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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-2 are drawn to a system, while claims 3-6 are drawn to a method, each of which is within the four statutory categories.
Step 2A(1)
Claim 1 recites, in part, performing the steps of
performing preprocessing on sperm motility video data,
determining whether male infertility is present based on the preprocessed video data.
These elements constitute concepts performed in the human mind and therefore fall within the scope of an abstract idea in the form of a mental process. Fundamentally the process is that of preprocessing video data of sperm motility and determining whether male infertility is present based on that preprocessed video data. Each of these functions may be performed in the human mind through observation and evaluation of the video data and using that information to render an opinion on the presence of male infertility. Examiner notes that Applicant’s specification describes preprocessing of the video data as extracting sperm motility trajectories and sperm motility patterns such as linear motility or strong rotational movement (see e.g. page 10 of Applicant’s specification), which a human is capable of performing via observation and judgement.
Step 2A(2)
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
Claim 1 further recites additional elements including a) a camera unit used to capture the sperm motility videos, b) a male infertility automatic diagnosis server comprising the subsequently recited units used to perform data capture and analysis functions including obtaining the sperm motility videos, inputting the videos into the artificial intelligence model, obtaining and preprocessing the video of sperm motility, determining whether male infertility is present and providing the male infertility diagnosis results, c) a cloud platform used to receive and provide data such as providing the motility videos and receiving the male infertility diagnosis results, and d) an artificial intelligence model recited as receiving the motility videos at an input node and male infertility diagnosis results at an output node to perform training.
Pages 9 and 10 of Applicant’s specification as originally filed describe the camera unit in terms of its recited function of capturing video data of sperm motility. The camera unit is construed as encompassing generic forms of video capture devices.
Page 5 of Applicant’s specification states that “each block of the flowchart drawings and combinations of the flowchart drawings may be performed by computer program instructions” where “[t]he computer program instructions may be mounted on a processor of a general-purpose computer,” and “the processor may perform the operations described in the embodiments that follow, and the operations described as being performed by the server in the embodiments that follow may be regarded as being performed by the processor…”. Claim 1 recites the sever as comprising each of the data collection unit, data preprocessing unit, machine learning unit, infertility determination unit, and data providing unit, and page 7 of Applicant’s specification likewise states that the server “includes” these units. The server is therefore construed as encompassing generic computing elements while each of the units is construed as encompassing software elements operating on said generic server device.
Page 9 states that “[t]he cloud platform 300 provides computing services such as servers, storage, databases, networking, software, analytics, and intelligence via the Internet ("cloud").” The cloud platform is construed accordingly as encompassing generic networked server and database elements.
Page 9 further states that the machine learning unit “may receive the sperm motility video acquired from the cloud platform from the data collection unit, input it into the input node of the artificial intelligence model, and input the male infertility diagnosis result acquired from the cloud platform into the output node of the artificial intelligence model to perform training,” and that the training “may be performed using one or more selected from the group consisting of machine learning (for example, Support Vector Machine SVM, Artificial Neural Network ANN, Recurrent Neural Network RNN) and deep learning (for example, Deep Neural Network DNN).” No further disclosure is provided specifying the training, the artificial intelligence model, or the input/output nodes. The artificial intelligence model and training are construed accordingly as encompassing general forms of machine learning algorithms and training techniques.
Each of the above elements only amounts to mere instructions to implement the claimed functions using computing elements as tools. For example, each of the sever and cloud platform are recited at a high level of generality as used to perform corresponding data processing, transmission, and receiving functions and are disclosed broadly in the specification, while the artificial intelligence model is likewise only recited at a high level of generality as receiving the videos and diagnosis results “to perform training.”
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
Claim 1 recites the further additional elements of a) capturing and obtaining the sperm motility videos from the camera and cloud platform, b) receiving the preprocessed video, and c) providing the male infertility diagnosis results.
However, these elements only amount to insignificant extra-solution activity in the form of data gathering and post-solution activity. Specifically, capturing and obtaining the sperm motility videos and receiving the preprocessed video amounts to mere gathering of data for use in performing the idea as well as transmission of data over a network, while providing the determined male infertility results amounts to transmitting the results over a network subsequent to performing the abstract idea.
The above additional elements, considered in the context of the claim as a whole, are not sufficient to integrate the abstract idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claim 1 only recites the camera unit, male infertility automatic diagnosis server and component units, cloud platform, and artificial intelligence model as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f)
B. Insignificant Extra-Solution Activity. MPEP 2106.05(g)
As set out above, claim 1 recites the further additional elements of a) capturing and obtaining the sperm motility videos from the camera and cloud platform, b) receiving the preprocessed video, and c) providing the male infertility diagnosis results. These elements only amount to insignificant extra-solution activity in the form of data gathering and post-solution activity. Specifically, capturing and obtaining the sperm motility videos and receiving the preprocessed video amounts to mere gathering of data for use in performing the idea as well as transmission of data over a network, while providing the determined male infertility results amounts to transmitting the results over a network subsequent to performing the abstract idea.
C. Well-Understood, Routine and Conventional Activities. MPEP 2106.05(d)
In addition to constituting insignificant extra-solution activity, the elements of capturing and obtaining the sperm motility videos from the camera and cloud platform, receiving the preprocessed video, and providing the male infertility diagnosis results only amount to well-understood routine and conventional activity. These elements constitute forms of extracting data and receiving or transmitting data over a network and are received both at a high level of generality and as insignificant extra-solution activity.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Depending Claims
Claim 2 recites the additional elements of wherein the machine learning unit performs training using a machine learning technique or a deep learning technique.
As cited above, page 9 states that the machine learning unit “may receive the sperm motility video acquired from the cloud platform from the data collection unit, input it into the input node of the artificial intelligence model, and input the male infertility diagnosis result acquired from the cloud platform into the output node of the artificial intelligence model to perform training,” and the training “may be performed using one or more selected from the group consisting of machine learning (for example, Support Vector Machine SVM, Artificial Neural Network ANN, Recurrent Neural Network RNN) and deep learning (for example, Deep Neural Network DNN).” No further disclosure is provided of the machine learning technique or deep learning technique. The training using a machine learning technique or a deep learning technique is construed accordingly as encompassing general forms of these machine learning training techniques.
The recitation of performing training using a machine learning technique or a deep learning technique only amounts to mere instructions to implement the claimed functions using computing elements as tools. Each of the machine learning technique or deep learning technique is recited at a high level of generality as used to perform the training, and are disclosed broadly in the specification only in terms of corresponding example types of models. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 3 recites preprocessing the obtained sperm motility video, using the preprocessed sperm motility video to determine whether male infertility is present. These limitations fall within the scope of the abstract idea as set out above.
Claim 3 further recites the additional elements of a) a male infertility automatic diagnosis server used to perform data capture and analysis functions including obtaining the sperm motility videos and data on male infertility, inputting the videos into the artificial intelligence model, obtaining and preprocessing the video of sperm motility, and providing the male infertility diagnosis data, b) a cloud platform used to provide the videos and data on male infertility and to receive the male infertility diagnosis data, c) an artificial intelligence model used to receive the motility videos and male infertility status at an input node and output node to perform training and to determine whether male infertility is present, and d) the camera unit recited as capturing the video of sperm motility.
Examiner refers Applicant to the citations to relevant portions of the disclosure and analysis of each of these elements already provided above with respect to claim 1. As explained, each of these elements only amounts to mere instructions to implement the claimed functions using computing elements as tools. For example, each of the sever and cloud platform are recited at a high level of generality as used to perform corresponding data processing, transmission, and receiving functions and are disclosed broadly in the specification, while the artificial intelligence model is likewise only recited at a high level of generality as receiving the videos and diagnosis results “to perform training.”
As also set out above, the further additional elements of a) capturing and obtaining the sperm motility videos from the camera and cloud platform, b) receiving the preprocessed video, and c) providing the male infertility diagnosis results only amount to insignificant extra-solution activity in the form of data gathering and post-solution activity. Specifically, capturing and obtaining the sperm motility videos and receiving the preprocessed video amounts to mere gathering of data for use in performing the idea as well as transmission of data over a network, while providing the determined male infertility results amounts to transmitting the results over a network subsequent to performing the abstract idea.
In addition to constituting insignificant extra-solution activity, these elements only amount to well-understood routine and conventional activity, constituting forms of extracting data and receiving or transmitting data over a network and are received both at a high level of generality and as insignificant extra-solution activity.
These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claim 4 recites wherein the preprocessing of the sperm motility video comprises: extracting sperm motility trajectory; and classifying sperm motility patterns. These limitations fall within the scope of the abstract idea as set out above.
Claim 5 recites wherein the determining of whether male infertility is present comprises deriving a class, number, speed and motility distance of sperm. These limitations fall within the scope of the abstract idea as set out above.
Claim 6 recites the additional element of wherein the training is performed using a machine learning technique or a deep learning technique.
As cited above, page 9 states that the machine learning unit “may receive the sperm motility video acquired from the cloud platform from the data collection unit, input it into the input node of the artificial intelligence model, and input the male infertility diagnosis result acquired from the cloud platform into the output node of the artificial intelligence model to perform training,” and the training “may be performed using one or more selected from the group consisting of machine learning (for example, Support Vector Machine SVM, Artificial Neural Network ANN, Recurrent Neural Network RNN) and deep learning (for example, Deep Neural Network DNN).” No further disclosure is provided of the machine learning technique or deep learning technique. The training using a machine learning technique or a deep learning technique is construed accordingly as encompassing general forms of these machine learning training techniques.
The recitation of performing training using a machine learning technique or a deep learning technique only amounts to mere instructions to implement the claimed functions using computing elements as tools. Each of the machine learning technique or deep learning technique is recited at a high level of generality as used to perform the training, and are disclosed broadly in the specification only in terms of corresponding example types of models. These elements are not sufficient to integrate the abstract idea into a practical application or to amount to significantly more than the abstract idea.
Claims 1-6 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Ozkosem (EP 4005672A1) in view of Letterie et al (US Patent Application Publication 2019/0042958) and Hsu et al (US Patent Application Publication 2017/0329120).
With respect to claim 1, Ozkosem discloses the claimed male infertility automatic diagnosis system, comprising:
a camera unit that captures sperm motility videos (Figure 1, [164], and [181] describe an optical device having a lens and sensor which captures images of the sperm sample);
a male infertility automatic diagnosis server ([194] and [195] describe the system having a server); and
wherein the male infertility automatic diagnosis server comprises:
a data collection unit that obtains sperm motility videos from the camera unit and memory (Figure 1, [180], [181], and [189] describe the system obtaining the video data from the optical device);
a data preprocessing unit that performs preprocessing on video data obtained by the data collection unit ([180] and [182]-[184] describe performing processing steps on the video images such as determinations of motility and morphology);
a machine learning unit that inputs the sperm motility videos obtained from the memory into an input node of an artificial intelligence model, and inputs male infertility diagnosis results obtained from the memory into an output node of the artificial intelligence model to perform training ([106], [112], [114], [118], [186], [192], [196], and [203] describe training a deep learning model using the video data and known fertility diagnosis data; [128], [131]-[140], and [203] describe the training process including applying the training data to the nodes of the model and adjusting the bias and weights using back propagation based on the training data);
an infertility determination unit that receives the preprocessed video data and determines whether male infertility is present ([101], [121]-[124], [184], [185], [192], and [206] describe the system applying the trained deep learning model to data from a test subject or patient and determining a fertility diagnosis); and
a data providing unit that provides the male infertility diagnosis results ([110], [192], and [206] describe the system providing the fertility diagnosis);
but does not expressly disclose:
the memory being a cloud platform for transmitting and receiving data with the server;
providing the male infertility diagnosis results to the cloud platform.
However, Letterie teaches that it was old and well known in the art of fertility assessment before the effective filing date of the claimed invention to incorporate a cloud platform and retrieve images and fertility outcome data from the cloud server for use in training a model (Figure 2, [26], [35], and [96] describe a system in communication with a cloud platform storing images and outcomes which are retrieved to train a machine learning model).
Therefore it would have been obvious to one of ordinary skill in the art of fertility assessment before the effective filing date of the claimed invention to modify the system of Ozkosem to have the memory be a cloud server for transmitting and receiving the data used to train the model as taught by Letterie since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Ozkosem already discloses memory devices in communication with the server as well as using video data and diagnosis data retrieved from the memory to train the model, and having the memory be a cloud platform as taught by Letterie would serve that same function in Ozkosem, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Hsu further teaches that it was old and well known in the art of fertility assessment before the effective filing date of the claimed invention to provide male infertility results to a cloud platform (Figure 27, [60], [112], and [137] describe performing testing on sperm samples and outputting the fertility results to a cloud server).
Therefore it would have been obvious to one of ordinary skill in the art of fertility assessment before the effective filing date of the claimed invention to modify the system of Ozkosem to provide the male infertility results to a cloud platform as taught by Hsu since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Ozkosem already discloses outputting the male infertility diagnosis results, and outputting them to a cloud platform as taught by Hsu would serve that same function in Ozkosem, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 2, Ozkosem/Letterie/Hsu teach the system of claim 1. Ozkosem further discloses:
wherein the machine learning unit performs training using a machine learning technique or a deep learning technique ([99]-[100], [114], [185], [186], and [192] describe the training process involving training a neural network using deep learning).
With respect to claim 3, Ozkosem/Letterie/Hsu teach the claimed method for diagnosing male infertility by using the male infertility automatic diagnosis system of claim 1, the method comprising:
obtaining, by a male infertility automatic diagnosis server, sperm motility videos and data on male infertility from a memory ([103], [106], [107], and [196] describe the system obtaining the video data from a memory);
inputting, by the male infertility automatic diagnosis server, sperm motility videos into an input node of an artificial intelligence model and inputting a male infertility status into an output node of an artificial intelligence model to perform training ([106], [112], [114], [118], [186], [192], [196], and [203] describe training a deep learning model using the video data and known fertility diagnosis data; [128], [131]-[140], and [203] describe the training process including applying the training data to the nodes of the model and adjusting the bias and weights using back propagation based on the training data);
capturing, by the camera unit, a video of sperm motility (Figure 1, [164], and [181] describe an optical device having a lens and sensor which captures images of the sperm sample);
obtaining, by the male infertility automatic diagnosis server, the video of sperm motility captured by the camera unit (Figure 1, [180], [181], and [189] describe the system obtaining the video data from the optical device);
preprocessing, by the male infertility automatic diagnosis server, the obtained sperm motility video ([180] and [182]-[184] describe performing processing steps on the video images such as determinations of motility and morphology);
inputting, by the male infertility automatic diagnosis server, the preprocessed sperm motility video into the trained artificial intelligence model, to determine whether male infertility is present ([101], [121]-[124], [184], [185], [192], and [206] describe the system applying the trained deep learning model to data from a test subject or patient and determining a fertility diagnosis); and
providing, by the male infertility automatic diagnosis server, the male infertility diagnosis data ([110], [192], and [206] describe the system providing the fertility diagnosis);
but does not expressly disclose:
the memory being a cloud platform for transmitting and receiving data with the server;
providing the male infertility diagnosis results to the cloud platform.
However, Letterie teaches that it was old and well known in the art of fertility assessment before the effective filing date of the claimed invention to incorporate a cloud platform and retrieve images and fertility outcome data from the cloud server for use in training a model (Figure 2, [26], [35], and [96] describe a system in communication with a cloud platform storing images and outcomes which are retrieved to train a machine learning model).
Therefore it would have been obvious to one of ordinary skill in the art of fertility assessment before the effective filing date of the claimed invention to modify the system of Ozkosem to have the memory be a cloud server for transmitting and receiving the data used to train the model as taught by Letterie since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Ozkosem already discloses memory devices in communication with the server as well as using video data and diagnosis data retrieved from the memory to train the model, and having the memory be a cloud platform as taught by Letterie would serve that same function in Ozkosem, making the results predictable to one of ordinary skill in the art (MPEP 2143).
Hsu further teaches that it was old and well known in the art of fertility assessment before the effective filing date of the claimed invention to provide male infertility results to a cloud platform (Figure 27, [60], [112], and [137] describe performing testing on sperm samples and outputting the fertility results to a cloud server).
Therefore it would have been obvious to one of ordinary skill in the art of fertility assessment before the effective filing date of the claimed invention to modify the system of Ozkosem to provide the male infertility results to a cloud platform as taught by Hsu since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Ozkosem already discloses outputting the male infertility diagnosis results, and outputting them to a cloud platform as taught by Hsu would serve that same function in Ozkosem, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 4, Ozkosem/Letterie/Hsu teach the method of claim 3. Ozkosem does not expressly disclose wherein the preprocessing of the sperm motility video comprises: extracting sperm motility trajectory; and classifying sperm motility patterns.
Hsu further teaches that it was old and well known in the art of fertility assessment before the effective filing date of the claimed invention to process sperm motility video by extracting sperm motility trajectory and classifying sperm motility patterns (Figures 15A and 15F, [86], [93]-[94], and [103]-[104] describe processing video frame data to identify sperm trajectories and classify the motility based on parameters such as curvilinear velocity, straight-line velocity, and linearity).
Therefore it would have been obvious to one of ordinary skill in the art of fertility assessment before the effective filing date of the claimed invention to modify the combination of Ozkosem, Letterie, and Hsu to preprocess sperm motility video by extracting sperm motility trajectory and classifying sperm motility patterns as taught by Hsu since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Ozkosem, Letterie, and Hsu already teach preprocessing the video by determining motility, and further extracting sperm motility trajectory and classifying sperm motility patterns as taught by Hsu would serve that same function in Ozkosem, Letterie, and Hsu, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 5, Ozkosem/Letterie/Hsu teach the method of claim 3. Ozkosem further discloses:
wherein the determining of whether male infertility is present comprises deriving a number and speed of sperm (Figure 2, [92], [164], [183], and [184] describe the determination of infertility involving deriving a sperm count and motility rate, i.e. speed);
but does not expressly disclose:
further deriving a class and motility distance of the sperm.
However, Hsu teaches that it was old and well known in the art of fertility assessment before the effective filing date of the claimed invention to derive a class and motility distance of sperm as part of a fertility analysis (Figures 15A and 15F, [86], [93]-[94], and [103]-[104] describe processing video frame data to classify sperm and determine movement distance).
Therefore it would have been obvious to one of ordinary skill in the art of fertility assessment before the effective filing date of the claimed invention to modify the combination of Ozkosem, Letterie, and Hsu to derive a class and motility distance of the sperm as part of a fertility analysis as taught by Hsu since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Ozkosem, Letterie, and Hsu already teach deriving a number and speed of sperm as part of determining whether male infertility is present, and further deriving a class and motility distance of the sperm as taught by Hsu would serve that same function in Ozkosem, Letterie, and Hsu, making the results predictable to one of ordinary skill in the art (MPEP 2143).
With respect to claim 6, Ozkosem/Letterie/Hsu teach the method of claim 3. Ozkosem further discloses:
wherein the training is performed using a machine learning technique or a deep learning technique ([99]-[100], [114], [185], [186], and [192] describe the training process involving training a neural network using deep learning).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hicks et al, Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction;
Iqbal et al, Deep Learning-Based Morphological Classification of Human Sperm Heads;
Larsen et al, Computer-assisted semen analysis parameters as predictors for fertility of men from the general population;
Shafiee (US Patent Application Publication 2018/0181792);
Cassuto et al (US Patent Application Publication 2023/0061402);
Barnea et al (US Patent Application Publication 2021/0041336);
Schnorr et al (US Patent Application Publication 2020/0395117).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM G LULTSCHIK whose telephone number is (571)272-3780. The examiner can normally be reached 9am - 5pm.
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, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gregory Lultschik/Examiner, Art Unit 3682
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