DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-9 are pending and examined on merits.
Claims 1-9 are rejected.
Priority
This application claims the benefit of and priority to the prior-filed application PCT/IN2022/051091, filing date 2022-12-17. Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
No Information Disclosure Statement (IDS) filed herein.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 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.
Claims 1-9 recite limitations encompassing numbers surrounded by parenthesis throughout the claims. The metes and bounds of the limitations are unclear with regards to the definitions of the recited limitations and whether the numbers within the parentheses are intended to be positive limitations of the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO' s patent eligibility guidance.
Subject Matter Eligibility Test For Products And Process
Step 1: Statutory Category?
35 U.S.C. 101 enumerates four categories of subject matter that Congress deemed to be appropriate subject matter for a patent: processes, machines, manufactures and compositions of matter. As explained by the courts, these "four categories together describe the exclusive reach of patentable subject matter. If a claim covers material not found in any of the four statutory categories, that claim falls outside the plainly expressed scope of § 101 even if the subject matter is otherwise new and useful." In re Nuijten, 500 F.3d 1346, 1354, 84 USPQ2d 1495, 1500 (Fed. Cir. 2007).(MPEP 2106.03)
With respect to step 1: YES, the claims are drawn to statutory categories: machine , process.
Claims 1-8 recites a system, which is a machine “concrete thing, consisting of parts, or of certain devices and combination of devices”. (Step 1, YES).
Claim 9 recites a series of method steps, therefore is a process. (Step 1, YES).
Step 2A - Prong One: Judicial Exception (JE) Recited?
With respect to Step 2A - Prong One, YES, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more JE.
Claim 1 is directed to a system which execute following method steps in claim 9:
Independent claims 1 and 9 recite, “by a sample coding device, a pooling matrix for pooling and testing the plurality of biological samples”, which is gathering and grouping information, the concept can be performed in human mind or by a human using a pen and paper (mental process). However, a coding device executes the step of pooling using pooling matrix (mathematic equation/algorithm), therefore is a mathematic concept.
“Generating linear equations based on the output data”, which is creating a mathematic representation of information/data, the step can be performed in human mind or by a human using a pen and paper. Generating a linear equation results in an equation. An equation is a mathematical concept. therefore, it’s a mental step and a mathematical concept.
“convert a set of linear equations into a set of nonlinear equations…using a compressed sensing algorithm”, which involve mathematical equation/algorithm/calculation, therefore is a mathematical step.
“invoke at least one regularity condition to obtain a unique solution of the set of nonlinear equations”, which is evaluation of information against criteria through observation, judgment, or comparison that a person could conceptually complete using just their mind or pen and paper, therefore is a mental step. Invoking a solution of an equation means solving an equation to yield a solution, i.e. a calculation. So, the limitation can also be a mathematical concept.
“the regulatory condition is selected from one of (a) sparsity with respect to a presence or an absence of each analyte separately, or (b) sparsity with respect to a disproportionate number of samples…”. This is a mental process of choosing a condition of either (a) or (b).
Dependent claims 4, 6-8 recite following mathematic concept of abstract idea: “linear equation is y = A x”, “A = (a1) m x n is a pooling matrix of dimension m x n”, “x = (x)nx d is a matrix of dimension n x d with entries x”, “y=(yk)m xd is a matrix of dimension m x d”, “nonlinear equation is generated based on a plurality”, “statistical correlation between the measurement”, “pooling matrix is generated”.
(Step 2A - Prong One, YES)
Step 2A - Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception.
With respect to Step 2A - Prong Two: NO, the claims do not integrate the JE into a practical application (MPEP 2106.04(d)).
Claims 1 and 9 recite additional element “obtaining output data”. Data gathering steps are not an abstract idea, they are insignificant, extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g). The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The data gathering steps constitute a general link to a technological environment: the trait prediction methods are intended to be applied to plant populations. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide integration into a practical application. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.).
Claim(s) 1-3, recite the additional non-abstract element (EIA) of computer system or parts (processor, memory, testing machine) in high level of generality. The testing machines (PCR, HPLC, NGS, Mass Spectrometer, NMR, Raman Spectroscope) generates data of sample testing based on the instructions (sample pool and testing instructions) received. These data gathering steps by testing machines are no more than insignificant extra-solution activities, where the data gathered is further analyzed per judicial exceptions. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978), (2106.05(g)).
The claims provide structures of the computer elements in high level of generality, are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int' l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole
amount to significantly more than the recited exception i.e., whether any additional
element, or combination of additional elements, adds an inventive concept to the claim
(MPEP 2106.05).
With respect to Step 2B: NO, the claims do not recite a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05).
“… an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself. Alice Corp…”
With respect to claim(s) 1, 3 and 9: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
Waldstein et al. [PLOS Computational Biology, 2022, p1-20; published date: Oct24,2022; title: Use of compressed sensing to expedite high throughput diagnostic testing for COVID-19 and beyond] discloses data gathering in computer environment (testing instrument machine) and use of compressed sensing for routine laboratory testing.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art. MPEP 2106.05(d): “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data necessary to carry out the JE. MPEP 2106.05(g).
The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. MPEP 2106.05(g).
The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception to provide an inventive concept. (MPEP 2106.05(g) citing Mayo, PerkinElmer, Inc. v. Interna Ltd, Intellectual Ventures LLC v. Erie Indem. Co., Electric Power Group LLC v. Alstom S.A.)
The data gathering steps constitute a general link to a technological environment: the trait prediction methods are intended to be applied to plant populations. (MPEP 2106.05(h), citing Mayo, Bilski, electric Power Group, Genetic Techs Ltd v Merial LLC.)
Therefore, 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 (as discussed in Alice Corp.,).
As such, the prior art [Sebastian et al. review article, Nature Nanotechnology, 2020, 15,
529-544, Mar30, 2020] recognizes that these computing elements (memory device and processor) are routine, well understood and conventional in the art.
As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
The claims do not provide any details of how specific structures of the computer elements are used to implement the JE. MPEP 2106.05(a), contrasting decisions identifying how the computer implements an abstract idea, such as in McRo to decisions which found no specific interaction with the computer, such as in Affinity Labs of Tex v. DirecTV, LLC.
The computer elements of the claims do not provide improvements to the functioning of the computer itself. MPEP 2106.05(a) I, contrasting decisions indicating an improvement to the computer, such as DDR Holdings, LLC v. Hotels.com LP, with decisions that did not identify an improvement to the computer, such as FairWarning IP, LLC v. Iatrix Sys.
The computer elements of the claims do not provide improvements to any other technology or technical field. MPEP 2106.05(a) II: contrasting decisions indicating an improvement to the technology, such as Diamond v. Diehr, Trading Techs. Int' l v. CQG Inc, or Intellectual Ventures I v. Symantec Corp, with decisions that did not identify an improvement to the technology, such as Alice Corp, Versata Dev. Group, Inc. v. SAP AM. Inc, or TLI Communications.
The computer elements of the claims do not utilize a particular machine. MPEP 2106.05(b): contrasting decisions wherein a particular machine was identified, such as MacKay Radio & Tel. Co. v. Radio Corp. of America, Eibel Process Co. v. Minn. & Ont. Paper Co., with decisions where a general-purpose computer does not qualify as a particular machine, such as Ultramercial, Inc. v. Hulu, LLC, TLI communications, or Eon Corp. IP holdings LLC v. AT&T Mobility LLC.
Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not provide significantly more.
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
The data gathering elements recited in Step 2A - Prong Two are further found to be well-understood, routine, and conventional per Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) for using PCR; and Ghasemi et al. (Am J Nucl. Med. Mol. Imaging; Vol. 6: 6; 2016) for utilizing the other testing machines as conventional systems biology tools for obtaining and analyzing samples within a precision medicine pipeline (pages 311, column 2; 313, column 1; and 320, table 2).
The computer components are further found to be well-understood, routine, and conventional per 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 for storing and retrieving information in memory and Alice Corp., 573 U.S. at 225,110 USPQ2d at 1984 (MPEP 2106.05 (a)).
Therefore, the additional elements taken individually, and also taken as a
combination, do not integrate the judicial exception into a practical application and do not result in the claim as a whole amounting to significantly more than the judicial
exception. (Step 2B, NO).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Gopalkrishnan et al. [US 2022/0170118 A1, published date: Jun 02, 2022](referred as “Gopalkrishnan”) further in view of Yi et al. [arXiv:2004.05759v1 [q-bio.QM], online published date: Apr03, 2020] (referred as “Yi”).
Regarding claim 1 and 9, Gopalkrishnan teaches a sample decoding device comprising: a memory and a processor coupled to the memory configured to: create a pooling matrix for pooling and testing of a plurality of biological samples, wherein the pooling matrix indicates a plurality of pools for the plurality of pools for the plurality of biological samples to be tested and at least two pools for each biological sample, wherein a pooling is performed to include each of the biological sample in the determined at least two pools of the plurality of pools and tests are performed on the plurality of pools [page 10:col1:11][0094] as in claim 1 and 9 teach a memory stores a set of instructions, and a processor that is configured to execute, by a sample coding device, a pooling matrix for pooling and testing the plurality of biological samples, wherein the pooling matrix indicates a plurality of pools for the plurality of biological samples to be tested and at least two pools for each biological sample, wherein a pooling is performed to include each of the biological samples in the determined at least two pools of the plurality of pools and tests are performed on the plurality of pools.
Gopalkrishnan discloses creating sparse pooling matrix for pooling the biological samples where each biological sample may be combined into 3 pools, as opposed to 6 pools in conventional approaches which reduces testing rounds [0097] as in claim 1 and 9 teach reducing a number of testing for plurality of biological samples.
Regarding claim 1 and 9, Gopalkrishnan teaches pooled samples test result data is in vector [0097][0037] as in claim 1 and 9 teach an output data on completing assay in each of the plurality of pools with reduced number of tests is a row vector.
Regarding claim 1 and 9, Gopalkrishnan teaches performing a quantitative reverse transcription Polymerase Chain Reaction (PCR) (RT-qPCR) test on each pool of the biological samples to obtain input cycle threshold values (Ct values) of each pool, wherein the Ct values of each pool are converted into a quantitative measure of viral load of each pool [0026-0027]. Gopalkrishnan’s teaching determines absence of viruses (analytes) in biological samples when converted Ct value is negative. Pools with converted positive Ct values has presence of viruses (analytes) in the corresponding pooled biological samples [0065] as in claim 1 and 9 teach categorical values indicating an absence, a presence or a category of at least one analyte in the plurality of biological samples, wherein the output data of each pool comprises a measure or the category of each analyte in that pool.
Regarding claim 1 and 9, Gopalkrishnan teaches decoding device constructs a noisy linear equation based on the created for testing of the biological samples and the quantitative measure of the viral load associated with each pool [0053] as in claim 1 and 9 teach, the output data of each pool comprises a measure or the category of each analyte in that pool; generate, a set of linear equations based on the output data and the generated pooling matrix.
Regarding claims 1 and 9, Gopalkrishnan discloses solving the noisy linear equation for each of the biological samples to recover the status of each biological sample using the compressed sensing method [0053][18][8][0065][0076] as in claims 1 and 9 teach to solve the set of linear equations using a compressed sensing algorithm, convert the set of linear equations into a set of nonlinear equations.
Gopalkrishnan does not teach explicitly regularity condition to obtain a unique solution of the set of nonlinear equations to detect, identify, and quantify the plurality of analytes in the plurality of biological samples, wherein the regulatory condition is selected from one of (a) sparsity with respect to a presence or an absence of each analyte separately.
However, Yi teaches development of robust real time PCR conditions (conditions for temperature, end points, threshold cycle (Ct), or quantification cycle (Cq)) for DNA quantification [page 3, col 1:para5, col2]. Yi discloses using compressed sensing detection algorithm at various conditions for detecting virus and antibodies (such as decoding algorithms to infer x from y, such as basis pursuit (l1 minimization), LASSO, message passing style algorithms, and greedy algorithms). It has been shown that the optimal solution of l0 minimization can be obtained by solving l1 minimization under certain conditions (e.g. Restricted Isometry Property or RIP). A necessary and sufficient condition under which a vector x with no more than k nonzero elements can be uniquely obtained via l1 minimization is Null Space Condition (NSC) [page 4-6, sections: 2.1 -203] .
It would have been obvious to a person having ordinary skill in the art to incorporate Yi’s sparsity regularity conditions with the teachings of Gopalkrishnan to arrive at the claimed invention. Incorporating standard sparsity regularity conditions is a known, routine optimization in compressed sensing, person having ordinary skill in the art would have motivated at the time of invention to apply the known regularity to arrive at the claimed invention with reasonable success.
Regarding claim 2, Yi teaches real time PCR instrument conditions (example, temperature conditions for each cycle reaction, detection threshold 35times of Standard Deviation) for robust quantification of DNA (analyte) [page 3, col 1, para 4, and col 2]. Yi teaches compress sensing approach and uses decoding algorithms detecting viruses or antibodies (such as decoding algorithms to infer x from y, such as basis pursuit (l1 minimization), LASSO, message passing style algorithms , and greedy algorithms). It has been shown that the optimal solution of l0 minimization can be obtained by solving l1 minimization under certain conditions (e.g. Restricted Isometry Property or RIP). A necessary and sufficient condition under which a vector x with no more than k nonzero elements can be uniquely obtained via l1 minimization is Null Space Condition (NSC) [page 4-6, sections: 2.1 -203] as in claim 2 teaches detect, identify, and quantify a condition of interest based on the detected, identified, and quantified analytes in the plurality of biological samples.
Gopalkrishnan or/and Yi does not teach “a condition of quality assurance, a condition of food safety …. next generation sequencing (NGS) targeted panels”. However, applying data processing to categorize or flag specific conditions—such as quality assurance, food safety, or medical screening—represents an obvious application of known diagnostic evaluation techniques to routine operational parameters. A POSITA (a person of ordinary skill in the art) would have motivated to perform the data processing according to quality conditions to ensure the complete process meets quality requirements.
Regarding claim 3, Yi and Gopalkrishnan both teaches performing assays using PCR machine. Yi and Gopalkrishnan, independently and in combination, do not teach specifically performing assays using instruments a high-performance liquid chromatography column (HPLC), microarrays, a next generation sequencing (NGS) device, a mass spectrometer, a nuclear magnetic resonance (NMR) spectroscope, or a Raman spectroscope. However, performing biological sample testing in those instruments are well known and conventional. Therefore, a POSITA would have motivated to use well known instruments to test these pooled biological samples with reasonable expectation of success to arrive at the claimed invention.
Regarding claim 4, Yi teaches concept of sample pooling and group testing as a system of linear equations. Yi discloses or renders obvious element of the claimed linear matrix equation system for sample pooling and formulating group testing (pooling) as a system of linear matrix equations of the exact form (y = Ax) (or equivalently), where test outcomes are represented as a linear combination of sample properties mapped via a design matrix {page 5-6] as claim 4 teaches y = A x and its limitations.
To the extent claim 4 uses specific variations “pooling matrix of dimension” in mathematic equation. A PHOSITA, seeking sample testing workflows would have motivated to evaluate each variation to optimize the sample testing workflow. Optimization of sample pooling and group testing is well known in the art, therefore a PHOSITA would have arrived at the claimed invention with reasonable success, by using steps well known in the art.
Regarding claim 5, Yi discloses transforming and converting linear mathematical structures into nonlinear equations (e.g., utilizing non-linear functions or mapping structures) and subsequently using mathematic regularity conditions (e.g., restricted isometry property, sparsity constraints, or optimization regularities) to reconstruct and solve for the unknown target matrix [page 5-8] as in claim 5 teaches processor is configured to convert the linear equation yk = A xk into a nonlinear equation and then to use the regularity conditions to solve for the matrix x, wherein xk is the kth column of the x matrix and yk is the kth column of the y matrix.
Regarding claim 6, Yi teaches a functional modeling non-linearity and randomness associated with the measurement process [page 4] as in claim 6 teaches nonlinear equation is generated based on a plurality of variables that comprise the generated pooling matrix, a plurality of output data of the plurality of pools, and a quantitative measurement of each analyte.
Regarding claim 7, Yi et al. disclose utilizing statistical properties, historical data patterns, and probabilistic models of analyte/viral distributions to regularize and optimize decoding algorithms in pooled and compressed sensing biological measurements [page 5-8] as in claim 7 teaches a statistical correlation between the measurement of the different analytes from previous data is used as a part of the regularity condition.
Regarding claim 8, Gopalkrishnan teaches creating the includes: receiving a request from a user for the pooling and testing of the plurality of biological samples, wherein the request includes information about at least one of, a name of a test, and a size of the test, wherein the size of the test indicates a total number of biological samples to be tested and a number of biological samples estimated as positive out of the total number of biological samples; creating the for there quested size of the test [3][13] as in claim 8 teaches pooling matrix is generated based on an at least one input from a user, comprising at least one of a name of the assay, and a size of the assay, wherein the size of the assay indicates a total number of biological samples to be tested and a number of biological samples estimated as positive out of the total number of biological samples.
The prior art made of record and not relied upon is considered pertinent to applicants’ claims 1 and 9.
Furstenau et al., PLoS ONE 2020, 15(11), 1-29: e0236849. https://doi.org/
10.1371/journal.pone.0236849, title: Sample Pooling Methods For Efficient
Pathogen Screening: Practical Implications.
Ghosh et al. Open Journal of Signal Processing, 2021, 2, 248-264, title: A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection.
Conclusion
Claims 1-9 are not allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PRAMOD KUMAR MOHANTA whose telephone number is (571)272-8775. The examiner can normally be reached Mon-Fri 9:00am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PKM/Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
