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 .
Response to Preliminary Amendment
This Office Action is responsive to a preliminary amendment filed on 12/23/2024. Claims 1, 3-8, and 11-15 have been amended to remove multiple dependencies and to place the claims in better condition for U.S. prosecution. The amendments are made to reduce filing fees and not for any other reason related to patentability of such claims. Applicant submits that support for these amendments can be found throughout the specification and in the original claims. Applicant reserves the right to pursue amended and cancelled subject matter in one or more related applications. Applicant submits no new matter has been added. An Office Action on the merits follows here below.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Specification
The abstract of the disclosure is objected to because the patent abstract should be concise statement that does not recite legal phraseology and should not merely recite an instant claim. Correction is required. See MPEP § 608.01[b].
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/23/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Shafiee (US 20210374952 A1) in combination with Epstein (US 20240136012 A1).
Regarding Claim 1: Shafiee discloses a method for sperm quality determination (Refer to para [002]; “The present invention relates generally to the field of medical decision support, and more particularly to automated evaluation of sperm morphology.”) comprising the computer-implemented steps of: a) locating and tracking image data representative of at least one spermatozoon in a plurality of sequentially acquired volumetric groups of images of a sample that includes a plurality of freely swimming spermatozoa (Refer to para [028]; “For example, a cross-correlation between a template representing at least a portion of a sperm and each of a plurality of locations on the image can be computed and locations having a cross-correlation output above a threshold value can be selected. Alternatively, the template matching can be performed using a convolutional neural network trained on template images of sperm.”) wherein said sequential acquisition has been carried out using non-point scanning optical sectioning microscopy (Refer to para [019, 020 and 036]; “In another implementation, the imager 102 can be implemented as a stand-alone system an optical housing containing an electronic circuit with a light source, a power source, and appropriate optics for magnifying the sample. The semen sample can be trans-illuminated or epi-illuminated, with a set of one or more objective lenses for image magnification and an image sensor, for example a complementary metal-oxide-semiconductor (CMOS), for image data acquisition. In one example, the objective lenses include a 4× lens, a 10× lens, and a 40× lens that can be exchanged via a servo motor. The image sensor can be connected to a single-board computer to process the captured images. The imager 102 can be connected to a mobile device via a wireless connection (e.g., Wi-Fi, Bluetooth, or a similar connection) for data processing and visualization”) b) extracting features from said image data, said extracted features being three-dimensional morphological (Refer to para [021]; “For example, the images can be reduced to a binary image, an inverted binary image, or a grayscale image, such that some color information is deliberately excluded from the image. Other techniques include dilution, segmentation, filtering the image with low-pass or high-pass filters. In particular, the use of a low-pass filter for reducing noise in the image has been found to be useful in improving the quality of images for morphology analysis. It will be noted, however, that the convolutional neural network 104 can include convolutional layers that replicate several of these image processing functions, and preprocessing of the image is not necessary for a system in accordance with the invention described herein.”) and dynamics features of the head and flagellum of said at least one spermatozoon (Refer to para [022]; “The convolutional neural network 104 calculates, from the preprocessed images of the semen sample, at least one output value representing the morphology of the sperm within the image. For example, the convolutional neural network 104 can classify the image into one of a plurality of classes representing normal morphology, abnormal morphology, or a particular type of abnormality (e.g., head defects, midpiece defects, tail defects, missing acrosome, etc.). In this example, the convolutional neural network 104 can be trained on a plurality of images of sperm that have been classified into the selected classes by one or more experts.”) c) determining a quality score for the at least one spermatozoon based on said extracted three-dimensional features, for at least part of said extracted three-dimensional dynamics features after applying statistics thereon or on parameters derived therefrom, and on a comparison with benchmarks referring to spermatozoa morphology and dynamics (Refer to para [028]; “the image processor 264 can use other image processing techniques to select tiles for segmentation. For example, the image processor 264 can apply one of dilution and filtering (e.g., high pass or low pass filtering) to the image to generate the representation of the image. These image processing techniques can be applied to the original image or to a representation of the image, generated as one of a binary, an inverse binary, and a grayscale representation of the image. Tiles representing individual sperm or selected groupings of sperm can be provided to the convolutional neural network 261. In still another implementation, a template matching approach can be utilized. In one implementation, the template matching can be performed via a normalized cross-correlation or a sum of absolute differences approach. For example, a cross-correlation between a template representing at least a portion of a sperm and each of a plurality of locations on the image can be computed and locations having a cross-correlation output above a threshold value can be selected. Alternatively, the template matching can be performed using a convolutional neural network trained on template images of sperm.”)
Epstein teaches “At step/operation 610, the user computing entity 30 processes the specimen response to generate respective graphical representations of one or more specimen scores provided by the specimen response. In an example embodiment, the graphical representation of a specimen score is a numerical, alphanumerical, color-coded and/or other human discernable indication of the predicted probability of a successful outcome corresponding to a respective specimen.”
More specifically, Epstein teaches a non-point microscopy (Refer to para [062]; “These modalities can be extended to include input features from bright field images or videos (e.g., time ordered sequences of imaging data) derived from an ordinary light microscopy and non-imaging sample data both from the sperm donor or if known the intend egg donor.”) and d) providing said quality score (Refer to para [121]; “At step/operation 612, the user computing entity 30 causes display of the graphical representation of the specimen scores (e.g., via display 316). For example, the user computing entity 30 (e.g., processing device 308) may render the graphical representation of the specimen scores and cause the display 316 to display the graphical representation of the specimen scores.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Shafiee by adding a quality score processor for microscopy differentiation as taught by Epstein as rejected above.
The suggestion/motivation for combining the teachings of Shafiee and Epstein would have been in order to more “…effectively identify sperm that are likely to achieve successful fertilization, healthy embryo development, and/or a successful live birth outcome when the specimen is used to perform a fertilization event without rendering the individual sperm unusable. ” (at para [123], Epstein).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Shafiee and Epstein in order to obtain the specified claimed elements of Claim 1. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding Claim 12: Shafiee discloses a non-transitory computer program product, including code instructions that, when executed on at least one processor (Refer to para [008]; “a system includes a camera that produces an image of a semen sample, a processor, and a non-transitory computer readable medium storing instructions executable by the processor.”) implement the computer-implemented steps of the method of claim 1 (For the sake of brevity, refer to the rejection of claim 1).
Regarding Claim 13: Shafiee discloses a system for sperm quality determination, comprising a processor and a memory storing instructions that when executed by the processor (Refer to para [008]; “a system includes a camera that produces an image of a semen sample, a processor, and a non-transitory computer readable medium storing instructions executable by the processor. The executable instructions provide an image processor that segments the image to provide a plurality of image tiles and a convolutional neural network that evaluates a subset of the plurality of image tiles and assigns each of the image tiles to one of a plurality of output classes associating them with the morphology of sperm within the image tile. A sample evaluator records the output of the convolutional neural network and assigns a metric representing the overall morphological quality of the semen sample from the respective outputs for the plurality of image tiles.”) cause the processor to implement the computer-implemented steps of the method of claim 1 (For the sake of brevity, refer to the rejection of claim 1).
Allowable Subject Matter
Claims 2-11, 14 and 15 are 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.
The prior art either singly or in combination does not teach, disclose or suggest at least the following claim limitation(s): “… step b) comprises: b1) for the three-dimensional morphological features: b1a) identifying in the image data the head and tail of the at least one spermatozoon; and b1b) extracting the following three-dimensional morphological features: head length, head width, head thickness, and tail length of the at least one spermatozoon; b2) for the three-dimensional dynamics features: b2a) identifying in the image data the cartesian coordinates in each time in three dimensions of the centroid of the head and different points along the tail of the at least one spermatozoon; and b2b) extracting the following three-dimensional dynamics features: a total forward swimming speed of the at least one spermatozoon and average tail displacements of those tail different points relative to a centre of movement, and deriving a motility parameter therefrom.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIA M THOMAS whose telephone number is (571)270-1583. The examiner can normally be reached M-Th 8:30am-4:30pm.
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MIA M. THOMAS
Primary Examiner
Art Unit 2665
/MIA M THOMAS/Primary Examiner
Art Unit 2665
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