DETAILED ACTION
Claims 1-20 are pending in this application. Applicant’s claim for foreign priority is acknowledged and claims 1-20 have been examined under the priority date of 09/29/2022. Claims 1 and 10 have been amended in this application.
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.
Response to Arguments
35 U.S.C. 112(f)
The applicant’s arguments (see Remarks filed 1/20/2026) regarding the claim interpretation under 35 U.S.C. 112(f) have been fully considered by the examiner and are persuasive in view of the amendments made to the claims. The examiner has withdrawn the interpretations under 35 U.S.C. 112(f) accordingly.
35 U.S.C. 102
The applicant’s arguments (see Remarks filed 1/20/2026) regarding the claim rejections made under 35 U.S.C. 102 have been fully considered by the examiner and are not persuasive. The broadest reasonable interpretation of the newly added claim limitation of claims 1 and 10 “the positioning score indicating a quality of positioning for the X-ray image” is that the generated “positioning score” can indicate any metric related to an angle, direction, or view of the X-ray image, or any position of a region of interest on the X-ray image. Lyman teaches that the system uses an inference step which comprises multiple heatmap generations to detect a location of an abnormality, as well as selecting similar scans based on localization of the abnormality on the scan (see Lyman, [0104]-[0105], Figure 7A and [0132]-[0135]). Further, in Lyman [0260]-[0263] the system localized the abnormality locations in the inference steps, and patches in the image can be determined as subregions by this step, further, similar scans are identified based on the generated probability score where the regions must positionally match those in the image. This means there is an assessment of region positioning for determining the matching of regions which is analogous to a “quality of positioning” as understood by one of ordinary skill in the art. Therefore, for at least the above reasons, the examiner is maintaining the rejections made under Lyman. The applicant is encouraged to amend the claims to narrow the scope of “quality of positioning” to further distinguish the claimed positioning score over the prior art of record.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, and 9-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lyman (US 20220156934A1).
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 (Lyman, [0295] following the saliency map being pooled using a generalized mean function (weighted heatmap/saliency map) a LSE function (third function) is used to generate a score which is corresponding to a probability of abnormality, where [0104]-[0105] the inference function which is used to generate the abnormality score localize the positioning of the abnormality, figure 7A shows this method of localizing the positioning of the abnormality detected, [0132]-[0134 the inference data is used to set the region/position of interest for the abnormality which is analogous to a positioning score);
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).
Regarding claim 2 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 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);
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);
the positioning score indicating a quality of positioning for the X-ray image Lyman, [0295] following the saliency map being pooled using a generalized mean function (weighted heatmap/saliency map) a LSE function (third function) is used to generate a score which is corresponding to a probability of abnormality, where [0104]-[0105] the inference function which is used to generate the abnormality score localize the positioning of the abnormality, figure 7A shows this method of localizing the positioning of the abnormality detected, [0132]-[0134 the inference data is used to set the region/position of interest for the abnormality which is analogous to a positioning score);
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).
Regarding claim 11 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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 Lyman discloses; 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).
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.
2. Claims 8 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lyman (US 20220156934 A1) in view of Sun (US 20210321968 A1).
Regarding claim 8 Lyman fails to teach; The method of claim 1, wherein the positioning score describes a rotation of the examining region.
However, in the same field of endeavor, Sun teaches; wherein the positioning score describes a rotation of the examining region (Sun, [0035] the system extracts feature of the position and orientation of the bones and tissue in the images, this is based on the calibration and features, these are then classified based on the ROI position feature data where the classification based on the position data is the score).
The combination of Sun and Lyman 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, Sun however teaches this deficiency. The motivation for the combination is that specifically 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. (Sun, [0035] and [0009]-[0020])
Regarding claim 20 the combination of Lyman and Sun teaches; The method of claim 19, wherein the positioning score describes a rotation of the examining region (Sun, [0035] the system extracts features of the position and orientation of the bones and tissue in the images, this is based on the calibration and features, these are then classified based on the ROI position feature data where the classification based on the position data is the score.).
The combination of Sun and Lyman 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, Sun however teaches this deficiency. The motivation for the combination is that specifically 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. (Sun, [0035] and [0009]-[0020])
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.
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.
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/J.M.E./Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666