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
A Preliminary Amendment was made 03/17/2025 to amend claims 4, 9, 11, 13, 14, 17-20, 36 and cancel claims 12, 21-35, 37-59.
Election/Restrictions
Applicant’s election without traverse of Group 1: Claims 1-11, 13-20 in the reply filed on March 6, 2026 is acknowledged. The claims submitted with the response are used in the rejections below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is considered by examiner.
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 1-11, 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gedraitis et al (US 2021/0278654) in view of Chiu et al (US 2020/0208202).
Regarding Claim 1, Gedraitis et al teach a method for detecting presence of a target in a biological sample, comprising:
obtaining an image representing an array of partitions disposed in a container (an image 200a of biological samples, with samples located at evenly spaced locations 118 arranged in an array 150 of a slide (container) 116, is captured with a high-content imaging system (HCIS) 100 via the image capture device 110; Fig 1, 2, 3A and ¶ [0015], [0018], [0024]-[0025]);
determining, based on the image representing the array of partitions, locations associated with a plurality of corners of the array of partitions (the image 200a represents the partitions 118 of the biological samples, including locations 202a representing the reference marks 152, positioned in the corners 118 of array 150; Fig 2, 3A and ¶ [0024]-[0026]); and
quantifying, based on the locations associated with the plurality of corners, a first target concentration in the biological sample (based on the locations 118 using reference marks 152, the biological sample locations is identified for further analysis; Fig 2, 3A and ¶ [0026]-[0026], [0034]).
Gedraitis et al does not explicitly teach quantifying a first target concentration of the biological sample.
Chiu et al is analogous art pertinent to the technological problem addressed in the current application and teaches quantifying a first target concentration of the biological sample (the image data of the array is used to quantify the target concentration for the given biological sample, step 145, 150, 155; Fig 2C and ¶ [0292]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gedraitis et al with Chiu et al including quantifying a first target concentration of the biological sample. By performing image analysis to quantify the target concentration, in combination with PCR, the concentration of the analyte (target) may be more accurately determined using the size of sample and may be performed in an efficient manner with higher accuracy in measuring the concentration, as recognized by Chiu et al (¶ [0075]).
Regarding Claim 2, Gedraitis et al in view of Chiu et al teach the method of claim 1 (as described above), wherein the image representing the array of partitions comprises a pre-PCR image representing the array of partitions before amplification of one or more targets in the biological sample (Chiu et al, droplets can be extracted before amplification and imaged for the given reaction; ¶ [0124]).
By imaging the fluorescence before amplification, it is possible to back-calculate the original concentration before amplification, thereby measuring volume of the sample and analysis of changes in the fluorescence (associated with background signal), thereby improving accuracy in analysis and reducing undue experimental error, as recognized by Chiu et al (¶ [0010], [0124]).
Regarding Claim 3, Gedraitis et al in view of Chiu et al teach the method of claim 1 (as described above), wherein the image representing the array of partitions comprises a post-PCR image representing the array of partitions after amplification of one or more targets in the biological sample (Chiu et al, an image is taken of the array after PCR amplification (thermal cycling) of a given target in the biological samples, with the target concentration of a given sample calculated 155; Fig 2C, 4 and ¶ [0292], [0329]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gedraitis et al with Chiu et al including wherein the image representing the array of partitions comprises a post-PCR image representing the array of partitions after amplification of one or more targets in the biological sample. By quantifying image data linked to the PCR data of a biological sample, analysis of low sample concentrations may be performed, thereby advancing a means to detect and quantify the presence or absence of a target agent in droplets with reduced introduction of undue experimental error, as recognized by Chiu et al (¶ [0010], [0049]).
Regarding Claim 4, Gedraitis et al in view of Chiu et al teach the method of claim 1 (as described above), wherein determining the locations associated with the plurality of corners of the array of partitions (the image 200a represents the partitions 118 of the biological samples, including locations 202a representing the reference marks 152, positioned in the corners 118 of array 150; Fig 2, 3A and ¶ [0024]-[0026]) comprises:
selecting, using the image representing the array of partitions, a first corner area comprising a first corner formed by a first edge of the array and a second edge of the array (Gedraitis et al, the image 200a includes a first corner 160 formed by pixel location 210, determined based on reference marks 152, representing array location 118, combined with reference marks 154; Fig 2, 3A and ¶ [0026]);
obtaining a first template image (Gedraitis et al, a first binary image (first template image) of image 200a is created; Fig 3A and ¶ [0029]-[0031]) and a second template image (Gedraitis et al, a second binary image (second template image) of image 200b is created; Fig 3B and ¶ [0029]-[0031]); and
determining, based on the first template image and the second template image, a location of the first corner (Gedraitis et al, the first binary image (first template image) of image 200a is aligned with second binary image (second template image) of image 200b and used to identify the point 160 of the array 150 with alignment of points 210a, 210b, to identify the corner position 118 and marker 152; Fig 2, 3A, 3B and ¶ [0031]-[0034]).
Regarding Claim 5, Gedraitis et al in view of Chiu et al teach the method of claim 4 (as described above), wherein selecting the first corner area (Gedraitis et al, the first corner 118, 152 based on point location 160; Fig 2 and ¶ [0026]) comprises:
obtaining dimensions of an area to-be-selected (Gedraitis et al, distances 156a,b,c and 158a,b,c are measured to the respective reference mark 154, representing the horizontal and vertical dimensions of array 150; Fig 2 and ¶ [0033]-[0034]);
selecting the first corner area based on the dimensions of the area to-be-selected (Gedraitis et al, from the measured distances 154, 156, the point 160 is calculated, used to identify nearest corner 118, 152 of array 150; Fig 2 and ¶ [0033]-[0035]); and
displaying an annotation of the first corner area, the annotation overlaying the image representing the array of partitions (Gedraitis et al, an annotation 210a, 210b is marked with the pixel point representing the array 150 corner point 160 and may be displayed to a user for visual analysis in sub-images; ¶ [0033]-[0035]).
Regarding Claim 6, Gedraitis et al in view of Chiu et al teach the method of claim 4 (as described above), wherein determining the location of the first corner (Gedraitis et al, determining location of corner 118 (marker 152) of array 150 closest to point 160, determined with additional markers 154; Fig 2, 3A and ¶ [0033]-[0034]) comprises:
determining a location of the first edge using the first template image (Gedraitis et al, a first edge 212a of the first binary (template) image of image 200a can be determined; Fig 3A and ¶ [0033]);
determining a location of the second edge using the second template image (Gedraitis et al, a corresponding first edge 212b of the second binary (template) image of image 200b can be determined; Fig 3A and ¶ [0033]); and
determining the location of the first corner based on the location of the first edge and the location of the second edge (Gedraitis et al, the first binary (template) image of image 200a is aligned with second binary (template) image of image 200a to identify point 160 and location of nearby array corner 118, 152, including based on alignment of boundaries 212a, 212b between the binary images; Fig 2-4 and ¶ [0033]-[0034]).
Regarding Claim 7, Gedraitis et al in view of Chiu et al teach the method of claim 6 (as described above), wherein determining the location of the first edge using the first template image (Gedraitis et al, a first edge 212a of the first binary (template) image of image 200a can be determined; Fig 3A and ¶ [0033]) comprises:
performing one or both of moving and rotating (interpreted as “performing moving and/or rotating”) the first template image (the first binary (template) image of image 200a may be brought into register alignment by shifting the template horizontally and vertically; ¶ [0033]);
correlating, based on one or both of moving and rotating the first template image, the first template image with the first corner area to find the first edge by matching at least a part of the first template image with the first edge (the first binary (template) image of image 200a is aligned for the pixel point 210a representing point 160 and respective boundary edge 212; Fig 2, 3A and ¶ [0033]-[0037]); and
determining the location of the first edge based on a result of correlating the first template image with the first edge (the location of edge 212a is determined for first binary (template) image of image 200a based on pixel 210a, related to markers 152, 154; Fig 2, 3A and ¶ [0033]-[0037]).
Regarding Claim 8, Gedraitis et al in view of Chiu et al teach the method of claim 6 (as described above), wherein determining the location of the second edge using the second template image (Gedraitis et al, a corresponding first edge 212b of the second binary (template) image of image 200b can be determined; Fig 3A and ¶ [0033]) comprises:
performing one or both of moving and rotating (interpreted as “performing moving and/or rotating”) the second template image (the second binary (template) image of image 200b may be brought into register alignment by shifting the template horizontally and vertically; ¶ [0033]);
correlating, based on one or both of moving and rotating the second template image, the second template image with the first corner area to find the second edge by matching at least a part of the second template image with the second edge (the second binary (template) image of image 200b is aligned for the pixel point 210b with the point 210a (first corner) representing point 160 and respective boundary edge 212; Fig 2, 3B and ¶ [0033]-[0037]); and
determining the location of the second edge based on a result of correlating the second template image with the second edge (the location of edge 212b is determined for second binary (template) image of image 200b based on pixel 210b, related to markers 152, 154; Fig 2, 3A and ¶ [0033]-[0037]).
Regarding Claim 9, Gedraitis et al in view of Chiu et al teach the method of claim 4 (as described above), wherein:
the first template image (Gedraitis et al, the first binary (template) image of image 200a; Fig 3A and ¶ [0033]) comprises a first portion and a second portion (Gedraitis et al, the first binary (template) image of image 200a includes an array 150 (appears as a typo as 120 in reference) region with sample locations 118 (first portion) with the multiple markers 152 and second portion 204a, 206a, 208a representing markers 154 located outside of the array 150; Fig 2, 3A and ¶ [0024]-[0025]);
the first portion represents a plurality of predetermined partitions forming a first pattern (Gedraitis et al, array 150 includes locations 118 organized with predetermined partitions (via printed, etched or raised) for different sample deposits (partitioned such that samples are separated) and organized in an array pattern; Fig 2 and ¶ [0117]-[0018], [0021], [0027]); and
the second portion represents an area predetermined to have no partitions (Gedraitis et al, the region outside of the array 150 on the slide 116 does not include the locations 118 for the biological samples; Fig 2 and ¶ [0117]-[0019]), the second portion being immediately adjacent to the first portion (Gedraitis et al, the region (second portion) adjacently surrounds the array 150 (first portion) on the slide 116; Fig 2 and ¶ [0117]-[0019]).
Regarding Claim 10, Gedraitis et al in view of Chiu et al teach the method of claim 9 (as described above), wherein the first pattern comprises a single line pattern formed by the plurality of predetermined partitions (Gedraitis et al, locations 118 are a predetermined pattern, organized in an array 120, and used for the sample deposits; Fig 2 and ¶ [0018]).
Regarding Claim 11, Gedraitis et al in view of Chiu et al teach the method of claim 4 (as described above), wherein
the second template image (Gedraitis et al, the second binary (template) image of image 200b; Fig 3B and ¶ [0033]) comprises a third portion and a fourth portion (Gedraitis et al, the second binary (template) image of image 200b includes an array 150 (appears as a typo as 120 in reference) region with sample locations 118 (first portion) with the multiple markers 152 and second portion 204b, 206b, 208b representing markers 154 located outside of the array 150; Fig 2, 3B and ¶ [0024]-[0025]), wherein:
the third portion represents a plurality of predetermined partitions forming a second pattern (Gedraitis et al, array 150 includes locations 118 organized with predetermined partitions (via printed, etched or raised) for different sample deposits (partitioned such that samples are separated) and organized in an array pattern (represented in the second binary (template) image of image 200b; Fig 3B); Fig 2 and ¶ [0117]-[0018], [0021], [0027]);
the fourth portion represents an area predetermined to have no partitions (Gedraitis et al, the region outside of the array 150 on the slide 116 does not include the locations 118 for the biological samples; Fig 2 and ¶ [0117]-[0019]), the fourth portion being immediately adjacent to the third portion (Gedraitis et al, the region (second portion) adjacently surrounds the array 150 (first portion) on the slide 116; Fig 2 and ¶ [0117]-[0019]); and
the second pattern comprises a first line pattern and a second line pattern (Gedraitis et al, the array 150 contains locations 118 organized along a vertical (first line) and horizontal (second line) pattern; Fig 2 and ¶ [0018]), the first line pattern being formed by a part of the predetermined partitions (Gedraitis et al, the array 150 vertical (first line) pattern represents the partitioned separated locations 118 for samples; Fig 2 and ¶ [0017]-[0018]), and the second line pattern being formed by another part of the predetermined partitions (Gedraitis et al, the array 150 horizontal (second line) pattern represents the partitioned separated locations 118 for samples; Fig 2 and ¶ [0017]-[0018]), the first line pattern and the second line pattern being offset from each other (Gedraitis et al, the array 150 contains evenly spaced locations 118 organized along a vertical (first line) and horizontal (second line) pattern; Fig 2 and ¶ [0018]).
Regarding Claim 13, Gedraitis et al in view of Chiu et al teach the method of claim 4 (as described above), further comprising:
selecting, using the image representing the array of partitions, one or more additional corner areas comprising one or more corresponding additional corners (Gedraitis et al, the image 200a includes additional reference marks 152, representing array location 118, representing additional corners of the array 150; Fig 2, 3A and ¶ [0023]);
obtaining one or more additional template images (Gedraitis et al, a third binary image (third template image) of image 200c is created; Fig 3C and ¶ [0029]-[0031]); and
determining, based on two or more of the first template image, the second template image, and the one or more additional template images, one or more locations of the one or more additional corners (Gedraitis et al, the first template image 200a is aligned with second template image 200b and the third template image 200c used to identify the point 160 of the array 150 with alignment of points 210a, 210b, 210c to identify the corner position 118 and marker 152 of additional corners (top left, top right, bottom right in relation to bottom left corner closest to point 160); Fig 2, 3A, 3B, 3C and ¶ [0031]-[0034]).
Regarding Claim 14, Gedraitis et al in view of Chiu et al teach the method of claim 1 (as described above), wherein the image is associated with a first fluorescence channel of a PCR apparatus (Chiu et al, the image is taken of a fluorescence channel to measure a target concentration, step 150, 155, associated with a PCR analysis; Fig 2C, 8 and ¶ [0292], [0420]); comprising a plurality of fluorescence channels including the first fluorescence channel (Chiu et al, the PCR fluorescence reaction is dependent on the given fluorescence wavelength of the given agent for a target (indicator fluorescence) and reference (reference fluorescence), with the image obtained using associated wavelengths; Fig 2C, 8 and ¶ [0292], [0421]-[0423]), the plurality of fluorescence channels being associated with different spectral wavelengths (Chiu et al, the separate fluorescence wavelength channels of the image are obtained with different wavelength channels (example 543 nm and 488 nm for the respective ROX and FAM dye; Fig 2C, 8 and ¶ [0292], [0421]-[0423]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gedraitis et al with Chiu et al including wherein the image is associated with a first fluorescence channel of a PCR apparatus comprising a plurality of fluorescence channels including the first fluorescence channel, the plurality of fluorescence channels being associated with different spectral wavelengths. By quantifying image data linked to the PCR data of a biological sample, analysis of low sample concentrations may be performed, thereby advancing a means to detect and quantify the presence or absence of a target agent in droplets with reduced introduction of undue experimental error, as recognized by Chiu et al (¶ [0010], [0049]).
Regarding Claim 15, Gedraitis et al in view of Chiu et al teach the method of claim 14 (as described above), further comprising: obtaining one or more additional images, the one or more additional images being associated with one or more fluorescence channels of the PCR apparatus (Chiu et al, the PCR fluorescence reaction is dependent on the given fluorescence wavelength of the given agent for a target (indicator fluorescence) and reference (reference fluorescence), with a second image for a second fluorescence wavelength channels obtained (example 488 nm for FAM dye), with multiple images acquired, step 140; Fig 2C, 4, 8 and ¶ [0292], [0422]-[0423]); determining, based on the one or more additional images, additional locations associated with the plurality of corners of the array of partitions (Chiu et al, in acquiring additional images, step 140, the horizontal and vertical location is determined 125, which is used to align a second fluorescence image to the first fluorescence image 150a of the given well; Fig 2C, 4, 8 and ¶ [0292], [0422]-[0423]); and quantifying, based on the additional locations, one or more additional target concentrations in the biological sample (Chiu et al, the image data of the array is used to quantify the target concentration for the given biological sample, step 145, 150, 155; Fig 2C, 4, 9 and ¶ [0292], [0422]-[0425]).
Regarding Claim 16, Gedraitis et al in view of Chiu et al teach the method of claim 14 (as described above), wherein one of the plurality of fluorescence channels comprises a channel using a 6-carboxy-X-rhodamine (ROX) based dye (Chiu et al, one fluorescence channel is at the 543 nm wavelength for the 6-carboxy-X-rhodamine (ROX) dye; Fig 8A and ¶ [0421]-[0423]).
Regarding Claim 17, Gedraitis et al in view of Chiu et al teach the method of claim 1 (as described above), wherein quantifying the first target concentration in the biological sample comprises: determining, based on the locations associated with the plurality of corners, partition locations of the array of partitions (Gedraitis et al, the image 200a represents the partitions 118 of the biological samples, including locations 202a representing the reference marks 152, positioned in the corners 118 of array 150; Fig 2, 3A and ¶ [0024]-[0026]); classifying each partition image representing a partition of the array of partitions as a positive partition image or a non-positive partition image (Gedraitis et al, the partitions regions 118 to contain the biological sample are determined; Fig 3A and ¶ [0026], [0034]-[0035]); and quantifying, based on a classification result of at least some of the partition images, the first target concentration in the biological sample (Gedraitis et al, based on the locations 118 using reference marks 152, the biological sample locations is identified for further analysis; Fig 2, 3A and ¶ [0026]-[0026], [0034]; Chiu et al the image data of the array is used to quantify the target concentration for the given biological sample, step 145, 150, 155; Fig 2C and ¶ [0292]).
Regarding Claim 18, Gedraitis et al in view of Chiu et al teach the method of claim 1 (as described above), wherein the container comprises a microfluidic array plate (a microfluidic 96-well array plate is used; Fig 4 and ¶ [0291], [0329], [0422]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gedraitis et al with Chiu et al including the container comprises a microfluidic array plate. By using a multi-well plate, the plate may be used for both PCR assay amplification and imaging to identify the presence or absence of he detectable agent while using a small sample size while maintaining a high accuracy quantification for a substantial number of samples, as recognized by Chiu et al (¶ [0049]).
Regarding Claim 19, Gedraitis et al teach a non-transitory computer readable medium comprising a memory storing one or more instructions (a memory contains instructions for implementing logical functions; ¶ [0052]-[0053]) which, when executed by one or more processors of at least one computing device (processor executes instructions; ¶ [0052]), perform steps according to claim 1 (as described above).
Gedraitis et al does not teach quantification of one or more target concentrations in a biological sample using an analyte detection apparatus configured to analyze an array of partitions of the biological sample.
Chiu et al is analogous art pertinent to the technological problem addressed in the current application and teaches quantification of one or more target concentrations in a biological sample using an analyte detection apparatus configured to analyze an array of partitions of the biological sample(a PCR cycler is used for amplification measurements of the biological samples; Fig 4 and ¶ [0329]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gedraitis et al with Chiu et al including quantification of one or more target concentrations in a biological sample using an analyte detection apparatus configured to analyze an array of partitions of the biological sample. By using PCR techniques to quantify small biological samples, allowing for identification of target molecules in small volumes through amplification techniques, as recognized by Chiu et al (¶ [0012]).
Regarding Claim 20, Gedraitis et al teach a system (system to implement template-based image analysis; ¶ [0052]) comprising:
one or more processors of at least one computing device (processor executes instructions; ¶ [0052]); and
a memory storing one or more instructions (a memory contains instructions for implementing logical functions; ¶ [0052]-[0053]), when executed by the one or more processors, cause the one or more processors (processor executes instructions; ¶ [0052]) to perform steps according to claim 1 (as described above).
Gedraitis et al does not teach quantifying of one or more target concentrations in a biological sample using an analyte detection apparatus configured to analyze an array of partitions of the biological sample.
Chiu et al is analogous art pertinent to the technological problem addressed in the current application and teaches quantifying of one or more target concentrations in a biological sample using an analyte detection apparatus configured to analyze an array of partitions of the biological sample(a PCR cycler is used for amplification measurements of the biological samples; Fig 4 and ¶ [0329]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Gedraitis et al with Chiu et al including quantification of one or more target concentrations in a biological sample using an analyte detection apparatus configured to analyze an array of partitions of the biological sample. By using PCR techniques to quantify small biological samples, allowing for identification of target molecules in small volumes through amplification techniques, as recognized by Chiu et al (¶ [0012]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hallock et al (US 2018/0023045) teach a method and system for using a microfabricated device with a plurality of microwells used for a plurality of samples that may quantified and compared, including image analysis.
Whitman et al (US 2019/0226008) teach a method and system for quantifying target samples contained on an array including imaging before amplification and post amplification of the samples and quantifying the intensity of the droplets before and after amplification.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHLEEN M BROUGHTON whose telephone number is (571)270-7380. The examiner can normally be reached Monday-Friday 8:00-5:00.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661