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 .
Response to Amendment
The Amendment filed on April 23, 2026 has been entered. Claims 1–21 are currently pending. Claims 1, 2, 5–8, and 19 have been amended. New claim 21 has been added.
Response to Arguments
Applicant's arguments filed 04/23/2026 have been fully considered. Some arguments are persuasive with respect to the rejections under 35 U.S.C. § 112(b) and the claim interpretation under 35 U.S.C. § 112(f), while others regarding the 35 U.S.C. § 101 and § 103 rejections are not persuasive, as explained below.
Applicant's arguments filed 04/23/2026 are persuasive with respect to the claim interpretation under 35 U.S.C. § 112(f). Applicant's arguments, see page 6 of the Remarks, state that amended claim 1 recites a sensor, at least one processor, and at least one memory storing instructions, rather than purely functional "unit" language, and therefore no longer invokes means-plus-function treatment. Upon reconsideration, the Examiner agrees that the amended claim language recites sufficient structure to avoid interpretation under § 112(f). Therefore, the prior claim interpretation under 35 U.S.C. § 112(f) has been withdrawn.
Applicant's arguments filed 04/23/2026 are persuasive with respect to the rejection of claim 7 under 35 U.S.C. § 112(b). Applicant's arguments, see page 6 of the Remarks, state that claim 7 has been amended to replace the unlabeled symbols "μ" and "σ" with "μv" and "σv," which are properly antecedent to the average and standard deviation values recited in claim 6. The Examiner finds this amendment resolves the indefiniteness issue previously identified. Therefore, the rejection under 35 U.S.C. § 112(b) has been withdrawn following Applicant's amendments. It is noted the amendments do raise new § 112(b) rejection regarding new issue as fully disclosed below.
Applicant's arguments filed 04/23/2026 have been fully considered but are not persuasive with respect to the rejection under 35 U.S.C. § 101. Applicant's arguments, see pages 9–10 of the Remarks, state that amended claims 1 and 19 are directed to significantly more than an abstract idea because the claims recite detecting a target with "greater measurement accuracy" by determining an exclusion region containing a "microscopic abnormality" and that such abnormalities are "not detectable by the human visual system". The Examiner respectfully disagrees.
The amended claims recite generic, off-the-shelf computing hardware "a sensor", "at least one processor" and "at least one memory" of the type expressly disclosed in the specification as a conventional CPU, RAM, ROM, and HDD configuration. These generic components are merely used as tools to perform the underlying mathematical operations of setting a characteristic value, comparing that value to a statistically-derived threshold (e.g., Vt < μv − 3σv, as recited in claim 7), and excluding regions that fall outside the threshold. This is a mathematical concept (Step 2A, Prong One) implemented on generic computer components (Step 2A, Prong Two), and does not reflect an improvement to the sensor, the imaging hardware, or the underlying assay chemistry itself. The claimed "greater measurement accuracy" is the result of performing a statistical exclusion calculation more selectively, an advancement to an abstract idea, NOT a technological improvement of the type found to integrate a judicial exception into a practical application. Applicant's assertion that microscopic abnormalities are undetectable by the human eye is not commensurate with the claim scope, because the claims do not require any specific imaging resolution or hardware capable of resolving microscopic features beyond what is already used to capture ordinary brightness/ intensity values for each individual separated compartment. Further, it is noted the claims still recite calculating, collecting, and detecting data, but doesn’t disclose what happens once it is collected and detected. Therefore, the rejection under 35 U.S.C. § 101 is maintained.
Applicant's arguments filed 04/23/2026 have been fully considered but are not persuasive with respect to the rejection under 35 U.S.C. § 103. Applicant's arguments, see pages 11–13 of the Remarks, state that amended claim 1 requires an exclusion region comprising "a plurality of individual separated compartments", whereas Betschart allegedly only performs invalidation on a per-reaction-area basis, and that a person of ordinary skill would be discouraged from modifying Betschart to exclude a region containing multiple reaction areas because doing so would remove valid reaction areas along with invalid ones. The Examiner respectfully disagrees, and further notes that the rejection has been updated to rely on the combined teachings of Betschart and Yip, rather than Betschart alone, for this limitation as reflected below. This modification to the rejection is necessitated by Applicant's amendment adding new limitations to independent claims 1 and 19.
Applicant's "teaching away" argument is not persuasive because Betschart does not criticize, discredit, or discourage grouping reaction areas into a common exclusion unit; Betschart's disclosure of rim/ environmental sub-area analysis (recognizing that artifacts extend beyond single reaction-area boundaries) and its disclosure of subarray-organized dPCR platforms are affirmatively consistent with, rather than contrary to, a region-based exclusion approach. Betschart expressly recognizes that microscopic optical artifacts "have larger lateral size than the reaction area dimensions" and teaches that sub-areas "surrounding the reaction area" (rim areas) should be considered in artifact recognition. A reference does not teach away merely by disclosing a different embodiment than the one claimed. See In re Fulton, 391 F.3d 1195, 1201 (Fed. Cir. 2004). Because Betschart already contemplates cross-compartment artifact analysis, applying Yip's tile-based exclusion framework to Betschart's arrays to efficiently exclude broad localized optical artifacts spanning multiple compartments is highly obvious and produces predictable results.
Applicant further argues, see page 4 of the Remarks, that Betschart teaches away from new claim 21, which requires that each target in the compartment is "limited to one molecule or less." Applicant points to Betschart's disclosure that some areas may contain an average of 2.2 molecules. The Examiner respectfully disagrees. A reference does not teach away simply by describing one embodiment or dynamic range limit (e.g., 2.2 molecules). Betschart is directed to digital PCR (dPCR), which inherently utilizes limiting dilutions to partition samples such that the vast majority of partitions contain zero or one molecule to allow for Poisson-based absolute quantification. Moreover, this limitation is also taught by Duffy, which is explicitly directed to "Single Molecule Arrays" (Simoa) where target analytes are intentionally diluted so that each reaction vessel contains zero or one analyte molecules [0254]. Therefore, both Betschart or Duffy renders claim 21 obvious.
The rejections of claims 4, 8, 9, and 16 over Betschart in view of Duffy, and of claim 18 over Betschart in view of Noji, remain unaffected by the amendment and are maintained on the grounds and rationale previously set forth. The rejections of claims 12–15 over Betschart in view of Yip are likewise maintained, and the rationale for combining Betschart and Yip set forth above with respect to claim 1 is equally applicable to and consistent with the rejection of claims 12–15.
Applicant’s addition of new claim 21 and the amendments to claims 1, 2, 5–8, and 19 have been fully considered. The newly added limitations from the amended and new claims have been considered and addressed in the updated rejections utilizing the prior art of record (Betschart, Yip, Duffy, Noji).
Based on these facts, this action is made FINAL, in accordance with MPEP § 706.07(a).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1–21 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1, 19, and 21 recite “detecting the target from the image obtained by excluding the exclusion region with greater measurement accuracy”. Even though the specification describes "-- to improve the measurement accuracy" [0064] to not performing the exclusion, however the claim itself just says "with greater measurement accuracy" without stating what it is greater than. The term “greater measurement accuracy” is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree or baseline for comparison, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term "greater" is indefinite because neither the claims nor the specification provides any standard, metric, or objective baseline criterion by which a POSITA could determine what the accuracy is being compared to (e.g., greater than human visual observation, greater than conventional prior art systems, or greater than the system's own baseline before the exclusion region is omitted). Under Nautilus, Inc. v. Biosig Instruments, Inc., 572 U.S. 898 (2014), a claim is indefinite if it fails to inform those skilled in the art about the scope of the invention with reasonable certainty. Because one of ordinary skill in the art cannot determine with reasonable certainty what baseline measurement standard applies, the claim term "greater measurement accuracy" renders claims 1, 19, and 21 indefinite. Because claims 2–18 depend from claim 1, and claim 20 depends from claim 19, they inherit this ambiguity and fail to cure the deficiency.
Claims 1 and 21 further recite "at least one memory in communication with the at least one processor, storing instructions that, when executed by the at least one processor, cause the at least one processor to:
set, …;
calculate …; and
detecting the target …."
The introductory clause "cause the at least one processor to:" grammatically requires each subsequent recited action to be stated in the infinitive form in order to be understood as an instruction executed by the processor, consistent with "set" and "calculate". However, the final clause recites "detecting" in the gerund form rather than "detect". This inconsistency renders it unclear (i) whether "detecting the target..." is a third instruction executed by the at least one processor as part of the recited "instructions", in which case the claim should recite "detect" for grammatical and antecedent consistency, or (ii) whether "detecting the target..." is a separate, freestanding limitation of the claimed system that exists outside of and is not performed by the instructions executed by the at least one processor, in which case it is unclear what structure of the claimed system performs this detecting operation and how it relates structurally to the previously recited sensor, processor, and memory. Because the claim does not clearly indicate which interpretation is intended, a person of ordinary skill in the art cannot ascertain the metes and bounds of the claim with reasonable certainty. See MPEP § 2173.05(e); Nautilus, Inc. v. Biosig Instruments, Inc., 572 U.S. 898, 910 (2014) (a claim is indefinite if it fails to inform, with reasonable certainty, those skilled in the art about the scope of the invention). Accordingly, claims 1 and 21 fail to clearly define the processor-executed operations. If Applicant intends the processor to perform the target-detection operation, replace “detecting the target” with “detect the target”. Any such amendment must find support in the specification as originally filed and must not introduce new matter under 35 U.S.C. § 112(a). Because claims 2–18 depend from claim 1, they inherit this ambiguity and fail to cure the deficiency.
Claim 19 recites a similar grammatically deficiency. The claim recites a series of method steps each expressly introduced by a "step of" after phrases "an image acquisition step of acquiring...", "a characteristic value setting step of setting...", "an exclusion region determination step of determining..." and "a calculation step of calculating...", then last step followed by "detecting the target …". Unlike the other recited steps, the "detecting" clause is not introduced by a corresponding "step of" phrase (e.g., "a detection step of detecting..."). It is therefore unclear whether "detecting the target..." constitutes a distinct, fifth positively recited step of the claimed method, or whether it is intended merely as a description of a result that flows automatically from the previously recited "calculation step" without itself being a separately performed step. This ambiguity is compounded by the fact that "detecting a target" also appears in the claim's preamble ("A detection method using an individual separated compartment for detecting a target..."), such that it is unclear whether the final clause further defines the preamble's "detecting" or recites an entirely separate operation. Applicant may amend the clause to recite "a detection step of detecting the target..." consistent with the "step of [gerund]" format used for the claim's other recited steps, or otherwise clarify the relationship between the "detecting" clause and the previously recited "calculation step". Any such amendment must find support in the specification as originally filed and must not introduce new matter under 35 U.S.C. § 112(a). Because claim 20 depends from claim 19, it inherits this ambiguity and fails to cure the deficiency.
Claim 8 recites "wherein the characteristic value is set to the target-capturing substance filling rate and determine, as the exclusion region, each of a region in which the target-capturing substance filling rate is less than 4% and a region in which the target-capturing substance filling rate exceeds 40%.". Prior to amendment, this limitation recited "wherein the exclusion region determination unit is configured to set the characteristic value to the target-capturing substance filling rate and determine, as the exclusion region, each of a region..." In that prior version, the phrase "the exclusion region determination unit is configured to" served as a common grammatical subject governing both infinitive verbs "set" and "determine" as parallel actions performed by the same recited structural component. Applicant's amendment deleted "the exclusion region determination unit is configured to set the" and replaced it with the passive construction "the characteristic value... is set to", but left the subsequent clause "and determine, as the exclusion region, each of a region..." unchanged in its original bare infinitive form. As a result, the "determine" clause no longer has any governing subject, since the antecedent phrase that previously supplied its grammatical subject ("...is configured to") was removed, and the clause was not correspondingly revised to passive voice (e.g., "and the exclusion region is determined...") to match the amended "set" clause. This defect renders the scope of claim 8 unclear for at least the following reasons: (i) it is unclear whether "determine" is performed by "the at least one processor" recited in parent claim 1, (ii) whether it further limits the "determine the exclusion region based on the characteristic value of the plurality of regions" instruction already recited in claim 1, or (iii) whether it is an orphaned limitation with no clear effect on claim scope, since claim 8's "determine" clause is not clearly tied by antecedent basis to any previously recited claim element. Second, the claim inconsistently mixes passive voice ("the characteristic value is set to...") with active/infinitive voice ("and determine..."), such that a person of ordinary skill in the art cannot determine whether these are intended as two parallel actions performed by the same actor, two separate actions performed by different actors, or a single combined limitation improperly split across inconsistent grammatical forms. Under Nautilus, Inc. v. Biosig Instruments, Inc., 572 U.S. 898, 910 (2014), a claim is indefinite if it fails to inform, with reasonable certainty, those skilled in the art about the scope of the invention. Because the grammatical structure of claim 8 does not permit a person of ordinary skill in the art to determine with reasonable certainty what component performs the recited "determine" action or how that action relates to the previously recited processor instructions of claim 1, claim 8 is indefinite. For purposes of examination on the merits, claim 8 is being interpreted as the same context with the original one.
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 1–21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without significantly more.
This rejection has been made in accordance with the current USPTO subject matter eligibility framework, including MPEP §§ 2103–2106.07, the 2019 Revised Patent Subject Matter Eligibility Guidance, the October 2019 Patent Eligibility Guidance Update, the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the July 2024 AI Subject Matter Eligibility Examples, the August 4, 2025 USPTO memorandum titled “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. § 101,” and the USPTO’s guidance concerning Ex parte Desjardins, Appeal No. 2024-000567. The claims have been evaluated under the broadest reasonable interpretation, and the claims have been considered as a whole.
Step 1: Statutory category
Independent claim 1 is directed to an information processing system and therefore falls within the statutory category of a machine. Independent claim 19 is directed to a detection method and therefore falls within the statutory category of a process. Claim 20 is directed to a non-transitory storage medium and therefore falls within the statutory category of a manufacture. Independent claim 21 is directed to an information processing system and therefore falls within the statutory category of a machine. Accordingly, the analysis proceeds to Step 2A.
Step 2A, Prong One (Judicial exception)
Independent claim 1 recites: setting, based on an image, a characteristic value for determining an exclusion region among a plurality of regions of the image, wherein each region includes a plurality of individual separated compartments; determining the exclusion region based on the characteristic value of the plurality of regions;calculating information relating to a target from an image of individual separated compartments included in each region for calculation obtained by excluding the exclusion region; and detecting the target from the image obtained by excluding the exclusion region with greater measurement accuracy. These limitations recite an abstract idea, namely evaluating regions of previously-acquired image data, computing a statistical characteristic value for each region, comparing that value against a threshold to make an exclusion decision, and calculating a result from the non-excluded data.
The claim recites data evaluation, statistical calculation, comparative decision-making, and selective exclusion of a subset of data prior to a final calculation. The "characteristic value", "exclusion region", "Vt", "μv", and "σv" are used as mathematical constructs in a statistical outlier-detection and data-filtering process. The claim does NOT recite an improvement to the way images are captured, digitized, encoded, stored, or transmitted. Rather, the claim uses a generic sensor and generic computer components to obtain an already-existing image, mathematically evaluate portions of that image, and exclude some portions from a downstream calculation.
The claim is similar in character to claims that courts have found abstract where the focus is collecting information, analyzing the information mathematically, and acting on the results of the analysis. In Electric Power Group, LLC v. Alstom S.A., the Federal Circuit recognized claims directed to collecting, analyzing, and displaying information as abstract. The present claims similarly collect image data, mathematically analyze regions of that data to compute characteristic values, and use those values to select which regions to exclude from a final calculation. Furthermore, observing a region of an assay image, judging whether its signal appears unusual relative to surrounding regions, and disregarding that region accordingly are mental processes historically performed by laboratory personnel with the aid of pen and paper.
Claims 5–7 further limit the apparatus and method to determining the exclusion region "through statistical processing" and specifically based on the relationship of Vt, μv, and σv, including the explicit inequality Vt < μv − 3σv and Vt > μv + 3σv. These limitations recite pure mathematical concepts, specifically the application of a statistical outlier-detection formula (the "3-sigma rule") to numerical data.
Claims 3 and 4 recite that the characteristic value comprises any one of a list of purely numerical quantities, including a number of positive compartments, a ratio, an average, a median, a maximum, a minimum, or a standard deviation of brightness values. These limitations recite explicit mathematical and statistical calculations performed on data values.
Claim 8 recites setting the characteristic value to a numerical ratio (a "target-capturing substance filling rate") and comparing that ratio to fixed numerical thresholds of 4% and 40%. This limitation recites a mathematical comparison of a calculated ratio to predetermined numerical bounds.
Claims 12–15 recite geometric and spatial relationships among the plurality of regions, namely equivalent areas, overlapping portions, full coverage, or inhibited full coverage of the image. These limitations describe how the abstract data set is subdivided for the purpose of performing the abstract exclusion analysis, and do not recite any technical mechanism beyond partitioning data for subsequent mathematical evaluation.
Independent claim 19 recites substantially the same abstract idea in method form using generic process steps. Merely implementing the same statistical exclusion and calculation process as a series of method steps does not avoid the judicial exception. Claim 21 recites the same abstract idea as claim 1, further reciting that the compartments include a sample solution limiting each target to one molecule or less, a physical sample-preparation characteristic that does not alter the fundamentally mathematical and mental character of the setting, determining, calculating, and detecting limitations.
The claims are also consistent with the reasoning of Interval Licensing LLC v. AOL, Inc., where the Federal Circuit looked to the character of the claims as a whole and affirmed ineligibility where the claims were directed to acquiring, evaluating, and selectively using information at a high level of generality rather than to a specific improvement in computer functionality. Here, the character of the claims as a whole is image data acquisition, region-wise statistical evaluation, threshold-based exclusion, and downstream calculation, not an improvement to camera technology, computer memory, sensor operation, or assay-chamber structure itself.
Accordingly, claims 1–21 recite an abstract idea under Step 2A, Prong One.
Step 2A, Prong Two (Practical Application)
The additional elements, considered individually and in combination, do not integrate the abstract idea into a practical application.
The recited "sensor", "at least one processor", and "at least one memory" amount to generic computer hardware and generic computer implementation of the abstract characteristic-value calculation and exclusion-decision concept.
The claims do not recite a particular improvement to computer or imaging technology. They do not improve how an image is physically captured, sensed, focused, or stored. The claims merely require obtaining an image that has already been captured of compartments that have already been physically prepared. The claims also do not recite a particular improvement to sensor or optical hardware. Rather, the claims use a standard captured image as input data for generic mathematical filtering and calculation.
The claims further do not recite a particular improvement to sensor, processor, or memory technology. The claims do not modify sensor architecture to improve resolution, alter processor architecture to reduce computational load, modify memory structure to save storage, or improve detection speed by a specific claimed technical mechanism. The recited "greater measurement accuracy" and "microscopic abnormality" are recited functionally as the intended outcome of performing generic statistical filtering, not as a specific technical means for achieving that outcome.
This analysis is consistent with the reasoning of Electric Power Group, which emphasizes that claims focused on collecting, analyzing, and using information, without any particular assertedly inventive technology for performing those functions, are not patent eligible. The present claims do NOT recite such a specific technological improvement. Instead, the claims use generic sensor and computer components to acquire image data, mathematically evaluate portions of that data, and exclude some portions from a calculation.
This case is distinguishable from cases finding eligibility based on a specific improvement to underlying technology. The present claims do NOT recite a particular sensor configuration, memory-saving arrangement, or data-acquisition mechanism that improves the physical or computational operation of the imaging or computing hardware itself. The claimed sensor, processor, and memory merely automate the mathematical task of comparing regional characteristic values against a threshold. Nor does limiting the abstract idea to the environment of digital assay imaging using "individual separated compartments" make the claims eligible. See Recentive Analytics, Inc. v. Fox Corp. (applying a known analytical technique to a new field of use is insufficient for eligibility where the claims do not recite a technical improvement to the analytical process itself). Similarly here, applying statistical exclusion analysis to compartmentalized assay images is a field-of-use limitation, not an integration of the abstract idea into a practical application.
Accordingly, the claims do not integrate the judicial exception into a practical application under Step 2A, Prong Two.
Step 2B: (Inventive Concept)
The additional elements, considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The claims use a generic sensor, processor, and memory to perform ordinary computer functions, including acquiring image data, computing statistical values for regions, comparing computed values to thresholds, excluding data based on that comparison, and calculating a result from the remaining data. These are conventional data-processing and mathematical operations performed using generic computer technology.
The ordered combination also does not provide an inventive concept. The ordered combination follows the abstract idea itself: acquire an image, divide it into regions, compute a characteristic value for each region, compare that value to a threshold, exclude outlying regions, and calculate a result from the rest. This is no more than the abstract mathematical idea implemented on generic computer components.
Dependent claims 2–18 recite additional limitations specifying particular characteristic values, particular statistical formulas and thresholds, particular physical parameters of the target-capturing substance, particular image types, a particular number of regions, particular region geometries, a particular number of compartments per region, particular compartment types, and a particular compartment volume. These limitations merely specify particular numerical parameters, known statistical formulas, and physical characteristics of the pre-existing sample environment used in the abstract evaluation and do not add significantly more.
The independent method and storage-medium claims recite generic counterparts using basic computing logic to perform substantially the same operations. The recitation of a generic sensor, processor, and memory does not transform the abstract idea into patent-eligible subject matter.
Accordingly, claims 1–21 are directed to a judicial exception without significantly more and are therefore rejected under 35 U.S.C. § 101.
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.
Claims 1-3, 5-7, 10-15, 17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Betschart (Betschart et al, US 2018/0230515 A1, 2018) in view of Yip (Yip et al, US 2020/0258223 A1, 2017).
Regarding claim 1, Betschart teaches an information processing system using an individual separated compartment for detecting a target through use of the individual separated compartment ( [0007-0008]: Betchart performs dPCR in each reaction area (partition/ compartment) of an array of reaction areas and quantifies nucleic acid concentration from the set of reaction area, where each reaction location is separated interrogated by imaging to determine presence of a target analyte. ), the information processing system comprising:
a sensor configured to acquire an image including, as an object, a plurality of individual separated compartments in which a target is includable;
( [0038–0040]: Betschart teaches reaction areas suitable for dPCR, including microwells, nanowells, microfluidic chambers, and droplets, and further teaches dPCR platforms having many reaction areas/ partitioned droplets; [0043–0045]: Betschart teaches obtaining optical signals from sub-areas/pixels of each reaction area, wherein the image is a dot matrix data structure and image processing/ filtering may produce an image in which partition center pixels have peak values; [0056]: Betschart expressly teaches that a sensor is placed in focus position to the reaction areas of the array and an image is captured, and that the complete sensor/camera pixel image may be read and evaluated. )
at least one processor; and
at least one memory in communication with the at least one processor, storing instructions that, when executed by the at least one processor, cause the at least one processor to:
( [0043], [0047-0050]: Betschart teaches recording values for image sub-areas, comparing optical-signal values, using statistical procedures, and executing different algorithms using pixel/optical-signal values to determine signal-intensity distribution, artifacts, and artifact type. Betschart discloses that the disclosed method, including image acquisition, signal-distribution analysis, and concentration calculation, is performed by an image analysis/ processing system, which necessarily comprises at least one processor and at least one memory storing instructions for executing different algorithms. )
set, based on the image, a characteristic value for determining an exclusion region to be excluded from among a plurality of regions of the image, and determine the exclusion region based on the characteristic value of the plurality of regions, wherein each of the plurality of regions includes at least one of the individual separated compartments;
( [0043]: Betschart teaches that the reaction area is subdivided into sub-areas for analysis, optical signals are obtained for each sub-area, and the image is a dot matrix data structure; [0047–0050]: Betschart teaches recording values for sub-areas/ pixels, assessing distribution by comparing values, using statistical procedures, and calculating parameters such as mean signal level, median, standard deviation, largest and lowest signal steps, and distribution shape to determine artifacts and invalidation; [0051–0053]: Betschart teaches selecting thresholds, determining average/ mean and standard deviation values, and identifying a reaction area as invalid when the optical-signal distribution satisfies abnormal/ threshold conditions; [0056]: Betschart further teaches evaluating the captured image and calculating the mean signal level, median, standard deviation, largest and lowest signal steps, and distribution shape, and treating inappropriate filling or abnormal signal values as an artifact based on threshold values. The reaction area/ partition corresponds to a region of the image and includes at least one individual separated compartment. )
calculate information relating to the target from an image of individual separated compartments included in each region for calculation obtained by excluding the exclusion region from the image; and
( [0009–0019]: Betschart teaches quantifying the amount or concentration of a nucleic acid of interest in an array of reaction areas, identifying invalid reaction areas based on unequal optical-signal distribution, eliminating invalid reaction areas, and calculating the amount or concentration based on dPCR results of reaction areas not identified as invalid; [0057]: Betschart teaches that the reaction area identified as invalid is eliminated from calculating the amount or concentration of the nucleic acid of interest, such that neither the signal nor the volume of the invalid reaction area is considered; [0060–0063]: Betschart teaches performing dPCR in each reaction area, determining optical-signal distributions, identifying invalid reaction areas, and calculating the amount or concentration of the nucleic acid of interest based on reaction areas not identified as invalid. )
detecting the target from the image obtained by excluding the exclusion region with greater measurement accuracy, wherein the exclusion region contains a microscopic abnormality that causes deterioration in measurement accuracy.
( [0007–0008]: Betschart teaches excluding reaction areas with optical artifacts from calculating the nucleic-acid amount or concentration, thereby providing a highly accurate and precise dPCR quantification method; [0026–0030]: Betschart teaches that optical artifacts arise from undesired optical-signal alterations, including impurities, dirt, dust, scratches, fluid splash, hair, fiber, fingerprints, incorrect filling, defects in partition structure, and technical imaging problems; [0052–0053]: Betschart teaches that reaction areas with dust artifacts exhibit increased standard deviation, inappropriate distribution shape, increased deviation of minimum/ maximum signals from the mean, and mean deviation from reaction areas without artifacts; [0103–0108]: Betschart teaches that accuracy and precision of dPCR quantification can be increased by classifying reaction areas as valid positive, valid negative, or invalid, where an invalid reaction area may be insufficiently filled or eliminated because of an optical artifact, and such reaction areas are eliminated or discarded from target-concentration calculation. )
Betschart teaches determining an exclusion region based on image-derived characteristic values, where the “region” corresponds to a reaction area/ partition or pixels/ sub-areas associated with that reaction area. However, Betschart does not expressly teach the amended requirement that each of the plurality of image regions includes a plurality of individual separated compartments where Yip teaches:
from the plurality of regions, wherein each of the plurality of regions includes a plurality of the individual separated compartments;
( [0022–0023]: Yip teaches separating a digital image into a plurality of tile images by applying a tiling mask, where the tiling mask may comprise same-size and rectangular tiles; [0295]: Yip teaches a medium square tile containing approximately 225 small square tiles; [0302], [0306]: Yip teaches a large tile formed by multiple overlapping medium square tiles and a large square tile formed from 16,384 non-overlapping small square tiles. Therefore, applying Yip’s tile/ region framework to Betschart’s dPCR image causes each image region/ tile to include a plurality of Betschart’s individual separated compartments/ reaction areas. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Betschart’s image-based dPCR artifact-exclusion system by applying Yip’s known image tiling/ region-processing framework, such that Betschart’s acquired image of reaction areas/ partitions is divided into a plurality of image regions/ tiles, each including a plurality of individual separated compartments. Betschart teaches that quantification accuracy is improved by detecting image-derived optical artifacts and excluding invalid reaction-area data from target calculation, while Yip teaches dividing digital images into plural tiles/ regions, including larger tiles made from multiple smaller image portions, for region-based image analysis. Applying Yip’s tile/ region framework to Betschart’s dPCR image would have been a predictable implementation choice to localize and exclude abnormal image regions caused by dust, improper filling, or other microscopic abnormalities, thereby improving the reliability of Betschart’s image-based target quantification without changing the underlying assay chemistry or the ordinary functions of the references.
Regarding claim 2, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the characteristic value is acquired for each of the plurality of regions.
( in [0050], Betschart explicitly determines characteristic value of distribution for each reaction area and computes values used for exclusion or invalidation. )
Regarding claim 3, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the characteristic value comprises any one selected from a group consisting of:
a number of positive individual separated compartments;
( [0003], [0025]: Betschart teaches that dPCR partitions (reaction areas) yield positive reactions when the target molecule is present and that nucleic acids may be quantified by counting the regions that contain PCR end-product positive reactions. )
a ratio of the number of positive individual separated compartments to the number of individual separated compartments;
an average value of brightness of the individual separated compartments;
a median value of brightness of the individual separated compartments;
a maximum value of brightness of the individual separated compartments; and
a minimum value of brightness of the individual separated compartments,
where the positive individual separated compartment represents an individual separated compartment indicating that the individual separated compartment includes a target and a negative individual separated compartment represents an individual separated compartment indicating that the individual separated compartment does not include a target.
( Given claim 3 is satisfied by any one of the listed characteristics values, Betschart fully teaches the limitations of claim 3. Furthermore, Applicant is directed to Betschart’s other discussions, from which it is readily apparent that Betschart also discloses other listed types of characteristic values for identifying invalid reaction areas. )
Regarding claim 5, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the exclusion region is determined through statistical processing from the characteristic value of each of the plurality of regions and the characteristic value of the plurality of regions.
( in [0050-0054], Betschart uses statistical parameter processing (e.g., mean, standard deviation, distribution-shape assessment, deviations vs thresholds; deriving thresholds from typical values in the same array) to determine which regions are invalid or excluded.)
Regarding claim 6, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the exclusion region is determined based on Vt, μv, and δv, where Vt represents the characteristic value of each of the plurality of regions, μv represents an average of the characteristic value of the plurality of regions, and δv represents a standard deviation of the characteristic value of the plurality of regions.
( in [0050-0054], Betschart uses statistical parameter processing (e.g., mean, standard deviation, distribution-shape assessment, deviations vs thresholds; deriving thresholds from typical values in the same array) to determine which regions are invalid or excluded. Betschart further teaches that the statistical characteristics include mean & standard deviation to determine the exclusion area. )
Regarding claim 7, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the exclusion region is determined as each of a region that satisfies Vt < μv − 3δv and a region that satisfies Vt > μv + 3δv, where Vt represents the characteristic value of each of the plurality of regions, μv represents an average of the characteristic value of the plurality of regions, and δv represents a standard deviation of the characteristic value of the plurality of regions.
( Betschart teaches an exclusion/ invalid-region determination based on image-derived characteristic values and teaches characterizing optical-signal distributions using statistical parameters including mean (μ) and standard deviation (σ), and determining invalid reaction areas using a threshold selected based on such characteristic values. [0050–0053].
Betschart further teaches that, with respect to threshold selection, a POSITA would be able to select a suitable threshold, and that invalid reaction areas (exclusion regions) can be identified when the distribution deviates from expected behavior ([0051]–[0052]). It would have been obvious to a POSITA, when implementing Betschart’s taught threshold-based invalidation using μ and σ, to define the threshold as μ±3σ (i.e., to designate as exclusion regions those regions having characteristic values Vt satisfying Vt < μ−3σ or Vt > μ+3σ), because the 3-sigma rule is a well-known, standard outlier-rejection criterion in statistical quality control and analytical data processing for removing anomalous data points or regions. Applying that conventional 3σ rule in Betschart’s framework would have been a predictable choice for excluding aberrant regions while leaving the underlying assay and image-processing approach unchanged, thereby improving robustness of Betschart’s quantification by rejecting statistical outliers. )
Regarding claim 10, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the image comprises at least any one of a bright-field image or a fluorescent image.
( in [0043-0045], Betschart’s optical signals are imaged derived from pixel values and Betschart discloses imaging method via bright-field or fluorescence mode. )
Regarding claim 11, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the image includes 10 or more and 1,000 or less regions among the plurality of regions.
( in [0041], Betschart uses target regions are quantified approximately 100 to 200, 200 to 300, 300 to 400, 700 or more reaction areas, and further teaches that each reaction region is analyzed by detecting optical-signals from sub-areas arranged as pixels where the image is a dot matrix data structure [0043]. )
Regarding claim 12, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the plurality of regions of the image have mutually equivalent areas.
( Yip, [0358], [0359], [0368]: Yip explicitly teaches dividing an imaged tissue region into fixed-size regions, including large non-overlapping 4096×4096 input windows and a grid of 32×32 tiles within the windows (uniform, mutually equivalent areas. )
Regarding claim 13, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the plurality of regions of the image have overlapping portions that overlap with each other.
( Yip teaches that annotated regions of the image are tiled into overlapping tiles (466×466 pixels) with a stride of 32 pixels, which necessarily creates overlap between neighboring regions/tiles [0367]; and Yip also describes overlap context in multi-tile processing as part of its region analysis [0353]. )
Regarding claim 14, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the image is covered with all the plurality of regions of the image.
( Yip teaches dividing the tissue region into non-overlapping 4096×4096 input windows, and that “typically, between 10–30 input windows are needed to cover the tissue,”, the set of regions collectively covers the (target) image or tissue region [0358]. )
Regarding claim 15, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the image is inhibited from being covered with all the plurality of regions of the image.
( Yip teaches performing tissue segmentation using a tissue masking algorithm to contour tissue and produce a bounding box around the tissue of interest, and dividing the tissue region (not the entire slide/ image background) into windows [0358]; Yip also teaches passing an assembled probability map through a tissue mask to remove background and marker area; both inhibiting full-image coverage [0360]. )
Regarding claim 17, Betschart [as modified by Yip] teaches the information processing system according to claim 1, wherein the individual separated compartment comprises one of a well or a liquid droplet.
( Betschart explicitly lists reaction areas including microwell or nanowell and also discusses droplet-based dPCR (droplets); [0038], [0039]. )
Regarding claims 19–20. The rationale provided for claim 1 is incorporated herein. In addition, the information processing system of claim 1 corresponds to the method of claim 19, as well as the non-transitory storage medium of claim 20, and performs the steps disclosed herein. Therefore, the claims are all ineligible.
Regarding claim 21. The rationale provided for claim 1 is incorporated herein. Claim 21 recites substantially similar limitations to the system of claim 1. In addition, claim 21 further recites the limitation:
wherein the plurality of individual separated compartments includes a sample solution so that each target in the individual separated compartment from the plurality of individual separated compartments is limited to one molecule or less;
( [0076–0078]: Betschart teaches that the sample provided may be liquid; dPCR is performed with the sample in each reaction area of an array of reaction areas; the sample is subjected to limiting dilution across separate PCR reactions so that part of the reactions have no template molecules; the nucleic acid is distributed into different reaction areas such as wells, beads, emulsions, gel spots, or chambers; typically, each reaction area contains one or zero molecules; and Poisson distribution is used to predict the digital regime where only a single DNA amplicon occurs in a randomly discretized reactor volume, such that each reactor volume is limited to no more than a single DNA strand in the digital regime. )
Claims 4, 8-9 and 16 are rejected under 35 U.S.C. §103 as being unpatentable over Betschart [ as modified by Yip ] in view of Duffy (Duffy et al, US 2012/0277114 A1, 2012).
Regarding claim 4, Betschart [as modified by Yip] teaches the information processing system according to claim 1,
wherein the characteristic value comprises any one selected from a group consisting of:
a number of positive individual separated compartments;
( Betschart teaches that dPCR partitions (reaction areas) yield positive reactions when the target molecule is present and that nucleic acids may be quantified by counting the regions that contain PCR end-product positive reactions [0003], [0025]). )
a ratio of the number of positive individual separated compartments to the number of individual separated compartments that include target-capturing substances;
an average value of brightness of the individual separated compartments;
a median value of brightness of the individual separated compartments;
a maximum value of brightness of the individual separated compartments;
a minimum value of brightness of the individual separated compartments;
an average value of brightness of the individual separated compartments that do not include target-capturing substances;
a median value of brightness of the individual separated compartments that do not include target-capturing substances;
a maximum value of brightness of the individual separated compartments that do not include target-capturing substances;
a minimum value of brightness of the individual separated compartments that do not include target-capturing substances;
a standard deviation of brightness of the individual separated compartments that do not include target-capturing substances;
an average value of brightness of the individual separated compartments that include target-capturing substances;
a median value of brightness of the individual separated compartments that include target-capturing substances;
a maximum value of brightness of the individual separated compartments that include target-capturing substances;
a minimum value of brightness of the individual separated compartments that include target-capturing substances;
a standard deviation of brightness of the individual separated compartments that include target-capturing substances;
a number of target-capturing substances;
a value obtained by dividing the number of positive individual separated compartments by the number of individual separated compartments that include target-capturing substances; and
a target-capturing substance filling rate,
where the positive individual separated compartment represents an individual separated compartment indicating that the individual separated compartment includes a target-capturing substance and includes a target, a negative individual separated compartment represents an individual separated compartment indicating that the individual separated compartment includes a target-capturing substance and does not include a target, and the target-capturing substance filling rate represents a value obtained by dividing the number of individual separated compartments that include target-capturing substances by the number of individual separated compartments.
( Given claim 4 is satisfied by any one of the listed characteristics values, Betschart fully teaches the limitations of claim 4. Furthermore, Applicant is directed to Betschart’s other discussions, from which it is readily apparent that Betschart also discloses other listed types of characteristic values for identifying invalid reaction areas. )
Betschart [as modified by Yip] teaches the image-based dPCR artifact-detection and exclusion framework of claim 1, including determining image-derived characteristic values and excluding abnormal/ invalid reaction areas from target quantification. However, Betschart [as modified by Yip] does not expressly teach where Duffy teaches:
wherein the information processing system is configured to use a target-capturing substance for capturing a target
( [0008]: Duffy teaches capturing substances as beads, exposing them to analyte molecules so some beads bind analyte while others do not, and spatially segregating beads into reaction vessels/wells for optical interrogation, a target-capturing substance used to capture a target as analyte. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify Betschart [as modified by Yip]’s image-based dPCR artifact-exclusion system and image-region/ tiling framework, to incorporate Duffy’s capture-object/ bead based digital assay technique. Betschart [as modified by Yip] teaches improving target quantification accuracy by calculating image-derived characteristic values and excluding abnormal or invalid image regions/ reaction areas from target calculation. Duffy teaches a closely related compartment-based digital assay in which capture beads/ capture objects bind target analytes, are spatially segregated into wells/ reaction vessels, are optically interrogated, and are counted or ratioed based on bead presence and analyte-associated bead presence to determine analyte concentration. A person of ordinary skill would have been motivated to use Duffy’s capture beads in Betschart [as modified by Yip]’s image-based quantification workflow so that Betschart [as modified by Yip]’s region-level characteristic-value and exclusion logic could evaluate bead-related conditions, including bead presence, analyte-associated bead presence, and bead filling rate, because such conditions directly affect the reliability of compartment-based target quantification. The combination would have produced the predictable result of excluding unreliable bead/ compartment regions before calculating target information, thereby improving measurement accuracy while preserving the ordinary functions of Betschart [as modified by Yip]’s artifact exclusion, image-region tiling, and Duffy’s capture-bead analyte detection.
Regarding claim 8, Betschart [as modified by Yip and Duffy] teaches the information processing system according to claim 4, wherein the characteristic value is set to the target-capturing substance filling rate and determine, as the exclusion region, each of a region in which the target-capturing substance filling rate is less than 4% and a region in which the target-capturing substance filling rate exceeds 40%.
( Duffy teaches capture-object's filling rate is “about 4% … about 40% … of the capture objects are spatially segregated into the plurality of locations” [0078], that there is an optimal bead occupancy window to supports selecting lower and upper filling-rate thresholds and excluding regions outside those bounds [0076]. )
Regarding claim 9, Betschart [as modified by Yip and Duffy] teaches the information processing system according to claim 4, wherein the target-capturing substance comprises a magnetic particle, wherein the particle has a particle diameter of 1 μm or more and 10 μm or less.
( Duffy teaches using magnetic beads (capture objects) having diameter ~2.8 μm and coated with capture antibodies, which fall within 1–10 μm, as target-capturing substances [0228]. )
Regarding claim 16, Betschart [as modified by Yip] teaches the information processing system according to claim 1,
Betschart [as modified by Yip] teaches that each image region includes a plurality of individual separated compartments, but does not expressly disclose a specific numerical range for the number of compartments per region where Duffy teaches:
wherein each of the plurality of regions includes 100 or more and 100,000 or less individual separated compartments.
( Duffy teaches arrays comprising between about 10,000 and about 100,000 reaction vessels [0140], and also provides a concrete example of an array having 50,000 microwells, which is within the claimed 100–100,000 range [0239]. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to size each image region of the Betschart [as modified by Yip]'s system to include a number of compartments within Duffy's disclosed array-density range. Betschart [as modified by Yip] teaches grouping a plurality of compartments into each image region but does not specify a particular compartment count. Duffy teaches digital assay arrays of about 10,000 to 100,000 reaction vessels, including a 50,000-microwell embodiment. A person of ordinary skill would have been motivated to apply Duffy's known, commercially-relevant compartment density to each Betschart [as modified by Yip]'s region to balance statistical reliability against computational granularity, yielding the predictable result of a region size within Duffy's established range.
Claim 18 is rejected under 35 U.S.C. §103 as being unpatentable over Betschart [ as modified by Yip ] in view of Noji (Noji et al, US 2013/0345088 A1, 2013).
Regarding claim 18, Betschart [as modified by Yip] teaches the information processing system according to claim 1,
Betschart, as modified by Yip, teaches individual separated compartments grouped into image regions for characteristic-value calculation and exclusion determination, but does not expressly disclose a specific volume range for the individual separated compartments where Noji teaches:
wherein the individual separated compartment has a volume of 0.1 fL or more and 1,000 fL or less.
( Noji teaches femtoliter scale chambers used as individual separated compartments, including receptacles with a capacity up to 1,000 fL, which falls within the claimed range [0003]. )
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to use Noji’s known femtoliter-scale chambers/ droplets in Betschart [as modified by Yip]’s image-based digital assay system, region/ tiling framework, because Betschart [as modified by Yip] and Noji both use optically interrogated separated compartments for target detection/ quantification, and selecting a known femtoliter compartment volume would predictably support single-molecule or low-copy-number target detection while preserving Betschart [as modified by Yip]’s image-based abnormal-region exclusion.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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