DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
2. Claims 1-78, 81, 83, 92, 99-101 and 105 are cancelled.
Claims 79-80, 82, 84-91, 93-98 and 102-104 are currently pending and under exam herein.
Claims 79-80, 82, 84-91, 93-98 and 102-104 rejected.
Claim 93 is objected to.
Priority
3. The claimed benefit of U.S. Provisional Application No. 63/386,814, filed 9 December 2022, and U.S. Provisional Application No. 63/295,160, filed 30 December 2021 is acknowledged. In this action, all claims are examined as though they had an effective filing date of 30 December 2021. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
4. The information disclosure statements (IDSs) submitted on 14 November 2024 and 05 November 2025 are being considered by the examiner. On the IDS submitted 14 November 2024, an error in a referenced patent number was identified and corrected by the examiner: US20140065647A1 (Momenta et al.) has been corrected to US2014065647A1 (Momenta et al.).
Drawings
5. The drawings submitted on 30 December 2022 are accepted by the examiner.
Claim Objections
6. Claim 93 is objected to because of the following informalities:
Claim 93 recites ‘wherein the data table comprises the calculates molecular weight’, which should be corrected to: ‘wherein the data table comprises the calculated molecular weight’.
Appropriate correction is required.
Claim Interpretation
7. No limiting definition of an immunoassay image was found in the disclosure. With broadest reasonable interpretation, an immunoassay image is interpreted to include any spatial representation of immunoassay data including a spatial representation of a gel (for example from SDS PAGE analysis) or a spatial representation of an electropherogram (for example from capillary electrophoresis) or a spatial representation of flow-based assay (for example flow assay membrane).
No limiting definitions for the terms ‘band’ and ‘degree of shift’ were disclosed in the specification. However, a broad range of immunoassays are embodied other than simple western blots including bead-based assays, flow-based assays (including those using colored particles) and capillary gel-based assays (para. 0291, 0102). For the purpose of review, and with the broadest reasonable interpretation, a ‘band’ is interpreted to mean a physical signal of a protein in an immunoassay readout and a ‘shift’ of a band is interpreted to mean a change in the physical signal of the protein. Thus, a ‘band’ includes a band on a gel, a peak on an electropherogram or a spot on a lateral or vertical flow assay, and a shift includes a shift in mobility or a shift in intensity.
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.
8. Claims 79-80, 82, 84-91, 93-98 and 102-104 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 79 recites: analyzing the experimental data
Claim 79 recites: by a machine learning process trained to determine a degree that a band of the analyte shifts in immunoassays
Claim 79 recites: determining, based on the analyzing, the degree of shift for a band in the immunoassay experiment, wherein the band comprises the analyte of interest
Claim 82 recites: the method of claim 79, wherein: the analyzing comprises analyzing an immunoassay image of the immunoassay experiment
Claim 82 recites: the determining comprises marking the degree of shift for the band on the immunoassay image
Claim 82 recites: wherein the immunoassay image comprises a stained gel or a capillary gel where the analyte is loaded
Claim 84 recites: the method of claim 79, wherein the immunoassay experiment is a bead-based immunoassay or a flow-based immunoassay
Claim 86 recites: the method of claim 79, wherein the machine learning process has been trained using an immunoassay dataset comprising at least one of glycosylation, disulfide bonds, modified residue, 3-(N-morpholino)propanesulfonic acid (MOPS), ubiquitination, lipidation, 2-(N-morpholino)ethanesulfonic acid (MES), isoelectric point (pI), SUMOylation, Tris acetate, interchain or polymer cross links, gel type, buffer type, or degree of shift
Claim 88 recites: the method of claim 79, wherein the degree of shift of the band is caused by a shift in the molecular weight corresponding to the analyte of interest
Claim 89 recites: the method of claim 79, further comprising: determining whether the experimental data should be modified
Claim 90 recites: analyzing the immunoassay image to mark features of the immunoassay image, wherein the features comprises at least one band
Claim 90 recites: determining a degree of shift for a band on the immunoassay image, wherein the band comprises an analyte of interest that corresponds to the identifier
Claim 90 recites: by a machine learning process
Claim 93 recites: the system of claim 90, wherein the processor performs further operations comprising: calculating a molecular weight data for the band
Claim 95 recites: the system of claim 90, wherein the processor performs further operations comprising: determining whether the experimental data should be modified
Claim 96 recites: the system of claim 90, wherein the immunoassay image is an image of a bead- based immunoassay or a flow-based immunoassay
Claim 98 recites: the system of claim 90, wherein the features further comprise at least one of a frame of the immunoassay image and at least one lane of the immunoassay image, wherein the at least one lane is analyzed to determine the shift of the band in each lane
Claim 103 recites: the system of claim 90, wherein the machine learning process has been trained with an immunoassay dataset to determine a degree that a band of an analyte shifts in an immunoassay experiment
Claim 104 recites: the system of claim 103, wherein the immunoassay dataset comprises at least one glycosylation, disulfide bonds, modified residue, 3-(N- morpholino)propanesulfonic acid (MOPS), ubiquitination, lipidation, 2-(N- morpholino)ethanesulfonic acid (MES), isoelectric point (pI), SUMOylation, Tris acetate,interchain or polymer cross links, gel type, and buffer type
The limitations regarding ‘calculating a molecular weight data’ is a verbal equivalent that describe a mathematical calculation that is performed as the limitation and is so simple that it could be performed in the human mind or with pen and paper. Therefore, this limitation fall under the "Mathematical concepts" and "Mental processes" groupings of abstract ideas.
The limitations for ‘determining whether the experimental data should be modified’, ‘analyzing the immunoassay image’, ‘determining a degree of shift of a band in an immunoassay’ are generically recited data analysis steps that can be practically performed in the human mind because the human mind is capable of identifying relevant information, comparing values, and determining information from other values. Therefore, these limitations fall under the "Mental processes" groupings of abstract ideas.
The limitations in claims 79 and 90 directed to ‘analyzing experimental data’ (to determine the degree of shift of a band on an immunoassay) further recite using a ‘machine learning process’ to perform the analyzing. The limitations do not limit how the analysis or evaluation is performed, but the process of determining the degree of shift involves digital image quantification to determine the center of mass or intensity of profiles of bands and distances between the centers; therefore these limitations fall into the ‘Mathematical concepts’ grouping of abstract ideas because this image-based application of machine learning analysis is mathematical in nature.
The limitations that further limit the image type analyzed, what feature is determined on the image, the immunoassay experiment type, the analyte measured, how much of the image is analyzed, and the immune assay dataset features further limit the abstract idea, but do not integrate the judicial exception into a practical application because they merely further limit the mental processes and mathematical concepts but do not change their position as abstract ideas.
While claims 90, 93, 95 and 102 recite performing some aspects of the analysis with a processor, there are no additional limitations that indicate that this processor requires anything other than carrying out the recited mental process or mathematical concept in a generic computer environment. Merely reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. As set forth in the MPEP section 2106.04(a)(2)(III)(C), if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components, then it falls within the "Mental processes" grouping of abstract ideas. As such, claims 79-80, 82, 84-91, 93-98 and 102-104 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment or insignificant extra-solution activity. Specifically, the claims recite the following additional elements:
Claim 79 recites: acquiring experimental data corresponding to an immunoassay experiment, wherein the experimental data is based on an analyte of interest
Claim 80 recites: the method of claim 79, wherein the machine learning process comprises a neural network, wherein the neural network comprises a feedforward network or a deep neural network
Claim 85 recites: the method of claim 79, wherein the analyte is at least one of a protein, a hapten, a hormone, a nucleic acid, a peptide, a modified peptide, or a modified form of any of the foregoing analytes
Claim 87 recites: the method of claim 79 or claim 86, wherein the experimental data comprises at least one of glycosylation, disulfide bonds, modified residue, 3-(N- morpholino) propanesulfonic acid (MOPS), ubiquitination, lipidation, 2-(N- morpholino) ethanesulfonic acid (MES), isoelectric point (pI), SUMOylation, Tris acetate, or interchain or polymer cross links for the analyte of interest and gel type and buffer type for the immunoassay experiment
Claim 89 recites: displaying the determined modifications
Claim 90 recites: a system, comprising: at least one processor; and a memory coupled to the at least one processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to perform operations
Claim 90 recites: acquiring experimental data, wherein the experimental data comprises an identifier of an analyte and an immunoassay image
Claim 90 recites: displaying the degree of shift for the band on the immunoassay image
Claim 91 recites: the system of claim 90, wherein the machine learning process comprises a neural network
Claim 93 recites: displaying a data table, wherein the data table comprises the calculates molecular weight
Claim 94 recites: the system of claim 90, wherein the experimental data further comprises at least one identifier of a cell line, an identifier of a molecular marker, a lysate type, a loading concentration, and a gel type
Claim 95 recites: displaying the determined modifications
Claim 97 recites: the system of claim 90, wherein the analyte is at least one of a protein, a hapten, a hormone, a nucleic acid, a peptide, a modified peptide, or a modified form of any of the foregoing analytes
Claim 102 recites: the system of claim 90, wherein the at least one processor performs operations further comprising: displaying whether the band was found, found with non-specific bands, not found, or there were no bands
The limitations for ‘acquiring experimental data’ merely serve to gather data that is used an input for the judicial exception. Therefore, these limitations are mere data gathering activities. As set forth in MPEP 2106.05(g), mere data gathering activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application. The limitations in claims 79, 87, 90 and 94 and the limitations in claims 97 and 85 limit the type of experimental data acquired, and analyte of interest, respectively, but do not integrate the judicial exception into a practical application because they just further limit data gathering activities but don’t change their position as data gathering activities.
The limitations for ‘displaying the determined modifications’, ‘displaying if the band was found’, ‘displaying a data table’ and displaying the ‘degree of shift’ are post-solution activity steps that merely serve to output data from the judicial exception. As set forth in MPEP 2106.05(g), output activity that is incidental to the primary process are insignificant extra-solution activities that do not have a practical application. The limitations that limit the type of data output fail to integrate the judicial exception into a practical application because they merely further limit the tangential output activities but do not change their position as output activities.
There are no limitations that indicate that the processor requires anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract idea in a generic computing environment, which as set forth in MPEP section 2106.05(f), does not render an abstract idea eligible. For example, see the court decisions of Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983 and 573 U.S. at 224, 110 USPQ2d at 1984.
Similarly, the limitations of claims 80 and 91, directed to using a deep or feed forward neural network to generate a score, are describing an algorithm at a high level of generality. Instructions to apply a generic neural network without reciting a particular structure or configuration that improves technology is akin to performing the abstract idea in a generic computing environment, which, as indicated above, do not render the abstract idea eligible (see MPEP 2106.05(f)).
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 79-80, 82, 84-91, 93-98 and 102-104 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, and conventional activity.
As set forth in MPEP section 2106.05(g), the courts have decided that limitations that merely add an insignificant extra-solution activity, do not amount to an inventive concept, particularly when the activities are well-understood and conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). As set forth in MPEP section 2106.05(d), the courts have recognized that limitations directed to data gathering and output that are claimed as insignificant extra-solution activity are routine, well understood and conventional.
As discussed above, there are no additional limitations to indicate that the claimed processor requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Similarly, generic neural network implementation does not provide an inventive concept. The application of neural network machine learning for image classification was routine, well understood and conventional before the effective filing date of the instant application as evidenced by Rwat et al. (Neural Computation, vol. 29, p. 2352 – 2449 (2017)). Rwat et al. discloses that convolution neural networks have been applied to visual tasks since the late 1980s (abstract) and that in 2017, 8 data sets of images were publicly available that were commonly used for evaluation and benchmarking of neural network-based classification studies (p. 2368, para. 4 – p. 2370, para. 1, Table I).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 79-80, 82, 84-91, 93-98 and 102-104 are not patent eligible.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
9. Claims 79, 80, 84 and 89, 90-91, 94-98, and 102-104 are rejected under 35 U.S.C. 102 (a)(1) and 35 U.S.C. 102 (a)(2) as being unpatentable over Ozcan et al. (WO2020242993A1, 11/14/2024 IDS) The italicized text corresponds to the instant claim limitations.
Regarding claim 79, Ozcan et al. discloses a machine learning-based system that reads immunoreaction spots of a vertical flow assay (VFA) or a lateral flow assay (LFA), wherein each spot has a reaction of a different condition, to determine optical configurations for the assay and infer the target analyte concentration. Ozcan et al. discloses generating immunoassay data by loading a sample and reagent mixture into a flow assay cartridge, allowing the mixture to react with the spots of a multiplexed sensing membrane (to allow the immunodetection of the analyte of interest) followed by imaging with a reader device and processing the image. Ozcan et al. further discloses normalizing the image to a universal blank background image (a blank sensing membrane with no spots). Ozcan et al. discloses analyzing the normalized image using a trained neural network to analyze the pattern of detected intensity across the spots (spatial locations and intensities) to determine the best combination of conditions for the assay and to determine the concentration/abundance of the target analyte. In this example, the degree of intensity of a spot normalized to a blank corresponds to the ‘degree of shift’ for a band in the immunoassay experiment (para. 0003, 0010-0013, 0040, Fig. 6; acquiring experimental data corresponding to an immunoassay experiment, wherein the experimental data is based on an analyte of interest; analyzing, by a machine learning process trained to determine a degree that a band of the analyte shifts in immunoassays, the experimental data and determining, based on the analyzing, the degree of shift for a band in the immunoassay experiment, wherein the band comprises the analyte of interest).
Pertaining to claim 80, Ozcan et al. discloses generating and outputting the results of the test using a deep neural network (para. 0039 and 0090; the method of claim 79, wherein the machine learning process comprises a neural network, wherein the neural network comprises a feedforward network or a deep neural network).
Pertaining to claim 84, Ozcan et al. discloses that the immunoassay is a flow assay (para. 0010-0013; wherein the immunoassay experiment is a bead-based immunoassay or a flow-based immunoassay).
With respect to claim 89, Ozcan et al. teaches that several spots, each representing a different assay condition, are analyzed by the machine learning process and each spot is evaluated for performance (based for example on linearity of the signal) and only a subset of the spots are selected as features in determining the diagnosis of the patient. Ozcan et al. further discloses modifying future experimental conditions based on the readout from the machine learning analysis in that future multiplexed sensing membranes are manufactured or fabricated with only those spots that were selected to be used as features based on the performance assessment.
Ozcan et al. further discloses that the screen or display of the mobile phone device illustrates an image of the multiplexed sensing membrane that was obtained with the camera of the mobile phone and the output can be displayed on a graphical user interface (GUI) that displays the results including an image of the membrane before and after processing, the quantified intensity values of the spots and antigen/antibody concentrations or other information. This is inclusive of displaying which spots were used in the diagnosis and which spots were omitted (due to suboptimal conditions. Ozcan et al. also discloses Fig. 8, which shows a display of omitted/suboptimal conditions (dark) and selected conditions (light). Ozcan et al. further discloses Fig. 11A, which shows that the optimal quantification performance (i.e. optimal and suboptimal subset of spots) is indicated by the solid red marker and a heatmap. Ozcan et al. further discloses that the spots (conditions) selected by the machine learning classifier as being of adequate performance can be contained as part of a test information that is included in the form of a QR code, bar code or serial number withing the flow assay cartridge (para. 0017; 0029-0030, 0048, 0051-0052, Fig. 8, Fig. 11; the method of claim 79, further comprising: determining whether the experimental data should be modified; displaying the determined modifications).
Pertaining to claim 90, Ozcan et al. discloses a system for performing a immunoreaction-based flow assay including one or more processors that are used in conjunction with driving circuitry to control operations of a reader device and to perform image processing of the images obtained from the camera, generating or outputting the results of the test, operating the trained deep neural network and generating output results. The system is configured to per (para. 0010; 0039; a system, comprising: at least one processor; and a memory coupled to the at least one processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to perform operations).
Regarding Claim 90, Ozcan et al. discloses a multi-spot immunoreaction-based flow assay to detect the presence of and/or quantify the amount or concentration of one or more analytes in a sample. Ozcan et al. further discloses that data are acquired by inserting a sample and reagent mixture into a flow assay cartridge, incubating and then imaging using a reading device. Ozcan et al. further discloses that analytes of interest can be particular proteins (antigens) that have identifiers (antigen names), for example, CRP antigen (para. 0010-0013, Fig. 6; acquiring experimental data, wherein the experimental data comprises an identifier of an analyte and an immunoassay image).
With respect to claim 90, Ozcan et al. discloses that the image obtained by the reader is subject to image processing to obtain normalized pixel intensity values of the plurality of immunoreaction spots. Ozcan et al. further discloses that normalized pixel intensity values may be obtained by segmentation to identify spot locations (i.e. marking features) (para. 0011-0012; analyzing the immunoassay image to mark features of the immunoassay image, wherein the features comprises at least one band).
Pertaining to claim 90, Ozcan et al. discloses that normalized pixel intensity values from the immunoassay image are input to one or more trained neural networks configured to generate one or more outputs that (i) quantify the amount or concentration of the one or more analytes in the sample, and/or (ii) indicate the presence of the one or more analytes in the sample, and/or (iii) determine a diagnostic decision or classification of the sample. In this assay, the change in intensity of the reaction spots on the flow assay corresponds to “the degree of shift of a band”. Ozcan et al. further discloses quantifying the level of a specific analyte corresponding to an identifier (i.e. CRP antigen) (para. 006, 0012; determining, by a machine learning process, a degree of shift for a band on the immunoassay image, wherein the band comprises an analyte of interest that corresponds to the identifier).
Pertaining to claim 90, Ozcan et al. discloses a screen display on a portable electronic device showing an obtained image of the multiplexed sensing membrane showing intensity of each signal using color or intensity signal. Ozcan et al. further disclose outputting the quantitative results of the machine learning analysis and that the image of the membrane can be shown with either raw data or after image processing as well as the quantified intensity values of the spots or locations (para. 0047-0048; displaying the degree of shift for the band on the immunoassay image).
Regarding claim 91, Ozcan et al. discloses that the method includes using a neural network trained to generate an output that quantifies the amount or concentration of one or more analytes in a sample (para. 0053; the system of claim 90, wherein the machine learning process comprises a neural network).
Regarding claim 94, Ozcan et al. disclose that each of the plurality of spatially multiplexed immunoreaction spots or locations may include one more of a protein, antigen, antibody, nucleic acid, aptamer, or enzyme. Ozcan et al. discloses that the spots can be disease-specific antigens and/or antibodies, which are biomarkers for a particular condition, or disease state (i.e. a molecular marker). Ozcan et al. further discloses that for CRP testing, the spots include CRP capture antibodies, CRP antigen, combinations of these and secondary CRP antibodies (i.e. an identifier of a molecular marker) (para. 0044; the system of claim 90, wherein the experimental data further comprises at least one identifier of a cell line, an identifier of a molecular marker, a lysate type, a loading concentration, and a gel type).
With respect to claim 95, Ozcan et al. teaches that several spots, each representing a different assay condition, are analyzed by the machine learning process and each spot is evaluated for performance (based for example on linearity of the signal) and only a subset of the spots are selected as features in determining the diagnosis of the patient. Ozcan et al. further discloses modifying future experimental conditions based on the readout from the machine learning analysis in that future multiplexed sensing membranes are manufactured or fabricated with only those spots that were selected to be used as features based on performance (para. 0029-0030, 0052, Fig. 8, Fig. 11 ; the system of claim 90, wherein the processor performs further operations comprising: determining whether the experimental data should be modified).
Regarding Claim 95, Ozcan et al. further discloses that the screen or display of the mobile phone device illustrates an image of the multiplexed sensing membrane that was obtained with the camera of the mobile phone and the output can be displayed on a graphical user interface that displays the results including an image of the membrane before and after processing, the quantified intensity values of the spots and antigen/antibody concentrations or other information. Thus, this is inclusive of displaying which spots were used in the diagnosis and which spots were omitted. Ozcan et al. discloses Fig. 8, which shows a display of omitted/suboptimal conditions (dark) and selected conditions (light). Ozcan et al. further discloses Fig. 11A, which shows that the optimal quantification performance (i.e. optimal and suboptimal subset of spots) is indicated by the solid red marker and a heatmap. Ozcan et al. further discloses that the spots (conditions) selected by the machine learning classifier as being of adequate performance can be contained as part of a test information that is included in the form of a QR code, bar code or serial number withing the flow assay cartridge (para. 0017, 0029, 0048, 0051, Fig. 8, Fig. 11 displaying the determined modifications).
Pertaining to claim 96, Ozcan et al. discloses the system includes a flow assay cartridge with the multiplexed sensing membrane and that the multiplexed sensing membrane has a plurality of immunoreaction or biological reaction spots. Ozcan et al. further discloses that after the assay is run, the sensing membrane is imaged with a reader device (para. 0010-0011; the system of claim 90, wherein the immunoassay image is an image of a bead-based immunoassay or a flow-based immunoassay).
With respect to claim 97, Ozcan et al. teaches that the one or more analytes analyzed in the system can include C-Reactive-Protein (CRP) (para. 0003; 0008; the system of claim 90, wherein the analyte is at least one of a protein, a hapten, a hormone, a nucleic acid, a peptide, a modified peptide, or a modified form of any of the foregoing analytes).
Regarding Claim 98, Ozcan et al. discloses that the multiplexed sensing membrane has a plurality of immunoreaction or biological reaction spots of varying conditions spatially arranged across the surface of the membrane and that the pixel intensity values are input to a neural network configured to generated an output that quantifies the amount or concentration of the one or more analytes in the sample. Ozcan et al. further discloses that the signal intensity of spots is normalized to a blank sensing membrane with no spots. Ozcan et al. further discloses Fig. 11 showing predicted concentrations from multiple spots on the immunoassay. In this assay, each spot is equivalent to an ‘immunoassay frame’, and the shift in signal in each spot from the blank condition to the signal from assay readout is the ‘shift of the band’ (para. 0010, 0040, 0053; the system of claim 90, wherein the features further comprise at least one of a frame of the immunoassay image and at least one lane of the immunoassay image, wherein the at least one lane is analyzed to determine the shift of the band in each lane).
Pertaining to claim 102, Ozcan et al. discloses that specificity is addressed by analyzing spots from control conditions including activating the cartridge with varying CRP concentrations, which were spiked into CRP-free serum. Including primary CRP antibody, the CRP antigen itself, a mix of the antibody and antigen at various concentrations and the CRP secondary antibody. Ozcan et al. further discloses that the capture antibody (Ab) condition shows false reporting of high analyte concentrations and inclusion of a monotonically responsive CRP antigen (Ag) spotting condition (showing non-specific binding) in the experiment to control for false reporting of high analyte conditions. Ozcan et al. further discloses Figure 9, displaying spot segmentation on the immunoassay image (i.e. indicating whether a spot was found or not) as well as local background region in a doughnut shape (showing local background that was subtracted in the normalization procedure) (para. 0036, 0040, 0092; Fig. 14, Fig. 9; the system of claim 90, wherein the at least one processor performs operations further comprising: displaying whether the band was found, found with non-specific bands, not found, or there were no bands).
Pertaining to claim 103, Ozcan et al. discloses that a certain subset of the total number of spots in the multiplexed sensing membrane 42 are used as the input to the trained neural network and that the trained neural networks are configured to generate an output that quantifies the amount or concentration of one or more analytes in the sample (para. 0051-0053; the system of claim 90, wherein the machine learning process has been trained with an immunoassay dataset to determine a degree that a band of an analyte shifts in an immunoassay experiment).
Regarding Claim 104, Ozcan et al. discloses that their machine learning driven condition selection approach could be used to select the best buffer condition by comparing varying buffer conditions (para. 0096; the system of claim 103, wherein the immunoassay dataset comprises at least one glycosylation, disulfide bonds, modified residue, 3-(N- morpholino)propanesulfonic acid (MOPS), ubiquitination, lipidation, 2-(N- morpholino)ethanesulfonic acid (MES), isoelectric point (pI), SUMOylation, Tris acetate, interchain or polymer cross links, gel type, and buffer type).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
10. Claims 79 and 85-87 are rejected under 35 U.S.C. 103 as being unpatentable over Wiesner et al. (Electrophoresis, 2021, Vol. 42, p. 206-218), in view of Yu et al. (Talanta, Vol. 71, 2007, p. 676-682) as evidenced by Compass (Compass for Simple Western User Guide, 2021, pl 1-358). The italicized text corresponds to the instant claim limitations.
Regarding claim 79, Wiesner et al. discloses generating data corresponding to an immunoassay using capillary electrophoresis (CE) called Simple Western to detect 6 proteins of interest. Wiesner et al. discloses that In Simple Western, proteins are separated by CE-SDS followed by a total protein detection assay inside the capillary, wherein the protein is covalently bound to the inside of the capillary after separation, followed by a biotin streptavidin-horseradish peroxidase (HRP) detection and a fluorescence readout. Wiesner et al. further disclose that the proteins of interest detected in the experiment were non-glycosylated proteins (lysozyme, myoglobin, carbonic anhydrase, BSA, phosphorylase phosphorylase B, and β-galactosidase), two glycosylated proteins (ovalbumin and α-2-macroglobulin) and an antibody (Matuzumab). As evidenced by Compass, Simple Western data can be represented as peaks in electropherograms or as bands in virtual lane views (Compass, p. 5, para. 1; ; Wiesner et al. p. 207, col. 2, para. 2-4, p. 209, col. 1, para. 3, p. 210, col. 2, para. 2; Table S4 and S5; acquiring experimental data corresponding to an immunoassay experiment, wherein the experimental data is based on an analyte of interest).
Regarding claims 79 and 86, Wiesner et al. is silent to analyzing, by a machine learning process trained to determine a degree that a band of the analyte shifts in immunoassays, the experimental data and determining, based on the analyzing, the degree of shift for a band in the immunoassay experiment, wherein the band comprises the analyte of interest (claim 79); the method of claim 79, wherein the machine learning process has been trained using an immunoassay dataset comprising at least one of glycosylation, disulfide bonds, modified residue, 3-(N-morpholino) propanesulfonic acid (MOPS), ubiquitination, lipidation, 2-(N-morpholino) ethanesulfonic acid (MES), isoelectric point (pI), SUMOylation, Tris acetate, interchain or polymer cross links, gel type, buffer type, or degree of shift). (claim 86). However, these limitations were known in the art at the time of the effective filing date of the invention as taught by Yu et al.
Pertaining to claim 79, Yu et al. discloses developing 3 machine learning models for the prediction of the mobility of peptides in capillary zone electrophoresis (CZE). Yi et al. discloses generating a dataset of CZE mobilities of 102 peptides with varying numbers of amino acids and sequences, dividing the set into training, test and validation sets consisting of 70, 20 and 12 peptides, respectively. Yi et al. further discloses that the training set was used to develop the calibration model, adjust and optimize the modeling parameters and the test set was used to evaluate the prediction accuracy of the developed models, while the external validation set was used to assess the generalization performance of the models (p. 677, col. 1, para. 2-3, Table 1; analyzing, by a machine learning process trained to determine a degree that a band of the analyte shifts in immunoassays, the experimental data).
Regarding claim 79, Yu et al. discloses using the three models to predict the degree of shift of each peptide in CZE in the training, test and validation sets (p. 680, col. 1, para. 3 – p. 682, col. 1, para. 1; Table 1; determining, based on the analyzing, the degree of shift for a band in the immunoassay experiment, wherein the band comprises the analyte of interest).
Pertaining to claim 85, Wiesner et al. disclose that the proteins detected in the experiment were non-glycosylated proteins (lysozyme, myoglobin, carbonic anhydrase, BSA, phosphorylase phosphorylase B, and β-galactosidase), two glycosylated proteins (ovalbumin and α-2-macroglobulin) and an antibody (Matuzumab) (p. 207, col. 2, para. 4; the method of claim 79, wherein the analyte is at least one of a protein, a hapten, a hormone, a nucleic acid, a peptide, a modified peptide, or a modified form of any of the foregoing analytes).
Regarding Claim 86, Yu et al. discloses training the machine learning classifier on the mobility (degree of shift) of various known peptides to develop quantitative structure–mobility relationship (QSMR) models (p. 677, col. 1, para. 2; the method of claim 79, wherein the machine learning process has been trained using an immunoassay dataset comprising at least one of glycosylation, disulfide bonds, modified residue, 3-(N-morpholino) propanesulfonic acid (MOPS), ubiquitination, lipidation, 2-(N-morpholino) ethanesulfonic acid (MES), isoelectric point (pI), SUMOylation, Tris acetate, interchain or polymer cross links, gel type, buffer type, or degree of shift).
With respect to claim 87, Wiesner et al. discloses that two of the proteins of interest (ovalbumin and α-2-macroglobulin) were glycosylated and that the heavy chain of the antibody of interest were glycosylated. Wiesner et al. further discloses that the glycosylation affected mobility of the heavy chain (p. 207, col. 2, para. 4; p. 213, col. 2, para. 3; the method of claim 79 or claim 86, wherein the experimental data comprises at least one of glycosylation, disulfide bonds, modified residue, 3-(N- morpholino) propanesulfonic acid (MOPS), ubiquitination, lipidation, 2-(N- morpholino) ethanesulfonic acid (MES), isoelectric point (pI), SUMOylation, Tris acetate, or interchain or polymer cross links for the analyte of interest and gel type and buffer type for the immunoassay experiment).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Yu et al. taught that machine learning techniques, were effective and efficient for the development of the accurate and reliable quantitative structure–mobility relationship (QSMR) models, which were helpful predicting peptide separation. Yu et al. further disclose that machine learning approach successfully modeled non-linear characteristics withing the data (p. 677, col. 1, para. 2; p. 682, col. 1, para. 2). Therefore, one of ordinary skill in the art would have been motivated to utilize the machine learning based QSMR modelling approach taught by Yu et al. in the immunoassay experiments to detect protein mobility of Wiesner et al., in order to predict mobility patterns of peptides in capillary electrophoresis-based immunoassay experiments. Furthermore, one of ordinary skill in the art would predict that the machine-learning modeling taught by Yu. et al. could be readily added to the immunoassay method of detecting protein mobility taught by Wiesner et al. with a reasonable expectation of success because they both pertain to analysis of physical representations of mobility shift data of proteins (or peptides) to generate numerical values for understanding the relationship between physical properties of the proteins and mobility shift in the experiment. The invention is therefore prima facie obvious.
11. Claim 88 is rejected under 35 U.S.C. 103 as being unpatentable over Wiesner et al. (Electrophoresis, 2021, Vol. 42, p. 206-218), in view of Yu et al. (Talanta, vol. 71, 2007, p. 676-682) as applied to claims 79 and 85-87 above, and further in view of Schultz et al. (Anal. Chem. 1993, Vol. 65, p. 3161-3165). The italicized text corresponds to the instant claim limitations.
The limitations of claims 79 and 85-87 have been taught by Wiesner et al. and Yu et al. above.
Regarding Claim 88, Wiesner et al. and Yu et al. are silent to the method of claim 79, wherein the degree of shift of the band is caused by a shift in the molecular weight corresponding to the analyte of interest. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Schultz et al.
Pertaining to Claim 88, Schultz et al. teaches an immunoassay using capillary electrophoresis with fluorescence detection for quantifying analytes of interest in solution. Schultz et al. teaches an assay where a fluorescently labeled antigen and the sample antigen compete to form a complex with an antibody. Schultz et al. further discloses that the assay is based on separation by capillary electrophoresis to yield two zones, one due to the formation of a complex with the fluorescently labeled antigen and the antibody and other due to free fluorescently labeled antigen. Thus the assay is based on detecting a shift in the migration of the fluorescently labeled antigen due to a molecular weight difference (i.e. due to forming a complex with the antibody) (p. 3161, col. 2, para. 5 – p. 3162. Col. 1, para. 1; Fig. 1; the method of claim 79, wherein the degree of shift of the band is caused by a shift in the molecular weight corresponding to the analyte of interest.
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Schultz et al. taught that the major advantage of this capillary based immunodetection approach compared to other approaches are low mass detection limits, speed of analysis and potential for automation (p. 3162, col. 1, para. 2). Therefore, one of ordinary skill in the art would have been motivated to utilize the approach of detecting mobility shift in molecular weight taught by Schultz et al. in the immunoassay experiments to detect protein mobility of Wiesner et al. and Yu et al. in order to increase the speed of analysis. Furthermore, one of ordinary skill in the art would predict that the mobility shift approach of immunodetection by Schultz et al. could be readily added to the immunoassay method of detecting protein mobility taught by Wiesner et al. and Yu et al. with a reasonable expectation of success because they both pertain to analysis of mobility shift data of proteins (or peptides) using capillary electrophoresis. The invention is therefore prima facie obvious.
12. Claim 82 is rejected under 35 U.S.C. 103 as being unpatentable over Wiesner et al. (Electrophoresis, 2021, Vol. 42, p. 206-218), in view of Yu et al. (Talanta, vol. 71, 2007, p. 676-682) as applied to claims 79 and 85-87 above, and further in view of Taylor et al. (Forensic Science International; Genetics, Vol. 25, 2016, p. 10-18). The italicized text corresponds to the instant claim limitations.
The limitations of claims 79 and 85-87 have been taught by Wiesner et al. and Yu et al. above.
Regarding claim 82, Wiener et al. and Yu et al. are silent to the method of claim 79, wherein: the analyzing comprises analyzing an immunoassay image of the immunoassay experiment; wherein the immunoassay image comprises a stained gel or a capillary gel where the analyte is loaded. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Taylor et al.
Regarding claim 82, Taylor et al. teaches training a feed-forward artificial neural network to classify electrophoretic DNA data as allelic or artifactual, wherein the electropherogram profile data are used by the neural network to classify the samples. Taylor et al. further teaches training the classifier to recognize and classify 5 aspects in the data such as artifacts including Baseline, Allele, Stutter, Pull-up and Forward Stutter by examining the 6-FAM dye lane in the electropherogram. This work demonstrates training a neural network to analyze electrophoretic image data to classify analytes of interest. (Fig. 3; p. 14, col. 2, para. 4 – p. 15, col. 1, para. 2; abstract; Fig. 5; the method of claim 79, wherein: the analyzing comprises analyzing an immunoassay image of the immunoassay experiment).
Regarding claim 82, as evidenced by Compass, annotation of gel and blot images with analytical information was well known at the time of the effective filing date. Compass discloses that Simple Western software associates electrophoretic peak information onto a virtual lane/gel image and display the data as bands and as text when a mouse is hovered over a band. Compass further discloses that the software can mark bands with boxes. Therefore, once a predicted band shift has been calculated by the machine learning analysis, displaying that result on the immunoassay image is merely presenting the analysis result to the user in the context of the image being analyzed either as virtual bands or boxes (image and text on p. 137; image and text on p. 173; the determining comprises marking the degree of shift for the band on the immunoassay image).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention if some motivation in the prior art would have led that person to combine the prior art teachings to arrive at the claimed invention. Taylor et al. taught that applying neural network processes to assess electrophoretic data reduces the subjective and laborious task of manually classifying data as allelic or artefactual (p. 18, col. 2, para. 1-2). Therefore, one of ordinary skill in the art would have been motivated to utilize the approach of detecting mobility shift in molecular weight by Wiesner et al. and Yu et al. in the immunoassay experiments to detect protein mobility of Wiesner et al. and Yu et al. in order to increase the speed of analysis. Furthermore, one of ordinary skill in the art would predict that the mobility shift approach of immunodetection by Taylor et al. could be readily added to the immunoassay method of detecting protein mobility taught by Wiesner et al. and Yu et al. with a reasonable expectation of success because they both pertain to analysis of mobility shift data of proteins (or peptides) using capillary electrophoresis. The invention is therefore prima facie obvious.
Pertaining to claim 82, Taylor et al. teaches that the image data analyzed by the neural network process is an electropherogram, but because Simple Western software used by Wiesner et al. allows the user to view and output the mobility shift data as either an electropherogram or a gel image (as evidenced by Compass), it would be obvious to try using the virtual lane/gel image instead of the electropherogram image in training the neural network process (images on p. 166; wherein the immunoassay image comprises a stained gel or a capillary gel where the analyte is loaded).
One of ordinary skill in the art would predict that the Simple Western gel image taught by Wiesner et al. (as evidenced by Compass) could be readily added to the neural network analysis of immunoassay data taught by Wiesner et al., Yu et al. and Taylor et al. with a reasonable expectation of success because they are simply two different visual representations of the same underlying data. The invention is therefore prima facie obvious.
13. Claim 93 is rejected under 35 U.S.C. 103 as being unpatentable over Ozcan et al. (WO2020242993A1, 11/14/2024 IDS) as applied to claims 79, 80, 84, 89-91, 94-98 and 102-104 above, further in view of Wilkins et al. (Chapter Protein identification and analysis tools in the ExPASy server in Methods in Molecular Biology, Humana press, 1999, Vol. 112, p. 531-552). The italicized text corresponds to the instant claim limitations.
Ozcan et al. teaches the limitations of claims 79, 80, 84, 89-91, 94-98 and 102-104 above.
Pertaining to claim 93, Ozcan et al. is silent to the system of claim 90, wherein the processor performs further operations comprising: calculating a molecular weight data for the band. However, this limitation was known in the art at the time of the effective filing date of the invention as taught by Jang et al.
Regarding Claim 93, Wilkins et al. discloses protein molecular weight is calculated by the addition of average isotopic masses of amino acids in the protein and the average isotopic mass of one water molecule (p. 549, note 2; displaying a data table, wherein the data table comprises the calculates molecular weight).
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the invention because the molecular weight calculation taught by Wilkins et al. and the lateral flow immunoassay taught by Ozcan et al. were both known in the art before the effective filing date, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. One of ordinary skill in the art could have combined the protein molecular weight calculation taught by Wilkins et al. with the flow-based immunoassay taught by Ozcan et al. because each element merely performs the same function as it does separately (see MPEP 2143, Rationale A). One of ordinary skill in the art at the effective filing date would have recognized that the results of the combination were predictable because the protein molecular weight calculation taught by Wilkins et al. is well known and widely used for determining molecular weights of proteins in the process of protein analyses like immunoassay development (for example, the generation and validation of protein standards and controls in the assay). The invention is therefore prima facie obvious.
References considered but not cited
14. Wilkins et al., which was cited as prior art regarding the limitation of claim 93 directed to molecular weight calculation, also teaches that protein glycosylation can affect the migration of a protein on a 2D gel (both pI and molecular weight dimensions). Wilkins et al. further discloses predicting the degree of shift of a protein on a 2D PAGE gel as a result of a protein modification (such as glycosylation) and marking it on the image of the gel (pertaining to claims 88 and 90). Specifically, Wilkins et al. discloses a function in software called SWISS-2DPAGE uses the Compute pI/Mw algorithm to highlight the region on a 2-D gel to where an unmodified protein should run, and suggests a region where the modified protein might be found if it has modifications documented in the Swiss-PROT database (p. 549-550, note 2).
Conclusion
15. No claims are allowed.
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/J.J.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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