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 1-20 are pending in the present application with claims 1, 10, and 19 being independent.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. PCT/US24/26259 ("the '259 Application"), fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Specifically, the '259 Application does not appear to provide adequate support or enablement at least for predicting a plurality of values using a plurality of machine learning models that each take as input a respective segment of the plurality of segments of the image or augmented image as required in each of independent claims 1, 10, and 19.
The disclosure of the prior-filed application, Application No. 17/508,517 ("the '517 Application"), fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Specifically, the '517 Application does not appear to provide adequate support or enablement at least for retraining the trained object detection model based on the one or more regions of interest and the augmented image as required in each of independent claims 1, 10, and 19.
Accordingly, the effective filing date of the present application is May 5, 2025, the actual filing date of the present application.
Claim Objections
Claims 1 and 19 are objected to because of the following informalities:
In claim 1, the sixth to last line, "in input" should be changed to --an input--.
In claim 19, the sixth to last line, "in input" should be changed to --an input--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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 1-20 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.
Claim 1 recites "a plurality of machine learning trained models" in line 21 and "a plurality of machine learning models" in lines 25-26 thus leading to confusion as to whether or not they are the same. For purposes of examination, the Examiner will assume they are the same.
Claim 10 recites the limitation "the trained object detection model" in line 13. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation "the plurality of machine learning models" in line 18. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites "a plurality of machine learning trained models" in lines 21-22 and "a plurality of machine learning models" in lines 26-27 thus leading to confusion as to whether or not they are the same. For purposes of examination, the Examiner will assume they are the same.
Claims 1, 10, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential steps, such omission amounting to a gap between the steps. See MPEP § 2172.01. Each of independent claims 1, 10, and 19 call for identifying/detecting an anatomical object from the image using a trained object detection model and then segmenting the image or augmented image into segments and inputting the segments into respective ones of a plurality of ML models to predict values. However, these claims do not disclose any relation between the segmentation of the image and the detected/identified anatomical object thus leading to a gap between the steps. Furthermore, Applicant's specification appears to disclose the essential nature of performing the segmentation based on the detected anatomical object (e.g., [0096] discloses segmenting the image into quadrants based on object detection of a Malleus Handle and [0108] discloses how image segmentation may include object detection). In this regard, it is recommended that Applicant amends each of independent claims 1, 10, and 19 to recite "segmenting, based on the identified/detected anatomical object, the image… into a plurality of segments" or similar language.
The remaining claims are rejected based on their dependency from the above claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more:
Subject Matter Eligibility Criteria - Step 1:
Claims 1-9 are directed to a method (i.e., a process), claims 10-18 are directed to a system (i.e., a machine), and claims 19-20 are directed to a non-transitory computer-readable medium (i.e., a manufacture). Accordingly, claims 1-20 are all within at least one of the four statutory categories. 35 USC §101.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test (which collectively includes the guidance in the January 7, 2019 Federal Register notice and the October 2019 and July 2024 updates issued by the USPTO as incorporated into the MPEP, as supported by relevant case law), the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 19 includes limitations that recite at least one abstract idea. Specifically, independent claim 19 recites:
At least one machine-readable medium including instructions for generating an ear disease state prediction to assist diagnosis of an ear disease, which when executed by processing circuitry, cause the processing circuitry to perform operations to:
receive, at one or more processors, an image captured by an otoscope of an inner portion of an ear of a patient;
identify, at the one or more processors, an anatomical object of the ear from the image by using a trained object detection model, wherein identifying the anatomical object comprises:
augment, by an augmentation engine and based at least in part on the image captured by the otoscope, the image captured by the otoscope to generate an augmented image;
identify, by a bounding engine and based at least in part on the image captured by the otoscope, one or more regions of interest of the image captured by the otoscope;
retrain, at the one or more processors, the trained object detection model based on the one or more regions of interest and the augmented image; and
identify, by the trained object detection model, the anatomical object;
in response to identifying the anatomical object, predict, at the one or more processors, an image-based confidence level of a disease state in the ear by using a plurality of machine learning models, wherein predicting the image-based confidence level comprises:
segment the image captured by the otoscope or the augmented image into a plurality of segments;
predict a plurality of values using a plurality of machine learning models, wherein each of the plurality of machine learning models takes a respective segment of the plurality of segments as input; and
generate the image-based confidence level based on the plurality of values;
receive text corresponding to a symptom of the patient;
predict a symptom-based confidence level of the disease state in the ear by using the text as in input to a classifier;
combine the image-based confidence level and the symptom-based confidence level to generate an overall confidence level of presence of an ear infection in the ear of the patient; and
output an indication including the confidence level for display on a user interface.
The Examiner submits that the foregoing underlined limitations constitute "a mental
process" because they are observations/calculations/judgments/analyses that can, at the currently
claimed high level of generality, be practically performed in the human mind (e.g., with pen and
paper). For instance, a medical professional (e.g., otolaryngologist) can practically augment an image captured by an otoscope (e.g., via labeling the image, etc.) to generate an augmented image (e.g., with pen and paper), identify one or more ROIs in the image (e.g., regions that have a particular color, contrast, structure, etc.), identify an anatomical object in the image (e.g., Malleus Handle), segment the image into a plurality of segments (e.g., using a pen to divide the identified object into segments/regions), predict based on his/her experience a likelihood of disease in each of the segments to generate a plurality of values, generate an "image-based" confidence/probability level of disease in the object based on the plurality of values (e.g., calculating an average of the values), predict based on his/her experience a likelihood a "symptom-based" confidence level of ear disease using textual symptoms reported by a patient using (e.g., ear pain, fever, etc.), combine the image-based and symptom-based confidence levels to generate an overall confidence level of presence of an ear infection in the ear of the patient, and output an indication including the confidence level for display. For instance, in the case where the image-based confidence level is 80% but the symptom-based confidence level is only 20%, the medical professional could mentally easily average the two confidence levels to obtain an overall confidence level of 50%. As another example, if the image-based confidence level is 50% and the symptom-based confidence level is below 50%, then the medical professional could make a determination to not use the symptom-based confidence level as part of determining the overall confidence level. These recitations, under their broadest reasonable interpretation, are similar to the concepts of collecting information, analyzing it and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQe2d 1739 (Fed. Cir. 2016)).
Furthermore, the foregoing underlined limitations constitute "certain methods of
organizing human activity" because they relate to managing personal behavior or relationships or
interactions between people (e.g., following rules or instructions). For instance, the steps are
similar to a mental process that a neurologist should follow when testing a patient for nervous
system malfunctions. In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982).
MPEP 2106.04(a)(2)(II)(C).
Accordingly, the claim recites at least one abstract idea.
Furthermore, dependent claims 4-7 and 13-16 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract) as set forth below:
-Claims 4 and 13 recite how the anatomical object comprises a Malleus Handle of a human ear, wherein segmenting the image includes using the identified Malleus Handle as an axis for segmentation, and wherein the image-based confidence level is predicted for a respective segment of the plurality of segments based on the identified Malleus Handle. These limitations just further define the abstract idea(s) discussed above.
-Claims 5 and 14 recite how identifying the anatomical object of the ear includes identifying an entirety of an ear drum of the ear such that the image is augmented in response to identifying the entirety of the ear drum of the ear. These limitations just further define the abstract idea(s) discussed above.
-Claims 6 and 15 recite how determining the overall confidence level includes multiplying the image-based confidence level output by the symptom-based confidence level which again is practically performable in the human mind with pen and paper and thus just further defines the abstract idea(s) discussed above.
-Claims 7 and 16 recite how receiving the text includes receiving a selection from a list of symptoms which just further defines the abstract idea(s) discussed above.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
At least one machine-readable medium including instructions for generating an ear disease state prediction to assist diagnosis of an ear disease, which when executed by processing circuitry, cause the processing circuitry to perform operations to (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)):
receive, at one or more processors (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), an image captured by an otoscope of an inner portion of an ear of a patient;
identify, at the one or more processors (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), an anatomical object of the ear from the image by using a trained object detection model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), wherein identifying the anatomical object comprises:
augment, by an augmentation engine (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) and based at least in part on the image captured by the otoscope, the image captured by the otoscope to generate an augmented image;
identify, by a bounding engine and (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)) based at least in part on the image captured by the otoscope, one or more regions of interest of the image captured by the otoscope;
retrain, at the one or more processors, the trained object detection model based on the one or more regions of interest and the augmented image (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)); and
identify, by the trained object detection model (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), the anatomical object;
in response to identifying the anatomical object, predict, at the one or more processors (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)), an image-based confidence level of a disease state in the ear by using a plurality of machine learning models (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)), wherein predicting the image-based confidence level comprises:
segment the image captured by the otoscope or the augmented image into a plurality of segments;
predict a plurality of values using a plurality of machine learning models, wherein each of the plurality of machine learning models (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f)) takes a respective segment of the plurality of segments as input; and
generate the image-based confidence level based on the plurality of values;
receive text corresponding to a symptom of the patient;
predict a symptom-based confidence level of the disease state in the ear by using the text as in input to a classifier (merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished, see MPEP § 2106.05(f));
combine the image-based confidence level and the symptom-based confidence level to generate an overall confidence level of presence of an ear infection in the ear of the patient; and
output an indication including the confidence level for display on a user interface (using computers or machinery as mere tools to perform the abstract idea as noted below, see MPEP § 2106.05(f)).
For the following reasons, the Examiner submits that the above-identified additional limitations, when considered as a whole with the limitations reciting the at least one abstract idea, do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the machine-readable medium including instructions for execution by processing circuitry including one or more processors, the augmentation and bounding engines, and the user interface, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of the trained object detection model, the retraining of the trained object detection model using the ROIs and augmented image, and the plurality of ML models, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Furthermore, looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 19 and analogous independent claims 1 and 10 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, representative independent claim 19 and analogous independent claims 1 and 10 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
-Claims 2, 3, 11, and 12: These claims recite different high level augmentation techniques (e.g., contrast correction including pixel interpolation, spatial distortion via cropping, scaling, etc.) and thus amount to using known computer-based techniques as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
-Claims 4 and 13 recite how each of the ML models is generically trained to perform the (abstract idea of) predicting the image-based confidence level for a respective segment of the plurality of segments based on the identified Malleus Handle which again amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
-Claims 8, 17, and 20 recite how the classifier is a support vector machine (SVM) classifier, a Logistic Regression model classifier, or Naives Bayes classifier which amounts to using known computer-based techniques as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
-Claims 9, 18, and 20 recite how the ML model is a CNN which amounts to using known computer-based techniques as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
When the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 19 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations of the machine-readable medium including instructions for execution by processing circuitry including one or more processors, the augmentation and bounding engines, and the user interface, the Examiner submits that these limitations amount to merely using a computer or other machinery as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitations of the trained object detection model, the retraining of the trained object detection model using the ROIs and augmented image, and the plurality of ML models, the Examiner submits that these limitations amount to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)). Claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), pp. 10, 14. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Id.
Requirements that the machine learning model be “iteratively trained” or dynamically adjusted do not represent a technological improvement because iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), p. 12. “[T]he way machine learning works is the inputs are defined, the model is trained, and then the algorithm is actually updated and improved over time based on the input.” Id.
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
-Claims 2, 3, 11, and 12: These claims recite different high level augmentation techniques (e.g., contrast correction including pixel interpolation, spatial distortion via cropping, scaling, etc.) and thus amount to using known computer-based techniques as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
-Claims 4 and 13 recite how each of the ML models is generically trained to perform the (abstract idea of) predicting the image-based confidence level for a respective segment of the plurality of segments based on the identified Malleus Handle which again amounts to merely reciting the idea of a solution or outcome without reciting details of how a solution to a problem is accomplished which is equivalent to the words “apply it” (see MPEP § 2106.05(f)).
-Claims 8, 17, and 20 recite how the classifier is a support vector machine (SVM) classifier, a Logistic Regression model classifier, or Naives Bayes classifier which amounts to using known computer-based techniques as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
-Claims 9, 18, and 20 recite how the ML model is a CNN which amounts to using known computer-based techniques as tools performing their typical functionality in conjunction with performing the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Therefore, claims 1-20 are ineligible under 35 USC §101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The references cited on the attached PTO-892 disclose various systems for obtaining otoscopic images of the inside of a patient's ear; augmenting the images using various augmentation techniques such as labeling, corrections, etc.; detecting anatomical objects/structures in the images; segmenting the images; and using various ML techniques to predict diseases/disorders such as otitis media and the like in patient's ear.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHON A. SZUMNY whose telephone number is (303) 297-4376. The examiner can normally be reached Monday-Friday 7-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason Dunham, can be reached at 571-272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JONATHON A. SZUMNY/Primary Examiner, Art Unit 3686
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