DETAILED ACTION
Claims 1-7 and 9-19 are pending in this application. Applicant’s claim for foreign priority is acknowledged and claims 1-7 and 9-19 have been examined under the priority date of 09/29/2022. Claims 1 and 10 have been amended in this application and claims 8 and 20 have been canceled.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/28/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/30/2026 has been entered.
Response to Arguments
35 U.S.C. 102
The applicant’s arguments (see Remarks filed 04/30/2026) regarding the claim rejections made under 35 U.S.C. 102 have been fully considered by the examiner and are persuasive. However, given the change in scope to independent claims 1 and 10, a new grounds of rejection is presented over Lyman in view of Rao and is fully discussed below.
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.
1. Claims 1-7, and 9-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lyman (US 20220156934 A1) in view of Rao (US 12020428 B2).
Regarding claim 1 Lyman discloses; A computer-implemented method for providing a positioning score regarding a positioning of an examining region in an X-ray image, comprising (Lyman, [0053] the system takes medical scans and analyzes them for a region of interest):
receiving input data, the input data comprising an X-ray image including the examining region (Lyman, [0059] medical scan image data is taking as an input, which can correspond to an x-ray image data, [0060] the scans may include an anatomical region based upon the area of the body scanned);
applying a first trained function to the input data to detect at least one region of interest in the X-ray image and to generate a heatmap comprising the at least one region of interest (Lyman, [0261]-[0262] the system takes in a set of scans, and [0263] the inference function (which is part of the medical scan analysis system and may be a type of model or neural network per [0046]) uses the scans to generate probability matrices which it then uses to create multiple saliency maps (a type of heat map) for each region of interest);
applying a second trained function to the input data and the heatmap to generate an individual score for each of the at least one region of interest (Lyman [0287] the system uses multiple saliency maps and the input X-ray to generate a preliminary heat map, [0289] this is done using either the inference function or the medical labeling function (at least a first and second trained function) which aggregates the scores to output/ predict a score for the scan/region of interest, further [0294] a saliency map of a class can be used to produce a final score for that map, where each map corresponds to a region of interest) and to generate a score-weighted heatmap based on the at least one region of interest and the individual scores (Lyman, [0295] the saliency maps can be pooled using a generalized mean function, which is a function that uses weighting, and would therefore result in a weighted saliency map);
applying a third trained function to the input data and the score-weighted heatmap to generate a positioning score, the positioning score indicating a quality of positioning for the X-ray image and describing a rotation of the examining region;
and providing the positioning score (Lyman, [0295] following the saliency map being pooled using a generalized mean function (weighted heatmap/saliency map) an LSE function is used to generate a score which is corresponding to a probability of abnormality, the score is provided at the end of the process).
Lyman fails to teach; applying a third trained function to the input data and the score-weighted heatmap to generate a positioning score, the positioning score indicating a quality of positioning for the X-ray image and describing a rotation of the examining region;
In the same field of endeavor Rao teaches; applying a third trained function to the input data and the score-weighted heatmap to generate a positioning score, the positioning score indicating a quality of positioning for the X-ray image and describing a rotation of the examining region (Rao, column 2, line 55 - column 3 line 5, the system uses multiple deep neural networks to generate quality metrics (positioning score) which pertain to the positioning and rotation angles of the patient on the image, column 9 line 310 column 10 line 5 and column 5 lines 27-47, multiple deep learning neural networks may generate mapped positions/feature maps of anatomical landmarks to generate the quality metrics, column 17 lines 40-67, the feature maps are weighted based on the positions, since the feature maps are weighted and used to determined quality metrics pertaining to the positioning and rotation of the image, this would be analogous to generation of a positioning score as claimed);
PNG
media_image1.png
184
340
media_image1.png
Greyscale
PNG
media_image2.png
80
336
media_image2.png
Greyscale
(Rao, columns 2 and 3, emphasis added)
PNG
media_image3.png
274
342
media_image3.png
Greyscale
(Rao, column 5, emphasis added)
The combination of Lyman and Rao would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Lyman teaches a method of generating heatmaps to process position data and generate score from x-ray images. It however does not teach a position score which describes the rotation or orientation of the region of interest, Rao however teaches this deficiency. The motivation for the combination is that scoring, or extracting values describing the orientation or rotation of the image would be advantageous for determining if the body part of interest is imaged correctly and to accurately assess features associated with the region. (Rao, columns 2,3,5 and 10)
Regarding claim 2 the combination of Lyman and Rao teaches; The method of claim 1, wherein the X-ray image is a two-dimensional X-ray image (Lyman, [0253] the input image data may be 2D x-ray data/image).
Regarding claim 3 the combination of Lyman and Rao teaches; The method of claim 1, wherein the first trained function is based on an object detection network (Lyman, [0046] the medical scan image analysis system may use multiple neural networks to carry out tasks/functions, where one function is the inference function (first function), where a neural network is a type of object detection network).
Regarding claim 4 the combination of Lyman and Rao teaches; The method of claim 1, wherein at least one of the second trained function or the third trained function is based on a classifier or a regression model (Lyman, [0295] an LSE function (third function) is used to pool the saliency map to create a score, where an LSE model is a type of regression model/function).
Regarding claim 5 the combination of Lyman and Rao teaches; The method of claim 4, further comprising: modifying an initial convolution layer to focus network attention based on at least one of the heatmap or the score-weighted heatmap (Lyman, [0290] the network contains several modules where are parametrized, where the modules/functions can be a series of convolutions, [0296] the system reparametrizes hyperparameters as a result of the LSE function, which utilizes the saliency maps to be trained, and therefore this step would be based on a heat map at least in part).
Regarding claim 6 the combination of Lyman and Rao teaches; The method of claim 1, further comprising: displaying at least one of the heatmap or the at least one region of interest (Lyman, [0310] the system has a heat map display system to display the head maps and associated data).
Regarding claim 7 the combination of Lyman and Rao teaches; The method of claim 1, wherein at least one of the heatmap or the at least one region of interest is adjustable (Lyman, [0284] the heatmaps can be customized using the heat map post processing function where the user may choose custom visualization criteria for the heat map [0312] the heat map setting are adjustable).
Regarding claim 9 the combination of Lyman and Rao teaches; The method of claim 1, wherein the examining region comprises a knee, a thorax, or a breast (Lyman, [0060] the scan may be a scan of a knee, chest, head or other anatomical region).
Regarding claim 10 the combination of Lyman and Rao teaches; A scoring system, comprising:
a first interface configured to receive input data (Lyman, [0032] the system has multiple interfaces/client devices (first and a second) for sending and receiving data), the input data comprising an X-ray image including the examining region (Lyman, [0059] medical scan image data is taking as an input, which can correspond to an x-ray image data, [0060] the scans may include an anatomical region based upon the area of the body scanned);
at least one computer processor (Lyman, [0032] a processor executes the program instructions) configured to cause the scoring system to, apply a first trained function to the first input data to generate first output data (Lyman, [0261]-[0262] the system takes in a set of scans, and [0263] the inference function (which is part of the medical scan analysis system and may be a type of model or neural network per [0046] and is a first trained function) uses the scans to generate probability matrices which it then uses to create multiple saliency maps (a type of heat map) for each region of interest),
to detect at least one region of interest in the X-ray image and to generate a heatmap comprising the at least one region of interest (Lyman, [0261]-[0262] the system takes in a set of scans, and [0263] the inference function (which is part of the medical scan analysis system and may be a type of model or neural network per [0046]) uses the scans to generate probability matrices which it then uses to create multiple saliency maps (a type of heat map) for each region of interest);
apply a second trained function to the input data and the heatmap to generate an individual score for each of the at least one region of (Lyman [0287] the system uses multiple saliency maps and the input X-ray to generate a preliminary heat map, [0289] this is done using either the inference function or the medical labeling function (at least a first and second trained function) which aggregates the scores to output/ predict a score for the scan/region of interest, further [0294] a saliency map of a class can be used to produce a final score for that map, where each map corresponds to a region of interest) and to generate a score-weighted heatmap based on the at least one region of interest and the individual scores (Lyman, [0295] the saliency maps can be pooled using a generalized mean function, which is a function that uses weighting, and would therefore result in a weighted saliency map);
apply a third trained function to the input data and the score-weighted heatmap and to generate a positioning score, the positioning score indicating a quality of positioning for the X-ray image and describing a rotation of the examining region (Rao, column 2, line 55 - column 3 line 5, the system uses multiple deep neural networks to generate quality metrics (positioning score) which pertain to the positioning and rotation angles of the patient on the image, column 9 line 310 column 10 line 5 and column 5 lines 27-47, multiple deep learning neural networks may generate mapped positions/feature maps of anatomical landmarks to generate the quality metrics, column 17 lines 40-67, the feature maps are weighted based on the positions, since the feature maps are weighted and used to determined quality metrics pertaining to the positioning and rotation of the image, this would be analogous to generation of a positioning score as claimed);
and a second interface (Lyman, [0032] the system has multiple interfaces/client devices (first and a second) for sending and receiving data) configured to provide the positioning score (Lyman, [0295] following the saliency map being pooled using a generalized mean function (weighted heatmap/saliency map) an LSE function is used to generate a score which is corresponding to a probability of abnormality, the score is provided at the end of the process).
The combination of Lyman and Rao would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Lyman teaches a method of generating heatmaps to process position data and generate score from x-ray images. It however does not teach a position score which describes the rotation or orientation of the region of interest, Rao however teaches this deficiency. The motivation for the combination is that scoring, or extracting values describing the orientation or rotation of the image would be advantageous for determining if the body part of interest is imaged correctly and to accurately assess features associated with the region. (Rao, columns 2,3,5 and 10)
Regarding claim 11 the combination of Lyman and Rao teaches; A non-transitory computer program product comprising instructions which, when executed by a scoring system, cause the scoring system to perform the method of claim 1 (Lyman, [0032] a processor executes the program instructions which runs the program of claim 1).
Regarding claim 12 the combination of Lyman and Rao teaches; A non-transitory computer-readable medium comprising instructions which, when executed by a scoring system, cause the scoring system to perform the method of claim 1 (Lyman, [0032] a processor executes the program instructions which runs the program of claim 1).
Regarding claim 13 the combination of Lyman and Rao teaches; An X-ray system comprising the scoring system of claim 10 (Lyman, [0040] the system is part of a system allowing radiologists to analyze medical scans, [0053] system uses x-ray scans, [0057]-[0059] the system collects medical scans and associated data of the scan, which can be from a variety of modalities including x-ray, making it a radiography system or x-ray system).
Regarding claim 14 the combination of Lyman and Rao teaches; The X-ray system of claim 13, wherein the X-ray system is a radiography system or a mammography system (Lyman, [0040] the system is part of a system allowing radiologists to analyze medical scans, [0053] system uses x-ray scans, [0057]-[0059] the system collects medical scans and associated data of the scan, which can be from a variety of modalities including x-ray, making it a radiography system).
Regarding claim 15 the combination of Lyman and Rao teaches; The method of claim 2, wherein the first trained function is based on an object detection network (Lyman, [0046] the medical scan image analysis system may use multiple neural networks to carry out tasks/functions, where one function is the inference function (first function), where a neural network is a type of object detection network).
Regarding claim 16 the combination of Lyman and Rao teaches; The method of claim 15, wherein at least one of the second trained function or the third trained function is based on a classifier or a regression model (Lyman, [0295] an LSE function (third function) is used to pool the saliency map to create a score, where an LSE model is a type of regression model/function).
Regarding claim 17 the combination of Lyman and Rao teaches; The method of claim 16, further comprising: modifying an initial convolution layer to focus network attention based on at least one of the heatmap or the score-weighted heatmap (Lyman, [0290] the network contains several modules where are parametrized, where the modules/functions can be a series of convolutions, [0296] the system reparametrizes hyperparameters as a result of the LSE function, which utilizes the saliency maps to be trained, and therefore this step would be based on a heat map at least in part).
Regarding claim 18 the combination of Lyman and Rao teaches; The method of claim 17, further comprising: displaying at least one of the heatmap or the at least one region of interest (Lyman, [0310] the system has a heat map display system to display the head maps and associated data).
Regarding claim 19 the combination of Lyman and Rao teaches; The method of claim 18, wherein at least one of the heatmap or the at least one region of interest is adjustable (Lyman, [0284] the heatmaps can be customized using the heat map post processing function where the user may choose custom visualization criteria for the heat map [0312] the heat map setting are adjustable).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous art as cited by the examiner please see the attached PTO-892 Notice of References cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN M ELLIOTT whose telephone number is (703)756-5463. The examiner can normally be reached M-F 8AM-5PM 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, Emily Terrell can be reached at (571) 270-3717. 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.
/J.M.E./Examiner, Art Unit 2666
/Molly Wilburn/Primary Examiner, Art Unit 2666
Prosecution Insights