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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 17 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 12 of copending Application No. 19/025,270 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because every feature and element of the claim of the instant Application is recited in claim of the copending Application. Since the word “comprising” in claim of the instant Application does not preclude further limitations of the claim of the copending Application, the claim of the instant Application would be obvious in view of the claim of the copending Application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an imaging device configured to detect . . .” in claims 1 and 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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, 2, 6, 10, 11 and 13-17 are rejected under 35 U.S.C. 102(a)(1) as anticipated by US 2019/0384962 A1 to Hayut et al (hereinafter ‘Hayut’) or, in the alternative, under 35 U.S.C. 103 as obvious over US 10,996,214 B2 to Shaked et al (hereinafter ‘Shaked’).
Regarding claim 1, Hayut discloses an imaging system for automated sperm analysis (Para [0009], wherein the processor further comprises instructions to analyze the spatial field of the high-resolution image for identifying sperm morphology), the system comprising: an imaging device configured to detect optical signals encoded with information associated with a sperm sample (Para [0062] and Fig. 7, step 701, wherein after the spatial field analysis of the high-resolution image is performed, the processor may further comprise instructions to, using the spatial field analysis of the high-resolution image, perform one or more of a variety of diagnostic tests, such as screening for cancer or pre-cancerous cells, white blood cells differential count, a CBC test, a platelet count, cytology, or cell morphology (e.g., sperm morphology) identification); a processor operatively coupled with the imaging device and configured to receive the detected optical signals and generate a plurality of images of the sperm sample based at least in part on the detected optical signals (Para [0069] and Fig. 7, wherein the method of cell identification may further comprise, in step 703, reconstructing a high-resolution image of the region of the sample using the plurality of low-resolution images); and a machine learning model configured to receive the plurality of images of the sperm sample as input from the processor and determine an output indicative of a health of the sperm sample based at least in part on the received plurality of images of the sperm sample (Para [0061], wherein the processor may comprise instructions to perform machine learning, deep learning, supervised learning, unsupervised learning, or other image recognition or pattern recognition algorithms for analyzing the spatial field of the high-resolution image, e.g., to identify the at least one of a cell type or a cell structure based on sub-cellular features, . . . and wherein Identifying the at least one of a cell type or a cell structure based on sub-cellular features may comprise classifying the objects of interest (e.g., putative cells) detected from the high-resolution image into one of a plurality of classes or categories (e.g., WBC types, cancerous or non-cancerous cells, healthy or abnormal sperm cells, etc). Hayut also discloses in some embodiments, detecting lights of different wavelengths, as an encoded optical signals (Para [0049], wherein as another example, illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths, inherently as generating an encoded light specific to the sample). In as much as, Applicant disagrees with the Examiner’s assessment, Shaked also discloses capturing encoded optical signal associated with sperm (column 8, lines 22-29, wherein the imaging of the labeled sperm cell is made with interferometric phase microscopy (IPM), while immersing said at least one labeled sperm cell in a medium with a known refractive index and sequentially illuminating said at least one labeled sperm cell with light having first and second different light properties, said image data comprising first and second image data indicative of first and second phase map data, respectively, as encoded optical signal). Hayut and Shaked are combinable because they both disclose sperm sample analysis. Therefore, before the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the IPM microscopy of Shaked’s system with Hayut’s in order to generate plurality of image data indicative of sperm properties i.e. physical thickness of the sperm cell (column 8, lines 36-38).
Regarding claim 2, in the combination of Hayut and Shaked, Hayut discloses wherein: the machine learning model is trained at least in part using training images having a higher resolution than a resolution of the received plurality of images (Para [0006], wherein the imaging device may be configured to capture a plurality of low-resolution images of a region of a sample viewed by the microscope. The region of the sample may comprise a plurality of cells. The diagnostic system may further comprise a processor coupled to the imaging device. The processor may comprise instructions configured to reconstruct a high-resolution image of the region of the sample using the plurality of low-resolution images, and wherein instructions to apply at least one of image recognition or image segmentation upon the high-resolution image for analyzing the spatial field of the high-resolution image to identify the at least one of a cell type or a cell structure based on sub-cellular features. In some embodiments, the processor further comprises instructions to perform machine learning for analyzing the spatial field of the high-resolution image to identify the at least one of a cell type or a cell structure based on sub-cellular features, as inherently machine learning has been trained with high resolution images to identify and analyze the cells).
Regarding claim 6, in the combination of Hayut and Shaked, Hayut discloses wherein: the machine learning model is a deep learning model configured to determine data indicative of the health of the sperm sample (Para [0077], wherein examples of machine learning algorithms may include support vector machines (SVMs), neural networks, convolutional neural networks (CNNs), k-Nearest Neighbor (k-NN) classification, and random forests (RFs). Identifying the at least one of a cell type or a cell structure based on sub-cellular features may comprise classifying the objects of interest (e.g., putative cells) detected from the high-resolution image into one of a plurality of classes or categories (e.g., WBC types, cancerous or non-cancerous cells, healthy or abnormal sperm cells, etc.).
Regarding claim 10, in the combination of Hayut and Shaked, Hayut discloses wherein: the imaging device comprises: an emitter comprising a light source configured to illuminate the sperm sample (Para [0049] and Figh. 1, wherein illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths); a sample holder to hold the sperm sample (Para [0051] and Fig. 1, wherein Stage 116 includes any horizontal rigid surface where sample 114 may be mounted for examination.); and a sensor configured to detect the optical signals encoded with information associated with the sperm sample (Para [0040] and Fig. 1, camera 102, wherein the diagnostic system may be configured to acquire a plurality of images of the sample under a plurality of different illumination conditions (e.g., at different illumination angles, different illumination patterns, different wavelengths, inherently as encoded signal by wavelength of light required by the specific sample, or a combination thereof).).
Regarding claim 11, in the combination of Hayut and Shaked, Hayut discloses wherein: the light source is a first light source of a plurality of light sources arranged in an array and is configured to vary at least one illumination condition of the plurality of light sources (Para [0040] and Fig. 1, camera 102, wherein the diagnostic system may be configured to acquire a plurality of images of the sample under a plurality of different illumination conditions (e.g., at different illumination angles, different illumination patterns, different wavelengths).
Regarding claim 13, in the combination of Hayut and Shaked, Hayut discloses wherein: at least two of the imaging device, processor, and machine learning model are integrated on the same device (Fig. 1, camera 102 and the computer 106).
Regarding claim 14, in the combination of Hayut and Shaked, Hayut discloses wherein: the optical signals detected by the imaging device are a first set of optical signals associated with a first illumination condition of the imaging device; and the imaging device is further configured to detect a second set of optical signals associated with a second illumination condition of the imaging device (Para [0049], wherein illumination assembly 110 may include a plurality of light sources configured to emit light in different wavelengths. In this case, the different illumination conditions may include different wavelengths. In yet another example, illumination assembly 110 may configured to use a number of light sources at predetermined times. In this case, the different illumination conditions may include different illumination patterns).
Regarding claim 15, in the combination of Hayut and Shaked, Hayut discloses wherein: generating the plurality of images comprises generating a plurality of high-resolution images based at least in part on the first and second sets of optical signals (Para [0040], wherein the diagnostic system may be configured to acquire a plurality of images of the sample under a plurality of different illumination conditions (e.g., at different illumination angles, different illumination patterns, different wavelengths, or a combination thereof). The diagnostic system may be further configured to reconstruct a higher-resolution image and/or a phase image from the plurality of images acquired under the plurality of different illumination conditions.).
Regarding method claim 16, Please refer to the corresponding system claim 1 for further teachings.
Regarding claim 17, in the combination of Hayut and Shaked, Hayut discloses wherein detecting optical signals encoded with information associated with the sperm sample comprises: illuminating the sperm sample using an emitter of the imaging device, the emitter disposed on a first side of the sperm sample; and detecting the optical signals with an optical sensor the imaging device, the optical sensor disposed on a second side of the sperm sample opposite the first side (Fig. 1, wherein the stage is positioned between the light source 110 and camera 102).
Claims 3, 7-9 and 18 are rejected under 35 U.S.C. 102(a)(1) as anticipated by US Hayut in view of Shaked and further in view of CN 116311234 A to Zhang et al (hereinafter ‘Zhang’) (Please refer to the attached USPTO translation version).
Regarding claim 3, in the combination of Hayut and Shaked, Shaked discloses wherein: the data indicative of the health of the sperm sample comprises an indication of whether sperm in the sperm sample is a normal shape (column 12, line 66 through column 13, line 3, wherein using a predetermined number of images from of heads of cells possessing good head shapes as determined by a trained clinician, an average OPD image for good sperm heads is found and used as a template) however, Hayut and Shaked do not specifically disclose a machine learning model generating the data indicative of the health of the sperm sample comprises an indication of whether sperm in the sperm sample is a normal shape. Zhang discloses a machine learning model generating the data indicative of the health of the sperm is a normal shape (Page 3, Para 3, wherein respectively loading a video interface and a result display interface, the rear end is designed to pack the trained deep learning network and the trained weight, writing the rear end frame, loading any semen sample video, calling the trained convolutional neural network predicting the movement activity index of the semen and the shape index of the sperm, inherently as one indicative of the condition of the shape). Hayut, Shaked and Zhang are combinable because they all disclose sperm sample analysis. Therefore, before the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the a machine learning model generating the data indicative of the health of the sperm sample comprises an indication of whether sperm in the sperm sample is a normal shape, of Zhang’s system with Hayut’s and Shaked’s because it is a sperm property factor in order to determine male fertility (Page 2, Para 1).
Regarding claim 7, Hayut and Shaked do not specifically disclose wherein: the data indicative of the health of the sperm sample includes at least one of: concentration of the sperm sample and motility of the sperm sample. Zhang discloses data indicative of the health of the sperm sample motility of the sperm sample (page 4, para 6 through page 5, para 1, wherein calling the trained convolutional neural network predicting the movement activity index, as motility, of the semen and the shape index of the sperm, and transmitting to the front page). Hayut, Shaked and Zhang are combinable because they all disclose sperm sample analysis. Therefore, before the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the a machine learning model generating the data indicative data indicative of the health of the sperm sample motility of the sperm sample, of Zhang’s system with Hayut’s and Shaked’s because it is a sperm property factor in order to determine male fertility (Page 2, Para 1).
Regarding claim 8, in the combination of Hayut, Shaked and Zhang, Zhang discloses motility of the sperm sample comprises data indicative of the motion of at least one sperm in the sperm sample (page 4, para 6 through page 5, para 1, wherein calling the trained convolutional neural network predicting the movement activity index, as motility, of the semen and the shape index of the sperm, and transmitting to the front page).
Regarding claim 9, Hayut and Shaked do not specifically disclose the machine learning model is a first machine learning model configured to determine at least one of a concentration of the sperm sample and a motility of the sperm sample; the system further comprises a second machine learning model configured to determine whether sperm in the sperm sample is a normal shape; and the output indicative of a health of the sperm sample includes a first determination from the first machine learning model and a second determination from the second machine learning model. Zhang discloses wherein: the machine learning model is a first machine learning model configured to determine at least one of a concentration of the sperm sample and a motility of the sperm sample (Page 3, Para 3, wherein calling the trained convolutional neural network predicting the movement activity index, as the motility, of the semen and the shape index of the sperm, and transmitting to the front page); the system further comprises a second machine learning model configured to determine whether sperm in the sperm sample is a normal shape (Page 3, Para 3, wherein calling the trained convolutional neural network predicting the shape index of the sperm, inherently as inherently indicating a normal and abnormal shape indexes); and the output indicative of a health of the sperm sample includes a first determination from the first machine learning model and a second determination from the second machine learning model (Page 3, Para 3, wherein calling the trained convolutional neural network predicting the movement activity index, as the motility, of the semen and the shape index of the sperm, and transmitting to the front page, as inherently transmitting indicative of combination of health parameters of the sperm). Hayut, Shaked and Zhang are combinable because they all disclose sperm sample analysis. Therefore, before the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the a machine learning model generating the data indicative of the sperm sample motility and shape, of Zhang’s system with Hayut’s and Shaked’s because the two property factors of the sperm are indicative of male fertility (Page 2, Para 1).
Regarding claim 18, Hayut and Shaked do not specifically disclose wherein determining the output indicative of the health of the sperm sample using the machine learning model comprises: generating, using a first portion of the machine learning model, data indicative of the health of the sperm sample based on the plurality of images of the sperm sample; and generating, using a second portion of the machine learning model, new data indicative of the health of the sperm sample based at least in part on the data generated by the first portion. Zhang discloses wherein determining the output indicative of the health of the sperm sample using the machine learning model comprises: generating, using a first portion of the machine learning model, data indicative of the health of the sperm sample based on the plurality of images of the sperm sample; and generating, using a second portion of the machine learning model, new data indicative of the health of the sperm sample based at least in part on the data generated by the first portion (Page 3, Para 3, wherein calling the trained convolutional neural network predicting the movement activity index, as the first portion generating, of the semen and the shape index of the sperm, as the second portion generating, and transmitting to the front page). Hayut, Shaked and Zhang are combinable because they all disclose sperm sample analysis. Therefore, before the effective filing data of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the first portion of the machine learning model and the a second portion of the machine learning model, indicative of the health of the sperm sample, of Zhang’s system with Hayut’s and Shaked’s because the combination of health data of the sperm are indicative of male fertility (Page 2, Para 1).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hayut in view of Shaked and further in view of US 11,708,563 B2 to Wagner et al (hereinafter ‘Wagner’).
Regarding claim 12, Hayut and Shaked do not specifically disclose wherein the emitter is configured to operate in a pulse mode and operation of the emitter is configured to be synchronized with operation of the sensor. Wagner discloses the emitter is configured to operate in a pulse mode and operation of the emitter is configured to be synchronized with operation of the sensor (column 41, lines 16-20, wherein central pulsed laser is run at a consistent pulse rate, the photodetector 1852 may be used to acquire the laser pulse signal timing and synchronize the optical scanning subsystem to the laser pulse timing). Hayut, Shaked and Wagner are combinable because they all disclose sample microscopy imaging. It would have been obvious to one of ordinary skill in the art to combine the emitter is configured to operate in a pulse mode and operation of the emitter is configured to be synchronized with operation of the sensor, of Wagner’s system with Hayut’s and Shaked’s so that alleviate the need for a separate electronic synchronization system and wiring (column 41, lines 21-22).
Allowable Subject Matter
Claims 4, 5, 19 and 20 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 following is a statement of reasons for the indication of allowable subject matter: the prior art or the prior art of record specifically, Hayut, Shaked, Zhang and US 10,871,745 B2 to Ozcan et al, does not disclose:, does not disclose:
. . . . wherein: the machine learning model is an adversarial neural network comprising: a first portion, trained using the training images having the higher resolution, configured to generate data indicative of the health of the sperm sample based on the received plurality of images of the sperm sample; and a second portion configured to generate new data indicative of the health of the sperm sample based at least in part on the data generated by the first portion; and the output is determined based at least on the data generated by the first portion and the new data generated by the second portion, of claim 4 combined with other features and elements of the claim;
. . . . wherein: the machine learning model is an adversarial neural network comprising: a first portion, trained using the training images having the higher resolution, configured to extract image features based on the received plurality of images of the sample; a second portion configured to generate data indicative of the one or more attributes of the sample based on the features received from the first portion; and a third portion configured to generate new data indicative of the one or more attributes of the sample based at least in part on the data generated by the first portion; and the output is determined based at least on the data generated by the second portion and the new data generated by the third portion, of claim 5 combined with other features and elements of the claim;
. . . . wherein generating the plurality of images of the sperm sample comprises: determining first amplitude values and first phase values for the detected optical signals at a first plane of the sensor; determining second amplitude values and second phase values for the detected optical signals at a second plane of the sperm sample based on the first amplitude values and first phase values; and updating the first amplitude values and first phase values at the first plane based on the second amplitude values and second phase values, of claim 19 combined with other features and elements of the claim;
Claim 20 depends from an allowable base claim and is thus allowable itself.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHERVIN K NAKHJAVAN whose telephone number is (571)272-5731. The examiner can normally be reached Monday-Friday 9:00-05:00 PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sue Lefkowitz can be reached at (571)272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHERVIN K NAKHJAVAN/ Primary Examiner, Art Unit 2672
