DETAIL OFFICE ACTIONS
The United States Patent & Trademark Office appreciates the response filed for the current application that is submitted on 10/14/2025. The United States Patent & Trademark Office reviewed the following documents submitted and has made the following comments below.
Amendment
Applicant submitted amendments on 10/14/2025. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Applicant Arguments:
Applicant/s state/s that the cited prior arts do not teach the amended claims; therefore, the rejection under 35 U.S.C. 103 should be withdrawn.
Examiner’s Responses:
Applicant’s arguments and amendments, see Remarks, filed 10/14/2025, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration of amendments, a new ground(s) of rejection is made in view of Chernyak et al. (US-20030223037-A1, cited in IDS) in view of Fea et al. (Fea, Antonio Maria, et al. "Cyclotorsional eye movements during a simulated PRK procedure." Eye 20.7) in view of Hamza (US20100142765A1) and further in view of Sarver et al. (US20220218197A1).
Claims Status:
Claims 7 and 14-15 are rejected under 35 U.S.C. 112 (b).
Claims 1-4, 6-20 are rejected under 35 U.S.C. 103 in view Chernayk in view of Fea, in view of Hamza, and further in view of Sarver.
Claim 5 is rejected under 35 U.S.C. 103 in view Chernayk in view of Fea, in view of Hamza in view of Sarver, and further in view of Wang.
Claim 13 is objected.
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.
Claim 7 and 14-15 is 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. The examiner strongly suggested that appropriate corrections be made to clarify the claim scope.
With respect to Claim 7, the claim recites the following, each of which renders the claim indefinite:
“the reference image” on lines 4-6 (unclear antecedent basis).
With respect to Claim 14, the claim recites the following, each of which renders the claim indefinite:
“the reference image” on line 6 (unclear antecedent basis).
With respect to Claim 15, the claim recites the following, each of which renders the claim indefinite:
“the reference image” on lines 10-11 (unclear antecedent basis).
Claim Rejections - 35 USC § 103
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 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.
Claim(s) 1-4, 6-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Chernyak et al. (US-20030223037-A1, cited in IDS, published 12/04/2003, hereinafter Chernyak) in view of
Fea et al. (Fea, Antonio Maria, et al. "Cyclotorsional eye movements during a simulated PRK procedure." Eye 20.7, published 2006, hereinafter Fea) in view of
Hamza (US20100142765A1, hereinafter Hamza) and further in view of
Sarver et al. (US20220218197A1, filed 2021, hereinafter Sarver).
CLAIM 1
In regards to Claim 1, Chernyak teaches an ophthalmological image processing device (Chernyak, Abstract: “Methods and systems for tracking a position and torsional orientation of a patient's eye”) comprising a processor (Chernyak, ¶ [0018-0019]: “The system includes a computer processor”) configured to: receive a reference image of an eye of a person (Chernyak, ¶ [0017-0018]: “An image of the patient's eye is obtained…a computer processor configured to receive a first image of an eye…”).
Chernayk does not explicitly disclose analyze the first reference image by calculating a quality measure of the first reference image, the quality measure indicative of a suitability of the first reference image for a cyclorotation assessment.
Fea is in the same field of art of cyclorotation assessment. Further, Fea teaches analyze the first reference image by calculating a quality measure of the first reference image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”), the quality measure indicative of a suitability of the first reference image for a cyclorotation assessment (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernayk by incorporating quality criteria for image data in cyclorotation assessment that is taught by Fea, to make an ophthalmology device that performs quality check for cyclorotation assessment; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for high quality input image to achieve correct measurement (Fea, page 767, “The image quality index was constantly high, assuring that the measure of torsion was correct throughout the experimental settings”).
The combination of Chernyak and Fea does not explicitly disclose the quality measure comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye or an iris structure measure indicative of a level of structuring of the iris.
Hamza is in the same field of art of assessing quality of eye images. Further, Hamza teaches the quality measure (Hamza, ¶ [0014]: “The present invention may include an implementation of a set of appropriate quantitative iris image quality metrics (IQM's)”) comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye (Hamza, ¶ [0021-0022]: “IQM4 is a simple test of the location of the eye within the eye image… it can be considered an offset eye as it may not contain the entire bounds of the iris… IQM5 is an amount of iris exposure within the iris map”, ¶ [0037-0038]: “The visibility measure according to IQM5…”, see statement 68 in [0038]) or (The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required.) an iris structure measure indicative of a level of structuring of the iris. (Hamza, ¶ [0017-0018]: “IQM1 is eye validation. Eye validation may be assessed using the pupil edges (i.e., inner border of the iris) and determining how they fit to an elliptic model… IQM2 is blur amount. Properties of a neighborhood pixel distribution may be considered using a gradient of the iris texture … for non-blur images, one may expect additional detected edges which exceed the amount associated with the inner borders”, ¶ [0022]: “IQM6 is similar to IQM1 but is applied to the outer border of the iris rather than the inner border.” Hamza teaches quality metrics considering visible anatomical features of the iris (inner, outer border, and edges at the borders of the iris))
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak and Fea by incorporating iris image quality metrics that is taught by Hamza, to make a system to assessing quality of eye images considering visibility and intrinsic structures of iris; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for a good quantitative system to assessing quality of iris images in real-time (Hamza, ¶ [0014]: “It appears desirable to assess the quality of an eye image in real-time as a quality control procedure. This may allow poor image acquisition to be corrected through recapture and facilitate acquisition of the best possible image within the capture time window configured in the system”).
The combination of Chernyak, Fea and Hamza teaches evaluate, using the quality measure and without using a second reference image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”) (Hamza, ¶ [0017-0022] and ¶ [0032-0037]. The computation of the IQMs for an image does not require a second image.), whether the first reference image is suitable for a cyclorotation assessment. (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”. Fea teaches quality check eye images, and the eye images are used for cyclorotation assessment)
The combination of Chernyak, Fea and Hamza does not explicitly disclose generate a message indicating whether an image is suitable for a cyclorotation assessment.
Sarver is in the same field of art of analyzing eye images. Further, Sarver teaches generating a message indicating whether the reference image is suitable for a cyclorotation assessment. (Sarver, ¶ [0075]: “the controller may display a message indicating to the operator that the image was not used to generate measurements because the quality is too low”)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak, Fea, and Hamza by incorporating message-based communication method that is taught by Sarver, to make an ophthalmology system that can communicate with user through image; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for improved techniques for performing image data processing and analysis during ophthalmological procedures (Sarver, ¶ [0004]: “there is a need for improved techniques for performing image data processing and analysis during procedures”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 2
In regards to Claim 2, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver and Sarver teaches a display, wherein the processor is configured to render the message on the display. (Sarver, ¶ [0179]: “user interface devices… touch screens…display devices”)
CLAIM 3
In regards to Claim 3, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and teaches rendering a warning on the display if the message indicates that the first reference image is unsuitable for the cyclorotation assessment. (Sarver, ¶ [0072]: “the controller may provide simplified or generic warnings or prompts to the operator that the quality level threshold was not met by one or more images”)
CLAIM 4
In regards to Claim 4, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches the reference image was recorded with the person in an upright position (Fea, Page 765, left col, fourth paragraph: “Four different experimental settings were used to assess the cyclotorsional eye movements: the subjects were fixating in upright position (1) …”) (Chernyak, ¶ [0006]: “when the wavefront measurement is taken, the patient will generally be in a seated position”) by a camera of a diagnostic device and the processor is configured to receive the reference image from the diagnostic device. (Chernyak, ¶ [0077]: “wavefront measurement device includes an imaging assembly that can image the patient's eye during the wavefront measurement… The imaging assembly can be in communication with a computer system to deliver the image(s) of the patient's eye to a memory in the computer.”)
CLAIM 6
In regards to Claim 6, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches providing the message and the reference image to an ophthalmological laser treatment device (Chernyak, ¶ [0075]: “The wavefront data and/or the customized ablation profile can be loaded into a laser surgical system”, see FIG. 1) for use in the cyclorotation assessment, in which cyclorotation assessment an angle of cyclorotation of the eye (Chernyak, ¶ [0006]: A cyclotorsional rotation of the eye is estimated between the first image and second image “”) is determined using the reference image and a current image of the eye recorded by a camera of the ophthalmological laser treatment device (Chernyak, ¶ [0089-0096]: “an alignment algorithm that can torsionally register a reference image with a second image of the eye to determine the torsional displacement between the two images of the eye”, FIG. 5 and Claim 22) when the person is in a supine position. (Chernyak, ¶ [0006]: “when the laser eye surgery is being performed, the patient will generally be in a supine position”) (Fea, page 766, section result: “When moving from an upright to a supine position, the average change in cyclotorsion is 3.31”, see table 1 and 2.) (Both Chenryak and Fea discloses calculate cyclotorsion between two images, one is upright position, one is supine position)
CLAIM 7
In regards to Claim 7, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches generate the iris visibility measure by analyzing one or more of the following photographic characteristics of the first reference image : a global dynamic range of the entire reference image, a local dynamic range of one or more areas of the reference image, a global contrast of the entire reference image, a local contrast of one or more areas of the reference image, a global sharpness, a local sharpness of one or more areas of the reference image (Hamza, ¶ [0018]: “IQM2 is blur amount. Properties of a neighborhood pixel distribution may be considered using a gradient of the iris texture”), a noise level, a reflection indicator indicating whether a reflection of a light source is present (Hamza, ¶ [0046-0047]: “Cluster would then be noise such a blocked lower portion of the iris or a bright reflective spot on the iris which may be regarded as an outlier… There may be leaked pixels from a cluster to another due to poor segmentation or other artifacts, e.g., reflections”), or (The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required.) an artifact measure indicating whether visual artifacts are present. (Hamza, ¶ [0016]: “The present metrics may be … Based upon the amount of the artifacts, from obscuration, occlusion, or blurring or other effects, a process may be applied based upon the case based (CBR) reasoning approach.”)
CLAIM 8
In regards to Claim 8, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches generate the iris visibility measure by determining one or more of: a level of pupil dilation of the eye (Chernyak, ¶ [0088]: “The image of the patient's eye can be analyzed by an algorithm that locates the center of the pupil and/or iris, calculates the radius of the pupil and/or iris”) or (The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required.) an eyelid coverage of the iris. (Hamza, ¶ [0017]: “IQM1 is eye validation. Eye validation may be assessed using the pupil edges (i.e., inner border of the iris) … one may mask the upper lids and re-assess only the sclera and bottom lids against a model fit”) (Chernyak, ¶ [0098]: “the smallest distance between the edge of the pupil and the obstructing elements, such as eyelids, eyelashes, strong shadows or highlights should be sufficiently large to leave a portion of the iris completely exposed for the entire 360-degree range”)
CLAIM 9
In regards to Claim 9, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches generate the iris structure measure by determining whether the iris has global or local features which are not rotationally invariant. (Hamza, ¶ [0018]: “IQM5 is an amount of iris exposure within the iris map”, ¶ [0046]: “This process relates to map analysis involving a stage for extracting outliers in the iris map. The may be clusters and of pixels outliers. Cluster 37 may be set out by valleys and in the histogram. The iris pixels should be part of just one cluster as the color of the iris would tend to result in pixels having a similar intensity since there is generally one overall color in the iris” the Examiner notes color of iris is not rotationally invariant)
CLAIM 10
In regards to Claim 10, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches generate the iris structure measure by identifying one or more landmark features in the first reference image of the eye. (Hamza, ¶ [0017 and 0022]: “IQM1 is eye validation. Eye validation may be assessed using the pupil edges (i.e., inner border of the iris) and determining how they fit to an elliptic model… IQM6 is similar to IQM1 but is applied to the outer border of the iris rather than the inner border.” Hamza teaches identifying inner and outer border of the iris from the eye image) (Chernyak, ¶ [0099-0110]: “A radius and center of the pupil can be estimated by a standard weighted least-square estimation procedure”. The Examiner notes center of the pupil corresponds to landmark feature in an eye image.)
CLAIM 11
In regards to Claim 11, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches generate one or more of: the iris visibility measure or the iris structure measure, using a neural network. (Sarver, ¶ [0007 and 0199]: “a two-stage machine learning model that identifies artifacts in images …. an overall artifact value includes a total image quality score, a total number of artifacts in the image” Sarver teaches generating a image quality score regarding the number of visual artifacts in the image, the examiner notes artifacts affect iris visibility)
CLAIM 12
In regards to Claim 12, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 11. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches the neural network is trained using supervised learning and a training dataset comprising a plurality of training reference images of a plurality of eyes, each training reference image having an associated pre-determined quality measure, iris visibility measure (Sarver, ¶ [0106-0107]: “The machine learning model may be trained using a collection of images that have been labeled with respect to containing one or more artifacts…Each of the images in the collection may have been manually reviewed and labeled with respect to artifacts contained therein. For example, each image may be labeled to indicate whether or not the image includes one or more bubble regions, one or more floater regions, one or more glint regions, one or more artifacts, and the like”), or (The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required.) iris structure measure.
CLAIM 14
In regards to Claim 14, Chernyak teaches a processor (Chernyak, ¶ [0018-0019]: “The system includes a computer processor”) of an ophthalmological image processing device (Chernyak, Abstract: “Methods and systems for tracking a position and torsional orientation of a patient's eye”) performing the steps of: receive a reference image of an eye of a person (Chernyak, ¶ [0017-0018]: “An image of the patient's eye is obtained…a computer processor configured to receive a first image of an eye…”).
Chernayk does not explicitly disclose analyze the first reference image by calculating a quality measure of the first reference image, the quality measure indicative of a suitability of the first reference image for a cyclorotation assessment.
Fea is in the same field of art of cyclorotation assessment. Further, Fea teaches analyze the first reference image by calculating a quality measure of the first reference image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”), the quality measure indicative of a suitability of the first reference image for a cyclorotation assessment (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernayk by incorporating quality criteria for image data in cyclorotation assessment that is taught by Fea, to make an ophthalmology device that performs quality check for cyclorotation assessment; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for high quality input image to achieve correct measurement (Fea, page 767, “The image quality index was constantly high, assuring that the measure of torsion was correct throughout the experimental settings”).
The combination of Chernyak and Fea does not explicitly disclose a method for determining a quality measure of a reference image of an eye; the quality measure comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye or an iris structure measure indicative of a level of structuring of the iris.
Hamza is in the same field of art of assessing quality of eye images. Further, Hamza teaches a method for determining a quality measure of a reference image of an eye (Hamza, ¶ [0011]: “methods and apparatus for developing quantitative measures that can automatically assess the quality of iris images”); the quality measure (Hamza, ¶ [0014]: “The present invention may include an implementation of a set of appropriate quantitative iris image quality metrics (IQM's)”) comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye (Hamza, ¶ [0021-0022]: “IQM4 is a simple test of the location of the eye within the eye image… it can be considered an offset eye as it may not contain the entire bounds of the iris… IQM5 is an amount of iris exposure within the iris map”, ¶ [0037-0038]: “The visibility measure according to IQM5…”, see statement 68 in [0038]) or (The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required.) an iris structure measure indicative of a level of structuring of the iris. (Hamza, ¶ [0017-0018]: “IQM1 is eye validation. Eye validation may be assessed using the pupil edges (i.e., inner border of the iris) and determining how they fit to an elliptic model… IQM2 is blur amount. Properties of a neighborhood pixel distribution may be considered using a gradient of the iris texture … for non-blur images, one may expect additional detected edges which exceed the amount associated with the inner borders”, ¶ [0022]: “IQM6 is similar to IQM1 but is applied to the outer border of the iris rather than the inner border.” Hamza teaches quality metrics considering visible anatomical features of the iris (inner, outer border, and edges at the borders of the iris))
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak and Fea by incorporating iris image quality metrics that is taught by Hamza, to make a system to assessing quality of eye images considering visibility and intrinsic structures of iris; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for a good quantitative system to assessing quality of iris images in real-time (Hamza, ¶ [0014]: “It appears desirable to assess the quality of an eye image in real-time as a quality control procedure. This may allow poor image acquisition to be corrected through recapture and facilitate acquisition of the best possible image within the capture time window configured in the system”).
The combination of Chernyak, Fea and Hamza teaches evaluate, using the quality measure and without using a second reference image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”) (Hamza, ¶ [0017-0022] and ¶ [0032-0037]. The computation of the IQMs for an image does not require a second image.), whether the first reference image is suitable for a cyclorotation assessment. (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”. Fea teaches quality check eye images, and the eye images are used for cyclorotation assessment)
The combination of Chernyak, Fea and Hamza does not explicitly disclose generate a message indicating whether an image is suitable for a cyclorotation assessment.
Sarver is in the same field of art of analyzing eye images. Further, Sarver teaches generating a message indicating whether the reference image is suitable for a cyclorotation assessment. (Sarver, ¶ [0075]: “the controller may display a message indicating to the operator that the image was not used to generate measurements because the quality is too low”)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak, Fea, and Hamza by incorporating message-based communication method that is taught by Sarver, to make an ophthalmology system that can communicate with user through image; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for improved techniques for performing image data processing and analysis during ophthalmological procedures (Sarver, ¶ [0004]: “there is a need for improved techniques for performing image data processing and analysis during procedures”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 15
In regards to Claim 15, Chernyak teaches a non-transitory computer-readable medium having stored thereon computer program code (Chernyak, ¶ [0071]: “a tangible storage media embodying steps or programming instructions for any of the methods of the present invention”) for controlling a processor (Chernyak, ¶ [0018-0019]: “The system includes a computer processor”) of an ophthalmological image processing device (Chernyak, Abstract: “Methods and systems for tracking a position and torsional orientation of a patient's eye”) to: receive a reference image of an eye of a person (Chernyak, ¶ [0017-0018]: “An image of the patient's eye is obtained…a computer processor configured to receive a first image of an eye…”).
Chernayk does not explicitly disclose analyze the first reference image by calculating a quality measure of the first reference image, the quality measure indicative of a suitability of the first reference image for a cyclorotation assessment.
Fea is in the same field of art of cyclorotation assessment. Further, Fea teaches analyze the first reference image by calculating a quality measure of the first reference image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”), the quality measure indicative of a suitability of the first reference image for a cyclorotation assessment (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernayk by incorporating quality criteria for image data in cyclorotation assessment that is taught by Fea, to make an ophthalmology device that performs quality check for cyclorotation assessment; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for high quality input image to achieve correct measurement (Fea, page 767, “The image quality index was constantly high, assuring that the measure of torsion was correct throughout the experimental settings”).
The combination of Chernyak and Fea does not explicitly disclose the quality measure comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye or an iris structure measure indicative of a level of structuring of the iris.
Hamza is in the same field of art of assessing quality of eye images. Further, Hamza teaches the quality measure (Hamza, ¶ [0014]: “The present invention may include an implementation of a set of appropriate quantitative iris image quality metrics (IQM's)”) comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye (Hamza, ¶ [0021-0022]: “IQM4 is a simple test of the location of the eye within the eye image… it can be considered an offset eye as it may not contain the entire bounds of the iris… IQM5 is an amount of iris exposure within the iris map”, ¶ [0037-0038]: “The visibility measure according to IQM5…”, see statement 68 in [0038]) or (The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required.) an iris structure measure indicative of a level of structuring of the iris. (Hamza, ¶ [0017-0018]: “IQM1 is eye validation. Eye validation may be assessed using the pupil edges (i.e., inner border of the iris) and determining how they fit to an elliptic model… IQM2 is blur amount. Properties of a neighborhood pixel distribution may be considered using a gradient of the iris texture … for non-blur images, one may expect additional detected edges which exceed the amount associated with the inner borders”, ¶ [0022]: “IQM6 is similar to IQM1 but is applied to the outer border of the iris rather than the inner border.” Hamza teaches quality metrics considering visible anatomical features of the iris (inner, outer border, and edges at the borders of the iris))
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak and Fea by incorporating iris image quality metrics that is taught by Hamza, to make a system to assessing quality of eye images considering visibility and intrinsic structures of iris; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for a good quantitative system to assessing quality of iris images in real-time (Hamza, ¶ [0014]: “It appears desirable to assess the quality of an eye image in real-time as a quality control procedure. This may allow poor image acquisition to be corrected through recapture and facilitate acquisition of the best possible image within the capture time window configured in the system”).
The combination of Chernyak, Fea and Hamza teaches evaluate, using the quality measure and without using a second reference image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”) (Hamza, ¶ [0017-0022] and ¶ [0032-0037]. The computation of the IQMs for an image does not require a second image.), whether the first reference image is suitable for a cyclorotation assessment. (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”. Fea teaches quality check eye images, and the eye images are used for cyclorotation assessment)
The combination of Chernyak, Fea and Hamza does not explicitly disclose generate a message indicating whether an image is suitable for a cyclorotation assessment.
Sarver is in the same field of art of analyzing eye images. Further, Sarver teaches generating a message indicating whether the reference image is suitable for a cyclorotation assessment. (Sarver, ¶ [0075]: “the controller may display a message indicating to the operator that the image was not used to generate measurements because the quality is too low”)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak, Fea, and Hamza by incorporating message-based communication method that is taught by Sarver, to make an ophthalmology system that can communicate with user through image; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for improved techniques for performing image data processing and analysis during ophthalmological procedures (Sarver, ¶ [0004]: “there is a need for improved techniques for performing image data processing and analysis during procedures”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 16
In regards to Claim 16, Chernyak teaches an ophthalmological image processing device (Chernyak, Abstract: “Methods and systems for tracking a position and torsional orientation of a patient's eye”) comprising a processor (Chernyak, ¶ [0018-0019]: “The system includes a computer processor”) configured to: receive one or more reference images of an eye of a person (Chernyak, ¶ [0017-0018]: “An image of the patient's eye is obtained…a computer processor configured to receive a first image of an eye…”), wherein the one or more reference images were recorded with the person in an upright position (Chernyak, ¶ [0006]: “when the wavefront measurement is taken, the patient will generally be in a seated position”) by a camera of a diagnostic device. (Chernyak, ¶ [0077]: “wavefront measurement device includes an imaging assembly that can image the patient's eye during the wavefront measurement… The imaging assembly can be in communication with a computer system to deliver the image(s) of the patient's eye to a memory in the computer.”)
Chernayk does not explicitly disclose analyze the one or more reference image by calculating a quality measure of the one or more reference image, the quality measure indicative of a suitability of the one or more reference image for a cyclorotation assessment.
Fea is in the same field of art of cyclorotation assessment. Further, Fea teaches analyze the one or more reference image by calculating a quality measure of the one or more reference image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”), the quality measure indicative of a suitability of the one or more reference image for a cyclorotation assessment (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernayk by incorporating quality criteria for image data in cyclorotation assessment that is taught by Fea, to make an ophthalmology device that performs quality check for cyclorotation assessment; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for high quality input image to achieve correct measurement (Fea, page 767, “The image quality index was constantly high, assuring that the measure of torsion was correct throughout the experimental settings”).
The combination of Chernyak and Fea does not explicitly disclose the quality measure comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye or an iris structure measure indicative of a level of structuring of the iris.
Hamza is in the same field of art of assessing quality of eye images. Further, Hamza teaches the quality measure (Hamza, ¶ [0014]: “The present invention may include an implementation of a set of appropriate quantitative iris image quality metrics (IQM's)”) comprises one or more of: an iris visibility measure indicative of a level of visibility of the iris of the eye (Hamza, ¶ [0021-0022]: “IQM4 is a simple test of the location of the eye within the eye image… it can be considered an offset eye as it may not contain the entire bounds of the iris… IQM5 is an amount of iris exposure within the iris map”, ¶ [0037-0038]: “The visibility measure according to IQM5…”, see statement 68 in [0038]) or (The Examiner notes since a listing with “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required.) an iris structure measure indicative of a level of structuring of the iris. (Hamza, ¶ [0017-0018]: “IQM1 is eye validation. Eye validation may be assessed using the pupil edges (i.e., inner border of the iris) and determining how they fit to an elliptic model… IQM2 is blur amount. Properties of a neighborhood pixel distribution may be considered using a gradient of the iris texture … for non-blur images, one may expect additional detected edges which exceed the amount associated with the inner borders”, ¶ [0022]: “IQM6 is similar to IQM1 but is applied to the outer border of the iris rather than the inner border.” Hamza teaches quality metrics considering visible anatomical features of the iris (inner, outer border, and edges at the borders of the iris))
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak and Fea by incorporating iris image quality metrics that is taught by Hamza, to make a system to assessing quality of eye images considering visibility and intrinsic structures of iris; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for a good quantitative system to assessing quality of iris images in real-time (Hamza, ¶ [0014]: “It appears desirable to assess the quality of an eye image in real-time as a quality control procedure. This may allow poor image acquisition to be corrected through recapture and facilitate acquisition of the best possible image within the capture time window configured in the system”).
The combination of Chernyak, Fea and Hamza teaches calculating a quality measure of the one or more reference images using exclusively the one or more reference images (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”) (Hamza, ¶ [0017-0022] and ¶ [0032-0037]. The computation of the IQMs for an image does not require a second image.); evaluate whether the one or more reference image is suitable for a cyclorotation assessment. (Fea, page 765, right col, second paragraph: “In the analysis, because excyclotorsions are reported as negative and incyclotorsions as positive and averaging the values would have underestimated the torsional movements, absolute values were used. The average cyclorotation in the four experimental settings was analyzed”. Fea teaches quality check eye images, and the eye images are used for cyclorotation assessment)
The combination of Chernyak, Fea and Hamza does not explicitly disclose generate a message indicating whether an image is suitable for a cyclorotation assessment.
Sarver is in the same field of art of analyzing eye images. Further, Sarver teaches generating a message indicating whether the reference image is suitable for a cyclorotation assessment. (Sarver, ¶ [0075]: “the controller may display a message indicating to the operator that the image was not used to generate measurements because the quality is too low”)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak, Fea, and Hamza by incorporating message-based communication method that is taught by Sarver, to make an ophthalmology system that can communicate with user through image; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for improved techniques for performing image data processing and analysis during ophthalmological procedures (Sarver, ¶ [0004]: “there is a need for improved techniques for performing image data processing and analysis during procedures”).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
CLAIM 17
In regards to Claim 17, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 16. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches calculate the quality measure of the one or more reference images without using an image (Fea, page 765, right col, first paragraph: “The SMI program provides a quality index that relates to each frame. Low-quality index frames (=<0.7) were excluded from the analysis”) (Hamza, ¶ [0017-0022] and ¶ [0032-0037]. The computation of the IQMs for an image does not require a second image.) recorded by a camera when the person is in a supine position. (Fea, Page 765, left col, fourth paragraph: “Four different experimental settings were used to assess the cyclotorsional eye movements: the subjects were fixating in upright position (1) …”)
CLAIM 18
In regards to Claim 18, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 16. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches the processor is configured to render a warning on a display if the message indicates that at least one of the one or more reference images is unsuitable for the cyclorotation assessment. (Sarver, ¶ [0072]: “the controller may provide simplified or generic warnings or prompts to the operator that the quality level threshold was not met by one or more images”)
CLAIM 19
In regards to Claim 19, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 16. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches the processor is configured to receive the one or more reference images from the diagnostic device. (Chernyak, ¶ [0077]: “wavefront measurement device includes an imaging assembly that can image the patient's eye during the wavefront measurement… The imaging assembly can be in communication with a computer system to deliver the image(s) of the patient's eye to a memory in the computer.”)
CLAIM 20
In regards to Claim 20, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 16. In addition, the combination of Chernyak, Fea, Hamza and Sarver teaches providing the message and the one or more reference images to an ophthalmological laser treatment device (Chernyak, ¶ [0075]: “The wavefront data and/or the customized ablation profile can be loaded into a laser surgical system”, see FIG. 1) for use in the cyclorotation assessment, in which cyclorotation assessment an angle of cyclorotation of the eye (Chernyak, ¶ [0006]: A cyclotorsional rotation of the eye is estimated between the first image and second image “”) is determined using the one or more reference images and a current image of the eye recorded by a camera of the ophthalmological laser treatment device (Chernyak, ¶ [0089-0096]: “an alignment algorithm that can torsionally register a reference image with a second image of the eye to determine the torsional displacement between the two images of the eye”, FIG. 5 and Claim 22) when the person is in a supine position. (Chernyak, ¶ [0006]: “when the laser eye surgery is being performed, the patient will generally be in a supine position”) (Fea, page 766, section result: “When moving from an upright to a supine position, the average change in cyclotorsion is 3.31”, see table 1 and 2.) (Both Chenryak and Fea discloses calculate cyclotorsion between two images, one is upright position, one is supine position)
CLAIM 5
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chernayk in view of Fea, in view of Hamza in view of Sarver, and further in view of Wang et al. (US-20190038124-A1, published 02/07/2019, hereinafter Wang)
In regards to Claim 5, the combination of Chernyak, Fea, Hamza and Sarver teaches the device of Claim 1.
The combination of Chernyak, Fea, Hamza and Sarver and does not explicitly disclose if the reference image is unsuitable for the cyclorotation assessment, the processor is further configured to determine optimization instructions configured to direct a diagnostic device to record a new reference image, and to transmit the optimization instructions to the diagnostic device.
Wang is in the same field of art of ophthalmic device. Further, Wang teaches if the reference image is unsuitable for the cyclorotation assessment (Wang, ¶ [0062-0063]: “…if the image process device 112 determines that the image retrieved from the image capture device 110 are of insufficient quality although the image has been determined to represent the eye fundus…”), the processor is further configured to determine optimization instructions configured to direct a diagnostic device to record a new reference image (Wang, ¶ [0062-0063]: “the image process device can trigger the image capture device to capture another image of sufficient quality in real-time.”), and to transmit the optimization instructions to the diagnostic device. (Wang, ¶ [0062-0063]: “the image process device can save at least one of the images that have been retrieved from the image capture device if the image has one or more predetermined characteristics (e.g., desired image quality and/or size)”)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Chernyak, Fea, Hamza and Sarver by incorporating automated image capture process that is taught by Wang, to make an ophthalmic system that can capture image automatically; thus, one of ordinary skilled in the art would be motivated to combine the references since among its several aspects, the present invention recognizes there is a need for an automated image capture process (Wang, ¶ [0001-0006]).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHUT HUY (JEREMY) PHAM whose telephone number is (703)756-5797. The examiner can normally be reached Mo - Fr. 8:30am - 6pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, O'Neal Mistry can be reached on (313)446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
NHUT HUY (JEREMY) PHAMExaminerArt Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674