DETAILED ACTION
The Applicant’s filing, received 15 May 2023, has been fully considered. The following rejections and/or objections constitute the complete set presently being applied to the instant application.
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
There were two sets of claims received 15 May 2023. Both sets appear to be the same set of claims.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
This application is a 371 of PCT/US2021/072441, filed 16 November 2021
which claims benefit of 63/114,324, filed 16 November 2020.
Therefore, unless otherwise noted, the effective filing date of the claimed invention is 16 November 2020.
Information Disclosure Statement
The information disclosure statement (IDS) received 15 May 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Drawings
The drawings received 15 May 2023 have been accepted.
NOTE: There were two sets of drawings received 15 May 2023. Both sets appear to be the same set of drawings.
Specification
The amendment to the Specification received 15 May 2023 has been entered.
The disclosure is objected to because of the following informalities:
There were two copies of the Specification received 15 May 2023. Both copies are marked-up versions (i.e., there is not a clean copy in the application file) with a mark-up at least at para. [0046].
Appropriate correction is required.
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 1-20 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, 14, and 15 are indefinite for reciting “receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters” because the subsequent limitation recites “measuring an electrical impedance of the biological specimen” and therefore it is not clear as to whether the analyte biological specimen has been previously measured in order for there to be defined biophysical features characterized by corresponding electrical impedance parameters at the receiving step.
Claims 2-13 and 16-20 are indefinite for depending from either of claims 1 or 15 and for failing to remedy the indefiniteness of the independent claim from which they depend.
Claim 12 recites the limitation "the physical dielectric model" in line two. There is insufficient antecedent basis for this limitation in the claim, because claim 1 does not recite a physical dielectric model.
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 an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-13 recite a method of training a classifier (i.e., a process); claim 14 recites a method of automated classification of a biological specimen (i.e., a process); and claims 15-20 recite a method for inline classification of biological structures using a machine learning technique (i.e., a process).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
extracting at least two electrical impedance parameters from the measured electrical impedance (i.e., mental processes, e.g., read values directly from the plots); and
using the at least two electrical impedance parameters as an input to a trained classifier, training the classifier using training data from a plurality of other biological specimens and corresponding electrical impedance parameters of such training data (i.e., mathematical concepts, e.g., a classifier can be as simple as a logistic regression classifier, and broadly can be trained by calculating a linear combination of input features, passing it through a sigmoid function to predict a probability, measuring the error with a binary cross-entropy loss function, and optimizing the weights using gradient descent).
Independent claim 14 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
extracting at least two electrical impedance parameters from the measured electrical impedance (i.e., mental processes, e.g., read values directly from the plots);
labeling the biological specimen as a member of a subpopulation using the at least two electrical impedance parameters and a physical dielectric model (i.e., mental processes, e.g., adding tags or context to raw data); and
using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function (i.e., mathematical concepts, e.g., logistic regression is a fundamental supervised learning algorithm used for classification tasks that predicts the probability that a given input belongs to a specific category, and broadly works by passing a linear combination of input features through a sigmoid function to map any real-valued number into a probability value between 0 and 1, and then a decision threshold (usually 0.5) is applied to the predicted probability, where values above the threshold belong to one class and values below belong to a different class).
Independent claim 15 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
extracting at least two electrical impedance parameters from the measured electrical impedance (i.e., mental processes, e.g., read values directly from the plots); and
using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function (i.e., mathematical concepts, e.g., logistic regression is a fundamental supervised learning algorithm used for classification tasks that predicts the probability that a given input belongs to a specific category, and broadly works by passing a linear combination of input features through a sigmoid function to map any real-valued number into a probability value between 0 and 1, and then a decision threshold (usually 0.5) is applied to the predicted probability, where values above the threshold belong to one class and values below belong to a different class).
Dependent claims 2-4 and 9-13 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
the biological specimen is a heterogenous cellular system including a plurality of subpopulations exhibiting phenotypic differences from each other (i.e., mental processes, e.g., reading and evaluating data).
Dependent claim 3 further recites:
labeling the biological specimen as a member of a subpopulation using the at least two electrical impedance parameters and a physical dielectric model (i.e., mental processes, e.g., adding tags or context to raw data).
Dependent claim 4 further recites:
using the labeling as an input to a trained classifier, training the classifier using training data from a plurality of other biological specimens and corresponding associations of such training data with a specified disease state or biological function (i.e., mathematical concepts).
Dependent claim 9 further recites:
the at least two electrical impedance parameters comprise impedance phase values versus frequency, including at least two different frequencies (i.e., mental processes, e.g., reading and evaluating data).
Dependent claim 10 further recites:
the at least two electrical impedance parameters comprise impedance magnitude values versus frequency, including at least two different frequencies (i.e., mental processes, e.g., reading and evaluating data).
Dependent claim 11 further recites:
the at least two electrical impedance parameters comprise impedance phase values versus impedance magnitude values at a specified frequency (i.e., mental processes, e.g., reading and evaluating data).
Dependent claim 12 further recites:
one of the at least two electrical impedance parameters comprises an electrical size value determined using the physical dielectric model (i.e., mental processes, e.g., reading and evaluating data).
Dependent claim 13 further recites:
the physical dielectric model comprises a dielectric shell model (i.e., mental processes, e.g., reading and evaluating data; and mathematical concepts, e.g., a dielectric shell model is a mathematical way to study how electric fields interact with layered, spherical, or cellular objects, wherein the model framework treats a biological cell as a conductive interior surrounded by one or more insulating capacitive membranes, and by fitting multi-frequency bioimpedance data to this model, scientists extract intrinsic cellular parameters - such as membrane capacitance and cytoplasmic conductivity - to differentiate cell types, physiological states, or pathologies).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., extracting at least two electrical impedance parameters from the measured electrical impedance), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-20 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
In the instant application, the independent claims provide additional elements for ‘receiving an analyte biological specimen’ and ‘measuring an electrical impedance of the biological specimen’ (claims 1, 14, and 15) and ‘recycling at least a portion of the analyte biological specimen back through the test cell’ (claim 15), however once the measurement data is obtained, the subsequent steps only perform analysis and/or calculations using the data (e.g., training a classifier; and using the classifier to classify the data). Thus, the claims provide steps for obtaining data, but do not recite any steps or limitations to which the output of the judicial exception is practically applied. More specifically, the first part of the claims provide for known data gathering steps using impedance measurements, and the second part, i.e., the judicial exception, provides for the analysis of the data without any specific application or use of the classifier beyond obtaining data classification results. It is noted that independent claim 14 provides for ‘labelling’, however this is not a physical step (i.e., wet lab process) of providing a label to a molecule, but rather a data analysis step of adding informative tags or context to raw data (e.g., within the context of a supervised learning classifier, a labeled dataset comes paired with the correct output or category label (e.g., “cancer” or “not cancer”). For independent claim 15, there is an additional step added after the classification which requires ‘recycling at least a portion of the analyte’ however this is not tied to the analysis itself and given the guidance of the specification encompasses gathering data about the specimen under different conditions and/or times independent of what is analyzed and independent of the outcome of the analysis.
Dependent claims 2-4 and 9-13 do not further recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters; and
within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies.
The additional elements in independent claim 14 include:
receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters; and
within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies.
The additional elements in independent claim 15 include:
receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters;
within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies; and
recycling at least a portion of the analyte biological specimen back through the test cell.
The additional elements in dependent claims 5-8 and 16-20 include:
the analyte biological specimen comprises single cells (claim 5);
the analyte biological specimen comprises stem cells (claim 6);
the analyte biological specimen comprises neural progenitor cells (claim 7);
the analyte biological specimen comprises sub-cellular components (claim 8);
treating a recycled portion of the analyte biological specimen according to the association of the analyte biological specimen with the specified disease state or biological function (claim 16);
treating a recycled portion of the analyte biological specimen includes administration of a drug to the specimen (claim 17);
treating a recycled portion of the analyte biological specimen includes suppressing administration of a drug to the specimen (claim 18);
treating a recycled portion of the analyte biological specimen includes physically separating heterogenous specimen samples into two or more specimen groups (claim 19); and
recycling at least a portion of the analyte biological specimen includes selecting a portion of the analyte biological specimen according to the association of the portion with the specified disease state or biological function (claim 20).
The additional elements of receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters (claims 1, 14, and 15); and within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies (claims 1, 14, and 15); and recycling at least a portion of the analyte biological specimen back through the test cell (claim 15); are merely pre-solution activities of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of the analyte biological specimen comprises single cells (claim 5); the analyte biological specimen comprises stem cells (claim 6); the analyte biological specimen comprises neural progenitor cells (claim 7); and the analyte biological specimen comprises sub-cellular components (claim 8); are merely part of the pre-solution activities of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of treating a recycled portion of the analyte biological specimen according to the association of the analyte biological specimen with the specified disease state or biological function (claim 16); treating a recycled portion of the analyte biological specimen includes administration of a drug to the specimen (claim 17); treating a recycled portion of the analyte biological specimen includes suppressing administration of a drug to the specimen (claim 18); treating a recycled portion of the analyte biological specimen includes physically separating heterogenous specimen samples into two or more specimen groups (claim 19); and recycling at least a portion of the analyte biological specimen includes selecting a portion of the analyte biological specimen according to the association of the portion with the specified disease state or biological function (claim 20); are merely part of the pre-solution activities of gathering data for use in the claimed process – a nominal or tangential addition to the claims that does not meaningfully limit the claims, and therefore does not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or a field of use in which to apply a judicial exception; and as such, when all limitations in claims 1-20 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-20 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2-4 and 9-13 do not further recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claims 1, 14, and 15 and dependent claims 5-8 and 16-20 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters (claims 1, 14, and 15); and within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies (claims 1, 14, and 15); are conventional. Evidence of conventionality is shown by:
Hassan et al. (“Recent Advances in Monitoring Cell Behavior Using Cell-based Impedance Spectroscopy.” Micromachines, 2020, vol. 11, no. 590, pp. 1-23).
Hassan et al. reviews cell-based impedance spectroscopy (CBI) and shows that CBI is a powerful tool that uses the principles of electrochemical impedance spectroscopy (EIS) by measuring changes in electrical impedance relative to a voltage applied to a cell layer (Abstract). Hassan et al. further shows that given that the impedance of a system depends on the applied frequency, generally a range of frequencies are scanned, and in the case of CBI, the frequency ranges swept tend to be between 10 Hz – 100 kHz (page 6, para. 1). Hassan et al. further shows that CBI techniques can be used in a wide range of research fields for various applications, e.g., combining CBI with microfluidic systems and flow cytometry provide for the development of miniaturized devices for automated bioanalysis (page 7, para. 2).
The additional elements of the analyte biological specimen comprises single cells (claim 5); the analyte biological specimen comprises stem cells (claim 6); the analyte biological specimen comprises neural progenitor cells (claim 7); and the analyte biological specimen comprises sub-cellular components (claim 8); are conventional. Evidence of conventionality is shown by:
Petchakup et al. (“Advances in Single Cell Impedance Cytometry for Biomedical Applications.” Micromachines, 2017, vol. 8, no. 87, pp. 1-20); and
Krukiewicz. (“Electrochemical impedance spectroscopy.” Electrochemistry Communications, 2020, vol. 116, no. 106742, pp. 1-5).
Petchakup et al. reviews advances in single cell impedance cytometry for biomedical applications (Abstract), and shows that the characterization of electrical impedance at different frequencies provides important information about biological cells and the suspending medium, making it an attractive tool for single cell analysis (page 1, para. 2); and further shows biomedical applications of impedance cytometers based on cell types, e.g., the characterization of parameters of tumor cells and stem cells, e.g., membrane capacitance and cytoplasm conductivity (pages 8-9, Table 2: Tumors, Stem Cells).
Krukiewicz reviews electrochemical impedance spectroscopy (EIS) as a versatile tool for the characterization of neural tissue (Abstract); and shows that EIS has been used to develop a non-invasive system for sensitive and quantitative monitoring of the differentiation and maturation of human neural stem/progenitor cell lines (page 3, col. 2, para. 2).
The additional elements of recycling at least a portion of the analyte biological specimen back through the test cell (claim 15); treating a recycled portion of the analyte biological specimen according to the association of the analyte biological specimen with the specified disease state or biological function (claim 16); treating a recycled portion of the analyte biological specimen includes administration of a drug to the specimen (claim 17); treating a recycled portion of the analyte biological specimen includes suppressing administration of a drug to the specimen (claim 18); treating a recycled portion of the analyte biological specimen includes physically separating heterogenous specimen samples into two or more specimen groups (claim 19); and recycling at least a portion of the analyte biological specimen includes selecting a portion of the analyte biological specimen according to the association of the portion with the specified disease state or biological function (claim 20); are conventional. Evidence of conventionality is shown by:
Ahuja et al. (“Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised learning.” Microsystems & Nanoengineering, 2019, vol. 5:34, pp. 1-11);
Debski et al. (“Continuous Recirculation of Microdroplets in a Closed Loop Tailored for Screening of Bacteria Cultures.” Micromachines, 2018, vol. 9, no. 469, pp. 1-11);
Feng et al. (“A Microfluidic Device Integrating Impedance Flow Cytometry and Electric Impedance Spectroscopy for High-Efficiency Single-Cell Electrical Property Measurement.” Analytical Chemistry, 2019, vol. 91, pp. 15204-15212);
Jakobsson et al. (“Thousand-Fold Volumetric Concentration of Live Cells with a Recirculating Acoustofluidic Device.” Analytical Chemistry, 2015, vol. 87, pp. 8497-8502);
Shim et al. (“Two-way communication between ex vivo tissues on a microfluidic chip: application to tumor-lymph node interaction.” Lab Chip, 2019, vol. 19(6), pp, 1013-1026); and
Vulto et al. (“Selective sample recovery of DEP-separated cells and particles by phaseguide-controlled laminar flow.” Journal of Micromechanics and Microengineering, 2006, vol. 16, pp. 1847-1853).
Ahuja et al. teaches a tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised machine learning (Title; and Abstract) and shows that electrical impedance spectroscopy/cytometry enables measuring AC electrical properties of particles in suspension through which the frequency dependent dielectric parameters of the particles can be obtained, and that the primary advantage of impedance cytometry is that it is label free, and analysis can be performed at a single cell level (page 2, col. 1, para.3). Ahuja et al. further shows that microfluidic impedance cytometry has shown promising results in various fields such as analysis and differentiation of leukocytes and platelets, whole blood cell differentiation, nano-electronic barcoding of particles, and tumor cell characterization and classification (page 2, col. 2, para. 1) and further shows using multifrequency impedance cytometry to measure the response of tumor cells to a cancer drug across a broad range of frequencies for assessment of cellular response to a target drug (Fig. 1).
Debski et al. teaches a microfluidic device that provides a continuous recirculation of droplets in a closed loop for real-time optical characterization of bacterial growth in a droplet (Abstract) and shows that the closed loop system can be used for long-term experiments such as monitoring of antibiotic treatment (page 8, para. 8).
Feng et al. teaches a microfluidic device that integrates impedance flow cytometry (IFC) and electric impedance spectroscopy (EIS) and shows that the same cell can be measured via IFC and then subsequently measured by EIS (page 15206, col. 1, bottom through col. 2, top); and further shows that the same cell can be recycled back to the same electrodes (e.g., six times) and remeasured at a different frequency in order to obtain different IFC-based impedance data points (page 15210, col. 1, bottom).
Jakobsson et al. teaches a recirculating microfluidic device that can be used with a wide variety of different cell types (Title; Abstract; and Fig. 1).
Shim et al. teaches a multi-compartment microfluidic chip that continuously recirculates a small volume of media through two ex-vivo tissue samples (Title; and Abstract) and shows a device design and used for recirculating fluid flow (page 8, paras. 1-2).
Vulto et al. teaches a device for the selective recovery of particles after separation with dielectrophoretic (DEP) forces (Title; and Abstract).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-20 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 4-8, and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Javanmard et al. (“Use of multi-frequency impedance cytometry in conjunction with machine learning for classification of biological particles.” US 2020/0333235, as cited in the Information Disclosure Statement (IDS) received 15 May 2023).
Independent claim 1 encompasses steps for training a classifier comprising receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters; within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies; extracting at least two electrical impedance parameters from the measured electrical impedance; and using the at least two electrical impedance parameters as an input to a trained classifier, training the classifier using training data from a plurality of other biological specimens and corresponding electrical impedance parameters of such training data.
Dependent claims 2 and 4-8 further define the biological specimen and the data parameters used for the electrical impedance measurements.
Independent claim 14 encompasses a method of automated classification of a biological specimen, the method comprising: receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters; within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies; extracting at least two electrical impedance parameters from the measured electrical impedance; labeling the biological specimen as a member of a subpopulation using the at least two electrical impedance parameters and a physical dielectric model; and using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function.
Javanmard et al. teaches methods and systems for classifying biological particles, e.g., blood cells, microbes, circulating tumor cells (CTCs) using impedance flow cytometry, e.g., multi-frequency impedance cytometry, in conjunction with supervised machine learning (Abstract).
Regarding independent claim 1, Javanmard et al. shows the Classification Learner Toolbox in MATLAB, which contains several machine learning classifiers which can be readily trained on the impedance response data (para. [0059]) and further shows that the machine learning model may be trained using various datasets to perform specific pattern recognition, e.g., the algorithms for detecting amplitude and/or phase of the impedance response may be trained using the impedance response data at various frequencies obtained from multi-frequency impedance spectrometry (para. [0060]); and further shows that a neural network is initially trained or fed large amounts of data (para. [0061]) and the classifier was trained with labeled data described by many features (para. [0063]). Javanmard et al. further shows that any cell type, tissue, or bodily fluid may be utilized to obtain a sample (para. [0089]) and the term “sample” refers to a biological material being tested for and/or suspected of containing an analyte of interest (para. [0088]), and that a tissue or cell type may be provided by removing a sample of cells from an animal, but can also be accomplished by using previously isolated cells (para. [0089]). Javanmard et al. further shows (i) obtaining from the patient a sample comprising one or more biological particles; (ii) measuring an impedance response of the one or more biological particles in the sample at one or more frequencies using multi-frequency impedance cytometry to generate impedance response data associated with the one or more biological particles in the sample; and (iii) determining the presence of a CTC in the one or more biological particles based on the determined physical properties of the generated impedance response data at the one or more frequencies by applying a machine learning model to the generated impedance response data. Javanmard et al. further shows that the determined physical properties of the generated impedance response data comprise electrical properties, and that the electrical properties comprise amplitude of the impedance response, phase of the impedance response, or both (para. [0013]); and further shows determining the presence of circulating cancer cells in the one or more biological particles based on the determined physical properties of the generated impedance response data at the one or more frequencies by applying a machine learning model to the generated impedance response data (para. [0011]).
Regarding independent claim 14, Javanmard et al. shows that any cell type, tissue, or bodily fluid may be utilized to obtain a sample (para. [0089]) and the term “sample” refers to a biological material being tested for and/or suspected of containing an analyte of interest (para. [0088]), and that a tissue or cell type may be provided by removing a sample of cells from an animal, but can also be accomplished by using previously isolated cells (para. [0089]). Javanmard et al. further shows (i) obtaining from the patient a sample comprising one or more biological particles; (ii) measuring an impedance response of the one or more biological particles in the sample at one or more frequencies using multi-frequency impedance cytometry to generate impedance response data associated with the one or more biological particles in the sample; and (iii) determining the presence of a CTC in the one or more biological particles based on the determined physical properties of the generated impedance response data at the one or more frequencies by applying a machine learning model to the generated impedance response data. Javanmard et al. further shows that the determined physical properties of the generated impedance response data comprise electrical properties, and that the electrical properties comprise amplitude of the impedance response, phase of the impedance response, or both (para. [0013]). Javanmard et al. further shows the Classification Learner Toolbox in MATLAB, which contains several machine learning classifiers which can be readily trained on the impedance response data (para. [0059]) and further shows that the machine learning model may be trained using various datasets to perform specific pattern recognition, e.g., the algorithms for detecting amplitude and/or phase of the impedance response may be trained using the impedance response data at various frequencies obtained from multi-frequency impedance spectrometry (para. [0060]); the classifier was trained with labeled data described by many features (para. [0063]); and further shows determining the presence of circulating cancer cells in the one or more biological particles based on the determined physical properties of the generated impedance response data at the one or more frequencies by applying a machine learning model to the generated impedance response data (para. [0011]).
Regarding dependent claim 2, Javanmard et al. further shows a microfluidic device to analyze impedance cytometry data by using phase and amplitude properties for both cancer cells and blood, wherein the heterogeneity between cancer cells and blood allows for rapidly measuring their properties (para. [0085]).
Regarding dependent claim 4, Javanmard et al. further shows a classifier trained with labeled data described by many features (para. [0063]) and the model may be trained using various datasets (para. [0060]).
Regarding dependent claim 5, Javanmard et al. further shows that passage of cells through the microfluidic device pore results in the modulation of ionic resistance such that each peak in the measured results corresponds to a single cell being detected (para. [0052]; and Fig. 4).
Regarding dependent claim 6, Javanmard et al. further shows that an undifferentiated progenitor (including a somatic stem cell) or a fully differentiated mature cell may be used as a source of a somatic cell (para. [0035]).
Regarding dependent claim 7, Javanmard et al. further shows examples of the undifferentiated progenitor include tissue stem cells (somatic stem cells) such as neural stem cells (para. [0035]).
Regarding dependent claim 8, Javanmard et al. further shows tumor cell characterization and classification using impedance spectroscopy recorded significant differences in cytoplasm conductivity and cell membranes for paired high metastatic and low metastatic cells (para. [0044]).
Thus, Javanmard et al. anticipates instant claims 1, 2, 4-8 and 14.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-6 and 8-13 are rejected under 35 U.S.C. 103 as being unpatentable over Mahesh et al. (“Double-peak signal features in microfluidic impedance flow cytometry enable sensitive measurement of cell membrane capacitance.” Lab on a Chip, 2020, vol. 20, pp. 4296-4309) and Riordon et al. (“Deep learning with microfluidics for biotechnology.” Trends in Biotechnology, 2019, vol. 37, no. 3, pp. 310-324).
Independent claim 1 encompasses steps for training a classier comprising receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters; within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies; extracting at least two electrical impedance parameters from the measured electrical impedance; and using the at least two electrical impedance parameters as an input to a trained classifier, training the classifier using training data from a plurality of other biological specimens and corresponding electrical impedance parameters of such training data.
Dependent claims 2-6 and 8-13 further define the biological specimen and the parameters used for the electrical impedance measurements.
Mahesh et al. teaches an improved method over conventional approaches of measuring cell membrane capacitance via impedance flow cytometry (i.e., conventional equating to using multiple frequencies) by enabling the measurement of both cell size and cell membrane properties at a single frequency rather than using multiple frequencies, which simplifies the system and reduces the associated costs (Abstract).
Riordon et al. teaches a roadmap for integrating microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges (Abstract).
Regarding independent claim 1, Mahesh et al. shows microfluidic impedance flow cytometry (Title; and Abstract); sample collection and preparation (page 4307, col. 1, para. 3); passing cells through a device at a particular flow rate to obtain readings (Ibid.; and page 4296, col. 2, para. 2); probing a cell at two frequencies – one low, which gives size information from the cell, and one high, which gives information about the dielectric properties of the cell membrane (page 4296, col. 2, bottom to page 4297, col. 1, top); and using electrical impedance parameters including the impedance magnitudes and impedance phase for classification of similarly sized cells with varying dielectric properties (page 4297, col. 1, paras. 1-2).
Regarding independent claim 1, Mahesh et al. does not show using the at least two electrical impedance parameters as an input to a trained classifier, training the classifier using training data from a plurality of other biological specimens and corresponding electrical impedance parameters of such training data.
Regarding independent claim 1, Riordon et al. discusses how the amount of data that is required for training varies based on how many classes are being trained, how different these classes are, and whether tricks can be used to augment the data, and shows that extensive training can in some cases be avoided by using a pretrained network (termed transfer learning) and further shows that a CNN pretrained on the ImageNet database (>106 labeled everyday images such as dogs, trees, and cars) could be retrained to predict cell class at reasonable accuracy with as few as ~30 cell images (page 315, Box 2).
Regarding dependent claims 2, 5, 6, and 8, Mahesh et al. further shows identifying subpopulations amongst lymphocytes which are differentiated based on their peak ratios (page 4306, col. 1, para. 2; and Fig. 11(E)) (claim 2); measurement of changes in CMC using a frequency-dependent phenomenon (termed “double-peak behavior) observed in the reactive current response of single cells passing through a microfluidic impedance flow cytometer (claim 5); classification of cells with varying dielectric properties such as erythrocytes, CD4 T-cells, E. coli, yeast, mesenchymal stem cells as well as osteoblasts, and circulating tumor cells (page 4297, col. 1, para. 1) (claim 6); and dielectric properties of a cell including cytoplasm conductivity (claim 8).
Regarding dependent claims 3 and 4, Mahesh et al. further shows population cluster recognition using amplitude-based sizing to identify monocytes followed by a machine learning algorithm (K-means) to identify sub-populations in the lymphocytes (Fig. 11(E)); a double-shell model of lymphocytes (page 4306, col. 1, para. 1; and Fig. 11(A)); and further shows accurately recording changes in the CMC of a population of cells under diseased conditions or when exposed to different stresses such as chemical/drug treatment (page 4302, col. 2, para. 2).
Regarding dependent claims 3 and 4, Mahesh et al. does not show labeling the biological specimen (data) (claim 3) or using the labeling as an input to a trained classifier, training the classifier using training data from a plurality of other biological specimens and corresponding associations of such training data with a specified disease state or biological function (claim 4).
Regarding dependent claims 3 and 4, Riordon et al. further shows supervised learning is performed using labeled data, i.e., for every training input, one must provide an explicit target output (page 311, sidebar); and further shows using a pre-trained network (termed transfer learning) (page 315, box 2); and further shows that traditional machine learning has already been paired with microfluidics for biotechnology applications, e.g., in disease detection (page 311, para. 2).
Regarding dependent claims 9-13, Mahesh et al. further shows probing a cell at two frequencies (page 4296, col. 2, bottom) (claims 9 and 10); comparing the impedance magnitudes at different frequencies (page 4297, col. 1, para. 1) (claim 10); utilizing impedance phase response at high- and low frequencies (page 4297, col. 1, para. 2) (claim 9); impedance magnitude and impedance phase as metrics used to study the dielectric properties of cells (page 4297, col. 1, paras. 1-2); a physical dielectric shell model with electrical sizes (Fig. 11(A)) (claims 12 and 13).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Mahesh et al. by incorporating methods for pairing a classifier for analyzing data generated by using a microfluidics device, as shown by Riordon et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Mahesh et al. with the methods of Riordon et al., because Riordon et al. shows that data generated from high-throughput microfluidics can be combined with machine learning (e.g., deep learning) architectures for accurate and consistent cell classifications. This modification would have had a reasonable expectation of success given that both Mahesh et al. and Riordon et al. disclose methods for analyzing data generated via high-throughput microfluidics.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mahesh et al. and Riordon et al. as applied to claims 1-6 and 8-13 above, and Javanmard et al. (“Use of multi-frequency impedance cytometry in conjunction with machine learning for classification of biological particles.” US 2020/0333235, as cited above, and as cited in the Information Disclosure Statement (IDS) received 15 May 2023)
Dependent claim 7 further defines the biological specimen.
Javanmard et al. teaches methods and systems for classifying biological particles, e.g., blood cells, microbes, circulating tumor cells (CTCs) using impedance flow cytometry, e.g., multi-frequency impedance cytometry, in conjunction with supervised machine learning (Abstract).
Regarding dependent claim 7, Mahesh et al. and Riordon et al. as applied to claims 1-6 and 8-13 above, does not show the analyte biological specimen comprises neural progenitor cells.
Regarding dependent claim 7, Javanmard et al. shows examples of the undifferentiated progenitor include tissue stem cells (somatic stem cells) such as neural stem cells (para. [0035]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Mahesh et al. and Riordon et al. as applied to claims 1-6 and 8-13 above, by incorporating an analyte biological specimen comprising neural progenitor cells, as shown by Javanmard et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Mahesh et al. and Riordon et al. as applied to claims 1-6 and 8-13 above, with the methods of Javanmard et al., because Javanmard et al. shows methods for classifying biological specimens (e.g., various kinds of cells including neural progenitor cells) using impedance flow cytometry. This modification would have had a reasonable expectation of success given that both Mahesh et al. and Riordon et al. as applied to claims 1-6 and 8-13 above, and Javanmard et al. disclose methods for using impedance flow cytometry to measure properties of cells.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Spencer et al. (“High-speed single-cell dielectric spectroscopy.” ACS Sensors, 2020, vol. 5, pp. 423-430) and Riordon et al. (“Deep learning with microfluidics for biotechnology.” Trends in Biotechnology, 2019, vol. 37, no. 3, pp. 310-324, as cited above).
Independent claim 14 encompasses a method of automated classification of a biological specimen, the method comprising: receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters; within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies; extracting at least two electrical impedance parameters from the measured electrical impedance; labeling the biological specimen as a member of a subpopulation using the at least two electrical impedance parameters and a physical dielectric model; and using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function.
Spencer et al. teaches a method that uses multifrequency impedance measurements to determine the complete intrinsic electrical properties of thousands of single cells at high throughput.
Riordon et al. teaches a roadmap for integrating microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges (Abstract).
Regarding independent claim 14, Spencer et al. shows single-cell impedance cytometry for measuring the properties of red blood cells and red cell ghosts (i.e., an empty red blood cell membrane that has lost its hemoglobin), deriving the unique values of conductivity and permittivity of the membrane and cytoplasm for each individual cell, and a dielectric model (Abstract); collecting specimens and processing red blood cells (page 426, col. 2, para. 3); cells were pumped through the device channels at a particular flow rate and impedance was measured at either two or eight frequencies (page 426, col. 2, para. 2); and dielectric parameters, e.g., electrical radius, membrane capacitance, cytoplasm conductivity, and cytoplasm permittivity (Figure 5).
Regarding independent claim 14, Spencer et al. does not show labeling the biological specimen as a member of a subpopulation using the at least two electrical impedance parameters and a physical dielectric model; and using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function.
Regarding independent claim 14, Riordon et al. shows supervised learning is performed using labeled data, i.e., for every training input, one must provide an explicit target output (page 311, sidebar); and further shows using a pre-trained network (termed transfer learning) (page 315, box 2); and further shows that traditional machine learning has already been paired with microfluidics for biotechnology applications, e.g., in disease detection (page 311, para. 2) and cell classification (Figure 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Spencer et al. by incorporating methods for pairing a classifier for analyzing data generated by using a microfluidics device, as shown by Riordon et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Spencer et al. with the methods of Riordon et al., because Riordon et al. shows that data generated from high-throughput microfluidics can be combined with machine learning (e.g., deep learning) architectures for accurate and consistent cell classifications. This modification would have had a reasonable expectation of success given that both Spencer et al. and Riordon et al. disclose methods for analyzing data generated via high-throughput microfluidics.
Claims 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ahuja et al. (“Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised learning.” Microsystems & Nanoengineering, 2019, vol. 5:34, pp. 1-11, as cited above) and Riordon et al. (“Deep learning with microfluidics for biotechnology.” Trends in Biotechnology, 2019, vol. 37, no. 3, pp. 310-324, as cited above) and Debski et al. (“Continuous Recirculation of Microdroplets in a Closed Loop Tailored for Screening of Bacteria Cultures.” Micromachines, 2018, vol. 9, no. 469, pp. 1-11, as cited above).
Independent claim 15 encompasses a method for inline classification of biological structures using a machine learning technique informed by a biological specimen, the method comprising: receiving an analyte biological specimen defining biophysical features characterized by corresponding electrical impedance parameters; within a test cell through which the biological specimen is flowing, measuring an electrical impedance of the biological specimen using a specified range of frequencies; extracting at least two electrical impedance parameters from the measured electrical impedance; using the labeling, further applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function; and recycling at least a portion of the analyte biological specimen back through the test cell.
Dependent claims 16-20 further define the inline classification of the biological structures with regard to the recycled portion of the analyte.
Ahuja et al. teaches a device capable of rapidly assessing tumor cell sensitivity to drugs using multifrequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy (Abstract).
Riordon et al. teaches a roadmap for integrating microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges (Abstract).
Debski et al. teaches a microfluidic device that provides continuous recirculation of droplets in a closed loop, maintaining low consumption of oil phase, no cross-contamination, stabilized temperature, a constant condition of gas exchange, dynamic feedback control on droplet volume, and a real-time optical characterization of bacterial growth in a droplet (Abstract).
Regarding independent claim 15, Ahuja et al. shows obtaining cell samples (page 8, col. 2, bottom through page 10, col. 1, para. 1); pumping sample fluid through the microfluidic channel (page 10, col. 1, para. 2); the use of multiple frequencies simultaneously can provide a snapshot of a cell’s dielectric properties over a wide range of frequencies, resulting in higher classification accuracy (page 2, col. 1, para. 2); extracting the impedance parameters for amplitude change and phase change (page 4, col. 1, para. 2); a support vector machine classifier that can be provided a labeled training data set, and training data that included labeled features from live cells and dead cells (page 4, col. 1, para. 3 and col. 2, para. 1).
Regarding independent claim 15, Ahuja et al. does not show a classification model trained (i.e., ‘pre-trained’), or recycling at least a portion of the analyte biological specimen back through the test cell.
Regarding independent claim 15, Riordon et al. shows using a pre-trained network (termed transfer learning) (page 315, box 2); and further shows that traditional machine learning has already been paired with microfluidics for biotechnology applications, e.g., in disease detection (page 311, para. 2) and cell classification (Figure 2).
Regarding independent claim 15, Debski et al. shows a microfluidic device that provides continuous recirculation of droplets in a closed loop for real-time optical characterization of bacterial growth in a droplet (Abstract) and real-time monitoring of a reaction’s progress, including monitoring of antibiotic treatment (page 8, para. 8).
Regarding dependent claims 16 and 17, Ahuja et al. further shows that a patient’s cancer cells are treated with antibody-conjugated drugs, and then the impedance cytometer determines the percentage of live and dead cells in the sample (page 2, col. 2, para. 2 through page 3, col. 1, paras. 1-2; and Fig. 1).
Regarding dependent claims 18, 19 and 20, Debski et al. further shows the experiment was also conducted to discover different antibiotic concentrations in order to determine the effect of its presence on the growth of bacterial culture, by preparing a sequence of 30 droplets, containing chloramphenicol from 0.0 μg/mL to 0.9 μg/mL, with six droplets at each concentration (page 7, para. 2; and Figure 5).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Ahuja et al. by incorporating the use of a trained (i.e., pre-trained) classification model, as shown by Riordon et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Ahuja et al. with the methods of Riordon et al., because Riordon et al. shows that extensive training can be avoided by using a pre-trained network (termed transfer learning). This modification would have had a reasonable expectation of success given that both Ahuja et al. and Riordon et al. disclose methods for cell classification.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Ahuja et al. by incorporating methods for the continuous recirculation of an analyte, as shown by Debski et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Ahuja et al. with the methods of Debski et al., because Debski et al. shows a device for the continuous recirculation of microdroplets in a closed loop that is tailored for screening of bacteria cultures, particularly after administration of a drug. This modification would have had a reasonable expectation of success given that both Ahuja et al. and Debski et al. disclose methods for using a microfluidic device for cell analysis.
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 14 and 15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 of copending Application No. 19/296,473 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim 19 limitations reciting “wherein the trained ML model is trained using a training data set comprising a sequence of images, or associated data, of a set of biological particles flowing in viscoelastic extensional flow along a flow path of the microfluidic channel through the contoured constriction region and corresponding impedance signal data” would anticipate the instant claims 14 and 15 limitation reciting “applying a classification model trained using training data from a plurality of other biological specimens to associate the analyte biological specimen with a specified disease state or biological function.”
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
No claims are allowed.
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 application.
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/STEVEN W. BAILEY/Examiner, Art Unit 1687
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