DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-8 are pending and examined herein.
Priority
The present application, filed 09/25/2023, is a 371 of PCT/JP2022/009369, filed 03/04/2022, which claims foreign priority of JP2021-053011, filed 03/26/2021.
Information Disclosure Statement
The Information Disclosure Statement(s) filed 09/27/2023, 01/28/2025, and 07/14/2025 are acknowledged and have been considered.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea/mathematical concept. The claims recite determining and applying a mathematical relationship for a minimum average particle storage number based on variables such as particle concentration, liquid volume, and imaging pixel parameters, which constitutes a mathematical formula. The additional steps of preparing a test liquid, trapping a biological substance with particles, storing particles in wells, and detecting color development using an image pickup element merely involve conventional laboratory data-gathering and observation techniques performed with generic components, and thus do not integrate the judicial exception into a practical application or amount to significantly more than the abstract idea itself.
This rejection is made in accordance with Patent Subject Matter Eligibility as set forth in MPEP §2106. Analysis of subject-matter eligibility under 35 U.S.C. §101 requires consideration under these steps as followed:
I. Step 1: Are the claims to a statutory category (process, machine, manufacture, or composition
of matter)?
II. Step 2A (Prong 1): Are the claims directed to a judicial exception (law of nature, natural
phenomenon, or an abstract idea)?
III. Step 2A (Prong 2): Do the claims recite additional elements that integrate the judicial exception
into a practical application?
IV. Step 2B: Do the claims recite additional elements that amount to significantly more than the
judicial exception (inventive concept)?
Step 1 – Statutory Category (Refer to MPEP §2106.03): Claim 1-6 are drawn to a process, which falls within a statutory category under 35 U.S.C. §101.
Step 2A, Prong One – Recitation of a Judicial Exception (Refer to MPEP §2106.04): According to MPEP §2106.04(a)(2), the mathematical concepts grouping have been held by the courts to constitute as abstract ideas. The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.
Regarding claim 1, it recites the following: determining a minimum average storage number of particles in wells of a well array using a mathematical formula in which the minimum average particle storage number (Nmin) is calculated based on variables including particle concentration in a test liquid, liquid volume, and pixel parameters of an imaging element, and requiring that the calculated value satisfy a specified numerical condition. Such recitation constitutes a mathematical formula and calculation, and therefore claim 1 recites a judicial exception, in the form of a mathematical concept. Also, since the judicial exception identified in claim 1 is incorporated into each dependent claim, claims 2-6 recite a judicial exception namely - the mathematical concepts grouping. Even where dependent claims add additional limitations (e.g., particle size ranges, particle concentration ranges, well volume ranges, visual field fixation conditions, or color reaction features), such limitations merely represents routine experimental parameters and data-gathering condition and do not remove the mathematical relationship from the scope of the claims. Accordingly, claims 2-6 also recite a judicial exception in the form of a mathematical concept.
Step 2A, Prong Two – Integration into a Practical Application (Refer to MPEP §2106.04 (d)): Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include: an improvement in the functioning of a computer, or an improvement to other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; effecting a transformation or reduction of a particular article to a different state or thing; and applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Conversely, the courts have also identified limitations that did not integrate a judicial exception into a practical application include: merely reciting the words "apply it" (or an equivalent) with the judicial
exception, or merely including instructions to implement an abstract idea on a computer, or merely using
a computer as a tool to perform an abstract idea; adding insignificant extra-solution activity to the judicial exception; and generally linking the use of a judicial exception to a particular technological environment or field of use.
Regarding claim 1, the additional elements including preparing a test liquid containing particles, allowing the particles to trap a biological substance, depositing and storing the particles in wells of a well array, and detecting color development using an image pickup element represent conventional sample preparation, assay handling, and observational data-collection steps performed with generic laboratory components. These elements do not reflect any improvement in the functioning of imaging hardware, well array structures, particle interaction mechanisms, or biological detection technology, but instead use such components in their ordinary and expected manner as tools for gathering information. Further, the recited use of an image pickup element having a specified pixel capability merely defines a performance parameter and does not impose a meaningful technological limitation that integrates the mathematical relationship into a particular machine. The steps also do not effect a transformation of a particular article to a different state or thing beyond routine chemical or biological reactions inherent in conventional detection assays. Rather, the claim as a whole applies the mathematical calculation to optimize or evaluate particle storage conditions within a general laboratory testing environment, which amounts to insignificant extra-solution activity and a mere field-of-use limitation. Accordingly, the additional elements do not integrate the judicial exception into a practical application, and claim 1 is directed to the abstract mathematical concept.
Moreover, the additional limitations in claims 2–10 do not integrate the identified abstract idea—
mathematical concepts—into a practical application. These claims depend from claim 1 and merely further specify routine experimental conditions and performance parameters, such as particle size ranges, particle concentration ranges, well volume ranges, fixation of a visual field, and particular color development reactions. Such limitations represent conventional assay design choices and data-gathering features that use generic laboratory components in their ordinary manner. They do not reflect an improvement to imaging technology, particle detection technology, or any other technical field, nor do they impose a meaningful limit on the use of the mathematical relationship beyond generally linking it to a biological testing environment. Accordingly, the additional elements of claims 2-6, whether considered individually or in combination, do not integrate the judicial exception into a practical application.
Step 2B, Inventive Concept (Refer to MPEP §2106.05): According to MPEP §2106.05(d), one consideration
when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional actional activities previously known to
the industry. If, however, the additional element (or combination of elements) is no more than well understood, routine, conventional activities previously known to the industry, which is recited at a high
level of generality, then this consideration does not favor eligibility. For example, the additional elements in claim 1 consist of: preparing a sample containing particles capable of binding a biological substance, distributing or storing the particles in an array of discrete locations, generating a detectable optical signal, and capturing image data for analysis. Such activities were previously practiced in the field as demonstrated by Gite et al. (A Rapid, Accurate, Single Molecule Counting Method Detects Clostridium Difficile Toxin B in Stool Samples. Scientific Reports. Vol. 8, No. 1, May 2018 – IDS dated 01/28/2025). For example, Gite et al. teaches preparation of particle-containing assay mixtures in which “samples are first mixed with a diluent and target-specific immunoreagents, which consists of fluorescent and magnetic particles coated with complementary antibodies specific for the target (C. difficile toxin B in this work). The assay mixture is then added to a clear-bottomed microtiter well, the bottom of which has been coated with a dried dye-cushion reagent” (Results, paragraph 2, page 2). Gite et al. further discloses that “when a stool sample containing toxin B is combined with these reagents, the toxin B molecules will bind to and tether the magnetic and fluorescent particles” (Results, paragraph 3, page 2).
Additionally, Gite et al. describes generation of an optical detection signal from labeled particles, stating that “illuminating the fluorescent nanoparticle labels causes them to emit photons which are collected using a 1:1 f/4 relay lens. The light emitted by a particle impinges on a small cluster of pixels on the CMOS chip of a digital camera forming white spots in the resulting image” (Results, paragraph 1, page 2). Lastly, Gite et al. teaches capturing and processing image data from the arrayed wells, stating that “a series of fluorescent image frames are captured with a camera using a 3.1MP Sony IMX265 monochrome sensor with 12-bit per pixel quantization. The final image for each well is then formed by summing multiple frames” (Materials and Methods, paragraph 3, page 6). These disclosures demonstrate that the additional assay preparation, particle-binding detection, optical signal generation, and image acquisition steps recited in claim 1 correspond to well-understood, routine, and conventional laboratory detection practices previously known in the art. Accordingly, the ordered combination of additional elements does not amount to significantly more than the judicial exception.
Additionally, claims 2–6 do not recite additional elements that amount to significantly more than the abstract idea identified in claim 1. The additional limitations in claims 2–6 merely further define routine experimental parameters and assay configuration features, such as specifying particle size ranges, particle concentration ranges, well volume ranges, visual field fixation conditions during observation, and particular color-producing reaction features associated with biological detection. These limitations represent conventional optimization variables and data-gathering conditions that were well-understood and routinely selected by those of ordinary skill in the biological assay and imaging fields to achieve desired detection sensitivity or throughput. Such features do not provide a technological improvement to the functioning of the well array, imaging device, or particle-based detection methodology itself, nor do they impose a meaningful limitation on the use of the mathematical relationship beyond refining the general laboratory environment in which it is applied. When considered individually and as an ordered combination with the elements of claim 1, the additional limitations therefore amount to no more than well-understood, routine, and conventional activity previously known in the art. Therefore, claims 2–6 do not recite additional elements that amount to significantly more than the judicial exception.
Accordingly, claims 1–6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, namely a mathematical concept, and does not include additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. The recited additional steps represent well-understood, routine, and conventional laboratory assay preparation, particle handling, and optical imaging activities performed using generic components.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Duffy et al. (US 9482662 B2) in view of Gite et al. (A Rapid, Accurate, Single Molecule Counting Method Detects Clostridium Difficile Toxin B in Stool Samples. Scientific Reports. Vol. 8, No. 1, May 2018 – IDS dated 01/28/2025), Kim et al. (Large-Scale Femtoliter Droplet Array for Digital Counting of Single Biomolecules. Lab on a Chip. Vol. 12, No. 23, December 2012 – IDS dated 09/27/2023), Figueroa et al. (Large-Scale Investigation of the Olfactory Receptor Space Using a Microfluidic Microwell Array. Lab on a Chip. Vo1. 10, No. 9, January 2010), and Ahrberg et al. (Poisson Statistics-Mediated Particle/Cell Counting in Microwell Arrays. Scientific Reports. Vol. 8, No. 1, February 2018).
Regarding claim 1, Duffy et al. teaches a biological substance detection workflow using particles and a microwell array system. In particular, Duffy et al. states that “an exemplary embodiment of an inventive assay method is illustrated in FIG. 1. A plurality of capture objects 2, are provided (step (A)). In this particular example, the plurality of capture objects comprises a plurality of beads. The beads are exposed to a fluid sample containing a plurality of analyte molecules 3 (e.g., beads 2 are incubated with analyte molecules 3). At least some of the analyte molecules are immobilized with respect to a bead. In this example, the analyte molecules are provided in a manner (e.g., at a concentration) such that a statistically significant fraction of the beads associate with a single analyte molecule and a statistically significant fraction of the beads do not associate with any analyte molecules. For example, as shown in step (B), analyte molecule 4 is immobilized with respect to bead 5, thereby forming complex 6, whereas some beads 7 are not associated with any analyte molecules” (page 37, column 8, lines 47-62). Furthermore, “as shown in step (C), the plurality of locations is illustrated as substrate 8 comprising a plurality of wells/reaction vessels 9. In this example, each reaction vessel comprises either zero or one beads” (page 38, column 9, lines 1-5). In addition, Duffy et al. further teaches optical detection, stating that “at least some of the reaction vessels may then be addressed (e.g., optically or via other detection means) to determine the number of locations containing an analyte molecule. For example, as shown in step (D), the plurality of reaction vessels are interrogated optically using light source 15, wherein each reaction vessel is exposed to electromagnetic radiation (represented by arrows 10) from light source 15. The light emitted (represented by arrows 11) from each reaction vessel is determined (and/or recorded) by detector 15 (in this example, housed in the same system as light source 15)” (page 38, column 9, lines 5-15). Duffy et al. further teaches statistical loading regime, stating that “the method makes use of arrays of femtoliter-sized reaction chambers (FIG. 23) that can isolate and detect single enzyme molecules. In the first step, a sandwich antibody complex is formed on microscopic beads, and the bound complexes are labeled with an enzyme reporter molecule, as in a conventional bead-based ELISA. When assaying samples containing extremely low concentrations of protein, the ratio of protein molecules (and the resulting enzyme label complex) to beads is small (typically less than 1:1) and, as such, the percentage of beads that contain a labeled immunocomplex follows a Poisson distribution, leading to single immunocomplexes on individual beads” (page 68, column 69, lines 43-54). Moreover, Duffy et al. discloses explicit numerical loading reasoning, stating that “for example, if 50 aM of a protein in 0.1 mL (3000 molecules) was captured on 200,000 beads, then 1.5% of the beads would have one protein molecule and 98.5% would have zero protein molecules (FIG. 23B)” (page 68, column 69, lines 55-58).
Lastly, Duffy et al. teaches mathematical/statistical detection thresholds, stating that “a statistically significant fraction of capture objects that contain at least one analyte molecule (or no analyte molecules) will typically be able to be reproducibly detected and quantified using a particular system of detection and will typically be above the background noise (e.g., non-specific binding) that is determined when carrying out the assay with a sample that does not contain any analyte molecules, divided by the total number of objects (or locations) addressed. A “statistically significant fraction” as used herein for the present embodiments, may be estimated according to the Equation 1:
n
≥
3
n
,
wherein n is the number of determined events for a selected category of events. That is, a statistically significant fraction occurs when the number of events is greater than three times square root of the number of events” (page 38, column 9, lines 25-41). Additionally, Duffy et al. teaches concentration-volume calculation relationships, stating that “the total number of analyte molecules/binding ligands/capture objects/etc. in a solution may be determined using calculations with knowledge of the concentration of the analyte molecules/binding ligands/capture objects/etc. in solution. For example, the total number of binding ligands in a solution may be determined according to Equation 2:
#
o
f
b
i
n
d
i
n
g
l
i
g
a
n
d
s
=
N
A
×
[
b
i
n
d
i
n
g
l
i
g
a
n
d
]
×
v
o
l
u
m
e
,
wherein NA is Avogadro's number (6.022x1023 mol-), binding ligand is the concentration of the binding ligand in solution in moles per liter, and volume is the total volume of solution in liters employed. Similar calculations may be carried out for other components (e.g., analyte molecules (e.g., in a calibration sample), capture objects, etc.)” (page 41, column 15, lines 30-43).
Although Duffy et al. teaches the following: a plurality of wells, wells formed on a substrate, particles trapping biological substance, color/fluorescence development detection, statistical particle occupancy modeling, concentration-dependent loading, volume-dependent quantitative assay design, mathematical calculation of particle analyte numbers, and statistical detection threshold framework – Duffy et al. does not expressly disclose the following: specific particle concentration (at least 5×10⁷ particles/mL), specific minimum average particle storage formula, image pickup element having at least 300,000 pixels, and pixel-group imaging size (at least 9 pixels).
On the other hand, Gite et al. teaches the biological detection using particle-containing wells. Specifically, Gite et al. states that “we describe a new rapid and accurate immunoassay-based technology capable of counting single target molecules using digital imaging without magnification. Using the technology, we developed a rapid test for Clostridium difficile toxin B, which is responsible for the pathology underlying potentially fatal C. difficile infections (CDI)” (Abstract, page 1). Then, “to prepare the assay mixture, stool was diluted to 8% with a mix of stool diluent, 7e8 particles/mL of antibody conjugated magnetic particles, 1.1e7 particles/mL of antibody conjugated fluorescent particles, and the indicated amount of toxin B” (Materials and Methods, paragraph 6, page 6). Furthermore, Gite et al. discloses that “100 µL of the assay mixture was pipetted in to each dried dye cushion-containing well” (Materials and Methods, paragraph 6, page 6). Lastly, Gite et al. further teaches megapixel imaging, stating that “a series of fluorescent image frames are captured with a camera using a 3.1MP Sony IMX265 monochrome sensor with 12-bit per pixel quantization. The final image for each well is then formed by summing multiple frames. For the C. difficile toxin B tests, we used 470/40 nm excitation and 515/30 nm emission filters and captured 2 frames at a 20 msec exposure” (Materials and Methods, paragraph 3, page 6).
Kim et al. teaches statistical droplet imaging analysis. Specifically, Kim et al. discloses that “we present a novel device employing one million femtoliter droplets immobilized on a substrate for the quantitative detection of extremely low concentrations of biomolecules in a sample. Surface modified polystyrene beads carrying either zero or a single biomolecule-reporter enzyme complex are efficiently isolated into femtoliter droplets formed on hydrophilic-in-hydrophobic surfaces” (Abstract, page 4986). Next, for image acquisition and data analysis, Kim et al. states that “for obtaining bright-field and fluorescence images of droplet array, the device was mounted on the motorized x–y translational stage located on a focus drift compensating microscope (IX81-ZDC2, Olympus, Japan) for continuous autofocusing. The images were acquired with a Scientific CMOS camera. The stage and the camera were controlled by commercial software. Bright-field and fluorescence images of one droplet array block were acquired sequentially. Then, the stage was moved to acquire images of the next block in an array. With this setup, bright-field and fluorescence images for one million droplets in an array were acquired in 8 min. From the obtained images, the fraction of bright droplets was determined by the following equation:
N
b
D
N
t
B
×
100
%
,
where NbD and NtB are the number of bright droplets and trapped beads, respectively” (Experimental, paragraph 6, page 4988). Lastly, Kim et al. teaches multi-particle trapping statistics, stating that “another important parameter for efficient bead trapping was the number of introduced beads. Different numbers of beads were introduced into the device and trapped after settlement (5 or 10 min). The number of trapped beads was proportional to the number of introduced beads over a certain range. However, if very large numbers of beads were introduced, 2 or 3 beads became trapped within a single droplet. For instance, 34% of droplets contained 2 or 3 beads when 8 6 106 beads were introduced and allowed to settle for 10 min. These results illustrate that beads can be efficiently trapped by adjusting the number of introduced beads and controlling the settlement time” (Results and discussion, paragraph 2, page 4989).
Figueroa et al. teaches pixel-group imaging, stating that “images were acquired with a Hamamatsu Orca HR, 10 mega-pixel, cooled-CCD camera at 4 magnification, mounted onto a Nikon Eclipse inverted microscope. Images were captured in time-lapse sequence and processed with MetaMorph software (molecular imaging)” (Materials and methods, paragraph 4, page 1126). Figueroa et al. further teaches that “images were taken every 4 s (200 ms exposures) using a 4 objective, yielding approximately 9 pixels per cell ( 25 pixels per well)” (Results, paragraph 3, page 1121).
Ahrberg et al. teaches occupancy statistics, stating that “we present a fast, simple method for determining the number density of cells or microparticles using a microwell array. We analyze the light transmission of the microwells and categorize the microwells into two groups. As particles/cells contained in a microwell locally reduce the light transmission, these wells displayed a lower average transmission compared to unoccupied microwells. The number density of particles/cells can be calculated by Poisson statistics from the ratio of occupied to unoccupied microwells” (Abstract, page 1). Ahrberg et al. further discloses that “particles and cells are distributed into wells according to a Poisson distribution:
P
λ
k
=
λ
k
k
!
e
-
λ
,
where P is the probability of having k particles or less inside of a well. λ is the average number of particles or cells per well” (Supplemental Materials, page 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the microwell digital biological detection system of Duffy et al. by adopting the high-particle-concentration assay preparation and megapixel imaging conditions taught by Gite et al., further incorporating the quantitative droplet-fraction statistical analysis taught by Kim et al., further optimizing imaging resolution at the pixel-group level as taught by Figueroa et al., and further applying the average particle-per-well statistical modeling framework taught by Ahrberg et al., in order to improve detection sensitivity, quantitative reliability, and statistical robustness of single-particle digital biological detection systems. Duffy et al. establishes the basic assay architecture of isolating particle-bound analyte complexes within spatially separated microwell reaction chambers and determining analyte concentration by digitally interrogating the number of signal-positive wells relative to the total number of wells addressed. Duffy et al. further explains that bead loading and analyte occupancy follow predictable Poisson statistical behavior that depends on analyte concentration, assay volume, and bead number. Thus, Duffy et al. recognizes that assay performance is governed by controllable statistical loading parameters.
Gite et al. demonstrates practical implementation of similar digital microwell detection systems using high particle concentrations, defined assay volumes, and megapixel imaging hardware. Incorporating such concentration and imaging parameters into the Duffy et al. system would have been an obvious design choice motivated by the known need to increase signal-to-noise ratio and improve statistical confidence in digital counting assays. Adjusting particle concentration and imaging resolution are routine engineering strategies used to enhance detection sensitivity and reduce sampling variability. Kim et al. teaches that the fraction of signal-positive droplets or wells can be determined using mathematical relationships derived from imaging data, thereby confirming that practitioners routinely used quantitative equations linking particle occupancy and detected signal counts to determine analyte concentration. Integrating such quantitative image-analysis relationships into the Duffy et al. assay framework would have represented a predictable refinement consistent with ordinary optimization of digital counting techniques. Figueroa et al. further teaches that reliable interrogation of microwell arrays requires capturing multiple pixels per well or cell region, and that imaging magnification and detector resolution are selected to achieve sufficient pixel sampling density. Thus, adopting multi-pixel imaging resolution per well would have been an obvious step to improve spatial discrimination and detection accuracy in the Duffy et al. system. Ahrberg et al. reinforces that digital particle-in-well assays are commonly analyzed using statistical occupancy parameters such as the average number of particles per well (λ), which governs the probability distribution of loading outcomes. This confirms that assay designers recognized the need to quantitatively relate particle concentration, assay volume, and the number of available wells to achieve reliable detection thresholds. Expressing a minimum average stored particle number using such parameters therefore represents an explicit mathematical formulation of relationships that were already understood and routinely optimized in the art.
Taken together, these references show that all variables recited in the claimed formula—particle concentration, assay liquid volume, imaging interrogation resolution, and statistical occupancy thresholds—were recognized design variables whose interaction would have been routinely modeled and optimized. The claimed equation therefore reflects a predictable mathematical synthesis of known quantitative assay design principles rather than a new technological concept. Furthermore, the mathematical relationships recited in claim 1 merely express known statistical and physical dependencies inherent in digital particle-counting assays. Duffy et al. and Ahrberg et al. demonstrate that assay performance is governed by probabilistic particle occupancy behavior dependent on concentration, volume, and the number of detection locations. Kim et al. confirms that quantitative ratios derived from imaging data were routinely used to determine assay outcomes. Gite et al. and Figueroa et al. likewise show that imaging resolution and pixel sampling density were recognized quantitative design considerations affecting detectability. Accordingly, deriving a formula relating minimum detectable particle occupancy to known assay parameters would have constituted routine statistical modeling and engineering optimization. The claimed equations therefore represent an explicit mathematical expression of relationships already implicit in prior art systems and would have been within the ordinary skill level to derive. The claimed formula thus reflects the predictable use of prior art elements according to their established statistical and physical functions to achieve expected quantitative results.
Lastly, a PHOSITA would have had a reasonable expectation of success in combining the teachings of Duffy et al. with those of Gite et al., Kim et al. Figueroa et al., and Ahrberg et al. because all references operate within the same technological field of digital microwell-based biological detection and employ compatible assay architectures, detection modalities, and statistical analysis methods. Duffy et al. provides the foundational structure for isolating particles in femtoliter-scale reaction chambers and optically interrogating reaction outcomes. Gite et al. demonstrates that similar systems successfully operate using higher particle concentrations and megapixel imaging sensors, confirming that scaling assay loading density and imaging resolution are practical and effective modifications. Kim et al. shows that digital counting systems routinely use mathematical relationships derived from imaging data to quantify assay performance, indicating that applying formula-based detection thresholds would have been well within ordinary skill. Figueroa et al. further confirms that imaging resolution parameters, including pixel density per well or cell region, are routinely selected and optimized to ensure reliable signal detection. Ahrberg et al. demonstrates that practitioners already applied probabilistic occupancy models to predict assay performance based on particle loading density.
Together, these teachings provide strong evidence that combining concentration-volume calculations with statistical occupancy modeling and imaging resolution considerations would have predictably improved assay performance. Since these modifications involve applying known techniques to enhance the performance of an established assay platform, and because the references collectively demonstrate that such parameter adjustments function as intended, there would have been no technical uncertainty discouraging the skilled artisan from implementing the claimed relationships. Rather, the combination represents routine optimization of known assay design variables using well-understood statistical and imaging principles. Accordingly, the skilled artisan would have reasonably expected that integrating these teachings into the Duffy et al. system would yield a digital biological detection method capable of achieving statistically reliable particle storage thresholds and improved detection sensitivity as recited in the claim.
Regarding claim 2, Duffy et al. teaches that the particles used for analyte capture in digital microwell biological detection systems may be magnetic particles. Specifically, Duffy et al. discloses that “in some embodiments, the beads may be magnetic beads. The magnetic property of the beads may help in separating the beads from a solution (e.g., comprising a plurality of unbound analyte molecules) and/or during washing step(s) (e.g., to remove excess fluid sample, labeling agents, etc.)” (page 45, column 23, lines 49-54).
Duffy et al. further teaches that the capture objects (e.g., beads) may be provided within a broad size range that encompasses the particle size range recited in claim 2. Duffy et al. states that “the plurality of capture objects for analyte capture may be of any suitable size or shape. Non-limiting examples of suitable shapes include spheres, cubes, ellipsoids, tubes, sheets, and the like. In certain embodiments, the average diameter (if substantially spherical) or average maximum cross-sectional dimension (for other shapes) of a capture object may be greater than about 0.1 um (micrometer), greater than about 1 um, greater than about 10 um, greater than about 100 um, greater than about 1 mm, or the like” (page 44, column 21, lines 27-35). Duffy et al. also states that “the plurality of beads in certain embodiment have an average diameter between about 0.1 micrometer and about 100 micrometers and the size of the reaction vessels may be selected such that only either zero or one beads is able to be contained in single reaction vessels” (page 50, column 33, lines 32-36).
Regarding claim 3, Gite et al. expressly teaches preparation of an assay mixture for digital microwell-based biological detection using particle concentrations within the claimed range. Specifically, Gite et al. states that “to prepare the assay mixture, stool was diluted to 8% with a mix of stool diluent, 7e8 particles/mL of antibody conjugated magnetic particles, 1.1e7 particles/mL of antibody conjugated fluorescent particles, and the indicated amount of toxin B” (Materials and Methods, paragraph 6, page 6). Here this disclosure expressly teaches a particle concentration of 7 × 10⁸ particles/mL, which lies squarely within the claimed range of 5 × 10⁷ to 5 × 10⁹ particles/mL.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the biological substance detection method of Duffy et al. by adjusting the particle concentration of the test liquid to a value within the range taught by Gite et al. in order to optimize particle loading statistics, improve detection sensitivity, and enhance quantitative reliability in digital particle-counting assays. Adjusting assay particle concentration represents a result-effective variable routinely optimized in digital immunoassay systems to achieve suitable signal-to-noise characteristics and statistical confidence in counting-based detection methodologies. Lastly, a PHOSITA would have had a reasonable expectation of success in making this modification because both Duffy et al. and Gite et al. operate within the same technological field of digital microwell-based biological detection and rely on similar particle-loading and optical interrogation principles. The concentration value taught by Gite et al. represents a practical working condition demonstrated to function effectively in such assays, and therefore its incorporation into the Duffy et al. system would have constituted no more than the predictable use of known assay design parameters according to their established functions.
Regarding claim 4, Duffy et al. expressly discloses that that the microwell reaction vessels may be formed at femtoliter-to-picoliter scale volumes. In particular, Duffy et al. teaches that “in accordance with one embodiment of the present invention, the reaction vessels may have a volume between about 1 femtoliter and about 1 picoliter, between about 1 femtoliters and about 100 femtoliters, between about 10 attoliters and about 100 picoliters, between about 1 picoliter and about 100 picoliters, between about 1 femtoliter and about 1 picoliter, or between about 30 femtoliters and about 60 femtoliters. In some cases, the reaction vessels have a volume of less than about 1 picoliter, less than about 500 femtoliters, less than about 100 femto liters, less than about 50 femtoliters, or less than about 1 femtoliter. In some cases, the reaction vessels have a volume of about 10 femtoliters, about 20 femtoliters, about 30 femtoliters, about 40 femtoliters, about 50 femtoliters, about 60 femtoliters, about 70 femtoliters, about 80 femtoliters, about 90 femtoliters, or about 100 femtoliters” (page 48, column 29, lines 6-21). Here this disclosure encompasses well volumes squarely within the claimed range of 2 fL to 100 pL.
Regarding claim 5, Gite et al. teaches configuring an optical detection system so that the imaging field-of-view is selected relative to the physical geometry of the well region. Specifically, Gite et al. teaches that “the MultiPath laboratory imaging system is a custom-built instrument and software that is capable of automatically capturing image data from selected wells of a microtiter plate. It uses a high precision linear stage from Prior Scientific (Rockland, MA) to position each well over a fluorescence-based image acquisition subsystem. The instrument can image in 4 separate color channels and uses an objective lens, illumination LEDs, fluorescent filter sets, and camera. The objective lens has a field of view designed to capture the image of an entire microtiter plate well” (Materials and Methods, paragraph 3, page 6). Here this disclosure demonstrates that practitioners in the field of optical biological detection systems deliberately configure the observation visual field of the imaging optics so that the captured image includes the entire well region in which signal generation occurs.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the optical interrogation configuration of Duffy et al. by selecting and fixing an observation visual field having a size sufficient to include the well formation area as taught by Gite et al. in order to ensure reliable identification and counting of signal-positive wells, improve quantitative detection accuracy, and enhance throughput in digital biological detection assays. Selecting an imaging field-of-view based on the physical dimensions of the well region represents a predictable optical system design choice and constitutes routine optimization of imaging parameters to achieve complete spatial sampling of the reaction array. Lastly, a PHOSITA would have had a reasonable expectation of success in making this modification because both Duffy et al. and Gite et al. employ camera-based optical interrogation of microwell-based biological detection platforms and rely on accurate spatial imaging of wells to determine assay outcomes. Gite et al. provides empirical evidence that configuring the field-of-view to encompass the well region enables successful fluorescence-based detection and quantitative image analysis. Accordingly, adapting the Duffy et al. imaging configuration to incorporate the field-of-view design principles taught by Gite et al. would have predictably resulted in a functional detection system capable of imaging the well formation region during the color development detection step.
Regarding claim 6, Duffy et al. teaches that “the method makes use of arrays of femtoliter-sized reaction chambers (FIG. 23) that can isolate and detect single enzyme molecules. In the first step, a sandwich antibody complex is formed on microscopic beads, and the bound complexes are labeled with an enzyme reporter molecule, as in a conventional bead-based ELISA” (page 68, column 69, lines 43-48). Next, “for example if 50 aM of a protein in 0.1 mL (3000 molecules) was captured on 200,000 beads, then 1.5% of the beads would have one protein molecule and 98.5% would have zero protein molecules (FIG. 23B). It is typically not possible to detect these low numbers of proteins using conventional detection technology (e.g., a plate reader), because the fluorophores generated by each enzyme diffuse into a large assay volume (typically 0.1-1 mL), and it takes hundreds of thousands of enzyme labels to generate a fluorescence signal above background (FIG. 24A). The method of this Example enables the detection of very low concentrations of enzyme labels by confining the fluorophores generated by individual enzymes to extremely small volumes (~50 fl), leading to a high local concentration of fluorescent product molecules. To achieve this localization in an immunoassay, in the second step of the method the immunoassay beads are loaded into an array of femtoliter-sized wells (FIG. 23B). The loaded array is then sealed against a rubber gasket in the presence of a droplet of fluorogenic enzyme substrate, isolating each bead in a femtoliter reaction chamber. Beads possessing a single enzyme-labeled immunocomplex generate a locally high concentration of fluorescent product in the 50-fL reaction chambers. By using standard fluorescence imaging on a microscope, it is possible to detect single enzyme molecules, and to image tens to hundreds of thousands of immunocomplexes substantially simultaneously” (page 68, column 69, lines 55-67; and column 70, lines 1-14). Here, this disclosure teaches that optical signal development in the wells results from a reaction between the trapped analyte-associated complex and a reagent (enzyme substrate), thereby producing a detectable localized signal within individual wells. Duffy et al. further explains that analyte concentration may be determined by detecting and counting wells exhibiting such reaction-generated signal. Accordingly, the fluorescence signal generated by the enzyme-substrate reaction in Duffy et al. constitutes “color development” within the meaning of the claim.
Regarding claim 7, as set forth above with respect to claim 1, the cited combination teaches or at least suggests designing microwell array geometries, particle loading conditions, and imaging interrogation parameters based on predictable statistical occupancy principles governing digital particle-counting assays. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to configure the microwell array structure of Duffy et al. so that particles are stored in the wells at loading concentrations, assay liquid volumes, and imaging interrogation conditions consistent with the quantitative statistical design approaches taught by Gite et al., Kim et al., Figueroa et al., and Ahrberg et al. Such modification would have been motivated by the well-recognized need in digital biological detection systems to optimize statistical confidence, improve signal-to-noise ratio, and ensure that a sufficient number of particle-containing wells are available for reliable digital counting analysis. Since the cited references collectively demonstrate that particle concentration, assay volume, imaging sampling density, and statistical occupancy thresholds are known result-effective variables governing microwell assay performance, adjusting these parameters so that the average stored particle number meets a minimum statistical threshold represents merely routine optimization of known system design parameters rather than the application of a new structural concept. By implementing these known quantitative design principles within the Duffy et al. microwell array architecture, the resulting well array would inherently possess the capability of storing a plurality of particles in individual wells at an average storage level satisfying a predetermined statistical minimum expressed as a function of particle concentration, assay volume, and imaging sampling resolution. The claimed mathematical relationships therefore reflect an explicit formulation of predictable statistical occupancy behavior already recognized and routinely controlled in prior-art digital particle-counting assays.
Regarding claim 8, Duffy et al. teaches a detection device including a detection chip having a well array and a detection unit configured to optically interrogate the array. Specifically, Duffy et al. states that “FIG. 7 depicts an experimental set-up for detection using light, according to one embodiment of the present invention” (page 36, column 5, lines 20-21). Duffy et al. further teaches that the detection system includes optical analysis hardware associated with the microwell array, stating that “a non-limiting embodiment is illustrated in FIG. 7. A sealing component 300 is placed on top of mechanical platform 302. The assay solution 304 is placed on top of the sealing component 300. The mechanical platform is moved upwards towards the array 306 (e.g., fiber optic array) such that uniform pressure is applied. As shown in FIG. 8, the sealing component 300 forms a tight seal with the array 306. In other instances, varying pressure may be applied to the sealing component to form a tight seal between the sealing component and the array. The system may also comprise additional components 312 that may be utilized to analyze the array (e.g., microscope, computer, etc.) as discussed more herein” (page 49, column 32, lines 9-21). Although these disclosures teach a detection device comprising a microwell-array detection chip together with an optical detection unit configured to analyze signals generated from individual wells, Duffy et al. does not expressly teach that the image pickup element of the detection unit has a pixel number of at least 300,000 pixels, as required by claim 8.
On the other hand, Gite et al. teaches the use of a high-resolution image pickup element suitable for digital interrogation of microwell-based biological detection assays. Specifically, Gite et al. states that “a series of fluorescent image frames are captured with a camera using a 3.1MP Sony IMX265 monochrome sensor with 12-bit per pixel quantization” (Materials and Methods, paragraph 3, page 6). Gite et al. further explains the pixel-based imaging detection mechanism, stating that “the light emitted by a particle impinges on a small cluster of pixels on the CMOS chip of a digital camera forming white spots in the resulting image (Fig. 1b)” (Results, paragraph 1, page 2). Since a 3.1-megapixel sensor inherently comprises far more than 300,000 pixels, Gite et al. teaches an image pickup element meeting the claimed pixel-number limitation.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the digital microwell detection device of Duffy et al. by incorporating the high-resolution image pickup element taught by Gite et al. in order to improve signal detectability, spatial resolution, and statistical reliability in digital biological detection systems. Duffy et al. already establishes a detection architecture in which individual microwell reaction sites are optically interrogated to determine analyte presence. In such digital counting systems, the ability to reliably resolve signal-positive wells directly depends on imaging resolution and pixel sampling density. Gite et al. demonstrates that practitioners in the field routinely employed megapixel-class CMOS imaging sensors to digitally detect particle-generated fluorescence signals and to form pixel clusters corresponding to individual detection events. Increasing detector pixel density represents a predictable engineering refinement directed to improving signal-to-noise ratio, spatial discrimination of adjacent wells, and quantitative accuracy in counting-based assays. Accordingly, substituting or upgrading the imaging detector of Duffy et al. with the higher-pixel-count sensor taught by Gite et al. represents a predictable application of known imaging optimization techniques to improve performance of an established detection platform. Such substitution involves merely selecting a known optical component performing the same function with improved resolution characteristics, which would have been well within the routine skill of a skilled artisan.
Lastly, a PHOSITA would have had a reasonable expectation of success in making this modification because both Duffy et al. and Gite et al. operate within the same technological domain of digital microwell-based biological detection and employ compatible optical interrogation principles. Duffy et al. already demonstrates successful detection of analyte-dependent optical signals using microscope-based imaging systems, while Gite et al. confirms that similar microwell assay platforms can be reliably interrogated using megapixel CMOS imaging sensors that capture fluorescent signal patterns as clusters of pixels. The modification therefore requires only the substitution of one known imaging detector with another detector of higher pixel density performing the same fundamental optical detection function. Such hardware upgrades are routine engineering refinements that do not alter the underlying assay chemistry, microwell architecture, or signal-generation mechanism. Since increasing pixel count predictably enhances spatial sampling resolution and detection sensitivity, the skilled artisan would reasonably expect that incorporating a megapixel imaging sensor into the Duffy et al. detection device would improve digital counting accuracy and signal discrimination without introducing technical incompatibilities or undue experimentation. Consequently, the claimed detection device represents no more than the predictable use of prior-art imaging technology according to its established purpose.
Ultimately, Claims 1–8 are rejected under 35 U.S.C. 103 as being unpatentable over Duffy et al. in view of Gite et al., Kim et al., Figueroa et al., and Ahrberg et al., as set forth in detail above. Collectively, these references teach or render obvious the claimed biological substance detection method and associated detection device, including the use of particle-based digital detection within microwell arrays, preparation of assay liquids having defined particle concentrations and volumes, statistical modeling of particle occupancy and detection thresholds, optical interrogation using image pickup elements having high pixel counts, and imaging configurations that provide sufficient spatial sampling of individual wells. The combination represents the predictable use of known assay design parameters, imaging hardware selections, and statistical optimization techniques to achieve improved sensitivity and quantitative reliability in digital biological detection systems. Accordingly, the claimed subject matter as a whole would have been obvious to a PHOSITA at the time of the invention.
For the reasons cited above, all claims are rejected.
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.
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Claims 1-8 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-9 of copending Application No. 18/552,365 in view of Duffy et al., Gite et al., Kim et al., Figueroa et al., and Ahrberg et al.
Copending claim 1 teaches a virus detection method employing a substrate-based microwell array and optical detection of localized signals generated within individual wells, which corresponds to the fundamental detection architecture recited in instant independent claim 1. However, the copending claim differs from instant claim 1 in failing to teach preparation of a particle-containing test liquid at defined particle loading conditions, statistical control of minimum average particle occupancy within wells, and imaging-based sensitivity associated with pixel-resolution constraints.
The applied secondary references (see the discussion of Duffy et al., Gite et al., Kim et al., Figueroa et al., and Ahrberg et al. above) collectively teach particle-assisted analyte capture, statistical modeling of discrete detection events, micro-volume reaction well design, and high-resolution imaging analysis, thereby rendering the additional limitations of instant claim 1 obvious design refinements of the copending claimed platform.
Instant claims 2–4, which depend from claim 1 and recite further limitations relating to particle type, particle concentration, and microwell reaction volume, are not patentably distinct from copending claim 1, which fails to expressly teach these assay parameter selections. Duffy et al. teaches the use of magnetic capture beads in microwell digital detection platforms and further teaches microwell reaction volumes in the femtoliter-to-picoliter range, while Gite et al. teaches adjusting particle concentration levels to optimize digital detection performance. These teachings would have suggested the claimed parameter selections as predictable optimization variables affecting analyte capture efficiency and signal detectability.
Instant claim 5 is not patentably distinct from copending claim 2, which teaches defining the observation visual field of the optical detection unit relative to the microwell test region but fails to expressly teach selecting imaging field-of-view parameters in relation to the spatial geometry of the well region in the manner recited. Gite et al. teaches configuring imaging field-of-view parameters in relation to array geometry to ensure reliable signal acquisition, thereby rendering the claimed imaging configuration an obvious system-level optimization.
Instant claim 6 is not patentably distinct from copending claims 3–6, which teach generation of optically detectable signals in microwells through reactions involving luminescent or fluorescence-producing reactions but fail to expressly teach the specific reagent-induced color-development interaction recited. Duffy et al. teaches enzyme-substrate reactions producing localized fluorescence signals within microwells that are detected and counted to determine analyte presence and concentration, thereby rendering the claimed color-development limitation an obvious implementation of known digital assay signaling techniques.
Instant claim 7, directed to the well array article, is not patentably distinct from copending claim 7, which teaches a microwell array structure configured for optical biological detection but fails to expressly teach configuring the well array to achieve a defined minimum average stored particle number. The additional limitation relating to achieving a defined average particle storage condition represents the inherent structural result of implementing the obvious particle loading concentrations, well volumes, and statistical occupancy principles taught by the applied references.
Instant claim 8, directed to a biological detection device, is not patentably distinct from copending claims 8–9, which teach a detection device including a microwell detection chip and an optical detection unit configured to image the wells but fail to expressly teach employing an image pickup element having the claimed resolution in combination with a well array configured according to the recited statistical particle-loading conditions. Duffy et al. and Gite et al. collectively teach optical analysis hardware and high-resolution digital imaging systems suitable for microwell-based biological detection, rendering the claimed device configuration an obvious system-level refinement of the copending claimed detection platform.
Accordingly, claims 1–8 are not patentably distinct from claims 1–9 of copending Application No. 18/552,365 in view of the applied references. This is a provisional nonstatutory double patenting rejection.
Conclusion
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/E.O./Examiner, Art Unit 1677
/BAO-THUY L NGUYEN/Supervisory Patent Examiner, Art Unit 1677 March 23, 2026