DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/14/2026 has been entered.
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 .
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.
Response to Amendment
In the amendment dated 9/14/2026, the following has occurred: Claims 1, 2, 8, 9, 15, and 16 have been amended.
Claims 1 – 20 are pending.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claim(s) recite(s) subject matter within a statutory category as a process (claims 1 – 7), machine (claims 15 – 20), and manufacture (claims 8 – 14) which recite the abstract idea steps of
accessing diagnostic models of a plurality of diagnostic models, wherein at least some of the diagnostic models are trained using a plurality of training examples that were generated by, for each of a plurality of patients:
receiving a set of biomarker features extracted from measurement data taken from an ear of a patient by:
obtaining an ear image of a portion of the patient's ear; and
extracting one or more biomarker features from the image by:
obtaining a set of samples of the ear image, each sample corresponding to a location within the ear;
for each of the set of samples, output a likelihood of whether the sample contains an ear image object; and
storing an example for the patient, the example comprising at least the biomarker features for the patient and a label that indicates whether the patient has an ear malady
repeatedly applying given ones of the plurality of training examples to the diagnostic models of the plurality of diagnostic models, each respective diagnostic model trained to perform diagnosis on data from a respective anatomical location, causing each of the diagnostic models to be trained to output a diagnosis relating to ear maladies based on an input of a new set of biomarker features as generated by the trained feature detection model based on an input of a new ear image from a corresponding anatomical location.
These steps of claims 1 – 20, as drafted, under the broadest reasonable interpretation, includes mathematical concepts. In the instant invention, the math includes the feature detection model including the neural network and the training the neural network.. see July 2024 Subject Matter Eligibility Examples
These steps of claims 1 – 20, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. The Examiner understands the claimed invention in light of the Specification. As stated in paragraph 3, “Systems and methods are described herein for using an objective service for accurately assessing, diagnosing, and prescribing therapy for ear infections.” The instant invention applies the abstract idea to technology to achieve all the benefits of applying that abstract idea to technology. As stated in paragraph 4, “The processor synthesizes the patient data and the biomarker features into input data, and applies the synthesized input data to a trained diagnostic mode.” Similar language is found in paragraph 27 as, “The samples may be applied by sufficiency determination module 231 to a trained feature detection model (which may also be stored in sufficiency parameters database 234), the model comprising a neural network that is configured to output a likelihood of whether the sample contains an ear image object.” The result of the invention is data that has a potential usage and therefore there is no practical application.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2 – 7, 10 – 14, and 16 – 20, reciting particular aspects of how diagnosing an ear malady may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of computer program product amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receive a set of biomarkers amounts to mere data gathering, recitation of storing a training example and also repeatedly applying given ones of the plurality of training examples… amounts to insignificant application, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 7, 10 – 14, and 16 – 20, additional limitations which amount to invoking computers as a tool to perform the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as claims 1 – 20; training, and applying, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii))
Additional Elements:
Computer – paragraph 46
Computer readable medium – paragraph 47
Program – paragraph 45
Network – paragraph 24
Neural network – paragraphs 36 and 37 – The Examiner notes that the RNN is considered an additional element.
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 7, 10 – 14, and 16 – 20, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
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 – 20 are rejected under 35 U.S.C. 102(a)1 as being anticipated by Douglass et al., U.S. Pre-Grant Publication 2015/ 0065803.
As per claim 1,
Douglass teaches a method for training a diagnostic model for use in diagnosing an ear malady, the method comprising:
accessing diagnostic models of a plurality of diagnostic models, wherein at least some of the diagnostic models are trained using a plurality of training examples that were generated by (paragraphs 237 – 240 CBIR paragraph 26, training set and paragraph 176 training data),
for each of a plurality of patients:
receiving a set of biomarker features extracted from measurement data taken from an ear of a patient by (Abstract):
obtaining an ear image of a portion of the patient's ear (paragraph 166 and 167 otoscope ); and
extracting one or more biomarker features from the image by (figure 2A feature extraction):
obtaining a set of samples from within the ear image (figures 3A or 3B and paragraph 190),
each sample having metadata indicating a location within the ear to which the sample corresponds relative to an anatomical structure of the ear, (paragraph 370 location paragraph 372 landmark features, such as eardrum 379 feature – paragraph 155 where a smartphone captures image information with metadata – it should be emphasized that the limitation is toward “metadata indicating a location…” but not that the location is stored in the metadata. How that item is “indicated” is not disclosed),
the location being an anatomical sub-component of the ear that is one of a plurality of anatomical sub-components (paragraph 379);
for each of the set of samples (paragraphs 198 and 199),
applying the sample and its metadata indicating the location in the ear to which the sample corresponds to a trained feature detection model (paragraph 199)
the feature detection model comprising a neural network that is configured to output a likelihood of whether the sample contains an ear image object (figure 18 and paragraphs 175 and 218 ML techniques including neural network paragraphs 202 and 203 anatomical quality metric),
the likelihood of whether the sample contains an ear image object and the location in the ear to which the sample corresponds together forming a given biomarker feature (figure 48E, paragraphs 172 – 174, 202 segmentation likelihood map); and
storing a training example for the patient (paragraph 203 retain frames),
the training example comprising at least the biomarker features for the patient and a label that indicates whether the patient has an ear malady
the training example comprising at least the biomarker features for the patient from each sample of the set of samples and a label that indicates whether the patient has an ear malady based on a historical diagnosis corresponding to the ear image (paragraphs 69, 171, 172 image features paragraphs 176, 213, disease or malady feature including color, figure 11, clinical information paragraph 232 results of past exams); and
repeatedly applying given ones of the plurality of training examples to diagnostic models of the plurality of diagnostic models (figure 14a and paragraphs 175, 210, 218 alternate model and paragraph 266 continuous monitoring – paragraph 240),
each diagnostic model trained to perform diagnosis on data from a respective anatomical location (Figure 14A, paragraph 239 likely to be most useful in CBIR application – diagnosis information is anatomical image recognition paragraph 349)
causing the diagnostic model to be trained to output a diagnosis relating to ear maladies based on an input of a new set of biomarker features as generated by the trained feature detection model based on an input of a new ear
causing each of the diagnostic models to be trained to output a diagnosis relating to ear maladies based on an input of a new set of biomarker features as generated by the trained feature detection model based on an input of a new ear image from a corresponding anatomical location (paragraphs 175 – 177).
As per claim 2, Douglass teaches the method of claim 1 as described above.
Douglass further teaches the method comprising updating parameters of the diagnostic models to improve an objective performance threshold when repeatedly applying the given ones of the plurality of training examples to the diagnostic model (paragraph 175 – this is a machine learning system paragraph 210).
As per claim 3, Douglass teaches the method of claim 1 as described above.
Douglass further teaches the method wherein the ear image is at least one of a two-dimensional image or a three-dimensional optical coherence tomography image (paragraph 205, 217 2D and figure 52, paragraph 294 3D).
As per claim 4, Douglass teaches the method of claim 1 as described above.
Douglass further teaches the method wherein the portion of the patient's ear comprises at least one of a tympanic membrane, an anatomical structure adjacent to a tympanic membrane, an ear canal adjacent to the tympanic membrane, a malleus, an umbo, and a light reflex (paragraph 293).
As per claim 5, Douglass teaches the method of claim 1 as described above.
Douglass further teaches the method wherein receiving the set of biomarker features comprises:
applying a pressure stimulus to inside an ear of the patient (paragraph 393 insufflator applying pressure);
receiving an acoustic response from the applied pressure stimulus (paragraphs 337, 393);
extracting acoustic biomarker features from the received acoustic response (paragraphs 394 – 401); and
synthesizing, into input data, the acoustic biomarker features (paragraphs 394 – 401).
As per claim 6, Douglass teaches the method of claim 5 as described above.
Douglass further teaches the method wherein the pressure stimulus is applied using at least one of pneumatic otoscopy or tympanometry (paragraph 227).
As per claim 7, Douglass teaches the method of claim 1 as described above.
Douglass further teaches the method wherein the biomarker features each further indicate a confidence value that reflects a confidence that an indicated anatomical feature of the ear was accurately determined (Figure 35, paragraph 241 score or accuracy or confidence).
As per claim 8,
Douglass teaches a computer program product for training a diagnostic model for use in diagnosing an ear malady, the computer program product comprising a computer-readable storage medium containing computer program code as described above in claim 1.
As per claim 9, Douglas teaches the medium as described above in claim 8, as understood.
Douglass further teaches the medium, as understood, as described above in claim 2.
As per claim 10, Douglas teaches the medium as described above in claim 8, as understood.
Douglass further teaches the medium, as understood, as described above in claim 3.
As per claim 11, Douglas teaches the medium as described above in claim 8, as understood.
Douglass further teaches the medium, as understood, as described above in claim 4.
As per claim 12, Douglas teaches the medium as described above in claim 8, as understood.
Douglass further teaches the medium, as understood, as described above in claim 5.
As per claim 13, Douglas teaches the medium as described above in claim 12, as understood.
Douglass further teaches the medium, as understood, as described above in claim 6.
As per claim 14, Douglas teaches the medium as described above in claim 8, as understood.
Douglass further teaches the medium, as understood, as described above in claim 7.
As per claim 15,
Douglass teaches a system for training a diagnostic model for use in diagnosing an ear malady as described above in claim 1.
As per claim 16, Douglas teaches the system as described above in claim 15.
Douglass further teaches the system as described above in claim 2.
As per claim 17, Douglas teaches the system as described above in claim 15.
Douglass further teaches the system as described above in claim 3.
As per claim 18, Douglas teaches the system as described above in claim 15.
Douglass further teaches the system as described above in claim 4.
As per claim 19, Douglas teaches the system as described above in claim 15.
Douglass further teaches the system as described above in claim 5.
As per claim 20, Douglas teaches the system as described above in claim 19.
Douglass further teaches the system as described above in claim 6.
Response to Arguments
Applicant's arguments filed 9/14/2026 have been fully considered but they are not persuasive.
Rejection Under 35 U.S.C. § 101
The Applicant states, “Under at least a Prong 2 analysis, the amendment is patent-eligible. The claims as amended solve technical problems discussed in U.S. Patent No. 10,115,194, which is incorporated by reference in its entirety. There are several technical problems, some of which are reproduced below:” However, the instant specification is not disclosed as solving this problem. The instant specification describes solving a different problem as noted above.
The Applicant states, “The claims as amended solve the foregoing problems by constraining the detection task anatomically rather than applying a single neural network to whole images or undifferentiated patches.” The Applicant adds new reasons for creating the instant invention that are not found in the originally filed specification including incorporated matter. The incorporated matter may disclose many different technical problems that are separate from the instant specification.
The Applicant states, “The specification notes this result: dedicated models for different anatomical structures lead "to more precise outputs from the machine learning models." See Specification, [0037] ("[f]or example, different anatomical structures in the ear may each have dedicated machine learning models trained using training data from those anatomical structures in training data database 464, leading to more precise outputs from the machine learning models.").” Below is the Applicant’s quote in context, emphasis added,
[0037] Machine-learning classifier module 462 executes a machine learning model trained using data in training data database 464. In one embodiment, the machine learning model is a recurrent neural network (RNN) model, and training data in training data database 464 is updated on an ongoing basis (e.g., for subsequent points in time) to improve the diagnostic accuracy of the machine learning model. In an embodiment, machine-learning classifier module selects one or more machine learning models from several machine learning models to use depending on various factors. These factors may include which disease biomarker detection and/or segmentation is being evaluated, and which anatomy location/segmentation is being evaluated. For example, different anatomical structures in the ear may each have dedicated machine learning models trained using training data from those anatomical structures in training data database 464, leading to more precise outputs from the machine learning models. Similarly, different types of biomarkers ( e.g., fissures versus blood flow markers) may each have dedicated machine learning models for similar reasons. Such machine learning models do not have to be (statistically) independent and may in fact be partially overlapping in their combined solution space. Where multiple machine learning models are selected by machine-learning classifier module, output form one module may form input of another module. Further discussion of machine models, such as neural networks, and selection of the best machine models from a pool of candidate machine models, is discussed in further detail in commonly owned U.S. Patent No. 10,115,194, issued October 30, 2018, the disclosure of which is hereby incorporated by reference herein in its entirety.
The specification does not describe this invention as overcoming “a constrained detection problem.” Rather, the specification describes selecting “to improve the diagnostic accuracy of the machine learning model” as quoted from above. The Applicant’s opinion of what the Specification states is noted.
The Applicant states, “Like Ex Parte Desjardins, the claims describe how a particular training arrangement provides an improvement to a machine learning model. As noted in the previous office action,
The Applicant further states, “See Specification at [0037]. Similar to Desjardins, applicant's specification alludes to a technical problem of imprecise outputs from general diagnostic models.” The Specification does not describe a technological problem. The Background describes a human factor problem. The full paragraph of paragraph 37, included here for simplicity, shows using known techniques being applied.
The Applicant further states, “Applicant's specification describes a technical solution to this technical problem, of using dedicated machine learning models for different anatomical structures in the ear.” The Specification describes a technological solution applied to the abstract idea to obtain all of the benefits of applying the technological solution to the abstract idea.
The Specification still shows improving a human diagnostic problem by applying technology.
Rejection Under 35 U.S.C. § 102(a)1
The Applicant states, “Douglass is relied upon for allegedly teaching or describing each of the elements of the claim. Even though Douglass discloses training a feature-detection/ segmentation model (see Douglass, [0l71]-[0178]), Douglass fails to teach or describe a separate diagnostic model trained on the outputs of a feature-detection model.” Please see the updated rejection above.
The Applicant further states, “Moreover, Douglass' s cited metadata disclosures describe whole-image orientation normalization and a display-only feature-annotation UI. See Douglass, [0370], [0372], [0379]. These disclosures fail to describe metadata on each of a set of samples identifies an anatomical sub-component, as in the amended claim” The claim limitation is:
obtaining a set of samples from within the ear image, each sample having metadata indicating a location within the ear to which the sample corresponds relative to an anatomical structure of the ear, the location being an anatomical sub-component of the ear that is one of a plurality of anatomical sub-components;
The claim is written at a high level without limitations on “sub-components.” Further, these “sub-components” are descriptive in nature and not functional. Regardless, the Applicant appears to argue that an ear drum is not a sub-component of an ear which the Examiner finds spurious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Neal R Sereboff whose telephone number is (571)270-1373. The examiner can normally be reached M - T, M - F 8AM - 6PM.
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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626
