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 .
Status of the Claims
Claim set received 09 September 2022 has been entered into the application.
Claims 1-20 are pending.
Priority
This Application is a continuation of U.S Patent Application 17/389,565 (Now: U.S Patent: 11,450,412) filed 30 July 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04 January 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings were received on 09 September 2022. These drawings are accepted.
Specification
The Specification received 09 September 2022 has been entered into the application.
Claim Rejections - 35 USC § 112
35 USC § 112(b)
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 1-20 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 and 11 recites “determine an enhanced well count.” It is not clear the relationship between identifying predictive prevalence and determining an enhanced well count. It is not clear if the prevalence(s) are used for determining enhanced well counts or if well counts are utilized for determining prevalence. It is not clear how the predicted prevalence is used for determining a well count when determining a well-count is based on the amount of RNA in a sample(s) (i.e., individual and pooled samples) and the prevalence is based on feature datum (i.e., age, sex, hobbies). Here, there is a disconnection between using feature datum (i.e., age, weight, hobbies [Specification page 4 para 0011]) and determining well count. As such, it is recommended to amend the claims to clarify if the analysis of the feature datum for determining a predictive prevalence requires the RNA data or if the predicted prevalence is combination of the feature datum and the enhanced well counts.
Claims 2-10 and 12-20 are rejected because they fail to provide limitations to overcome the deficiencies of the base claim(s).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step I - Process, Machine, Manufacture or Composition
Claims 1-10 are drawn to a system, so a machine.
Claims 11-20 are drawn to a method, so a process.
Step 2A Prong I - Identification of an Abstract Idea
Claim 1 recites a system for smart pooling while claim 11 recites a method for smart pooling. Here, because claims 1 and 11 recite similar limitations the claims are examined similarly.
Claims 1 and 11 recites:
identify a predictive prevalence value as a function of the feature datum
This step can be performed in the human mind by observing and evaluating predictive prevalence value to identify the value as a function of the feature datum and is therefore an abstract idea.
wherein identifying the predictive prevalence value further comprises:
training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum
This step encompasses performing mathematical concepts for training a machine learning model as a function of the predictive training set which reads on abstract ideas. Here, even though the claimed steps utilize “training a predictive machine-learning model as a function of the predictive training set”, the machine learning models/algorithms are broadly and generically recited and read on mere instructions to implement an abstract idea on a generic computer and read on mathematical/statistical computations (i.e., k-nearest neighbors, support vector machine [Spec page 16-17 para 0030-0032]). See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.04(a)(2)(III)(C) (1-3) and 2106.05(f).
This step can be further be performed in the human mind by observing and evaluating information (i.e., predictive prevalence value) to identify the information as a function of the trained predictive machine-learning model and the feature datum and is therefore an abstract idea.
determine an enhanced well count.
This step can be performed in the human mind by following instructions to determine an enhanced well count and is therefore an abstract idea. Here, the claimed step is broad and generic and reads on abstract ideas.
Claims 2-10 and 12-20 are further drawn to limitations that describe the abstract ideas of claim 1 and are therefore also abstract ideas.
Step 2A Prong II - Consideration of Practical Application
Claims 1 and 11 do not recite any additional element which integrates the recited judicial exception into a practical application. Here, in the instant case, the claims merely set forth a method of data analysis for determining an enhanced well count. As such, practicing the claims merely result in the generation of numerical/quantitative data for determining an enhanced well count. Such a result only produces information and does not provide for a practical application in the physical-realm of physical things and acts, i.e., the claims do not utilize the data generated by the judicial exception to affect any type of change. See MPEP 2106.04(a)(2)(A)(iv). Therefore, the claims do not utilize the obtained feature datum, identified predictive prevalence value, and determined enhanced well-count and the abstract idea to construct a practical application such as treating a subject, transformation of matter, or improving upon an existing technology.
Furthermore, and for sake of compact prosecution, even if the machine learning algorithm/model is also considered an additional element, the machine learning model (MLM) (i.e., predictive machine learning model) are used to generally apply the abstract idea without limiting how the trained (MLM) functions. The MLM is described at a high level such that it amounts to using a computer with a generic MLM to apply the abstract idea. These limitations only recite the outcomes for “training a machine learning algorithm as function of the predictive training set” without any details about how the predictive machine learning model is utilized for processing feature datum and predictive prevalence values for determining enhanced well counts which does not integrate the judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See 2024 Subject Matter Eligibility Update (AI) [Example 47 Claim 2] and MPEP 2106.05(f).
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B - Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea.
The recited additional element of using computer elements (i.e., system, computing device) of claims 1, 7, and 11 does add significantly more than the recited judicial exception because using computer to process data and carried out abstract ideas is tangential to the claimed method. See MPEP 2106.04(a)(2)(III)(C)(1-3) and 21406.05(d).
The recited additional element of data gathering (i.e., obtaining feature datum) of claims 1, 3, and 11 does add significantly more than the recited judicial exception because obtaining data that is subsequently processes by the abstract ideas is deemed a routine and conventional extra-solution activity. See MPEP 21406.05(g).
The recited additional element of data receiving (i.e., receiving a predictive training set) of claims 1 and 11 does add significantly more than the recited judicial exception because receiving data that is subsequently processed by the abstract ideas is deemed a routine and conventional extra-solution activity. See MPEP 21406.05(g).
The recited additional element of using computing device configured to received lab samples (i.e., obtaining feature datum) of claims 7 and 17 does not add significantly more than the recited judicial exception because using a device (i.e., computing device) that can receive and analyze lab specimens is routine and conventional. See MPEP 21406.05(d). To provide evidence of conventionality for using computing device configured to received lab samples, Self et al. (Self) discloses a continuous-loading sample processing comprising a sample input adapted to receive at least one sample container rack manually provided by a user [Self, claim 1] (U.S Patent No.: 8,703,492, Patent Date: 22 April 2014).
The recited additional element of data gathering (i.e., producing a pool database) of claims 7,10, 17, and 20 does add significantly more than the recited judicial exception because outputting data is deemed a routine and conventional extra-solution activity. See MPEP 21406.05(g).
In conclusion and when viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
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(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Escobar et al. (Smart Pooling: AI-powered COVID-19 testing medRxivpreprint doi: https://doi.org/10.1101/2020.07.13.20152983 (2020)) in view of Ghosh et al. (medRxiv, 29 Apr 2020).
Claim 1 recites a system for smart pooling while claim 11 recites a method for smart pooling. Here, because claims 1 and 11 recite similar limitations the claims are examined similarly.
Claims 1 recites obtain a feature datum.
Claim 1 recites identify a predictive prevalence value as a function of the feature datum.
Claim 1 recites wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, and training a predictive machine-learning model as a function of the predictive training set.
Claim 1 recites identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum.
Claim 1 recites determine an enhanced well count
Escobar et al. (Escobar) teach using PCR machine [page 5 fig 2] which reads on a computing device configured to receive a sample. Escobar teaches having access to patients age, sex, date of onset, for example [page 6 section 3.1.1]. Escobar teach using positive and negative datasets containing factors such as sex, age, or symptoms, for example [page 7 section 3.2.1], as in claim 1 obtain a feature datum.
Escobar teaches training a machine learning algorithm to predict the probability that a sample will test positive for COVID-19 based on clinical and sociodemographic data (i.e., feature datum) [page 9 section 4.1]. Efficiency depends on the prevalence p of the sample (defined as the probability that an individual in the population is ill [page 12 section 4.2.2]. Escobar teaches the idea of smart pooling is maintaining a low p artificially low, high overall prevalence of COVID-19, by reordering the samples according to a priori estimates of prevalence, before the pooling takes place [page 9 third para]. Escobar teaches demonstrating simulated prevalences of upto 25% and 50% of the test center dataset and the patient dataset [page 9 third para]. as in claim 1 identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, and training a predictive machine-learning model as a function of the predictive training set.
Escobar teaches training a machine learning algorithm to predict the probability that a same will test positive for COVID-19 based on clinical and sociodemographic data (i.e., feature datum) [page 9 section 4.1]. Escobar teaches different prevalence intervals [page 14 table 1]. Escobar teaches the method provide 306% efficiency at disease prevalence of 5% and efficiency of 107% at disease a prevalence of 50% [page 2 findings], as in claim 1 identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum.
Dependent claim(s): 2-6 and 12-16
Escobar teaches predicting the probability a sample will test positive for COVID based on claim clinical and sociodemographic [page 9 section 4.1]. Escobar teaches creating positive and negative datasets using sex, age, or symptoms [page 7 section 3.2.1], as in claims 2 and 12.
Escobar teaches using patient datasets and accessing patient’s sex, age, date of onset of symptoms, medical consultation, comorbidities, and suspected COVID-19 case [page 6 section 3.1.1] Escobar teaches creating positive and negative datasets using sex, age, or symptoms [page 7 section 3.2.1], as in claims 3 and 13.
Escobar teaches the prevalence p of the sample (defined as the probability that an individual in the population is ill) [page 12 section 4.2.2]. Escobar teaches the model also be learning the different probability distributions of samples being positive in different parts of the city [page 11 top para], as in claims 4 and 14.
Escobar teaches pooling thresholds for organizing samples into pools [page 5 fig 2]. Escobar teaches prevalence increases, the efficiency of pooling without a priori information drops rapidly and makes the strategy unviable, mainly because the probability of having at least one positive sample in a pool increases [page 5 fig 2], as in claims 5 and 15.
Escobar teaches a method for smart pooling [page 5 fig 2]. Escobar teaches using different thresholds using probability [page 5 fig 1]. Escobar teaches smart Pooling model processes these data and returns an arrangement with the probability that each sample tests positive. Escobar teaches comparing this probability to a threshold and, if it is greater than the threshold, selecting the sample for individual testing. Escobar teaches in the laboratory samples are tested individually or pooled based on the defined arrangement. Escobar teaches samples from positive pools are tested individually. Escobar teaches the diagnostic outcome of each sample is fed to the Smart Pooling platform [page 5 fig 2], as in claims 6 and 16.
Escobar does not explicitly teach claim 1 determine an enhanced well count. Escobar does not explicitly teach claims 7-10 and 17-20.
Ghosh et al. (Ghosh) teaches the results of the pooled tests can be fed into the application to recover status and estimated viral load [abstract]. Ghosh teaches using 96-well PCR reaction plates [page 17 protocol for tapestry pooling]. Ghosh teaches ground truth DNA amounts for each of the 60 samples compared to the DNA amounts estimated in Tapestry decoding algorithm [page 16 table 5]. Ghosh teaches preparing the samples in wells and serializing the samples [page 20 step 5], as in claims 1 determine an enhanced well count.
Dependent claim(s): 9 and 19
Ghost teaches samples with similar prevalences. Ghost teaches an assignment (i.e., number of samples) [page 11 table 1]. Ghosh teaches a workflow for assigning samples to identifiers such A7, B5, and B7 [page 20 steps 6-7], as in instant claims 9 and 19.
Obvious claims: 7-8, 10, 17-18, and 20.
Escobar teaches training a machine learning algorithm to predict the probability that a same will test positive for COVID-19 based on clinical and sociodemographic data (i.e., feature datum) [page 9 section 4.1]. Ghosh teaches assigning samples A7, B5, B7 [page 20]. Ghosh teaches sample ID assigned to identifiers [page 16 table 5]. Ghosh teaches the number of samples indicates the number of samples to be tested and the number of tests indicates the number of pooled Real-Time RT-PCR tests that will be performed [page 19 step 4]. Ghost teaches Tapestry pooling scheme [page 9 Fig 1A]. Ghosh teaches a tables of RNA elements for each 40 samples with associated sample ID (i.e., database/table of assigned samples) [page 13-14 table 3]. Escobar teaches pooling thresholds for organizing samples into pools [page 5 fig 2], as in instant claims 7 and 17.
Escobar teaches the first group of data according to the outcome and had access to patient data such as sex, age, onset of symptoms, for example. Escobar teaches the median age of the sample was 36 [page 6 section 3.1.1]. Escobar teaches training a machine learning for each dataset [page 7 section 3.2.3]. Ghosh teaches a workflow using for assigning unique names for test [page 19-20 steps 1-7], as in claims 8 and 18.
Escobar teach a pooling strategy for high disease prevalences [page 5 fig 2]. Ghosh teaches a pooling strategy using Tapestry pooling [page 9 fig 1A]. Ghost teaches tables (i.e., database) pooling methods using number of samples in pools and prevalence [page 11 table 1]. Ghosh teaches laboratory protocol for pooling samples using BYOM smart testing application [page 17 equipment and consumables]. Ghost teaches entering unique name for test [pages 19-20 steps 1-8]. Ghosh teaches selecting the test size based on number of samples and the number of positives expected [page 19 steps 3-4]. Ghost teaches opening the app and showing results for Ct values [page 21 step 10], as in instant claims 10 and 20. Here, it is obvious databases are being produced to subsequently store and display results.
It would be obvious to one of ordinary skill in the art by the effective filing data of the claimed invention to modify Escobar in view of Ghosh because Ghosh teaches pooling methods for COVID-19 testing. One of ordinary skill in the art would recognize that Escobar and Ghosh are in similar fields of endeavor such as determining methods for pooling samples to detect COVID-19 in patients. One of ordinary skill in the art would be motivated to combine Escobar in view of Ghosh because Ghosh teaches a refined system for pooling samples and analyzing the ground truth and estimated RNA amounts of samples which provides amounts of RNA (i.e., well count) for indicating positive samples (i.e., positive COVID test) [Ghosh, page 12 table 2 and pages 13-14 table 3]. Here, although Escobar does not explicitly teach calculating or using well counts (i.e., RNA amount), Escobar does teach using PCR-based pooling analysis methods which can be combined with the direct teachings of Ghosh using PCR-based RNA analysis for producing RNA amounts (i.e., well counts) for identifying positive and negative COVID-19 tests from pooled samples. As such, one of ordinary skill in the art would have a reasonable expectation of success combining the smart pooling methods of Escobar and Ghosh because Ghosh teaches the steps for using pooled samples (i.e., 40 and 60 patient samples) for determining RNA amounts for determining whether a sample is positive for COVID-19. Thus, combining the smart pooling elements of Escobar with the defined PCR RNA-based smart pooling protocol analysis of Ghosh would yield a predictable smart pooling method using information based on clinical and sociodemographic data (i.e., feature datum) and machine learning models for determining disease prevalence (i.e., COVID-19) and well counts.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of U.S. Patent Application: 17/389,565 filed 30 July 2021(Now: U.S. Patent No.: 11,450,412) (‘412).
Claim 1 recites a system for smart pooling while claim 11 recites a method for smart pooling. Here, because claims 1 and 11 recite similar limitations the claims are examined similarly.
Claim 1 recites obtain a feature datum. Claim 1 recites identify a predictive prevalence value as a function of the feature datum. Claim 1 recites wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set. Claim 1 recites identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum. Claim 1 recites determine an enhanced well count.
(‘412) discloses obtaining feature data [(‘412), claims 1 and 11], as in instant claims 1 and 11 obtaining datum feature.
(‘412) discloses identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, wherein the probabilistic outcome includes a contagion factor training a predictive machine-learning model as a function of the predictive training set [(‘412), claims 1 and 11]. It is noted in the instant specification the “probabilistic outcome” can include a contagion factor [Spec page 14 para 0026], as in instant claims 1 and 11 identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set.
(‘412) discloses identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum [(‘412), claims 1 and 11], as in instant claims 1 and 11 identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum.
(‘412) discloses determining an enhance well count [(‘412), claims 1 and 11], as in claims 1 and 11 determine an enhanced well count
Dependent claims:
(‘412) discloses wherein obtaining the feature datum further comprises identifying a clinical element and obtaining the feature datum as a function of the clinical element [(‘412), claim 2], as in instant claims 2 and 12.
(‘412) discloses wherein obtaining the feature datum further comprises receiving a medical input and obtaining the feature datum as a function of the medical input [(‘412), claim 3], as in instant claims 3 and 13.
(‘412) discloses identifying the predictive prevalence value further comprises determining a probabilistic distribution and identifying the predictive prevalence value as a function of the probabilistic distribution [(‘412), claim 4], as in instant claims 4 and 14.
(‘412) discloses determine an enhanced well count, wherein determining the enhanced well count further comprises generating a pooling threshold and determining the enhanced well count as a function of the pooling threshold and the predictive prevalence value [(‘412), claims 1 and 11], as in instant claims 5 and 15.
(‘412) discloses pooling threshold further comprises receiving a probability limiter and generating the pooling threshold as a function of the probability limiter [(‘412), claim 5], as in instant claims 6 and 16.
(‘412) discloses the computing device is configured to receive a lab specimen associated with the feature datum, generate an assignment of the lab specimen to a well as a function of the enhanced well count; and produce a pool database as a function of assigning the lab specimen to the well [(‘412), claim 6], as in instant claims 7 and 17.
(‘412) discloses wherein generating the assignment further comprises receiving a grouping element and generating the assignment the lab specimen as a function of the grouping element and a grouping machine-learning model [(‘412), claim 7], as in instant claims 8 and 18.
(‘412) discloses wherein generating the assignment further comprises identifying a similar predictive prevalence and generating the assignment as a function of the similar predictive prevalence [(‘412), claim 8], as in instant claims 9 and 19.
(‘412) discloses wherein producing the pool database further comprises identifying a delegated pooling strategy and producing the pool database as a function of the delegated pooling strategy [(‘412), claim 9], as in instant claims 10 and 20.
Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims and the claims of (‘412) recite similar claim limitations for smart pooling.
Conclusion
Claims 1-20 are rejected.
No claims are allowed.
Finality
This Office action is a Non-Final action. A shortened statutory period for reply to this action is set to expire THREE MONTHS from the mailing date of this action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH C PULLIAM whose telephone number is (571)272-8696. The examiner can normally be reached 0730-1700 M-F.
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/J.C.P./Examiner, Art Unit 1687
/Anna Skibinsky/
Primary Examiner, AU 1635