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 .
Response to Amendment
The amendment filed 19 May, 2026 has been entered.
The amendment of claims 1, 4, 7, 10, 13, 19, and 22 has been acknowledged.
The cancellation of claims 6 and 15 has been acknowledged.
The addition of new claims 25 and 26 has been acknowledged.
Response to Arguments
Applicant’s arguments, see page 10, section “Section 103 Rejections”, filed 19 May, 2026 with respect to the rejection of claims 1 – 3, 5 – 12, and 14 - 24 have been fully considered and are persuasive. The rejection of claims 1 – 3, 5 – 12, and 14 – 24 under 35 U.S.C. § 103 has been withdrawn.
Claim Objections
Claims 1, 2, 4, 5, 7, 10, 11, 13, 14, 16, 19, 22, 23, 24, 25, and 26 are objected to because of the following informalities:
Claim 1 recites “a plurality of machine learning systems”, “a first machine learning model”, “a model ensemble comprised of two or more machine learning systems” and “wherein each machine learning system of the model ensemble is different than the first machine learning model”. ¶ 0021 of the applicant’s specification filed 7 March, 2023 states “As used herein, a “machine learning model” generally encompasses instructions, data, and/or a model configured to receive input, and apply one or more of a weight, bias, classification, or analysis on the input to generate an output.”, however a “machine learning system” is not presently defined in the specification or claims. It is unclear if the use of “model” and “system” is used interchangeably or if there are distinctions between the two labels. This should be made clearer so as to remove any confusion.
Claims 2, 4, 5, 7, 10, 11, 13, 14, 16, 19, 22, 23, 24, 25, and 26 similarly recite “machine learning model” and “machine learning system” throughout and are objected for the same rationale as claim 1.
Claim 22 is objected to because of the following informalities:
Claim 22 states on line 3 “the digital medical”, the examiner believes this is intended to say “the one or more digital medical images” as recited in line 2 of claim 22.
Claim 25 and 26 are objected to because of the following informalities:
Claims 25 and 26 state on line 4 “each of first machine learning model and the model ensemble”, the examiner believes this should read as “each of the first machine model and the model ensemble” as recited in line 2 of claim 22.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 – 5, 7, 10 – 14, 16, 18 – 20, and 22 – 26 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 1, 10, and 19 have been amended to recite the following limitations:
Wherein the plurality of machine learning systems includes a first machine learning model and a model ensemble comprised of two or more machine learning systems, wherein each machine learning system of the model ensemble is different than the first machine learning model;
Outputting the first machine learning model and the model ensemble to a user;
Applying the first machine learning model and the model ensemble to the one or more received digital medical images to determine one or more first processed digital medical images utilizing the first machine learning model and one or more second processed digital medical images utilizing the model ensemble;
Displaying the one or more first processed digital medical images and the one or more second processed digital medical images, wherein the one or more second processed digital medical images includes an overlay indicating where two or more machine learning systems of the model ensemble are in agreement; and
Receiving a selection from the user, the selection corresponding to a selected machine learning system from model ensemble
The specification, filed 7 March, 2023, fails to provide an adequate written description of “wherein the plurality of machine learning systems includes a first machine learning model and a model ensemble comprised of two or more machine learning systems, wherein each machine learning system of the model ensemble is different than the first machine learning model”, “Outputting the first machine learning model and the model ensemble to a user”, “Applying the first machine learning model and the model ensemble to the one or more received digital medical images to determine one or more first processed digital medical images utilizing the first machine learning model and one or more second processed digital medical images utilizing the model ensemble”, “Displaying the one or more first processed digital medical images and the one or more second processed digital medical images, wherein the one or more second processed digital medical images includes an overlay indicating where two or more machine learning systems of the model ensemble are in agreement”, and “Receiving a selection from the user, the selection corresponding to a selected machine learning system from either the first machine learning model or the model ensemble”. Applicant failed to show support in their instant specification for any of the amended claim features including those relating to the “wherein the plurality of machine learning systems includes a first machine learning model and a model ensemble comprised of two or more machine learning systems, wherein each machine learning system of the model ensemble is different than the first machine learning model”, “Outputting the first machine learning model and the model ensemble to a user”, “Applying the first machine learning model and the model ensemble to the one or more received digital medical images to determine one or more first processed digital medical images utilizing the first machine learning model and one or more second processed digital medical images utilizing the model ensemble”, “Displaying the one or more first processed digital medical images and the one or more second processed digital medical images, wherein the one or more second processed digital medical images includes an overlay indicating where two or more machine learning systems of the model ensemble are in agreement”, and “Receiving a selection from the user, the selection corresponding to a selected machine learning system from either the first machine learning model or the model ensemble” as amended in direct contradiction to the requirements of MPEP 2163(II)(A) and 2163.04. Furthermore, the support for these limitations is not apparent. Thus, the amended limitations relating to the “wherein the plurality of machine learning systems includes a first machine learning model and a model ensemble comprised of two or more machine learning systems, wherein each machine learning system of the model ensemble is different than the first machine learning model”, “Outputting the first machine learning model and the model ensemble to a user”, “Applying the first machine learning model and the model ensemble to the one or more received digital medical images to determine one or more first processed digital medical images utilizing the first machine learning model and one or more second processed digital medical images utilizing the model ensemble”, “Displaying the one or more first processed digital medical images and the one or more second processed digital medical images, wherein the one or more second processed digital medical images includes an overlay indicating where two or more machine learning systems of the model ensemble are in agreement”, and “Receiving a selection from the user, the selection corresponding to a selected machine learning system from either the first machine learning model or the model ensemble” are not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventors, at the time the application was filed, had possession of the claimed invention.
Claims 2 – 5, 7, 11 – 14, 16, 18, 20, and 22 – 26 are rejected due to their dependency upon claim 1, 10, and 19.
Additionally, new claims 25 and 26 recite:
… before receiving the selection from the user, determining, by a processor, model-accuracy data of each of the plurality of machine learning systems wherein the model-accuracy data indicates an accuracy of each of first machine learning model and the model ensemble, the model-accuracy data being output to the user.
The specification, filed 7 March, 2023, fails to provide an adequate written description of the determining model-accuracy data of each of the plurality of machine learning systems wherein the model-accuracy indicates an accuracy of each of the first machine learning model and the model ensemble, and the model-accuracy data being output to the user. Applicant failed to show support in their instant specification for any of the amended claim features including those relating to the “determining model-accuracy data of each of the plurality of machine learning systems wherein the model-accuracy indicates an accuracy of each of the first machine learning model and the model ensemble”, and “the model-accuracy data being output to the user” as amended in direct contradiction to the requirements of MPEP 2163(II)(A) and 2163.04. Furthermore, the support for these limitations is not apparent. Thus, the amended limitations relating to the “determining model-accuracy data of each of the plurality of machine learning systems wherein the model-accuracy indicates an accuracy of each of the first machine learning model and the model ensemble, and the model-accuracy data being output to the user” are not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventors, at the time the application was filed, had possession of the claimed invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 4, 7, 10, 12, 13, 16, 19, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the newly amended claim 1 limitation of “the selection corresponding to a selected machine learning system from either the first machine learning model or the model ensemble” is unclear. Specifically, the first machine learning model is not a machine learning system, nor is it understood to comprise machine learning systems. As such, how can a machine learning system being selected from a machine learning model? Previously claim 1 states “wherein the plurality of machine learning systems includes a first machine learning model”. It is unclear what constitutes a machine learning system versus a model or how a system can be selected from a model, if a model is included within a system.
Additionally, it is unclear if the selection of the machine learning system from the model ensemble is specifically choosing one of the two machine learning systems that are comprised of the model ensemble, or if the selection is of the entire model ensemble.
Claims 10 and 19 are rejected for the same rationale
Claims 3, 12, and 20 recite the limitation “wherein the display of the one or more images" in line 1 - 2. There is insufficient antecedent basis for this limitation in the claim. The step in claim 1 which this previously referenced has been amended and there are now “one or more first processed digital medical images” and “one or more second processed digital medical images”
Claims 4 and 13 recite the limitation “wherein the display of the one or more images" in line 1 - 2. There is insufficient antecedent basis for this limitation in the claim. The step in claim 1 which this previously referenced has been amended and there are now “one or more first processed digital medical images” and “one or more second processed digital medical images”.
Claims 7 and 16 recite the limitation “one or more medical images" in lines 2 – 3 and 3 - 4. There is insufficient antecedent basis for this limitation in the claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Trautwein et al (U.S. Patent Publication No. 2021/0174503 A1) teaches a system that provides the automatic extraction and processing of medical images from an image archive by selecting an algorithm for the image data analysis based on image generated metadata.
Gur et al (U.S. Patent Publication No. 2021/0019665 A1) teaches a machine learning framework that receives a user specification for which a machine learning model is to be trained, and converts the user specification to machine learning search criteria used to search the index to identify matching machine learning algorithms having a corresponding machine learning algorithm metadata model that matches the machine learning algorithm search criteria.
THIS ACTION IS MADE FINAL. 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.
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/ANDREW B. JONES/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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