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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 1, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Response to Amendment
The amendment filed February 13, 2026 has been entered. Claims 1-9, 11-14, and 16-22 remain pending in the application, and claims 10 and 15 were cancelled. Applicant’s amendments to the claims have overcome each and every objection to the claims and 112 rejections previously set forth in the Non-Final Office Action mailed November 13, 2025. Applicant’s amendments to the claims necessitate new grounds of rejection, as described in the Response to Arguments and 102 and 103 Rejections below.
Claim Rejections - 35 USC § 102
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.
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.
Claims 1-7, 9, 11-14, 16, and 18-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20150065803 A1 (Douglas et al.).
Regarding claim 1, Douglas teaches a system comprising:
at least one processor ([0030] “processor”);
an image sensor ([0034] “device for aiding in imaging the tympanic membrane that may be used, in particular, for use with a home or clinical device that includes an otoscope (e.g., speculum, lens/lenses, and video/image capture capability)”);
an output device ([0045] “Instructions may be visual (images, text, etc.) or audible, or both”); and
at least one non-transitory computer readable medium, storing executable instructions executable by the at least one processor ([0031] “a non-transitory computer-readable storage medium storing a set of instructions capable of being executed by a processor”) to provide:
a guidance component configured to receive a first image captured from the image sensor, identify a contour associated with a tympanic membrane of a subject via a segmentation algorithm, evaluate the shape of the contour to determine an appropriate movement of the image sensor to bring the identified contour into a center of a field of view of the image sensor, and provide an instruction for a user to adjust a position of the image sensor to bring the image sensor into alignment with a tympanic membrane of a subject ([0031] “receive an image of the subject's ear canal; select a subset of subregions from the image; extract, at a plurality of different scales, a set of feature values for each of the subregions in the subset of subregions; estimate, for each individual subregion within the subset of subregions, a probability that the individual subregion is part of a tympanic membrane based on the extracted sets of feature values for the individual subregion; and identify, on a representation of the image, a tympanic membrane region from the image using the estimated probabilities for the subregions within the subset of subregions.”; [0039] “a method or system may alternatively or additional guide a subject by providing one or more directions, including directions on a display screen showing the images, audible cues, textual cues or the like, so that the subject may move the otoscope device to adjust the view being taken”; [0045] “any of these methods and apparatuses may also include instructing the subject to straighten the ear canal.”; [0193-0194] “A "shape finding" method that can find one or more pre-determined shapes of varying sizes, locations and (possibly) orientations within an image may be used. The apparatus may then assess some quality metric of found matches depending on how well they match the shape prototype. The apparatus may use the found match of the highest quality as the "correct" field of view, as long as it exceeds some pre-determined quality threshold”; [0298] “Such a guidance system could provide "heads up display"-style cues (e.g., arrows, "locked on" indicator, or other graphics superimposed on the live video of the in-progress exam) or audio cues (e.g., a chime, or a voice instructing the user to move the otoscope left, right, into the ear, out of the ear, etc.) to guide the user during the exam”; [0320] “This example uses the depth of the field of view (darker regions) to guide a user toward the TM, which is deeper in the ear canal. FIG. 27B (parts 1 and part 2) shows exemplary code for performing such a method. In addition, or alternatively, the apparatus may be configured to examine the images (or a subset of the images) being received and/or displayed, to identify a TM or a portion of a TM, as described herein. The apparatus may be configured to determine directionality (e.g., to center the TM) based on the position of the identified probable TM region on the screen, and provide indicators (e.g., arrows, icons, audible instructions/guidance) to guide the subject in positioning the otoscope.”);
a user interface that provides the determined instruction to the user via the output device ([0045] “Instructions may be visual (images, text, etc.) or audible, or both”; [0340-0341] “interfaces are methods for presenting and interacting with the relevant information, as well part of the system of image collection, review, storage, analysis, and transmission.”); and
a predictive model that determines a clinical parameter representing otitis media from one of the first image or a second image of the tympanic membrane of the subject captured with the image sensor in alignment with the tympanic membrane ([0176] “The image subjects may include healthy TMs, pathological TMs (e.g., acute otitis media, otitis media with effusion, foreign bodies, etc.), or images that do not contain a TM”; [0185] “machine learning classification model (e.g., a support vector machine or random forest model) included as part of the apparatus to predict whether the given pixel is part of the tympanic membrane or not”; [0232]; [0256] “The machine-learning model outputs a diagnosis outcome (example: normal versus AOM).”);
wherein the clinical parameter is provided to the user via the output device ([0340-0341] “user-facing interface”; [0349] “interfaces for automated diagnosis results”).
Regarding claim 2, Douglas teaches the system of claim 1, further comprising an optical assembly, comprising a plurality of optical elements aligned with the image sensor to improve a quality of images acquired at the image sensor ([0006] “An image is seen by the user through means of a magnifying eyepiece located on the rear or proximal side of the instrument, with the ear being illuminated by means of an interior lamp or a lamp tethered to a bundle of optical fibers located in the instrument head to facilitate viewing”; [0010] “these systems and methods may make it possible for untrained persons to take and save high-quality images of the tympanic membrane”; [0031] “an apparatus may also include an otoscope or otoscope adapter (lens portion) to connected to a mobile telecommunciations device”).
Regarding claim 3, Douglas teaches the system of claim 2, wherein the optical assembly is removably attached to the image sensor ([0287] “acquisition device is a mobile phone with an otoscope attachment”; Figs. 29A-29B; [0336] “Otoscope device attached (left) and removed (right)”).
Regarding claim 4, Douglas teaches the system of claim 3, wherein each of the processor, the image sensor, the output device, and the non-transitory computer readable medium are part of a mobile device, the optical assembly being configured to attach to a surface of the mobile device ([0031] “an apparatus may also include an otoscope or otoscope adapter (lens portion) to connected to a mobile telecommunciations device and a non-transitory computer-readable storage medium storing a set of instructions capable of being executed by a processor”; [0155] “an otoscope and imaging apparatus in which the otoscope is modular is adapted to connect to a smartphone (e.g., iPhone.”; [0259] “the diagnosis classification model could be stored on the mobile device. In this case, the analysis could be performed completely on the mobile device without requiring any network communication with the server”; [0287] “acquisition device is a mobile phone with an otoscope attachment”).
Regarding claim 5, Douglas teaches the system of claim 2, wherein the plurality of optical elements comprise a first set of optical elements in a first module and a second set of optical elements in a second module, the first module being removably attached to the image sensor and the second module being configured to removably attach to the first module at a first location ([0167] “The otoscope attachment may be modular, and/or may include a lightguide (such as a lightpipe as described in more detail below) for transmitting light from the mobile telecommunications device (e.g., one or more "flash" LEDs on the phone) or an independent light source.” … “A speculum cone (e.g., disposable cover for speculum) may be placed over the speculum form of the otoscope component (referred to as a speculum, for convenience).”).
Regarding claim 6, Douglas teaches the system of claim 5, wherein the second module has an associated first optical property and the system further comprising a third module, having an associated second property and configured to removably attach to the first module at the first location, such that the second module can be replaced with the third module ([0191] “The left image (3A) shows an adult speculum, and the right (3B) is a pediatric speculum.”; [0192] “removable speculum”).
Regarding claim 7, Douglas teaches the system of claim 1, wherein the predictive model is implemented as an artificial neural network ([0175] “artificial neural networks”).
Regarding claim 9, Douglas teaches the system of claim 1, wherein a first non-transitory computer medium of the at least one non-transitory computer readable medium and a first processor of the at least one processor are local to the image sensor, and a second non-transitory computer medium of the at least one non-transitory computer readable medium and a second processor of the at least one processor provide a server in a location remote from the image sensor ([0170] “The one or more processors may receive the image(s) from the otoscope and may analyze the images and/or record the images. In particular, the processor may automatically detect and segment the TM from digital video or image(s) taken during an ear exam”; [0446] “the process is part of a mobile telecommunications device, which may efficiently locally process image data (e.g., for images taken with a scope (e.g., otoscope) coupled to the mobile telecommunications device such as a smartphone, and remotely communicate the information with a database, including an image database, and/or a medical server (e.g., medical records, hospital, clinic, or physician). The distribution of functions between the local (e.g., smartphone) and the remote (e.g., database) may allow more efficient processor usage, enhancing speed and reliability”), the system further comprising a first network interface associated with the first processor and a second network interface associated with the second processor, wherein the guidance component and the user interface are stored on the first non-transitory computer readable medium, the predictive model is stored on the second non-transitory computer readable medium, and the one of the first image or the second image is provided to the predictive model via the first and second network interfaces ([0258] “The ear exam in the doctor's office (query images or videos) would be transmitted to the data analytics server, which hosts the diagnosis classification model. After processing through the diagnosis classification model, the exam analysis (with diagnosis outcome) would in turn be transmitted back to the mobile phone”).
Regarding claim 11, Douglas teaches a method comprising:
receiving a first image captured from an image sensor ([0031] “receive an image of the subject's ear canal”);
determining an instruction for a user to adjust a position of the image sensor to bring the image sensor into alignment with a tympanic membrane of a subject by identifying a contour associated with the tympanic membrane via a segmentation algorithm and evaluating a shape of the contour to determine an appropriate movement of the image sensor to bring the contour associated with the tympanic membrane into a center of a field of view of the image sensor ([0039] “a method or system may alternatively or additional guide a subject by providing one or more directions, including directions on a display screen showing the images, audible cues, textual cues or the like, so that the subject may move the otoscope device to adjust the view being taken”; [0193-0194]; [0298] “Such a guidance system could provide "heads up display"-style cues (e.g., arrows, "locked on" indicator, or other graphics superimposed on the live video of the in-progress exam) or audio cues (e.g., a chime, or a voice instructing the user to move the otoscope left, right, into the ear, out of the ear, etc.) to guide the user during the exam”; [0031] “receive an image of the subject's ear canal; select a subset of subregions from the image; extract, at a plurality of different scales, a set of feature values for each of the subregions in the subset of subregions; estimate, for each individual subregion within the subset of subregions, a probability that the individual subregion is part of a tympanic membrane based on the extracted sets of feature values for the individual subregion; and identify, on a representation of the image, a tympanic membrane region from the image using the estimated probabilities for the subregions within the subset of subregions.”; [0045] “any of these methods and apparatuses may also include instructing the subject to straighten the ear canal.”; [0320]);
providing the determined instruction to the user via an output device ([0045] “Instructions may be visual (images, text, etc.) or audible, or both”; [0340-0341] “interfaces are methods for presenting and interacting with the relevant information, as well part of the system of image collection, review, storage, analysis, and transmission.”);
determining a clinical parameter representing otitis media from one of the first image or a second image captured with the tympanic membrane of the subject in the center of the field of view of the image sensor at a predictive model ([0176] “The image subjects may include healthy TMs, pathological TMs (e.g., acute otitis media, otitis media with effusion, foreign bodies, etc.), or images that do not contain a TM”; [0185] “machine learning classification model (e.g., a support vector machine or random forest model) included as part of the apparatus to predict whether the given pixel is part of the tympanic membrane or not”; [0232]; [0256] “The machine-learning model outputs a diagnosis outcome (example: normal versus AOM).”); and
providing the clinical parameter to the user via the output device ([0340-0341] “user-facing interface”; [0349] “interfaces for automated diagnosis results”).
Regarding claim 12, Douglas teaches the method of claim 11, wherein the clinical parameter is one of a continuous parameter representing a likelihood of otitis media within the subject and a categorical parameter having a first value representing the presence of otitis media and a second value representing the absence of otitis media ([0210] “For example, in an otoscopic image of a TM, one might describe three values: redness (from 0 to 1), texture (0 to 1), and shape (0 to 1). These features together comprise a "feature vector" that would be extracted from the image before being fed as input into a machine learning method to predict some response (e.g., diagnosis, prognosis, etc.).”; [0213] “in the case of many diseases affecting the TM, e.g., acute otitis media or otitis media with effusion, the TM may lose its translucency and, e.g., take on a reddish tint (compare FIG. 9A to FIG. 9B). Features related to color may therefore be useful for various machine learning tasks related to diagnosis, prognosis, etc.”; [0241]; [0387] “provide a simple and understandable analysis to a non-expert viewer (e.g., "ear infection detected", or "90% match with ear infection".”).
Regarding claim 13, Douglas teaches the method of claim 11, wherein the clinical parameter is a categorical parameter representing effusion ([0063] “extracting a plurality of image features from a first image of a subject's tympanic membrane, wherein the image features include color and texture data; combining the extracted features into a feature vector for the first image; applying the feature vector to a trained classification model to identify a probability of each of a plurality of different diseases; indicating the probability of each of a plurality different diseases.”; [0213] “However, in the case of many diseases affecting the TM, e.g., acute otitis media or otitis media with effusion, the TM may lose its translucency and, e.g., take on a reddish tint (compare FIG. 9A to FIG. 9B).”; [0215] “Pathological TMs tend to have rougher textures (e.g., due to prominent vascularization) or pockets of fluid (effusion). One would therefore expect that texture features, particularly those involving the TM, could prove relevant in the diagnosis or other knowledge extraction for otoscopic images.”), the categorical parameter having a first value representing the absence of effusion, a second value representing partial effusion, and a third value representing complete effusion ([0210] “in an otoscopic image of a TM, one might describe three values: redness (from 0 to 1), texture (0 to 1), and shape (0 to 1). These features together comprise a "feature vector" that would be extracted from the image before being fed as input into a machine learning method to predict some response (e.g., diagnosis, prognosis, etc.).”; The texture and color scores of 0 to 1 represents different levels of effusion in a patient.).
Regarding claim 14, Douglas teaches the method of claim 11, further comprising:
attaching an optical assembly to a mobile device ([0170] “an otoscope component that is coupled (directly or indirectly) with a mobile telecommunications device such as a smartphone”), the image sensor being contained within the mobile device ([0078] “The camera device may be a mobile telecommunications device (e.g., smartphone) having a built-in camera, as mentioned above”); and
inserting a distal end of the optical assembly into an ear of the patient ([0170] “an otoscope system may include an otoscope component, including one or more lenses and a speculum for insertion into a patient's ear.”).
Regarding claim 16, Douglas teaches a method comprising:
iteratively repeating the following steps until a contour associated with the tympanic membrane is completely within a field of view of an image sensor ([0171]; [0320] “identify a TM or a portion of a TM”);
receiving an image captured from the image sensor ([0031] “receive an image of the subject's ear canal”);
identifying the contour associated with the tympanic membrane via a segmentation algorithm ([0320] “uses the depth of the field of view (darker regions) to guide a user toward the TM”);
determining an appropriate movement of the image sensor to being the contour associated with the tympanic membrane into a center of the field of view of the image sensor by evaluating a shape of the contour ([0320] “determine directionality (e.g., to center the TM) based on the position of the identified probable TM region on the screen, and provide indicators (e.g., arrows, icons, audible instructions/guidance) to guide the subject in positioning the otoscope.”); and
providing the determined instruction to the user via an output device ([0045] “Instructions may be visual (images, text, etc.) or audible, or both”; [0340-0341] “interfaces are methods for presenting and interacting with the relevant information, as well part of the system of image collection, review, storage, analysis, and transmission.”);
providing the image captured from the image sensor to a predictive model ([0047]; [0064]; [0258] “The ear exam in the doctor's office (query images or videos) would be transmitted to the data analytics server, which hosts the diagnosis classification model. After processing through the diagnosis classification model, the exam analysis (with diagnosis outcome) would in turn be transmitted back to the mobile phone”);
determining a clinical parameter representing otitis media from the image captured at the image sensor at the predictive model ([0176] “The image subjects may include healthy TMs, pathological TMs (e.g., acute otitis media, otitis media with effusion, foreign bodies, etc.), or images that do not contain a TM”; [0185] “machine learning classification model (e.g., a support vector machine or random forest model) included as part of the apparatus to predict whether the given pixel is part of the tympanic membrane or not”; [0232]; [0256] “The machine-learning model outputs a diagnosis outcome (example: normal versus AOM).”); and
providing the clinical parameter to the user via the output device ([0340-0341] “user-facing interface”; [0349] “interfaces for automated diagnosis results”).
Regarding claim 18, Douglas teaches the method of claim 16, wherein providing the image captured from the image sensor to the predictive model comprises sending the image captured from the image sensor to a remote server via a network interface ([0170] “The one or more processors may receive the image(s) from the otoscope and may analyze the images and/or record the images. In particular, the processor may automatically detect and segment the TM from digital video or image(s) taken during an ear exam”; [0446] “the process is part of a mobile telecommunications device, which may efficiently locally process image data (e.g., for images taken with a scope (e.g., otoscope) coupled to the mobile telecommunications device such as a smartphone, and remotely communicate the information with a database, including an image database, and/or a medical server (e.g., medical records, hospital, clinic, or physician). The distribution of functions between the local (e.g., smartphone) and the remote (e.g., database) may allow more efficient processor usage, enhancing speed and reliability”; [0258] “The ear exam in the doctor's office (query images or videos) would be transmitted to the data analytics server, which hosts the diagnosis classification model. After processing through the diagnosis classification model, the exam analysis (with diagnosis outcome) would in turn be transmitted back to the mobile phone”).
Regarding claim 19, Douglas teaches the method of claim 16, further comprising:
attaching an optical assembly to a mobile device ([0170] “an otoscope component that is coupled (directly or indirectly) with a mobile telecommunications device such as a smartphone”), the image sensor being contained within the mobile device ([0078] “The camera device may be a mobile telecommunications device (e.g., smartphone) having a built-in camera, as mentioned above”); and
inserting a distal end of the optical assembly into an ear of the patient ([0170] “an otoscope system may include an otoscope component, including one or more lenses and a speculum for insertion into a patient's ear.”).
Regarding claim 20, Douglas teaches the method of claim 16, wherein the clinical parameter is one of a continuous parameter representing a likelihood of infection and a categorical parameter representing infection, the categorical parameter having a first value representing the absence of infection, a second value representing possible infection, and a third value representing the presence of infection ([0210] “For example, in an otoscopic image of a TM, one might describe three values: redness (from 0 to 1), texture (0 to 1), and shape (0 to 1). These features together comprise a "feature vector" that would be extracted from the image before being fed as input into a machine learning method to predict some response (e.g., diagnosis, prognosis, etc.).”; [0213] “in the case of many diseases affecting the TM, e.g., acute otitis media or otitis media with effusion, the TM may lose its translucency and, e.g., take on a reddish tint (compare FIG. 9A to FIG. 9B). Features related to color may therefore be useful for various machine learning tasks related to diagnosis, prognosis, etc.”; [0241]; [0387] “provide a simple and understandable analysis to a non-expert viewer (e.g., "ear infection detected", or "90% match with ear infection".”).
Regarding claim 21, Douglas teaches the system of claim 1, wherein the segmentation algorithm is an edge detection algorithm ([0198] “edge detection is applied to the original average intensity image and a binary or real-valued edge mask is calculated. Edge detection proceeds only within the detected outer circle; edges outside the detected outer circle boundary are discarded”).
Regarding claim 22, Douglas teaches the method of claim 11, wherein the segmentation algorithm is an edge detection algorithm ([0198] “edge detection is applied to the original average intensity image and a binary or real-valued edge mask is calculated. Edge detection proceeds only within the detected outer circle; edges outside the detected outer circle boundary are discarded”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20150065803 A1 (Douglas et al.) in view of US 20220104688 A1 (Hill, Courtney).
Regarding claim 8, Douglas teaches the system of claim 7, wherein the artificial neural network has a set of associated parameters generated from a stored set of training data ([0176] “To generate training data, the user generates a training set of images, which may be frames from one or more videos, where each image contains an outlined contour of the TM to yield the TM segmentation mask. Some images may not contain a valid TM, in which case the TM segmentation will be present, but empty. The image subjects may include healthy TMs, pathological TMs (e.g., acute otitis media, otitis media with effusion, foreign bodies, etc.), or images that do not contain a TM”)
Douglas does not explicitly teach the set of training data comprising images taken from subjects who later underwent a myringotomy, a class label for each image being determined from the myringotomy.
However,
Hill teaches the set of training data comprising images taken from subjects who later underwent a myringotomy, a class label for each image being determined from the myringotomy ([0009] “Accurate labeling of training images only occurs when the middle ear status is defined by findings when a myringotomy is made (incision in the ear drum)”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the method taught by Douglas to include training on subjects who later underwent a myringotomy. One would have been motivated to make this modification because after a myringtomony, the contents of the middle ear can be visualized for 100% accuracy in labeling the image as being normal, having fluid, or having infection, as suggested by Hill [0009].
Regarding claim 17, Douglas teaches the method of claim 16, determining the clinical parameter representing otitis media from the image captured at the image sensor comprises providing the image captured at the image sensor to a convolutional neural network ([0218] “image features would be learned "automatically" using convolutional neural networks or other techniques and may be combined with non-image features, such as semantic or clinical features (discussed below)”)
Douglas does not explicitly teach trained on images taken from subjects who later underwent a myringotomy, a class label for each image being determined from the myringotomy.
However,
Hill teaches images taken from subjects who later underwent a myringotomy, a class label for each image being determined from the myringotomy ([0009] “Accurate labeling of training images only occurs when the middle ear status is defined by findings when a myringotomy is made (incision in the ear drum)”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the invention to have modified the method taught by Douglas to include training on subjects who later underwent a myringotomy. One would have been motivated to make this modification because after a myringtomony, the contents of the middle ear can be visualized for 100% accuracy in labeling the image as being normal, having fluid, or having infection, as suggested by Hill [0009].
Response to Arguments
Applicant's arguments filed February 13, 2026 have been fully considered but they are not persuasive. With respect to the 102 Rejections in the Non-Final Office Action (See Pages 7-8 of Applicant’s Response), Applicant argues that Douglas uses geometric shape differentiation to differentiate the tip of an ear speculum from the ear below, which does not require identification or confirmation of tympanic membrane structures. Applicant states that the intention of the present invention is to actively guide the user to localize and orient the ear drum in the center of the image prior to permitting any diagnostic inference or submission of the image for analysis by evaluating the contour of the specific ear anatomy via a segmentation algorithm to separate the tympanic membrane from surrounding structures such as the ear canal to verify that the target anatomy is the tympanic membrane itself rather than a generic circular or reflective structure. Applicant argues that Douglas does not do this because no anatomical validation step is required prior to classification. Applicant also states that Douglas does not perform an analysis of a shape of the tympanic membrane within the image, as claimed.
MPEP § 2111 discusses proper claim interpretation, including giving claims their broadest reasonable interpretation in light of the specification during examination. Under broadest reasonable interpretation (BRI), the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification, and it is improper to import claim limitations from the specification into the claim. The requirements for anticipation are discussed in MPEP § 2131.
As written, independent claims 1, 11, and 16 require identifying the contour associated with the tympanic membrane via a segmentation algorithm. Under BRI, the segmentation algorithm can be interpreted to be any type of method that identifies any portion of the image. As written, the claims to not require any anatomical analysis or validation. The “contour associated with a tympanic membrane” can be any shape or color related to a tympanic membrane under BRI. Douglas teaches a segmentation algorithm using “a shape finding” method described in [0193] “that can find one or more pre-determined shapes of varying sizes, locations and (possibly) orientations within an image may be used. The apparatus may then assess some quality metric of found matches depending on how well they match the shape prototype. The apparatus may use the found match of the highest quality as the "correct" field of view”. The language of claims 1, 11, and 16 as written does not preclude a “circle within a circle” method from being utilized, as this may be considered a segmentation algorithm under BRI. Douglas also describes guiding the user toward the TM using the darker regions in the field of view ([0320] “identify a TM or a portion of a TM, as described herein. The apparatus may be configured to determine directionality (e.g., to center the TM) based on the position of the identified probable TM region on the screen, and provide indicators (e.g., arrows, icons, audible instructions/guidance) to guide the subject in positioning the otoscope.”). The “shape finding” segmentation algorithm to identify the TM can include edge detection [0198] as required by new claims 21 and 22.
Claims 2-9, 11-14, and 16-22 are rejected because the rejection of claims 1, 11, and 16 are proper and the prior art teaches or suggests all the features of these claims for the reasons described in the 102 and 103 Rejections.
Conclusion
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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/EVELYN GRACE PARK/Examiner, Art Unit 3791
/TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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