DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
No IDS has been submitted.
Priority
Instant application does claim the benefit of priority to an earlier application. The benefit of the priority date determined based on provisional application that was filed on 10/06/2021 (application No. 63/253,041) and effective filing date was considered as 10/06/2021.
Drawings
The Drawings filed on 04 October 2022 are accepted.
Specification
In paragraph [00203], the word characterizing is misspelled to charactering and advised to correct.
Overall, the specification of the application is in compliance with guidelines (37 CFR 1.71-1.77 and MPEP § 608), ensuring all sections, including the title, background, summary, brief description of drawings, detailed description, claims, and abstract are present, clearly structured, and formatted with appropriate margins and line spacing.
Claim Status
Claims 1-22 are pending and examined on the merits.
Claims 1-22 are rejected.
Claim Rejections - 35 USC § 112b
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.
Claim 2 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 2, the claim states the display “comprises 1,000,000-pixel values”. This is technically ambiguous. An arrangement of 1,000,000 pixels could have multiple values per pixel (RGB). Therefore, it is unclear if the invention requires exactly 1,000,000 pixels, or 1,000,000 specific data points mapped to pixels, which subjected to deemed indefinite and rejected under 35 U.S.C. 112(b). (MPEP § 2173.02)
The claim 2 states the arrangement comprises the pixel values, but it does not specify how the plurality of entities is linked to these pixel values. It shows a mere recitation of a quantity on a display, which can be interpreted as a data table rather than the technical structure to accomplish it, and render improper (MPEP § 2173.05(e)).
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.
Claim 1-22 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, the instant claims 1-20 are drawn to a (“A system”), claim 21 and 22 are drawn to CRM and method claim respectively, and therefore are found to recite statutory subject matter (Step 1: YES). The instant claims 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
Indexing a two-dimensional spatial arrangement of the plurality of entities. (Use of indexing involves incorporation of mathematical formula/algorithm to determine the spatial position. (Mathematical concept) Also, indexing can be performed through observation or evaluation and that can be done in the human mind or pen and paper (Mental process)
Determining each entity in the plurality of entities that is a member of the subset using the k-dimensional binary search tree. Use of k-d binary tree to generate subset requires mathematical algorithm and considered as mathematical calculation or concept. Also generating subsets by indexing can be performed by human mind (Mental process)
Assigning each entity in the subset of entities to a user provided category. (mental process) Explicitly list evaluations, judgments, and organizing information such as classification or sorting considered as mental processes.
Modifying the discrete attribute value dataset to store an association of each respective entity in the subset of entities to the user provided category. (Mental process or human organizing activity) Reviewing a list of entities and associating them with a category is a task a human could perform mentally or with pencil-and-paper.
Claim 3 recites clustering the discrete attribute value dataset using the discrete attribute value for each reference sequence. (Mathematical process and mental process) Clustering is a process of organizing data points, which considered an active step using mathematical algorithm or can be performed in human mind.
Claim 5 recites the step of assigning each respective cluster in the plurality of clusters a different graphic or color code. (Mental process))
Claim 11 recites about quantification of gene expression from a single entity in counts of transcript reads mapped to genes. (Mathematical concept) The claim focus on the calculation (counting) and considered as a mathematical concept or calculation.
For claim 21 and 22 same limitations as claim 1 equates to an abstract idea.
Claim 2, 4, 6-10, 12-20 provide information with further limitation without active steps.
As such, claims 1-22 recite 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). Specifically, the claims recite the following additional elements:
Claims 1 recites
Visualization system consisting of memory, processor and a display.
Obtaining a discrete attribute value dataset derived by nucleic acid sequencing.
Displaying the two-dimensional spatial arrangement of the plurality of entities on the display.
Claim 15 recites about obtaining a closed form shape drawn by a user on the display that is within the two-dimensional spatial arrangement.
Claim 21 is a computer-readable medium that stores instructions constituting a computer implemented method (process) for evaluating biological samples.
Claim 22 is a method claim, implemented using a computer system (processing cores, memory and display) to evaluate biological samples.
The limitations about a visualization system consisting of memory, processor and a display device serves as being merely an insignificant, routine, or conventional post-solution activity and used an input for the judicial exception. The gathering data and analysis of all steps (Claim 1-22) merely serve as calculation of mathematical calculations and does not add any significant practical application. Displaying output using higher resolution with high pixels is common practice and conventional and serve no practical application. Clustering entity data is routine bioinformatic application and at the time of the effective filing date, a POSITA would have been able to do clustering to generate 2D map. Using reference genome of regulatory elements for alignment of entities from biological samples are routine process in sequence data alignment process to reference genome and does not add any significant practical application.
Therefore, these limitations are mere data gathering or analyzing activities and displaying the results using a conventional display system. As set forth in MPEP 2106.05(g), mere data gathering and analyzing activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application.
There are no limitations that indicate that the processor, storage, input and output device require 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 above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-22 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, routine and conventional activity. Also, remaining additional elements are routine and conventional in bioinformatic pipelines and merely serve extra solution activity.
As discussed above, there are no additional limitations to indicate that the claimed processor requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
As such, the combination of additional elements recited in the claims is well-understood, routine and conventional. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that 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 1-22 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim 1, 3, 4, 5, 15, 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Matthew et al. (Cell Rep Methods 2022 Apr 25;2(4)) in view of Bentley et al. (Communications of the ACM, September 1975, Volume 18, Number 9), and Asadi et al. (Patent No. US 9,600,625 B2)
Matthew et al. provides the foundational framework, teaching the majority of the limitations for analyzing biological samples. Specifically, Matthew et al. teaches:
Use of “biological sample”, (Discussion, 3rd paragraph and figure1 and introduction 5th paragraph), which suggest the limitation of performing a method for evaluating one or more biological samples.
“Nucleic acid sequencing data” (Result, 1st paragraph), maps to obtaining a discrete attribute value dataset derived by nucleic acid sequencing.
“Two-dimensional spatial arrangement” (Figure 1), maps to displaying the two-dimensional spatial arrangement of the plurality of entities on the display.
“User provided selection of subset and category” (Figure 1) which suggests the limitation of assigning each entity in the subset of entities to a user provided category.
“Association of each entity in the subset to the user provided category” (Figure1, e.g., signature or module) maps to store an association of each respective entity in the subset of entities to the user provided category.
Matthew et al. does not explicitly disclose the use of a k-dimensional search tree (k-d tree) for indexing, nor does it explicitly mention a specific computing system comprising a processor, memory, and display device.
Bentley et al. teaches that the k-d binary search tree (Abstract) is a highly effective data structure for storing information and conducting efficient associative searches, which suggest the limitation of independently assigned a unique two-dimensional position, in a k-dimensional binary search tree; displaying the two-dimensional spatial arrangement of the plurality of entities on the display.
It would have been obvious to a person of having ordinary skill in the art (PHOSITA) at the time of the invention to combine the teachings of Matthew et al. with Bentley et al. Matthew teaches the analysis of large-scale genomic data, specifically involving 2D spatial coordinates (2D spatial arrangement). A PHOSITA facing computational challenges in processing such 2D data would be motivated to turn to Bentley et al., which teaches k-d binary search trees specifically designed to reduce computational burden for spatial and multidimensional searches.
Combining these references would be a simple substitution of one known element for another to obtain the predictable result of improved efficiency in processing spatial genomic data. The 2D spatial data in Matthew G is ideally suited for indexing via the 2D k-d binary search tree described by Bentley et al., providing a faster, more efficient search mechanism for genomic data Therefore, applying Bentley’s k-d tree to Matthew’s genomic data structure constitutes a predictable modification to achieve a more efficient system, yielding predictable results.
However, Bentley et al. does not explicitly disclose the use of a specific computing system comprising a processor, memory, and display device.
Asadi et al. teaches the application of a computing system (processor, memory, and display system) (claim 1) to process nucleic acid sequencing data and display results which maps to a visualization system comprising one or more processing cores, a memory, and a display, the memory storing instructions for performing a method for evaluating one or more biological samples.
It would have been obvious to a person having ordinary skill in the art (PHOSITA) to combine the teachings of Matthew (the core method- a method for analyzing biological samples), Bentley (the use of k-d trees for efficient data indexing and searching), and Asadi (a computing system with a processor and display for analyzing data) to arrive at the claimed invention.
A PHOSITA would have been motivated to combine these references to create a faster, more efficient system for processing complex biological data. Combining Matthew with Bentley’s k-d trees would have been a "known technique to improve similar devices... in the same way" (MPEP 2143, Example C). Bentley explicitly teaches that k-d trees provide optimized, efficient indexing for spatial data searches, which would be a direct solution to improve the processing efficiency of the genomic analysis method disclosed in Matthew Utilizing Bani Asadi’s processor/display system for the combined method of Matthew and Bentley is a "simple addition of one known element to obtain predictable results" (MPEP 2143, Example B). It is standard in laboratory procedures to automate analysis using computer processors, making the implementation on a standard computer platform a matter of ordinary creativity.
The resulting combination functions as expected. Matthew provides the genome analysis, Bentley improves the search efficiency, and Asadi provides the computing platform. Therefore, combining these elements is simply a "combination of prior art elements according to known methods to yield predictable results.
Regarding claim 3, Matthew et al. teaches clustering biological samples via dimension reduction to generate a 2D arrangement based on sample similarities, specifically in Figure 1, inset 1, suggesting the limitation of “clustering the discrete attribute value …. dimension reduction components derived therefrom” While Matthew does not explicitly recite all sub-steps of the specific clustering algorithm or spatial arrangement claimed, the combination of clustering and dimension reduction was well-known in the art at the time of filing to analyze and visualize sample similarities (e.g., WGCNA for correlation module). Therefore, it would have been obvious to a person of ordinary skill in the art (PHOSITA) to apply such known techniques to the 2D arrangement of Matthew et al., as this constitutes combining prior art elements according to known methods to yield predictable results.
Regarding claim 4, it further limits claim 3 by introducing different subsets of clusters generated by dimension reduction. Matthew et al. teaches clustering biological samples via dimension reduction and displaying unique subsets in 2D/3D arrangements and phylogenetic circles, often employing color-coding for visual distinction (Figure 1, inset 1). Therefore, Matthew et al. discloses all elements of claim 4, including the generation of subsets and their distinct visual representation. Therefore, it would have been obvious to a person of ordinary skill in the art to apply conventional clustering techniques to categorize and color-code different subsets of clusters to yield a predictable result.
Regarding claim 5, it further limits claim 3 by coloring subsets of clusters. As shown in Matthew et al.’s prior art (Figure 1, inset 1), suggesting the limitation of “coloring each respective entity in the two-dimensional spatial arrangement … respective entities.” Color-coding subsets of cluster for visualization is well-known in computational biology. It would have been obvious to a person of ordinary skill in the art (PHOSITA) at the time of filing date to combine the conventional clustering methods of the prior art with standard color-coding, as this represents a routine optimization for improving the representation of sequencing data from biological samples.
Regarding claim 15, it further limits claim 1 by drawing the subset of the two-dimensional spatial arrangement on the display using a closed form shape. Matthew et al. (Figure 1) teaches a method for visualizing data, including generating a 2D map from cluster analysis and allowing for user-selected subsets from the 2D map. Specifically, Matthew (Figure 1) showing the selection tool that enables user selection of clusters.
To the extent that lasso tools are not explicitly mentioned, such a tool is inherently disclosed by the selection techniques shown in Figure 1, as it is a conventional technique used for selecting subsets of data points within 2D maps, and would be immediately understood by one of ordinary skill in the art before the effective filing date.
Regarding claim 21 (a computer-readable medium) and 22 (a method) comprise the same limitations as claim 1 regarding the computer-implemented evaluation of biological samples using various functional elements, therefore, they are rendered obvious by the teachings of the above cited prior art for claim 1 rejection.
Claim 6, 7, 8 and 9 are rejected under 35 U.S.C. 103 as being obvious over Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21 and 22 above and further in view of Gupta et al. (EPRA International Journal of Multidisciplinary Research (IJMR)Volume: 7 | Issue: 8 | August 2021).
Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21 and 22.
Claim 6 further limits claim 3 by specifying types of clustering algorithms (hierarchical, agglomerative nearest-neighbor, farthest-neighbor, average linkage, centroid, or sum-of-squares).
Matthew et al., Bentley et al., and Asadi et al. does not explicitly teach clustering methods such as K-mean and agglomerative hierarchical clustering.
Gupta et al. teaches the comparison between use of K-mean and agglomerative hierarchical clustering on datasets. (Abstract, p. 415) which suggests the limitation of the use of clustering method.
Therefore, it would have been obvious to a person of ordinary skill in the art (PHOSITA) at the time of effective filing date to integrate such known hierarchical clustering techniques taught by Gupta to differentiate subsets for better sequencing data representation, as taught by Matthew et al. and the cited references.
It would be an expectation of success of using all these cited arts together because they are all in the same technology (Nucleic acid sequencing), and addressing the problem with efficient way of analysis of sequencing data of biological samples.
Claim 7 narrows the clustering method in claim 3 to a specific, finite set of options (Louvain modularity algorithm, k-means, fuzzy k-means, or Jarvis-Patrick clustering). Under the Broadest Reasonable Interpretation (BRI), prior art teaching any of these methods invalidates claim 7.
Gupta’s literature review teaches comparison between k-means and agglomerative hierarchical clustering on datasets (Abstract, p. 415), demonstrating that interchanging these techniques is standard. These variations are mere routine optimization and direct substitutions of known techniques. A PHOSITA would find it obvious to swap these clustering methods to achieve predictable results.
Claim 8 further limits claim 3 by introducing predetermined cluster number. Gupta teaches that k-mean clustering can utilize a predetermined number of clusters for a dataset (Abstract, p. 415) suggesting the limitations of “the clustering the discrete attribute … into a predetermined number of clusters” It would have been obvious to a POSITA to define a predetermined number of clusters taught by Gupta prior to running the algorithm to adjust clustering strictness and yield a predictable result.
Claim 9 limits claim 3 by using a user-provided, predetermined number of clusters (k) in k-means clustering. Gupta et al. teaches user provided input for k-means clustering (Abstract, p. 45) suggesting the limitation of “the clustering the discrete attribute value dataset …. the number is acquired based on user input.” It would have been obvious to a POSITA to use a user-defined number of clusters to analyze datasets and examine results with different inputs and yield a predictable result, as this is a known, predictable modification for optimizing k-means clustering.
Claim 10 is rejected under 35 U.S.C. 103 as being obvious over Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22 above and further in view of González et al. (Molecular Systems Biology 16: e9438 | 2020).
Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22.
Matthew et al., Bentley et al., and Asadi et al. teaches all limitations of claim 1, and the general concept of array-based hybridization and raw data alignment.
However, Matthew et al., Bentley et al., and Asadi et al. does not explicitly teach the specific combination of the claimed types of reference sequences (specifically regulatory DNA/RNA elements like enhancers and promoters).
González et al. teaches the mapping of regulatory DNA/RNA sequences, specifically enhancers and promoters, to genomic references. (The abstract and page 7 (middle paragraph)) González teach that using defined regulatory elements is crucial for accurate transcriptional profiling and analysis suggesting the limitation of “each reference sequence …. regulatory RNA, exon, or polymorphism.”
It would have been obvious to a person having ordinary skill in the art (PHOSITA) at the time of the effective filing date to use regulatory elements such as enhancers or promoters, with possible variants spotted on a sequencing array platform and align the sequenced data obtained from the biological samples to those references, as taught by González et al.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22 above and further in view of Aisha et al. (Molecular Therapy: Methods & Clinical Development Vol. 10 September 2018).
Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22.
Regarding claim 11, cited prior art by Matthew et al., Bentley et al., and Asadi et al. discloses the method of claim 1.
However, Matthew et al., Bentley et al., and Asadi et al. does not explicitly recite that the transcriptomic output is a count (discrete number) of reads.
Aisha et al. teaches that transcriptomic sequencing output is commonly generated as counts. Specifically, Aisha discloses that counts are generated from the sequencing of biological samples (Aisha, p. 190, middle paragraph) suggesting the limitation of “quantifies gene expression from a single entity in counts of transcript reads mapped to genes.”
Limiting the transcriptomic reads to a “count” (discrete number) as recited in claim 11 is an obvious design choice and a standard optimization in the art. Using raw counts, such as those generated by 10x Genomics, is a well-known, conventional, and expected practice in single-cell RNA/spatial transcriptomic data analysis at the time of the invention.
Therefore, it would have been obvious to a person having ordinary skill in the art (PHOSITA) to incorporate this standard, known output form (taught by Aisha et al.) into the processing method (genomic analysis) of Matthew , as it offers the predictable benefit of standardized quantification.
Regarding Claim 12, it limits the method of claim 1 by using unique barcodes to count distinct biological molecules associated with specific entities. As taught by Aisha (Fig. 1, p. 191), utilizing unique barcodes for generating libraries for sample hybridization is a standard technique in the art suggesting the limitation of “plurality of sequence reads from the … a unique barcode associated with the corresponding entities.”
It would have been obvious to a PHOSITA to employ barcoded library data (e.g., from 10x Genomics) for biological sample analysis as part of known, standard workflow optimizations process, as taught by Aisha The combination of Matthew and Aisha results in a predictable and expected outcome.
Claim 16 and 17 are rejected under 35 U.S.C. 103 as being obvious over Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22 above and further in view of Liu et al. (Genes 2021, 12, 311) teachings.
Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22 above
Claim 16 further limits claim 15 by introducing user-selected subsets that is outside the closed form shape on a 2D cluster map.
Matthew et al., Bentley et al., and Asadi et al. does not explicitly teach selection of subset of clusters is outside the closed form shape.
Liu et al. disclose selecting a subset of clusters within a closed shape (circle/ellipse) (Figure 2) while leaving other entities outside for analysis suggesting the limitation of “the subset is each entity in the plurality of entities that is outside the closed form shape.”
It would have been obvious to a person of ordinary skill in the art (PHOSITA) to apply existing shape-selection algorithms to encircle a subset on a 2D map taught by Liu et al., thereby visually distinguishing selected entities from those excluded, as this is a standard technique for cluster analysis.
Claim 17, which limits claim 15 to user-selected subsets within a closed shape from a 2D cluster map. Liu et al. teaches selecting a subset of clusters using a circle or ellipse closed shape. (Figure 2) suggesting the limitation of “the subset is each entity in the plurality of entities that is inside the closed form shape.” It would have been obvious to a POSITA to utilize such an algorithm to select a subset via a closed shape, thereby isolating desired entities from outliers for further analysis.
Claims 18-20 are rejected under 35 U.S.C. 103 as obvious over Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22 above and further in view of Bakken et al.
Matthew et al., Bentley et al., and Asadi et al. as applied for claims 1, 3, 4, 5, 15, 21, and 22.
Matthew et al., Bentley et al., and Asadi et al. does not explicitly teach the visualization system of claim 1, wherein an entity is a cell, probe spot or nucleus.
Bakken et al. teaches cells, nuclei, and probe spots as standard targets in single-cell RNA technologies. (Abstract; pg. 2 (results); pg. 18 (middle paragraph)) suggesting the limitation claims 18-20 “the visualization system of claim 1, wherein an entity is a cell, probe spot or nucleus.”
A person of ordinary skill in the art would have found it obvious to interpret the claimed entities as cells, nuclei, or probe spots based on the investigative context.
Regarding claim 2, the 2D spatial arrangement of entities comprising 1,000,000-pixel values provides a technical improvement by enabling high-resolution analysis of biological data that is not taught, suggested, or inherently disclosed by the cited prior art. The claimed 1,000,000-pixel density represents a non-obvious, specific technical limitation rather than a mere design choice. Claim 2 is considered potentially allowable for patent if incorporating this limitation into an independent claim would provide a definitive, non-obvious improvement of a technical nature.
Regarding claims 13 and 14, which recites specific sequence read counts of 100,000 and 1,000,000, are not rejected under 35 U.S.C. 103, as no prior art teaches these exact numbers. Although such counts can occur naturally in gene sequencing of biological samples, the specific limitations are not anticipated and are currently considered potentially allowable. if amended as independent claim with regard to claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARSHAD KHAN whose telephone number is (571)272-9812. The examiner can normally be reached Mon-Fri-7:30-5:00 PM.
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/AK/Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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