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 .
Acknowledgements
This communication is in response to Application No. 19/311,383 filed on 8/27/2025.
Claims 1-25 are currently pending and have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/27/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-25 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.
Claim 1, 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a device and method for positioning assessment.
The limitations of providing physical patient-data about at least one of the patient or prior exams of the patient;
generating a positioning-advice for the medical examination based on a type of the medical examination and the physical patient-data;
outputting and using the positioning-advice to position the patient for the medical examination and to acquire a number of examination-images of the patient;
providing an examination-image of the patient;
generating patient-specific feedback-data based at least on the number of examination- images and the positioning-advice; and
outputting the patient-specific feedback-data., as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a device and interface (computer), the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the device and interface, this claim encompasses a person providing patient data, generating positioning advice, output advice, providing an image, generating feedback and outputting feedback in the manner described in the identified abstract idea, supra. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer (see MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a device and interface that implements the identified abstract idea. The device and interface are not described by the applicant and is recited at a high-level of generality (i.e., a generic computer device and interface performing a generic computer functions of computing, determining, and selecting) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The claim does 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 device and interface to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Dependent Claims
Claims 2-4, 6-25 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 2 merely describes determine mispositioning of the patient and determine whether it was avoidable or not. Claim 4 merely describes receiving patient data. Claim 6 merely describes patient data. Claim 7 merely describes analyzing patient data. Claim 8 merely describes medical examination. Claim 9 merely describes generating feedback. Claim 10 merely describes determining misposition was avoidable or not. Claim 11 merely describes assigning mispositioning, determining avoidability, and output mispositioning type. Claim 12 merely describes positioning advice. Claim 13 merely describes a system comprises the device. Claim 14 merely describes a non-transitory computer program product carries out the method. Claim 15 merely describes a non-transitory computer readable storage medium storing instructions cause the computer to carry out the method. Claim 16 merely describes the classification unit. Claim 18 merely describes bin avoidability values into value bins to allocate avoidability statements to the number of mispositioning types based on value bins. Claim 19 merely describes the sensor is a camera. Claim 20 merely describes prior examinations. Claim 21 merely describes adding person specific advice to advice. Claim 22 merely describes searching for deficiency and the feedback includes notes with methods to avoid deficiency. Claim 24 merely describes the system is a is a mammography-system or a fluoroscopy-system. Claim 25 merely describes the system to perform the method.
Claim 3, 17, 23 includes the additional element of a machine learning model. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the machine learning model was found to represent mere instructions to implement the abstract idea on a generic computer. This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible. Claim 3 merely describes parsing earlier examinations and determining avoidability values. Claim 17 merely describes segment regions that show irregularities due to mispositioning. Claim 23 merely describes avoidability-value as a percentage of radiographers able to perform the positioning without mispositioning and describes avoidabilty-values.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8-18, 20-22, 25 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SHARMA (US 20210096934)
CLAIM 1
Sharma teaches A device for organ positioning assessment for acquiring images during a medical examination of a patient, the device comprising: (Sharma para 14-15 teaches methods and systems for positioning a patient for examination and acquiring a medical image)
a first data-interface configured to at least one of receive or automatically retrieve physical patient-data; (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position. )
a pre-assessment-unit configured to generate a positioning-advice for the medical examination based on a type of the medical examination and the physical patient-data; (Sharma para 17 teaches generating positioning instruction based on patient position and reference position and relative positioning as well as scanning protocol and patient information. )
a second data-interface configured to output the positioning-advice; (Sharma para 17 teaches generating a positioning instruction)
a third data-interface configured to at least one of receive or automatically retrieve a number of examination-images of the patient; (Sharma para 18 teaches acquiring image based on positioning instructions )
a post-assessment-unit configured to generate patient-specific feedback-data based at least on the number of examination-images and the positioning-advice; and (Sharma para 19 teaches quality assessment of the image received from the acquiring an image based on positioning instructions. Para 22 teaches feedback based on quality. )
a fourth data-interface configured to output the patient-specific feedback-data. (Sharma para 22 teaches generating feedback such as a score, true or false classification, a translational deviation matrix, a rotational deviation matrix, and/or a translational-rotational deviation matrix. )
CLAIM 2
Sharma teaches a classification-unit configured to determine a mispositioning of the patient in the number of examination-images; and determine whether the mispositioning was avoidable or not. (Sharma para 24 teaches changing parameters of a positioning neural network based on feedback and repeatedly changing parameters to update the network based on quality assessments and feedback)
CLAIM 3
Sharma teaches wherein the classification-unit is configured to parse image- data of earlier examinations and determine an avoidability-value for the mispositioning based on an output of a machine-learning model trained on experts' opinions or […]. (Sharma para 24 teaches changing parameters of a positioning neural network based on feedback and repeatedly changing parameters to update the network based on quality assessments and feedback such as unsatisfactory measures when compared to a threshold. Para 26 teaches training the network based on ground truth associated with the medical image. Examiner notes additional limitations interpreted as optional due to claim language “or”)
CLAIM 4
Sharma teaches wherein the first data-interface is configured to receive the physical patient-data via at least one of […], or sensors. (Sharma para 18 teaches scanning machine, scanning probe. Examiner notes additional limitations interpreted as optional due to claim language “at least one of … or” )
CLAIM 5
Sharma teaches A method for organ positioning assessment for acquiring images during a medical examination of a patient, the method comprising: providing physical patient-data about at least one of the patient or prior exams of the patient; (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position. )
generating a positioning-advice for the medical examination based on a type of the medical examination and the physical patient-data; (Sharma para 17 teaches generating positioning instruction based on patient position and reference position and relative positioning as well as scanning protocol and patient information. )
outputting and using the positioning-advice to position the patient for the medical examination and to acquire a number of examination-images of the patient; (Sharma para 17 teaches generating a positioning instruction. para 18 teaches acquiring image based on positioning instructions )
providing an examination-image of the patient; (Sharma para 27 teaches output image to patient. )
generating patient-specific feedback-data based at least on the number of examination- images and the positioning-advice; and (Sharma para 19 teaches quality assessment of the image received from the acquiring an image based on positioning instructions. Para 22 teaches feedback based on quality. )
outputting the patient-specific feedback-data. (Sharma para 22 teaches generating feedback such as a score, true or false classification, a translational deviation matrix, a rotational deviation matrix, and/or a translational-rotational deviation matrix. )
CLAIM 6
Sharma teaches The method according to claim 5, wherein at least one of the physical patient-data is general patient information, or the physical patient-data includes data about prior examinations of the patient. (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position for an image. )
CLAIM 8
Sharma teaches The method according to claim 5, wherein the medical examination is a […] a CT-examination, an MRI-examination, a radiography examination or an ultrasound examination. (Sharma para 18 teaches scanning machine, scanning probe. Para 2 teaches medical CT, MR, X-ray, or ultrasound scanners. Examiner notes additional limitations interpreted as optional due to claim language “at least one of … or” )
CLAIM 9
Sharma teaches The method according to claim 5, wherein the generating of the patient-specific feedback- data comprises: automatically assessing the number of examination-images including with regard to a quality of positioning. (Sharma para 19 teaches quality assessment of the image received from the acquiring an image based on positioning instructions. Para 22 teaches feedback based on quality. )
CLAIM 10
Sharma teaches The method according to claim 5, wherein the number of examination-images is analyzed by looking for a mispositioning, and determining whether the mispositioning was avoidable or not. (Sharma para 19 teaches quality assessment of the image received from the acquiring an image based on positioning instructions. Para 22 teaches feedback based on quality. para 24 teaches changing parameters of a positioning neural network based on feedback and repeatedly changing parameters to update the network based on quality assessments and feedback)
CLAIM 11
Sharma teaches The method according to claim 10, wherein, in response to finding the mispositioning in an examination image, the method further includes: assigning the mispositioning in the examination-image to a mispositioning-type from a list; (Sharma para 21 teaches identifying missing feature using a list of target features)
determining an avoidability for the mispositioning-type based on an avoidability-value or […]; (Sharma para 21 teaches comparing identified features to target features in order to identify missing features and the number of missing features result in a quality score. )
comparing the avoidability with a threshold-value indicating whether the mispositioning- type is avoidable or not; and (Sharma para 21 teaches comparing identified features to target features in order to identify missing features)
in case the comparing shows that the mispositioning-type is avoidable, outputting a dataset allocated to the mispositioning-type, the dataset including detailed information on how to avoid a future occurrence of the mispositioning. (Sharma para 21 teaches comparing identified features to target features in order to identify missing features. Para 22-23 teaches a feedback based on the quality assessment and changing positioning based on the feedback.)
CLAIM 12
Sharma teaches The method according to claim 11, wherein the positioning-advice is configured according to at least one of a physical nature of the patient and the positioning-advice is patient-specific, or based on at least one of information or prior data of an examiner for the medical examination. (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position. )
CLAIM 13
Sharma teaches A medical imaging system comprising the device according to claim 1. (Sharma Para 52-53 teaches a computing system including that implements the method including input/output interfaces, network adapters, hardware and software.)
CLAIM 14
Sharma teaches A non-transitory computer program product comprising a computer program that, when executed by a computer, causes the computer to carry out the method of claim 5. (Sharma Para 52-53 teaches a computing system including that implements the method including input/output interfaces, network adapters, hardware and software. Para 52 teaches For example, the one or more codes are stored in the one or more system memories 6004 (e.g., one or more non-transitory computer-readable media), and are readable by the computing system 6000 (e.g., readable by the one or more processing units 6002).)
CLAIM 15
Sharma teaches A non-transitory computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to carry out the method of claim 5. (Sharma Para 52-53 teaches a computing system including that implements the method including input/output interfaces, network adapters, hardware and software. Para 52 teaches For example, the one or more codes are stored in the one or more system memories 6004 (e.g., one or more non-transitory computer-readable media), and are readable by the computing system 6000 (e.g., readable by the one or more processing units 6002).)
CLAIM 16
Sharma teaches The device of claim 2, wherein the classification-unit is part of the post-assessment-unit. (Sharma para 24 teaches changing parameters of a positioning neural network based on feedback and repeatedly changing parameters to update the network based on quality assessments and feedback)
CLAIM 17
Sharma teaches The device of claim 2, wherein the classification-unit is a machine-learning model trained to segment regions in the number of examination-images that show irregularities due to mispositioning, and allocate the regions to a list of multiple mispositioning-types. (Sharma para 20 teaches a feature identifying module to identify features in an image using a neural network that is trained on the image to identify for example a landmark, visual feature, geometric shape and/or unwanted object. Para 21 teaches comparing identified features to target features to identify missing features. )
CLAIM 18
Sharma teaches The device of claim 3, wherein the classification-unit is configured to bin avoidability-values of the number of mispositioning-types into value-bins to allocate avoidability-statements to the number of mispositioning-types based on the value-bins. (Sharma para 20 teaches a feature identifying module to identify features in an image using a neural network that is trained on the image to identify for example a landmark, visual feature, geometric shape and/or unwanted object. Para 21 teaches comparing identified features to target features to identify missing features. )
CLAIM 20
Sharma teaches The method of claim 6, wherein the prior examinations are similar to at least one of the medical examination or […]. (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position. Para 18 teaches acquiring image according to positioning instruction. Examiner notes additional limitations interpreted as optional due to claim language “or”)
CLAIM 21
Sharma teaches The method of claim 7, further comprising: determining whether a person performing the medical examination has performed a prior similar examination on the patient; generating person specific advice based on the prior similar examination; and adding the person specific advice to the positioning-advice. (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position. Para 18 teaches acquiring image according to positioning instruction. Examiner notes the subsequent image positioning instruction would be person specific advice because it is based on the previous image that was performed on the patient which is the same patient. )
CLAIM 22
Sharma teaches The method of claim 9, wherein the automatically assessing includes searching for at least one of a number or a grade of positioning deficiencies, and the patient-specific feedback-data includes automatically generated notes concerning methods for how to avoid a future positioning deficiency. (Sharma para 19 teaches quality assessment of the image received from the acquiring an image based on positioning instructions. Para 22 teaches feedback based on quality. para 24 teaches changing parameters of a positioning neural network based on feedback and repeatedly changing parameters to update the network based on quality assessments and feedback)
CLAIM 25
Sharma teaches A medical imaging system configured to perform the method according to claim 5. (Sharma Para 52-53 teaches a computing system including that implements the method including input/output interfaces, network adapters, hardware and software. Para 52 teaches For example, the one or more codes are stored in the one or more system memories 6004 (e.g., one or more non-transitory computer-readable media), and are readable by the computing system 6000 (e.g., readable by the one or more processing units 6002).)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 20210096934) in view of Randall (US 20230270389)
CLAIM 7
Sharma teaches The method according to claim 5, wherein the generating of the positioning-advice includes analyzing the physical patient-data, and (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position for an image. Para 18 teaches generating a position instruction based on this data)
the analyzing includes parsing of radiology reports, (Sharma para 15 teaches acquiring a medical image (radiology report) of a patient. Para 2 teaches medical CT, MR, X-ray, ultrasound scanners. Para 17 teaches patient position and a relative position based on a reference position for an image. )
analyzing general patient information and (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position for an image. )
comparing the general patient information to a database of known positioning challenges, (Sharma para 21 teaches comparing features from the patient image to a target feature list and identify missing feature in order to assess quality)
[…] analyzing pixel data of images of prior exams of the patient. (Sharma para 15 teaches acquiring a medical image of a patient. Para 17 teaches patient position and a relative position based on a reference position for an image. )
Sharma does not teach, however Randall does teach analyzing compliance of the patient based on a room camera video recording, and (Randall para 186 teaches video feed of the user for positioning themselves)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the imaging as taught by Sharma with analyzing compliance of the patient based on video recording as taught by Randall because it would be beneficial to ensure the patient is positioned correctly as taught by Randall.
CLAIM 19
Sharma teaches The device of claim 4, wherein the sensors […]. (Sharma para 18 teaches scanning machine, scanning probe.)
Sharma does not teach, however Randall does teach wherein the sensors are cameras. (Randall para 185 teaches a camera. Para 186 teaches video feed of the user for positioning themselves)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the sensors as taught by Sharma with the sensor as a camera in order to obtain position data as taught by Randall because it would be beneficial to ensure the patient is positioned correctly as taught by Randall para 186.
Claims 23 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 20210096934) in view of Wong (US 20200222023)
CLAIM 23
Sharma teaches The method of claim 10, wherein an avoidability-value is calculated […] avoidability-values of a number of mispositioning-types are binned into value bins in order to allocate avoidability-states to the number of mispositioning-types based on the value bins, and (Sharma para 20 teaches a feature identifying module to identify features in an image using a neural network that is trained on the image to identify for example a landmark, visual feature, geometric shape and/or unwanted object. Para 21 teaches comparing identified features to target features to identify missing features. )
an avoidability-value for a mispositioning is based on an output of a machine-learning model trained on experts' opinions or […]. (Sharma para 24 teaches changing parameters of a positioning neural network based on feedback and repeatedly changing parameters to update the network based on quality assessments and feedback such as unsatisfactory measures when compared to a threshold. Para 26 teaches training the network based on ground truth associated with the medical image. Examiner notes additional limitations interpreted as optional due to claim language “or”)
Sharma does not teach, however Wong does teach The method of claim 10, wherein an avoidability-value is calculated as a percentage of radiographers able to perform the positioning without the mispositioning (Wong para 16 teaches comparing positioning scores of a technician scores to a threshold. Para 82 teaches thresholds may be percentages. )
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the avoidably value as taught by Sharma with calculation as a percentage of radiographers able to perform without misposition as taught by Wong because it would be beneficial to increase image quality as taught by Wong para 3.
Claims 24 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 20210096934) in view of HANNEMANN (US 20220030172)
CLAIM 24
Sharma teaches The medical imaging system of claim 13, wherein the medical imaging system is a mammography-system or a fluoroscopy-system.
Sharma does not teach, however HANNEMANN does teach wherein the medical imaging system is a mammography-system or a fluoroscopy-system. (HANNEMANN para 61 teaches mammography apparatuses. Para 3 teaches fluoroscopy apparatuses)
It would have been obvious to one or ordinary skill in the art, before the effective filing date of the claimed invention, to modify the imaging system as taught by Sharma with mammography-system or a fluoroscopy-system as taught by HANNEMANN because it would be beneficial to have correct positioning in all types of imaging apparatuses as taught by HANNEMANN para 4.
Prior Art Made of Record and Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20240206981 Knutson
Para 9 teaches determining a success rate of a current clinician relative to success rates of other clinicians. The method may include displaying an indication of the success rate of the current clinician relative to success rates of other clinicians.
US 20250069735 von Berg
Para 10 teaches compensating for image exam quality issues resulting from deviations in a patient positioning from an optimal positioning for a given image (e.g., X-ray) exam type (e.g., chest, musculoskeletal). In particular, exemplary embodiments describe a system and method for determining a certainty classification and/or a grading for a finding, when the patient positioning deviates from the optimal positioning for the given X-ray exam type. Grading may be a number (e.g., heart size, lung-heart ratio) or a verbal statement (e.g., no, mild, moderate, prominent, strong) indicating the severity of a finding. Certainty classifications may include attributes of a finding such as, for example, suspected, possible, probable, almost certain, and certain.
US20020085668 Blumhofer
Abstract teaches a method for accurately positioning a patient for radiotherapy and/or radiosurgery, comprising the following steps: the patient is pre-positioned as accurately as possible with respect to a linear accelerator; at least two x-ray images of the patient and/or one of the parts of his body in the vicinity of the radiation target point are produced from different respective recording angles on a single image recorder; the x-ray image is spatially localised; at least one reconstructed image, corresponding to each x-ray image and deriving from a three-dimensional patient scan data set, is produced, the reconstructed images containing the desired image contents of the x-ray images when the patient is correctly positioned; and the real x-ray images are superimposed, and the positioning error is determined electronically and/or with computer guidance by way of particular landmarks and/or the intensity gradient or the contours in the two images; and the position of the patient is corrected by way of the determined positioning error
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW KYLE TAPIA whose telephone number is (703)756-1662. The examiner can normally be reached 830 - 530.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid 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.
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/A.K.T./Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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