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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/20/2025 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 10/20/2025, the following has occurred: Claims 1, 8, and 15 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 a plurality of 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
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
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 a plurality of training examples that were generated by, for each of a plurality of patients (paragraph 26, training set and paragraph 176 training data):
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 (paragraph 370 location paragraph 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 the diagnostic model (figure 14a and paragraphs 175, 210, 218 and paragraph 266 continuous monitoring).
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 image (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 model 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 10/20/2025 have been fully considered but they are not persuasive.
Rejection Under 35 U.S.C. § 101
The Applicant states, “Like Ex Parte Desjardins, applicant's background section describes a problem in technology resulting in over-diagnosis of ear maladies based on a lack of "an objective tool for ensuring an accurate diagnosis and require expert physician interpretation, lacking the capability of a fully-autonomous tool to automatically diagnosis ear infections."” Had the Applicant used the full sentence then the Examiner’s response would have better context. The complete sentence is, “Existing systems lack an objective tool for ensuring an accurate diagnosis and require expert physician interpretation, lacking the capability of a fully-autonomous tool to automatically diagnosis ear infections and prescribe antibiotics, which would reduce a cost burden on society caused by the over-diagnosis brought on by the existing systems.” The complete sentence does not state that the invention is overcoming a technological problem but rather states that using technology the invention will eliminate “expert physician interpretation” leading to “reduce a cost burden on society.” Therefore, the invention organizes human activity by applying technology to the abstract idea. That invention then obtains all the benefits of applying that technology to the abstract idea.
The applicant does not disclose technological invention. For example, the machine learning model is described generically in paragraph 27 as a neural network or paragraph 37 as a recurrent neural network. The invention is a technological application.
Rejection Under 35 U.S.C. § 102(a)1
The Applicant states, “The Office action takes the position that the term "location", as previously recited, is being broadly read as meaning "an ear". As clarified, the claimed location is a sub-component, of a plurality of sub-components, within an ear.” However, the Applicant uses the broad idea of “indicating a location” but does not specify how that location is indicated. The extensive use of metadata within smartphone images.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571)272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626