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 .
Status of Claims
Claims 1 – 6, 8 – 12, 14 – 18 and 20 remain pending.
Claims 1, 9 and 15 are Amended
Claims 7, 13 and 19 have been canceled.
Response to Arguments
Applicant's arguments filed June 15, 2026 with respect to claims 1 – 6, 8 – 12, 14 – 18 and 20 have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 9 and 15 are rejected under 35 U.S.C 103 as being unpatentable over Teixeira et al. Patent Publication No. US-10849585-B1 (hereinafter Teixeira) in view of Neumann Patent Application Publication No. US-20210004714-A1 (hereinafter Neumann).
Regarding claim 1, Teixeira discloses a computer-implemented method, comprising: receiving obtaining a diagnostic image relating to a condition of a patient, the diagnostic image reflecting one of a normal state or an abnormal state of the condition (Teixeira in [Column – 10, Line 31 – 33] discloses about receiving an image, “Referring again to FIG. 1, an X-ray image is acquired in act 14. The X-ray image may be acquired by an X-ray device coupled to the medical imaging system”. Furthermore, Teixeira in [Column – 1, Line 46 – 50] discloses the diagnostic image reflecting one of a normal state or an abnormal state, “The image processor compares the generated topogram with the acquired X-ray image. The image processor then detects an anomaly of the patient from the comparison between the generated topogram and the acquired X-ray image”); and generating a baseline image via a neural network using the diagnostic image; wherein the neural network is trained to generate a prediction of the diagnostic image reflecting a normal state of the condition (Teixeira in [Column – 1, Line 42 – 50] discloses, “An image processor generates the topogram by a machine-learned network in response to input of the surface data to the machine-learned network. The machine-learned network is trained with surface data from healthy patients. An X-ray image of the patient is acquired. The image processor compares the generated topogram with the acquired X-ray image. The image processor then detects an anomaly of the patient from the comparison between the generated topogram and the acquired X-ray image”. Furthermore, Teixeira in [Column – 4, Line 67; Column 5, Line 1 – 5] discloses, “the machine-learned networks of the proposed architecture is trained on healthy patients only. Thus, since the topogram is predicted from the skin surface, the generated topogram will not present any pathologies and may thus be compared to a real topogram from the patient for anomaly detection”).
Teixeira doesn’t disclose about the following limitation as further recited in the claim.
Neumann discloses the neural network is configured to receive training image data comprising a plurality of conditions, select a condition of the plurality of conditions, remove the selected condition from the training image data, and perform training based on the training imaging data with the selected condition removed (Neuman in [0111] discloses, “generating the at least a diagnostic output as a function of the at least an expert submission may include filtering the training data 120 according to the at least a diagnostic constraint. As a further non-limiting example, generating the at least a diagnostic output as a function of the at least an expert submission may include generating a plurality of machine-learning models, selecting a machine-learning model as a function of the at least a diagnostic constraint” wherein filtering the training data according to the at least a diagnostic constraint equates to remove the selected condition. Furthermore, Neuman in [0091] discloses about neural network).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Neumann into the system of Teixeira because it would allow the system to improve the output accuracy of the models targeted condition.
Summary of Citations (Teixeira)
[Column – 1, Line 42 – 50]; “An image processor generates the topogram by a machine-learned network in response to input of the surface data to the machine-learned network. The machine-learned network is trained with surface data from healthy patients. An X-ray image of the patient is acquired. The image processor compares the generated topogram with the acquired X-ray image. The image processor then detects an anomaly of the patient from the comparison between the generated topogram and the acquired X-ray image”.
[Column – 1, Line 46 – 50]; “The image processor compares the generated topogram with the acquired X-ray image. The image processor then detects an anomaly of the patient from the comparison between the generated topogram and the acquired X-ray image”.
[Column – 4, Line 67; Column 5, Line 1 – 5]; “the machine-learned networks of the proposed architecture is trained on healthy patients only. Thus, since the topogram is predicted from the skin surface, the generated topogram will not present any pathologies and may thus be compared to a real topogram from the patient for anomaly detection”.
[Column – 10, Line 31 – 33]; “Referring again to FIG. 1, an X-ray image is acquired in act 14. The X-ray image may be acquired by an X-ray device coupled to the medical imaging system”.
Summary of Citations (Neumann)
Paragraph [0091]; “Still referring to FIG. 1, prognostic label learner 148 may generate prognostic output using alternatively or additional artificial intelligence methods, including without limitation by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network”.
Paragraph [0111]; “generating the at least a diagnostic output as a function of the at least an expert submission may include filtering the training data 120 according to the at least a diagnostic constraint. As a further non-limiting example, generating the at least a diagnostic output as a function of the at least an expert submission may include generating a plurality of machine-learning models, selecting a machine-learning model as a function of the at least a diagnostic constraint, and generating the at least a diagnostic output using the selected machine-learning model, for instance as described above in reference to FIGS. 1-8”.
Regarding claim 9, apparatus claim 9 corresponds to method claim 1. Therefore, the
rejection analysis and motivation to combine of claim 1 is applicable to claim 9.
Regarding claim 15, is a non-transitory computer readable storage medium claim
corresponds to method claim 1. Therefore, the rejection analysis of claim 1 is applied in claim
15.
Claims 2, 3, 5, 10 and 16 are rejected under 35 U.S.C 103 as being unpatentable over Teixeira in view of Neumann and further in view of Shanbhag US Patent Application Publication No. US-20200364864-A1 (hereinafter Shanbhag).
Regarding claims 2 and 3, the combination of Teixeira and Neumann as a whole teaches claim 1 but fails to teach the further limitations as recited in claims 2 and 3. Shanbhag teaches claims 2 and 3 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
Regarding claim 5, the combination of Teixeira and Neumann and Shanbhag as a whole teaches claim 1, and Teixeira teaches claim 5 for the same grounds of rejection from the Non-Final Office Action of 03/20/2026.
Regarding claim 10, apparatus claim 10 corresponds to method claim 2. Therefore, the
rejection analysis and motivation to combine of claim 2 is applicable to claim 10.
Regarding claim 16, is a non-transitory computer readable storage medium claim
corresponds to method claim 2. Therefore, the rejection analysis of claim 2 is applied in claim
16.
Claims 4, 11 and 17 are rejected under 35 U.S.C 103 as being unpatentable over Teixeira in view of Neumann and Shanbhag and further in view of Alemi US Patent Application Publication No. US-20230098732-A1 (hereinafter Alemi).
Regarding claims 4, 11 and 17, the combination of Teixeira, Neumann and Shanbhag as a whole teaches claim 1 but fails to teach the further limitations as recited in claims 4, 11 and 17. Alemi teaches claims 4, 11 and 17 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
Claims 6, 12, 18 are rejected under 35 U.S.C 103 as being unpatentable over Teixeira in view of Neumann and further in view of Tominaga US Patent Application Publication No. US-20230238148-A1 (hereinafter Tominaga).
Regarding claims 6, 12 and 18, the combination of Teixeira and Neumann as a whole teaches claim 1 but fails to teach the further limitations as recited in claims 6, 12 and 18. Tominaga teaches claims 6, 12 and 18 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
Claims 8, 14 and 20 are rejected under 35 U.S.C 103 as being unpatentable over Teixeira in view of Neumann and further in view of Nataraj US Patent Application Publication No. US-20210209415-A1 (hereinafter Nataraj).
Regarding claims 8, 14 and 20, the combination of Teixeira and Neumann as a whole teaches claim 1 but fails to teach the further limitations as recited in claims 8, 14 and 20. Nataraj teaches claims 8, 14 and 20 for the same grounds of rejection and motivation established in the Non-Final Office Action of 03/20/2026.
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.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684. The examiner can normally be reached M-F 8 am - 5 pm ET.
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
7/08/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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