularity in the controller. The controller may also store additional information, such as the type of medical device 30 being examined, the date, the time, the identity of the personnel conducting the examination, how many times the particular fiber scope has been used to inspect medical devices) (see JACOBS, paragraph 62, the computer system can compare the one or more characteristics of a given medical device to the rule associated with the given medical device. Based on the comparison of the one or more characteristics of the medical device to the rule set corresponding to the medical device, the medical device computer system can identify an anomaly such as a flaw, defect, damage, or contamination associated with the medical device).
Regarding claim 41. SUNDET teaches the method of claim 35, wherein the inspection assistance information includes any one or more of:
(a) one or more historical images of the medical device (see paragraph 38, the system may identify the abnormality in the lumen based on learned shapes of images of medical device lumens stored in the system's processor);
(b) one or more historical analysis data from previous inspections (see paragraph 38, the system may identify the abnormality in the lumen based on learned shapes of images of medical device lumens stored in the system's processor);
(c) one or more landmarks for the medical device (see paragraph 43, The artificial intelligence may also be used to record an image, a location, a description, a date, a time, a name of a person operating the system, and/or a recommended course of corrective action pertaining to an identified defect in the lumen of the medical device);
(d) at least some instructions for use (IFU) for the medical device (see paragraph 8, the controller may be further in communication with the fiber scope, and the processor may be configured to instruct the fiber scope to capture images at multiple positions within the lumen of the medical device);
(e) one or more reference images (see paragraph 38, The system may identify the abnormality in the lumen based on learned shapes of images of medical device lumens stored in the system's processor); and
(f) combinations of (a)-(e) (see claim 41 above).
Response to Arguments
Applicant’s arguments with respect to claim(s) 9 has been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The argument for Rejection Under 35 U.S.C. & 112(d) is persuasive. Therefore, the rejection is withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIN JIA whose telephone number is (571)270-5536. The examiner can normally be reached 9:00 am-7:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at (571)272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/XIN JIA/Primary Examiner, Art Unit 2663
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