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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 30 March 2026 has been entered.
Status of the Claims
Claims 6, 9-10, 12, and 16 are pending and examined herein.
Claims 1-5, 7-8, 11, and 13-15 are canceled.
Priority
As detailed on the 11 August 2022 filing receipt, the application claims priority as early as 10 July 2019. At this point in examination, all claims have been interpreted as being accorded this priority date as the effective filing date.
Withdrawn Objections and Rejections
The claim objection regarding the duplicated “the” is withdrawn in view of the amendment removing the duplicated word.
The following objections and/or rejections are maintained or newly applied and constitute the full set of objections and/or rejections for the instant claims.
Claim Interpretation
Claims 6 and 12 recites "the machine learning model includes parameters trained on a training data set." The past tense of the trained parameter suggests the training occurs prior to the required steps of the claims. In a product-by-process claim, the product, rather than the process, is evaluated for patentability (MPEP 2113). A model trained by other means is treated as equivalent for the purposes of prior art.
Furthermore, the “selecting” step of claim 6 is interpreted as contingent. The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met (MPEP 2111.04). Claim 6 recites selecting the target gene as a candidate for neoantigen screening when the first value indicates death of the tumor cell and the second value indicates survival of the normal cell, and thus an embodiment in which the first value does not indicate death of the tumor cell and/or the second value does not indicate survival of the normal cell would not require making the selection.
In response to the 30 March 2026 remarks, it is not persuasive that product-by-process interpretation only applies to machine or manufacture (product) claims, as Biogen v. Serono is directed to methods. The structure of the model is interpreted as the product, determined by the process of training. Here, the model is interpreted as relating cell survival based on gene expression patterns. The contents of the “wherein” clauses are further limiting the data being associated with a network and simulating perturbation, but the data in the active “inputting” steps are expression data, which naturally have a relationship in a network and has perturbed expression based on tumor presence. Actively claiming aspects of the data found in the “wherein” clauses may change their interpretation.
35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 6, 9-10, 12, and 16 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). The claims as a whole, considering all claim elements individually and in combination, are directed to a judicial exception at Step 2A, Prong 2, and the additional elements of the claims, considered individually and in combination, do not provide significantly more at Step 2B than the abstract idea of essential gene identification.
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of
nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The claims are directed to methods (claims 6 and 9-10) and a computer system (claims 12 and 16), each of which falls within one of the categories of statutory subject matter. [Step 1: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Mathematical concepts recited in the independent claims include a machine learning model used to generate a value (claims 6 and 12), which is interpreted as a mathematical construct under a broadest reasonable interpretation in light of the specification, which discloses the model as an artificial neural network, decision tree, or deep learning model, which include mathematical concepts. Furthermore, the model is disclosed as associated with a loss function (Fig. 5C) and backpropagation (Fig. 6C), which are mathematical concepts.
Mental processes, defined as concepts practically performed in the human mind such as steps of observing, evaluating, or judging information, recited in the independent claims include comparing values to determine whether a target gene is an essential gene (claims 6 and 12), where comparison of values is data evaluation and/or judgment and thus a step the human mind is practically equipped to perform. The machine learning model may also be interpreted as performing a mental step under a broadest reasonable interpretation. Claim 6 recites selecting a gene as a neoantigen target, where making a selection is a step practically performed by the human mind. The pattern information of the expression information from the tumor and normal cells for the same gene are recited as including a first and second gene regulation network, respectively, where a gene network is interpreted as data including genes and their expression values. The “inputting” step requires expression values and the “wherein” clauses further describe the data, where expression values are naturally related to one another in a network and perturbation is assessed based on the expression in the tumor versus normal cell.
Claims 9-10 recite additional information about the training data, where the training occurs outside the metes and bounds of the claimed invention as discussed in the Claims Interpretation section above.
Claim 16 converting expression data into a vector for the machine learning model and information about the vector, where converting data into a vector is a mathematical concept.
Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas. The claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). [Step 2A: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
At Step 2A Prong Two, it is determined whether the judicial exception is integrated into a practical application by an additional element (MPEP 2106.04(d)). Additional elements are elements of the claims beyond the abstract ideas. Claim 6 recites receiving expression data and inputting data into an algorithm. Claim 12 recites these elements as well as an input device, storage device, and processor as part of an analysis apparatus, interpreted as a general purpose computer. These additional elements are interpreted as data collection steps, which are insignificant extra solution activity (MPEP 2106.05(g)) and performing the steps by invoking a general purpose computer (MPEP 2106.05(f)), further explained below.
Steps reciting receiving or inputting data are interpreted as data collecting steps for use in the abstract determination of essential genes step using a machine learning model. Data collecting is insignificant extra solution activity and does not integrate the abstract idea into a practical application (MPEP 2106.05(g)).
The analysis apparatus and constituent components are interpreted as a generic computer. The claims do not describe any specific computational steps by which the computer performs or carries out the abstract idea, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than that a generic computer performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
[Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
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 of 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05). The claims recite a computer, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions, which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Additional elements beyond the abstract ideas are recites receiving expression data and inputting data into an algorithm (claims 6 and 12), an input device, storage device, and processor (claim 12) as part of an analysis apparatus (claims 6 and 12), together interpreted as a general purpose computer.
Storing data on a computer in memory is a conventional computer function (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; MPEP 2106.05(d)).
The courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (storing and retrieving information in memory), as discussed in MPEP 2106.05(d)(II)(i)).
Therefore, the recited additional elements, alone or in combination with the judicial exceptions, do not appear to provide an inventive concept. [Step 2B: No]
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to the 30 March 2026 Applicant Remarks
Applicant remarks state the amended claims recite a concrete technical process involving: 1) simulation of gene perturbation using gene regulatory network models; 2) generation of simulated gene expression patterns based on the perturbation; and 3) prediction of cell survival using a trained machine learning mode (pg. 9, first paragraph). This is interpreted as directed to Step 2A Prong Two of 101 analysis, and the remarks suggest these are not abstract ideas. This argument is not persuasive because simulating perturbation and simulating gene expression patterns are relationships between data understood in a node-and-edge framework, which can be understood as a mental process or performed with pen-and-paper, and relationships between expression values, which are numerical relationships. Similarly, the prediction is a mental step performed based on comparison of numerical values, where the comparison may be interpreted as a mental step or mathematical step as determining a difference.
Applicant remarks assert an alleged improvement in the form of replacing or complimenting experimental screening techniques such as RNAi or CRISPR-based functional screening (pg. 9, second paragraph). This is interpreted as being directed to the abstract steps performed by the invention and not elements in addition to the abstract ideas. Abstract ideas integrate the abstract idea(s) into a practical application at Step 2A Prong Two (MPEP 2106.04(d)) and are used to evaluate whether the claims recite an inventive concept that provides significantly more than the judicial exception itself at Step 2B (MPEP 2106.05). At Step 2A Prong Two, the elements in addition to the abstract ideas are data collection steps, which are insignificant extra solution activity (MPEP 2106.05(g)) and performing the steps by invoking a general purpose computer (MPEP 2106.05(f)). Further analysis at Step 2B of the 101 framework explained in the rejection above considered the additional elements to be conventional, and thus do not provide significantly more than the abstract ideas.
Applicant remarks state a practical application in the form of identifying tumor-specific essential genes and selecting data for later screening (pg. 9, third paragraph). This argument is not persuasive because the technology – that is, the elements in addition to the abstract ideas recited – is not improved; the improvement lies in the abstract idea in the form of data analysis. An inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05). Finally, selection of genes for screening is merely a mental process as the screening itself is not required. Therefore, the improvement is not realized in the form of an additional element.
Therefore, the rejection under 35 USC 101 is maintained.
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.
Claims 6, 9-10, and 12
Claims 6, 9-10, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (Bioinformatics 30(17): 2393-2398, 2014; previously cited on the 04 September 2025 PTO-892 form) in view of Plaimas (BMC Systems Biology 4(56): 1-16, 2010; previously cited on the 04 September 2025 PTO-892 form), Cao (American Journal of Cancer Research 5(9): 2605-2625, 2015; previously cited on the 31 December 2025 PTO-892 form), and Gelman (Oncogene 23: 8158-8170, 2018; newly cited).
Claim 6 recites receiving, by an analysis apparatus, gene expression data of each of a normal
cell and a tumor cell of a same target patient.
Kim teaches generating and thus receiving expression data from cancer cells and normal cells (Fig. 1, “Kinome RNAseq”) as well as essential gene screening from normal cells and cell with a gene of interest knocked out (pg. 2394, col. 1, last paragraph to col. 2, first paragraph).
Claim 6 recites inputting, by the analysis apparatus, first gene expression pattern information of
the tumor cell, to a machine learning model to generate a first value, wherein the first value indicates information on whether the tumor cell survives or dies, and inputting, by the analysis apparatus, second gene expression pattern information of the normal cell, to the machine learning model to generate a second value, wherein the second value indicates information on whether the normal cell survives or dies.
Kim teaches a workflow which incorporates gene expression data (RNAseq analysis) based on cells with cancer (interpreted as tumor cells) and normal cells (Fig. 1) with an “essential screen” (pg. 2394, col. 2, second paragraph).
Kim does not teach a machine learning model.
Claim 6 recites comparing, by the analysis apparatus, the first value with the second value to
determine whether a target gene is an essential gene specific to the tumor cell.
Kim teaches a bioinformatics pipeline to analyze and interpret functional genomic screening data to identify essentiality of kinases in an EGFR mutant lung cancer line cell (pg. 2394, col. 1, second paragraph). Kim teaches pairwise comparison for each gene (pg. 2394, col. 2, last paragraph) to classify if a gene is essential for cell survival (pg. 2395, col. 1, first paragraph).
Claim 6 recites selecting, by the analysis apparatus, the target gene determined to be the essential gene specific to the tumor cell as a candidate gene for neoantigen screening when the first value indicates death of the tumor cell and the second value indicates survival of the normal cell.
Kim teaches a combination of tests which teach differential expression of genes in a tumor and normal cell and screening for cell survival (pg. 4, col. 1, third paragraph) as well as validation of essential genes determined in the assay (pg. 4, col. 2, second paragraph). A neoantigen screen is not taught.
Kim does not teach using these data as part of training or using a machine learning model, a gene regulation network, or neoantigen screening.
Claim 6 recites the machine learning model includes parameters trained based on a training data set, and the training data set includes gene expression data of a specific cell as an input data and a label value for whether the specific cell dies with the gene expression data of the specific cell.
Plaimas teaches using a machine learning system to identify essential genes (abstract), trained on a specific cell – in this case, E. coli (pg. 2, col. 1, second paragraph) – and predicting essential genes (Fig. 1), where the genes were classified, or labeled, as essential or not.
Claim 6 recites the first gene expression pattern information includes expression data of genes when an expression of the target gene is perturbed using a first gene regulation network for the tumor cell, and the second gene expression pattern information includes expression data of genes when the expression of the target gene is perturbed using a second gene regulation network for the normal cell.
Plaimas also teaches machine learning based knock outs to determine essentiality (pg. 11, col. 1, second paragraph), which is interpreted as a simulation of perturbation.
Cao teaches perturbation tests for differential regulation (pg. 2613, col. 1, last paragraph) and sub-networks around those differentially expressed genes lead to different patterns in the cancer and normal cells (pg. 2606, col. 2, second paragraph).
Claim 6 recites the first gene regulation network simulates gene perturbation by the target gene in the tumor cell, and the first gene regulation network includes nodes representing genes and edges representing interactions between the nodes and the second gene regulation network simulates gene perturbation by the target gene in the normal cell, and the second gene regulation network includes nodes
representing genes and edges representing interactions between the nodes.
Cao teaches networks with nodes and edges in the perturbation test (pg. 2607, col. 2, second paragraph).
Cao also teaches genes exhibiting differential expression being candidates for further investigation (abstract)
Gelman teaches identifying essential genes and using said identification for targets for therapeutic antibodies (abstract), where personalized antibody design and application is interpreted as reading on antigen screening.
Claim 12 teaches the requirements of claim 6, taught by Kim, Plaimas, Cao, and Gelman, implemented using an “analysis apparatus,” interpreted as a computer system, comprising an input device, a storage device, and a processor.
Plaimas teaches computational techniques to determine essential genes in silico (pg. 1, col. 2, second paragraph), where determining essential genes in silico is interpreted as using a computer, where computers generally include components such as an input device, storage, and a processor. Furthermore, generally automating an activity, already known to be performed without automation, and the related automation and computational environment would have been prima facie obvious to PHOSITA (MPEP 2114(III) pertains).
Claim 9 recites the gene expression data of the training data set is a gene expression of a specific cell measured experimentally, and the label value is a value for whether the specific cell having the gene expression dies or not.
Kim more explicitly teaches genes coding for essential proteins in which cell death and survival are noted (pg. 2394, col. 2, first paragraph), where the data is determined experimentally (pg. 2394, Section 2).
Claim 10 recites the gene expression data of the training data set is expression data of the genes of the specific cell predicted when an expression of a specific gene is knocked-down using a gene regulation network, and the label value is a value for whether a cell observed experimentally dies when the expression of the specific gene is knocked-down or inhibited.
Plaimas teaches an experimental study used for training in which genes were knocked out to test for essentiality (pg. 2, col. 2, second paragraph), and Kim teaches experimental screening for cell survival or death when an essential genes are knocked down (pg. 2394, col. 2, first paragraph).
Combining Kim, Plaimas, Cao, and Gelman
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention.
One would have been motivated to combine the work of Plaimas, which teaches analyzing gene expression data to determine whether a gene is essential or not, with the work of Kim, which explicitly teaches essentiality in terms of cell death and survival, because while Plaimas teaches essential genes as being necessary to live (pg. 1, col. 1, paragraph 1), Kim clarifies that survival and death are the options for analyzing essential genes, particularly in terms of cancer cells (pg. 2397, col. 1, last paragraph). Kim teaches knocking down genes as perturbing them and observing the downstream effect of the knockdown on cell survival. One would be further motivated to combine with the work of Cao because Cao teaches gene regulation networks and interconnection of genes in a disease state such as cancer found in adenoma and carcinoma samples (abstract), and genes enriched in a cancer cell were associated with negative regulation of cell death (pg. 2610, col. 1, first paragraph).
Cao goes on to teach differentially expressed genes, as indicated by the networks, start from disruption or switch of regulation relationships, results in changes of gene expression and thus functional alterations of crucial processes (pg. 2620, col. 2, first paragraph) and thus relevant for the gene essentiality research taught by Kim and Plaimas.
Gelman is further relied on because Gelman teaches identification of genes essential for tumor cell survival and using selected genes as targets for therapeutic antibodies (title, abstract). Gelman teaches that essential genes in tumors – that is, genes which induce apoptosis – can be targeted and used for cancer therapeutics (pg. 8164, col. 2, second paragraph), which would be a valuable contribution to cancer screening and potential treatment following the essential gene determination as taught by the previously applied art.
The applied prior art is directed to share field of endeavor of applying gene expression to therapeutic outcomes. Thus the invention is prima facie obvious.
Claim 16
Claims 16 is rejected under 35 U.S.C. 103 as being unpatentable over Plaimas, Kim, Cao, and Gelman as applied to claims 6, 9-10, and 12 above and further in view of Tamayo (US 2003/0073083 A1; previously cited on the 06 July 2023 IDS form).
Claim 16 recites the processor converts the gene expression pattern into a vector and inputs the vector to the machine learning model, and the vector includes an order of a gene sequence and information on an expression of each gene.
Plaimas teaches using expression data in a machine learning model but not explicitly using a vector including order of genes and expression information.
Tamayo teaches converting the data to an expression vector where each gene is representing by an expression level in the vector (paragraph [140]).
Combining Plaimas, Kim, Cao, Gelman, and Tamayo
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined works, which teaches analyzing gene expression data to determine whether a gene is essential or not, with the work of Tamayo, which teaches comparison of normal and tumor cells and specifically a vector with an order and expression level of genes because Tamayo teaches this facilitates distinction between classes of genes and correlating expression (pg. 17, paragraph [140]). Tamayo is directed to determining classes of gene expression, which is related to the previously combined art which is also directed to gene expression level and thus share a field of endeavor. Therefore, the invention is prima facie obvious.
Response to the 26 November 2025 Applicant Remarks
Applicant remarks state the combination of Plaimas and Kim does not teach all requirements of the independent claims. This is agreed as the amended claims recent information about a gene regulatory network, which is not explicitly taught by Plaimas or Kim. Kim generally teaches determining information about gene importance to essentiality in a cancer cell and non-cancer cell while Plaimas teaches identifying essential genes using machine learning methods. The newly applied combination also incorporating Cao, which teaches the requirement of a gene expression data based on perturbation of a gene regulation network for the tumor cell and normal cell, which would be obvious to include with the other combined prior art because Cao teaches gene regulation network differences in cancer and normal cell systems.
Applicant remarks state the previously applied combination of prior art does not teach selecting candidate genes for neoantigen screening (pg. 9, third paragraph). Neoantigen screening is understood as a targeted cancer immunotherapy based on specific genes. It is agreed the previously applied art does not teach such a step, Gelman is combined with the previously combined art to teach use of essential genes in targets for cancer therapeutics (pg. 8164, col. 2, second paragraph), and thus the limitations of instant claims are considered to be taught by the applied art.
Conclusion
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/Robert J. Kallal/Examiner, Art Unit 1685
Prosecution Insights