DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The preliminary Amendment filed 23 February 2025 has been entered and considered. Claims 1 and 9 have been amended. Claims 1-22 are all the claims pending in the application.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of Application No. DE1020221314499, filed in Germany on 11/28/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/27/2023 was considered by the examiner.
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.
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 limitations 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.
Claims 19-22 recites the limitations “evaluation unit”. These limitations have been interpreted under 112(f) as a means plus function because of the combination of the non-structural, generic placeholder “evaluation unit”, as well as their respective functional languages “for evaluating images of multiple coloring rounds” and is being interpreted respectively as “processing model 5, memory module 18, and modules that exchange data via channels 20” that corresponds to the structure found in the disclosure (Par. [0310]).
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 22 is treated as a product-by-process claim, requiring the performance of the steps of its parent claim.
Claim Rejections - 35 USC § 101
Claim 21 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. In particular, Claim 21 is directed to a computer-readable memory medium. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893F.2d 319 (Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of Claim 21 drawn to a computer-readable medium covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). Applicants are advised to amend Claim 21 to recite “A non-transitory computer-readable memory medium…” in order to overcome the rejection.
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.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 1, 13, 20, and 22 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 1, the phrases “on the one hand” and “on the other hand” render the claim indefinite because the phrases are unclear and can be up to interpretation. The Examiner recommends removing the phrases entirely or using sequential phrases like “first” and “second”.
Regarding Claim 13, the phrase "for example" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d). The Examiner recommends replacing the term “such as” with “including”, “comprising”, or “at least one of” in order to clearly convey that the items which follow the term “such as” are actual limitations.
Regarding Claims 20 and 22, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). The Examiner recommends replacing the term “such as” with “including”, “comprising”, or “at least one of” in order to clearly convey that the items which follow the term “such as” are actual limitations.
Claim 21 is rejected under 35 U.S.C. 112(b) because the claimed invention is not clearly a device nor a method. In Ex Parte Miyazaki, the BPAI held “that if a claim is amenable to two or more plausible claim constructions, the USPTO is justified in requiring the applicant to more precisely define the metes and bounds of the claimed invention by holding the claim unpatentable under 35 U.S.C. § 112, second paragraph, as indefinite.” Ex Parte Miyazaki, 89 USPQ2d 1207, 11-12 (Bd. Pat. App. & Int. 2008).
Claims 19-21 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claims 19-21 are apparatus claims that depend from method claims, but they do not require performance of the method itself and therefore remove limitations (i.e they do not further limit the parent claim), Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-15, 17, and 19-22 rejected under 35 U.S.C. 103 as being unpatentable over Gerdes et al. (U.S. Patent Pub No. 2008/0118934 A1, hereafter referred as Gerdes) in view of Mueller et al. (U.S. Patent App. Pub No. 2013/0089249 A1, hereafter referred as Mueller) and Aidt et al. (U.S. Patent App. Pub No. 2024/0029409 A1, hereafter referred as Aidt).
Regarding Claim 1:
Gerdes teaches a method for preparing data for identifying analytes in a sample (Gerdes: Par. [0002] and [0078]; methods for sequentially analyzing a biological sample; a target may provide information about the presence or absence of an analyte in the biological sample), one or more analytes being colored with markers in multiple coloring rounds in an experiment (Gerdes: Par. [0069]; providing a biological sample containing multiple targets adhered to a solid support and binding at least one fluorescent probe to one or more target present in the sample), the markers in each case being specific for a certain set of analytes (Gerdes: Par. [0141]; the plurality of probes may be capable of binding different targets in the biological sample; for example, a biological sample may include two targets: target1 and target2 and two sets of probes may be used in this instance: probe1 (having binder1 capable of binding to target1) and probe2 (having binder2 capable of binding to target2); a plurality of probes may be contacted with the biological sample simultaneously (for example, as a single mixture) or sequentially (for example, a probe1 may be contacted with the biological sample, followed by washing step to remove any unbound probe1, followed by contacting a probe2 with the biological sample, and so forth)), the multiple markers being detected using a camera (Gerdes: Par. [0213]; monochromatic Leica DFC 350FX monochromatic high-resolution camera mounted in a Leica DMRA2 fluorescent microscope), which for each coloring round generates at least one image that may contain color information of one or more markers (Gerdes: Par. [0208]; multiple staining is obtained by staining, imaging, chemically destroying the fluorophore, restaining, imaging, and repeating the steps), and the color information of the particular coloring rounds being stored for the evaluation (Gerdes: Par. [0232] and Fig. 22; shows a plot of average pixel intensity of the background for each cycle in the imaging as well as a small image of what the background looked like prior to staining), and a difference image is formed on the one hand from an actually detected image or from an actually detected image plane of the present coloring round (Gerdes: Par. [0189]; some embodiments, images (e.g., signals from the probe(s) and morphological stains) may be overlaid using computer-aided superimposition to obtain complete information of the biological sample, for example topological and correlation information).
Gerdes fails to further teach wherein for an nth coloring round, an expected predicted image is predicted based on predicted image data of one or more preceding coloring rounds and/or based on predicted image data of the present coloring round, and on the other hand a difference image is formed from the predicted image, the difference image being stored as color information.
Mueller, like Gerdes, is directed to preparing data for biological samples. Mueller does teach and on the other hand a difference image is formed from the predicted image, the difference image being stored as color information (Mueller: Par. [0050]; the transform function is adapted to highlight in the sub-region a difference between the first and the second digital images).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gerdes to utilize the difference technique, as taught by Mueller, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. As taught by Mueller, the proposed modification would allow for hardware or image processing algorithm optimization (Mueller: Par. [0023]).
Aidt, like Gerdes, is directed to preparing data for biological samples. Aidt does teach wherein for an nth coloring round, an expected predicted image is predicted based on predicted image data of one or more preceding coloring rounds and/or based on predicted image data of the present coloring round (Aidt: Par. [0015]; creating a ground truth multi-record training dataset wherein a record comprises: a first image of a sample of a tissue of a subject depicting a first group of biological objects, a second image of the sample of tissue depicting a second group of biological objects presenting at least one biomarker, and ground truth labels indicating a respective biological object category of a plurality of biological object categories for biological object members of the first group and the second group, and training the ground truth generator machine learning model on the ground truth multi-record training dataset for automatically generating ground truth labels selected from the plurality of biological object categories for biological objects depicted in an input set of images of the first type and the second type).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gerdes to utilize the predictive technique, as taught by Aidt, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. As taught by Mueller, the proposed modification would reduce the need for training data that may require tens of thousands of manually identified and annotated objects (e.g., tissue features, organelles, molecular markers, etc.) (Aidt: Par. [0014]).
In regards to Claim 2, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the predicted image corresponds to an image plane of a Z image made up of multiple image planes, and the predicted image data include one or more image planes of Z images made up of one or more preceding coloring rounds and/or one or more image planes of the Z image of the present coloring round (Aidt: Par. [0259]; the large image is analyzed as a whole. Images may be scanned along different x-y planes at different axial (i.e., z axis) depth).
In regards to Claim 3, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the preceding coloring rounds are coloring rounds of the same experiment, or coloring rounds of a different experiment with preferably a similar or identical sample (Gerdes: Par. [0208]; multiple staining is obtained by staining, imaging, chemically destroying the fluorophore, restaining, imaging, and repeating the steps).
In regards to Claim 4, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the predicted image data include subsets of the images of one or more preceding coloring rounds and/or of the present coloring round, wherein the subsets may be individual or multiple image planes of a Z image, or also excerpts in a plane of the images (Aidt: Par. [0259]; The images may be whole slide images (WSI), and/or patches extracted from the WSI, and/or portions of the sample; Each large image may be divided into smaller sized patches, which are then analyzed).
In regards to Claim 5, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the predicted image data are reconstructed image data from difference images, or only the difference images themselves from preceding coloring rounds (Mueller: Par. [0050-0051]; the transform function is adapted to highlight in the sub-region a difference between the first and the second digital images. This allows to determine any mismatch between the first and second images to be displayed or further analyzed; he second image may be either directly obtained from a library of images or generated from a library of images. The transform function may then be computed based on the retrieved or generated second image from the library. Such usage may include, but is not limited to, using the pixel data of the retrieved second image to compute the transform function; In the generation case, the second image may be generated from a library by, for example, computing the average of images meeting some search criteria).
In regards to Claim 6, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the predicted image data are kept in compressed form (Aidt: Par. [0803]; e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code.).
In regards to Claim 7, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the predicted image data originate solely from the immediately preceding coloring round and/or from the present coloring round (obvious to one skilled in the art to execute the trained model on the desired datasets).
In regards to Claim 8, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the difference image is compressed before being stored (Aidt: Par. [0803]; e.g., using any of a variety of generally available compilers, installation programs, compression/decompression utilities, etc.) then takes the form of executable code.).
In regards to Claim 9, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the prediction is carried out using a linear predictor (Aidt: Par. [0302]; Exemplary architectures of the machine learning models described herein include, for example, statistical classifiers and/or other statistical models, neural networks of various architectures (e.g., convolutional, fully connected, deep, encoder-decoder, recurrent, graph), support vector machines (SVM), logistic regression, other regressors, k-nearest neighbor, decision trees, boosting, random forest, a regressor, and/or any other commercial or open source package allowing regression, classification, dimensional reduction, supervised, unsupervised, semi-supervised or reinforcement learning.).
In regards to Claim 10, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the prediction is carried out using a processing model of a machine learning system, in particular of a neural network, for the image-to-image regression (Aidt: Par. [0302]; Exemplary architectures of the machine learning models described herein include, for example, statistical classifiers and/or other statistical models, neural networks of various architectures (e.g., convolutional, fully connected, deep, encoder-decoder, recurrent, graph), support vector machines (SVM), logistic regression, other regressors, k-nearest neighbor, decision trees, boosting, random forest, a regressor, and/or any other commercial or open source package allowing regression, classification, dimensional reduction, supervised, unsupervised, semi-supervised or reinforcement learning.).
In regards to Claim 11, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 9, wherein the processing model is retrained for each coloring round, or is retrained for each experiment, or a processing model is selected from multiple pretrained processing models, this selection preferably being made based on context information, which may include properties of the sample and/or of the experiment and/or of the expected analytes, and in particular parameters for coloring the sample and/or the expected number of analytes or also the expected ratio of the analytes contained in the sample (Aidt: Par. [0015]; training the ground truth generator machine learning model on the ground truth multi-record training dataset for automatically generating ground truth labels selected from the plurality of biological object categories for biological objects depicted in an input set of images of the first type and the second type).
In regards to Claim 12, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 10, wherein the processing model has been trained using annotated training data, the annotated training data in each case including an output image and a corresponding target image, the output image as well as the target image having been measured for a sample (Aidt: Par. [0083] and [0248]; there is provided an annotated image obtained by the method as described herein in any of the respective embodiments and any combination thereof; The automatically annotated first and second images (i.e., first image and virtual second image) may be fed in combination into the diagnosis and/or biological object machine learning model to obtain the diagnosis).
In regards to Claim 13, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the predicted image data are normalized prior to the prediction, for example to have a predetermined intensity range and/or a defined background signal (not explicitly stated but obvious to one skilled in the art to perform normalization in processing).
In regards to Claim 14, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the predicted image data are denoised prior to the prediction (not explicitly stated but obvious to one skilled in the art to perform normalization in denoising).
In regards to Claim 15, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein an image encompasses a two-dimensional depiction including multiple pixels as image points, or a three-dimensional depiction including multiple voxels as image points, wherein the images may include time information as an additional dimension (Aidt: Par. [0298]; Imaging device(s) 1212 may create two dimensional (2D) images of the samples, optionally whole slide images.).
In regards to Claim 17, Gerdes as modified by Mueller and Aidt further teaches the method according to Claim 1, wherein the analytes are one of the following: proteins, polypeptides, or nucleic acid molecules (Aidt: Par. [0046]; the target molecules are selected from the group consisting of nuclear proteins, cytoplasmic proteins, membrane proteins, nuclear antigens, cytoplasmic antigens, membrane antigens and nucleic acids.), and the markers couple to the analytes via analyte-specific probes and include a dye molecule that is coupled to the marker (Gerdes: Par. [0069]; providing a biological sample containing multiple targets adhered to a solid support and binding at least one fluorescent probe to one or more target present in the sample).
In regards to Claim 19, Gerdes as modified by Mueller and Aidt further teaches an evaluation unit for evaluating images of multiple coloring rounds, and which in particular is designed as a machine learning system, including the means for carrying out the method according to claim 1 (Aidt: Par. [0731]; a non-limiting example of evaluation of trained nuclei patch based classification deep neural network on validation FOVs from 11 WSIs, embodied by a confusion matrix. The output and target represent the predicted and ground truth classes).
In regards to Claim 20, Gerdes as modified by Mueller and Aidt further teaches an image processing system, including an evaluation unit according to preceding claim 19 (Aidt: Par. [0731]; a non-limiting example of evaluation of trained nuclei patch based classification deep neural network on validation FOVs from 11 WSIs, embodied by a confusion matrix. The output and target represent the predicted and ground truth classes), in particular including an image generation unit such as a microscope (Aidt: Par. [0296]; Exemplary imaging device(s) 1212 include: a scanner scanning in standard color channels (e.g., red, green blue), a multispectral imager acquiring images in four or more channels, a confocal microscope, a black and white imaging device, and an imaging sensor.).
In regards to Claim 21, Gerdes as modified by Mueller and Aidt further teaches a computer program product that includes commands which, when the program is executed by a computer, prompt the computer to carry out the method according to claim 1, the computer program product being in particular a computer- readable memory medium (Aidt: Par. [0282]; present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.).
In regards to Claim 22, Gerdes as modified by Mueller and Aidt further teaches a machine learning system that includes an evaluation unit, the evaluation unit including a processing model that has been trained according to the method according to claim 18 (Aidt: Par. [0731]; a non-limiting example of evaluation of trained nuclei patch based classification deep neural network on validation FOVs from 11 WSIs, embodied by a confusion matrix. The output and target represent the predicted and ground truth classes), in particular including an image generation unit such as a microscope (Aidt: Par. [0296]; Exemplary imaging device(s) 1212 include: a scanner scanning in standard color channels (e.g., red, green blue), a multispectral imager acquiring images in four or more channels, a confocal microscope, a black and white imaging device, and an imaging sensor.).
Allowable Subject Matter
Claims 16 and 18 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:
Claim 16 recites, wherein for identifying the analytes by use of the stored difference images, the actually detected image or the actually detected image plane is restored from same, at least for predetermined data points, wherein a data point in each case includes one or more contiguous pixels in the images of the multiple coloring rounds that are assigned to the same location in a sample. The cited art of record does not teach or suggest such a combination of features.
Claim 18 recites, a method for training a machine learning system, using a processing model for carrying out a method according to claim 1, comprising: providing an annotated data set, and optimizing an objective function by adapting the model parameters of the processing model, the objective function detecting a difference between a result output that is output by the processing model and a target output, characterized in that the annotated data set includes at least one target signal series of a candidate data point as well as a target signal series of a background data point, and the processing model processes a partial signal series of the target signal series of the annotated data set as input, and based on an output of the processing model, a data point corresponding to the particular target signal series is assessed as a background data point or a candidate data point. The cited art of record does not teach or suggest such a combination of features.
Because the cited art of record, alone or in combination, does not teach or suggest each and every feature of dependent Claims 16 and 18, these claims would be allowable.
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chefd'hotel et al. (U.S. Patent App. Pub No. 2017/0169567 A1) teaches methods for automatic immune cell detection that is of assistance in clinical immune profile studies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RENAE BITOR whose telephone number is (703)756-5563. The examiner can normally be reached Monday to Friday: 8:00 - 5:30 but off the 1st Friday of the biweek.
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/RENAE A BITOR/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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