DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. Claims 1-20 are currently pending and under exam herein.
Claims 1-20 are rejected.
Priority
3. There was no benefit of priority claimed herein. Therefore the effective filing date of claims 1-20 is considered to be to the filing date of 12 July 2023.
Information Disclosure Statement
4. The information disclosure statement submitted on 15 August 2023 is being considered by the examiner.
Drawings
5. The drawing submitted on 12 July 2023 are objected to by the examiner because Figures 4 and 5 are rendered in color.
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
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.
6. Claims 7 and 8 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.
Claim 7 recites “the medical information process of claim 6”, but claim 6 (and claim 1 from which claim 6 depends claim a ‘processing apparatus’. Therefore, statutory classes (an apparatus and a process) are mixed in a single claim rendering the scope of the claim unclear, as it is unclear if the claim is directed to an apparatus or the method of using it. Claim 8 is rejected by virtue of dependence on claim 7 and for failing to resolve the indefiniteness issue.
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.
7. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
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 an abstract idea:
Claim 1 recites: calculate a respective distance between each pair of biomolecules from the plurality of biomolecules
Claim 1 recites: apply a manifold learning method to the distances to obtain a respective position in a two-dimensional space mapped to each biomolecule of the plurality of biomolecules
Claim 1 recites: adjust the positions to achieve a more even distribution of the positions over the two-dimensional space
Claim 2 recites: The medical information processing apparatus of claim 1, wherein the processing circuitry is further configured to: after adjusting the positions and before storing the two-dimensional image format in the memory: apply an image morphology method to adjust at least some mapped positions into un-mapped adjacent positions
Claim 3 recites: The medical information processing apparatus of claim 2, wherein the image morphology method comprises dilation
Claim 4 recites: The medical information processing apparatus of claim 1, wherein the positions are adjusted by applying a spatial transformation to the positions in two-dimensional space
Claim 5 recites: wherein the respective distance between each pair of biomolecules from the plurality of biomolecules is calculated based on a correlation between said pair of biomolecules across the cohort
Claim 6 recites: The medical information processing apparatus of claim 1, wherein the respective distance between each pair of biomolecules from the plurality of biomolecules is calculated based on information from a biological database
Claim 7 recites: The medical information process of claim 6, wherein the biological database is a knowledge graph
Claim 8 recites: The medical information processing apparatus of claim 7, wherein the knowledge graph defines biomolecules as nodes, wherein the nodes are connected by edges, and wherein the distance between each pair of biomolecules is calculated based on the number and/or weight of the edges between the respective biomolecules
Claim 11 recites: calculating a respective distance between each pair of biomolecules from the plurality of biomolecules
Claim 11 recites: applying a manifold learning method to the distances to obtain a respective position in a two-dimensional space mapped to each biomolecule of the plurality of biomolecules
Claim 11 recites: adjusting the positions to achieve a more even distribution of the positions over the two-dimensional space
Claim 12 recites: generate a displaying image by mapping each value of the plurality of values onto an image using the two-dimensional image format, based on the display positions in the two-dimensional format
Claim 16 recites: The medical information processing apparatus of claim 15, wherein the displaying image is colored in accordance with the normalized values
Claim 18 recites: generate a further displaying image by mapping each value of the further plurality of image onto a further image using the two-dimensional image format, based on the display positions of the two-dimensional format
Claim 19 recites: generating a displaying image by mapping each value of the plurality of values onto an image using a two-dimensional image format of display positions for a plurality of biomolecules, based on the display position in the two-dimensional image format; wherein the two-dimensional image format is obtained using the method of claim 11
Claim 20 recites: calculate a respective distance between each pair of biomolecules from the plurality of biomolecules
Claim 20 recites: apply a manifold learning method to the distances to obtain a respective position in a two-dimensional space mapped to each biomolecule of the plurality of biomolecules
The limitations regarding calculating a respective distance, applying a manifold learning method, applying an image morphology method to adjust positions of biomolecules and applying a spatial transformation, are verbal equivalents that describe a mathematical calculation that is performed as the limitation and are so simple that they could be performed in the human mind or with pen and paper. Therefore, these limitations fall under the "Mathematical concepts" and "Mental processes" groupings of abstract ideas.
The remaining limitations in claim 1 and 11 for ‘adjusting the positions of the biomolecules in the two-dimensional space’ and the limitations of claims 12 and 18-19 for ‘mapping each value of the plurality of values onto an image using the two-dimensional image format’ are generically recited data analysis steps that can be practically performed in the human mind because the human mind is capable of adjusting positions of biomolecules to make the distribution more even. Therefore, these limitations fall under the "Mental processes" groupings of abstract ideas.
The limitation of claim 3 that limits the type of image morphology method applied and the limitations of claims 5-8 that limit the data used for the distance calculation, further limit the mathematical calculations, but do not change their position as mathematical concepts.
The limitation of claim 16 that limits how the displaying image is mapped (i.e. colored in accordance with the normalized values), further limits the mental process of mapping the values, but do not change their position as a mental process.
While claims 1 and 20 recite performing some aspects of the analysis with a processor, there are no additional limitations that indicate that this processor requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then if falls within the "Mental processes" grouping of abstract ideas.
Therefore, claims 1-11 and 20 explicitly recite elements that, individually and in combination, constitute an abstract idea (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 or not (Step 2A, Prong 2). This 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 or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or insignificant extra-solution activity. Specifically, the claims recite the following additional elements:
Claim 1 recites: A medical information processing apparatus comprising: a memory
Claim 1 recites: processing circuitry
Claim 1 recites: receive omics data, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a corresponding biomolecule of a plurality of biomolecules
Claim 1 recites: store, in the memory, a two-dimensional image format of display positions for each biomolecule of the plurality of biomolecules based on the adjusted positions
Claim 5 recites: The medical information processing apparatus of claim 1, wherein the omics data comprises data from a cohort of subjects
Claim 9 recites: The medical information processing apparatus of claim 1, wherein the omics data is transcriptome data, the biomolecules are genes, and the associated values are expression levels of said genes
Claim 10 recites: The medical information processing apparatus of claim 1, wherein the omics data is one of proteome, metabolome, or gene mutational data
Claim 11 recites: A medical information processing method, comprising: receiving omics data
Claim 11 recites: the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a corresponding biomolecule of a plurality of biomolecules
Claim 11 recites: storing, in a memory, a two-dimensional image format of display positions for each biomolecule of the plurality of biomolecules based on the adjusted positions
Claim 12 recites: A medical information processing apparatus, the medical information processing apparatus comprising a memory
Claim 12 recites: storing a two-dimensional image format of display positions for a plurality of biomolecules, wherein the two-dimensional image format is obtained using the method of claim 11
Claim 12 recites: processing circuitry
Claim 12 recites: receive omics data associated with a subject, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a respective biomolecule of the plurality of biomolecules
Claim 13 recites: The medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to display the displaying image for human inspection
Claim 14 recites: The medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to input the displaying image to a deep learning algorithm
Claim 15 recites: The medical information processing apparatus of claim 12, wherein the plurality of values in the omics data are normalized
Claim 16 recites: The medical information processing apparatus of claim 15, wherein the displaying image is colored in accordance with the normalized values
Claim 17 recites: The medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to: provide mouse-over functionality to the displaying image to display the names of the biomolecules mapped to the respective display positions
Claim 17 recites: and/or display functional information related to the biomolecules mapped to the respective display positions
Claim 18 recites: The medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to: receive further omics data associated with a further subject, each value of the further omics data comprising a further plurality of values, wherein each value of the further plurality of values is associated with a respective biomolecule of the plurality of biomolecules
Claim 18 recites: display the image and further image side by side, or display the image or further image in a window and allow a user to switch between display of the image and display of the further image
Claim 19 recites: A medical information processing method, the method comprising: receiving omics data associated with a subject, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a respective biomolecule of the plurality of biomolecules
Claim 20 recites: A medical information processing apparatus comprising: a memory
Claim 20 recites: processing circuitry
Claim 20 recites: receive omics data, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a corresponding biomolecule of a plurality of biomolecules
Claim 20 recites: store, in the memory, a two-dimensional image format of display positions for each biomolecule of the plurality of biomolecules based on the position; wherein the respective distance between each pair of biomolecules from the plurality of biomolecules is calculated based on information from a biological database
The additional elements of ‘receiving omics data’, ‘receiving further omics data’ and ‘inputting the displaying image to a deep learning algorithm’ merely serves to gather data that is used an input for the judicial exception or to perform machine learning, which is considered presolution activity. As set forth in MPEP 2106.05(g), mere data gathering activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application (In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989)). Dependent claims 5 and 15 directed to indicating the type of input with respect to consumption of the nutraceutical further limits the data gathering activities but don’t change their position as data gathering activities.
There are no limitations that indicate that the ‘processing circuitry’ or memory requires anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The limitations in claims 5, 9-11 and 15 further limit the type of omics data received, but do not integrate the judicial exception into a practical application because they just further limit data gathering activities but don’t change their position as data gathering activities. Furthermore, as set forth in the MPEP, limitations of data types amount to field-of-use (2106.05(h)), or mere instructions to apply the judicial exception (2106.05(f)).
The limitations for ‘displaying the displaying image, ‘displaying functional information’, ‘displaying the image and further image side by side’, ‘provide mouse-over functionality’ and ‘storing a two-dimensional image’ are considered post-solution activity steps that merely serve to output data from the judicial exception. As set forth in MPEP 2106.05(g), output activity that is incidental to the primary process are insignificant extra-solution activity that do not have a practical application. The limitations that limit the type of data output fail to integrate the judicial exception into a practical application because they merely further limit the tangential output activities but do not change their position as output activities.
Therefore the above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-20 are directed to an abstract idea (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)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, and conventional activity.
The limitations of claims 1, 11-14, 17-18 and 20, directed to ‘receiving omics data’, ‘receiving further omics data’, ‘storing a two-dimensional image format’, ‘display the displaying image’, ‘providing mouse-over functionality’, ‘display functional information’, ‘display the image and further image side by side’ and ‘input the displaying image to a deep learning algorithm’ do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As set forth in MPEP section 2106.05(g), the courts have decided that limitations that merely add an insignificant extra-solution activity, do not amount to an inventive concept, particularly when the activities are well-understood and conventional.
As set forth in MPEP section 2106.05(d), the courts have recognized that limitations directed to data gathering that are claimed as insignificant extra-solution activity are routine, well understood and conventional (Mayo Collaborative servs. V. Prometheus Labs., Inc., 566 U.S. at 79, 101 USPQ2d at 1968).
As evidenced by Krasowski et al. (Frontiers in Genetics, 2020, Vol. 11, p. 1-17), accessing omics data was widely available in 2020. Krasowski et al. cites various different public databases are in place aiming to store and share specific kinds of omics data types as public repositories [e.g., genomics data in NCBI-SRA, GEO and EBI-ENA, proteomics data at PRIDE and ProteomeXchange, or metabolomics data at MetaboLights, Metabolomics Workbench and GNPS-MASSIVE (p. 10, col. 1, para. 2- col. 2, para. 1).
Additionally, storing and retrieving information from memory has also been deemed well-understood, routine and conventional activity by the courts (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
As evidenced by Yao et al. (Multimedia Tools and Applications (2022) Vol. 81, p. 41361–41405), input and analysis of medical image data by neural networks was widely done by 2020. Yao et al. discloses that convolution neural networks has been extensively applied to medical image analysis as there are over 100 references from Google Scholar, PubMed, Web of Science and various sources published from 1958 to 2020 (abstract).
Furthermore, according to the courts, generic graphical user interface interactions (such as mouseover functionality) (DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 113 USPQ2d 1097 (Fed. Cir. 2014)) and displaying data (Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014)) are considered well-understood, routine and conventional.
The limitations of claims 1, 12 and 20, pertaining to the processing circuitry and memory used to execute the method, are directed to performing judicial exceptions with a generic computing system on a generic computer. These limitations are not sufficient to amount to significantly more than the judicial exception because, as set forth in the MPEP section 2106.05(d)(II)), using a generic computing environment or generic computer to perform the judicial exception, has been deemed well-understood, routine and conventional activity including receiving or transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362), performing repetitive calculations (Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)), and storing and retrieving information in memory (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)). Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms 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). As such, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
8. Claim 19 is rejected under 35 U.S.C. 102 (a)(1) as being unpatentable over Shen et al. (Nucleic Acids Research, 2022, Vol. 50, p. 1-23 8/15/2023 IDS document), as evidenced by Dell’Amico et al. (Discrete Applied Mathematics, 2000, vol 100, no. 3, p. 17-48). The italicized text corresponds to the instant claim limitations.
Pertaining to claims 19, Shen et al. discloses receiving several datasets and using them as AggMapNet inputs, including: 1) transcriptomic datasets (RNA-Seq gene expression data of cell-cycle data or pan-cancer data); 2) proteomics data (MALDI-MS signal peaks of COVID-19 data); and 3) proteomic and metabolomics data (protein and metabolomic biomarkers in blood of COVID-19 data) (Table 1; p. 8, col. 2, para. 1 – p. 9, col. 1, para. 1; a medical information processing method, the method comprising: receiving omics data associated with a subject, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a respective biomolecule of the plurality of biomolecules).
Pertaining to claim 19, Shen et al. discloses that AggMap displays two dimensional images based on the display positions in the two-dimensional format. For example, after AggMap was applied to the cell-cycle dataset, Shen et al. displayed the 2D embedding of the genes both after applying UMAP-mediated AggMap and overlaying cluster membership (Fig. 5C) and also after overlaying gene expression z-scores for each gene for each cell cycle stage (Fig. 5D-E). Shen et al. further discloses that z-score is a normalized value representing expression values (Fig. 5 C-E; p. 7, col. 1, para. 1; generating a displaying image by mapping each value of the plurality of values onto an image using the two-dimensional image format of display positions for a plurality of biomolecules, based on the display positions in the two-dimensional format;
Regarding claim 19, the formatting of the two-dimensional image disclosed by Shen et al. would be equivalent to the two-dimensional image format obtained from the method of claim 11 for the reasons described below (wherein the two-dimensional image format is obtained using the method of claim 11):
Shen et al. discloses receiving several datasets and using them as AggMapNet inputs, including: 1) transcriptomic datasets (RNA-Seq gene expression data of cell-cycle data or pan-cancer data); 2) proteomics data (MALDI-MS signal peaks of COVID-19 data); and 3) proteomic and metabolomics data (protein and metabolomic biomarkers in blood of COVID-19 data) (Table 1; p. 8, col. 2, para. 1 – p. 9, col. 1, para. 1). This step of the method is equivalent to the limitation of claim 11 directed to: receive omics data, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a corresponding biomolecule of a plurality of biomolecules.
Shen et al. discloses that AggMap aggregates and maps individual unordered omic feature points into spatial-correlated multi-channel 2DFmaps. Shen et al. further discloses that AggMap feature restructuring focuses on the spatial and channel dimension of the Fmaps and that feature points are embedded in a 2D space using the manifold learning method Uniform Manifold Approximation and Projection (UMAP) based on their pairwise correlation distances (p. 2, col. 1, para. 2; Fig. 1A). These steps of the method are equivalent to the limitations of claim 11 directed to: calculate a respective distance between each pair of biomolecules from the plurality of biomolecules; apply a manifold learning method to the distances to obtain a respective position in a two-dimensional space mapped to each biomolecule of the plurality of biomolecules.
Shen et al. discloses that the feature points are agglomerated into multiple feature clusters (feat-clusters) using the agglomerative hierarchical clustering method and that feature points are aggregated into 2D grids by the linear assignment algorithm LAPJV based on the embedding coordinates to form spatially-correlated Fmaps, and then the feat-clusters guide feature assignment into split channels. Figure 1A shows that after LAPJV adjustment, the features have an even distribution in the two-dimensional space and are in a 2D image format. As evidenced by Dell’Amico et al., LAPJV prevents multiple features from occupying the same positions and spreads features among discrete, regularly spaced grid cells to produce a more spatially uniform feature map than the original point cloud while aiming to preserve the original embedding’s neighbor relationship (Shen et al. p. 2, col. 1, para. 2; Fig. 1A; Dell’Amico et al. p. 17, para. 1 – p. 18, para. 1, p. 28, para. 3 – p. 29, para. 2). This step of the method is equivalent to the limitation of claim 11 directed to: adjust the positions to achieve a more even distribution of the positions over the two-dimensional space.
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.
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.
9. Claims 1, 4-6, 9-12, 15-16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (Nucleic Acids Research, 2022, Vol. 50, p. 1-23 8/15/2023 IDS document), as evidenced by Dell’Amico et al. (Discrete Applied Mathematics, 2000, vol 100, no. 3, p. 17-48), in view of Regev et al., 2021 (US2022/0180975A1; 08/15/2023 IDS). The italicized text corresponds to the instant claim limitations.
Regarding claims 1 and 20, Shen et al. teaches an unsupervised novel feature aggregation tool called AggMap, which was developed to aggregate and map omics features into multi-channel 2D spatial-correlated imagelike feature maps (Fmaps) based on their intrinsic correlations. Shen et al. further teaches AggMapNet, multi-channel CNN architecture AggMapNet for developing predictive models from AggMap Fmaps. As disclosed by Shen et al. AggMap and AggMapNet open-source code is available at Github repository, and the computation algorithms are designed for fast convergence and low memory consumption, thus the apparatus inherently includes a processor and a memory (Fig. 1A, abstract; p. 2, col. 1, para. 1 – col. 2, para. 1; p. 6, col. 1, para. 1; a medical information processing apparatus comprising: a memory and processing circuitry).
Pertaining to claims 1,11 and 20, Shen et al. discloses receiving several datasets and using them as AggMapNet inputs, including: 1) transcriptomic datasets (RNA-Seq gene expression data of cell-cycle data or pan-cancer data); 2) proteomics data (MALDI-MS signal peaks of COVID-19 data); and 3) proteomic and metabolomics data (protein and metabolomic biomarkers in blood of COVID-19 data) (Table 1; p. 8, col. 2, para. 1 – p. 9, col. 1, para. 1; receive omics data, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a corresponding biomolecule of a plurality of biomolecules).
With respect to claims 1, 11 and 20, Shen et al. discloses that AggMap aggregates and maps individual unordered omic feature points into spatial-correlated multi-channel 2DFmaps. Shen et al. further discloses that AggMap feature restructuring focuses on the spatial and channel dimension of the Fmaps and that feature points are embedded in a 2D space using the manifold learning method Uniform Manifold Approximation and Projection (UMAP) based on their pairwise correlation distances (p. 2, col. 1, para. 2; Fig. 1A; calculate a respective distance between each pair of biomolecules from the plurality of biomolecules; apply a manifold learning method to the distances to obtain a respective position in a two-dimensional space mapped to each biomolecule of the plurality of biomolecules).
Pertaining to claims 1 and 11, Shen et al. discloses that the feature points are agglomerated into multiple feature clusters (feat-clusters) using the agglomerative hierarchical clustering method and that feature points are aggregated into 2D grids by the linear assignment algorithm LAPJV based on the embedding coordinates to form spatially-correlated Fmaps, and then the feat-clusters guide feature assignment into split channels. Figure 1A shows that after LAPJV adjustment, the features have an even distribution in the two-dimensional space and are in a 2D image format. As evidenced by Dell’Amico et al., LAPJV prevents multiple features from occupying the same positions and spreads features among discrete, regularly spaced grid cells to produce a more spatially uniform feature map than the original point cloud while aiming to preserve the original embedding’s neighbor relationship (Shen et al. p. 2, col. 1, para. 2; Fig. 1A; Dell’Amico et al. p. 17, para. 1 – p. 18, para. 1, p. 28, para. 3 – p. 29, para. 2; adjust the positions to achieve a more even distribution of the positions over the two-dimensional space).
Regarding claim 20, Shen et al. teaches that the pairwise correlation used to position the feature points can be gene expression data or proteomic data from The Cancer Genome Atlas (TCGA) database (Table 1; wherein the respective distance between each pair of biomolecules from the plurality of biomolecules is calculated based on information from a biological database).
Pertaining to claim 12, Shen et al. discloses receiving several datasets and using them as AggMapNet inputs, including: 1) transcriptomic datasets (RNA-Seq gene expression data of cell-cycle data or pan-cancer data); 2) proteomics data (MALDI-MS signal peaks of COVID-19 data); and 3) proteomic and metabolomics data (protein and metabolomic biomarkers in blood of COVID-19 data) (Table 1; p. 8, col. 2, para. 1 – p. 9, col. 1, para. 1; processing circuitry configured to or a method comprising: receive omics data associated with a subject, the omics data comprising a plurality of values, wherein each value of the plurality of values is associated with a respective biomolecule of the plurality of biomolecules).
Pertaining to claim 12, Shen et al. discloses that AggMap displays two dimensional images based on the display positions in the two-dimensional format. For example, after AggMap was applied to the cell-cycle dataset, Shen et al. displayed the 2D embedding of the genes both after applying UMAP-mediated AggMap and overlaying cluster membership (Fig. 5C) and also after overlaying gene expression z-scores for each gene for each cell cycle stage (Fig. 5D-E). Shen et al. further discloses that z-score is a normalized value representing expression values; (Fig. 5 C-E; p. 7, col. 1, para. 1; generate a displaying image by mapping each value of the plurality of values onto an image using the two-dimensional image format, based on the display positions in the two-dimensional format; wherein the two-dimensional image format is obtained using the method of claim 11).
Regarding claims 1, 11-12 and 20, Shen et al. is silent to: store, in the memory, a two-dimensional image format of display positions for each biomolecule of the plurality of biomolecules based on the [adjusted] positions (claims 1, 11 and 20); and the medical information processing apparatus comprising a memory storing a two-dimensional image format of display positions for a plurality of biomolecules, wherein the two-dimensional image format is obtained using the method of claim 11 (claim 12). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Regev et al.
Regarding claims 1, 11 and 20, Regev et al. teaches a method and system of aligning gene expression data in 2D spatial arrangement. Regev et al. further teaches that one hardware-implemented module may perform an operation, and store the output of that operation in a memory device to which it is communicatively coupled. Consequently, another hardware-implemented module may, at some time later, access the memory device to retrieve and process the stored information (para. 0010; 0228; store, in the memory, a two-dimensional image format of display positions for each biomolecule of the plurality of biomolecules based on the adjusted positions).
Regarding claim 12, Regev et al. teaches a method and system of aligning gene expression data in 2D spatial arrangement. Regev et al. further teaches that one hardware-implemented module may perform an operation, and store the output of that operation in a memory device to which it is communicatively coupled. Consequently, another hardware-implemented module may, at some time later, access the memory device to retrieve and process the stored information (para. 0010; 0228; the medical information processing apparatus comprising a memory storing a two-dimensional image format of display positions for a plurality of biomolecules, wherein the two-dimensional image format is obtained using the method of claim 11).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Regev et al. taught their method and system of spatial transcriptomics can determine cell subtype identities from spatial multi-omic data (abstract; para. 0011). Regev et al. further taught that cell type predictions are made by analyzing 2D images representing spatial transcriptomics by machine learning algorithms (p. 0018; 00571). Therefore, one of ordinary skill in the art would have been motivated to utilize the spatial transcriptomic method and system taught by Regev et al. in the apparatus and method directed to image-based analysis of omics data taught by Shen et al., in order to distinguish cell types based on spatial transcriptomic profiles. Furthermore, one of ordinary skill in the art would predict that the method of Regev et al., and in particular the storage of 2D images, could be readily added to the method and apparatus of Shen et al. with a reasonable expectation of success because they both pertain to machine learning analysis of spatial transcriptomic data and both methods generate 2D representations of the data. The invention is therefore prima facie obvious.
Regarding claim 4, as per Figure 5 and lines 4-9 of page 11 of the specification, a spatial transformation comprises an image distortion technique that changes the shape of the 2D data (for example from a circle to a square). Shen et al. teaches that after feature points are embedded in a 2D space based on their pairwise correlation distances (using the UMAP manifold learning method), feature points are agglomerated into feature clusters using the agglomerative hierarchical clustering method, and then feature points are arranged into a 2D regular grid using AggMap’s linear assignment algorithm. Both the hierarchical clustering and linear assignment steps result in a spatial transformation of the features (Fig. 5A-C; p. 3, col. 1, para. 2; the medical information processing apparatus of claim 1, wherein the positions are adjusted by applying a spatial transformation to the positions in two-dimensional space).
Pertaining to claim 5, Shen et al. teaches that the omics data are data from cohorts of subjects (including either cohorts of patients that are positive and negative for SARS-CoV-2 or patients with various different cancers). Shen et al. further teaches that feature points (e.g. genes) are embedded in a 2D space using the manifold learning method Uniform Manifold Approximation and Projection (UMAP) based on their pairwise correlation distances (which are calculated using feature abundances from transcriptomic, proteomic or metabolomic data) (p. 2, col. 1, para. 2; Table 1; The medical information processing apparatus of claim 1, wherein the omics data comprises data from a cohort of subjects, and wherein the respective distance between each pair of biomolecules from the plurality of biomolecules is calculated based on a correlation between said pair of biomolecules across the cohort).
Regarding claim 6, Shen et al. teaches that the pairwise correlation used to position the feature points can be gene expression data or proteomic data from The Cancer Genome Atlas (TCGA) database (Table 1; wherein the respective distance between each pair of biomolecules from the plurality of biomolecules is calculated based on information from a biological database).
Pertaining to claim 9, Shen et al. discloses that the omics data used for AggMapNet can be transcriptomic data (e.g. the RNA-Seq gene expression data from the TCGA database that was used in the disclosed pan-cancer application). In this example, the feature points are genes and the values used for 2D embedding were gene expression values (Table 1; the medical information processing apparatus of claim 1, wherein the omics data is transcriptome data, the biomolecules are genes, and the associated values are expression levels of said genes).
Pertaining to claim 10, Shen et al. discloses that the data used for AggMapNet can be transcriptomic, proteomic or metabolomics data (Table 1; p. 8, col. 2, para. 1 – p. 9, col. 1, para. 1; the medical information processing apparatus of claim 1, wherein the omics data is one of proteome, metabolome, or gene mutational data).
With respect to claim 15, Shen et al. discloses that the RNA-seq data are normalized. Shen et al. discloses that the CCTD-U dataset of cell-cycle transcriptome data of U2OS cells consisting of expression levels across 5 different cell cycle stages was transformed using z-score standard scaling. Shen et al. further discloses that the multi-task pan-cancer transcriptomic benchmark dataset TCGA-T of 33 cancers
is from normalized-level3 RNA-Seq expression studies of normal and tumor conditions, and that other datasets were also normalized (p. 8, col. 2, para. 2 – p. 9, col. 1, para. 1; the medical information processing apparatus of claim 12, wherein the plurality of values in the omics data are normalized)
Regarding claim 16, Shen et al. teaches that the 2D displayed image output from AggMap display pixels/features colored based on expression z-scores (which is normalized expression) (Fig. 5E; p. 7, col. 1, para. 1; the medical information processing apparatus of claim 15, wherein the displaying image is colored in accordance with the normalized values).
Pertaining to claim 18, Shen et al. discloses receiving omics data from both case and control samples for each of their datasets analyzed using AggMap and AggMapNet. For example, Shen et al. discloses that for the SARS-CoV-2 project that analyzed proteomic data, data from both positive and negative samples analyzed, for the pan-cancer project that analyzed transcriptomic data, data from 33 different cancer types were analyzed; and for the cell cycle project that analyzed transcriptomic data, data were from 5 different phases of the cell cycle (Table 1; the medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to: receive further omics data associated with a further subject, each value of the further omics data comprising a further plurality of values, wherein each value of the further plurality of values is associated with a respective biomolecule of the plurality of biomolecules).
Regarding claim 18, Shen et al. discloses that for each AggMap 2D embedding, multiple 2D images are generated by overlaying different gene expression datasets onto the 2D image. For example, for the cell cycle dataset, Shen et al. generates multiple 2D display images displaying spatial gene expression data across 5 different phases of the cell cycle on 5 different images. Shen et al. further discloses displaying all 5 images side by side (Fig. 5E; generate a further displaying image by mapping each value of the further plurality of image onto a further image using the two-dimensional image format, based on the display positions of the two-dimensional format; and display the image and further image side by side, or display the image or further image in a window and allow a user to switch between display of the image and display of the further image).
10. Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (Nucleic Acids Research, 2022, Vol. 50, p. 1-23 8/15/2023 IDS document), as evidenced by Dell’Amico et al. (Discrete Applied Mathematics, 2000, vol 100, no. 3, p. 17-48), in view of Regev et al., 2021 (US2022/0180975A1; 08/15/2023 IDS), as applied to claims 1, 4-6, 9-12, 15-16, 18 and 20 above and further in view of Mund et al. (BioRxiv; 2021, p. 1-36). The italicized text corresponds to the instant claim limitations.
Regarding claims 2-3, Shen et al. and Regev et al. are silent to: the medical information processing apparatus of claim 1, wherein the processing circuitry is further configured to: after adjusting the positions and before storing the two-dimensional image format in the memory: apply an image morphology method to adjust at least some mapped positions into un-mapped adjacent positions (claim 2); and the medical information processing apparatus of claim 2, wherein the image morphology method comprises dilation (claim 3). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Mund et al.
With respect to claims 2 and 3, Mund et al. discloses an AI-driven deep visual proteomics (DVP) method, which performs AI image analysis of 2D images of spatial proteomics data to predict cellular phenotypes. In this method, Image preprocessing was followed by deep learning-based nucleus and cell segmentation modules (see segmentation methods and accuracy evaluation) further refined by unary and binary morphological operators (e.g.: dilation, erosion, cavity filling, addition and subtraction). (abstract; p. 29, para. 2; the medical information processing apparatus of claim 1, wherein the processing circuitry is further configured to: after adjusting the positions and before storing the two-dimensional image format in the memory: apply an image morphology method to adjust at least some mapped positions into un-mapped adjacent positions (claim 2); and the medical information processing apparatus of claim 2, wherein the image morphology method comprises dilation (claim 3).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Mund et al. taught that application of such operators can alter the image to aid in signal detection. For example, subtraction can be used to calculate the cytoplasm-only region from cell and nuclei masks (p. 29, para. 2). Therefore, one of ordinary skill in the art would have been motivated to utilize the morphological operators taught by Mund et al. in the image-based analysis of omics data taught by Shen et al. and Regev et al., in order to improve morphological signal detection in neural network-based image analysis. Furthermore, one of ordinary skill in the art would predict that the morphological operators taught by Mund et al., could be readily added to the system of Shen et al. and Regev et al. with a reasonable expectation of success because they both pertain to deep learning analysis of 2D spatial proteomic data. The invention is therefore prima facie obvious.
11. Claims 7-8, 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (Nucleic Acids Research, 2022, Vol. 50, p. 1-23 8/15/2023 IDS document), as evidenced by Dell’Amico et al. (Discrete Applied Mathematics, 2000, vol 100, no. 3, p. 17-48), in view of Regev et al., 2021 (US2022/0180975A1; 08/15/2023 IDS), ass applied to claims 1, 4-6, 9-12, 15-16, 18 and 20 above and further in view of Fernandez-Torras et al. (Nature communications, 2022, Vol. 13, p. 1-18), as evidenced by Bioteque website (https://bioteque.irbbarcelona.org). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 4-6, 9-12, 15-16, 18 and 20 have been taught by Shen et al. and Regev et al. above.
Pertaining to claims 7-8, 13 and 17, Shen et al. and Regev et al. are silent to: the medical information process of claim 6, wherein the biological database is a knowledge graph (claim 7); the medical information processing apparatus of claim 7, wherein the knowledge graph defines biomolecules as nodes, wherein the nodes are connected by edges, and wherein the distance between each pair of biomolecules is calculated based on the number and/or weight of the edges between the respective biomolecules (claim 8); the medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to display the displaying image for human inspection) (claim 13); and the medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to: provide mouse-over functionality to the displaying image to display the names of the biomolecules mapped to the respective display positions and/or display functional information related to the biomolecules mapped to the respective display positions (claim 17). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Fernandez-Torras et al.
Pertaining to claim 7, Fernandez-Torras et al. discloses a method for generating 2D embeddings representing 67 different types of relationships between features from knowledge graphs. Fernandez-Torras et al. further discloses making the knowledge graph and embeddings available to the community in a database called Bioteque. As an example, Fernandez-Torrez et al. further discloses a 2D projection (opt-SNE) of the 128-dimensional compound/disease embedding showing clusters of drugs and treatments which have identifiable targets (Fig. 3A; p. 2, col. 2, para. 2; p. 5, col. 2, para. 2; the medical information process of claim 6, wherein the biological database is a knowledge graph).
Pertaining to claim 8, Fernandez-Torras et al. discloses using the node2vec algorithm, which is based on random walk trajectories, to obtain embeddings of the knowledge graphs. Fernandez-Torras et al. further discloses that this method uses both weights of edges from the knowledge graphs and edge weights from random walk trajectories in node2vec (p. 13, col. 1, para. 9 ‘obtaining Bioteque embeddings’; the medical information processing apparatus of claim 7, wherein the knowledge graph defines biomolecules as nodes, wherein the nodes are connected by edges, and wherein the distance between each pair of biomolecules is calculated based on the number and/or weight of the edges between the respective biomolecules).
Regarding claim 13, Fernandez-Torres et al. discloses making biomedical knowledge embeddings available to the broad scientific community through Bioteque, a resource of unprecedented size and scope that contains pre-calculated embeddings derived from a gigantic heterogeneous network (more than 450k nodes and 30M edges). Fernendaz-Torres et al. further disclose that they have an online resource to facilitate access and exploration of the pre-calculated 2D embeddings at (https://bioteque.irbbarcelona.org) (p. 2, col. 2, para. 2; the medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to display the displaying image for human inspection).
Regarding claim 17, Fernandez-Torras et al. teaches that Bioteque, the database open to the public with 2D embeddings of knowledge graphs, has a public website (https://bioteque.irbbarcelona.org) wherein you can explore and download visualizations of networks which displays images of the various 2D embeddings. As evidenced by the Bioteque website, the website has mouse-over functionality (in the screen shot of the website, the ‘PGN’ node is moused over and thus the “perturbagen (PGN) box is displayed”). The website also indicates “click on the nodes of the network below to start exploring the available descriptors” (Fernandez-Torres et al. p. 10, col. 2, para. 1-2; Bioteque website, col. 1, para. 1; ‘Explore the Network figure’; ‘Analytical Card’ figure; the medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to: provide mouse-over functionality to the displaying image to display the names of the biomolecules mapped to the respective display positions and/or display functional information related to the biomolecules mapped to the respective display positions).
Regarding claim 17, the Bioteque website does not have a mouse over function for the display image of the 2D embedding. However, since they have demonstrated mouse-over technology for the parent Network with data pop-ups, it would be obvious to try also making mouse-over technology for the display of the 2D embeddings to display the names of the biomolecules also. This would be useful to users so they could see the relative positions/clustering of genes of interest relative to other nodes such as drugs in the 2D embedding to make hypotheses they can test through experimentation. One of ordinary skill in the art could have pursued the known potential expectation of success because the functionality was already demonstrated for the parent Network on the same webpage and because an image of the 2D embedding with legend and distance metrics are already displayed on the webpage (Fernandez-Torres et al. p. 10, col. 2, para. 1-2).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Fernandez-Torras et al. taught that Bioteque harmonizes data from 150 data sources, 12 distinct biological entities (e. g genes, disease and compounds) linked through 67 types of relationships). They further demonstrate that embeddings retain the information contained in the large biological network and show that this concise representation of the data can be used to evaluate, characterize and predict a wide set of experimental observations (p. 2, col. 2, para. 2). Therefore, one of ordinary skill in the art would have been motivated to utilize the knowledge graph embedding method and data taught by Fernandez-Torras et al. in the neural network analysis of image-based omics data taught by Shen et al. and Regev et al., in order to capture a wide range of feature interactions in their predictions. Furthermore, one of ordinary skill in the art would predict that the embedding method and data of Fernandez-Torras et al. could be readily added to the system of Shen et al. and Regev. et al with a reasonable expectation of success because they both pertain to mapping relationships between molecular features using existing data from databases by generating 2D embeddings of the data. Furthermore, Fernandez-Torraz discloses that the embeddings can be used off-the-shelf in downstream machine learning tasks (such as those disclosed by Shen et al.) without loss of performance with respect to using the original data (abstract). The invention is therefore prima facie obvious.
12. Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (Nucleic Acids Research, 2022, Vol. 50, p. 1-23 8/15/2023 IDS document), as evidenced by Dell’Amico et al. (Discrete Applied Mathematics, 2000, vol 100, no. 3, p. 17-48), in view of Regev et al., 2021 (US2022/0180975A1; 08/15/2023 IDS), ass applied to claims 1, 4-6, 9-12, 15-16, 18 and 20 above, and further in view of Elbashir et al. (2019, IEEE Access, Vol. 7, p. 185338 – 185349; 08/15/2023 IDS). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1, 4-6, 9-12, 15-16, 18 and 20 have been taught by Shen et al. and Regev et al. above.
Pertaining to claim 14, Shen et al. and Regev et al. teach inputting the 2D embedding into a convolution neural network, but they do it as a 3D vector in order to include another layer of information (p. 2, col. 1, para. 2 – col. 2, para. 1). Thus, Shen et al and Regev et al. are silent to: the medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to input the displaying image to a deep learning algorithm. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Elbashir et al. 2021.
Regarding claim 14, Elbashir et al. discloses a method and system for classifying breast cancer by analyzing RNA-seq gene expression data by converting it to into 2D images and analyzing them using a convolutional neural network (CNN) (Fig. 4-5; p. 185346, col. 1, para3 – col. 2, para. 1; p. 185339, col. 2, para. 2; p. 185341, col. 1, para. 1 – p. 185343, col. 2, para. 1; the medical information processing apparatus of claim 12, wherein the processing circuitry is further configured to input the displaying image to a deep learning algorithm.
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Elbashir et al. taught that the accuracy of their CNN method shows improvement compared with previous work on cancer classification (p. 185346, col. 2, para. 1). Therefore, one of ordinary skill in the art would have been motivated to utilize the CNN based classification using 2D images derived from RNA-seq data taught by Elbashir et al. in the neural network analysis of image-based omics data taught by Shen et al. and Regev et al., in order to improve cancer classification. Furthermore, one of ordinary skill in the art would predict that the CNN based classification using 2D images derived from RNA-seq data could be readily added to the method and system of Shen et al. and Regev. et al with a reasonable expectation of success because they both pertain to generating 2D images from RNA-seq data and applying CNN for classification based on this data. The only difference is that Shen et al. adds an additional step of stacking 2D image data to make a 3D tensor that is input into the CNN to add more information for the classification. The invention is therefore prima facie obvious.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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13. Claims 1, 10 and 11 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 15 and 16 of copending Application No. 18/350796 (reference application).
Although the claims at issue are not identical, they are not patentably distinct from each other because the apparatus of claim 1 and the method of claim 11 are taught by the apparatus of claim 15 of the reference application, and the apparatus of claim 10 is taught by the apparatus of claim 16. Both applications claim the same steps in the method, except that claims 15 and 16 of the reference application claim also training a convolution neural network via their dependence on claim 1. Therefore claims 1 and 11 of the instant application are anticipated by claim 15 of the reference application and claim 10 of the instant application is anticipated by claim 16 of the reference application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
14. No claims are allowed.
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/J.J.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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