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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/17/26 has been entered. Currently, claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see pages 9-11 of the remarks, filed 4/17/26, with respect to the rejection(s) of claim(s) 1, 8, and 15 under 35 USC 103(a) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Agaian (US 2014/0233826) in view of Sadeghi (US 2014/0365239) and Trautwein (US 2021/0174503).
Regarding claims 1, 8, and 15, Agaian discloses a non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic medical images, a system for processing electronic medical images, the system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to perform operations, and a computer-implemented method for processing electronic medical images comprising:
receiving one or more digital medical images of at least one pathology specimen, the pathology specimen being associated with a patient (see Fig. 18 and paras 271 and 346, medical images of a pathology specimen are acquired);
receiving an external designation of the one or more digital medical images (see Figs. 19 and 20 and paras 347-348, a pathologist can annotate an image and create a diagnostic report);
providing the one or more digital medical images to a plurality of machine learning systems, each of the plurality of machine learning systems having been trained to analyze medical images using one version of a plurality of versions of a protocol (see paras 151-154, a plurality of machine learning algorithms are trained on a specific type of Gleason pattern and an implementer selects the appropriate machine learning algorithm);
determining, by each of the plurality of machine learning systems, a machine learning system designation for the one or more digital medical images (see paras 263-265 and 346-348, a computer-aided diagnosis (CAD) system creates a diagnostic report and annotates images, just as a pathologist does); and
comparing the external designation to the machine learning system designations (see paras 274 and 346-348, an automatic algorithm analyzes the level of agreement between the pathologist report and the CAD report).
Agaian does not disclose expressly providing the one or more digital medical images to each machine learning system of a plurality of machine learning systems, determining whether the external designation matches any version of the plurality of versions of the protocol, and determining whether the external designation matches a predetermined version of the plurality of versions of the protocol.
Sadeghi discloses providing the one or more digital medical images to a plurality of machine learning systems, each of the plurality of machine learning systems having been trained to analyze medical images using one of a plurality of versions of a protocol (see paras 35, 41, and 63-64, decision engine 270 may identify one or more guidelines that are applicable to the input medical report and/or verify whether the input medical report complies with one or more guidelines, can be updated over time, such updates are analogous to “versions”) and
comparing the external designation to each machine learning system designation and, based on the comparison, determining whether the external designation matches a predetermined version of the plurality of versions of the protocol (see paras 9, 35, 37, and 63-64, medical reports are analyzed to determine if they are in compliance with current guidelines).
Trautwein discloses receiving an external designation of the one or more digital medical images (see paras 50-56, image metadata can include external designations, such as labels, diagnosis, etc.),
providing the one or more digital medical images to each machine learning system of a plurality of machine learning systems (see abstract and paras 57-60, images can be provided to a plurality of machine learning algorithms, algorithm selector 108 can, for example, select all algorithms suitable for examining hip joins prothesis for an image with an artificial hip join prothesis), and
determining whether the external designation matches any version of the plurality of versions of the protocol, and determining whether the external designation matches a predetermined version of the plurality of versions of the protocol (see paras 68 and 75-76, result validator 112 determines the confidence value of matching an algorithm).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the providing a plurality of machine learning algorithms image data, as described by Trautwein, and the comparing of designations to determine compliance with a predetermined protocol, as described by Sadeghi, with the system of Agaian.
The suggestion/motivation for doing so would have been to decrease the risk to a patient by ensuring up-to-date guidelines are being followed.
Therefore, it would have been obvious to combine Sadeghi and Trautwein with Agaian to obtain the invention as specified in claims 1, 8, and 15.
Regarding claims 2, 9, and 16, Sadeghi further discloses wherein the predetermined version of the plurality of versions of protocol corresponds to a most recently determined protocol (see paras 41 and 70, guidelines are kept up-to-date).
Regarding claims 3, 10, and 17, Sadeghi further discloses outputting to one or more users that the predetermined version of the plurality of versions of protocol was used in the external designation (see paras 63-64 and 68, decision engine 270 may identify one or more guidelines that are applicable to the input medical report and/or verify whether the input medical report complies with one or more guidelines, the decision engine 270 can also provide alerts to a user).
Regarding claim 4, 11, and 18, Sadeghi further discloses outputting whether the external designation does not match any of the machine learning system designations (see paras 38, 40, 68, and 80-83, any incomplete, missing, or incorrect recommendations may be alerted to a user).
Regarding claims 5, 12, and 19, Sadeghi further discloses upon determining the external designation does not match the predetermined version of the plurality of versions of protocol, outputting the predetermined version of the plurality of versions of protocol to a user or external system (see paras 80-83 and 109, any incomplete, missing, or incorrect recommendations may be alerted to a user).
Regarding claims 6, 13, and 20, Trautwein further discloses upon determining the external designation does not match any version of the plurality of versions of the protocol, outputting the version of the plurality of versions of the protocol in which the machine learning designation most closely matches the external designation (see para 68, if a result is not plausible or confident enough, then the result validator may communicate with the algorithm selector 108 to select a new algorithm or adjust certain parameters for the image analyzer 110 in the reference data structures 109, the result validator 112 may then cause the computer system 100 to restart the analysis or perform a parallel analysis beginning again at the algorithm selector 108).
Regarding claims 7 and 14, Sadeghi further discloses
upon determining a new version of the protocol has been developed (see paras 41, 70, and 86-89, monitoring of new guidelines is performed),
updating the predetermined version of the plurality of versions of the protocol to be the new version of the protocol (see paras 41, 70, and 89, when a new guideline is found, the system is updating with the new guideline); and
training a new machine learning system based on the new version of the protocol (see paras 153, 155, and 165, training of a machine learning model includes information based on the most up-to-date guidelines).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R MILIA whose telephone number is (571) 272-7408. The examiner can normally be reached Monday-Friday, 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached at 571-270-3438. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK R MILIA/ Primary Examiner, Art Unit 2681
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