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 .
Claim Status
Claims 1-13 are cancelled.
Claims 14-25 are newly added.
Claims 14-25 are currently pending and under exam herein.
Claims 14-25 are rejected.
Claim 25 is objected to.
Priority
Applicant’s claim for domestic benefit to the earlier filed international application PCT/EP2021/085187, filed December 10, 2021, which claims foreign priority under 35 U.S.C. 119 (a)-(d) to application DE10 2020 215 815.0, filed December 14, 2020, is acknowledged. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. At this point in the examination, the effective filing date of claims 14-25 is December 14, 2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on June 14, 2023 and September 21, 2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claim 25 is objected to because of the following informalities: In the preamble, the claim recites “A non-transitory machine-readable memory medium on is stored a computer program…” which has a grammatical error. It is suggested to amend the claim to recite “…storing a computer program…”, “…on which a computer program for training a classifier is stored…”, or the like to overcome the objection. Appropriate correction is required.
Claim Interpretation
MPEP 2111 states the scope of claims in pending patent applications are determined by giving claim language the broadest reasonable interpretation in light of the specification as understood by one of ordinary skill in the art. MPEP 2111.01 states words of a claim are given their plain meaning unless inconsistent with the specification and it is improper to import claim limitations from the specification. Thus, the following notes on claim interpretation:
The claim limitation “a molecular biological examination system” recited in claims 14, 19, 21, and 23-25 is being interpreted as any system that generates data based on measurements derived from biological molecules. The published specification gives some context and examples of what is encompassed by the limitation, but does not give a limiting definition (paragraph 0003, 0011, 0013-0014).
The following definition from the online Merriam-Webster dictionary will be used to interpret the claim limitation “ascertaining” recited throughout the claims: to find out or learn with certainty.
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:
Claim 23:
A training device configured for training a classifier…
Claim 24:
A system for data processing for ascertaining an output signal…
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. The sections in the published specification disclosing the structure for the above recited means-plus-function limitations are:
Claim 23:
The specification discloses a training system in paragraphs 0046-0053 and Figure 2 which appears to perform the claimed functions of the training device.
Claim 24:
The specification discloses a control system in paragraphs 0054-0058 and Figure 3 which appears to perform the claimed functions of the system for data processing.
The citations in the specification indicate that the training device and system for data processing are computers with software/hardware implements to perform the disclosed functions. MPEP 2181(II)(B) requires an algorithm be disclosed in the specification for computer-implemented means-plus-function limitations. The recited steps in claims 23 and 24 will be used to as the algorithms of the training device and system for data processing.
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 § 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.
Claims 14-25 are 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.
Regarding claim 14-20, the term “a desired output signal” in claim 14 is a subjective term which renders the claim indefinite. The specification does not provide an objective standard for ascertaining the scope of the term. Paragraph 0012 provides an example of what may be a desired output signal, but does not provide a limiting definition and one of ordinary skill in the art would need to rely on subjective opinion to define the scope of the invention (MPEP 2173.05(b)(iv)). Claims 15-20 are also rejected because they depend from claim 14 and do not resolve the issue of indefiniteness.
Regarding claims 21 and 22, the claims are rejected for the same reason as above for reciting the limitation “a desired output signal” in claim 21. Claim 22 depends from claim 21 and does not resolve the issue of indefiniteness.
Regarding claims 23-25, the same issue of the subjective term “a desired output signal” exists in each claim which renders them indefinite.
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 14-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
The first part of the eligibility analysis evaluates whether a claim falls within any statutory category (MPEP 2106.03). Claims 14-22 recite a series of steps taken using a computer to train a classifier on data and output a predicted classification of input data. The claims are directed to processes and fall within one of the statutory categories of invention. Claims 23-25 recite computer systems programed to train a classifier and output predicted classifications of input data and a non-transitory computer-readable storage medium with instructions that causes a computer to perform steps of training a classifier. The claims are directed to machines and fall within one of the statutory categories of invention (Step 1: YES).
Step 2A, prong 1:
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, prong 1). In the instant application, the claims recite the following limitations that equate to those concepts:
Claim 14 recites: ascertaining at least one first input signal, the first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocating a desired output signal, which characterizes a classification of the evaluation points, to the first input signal; subdividing the first input signal into a plurality of second input signals according to an arrangement of the evaluation points; ascertaining a plurality of first representations, a respective first representation being ascertained for each second input signal of at least a first subset of the plurality of second input signals, using the classifier; ascertaining an output signal using the classifier and based on the plurality of first representations, the output signal characterizing a classification of the first input signal; and adapting at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained output signal and the desired output signal.
Claim 15 recites: wherein the input signal is ascertained based on a sensor signal of an optoelectronic sensor, the sensor signal characterizing a measurement of the evaluation points.
Claim 16 recites: wherein the classifier includes at least one first neural network, using which the first representations are ascertained.
Claim 17 recites: wherein the classifier includes a plurality of first neural networks, the classifier including a respective first neural network for each second input signal of the first subset, using which the first representation of the second input signal is ascertained.
Claim 18 recites: wherein the output signal is ascertained using a second neural network including the classifier, and based on the first representations.
Claim 19 recites: wherein the molecular biological examination system includes a microarray, and the input signal characterizes an image of the evaluation points of the microarray.
Claim 20 recites: wherein each second input signal of the plurality of second input signals is an excerpt of the image, and the excerpt is selected according to the arrangement of the evaluation points of the microarray.
Claim 21 recites: training a classifier, including: ascertaining at least one third input signal, the third input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocating a desired output signal, which characterizes a classification of the evaluation points, to the third input signal, subdividing the third input signal into a plurality of fourth input signals according to an arrangement of the evaluation points, ascertaining a plurality of third representations, a respective third representation being ascertained for each fourth input signal of at least a first subset of the plurality of fourth input signals, using the classifier, ascertaining a first output signal using the classifier and based on the plurality of third representations, the output signal characterizing a classification of the third input signal, and adapting at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained first output signal and the desired output signal; subdividing the first input signal into a plurality of second input signals according to an arrangement of the plurality of evaluation points; and ascertaining the output signal based on the plurality of second input signals using the classifier.
Claim 22 recites: … displays the classification.
Claim 23 recites: ascertain at least one first input signal, the first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocate a desired output signal, which characterizes a classification of the evaluation points, to the first input signal; subdivide the first input signal into a plurality of second input signals according to an arrangement of the evaluation points; ascertain a plurality of first representations, a respective first representation being ascertained for each second input signal of at least a first subset of the plurality of second input signals, using the classifier; ascertain an output signal using the classifier and based on the plurality of first representations, the output signal characterizing a classification of the first input signal; and adapt at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained output signal and the desired output signal.
Claim 24 recites: subdivide the first input signal into a plurality of second input signals according to an arrangement of the plurality of evaluation points; and ascertain the output signal based on the plurality of second input signals using a trained classifier, the classifier being trained by … configured to: ascertain at least one third input signal, the third input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocate a desired output signal, which characterizes a classification of the evaluation points, to the third input signal, subdivide the third input signal into a plurality of fourth input signals according to an arrangement of the evaluation points, ascertain a plurality of third representations, a respective third representation being ascertained for each fourth input signal of at least a first subset of the plurality of fourth input signals, using the classifier, ascertain a first output signal using the classifier and based on the plurality of third representations, the output signal characterizing a classification of the third input signal, and adapt at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained first output signal and the desired output signal.
Claim 25 recites: ascertaining at least one first input signal, the first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocating a desired output signal, which characterizes a classification of the evaluation points, to the first input signal; subdividing the first input signal into a plurality of second input signals according to an arrangement of the evaluation points; ascertaining a plurality of first representations, a respective first representation being ascertained for each second input signal of at least a first subset of the plurality of second input signals, using the classifier; ascertaining an output signal using the classifier and based on the plurality of first representations, the output signal characterizing a classification of the first input signal; and adapting at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained output signal and the desired output signal.
The limitations of ascertaining an input signal, allocating a desired output signal, subdividing the input signal, ascertaining a plurality of representations using the classifier, ascertaining an output signal using the classifier, and adapting a parameter of the classifier recited in claims 14, 21, and 23-25 encompass mental processes of observing data and deciding which to use as input, evaluating the data to assign it a label, choosing how to parse the data into several subsets, observing the features representing the subsets assigned by the classifier, observing the output signal from the classifier, and changing a parameter of the classifier based on a loss function. Adapting a parameter of the classifier also encompasses mathematical calculations involved in training a machine-learning model.
The limitations in claim 15 only further limit the mental process of ascertaining an input signal in claim 14 by narrowing it to evaluate data from an optoelectronic sensor. Likewise, the limitations in claim 19 further limits the mental process of ascertaining an input signal by narrowing it to evaluate data of a microarray image.
The limitations in claim 16 further limit the mental process of ascertaining a plurality of representations by narrowing the type of classifier used to obtain features being observed. Similarly, the limitations in claim 17 also further limit the same mental process by narrowing the architecture of the classifier.
The limitations in claim 18 further limits the mental process of ascertaining an output signal by narrowing the architecture of the classifier used to obtain the output signal being observed.
The limitations in claim 20 further limits the mental process of subdividing the input signal in claim 14 by narrowing it to parse and image of a microarray into several excerpts.
The limitation in claim 22 of displaying the classification encompasses the mental process of displaying certain results of analyzing data (MPEP 2106.04(a)(2)(III)(A)).
Therefore, these limitations fall under the “Mathematical concepts” and “Mental processes” groupings of abstract ideas (Step 2A, prong 1: YES).
Step 2A, prong 2:
Claims found to recite a judicial exception under Step 2A, prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application (Step 2A, prong 2). The claims recite the following additional elements:
Claim 14 recites: the method is computer-implemented.
Claim 21 recites: the method is computer-implemented.
Claim 22 recites: wherein a display device is actuated based on the ascertained output signal…
Claim 23 recites: a training device.
Claim 24 recites: a system and a training device.
Claim 25 recites: a non-transitory machine-readable memory medium.
The additional elements recited in claims 14 and 21 of using a computer to implement the steps of the method are instructions to apply the judicial exception in a generic computer environment by invoking a computer to accomplish the process and the additional element in claim 22 of using a display device to show the classification results is also a computer used as a tool to perform the mental process (MPEP 2106.05(f)). In the same manner, the additional elements in claims 23-25 of a training device, a system, and a non-transitory machine-readable memory medium also invoke computers and instruct the judicial exception be applied to them.
Therefore, the judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies/uses the recited judicial exception in some other meaningful way and the claims are directed to the judicial exception (Step 2A, prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). Claims 14, 21-22, and 23-25 recite additional elements that equate to mere instructions to apply the recited judicial exception in a generic computing environment. Claims that amount to nothing more than instructions to apply the judicial exception using a generic computer do not render an abstract idea eligible. Alice Corp., 576 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
As such, the combination of additional elements recited in the claims is well-understood, routine, and conventional. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transform the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO) and claims 14-25 are not patent eligible.
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.
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.
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 14-25 are rejected under 35 U.S.C. 103 as being unpatentable over Stoughton et al. (US20180330056A1; IDS document 6/14/2023) in view of Wang et al. (Computer Methods and Programs in Biomedicine, vol. 111, no. 1, pp.189-98), as evidenced by Amari (Neurocomputing, vol. 5, nos. 4-5, pp. 185-96). The italicized text corresponds to the instant claim limitations.
Regarding claim 14, Stoughton et al. teach microanalysis methods and computer system using supervised machine learning and training data of well-characterized samples having known properties (paragraph 0010, 0037) which discloses the limitations a computer-implemented method for training a classifier, the method comprising the following steps: ascertaining at least one first input signal, the first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocating a desired output signal, which characterizes a classification of the evaluation points, to the first input signal. Stoughton et al. teach training supervised learning algorithms via a backpropagation method (paragraphs 0015-0016) which discloses the limitations ascertaining an output signal using the classifier …, the output signal characterizing a classification of the first input signal; and adapting at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained output signal and the desired output signal. The teaching of Stoughton discloses the limitations because Amari teaches that backpropagation is a version of the stochastic descent learning method for parameterized network (p. 185, 1. Introduction, paragraph 1) and it involves adjusting a parameter of a function to obtain the optimal value by inputting data of desired input-output pairs into the function, calculating a loss function value, and adjusting the parameter to reduce loss (p. 186-187, 2.1 Stochastic descent learning rule; p. 188-189, 3.2 Backpropagation).
Regarding claims 15 and 19, Stoughton et al. teach that an image of a microarray that measures light is used as the input data (paragraph 0011, 0032; Figure 3) which discloses the claim 15 limitation of wherein the input signal is ascertained based on a sensor signal of an optoelectronic sensor, the sensor signal characterizing a measurement of the evaluation points. Theses teaching also disclose the claim 19 limitations of wherein the molecular biological examination system includes a microarray, and the input signal characterizes an image of the evaluation points of the microarray.
Regarding claim 18, Stoughton et al. teach that supervised learning algorithms can be artificial neural networks and are used to output a determination of a pathogen parameter based on the training data (paragraph 0011, 0015) which discloses wherein the output signal is ascertained using a second neural network including the classifier…
Regarding claim 21, the above cited teachings of Stoughton et al. in regard to claims 14 and 18 of training a classifier on a set of training data (paragraphs 0010, 0015-0016) and using it to characterize input microarray data (paragraphs 0011, 0037) disclose the limitations of a computer-implemented method for ascertaining an output signal, the output signal characterizing a classification of a first input signal, and the first input signal characterizes a plurality of evaluation points of a molecular biological examination system, the method comprising the following steps: training a classifier, including: ascertaining at least one third input signal, the third input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocating a desired output signal, which characterizes a classification of the evaluation points, to the third input signal, …, ascertaining a first output signal using the classifier …, the output signal characterizing a classification of the third input signal, and adapting at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained first output signal and the desired output signal; …; and ascertaining the output signal … using the classifier.
Regarding claim 22, Stoughton et al. teach in an embodiment of their invention, the system generates outputs of the results of its’ analysis of microarray input data (paragraph 0084) which discloses the limitations of wherein a display device is actuated based on the ascertained output signal in such a way that the display device displays the classification.
Regarding claim 23, the above cited teachings of Stoughton et al. in regard to claims 14 and 18 of using a computer system for training a classifier on a set of training data (paragraphs 0010, 0015-0016) and using it to characterize input microarray data (paragraphs 0011, 0037) disclose the limitations of a training device configured for training a classifier, the training device configured to: ascertain at least one first input signal, the first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocate a desired output signal, which characterizes a classification of the evaluation points, to the first input signal; …; ascertain an output signal using the classifier …, the output signal characterizing a classification of the first input signal; and adapt at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained output signal and the desired output signal.
Regarding claim 24, the above cited teachings of Stoughton et al. in regard to claims 14 and 18 of using a computer system for training a classifier on a set of training data (paragraphs 0010, 0015-0016) and using it to characterize input microarray data (paragraphs 0011, 0037) disclose the limitations of a system for data processing for ascertaining an output signal, the output signal characterizing a classification of a first input signal, and the first input signal characterizes a plurality of evaluation points of a molecular biological examination system, the system configured to: …; and ascertain the output signal … using a trained classifier, the classifier being trained by a training device configured to: ascertain at least one third input signal, the third input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocate a desired output signal, which characterizes a classification of the evaluation points, to the third input signal, …, ascertain a first output signal using the classifier …, the output signal characterizing a classification of the third input signal, and adapt at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained first output signal and the desired output signal.
Regarding claim 25, since Stoughton et al. teach using a computer system to perform microarray image analysis (paragraph 0037), it is inherent that such system would contain a storage drive containing software/programs to perform the steps of training the machine learning algorithms and Stoughton et al. also teach that a central database can be used to store data that can be used to train new artificial neural networks (paragraph 0035). These teachings disclose the limitations of a non-transitory machine-readable memory medium on is stored a computer program for training a classifier, the computer program, when executed by a computer, causing the computer to perform the following steps: ascertaining at least one first input signal, the first input signal characterizing a plurality of evaluation points of a molecular biological examination system, and allocating a desired output signal, which characterizes a classification of the evaluation points, to the first input signal; …; ascertaining an output signal using the classifier …, the output signal characterizing a classification of the first input signal; and adapting at least one parameter of the classifier according to a loss value, the loss value characterizing a difference between the ascertained output signal and the desired output signal.
Stoughton et al. appears to be silent on the claims 14, 23, and 25 limitations of … subdividing the first input signal into a plurality of second input signals according to an arrangement of the evaluation points; ascertaining a plurality of first representations, a respective first representation being ascertained for each second input signal of at least a first subset of the plurality of second input signals, using the classifier and ascertaining an output signal using the classifier and based on the plurality of first representations. Stoughton et al. also appears to be silent on the limitations of claims 16-17, the claim 18 limitation of wherein the output signal is ascertained using a second neural network including the classifier, and based on the first representations, and the limitations of claim 20. Lastly Stoughton et al. appears to be silent on the claims 21 and 24 limitations of … subdividing the third input signal into a plurality of fourth input signals according to an arrangement of the evaluation points, ascertaining a plurality of third representations, a respective third representation being ascertained for each fourth input signal of at least a first subset of the plurality of fourth input signals, using the classifier, ascertaining a first output signal using the classifier and based on the plurality of third representations, subdividing the first input signal into a plurality of second input signals according to an arrangement of the plurality of evaluation points; and ascertaining the output signal based on the plurality of second input signals using the classifier. However, these limitations were known in the prior art prior to the effective filing date of the invention as taught by Wang et al.
Regarding claims 14, 23, and 25, Wang et al. teach a microarray segmentation method using neural networks by first using a gridding framework to divide a microarray into several spots (p. 191, 2.1 Creation of training sets) which discloses the limitations of … subdividing the first input signal into a plurality of second input signals according to an arrangement of the evaluation points. Then Wang et al. teach using neural networks to perform the spot segmentation task on different classes of spots, which is a method of assigning pixels into spot and non-spot classes (p. 191, 2. The proposed approach; p. 193-194, 2.4 Multiple neural networks implementation; Figure 6) which discloses the limitations of ascertaining a plurality of first representations, a respective first representation being ascertained for each second input signal of at least a first subset of the plurality of second input signals, using the classifier and ascertaining an output signal using the classifier and based on the plurality of first representations.
Regarding claims 16-17 and 20, since Wang et al. teach using multiple neural networks to perform spot segmentation from images of microarrays (p. 193-194, 2.4 Multiple neural networks implementation), the teachings disclose the claim 16 limitations of wherein the classifier includes at least one first neural network, using which the first representations are ascertained and the claim 17 limitations of wherein the classifier includes a plurality of first neural networks, the classifier including a respective first neural network for each second input signal of the first subset, using which the first representation of the second input signal is ascertained. The teachings also disclose the claim 20 limitations of wherein each second input signal of the plurality of second input signals is an excerpt of the image, and the excerpt is selected according to the arrangement of the evaluation points of the microarray.
Regarding claim 18, Wang et al. teach gene expression data derived from microarray images can be used for further steps of analysis (p. 198-190, 1. Introduction, paragraph 1). In combination with the cited teachings in regard to claims 14, 23, and 25, this discloses the limitation of wherein the output signal is ascertained using a second neural network including the classifier, and based on the first representations.
Regarding claims 21 and 24, the above cited teachings of Wang et al. in regard to claims 14, 23, and 25 of a microarray segmentation method using neural networks by first using a gridding framework to divide a microarray into several spots (p. 191, 2.1 Creation of training sets) and using neural networks to perform the spot segmentation task (p. 193-194, 2.4 Multiple neural networks implementation; Figure 6) disclose the limitations of … subdividing the third input signal into a plurality of fourth input signals according to an arrangement of the evaluation points, ascertaining a plurality of third representations, a respective third representation being ascertained for each fourth input signal of at least a first subset of the plurality of fourth input signals, using the classifier, ascertaining a first output signal using the classifier and based on the plurality of third representations, subdividing the first input signal into a plurality of second input signals according to an arrangement of the plurality of evaluation points; and ascertaining the output signal based on the plurality of second input signals using the classifier.
An invention would have been prima facie obvious to one of ordinary skill in the art at the time of the effective filing date of the inventio if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Stoughton et al. teach improving accuracy in microarray-based approaches would broaden the scope or diagnostic information and improve pathogen identification (paragraphs 0006-0007). Wang et al. teach the spot segmentation is a non-trivial issue necessary to remove background noise and artifacts (p. 190, 1. Introduction, paragraph 2). Additionally, Wang et al. teach there is a desire to automate the time-consuming manual processing of microarrays, but other technique for spot segmentation have several drawbacks, such as being too computationally intensive (p. 190, 1. Introduction, paragraph 3-5). Lastly, Wang et al. teach their neural network approach addressed several issues in the art and is very efficient in terms of runtime (p. 190-191, 1. Introduction paragraphs 6-7). Based on the foregoing teachings, one of ordinary skill in the art would be motivated to utilize the multiple neural networks taught in Wang et al. to perform spot segmentation in the microarray classification methods taught in Stoughton et al. in order to improve accuracy of classification and reduce the computational intensity of processing the microarray data. There would be a reasonable expectation of success because both references utilize microarray image data and neural networks to process the data. The invention of claims 14-25 is therefore prima facie obvious.
Conclusion
No claims are allowed.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (570-273-8300):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Written authorizations submitted to the examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (570-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMUR Y OLJUSKIN whose telephone number is (571)272-4006. The examiner can normally be reached Mon - Fri; 0800-1630 EST.
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, Olivia Wise can be reached at 571-272-2249. 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.
/T.Y.O./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
Prosecution Insights