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 .
Information Disclosure Statement(s)
The Information Disclosure Statement filed on August 28th, 2026 has been considered by the examiner.
Response to Remark(s)
Applicant's amendment filed June 17th, 2026 have been fully entered and considered. Applicant’s amendment to the claims have overcome each and every drawing objection, 101 rejection, 102 and 103 prior art rejection, previously set forth in the Non-Final Office Action mailed on March 18th, 2026. Regarding the prior art rejection, all new grounds of rejection set forth in the present action were necessitated by Applicants’ claim amendments, regarding the 112(f) issues and the 112(a) and 112(b) rejection, the examiner finds the Applicants’ arguments to be some non-persuasive. Accordingly, this action is made final.
Status of Claims
Claims 1-2, 5-6, 8-18, 23 and 64-67 are pending, claims 1, 17-18 and 23 have been amended, claims 3-4, 7, 19-22 and 24-63 have been canceled, claims 64-67 have been added. Claims 1-2, 5-6, 8-18, 23 and 64-67 remains rejected.
Response to Argument(s)
Regarding the prior art rejections:
In view of the Amendments to independent claim 1 the previously applied prior art rejections to the independent claim 1 are withdrawn. Applicants’ arguments are rendered moot in view of the new grounds of rejection set forth below. The newly added independent claim 64 has been considered and applied under the same prior arts for rejections, see 103 rejection below.
Regarding the 112(f) issues and the 112(a) and 112(b) rejections:
The examiner respectfully finds the amendment to not overcome the 112(f) issues and the corresponding 112(a) and 112(b) rejections. The examiner recognizes the Applicants’ amendment to further indicate in the claims, such as the claim 23 that “an image input layer is a model input layer for the image recognition model” wherein, its independent amended claim 1 recites a specific image recognition model to be a machine learning model generated through a specific training process involving specific training samples therefore, provides sufficient structure, material and/or act for the image input layer to be an input layer of the image recognition model, however, for the limitation of “an analysis layer is a classifier model”, this amendment further include another generic placeholder of a “classifier model” which requires 112(f) analysis, and the amended claim itself doesn’t provide structure, material and/or act for the placeholder to have sufficient support to perform the corresponding recited functions, see the 112(f) section below for more details.
Regarding the 101 rejection:
The examiner recognizes the independent claim 1 has been amended to further include a machine learning model that has a specific training process that is more than conventional, well-known routine using specific training samples including images and labels representing reference values of analysis parameters of the particles of concern within the sample images. Therefore, the examiner finds the amended claim 1 to integrate the additional elements into a practical application. The 101 rejection for all of the claims has been overcome.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitation(s) that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function.
Claim(s) 23, recite(s) limitation(s) that use words like “means” (or “step”) or similar terms with functional language but do not invoke 35 U.S.C. 112(f):
Claim 23; recites the limitation, “an image input layer¸ which is a model input layer for the image recognition model,” [Line 6].
Such claim limitation(s) is/are:
(i) “image input layer” has a structure associated with it, within the claimed limitation, a being a model input layer for the image recognition model, which is a machine learning model trained in a specific steps approach as recited in the independent claim 1.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Claim 23 , recite(s) limitation(s) that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claims 23 and 64-65; recites the limitation, “a feature extraction layer configured to extract….” [Line 8], [Lines 14-15], [Lines 21-22].
Claim 23; recites the limitation, “an analysis layer…configured to output.” [Line 10].
Claim 23; recites the limitation, “a classifier model, configured to output.” [Line 10].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 23:
(i) “feature extraction layer” (Par. 0057]- The specification, filed on November 28th, 2023, discloses sufficient structure, material and act for the recited features as mentioned above to perform the recited functions as disclosed in [0057] wherein the feature extraction layer include a convolutional neural network such as, ResNet, ResNeXt, SE-Net, DenseNet, MobileNet, ShuffleNet, RegNet, EfficientNet, Inception, etc., a recurrent neural network model, or the like, or a combination thereof, thus have sufficient structure or material wherein is a convolutional neural network such as, ResNet, ResNeXt, SE-Net, DenseNet, MobileNet, ShuffleNet, RegNet, EfficientNet.).
(ii) “analysis layer” - The specification, filed on November 28th, 2023, does not provide sufficient support for a structure, material and/or act for the recited analysis layer to perform the recited function. the closest support can be found in the instant specification’s [0059] to have the analysis layer to may be configured as a classifier model which still does not provide sufficient structure, material and/or act for the recited analysis layer to perform the recited function, thus have insufficient written support for a structure, material and/or act for the recited analysis layer to perform the recited function.
(iii) “classifier model” - The specification, filed on November 28th, 2023, does not provide sufficient support for a structure, material and/or act for the recited classifier model to perform the recited function. the closest support can be found in the instant specification’s [0059] to have the analysis layer to may be configured as a classifier model which still does not provide sufficient structure, material and/or act for the recited analysis layer to perform the recited function, thus have insufficient written support for a structure, material and/or act for the recited analysis layer to perform the recited function.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 23 is 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 pre-AIA the applicant regards as the invention.
Claim 23’s limitations:
Claim 23; recites the limitation, “an analysis layer…configured to output.” [Line 10].
Claim 23; recites the limitation, “a classifier model, configured to output.” [Line 10].
Claim 23 respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 23 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation.
Claim 23’s limitations:
Claim 23; recites the limitation, “an analysis layer…configured to output.” [Line 10].
Claim 23; recites the limitation, “a classifier model, configured to output.” [Line 10].
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 5, 8-10, 12, 14, 17-18 and 66 are rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”).
Regarding claim 1, Weng teaches a method for analyzing particles in a test sample, comprising (Abstract discloses of image processing for cell detection which is analogous to analyzing particles [cells]): obtaining a full volume image of the test sample using an image acquisition device (Par. [0012] discloses an image capturing device [image acquisition device] to capture a plurality of images to be stitched into a total chip image [analogous to full volume image]).
However, Weng does not explicitly teach and determining an analysis parameter of the particles in the test sample based on the full volume image.
Zhou teaches and determining an analysis parameter of the particles in the test sample based on the full volume image (Page 5026, 2nd column, discloses the images are stitched together [analogous to the total chip image and the full volume image as claimed] for quantitative analysis of single cell morphometric and proteomic signatures based on quantification of celomic parameters [2nd Par.] to identify cell periphery a number identity for cell is given for analysis, all of these information together are analogous to analysis parameter being determined).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, with the teachings of Zhou of having wherein determining an analysis parameter of the particles in the test sample based on the full volume image.
Wherein having Weng’s method of having wherein determining an analysis parameter of the particles in the test sample based on the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range. Since both Weng and Zhou perform cell analysis using image detection. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Zhou’s system improves cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range, (see Zhou’s Abstract).
However, Weng in view of Zhou do not explicitly teach wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
Kasdan teaches wherein the full volume image reflects an entire volume state of the test sample (Col. 3, lines 35-45, discloses “the user sets the maximum volume that the flow cell or the microscopic slide will analyze” wherein, the image obtain for analyzing is a max. volume image is analogous to the full volume image reflects an entire volume state of the test sample); wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume (Col. 5, lines 1-15, discloses “calculate the concentration of each type of the particle analyzed. The concentration calculation consists of simply taking the number of particles determined for each type of particle counted, divided by the volume of the fluid sample analyzed for each of the particular type of particle”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, and determining an analysis parameter of the particles in the test sample based on the full volume image with the teachings of Kasdan of having wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
Wherein having Weng’s method of having wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve particle analysis in fluid flow image capturing by considering different types of particles in fluid sample in low concentration by having maximum volume set low, to encourage greater throughput. Since both Weng and Kasdan perform cell analysis using image detection based on liquid flow and chip image setting. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Kasdan’s system improves particle analysis in fluid flow image capturing by considering different types of particles in fluid sample in low concentration by having maximum volume set low, to encourage greater throughput, (see Kasdan’s Col. 1, lines 37-44).
However, Weng in view of Zhou and Kasdan do not explicitly teach the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process including: iteratively training an initial image recognition model based on a plurality of training samples, wherein the plurality of training samples include sample images, labels for the plurality of training samples represent reference values of analysis parameters of the particles in the sample images, and the processing the full volume image based on the image recognition model further includes: extracting a color and a shape feature of the particles in the full volume image; and outputting the analysis parameter of the particles based on the color and the shape feature of the particles in the full volume image.
Randolph teaches the determining an analysis parameter of the particles (Abstract discloses “high-throughput flow imaging microscopy coupled with convolutional neural networks to analyze particles, such as aggregated biomolecules, and cells for use”) in the test sample based on the full volume image includes (Par. [0179] discloses “volume and analysis time requirement make this approach ideal for diagnosing neonatal sepsis. Larger blood samples may also be analyzed using this approach, increasing the analysis time due to the extra volume but yielding more reliable detection of trace concentrations of the pathogen” indicating a full volume image obtained): determining the analysis parameter of the particles by processing the full volume image based on an image recognition model (Par. [0125] discloses “whenever a parameter of the processing system is changed, the deep learning model is retrained”, Par. [0183] discloses “ConvNets for monitoring protein aggregates and other particles produced during the manufacture of therapeutic protein formulations may be detected and classified…monitor the particles in their product to ensure that the concentration and structure of particles present in each vial matches the product specification” wherein, the concentration and structure is analogous to the recited analysis parameter), wherein the image recognition model is a machine learning model generated through a training process including (Par. [0183] discloses “ConvNets for monitoring protein aggregates and other particles produced during the manufacture of therapeutic protein formulations may be detected and classified”, Par. [0184] discloses “the present inventors trained a ConvNet to identify aggregates of a polyclonal antibody”): iteratively training an initial image recognition model based on a plurality of training samples (Par. [0184] discloses “the network was then applied to synthetic FIM datasets containing mixtures of particles generated by stirring stress (a particle types the network was not shown during training)”), wherein the plurality of training samples include sample images (Par. [0184] discloses “the network was then applied to synthetic FIM datasets containing mixtures of particles generated by s tiring stress (a particle types the network was not shown during training)”; moreover, Par. [0185] discloses “color FIM images of these proteins were measured with a FlowCan VS device” and Par. [0189] discloses “the FIM images analyzed here were not contained in the training dataset”, wherein, the FIM dataset include color images), labels for the plurality of training samples (Par. [0186] discloses “C represents the net number of labeled classes in the training set”) represent reference values of analysis parameters of the particles in the sample images (Par. [0186] discloses “C represents the net number of labeled classes in the training set…indicator function for the sample xj belonging to class label “I”, ci represents a input parameter specifying the desired cluster center for class i samples”, moreover, Par. [0113] discloses “extracts relevant features of interest for a given classification…labeling…images may be analyzed to compile a database containing, count, size, concentration, as well as a range of shape and image contrast parameters…to product parameter distributions using histograms and scatter plots” wherein, the labels include information regarding size, concentration [analysis parameter]), and the processing the full volume image based on the image recognition model further includes (Par. [0183] discloses “ConvNets for monitoring protein aggregates and other particles produced during the manufacture of therapeutic protein formulations may be detected and classified…monitor the particles in their product to ensure that the concentration and structure of particles present in each vial matches the product specification”): extracting a color and a shape feature of the particles in the full volume image (Par. [0108] discloses “flow cytometry data may be presented in the form of single parameter histograms or as 20dimensional plots of parameters…the area, height, and width of the forward and side scatter signals can provide information about the size and granularity, or internal structure, for a cell…such as a dot plot, a pseudo-color dot plot…” furthermore, Par. [0132] discloses “a segmentation operation may be applied which may include converting the one or more images of the biological sample from color images to grayscale images”); and outputting the analysis parameter of the particles (Pars. [0144-0145] disclose “direct output from the machine learning model provides a condition, namely the model may identify a feature of interest…one class of host cell quantitation counts leukocytes. Cell count information may be absolute or differential” wherein the output of the machine learning model include information regarding the particle count [analysis parameter]) based on the color and the shape feature of the particles in the full volume image (Par. [0108] discloses “flow cytometry data may be presented in the form of single parameter histograms or as 20dimensional plots of parameters…the area, height, and width of the forward and side scatter signals can provide information about the size and granularity, or internal structure, for a cell…such as a dot plot, a pseudo-color dot plot…”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou and Kasdan of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, and determining an analysis parameter of the particles in the test sample based on the full volume image with the teachings of Randolph of having the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process including: iteratively training an initial image recognition model based on a plurality of training samples, wherein the plurality of training samples include sample images, labels for the plurality of training samples represent reference values of analysis parameters of the particles in the sample images, and the processing the full volume image based on the image recognition model further includes: extracting a color and a shape feature of the particles in the full volume image; and outputting the analysis parameter of the particles based on the color and the shape feature of the particles in the full volume image.
Wherein having Weng’s method of having the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process including: iteratively training an initial image recognition model based on a plurality of training samples, wherein the plurality of training samples include sample images, labels for the plurality of training samples represent reference values of analysis parameters of the particles in the sample images, and the processing the full volume image based on the image recognition model further includes: extracting a color and a shape feature of the particles in the full volume image; and outputting the analysis parameter of the particles based on the color and the shape feature of the particles in the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve assessment of imaged samples to detect particles by identifying of pathogens within samples to allow for effective treatment approach. Since both Weng and Randolph perform cell concentration determination through volume image. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Randolph’s system improves assessment of imaged samples to detect particles by identifying of pathogens within samples to allow for effective treatment approach, (see Randolph’s Par. [0004]).
Regarding claim 2, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 1, Weng teaches wherein before the obtaining a full volume image of the test sample, the method further comprises: performing an enrichment process on the test sample to obtain a processed test sample (Weng, Par. [0031], discloses the process including filling PBS solution in the chip into the injected cells [adding PBS solution is analogous to an enrichment process]), the particles in the processed test sample aggregating at a bottom of a sample container (Weng, Par. [0031], discloses the cells settle downward due to gravity to the lateral runner at the bottom [analogous to a bottom of a sample container, the particles aggregating]).
Regarding claim 5, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 1, Weng teaches wherein, when the particles include at least two types (Weng, Par. [0004], discloses a cell sorting process [indicating a more than one size of cells, a plurality of types of cells in terms of size]), before the obtaining the full volume image of the test sample, the method further comprises: removing other types of particles in the test sample except for one target type of particles (Weng, Par. [0004], discloses the cell sorting, indicating a type of cells of a certain desired size is kept while others are being sorted out [removed]).
Regarding claim 8, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 1, Weng teaches wherein the obtaining a full volume image of the test sample further includes (Par. [0012] discloses an image capturing device [image acquisition device] to capture a plurality of images to be stitched into a total chip image [analogous to full volume image]): obtaining the full volume image (Par. [0012] discloses an image capturing device) of the test sample based on a single imaging field on an imaging surface of a sample container, or obtaining the full volume image of the test sample by stitching detection images corresponding to a plurality of imaging fields on the imaging surface of the sample container (“or” indicates a selection, therefore, only one of the option is the instant scope of the claim, the examiner selects “obtaining the full…..by stitching detection images……the sample container” which is disclosed in Weng’s Par. [0016] wherein the images of the cell on the detection chip within the imaging fields of the camera are being stitched to form the total chip image).
Regarding claim 9, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 8, Weng teaches wherein the sample container is provided with a plurality of markers, and an overlap region between two adjacent detection images includes at least one same marker (Weng, Par. [0016], discloses the chip includes markers, according to Par. [0014], for the stitching of images adjacent overlapped with same marker to be stitched).
Regarding claim 10, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 9, Weng teaches wherein the plurality of markers are provided at equal intervals on an edge or a bottom of the sample container (“or” indicates a selection, therefore, only one of the options is the instant scope of the claim, the examiner selects “provided at a bottom of the sample container” which is disclosed in Weng’s Par. [0042] wherein the markers are increased at the bottom of the chip [provided at the bottom of the container as claimed]).
Regarding claim 12, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 8, Weng teaches wherein the obtaining the full volume image of the test sample (Par. [0012] discloses an image capturing device [image acquisition device] to capture a plurality of images to be stitched into a total chip image [analogous to full volume image]) by stitching detection images corresponding to a plurality of imaging fields on the imaging surface of the sample container (Weng’s Par. [0016] wherein the images of the cell on the detection chip within the imaging fields of the camera are being stitched to form the total chip image) further includes: determining the plurality of imaging fields of the image acquisition device on the imaging surface of the sample container (Weng, Par. [0016], discloses images of each of the regions in a detection region [plurality of imaging fields on the imaging surface of the sample container as claimed]); obtaining at least one detection image of the test sample within each of the plurality of imaging fields (Weng, Par. [0016] discloses the each of the regions is being obtained an image), wherein detection images collected in two adjacent imaging fields have an overlapping region (Weng, Par. [0016] discloses images sharing same markers [analogous to adjacent images] are stitched to be overlapped); and stitching the detection images based on the overlapping region in the detection images to obtain the full volume image of the test sample (Weng, Par. [0016] discloses the images after being stitched sharing overlapped region to obtain the total chip image).
Regarding claim 14, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 12, Weng teaches wherein the sample container is provided with a plurality of markers (Weng, Par. [0016] discloses the cell chip is marked with a plurality of markers), and the overlapping region between the two adjacent detection images includes at least one same marker (Weng, Par. [0016] discloses the overlapped potions includes same marker); and the stitching the detection images based on the overlapping region in the detection images to obtain the full volume image of the test sample further includes (Weng, Par. [0016] discloses the images after being stitched sharing overlapped region to obtain the total chip image): sequentially stitching the detection images based on the at least one same marker in the two adjacent detection images to obtain the full volume image of the test sample (Weng, Par. [0016] discloses the images after being stitched sharing overlapped region to obtain the total chip image).
Regarding claim 17, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 1, Weng teaches wherein the full volume image is obtained through one of bright field imaging, fluorescence imaging, or scattered light imaging (“one of….or…” indicates a selection, therefore, only one of the options is the instant scope of the claim, which the examiner finds Weng’s Par. [0004] to cover wherein the imaging is of fluorescence imaging).
Regarding claim 18, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 1.
However, Weng in view of Kasdan and Randolph do not explicitly teach wherein the analysis parameter of the particles includes at least one of a count of the particles, a percentage of each type of the particles, a concentration of the each type of the particles; or characterizing a state of the particles, wherein the state of the particles includes one or more of a type of the particles, a morphological parameter of the particles, or a distribution of the particles in different locations of the test sample.
Zhou teaches wherein the analysis parameter of the particles includes at least one of a count of the particles, a percentage of each type of the particles, a concentration of the each type of the particles; or (“or” here indicates a selection between the limitations, therefore, only one of the limitations between the “;” is the instant scope of the claim, mapping is chosen for the following limitation as shown in the sequential mapping) characterizing a state of the particles, wherein the state of the particles includes one or more of a type of the particles, a morphological parameter of the particles, or a distribution of the particles in different locations of the test sample (“or” here indicates a selection between the limitations, therefore, only one of the limitations between the “;” is the instant scope of the claim, as discussed in the previous statement, “or” indicates a selection, therefore, the examiner selects “characterizing a state of the particles….of the test sample” to be the instant scope of the claim, the examiner finds Zhou to disclose, in page 5026, 2nd column, wherein the method includes generate quantitative measurements of either morphometric or molecular signatures of all single cells [characterizing a state of the particles as claimed]; moreover, the state of the particles includes morphometric signatures [analogous to a morphological parameter of the particles]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Kasdan and Randolph of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, with the teachings of Zhou of having wherein the analysis parameter of the particles includes at least one of a count of the particles, a percentage of each type of the particles, a concentration of the each type of the particles; or characterizing a state of the particles, wherein the state of the particles includes one or more of a type of the particles, a morphological parameter of the particles, or a distribution of the particles in different locations of the test sample.
Wherein having Weng’s method of having wherein the analysis parameter of the particles includes at least one of a count of the particles, a percentage of each type of the particles, a concentration of the each type of the particles; or characterizing a state of the particles, wherein the state of the particles includes one or more of a type of the particles, a morphological parameter of the particles, or a distribution of the particles in different locations of the test sample.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range. Since both Weng and Zhou perform cell analysis using image detection. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Zhou’s system improves cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range, (see Zhou’s Abstract).
Regarding claim 66, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 8, Weng teaches wherein the plurality of imaging fields have different imaging magnification factors (Par. [0051] discloses “measurement is performed by using a pattern…having a known size to obtain the imaging region of the image capturing device at a certain magnification”).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”) and Sze Ying Lee et. al. (“Cell Separation and Disruption, Product Recovery, and Purification, July 2019, Part of the book series: Learning Materials in Biosciences, pp 237-271” hereinafter as “Lee”).
Regarding claim 6, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 5.
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach wherein the at least two types of particles include a transparent particle and a non-transparent particle, and the target type of particles is the non-transparent particle, the removing other types of particles in the test sample except for the target type of particles further includes: adding a lysis solution to the test sample to lyse the transparent particle.
Lee teaches wherein the at least two types of particles include a transparent particle and a non-transparent particle (Page 241, section 8.2, 2nd par., discloses the removal of the cell debris particles for purification, it can be understood there are cell particles [transparent particles] and cell debris particles [non-transparent particles, since these needs to be purified]), and the target type of particles is the non-transparent particle (Since the process is to remove the cell debris particles, it can be understood as the cell particles are being kept as target type of particles according to section 8.2, 2nd par.), the removing other types of particles in the test sample except for the target type of particles (Page 241, section 8.2, 2nd par., wherein the cell debris particles are being removed) further includes: adding a lysis solution to the test sample to lyse the transparent particle (Section 8.2, last par., of page 244, wherein the removal of debris include lysing the cells [transparent particles]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, with the teachings of Lee of having wherein the at least two types of particles include a transparent particle and a non-transparent particle, and the target type of particles is the non-transparent particle, the removing other types of particles in the test sample except for the target type of particles further includes: adding a lysis solution to the test sample to lyse the transparent particle.
Wherein having Weng’s method of having wherein the at least two types of particles include a transparent particle and a non-transparent particle, and the target type of particles is the non-transparent particle, the removing other types of particles in the test sample except for the target type of particles further includes: adding a lysis solution to the test sample to lyse the transparent particle.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve cell harvesting through cell disruption techniques in order to prepare an extract that is free of cell debris enabling efficient purification process. Since both Weng and Lee perform cell debris determination and removal. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Lee’s system improves cell harvesting through cell disruption techniques in order to prepare an extract that is free of cell debris enabling efficient purification process, (see Lee’s Page 238, 1st Par.).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”) and Laurens Nunnink et. al. (“US 2014/0340758 A1” hereinafter as “Nunnink”).
Regarding claim 11, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 8, Weng teaches wherein the obtaining the full volume image of the test sample based on a single imaging field on an imaging surface of a sample container further includes (Par. [0028] discloses “a runner about 5um thick is formed on the slide surface via photolithography” indicating an image obtained of the surface of the sample container).
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach controlling a maximum width of the sample container to be less than a long side width of the single imaging field of the image acquisition device, such that the single imaging field completely covers the width of the sample container.
Nunnink teaches controlling a maximum width of the sample container to be less than a long side width of the single imaging field of the image acquisition device (Pars. [0027-0029] disclose the width of the object with a sufficient height to fully image an ID within a given acquire image frame, indicating that the max. width of the object is covered/controlled to be within the field of view of the camera, hence, is smaller than the field of the view of the camera), such that the single imaging field completely covers the width of the sample container (Par. [0039] so that the camera can image the whole field of use with the width of the object being covered [analogous to the sample container or any object for imaging]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, with the teachings of Nunnink of having controlling a maximum width of the sample container to be less than a long side width of the single imaging field of the image acquisition device, such that the single imaging field completely covers the width of the sample container.
Wherein having Weng’s method of having controlling a maximum width of the sample container to be less than a long side width of the single imaging field of the image acquisition device, such that the single imaging field completely covers the width of the sample container.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve imaging system that can expand the field of view of an object to increase resolution and effective imaging. Since both Weng and Nunnink perform camera system for obtaining images for object analysis. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Nunnink’s system improves imaging system that can expand the field of view of an object to increase resolution and effective imaging, (see Nunnink’s Par. [0005]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”) and Frank Fuchs (foreign patent document “AU 2014253889 A1”/”WO 2014/172530 A1” hereinafter as “Fuchs”).
Regarding claim 13, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 8, Weng teaches wherein the obtaining the full volume image of the test sample (Par. [0012] discloses an image capturing device [image acquisition device] to capture a plurality of images to be stitched into a total chip image [analogous to full volume image]) by stitching detection images corresponding to a plurality of imaging fields on the imaging surface of the sample container (Par. [0016] wherein the images of the cell on the detection chip within the imaging fields of the camera are being stitched to form the total chip image) further includes: determining the plurality of imaging fields of the image acquisition device on the imaging surface of the sample container (Weng, Par. [0016], discloses images of each of the regions in a detection region [plurality of imaging fields on the imaging surface of the sample container as claimed]), obtaining at least one detection image of the test sample within each of the plurality of imaging fields (Weng, Par. [0016] discloses the each of the regions is being obtained an image); and directly stitching the detection images to obtain the full volume image of the test sample (Weng, Par. [0016] discloses the images after being stitched sharing regions to obtain the total chip image).
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach wherein the detection images collected in the adjacent two imaging fields have no overlapping region.
Fuchs teaches wherein the detection images collected in the adjacent two imaging fields have no overlapping region (Page 2, 2nd par., discloses the adjacent sub-images [analogous to the recited two imaging fields] that are adjacent and non-overlapping; Page 2, 2nd par., discloses stitching these two sub-images together).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, wherein the obtaining the full volume image of the test sample by stitching detection images corresponding to a plurality of imaging fields on the imaging surface of the sample container with the teachings of Fuchs of having wherein the detection images collected in the adjacent two imaging fields have no overlapping region.
Wherein having Weng’s method of having wherein the detection images collected in the adjacent two imaging fields have no overlapping region.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve sample imaging by using biological markers using fluorescent analysis enabling accurate optical scanning of the sample. Since both Weng and Fuchs perform camera system for obtaining images for object analysis. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Fuchs’ system improves sample imaging by using biological markers using fluorescent analysis enabling accurate optical scanning of the sample, (see Fuchs’ Background section, 2nd Par.).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”) and Fang-Yu Hwu et. al. (“Maintenance and Quantitative Phenotyping of the Oomycete-plant Model Pathosystem Hyaloperonospora Arabidopsidis-Aradidopsis, 2020, Bio-Protocol 10, e3661” hereinafter as “Hwu”).
Regarding claim 15, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 14, Weng teaches wherein the sequentially stitching the detection images based on the at least one same marker in the two adjacent detection images includes stitching two detection images in any adjacent imaging fields according to operations (Weng, Par. [0016] discloses the images after being stitched sharing overlapped region to obtain the total chip image, operation on the two detection images based on the at least one same marker to obtain the full volume image (Weng, Par. [0032] discloses the detection chip including the well is provided with mark] for image stitching according to Par. [0016]).
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach determining a stitching range for a first alignment operation on the two detection images; performing the first alignment operation on the two detection images based on the stitching range; and operation on the two detection images based on the at least one same marker to obtain the full volume image.
Hwu teaches determining a stitching range for a first alignment operation on the two detection images (Page 8, 1st par., bullet “b”, discloses the image stitching operation includes determining a “Stitching” window for the area to be included in the stitched image [first alignment operation using the stitching function], stitching function here to align images together without overlap hence is analogous aligning the images together); performing the first alignment operation on the two detection images based on the stitching range (Page 8, 1st par., bullet “b”, discloses the image stitching operation includes determining a “Stitching” window for the area to be included in the stitched image [first alignment operation using the stitching function], stitching function here to align images together without overlap hence is analogous aligning the images together); and performing a second alignment operation on the two detection images based on the at least one same marker to obtain the full volume image (Page 9 discloses in figure 5, the stitching can be perform additionally, additional stitching function is analogous to second alignment operation as claimed).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, wherein the obtaining the full volume image of the test sample by stitching detection images corresponding to a plurality of imaging fields on the imaging surface of the sample container with the teachings of Hwu of having determining a stitching range for a first alignment operation on the two detection images; performing the first alignment operation on the two detection images based on the stitching range; and operation on the two detection images based on the at least one same marker to obtain the full volume image.
Wherein having Weng’s method of having determining a stitching range for a first alignment operation on the two detection images; performing the first alignment operation on the two detection images based on the stitching range; and operation on the two detection images based on the at least one same marker to obtain the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve using stitching window to stitch images more efficiently and perform additional stitching to make sure stitching is more accurate. Since both Weng and Hwu perform image stitching to obtain target object full image. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Hwu’s system improves using stitching window to stitch images more efficiently and perform additional stitching to make sure stitching is more accurate, (see Hwu’s Page 2, 1st Par., and Page 9, last Par.).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”) and Fang-Yu Hwu et. al. (“Maintenance and Quantitative Phenotyping of the Oomycete-plant Model Pathosystem Hyaloperonospora Arabidopsidis-Aradidopsis, 2020, Bio-Protocol 10, e3661” hereinafter as “Hwu”) and Yu Wang et. al. (“Research on Stitching Technique of Medical Infrared Images, 2010, 2010 International Conference on Computer Application and System Modeling, ICCASM 2010” hereinafter as “Wang”).
Regarding claim 16, Weng in view of Zhou further in view of Kasdan and Randolph and Hwu teach the method of claim 15, Weng teaches wherein the determining a stitching range for the first alignment operation on the two detection images further includes (Par. [0052] discloses “position and stitch the images into a complete chip image according to the positions of the marks appearing in the images”)
However, Weng in view of Zhou further in view of Kasdan and Randolph and Hwu do not explicitly teach determining the stitching range for the first alignment operation based on an imaging field distance of two imaging fields corresponding to the two detection images, wherein the imaging field distance represents a distance between centers of the two imaging fields.
Wang teaches determining the stitching range for the first alignment operation based on an imaging field distance of two imaging fields corresponding to the two detection images (Section II.A, 1st 2 paragraphs, discloses the registration algorithm is based grid temple [including imaging field distance] such as shown in figure 1), wherein the imaging field distance represents a distance between centers of the two imaging fields (Section II.A, 2nd par., discloses the distance is of the distance between the grids narrowed with point A as the center to renew the registration template, hence, is based on a distance between the centers of the grids [imaging fields]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph and Hwu of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, wherein the obtaining the full volume image of the test sample by stitching detection images corresponding to a plurality of imaging fields on the imaging surface of the sample container further includes: determining the plurality of imaging fields of the image acquisition device on the imaging surface of the sample container, obtaining at least one detection image of the test sample within each of the plurality of imaging fields; and directly stitching the detection images to obtain the full volume image of the test sample with the teachings of Wang of having determining the stitching range for the first alignment operation based on an imaging field distance of two imaging fields corresponding to the two detection images, wherein the imaging field distance represents a distance between centers of the two imaging fields.
Wherein having Weng’s method of having determining the stitching range for the first alignment operation based on an imaging field distance of two imaging fields corresponding to the two detection images, wherein the imaging field distance represents a distance between centers of the two imaging fields.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve image stitching by removing seams in stitching images enabling improved registration speed and fusion performance. Since both Weng and Wang perform image stitching to obtain target object full image. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Wang’s system improves image stitching by removing seams in stitching images enabling improved registration speed and fusion performance, (see Wang’s Abstract).
Claims 23 and 64-65 are rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”) and David J. Williamson et. al. (“Machine Learning for Cluster Analysis of Localization Microscopy Data, March 2020, Nature Communications 11, article number 1493” hereinafter as “Williamson”).
Regarding claim 23, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 1.
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach wherein the image recognition model includes: an image input layer, which is a model input layer for the image recognition model, configured to obtain the full volume image; a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; and an analysis layer, which is a classifier model, configured to output the analysis parameter of the particle to be analyzed based on the color and the shape feature the full volume image.
Williamson teaches wherein the image recognition model includes: an image input layer, which is a model input layer for the image recognition model (Page 7, 2nd col., 1st par., discloses “a new model was configured using the same four-layer arrangement as Model XPILJZ (an input layer)” which is the input layer for the machine learning model), configured to obtain the full volume image (Figure 1, shows the neural network includes a layer to input data [the image input layer as claimed]; the input image can be understood as the full volume image, since the full volume image is already mapped to Weng, wherein the Weng’s complete chip image is used for subsequential processing here, Williamson discloses the subsequent processing, as further disclosed in Page 2, 1st col., last 3 Pars.); a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image (“or” indicates a selection, therefore, only one of the options is the instant scope of the claim, the examiner selects “shape” which is disclosed in Williamson’s page 3, 1st col., last par., including convolutional neural networks [according to page 2, 1st col., last par.]); and an analysis layer, which is a classifier model (Fig. 2 illustrate the classifier stating “a model and classified as either cluster or non-cluster” [according to page 2, 1st col., last par.] is a classifier model performing a model’s classifying task), configured to output the analysis parameter of the particle to be analyzed based on the color and the shape feature the full volume image (Figure 4 illustrates the output of the neural network include peripheral region of the cells and the shape of the cell morphometric identity of the cell which is analogous to the analysis parameter as claimed, and analogous to the mapped morphometric and peripheral identity of the cell [according to page 2, 1st col., last par.] furthermore, Fig. 1 illustrates and its caption discloses “a colored shapes indicate input or output files” indicating basing on color data/information regarding the image. or in another interpretation, in Page 2, 1st Par., discloses “whereas images from wide-field or confocal microscopes are composed of arrays of pixels” wherein, arrays of pixels indicating colors of the image).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, wherein the obtaining the full volume image of the test sample by stitching detection images corresponding to a plurality of imaging fields on the imaging surface of the sample container with the teachings of Williamson of having wherein the image recognition model includes: an image input layer, which is a model input layer for the image recognition model, configured to obtain the full volume image; a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; and an analysis layer, which is a classifier model, configured to output the analysis parameter of the particle to be analyzed based on the color and the shape feature the full volume image.
Wherein having Weng’s method of having wherein the image recognition model includes: an image input layer, which is a model input layer for the image recognition model, configured to obtain the full volume image; a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; and an analysis layer, which is a classifier model, configured to output the analysis parameter of the particle to be analyzed based on the color and the shape feature the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve particle localization using machine learning approach to cluster analysis enabling fast and accurate results. Since both Weng and Williamson perform particle localization using machine learning model. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Williamson’s system improves particle localization using machine learning approach to cluster analysis enabling fast and accurate results, (see Williamson’s Abstract).
Regarding claim 64, Weng teaches a method for analyzing particles in a test sample, comprising (Abstract discloses of image processing for cell detection which is analogous to analyzing particles [cells]): obtaining a full volume image of the test sample using an image acquisition device (Par. [0012] discloses an image capturing device [image acquisition device] to capture a plurality of images to be stitched into a total chip image [analogous to full volume image]).
However, Weng does not explicitly teach and determining an analysis parameter of the particles in the test sample based on the full volume image.
Zhou teaches and determining an analysis parameter of the particles in the test sample based on the full volume image (Page 5026, 2nd column, discloses the images are stitched together [analogous to the total chip image and the full volume image as claimed] for quantitative analysis of single cell morphometric and proteomic signatures based on quantification of celomic parameters [2nd Par.] to identify cell periphery a number identity for cell is given for analysis, all of these information together are analogous to analysis parameter being determined).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, with the teachings of Zhou of having wherein determining an analysis parameter of the particles in the test sample based on the full volume image.
Wherein having Weng’s method of having wherein determining an analysis parameter of the particles in the test sample based on the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range. Since both Weng and Zhou perform cell analysis using image detection. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Zhou’s system improves cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range, (see Zhou’s Abstract).
However, Weng in view of Zhou do not explicitly teach wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
Kasdan teaches wherein the full volume image reflects an entire volume state of the test sample (Col. 3, lines 35-45, discloses “the user sets the maximum volume that the flow cell or the microscopic slide will analyze” wherein, the image obtain for analyzing is a max. volume image is analogous to the full volume image reflects an entire volume state of the test sample); wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume (Col. 5, lines 1-15, discloses “calculate the concentration of each type of the particle analyzed. The concentration calculation consists of simply taking the number of particles determined for each type of particle counted, divided by the volume of the fluid sample analyzed for each of the particular type of particle”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, and determining an analysis parameter of the particles in the test sample based on the full volume image with the teachings of Kasdan of having wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
Wherein having Weng’s method of having wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve particle analysis in fluid flow image capturing by considering different types of particles in fluid sample in low concentration by having maximum volume set low, to encourage greater throughput. Since both Weng and Kasdan perform cell analysis using image detection based on liquid flow and chip image setting. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Kasdan’s system improves particle analysis in fluid flow image capturing by considering different types of particles in fluid sample in low concentration by having maximum volume set low, to encourage greater throughput, (see Kasdan’s Col. 1, lines 37-44).
However, Weng in view of Zhou and Kasdan do not explicitly teach the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process.
Randolph teaches the determining an analysis parameter of the particles (Abstract discloses “high-throughput flow imaging microscopy coupled with convolutional neural networks to analyze particles, such as aggregated biomolecules, and cells for use”) in the test sample based on the full volume image includes (Par. [0179] discloses “volume and analysis time requirement make this approach ideal for diagnosing neonatal sepsis. Larger blood samples may also be analyzed using this approach, increasing the analysis time due to the extra volume but yielding more reliable detection of trace concentrations of the pathogen” indicating a full volume image obtained): determining the analysis parameter of the particles by processing the full volume image based on an image recognition model (Par. [0125] discloses “whenever a parameter of the processing system is changed, the deep learning model is retrained”, Par. [0183] discloses “ConvNets for monitoring protein aggregates and other particles produced during the manufacture of therapeutic protein formulations may be detected and classified…monitor the particles in their product to ensure that the concentration and structure of particles present in each vial matches the product specification” wherein, the concentration and structure is analogous to the recited analysis parameter), wherein the image recognition model is a machine learning model generated through a training process (Par. [0183] discloses “ConvNets for monitoring protein aggregates and other particles produced during the manufacture of therapeutic protein formulations may be detected and classified”, Par. [0184] discloses “the present inventors trained a ConvNet to identify aggregates of a polyclonal antibody”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou and Kasdan of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, and determining an analysis parameter of the particles in the test sample based on the full volume image with the teachings of Randolph of having the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process.
Wherein having Weng’s method of having the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve assessment of imaged samples to detect particles by identifying of pathogens within samples to allow for effective treatment approach. Since both Weng and Randolph perform cell concentration determination through volume image. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]), and Randolph’s system improves assessment of imaged samples to detect particles by identifying of pathogens within samples to allow for effective treatment approach, (see Randolph’s Par. [0004]).
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach wherein the image recognition model includes a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image.
Williamson teaches wherein the image recognition model includes a feature extraction layer configured to extract at least one of the color or a shape feature of the particles in the full volume image (“or” indicates a selection, therefore, only one of the options is the instant scope of the claim, the examiner selects “shape” which is disclosed in Williamson’s page 3, 1st col., last par., including convolutional neural networks [according to page 2, 1st col., last par.]); outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image (Figure 4 illustrates the output of the neural network include peripheral region of the cells and the shape of the cell morphometric identity of the cell which is analogous to the analysis parameter as claimed, and analogous to the mapped morphometric and peripheral identity of the cell [according to page 2, 1st col., last par.]).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample using a machine learning model with the teachings of Williamson of having wherein the image recognition model includes a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image.
Wherein having Weng’s method of having wherein the image recognition model includes a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve particle localization using machine learning approach to cluster analysis enabling fast and accurate results. Since both Weng and Williamson perform particle localization using machine learning model. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Williamson’s system improves particle localization using machine learning approach to cluster analysis enabling fast and accurate results, (see Williamson’s Abstract).
Regarding claim 65, Weng teaches a method for analyzing particles in a test sample, comprising (Abstract discloses of image processing for cell detection which is analogous to analyzing particles [cells]): obtaining a full volume image of the test sample using an image acquisition device (Par. [0012] discloses an image capturing device [image acquisition device] to capture a plurality of images to be stitched into a total chip image [analogous to full volume image]).
However, Weng does not explicitly teach and determining an analysis parameter of the particles in the test sample based on the full volume image.
Zhou teaches and determining an analysis parameter of the particles in the test sample based on the full volume image (Page 5026, 2nd column, discloses the images are stitched together [analogous to the total chip image and the full volume image as claimed] for quantitative analysis of single cell morphometric and proteomic signatures based on quantification of celomic parameters [2nd Par.] to identify cell periphery a number identity for cell is given for analysis, all of these information together are analogous to analysis parameter being determined).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, with the teachings of Zhou of having wherein determining an analysis parameter of the particles in the test sample based on the full volume image.
Wherein having Weng’s method of having wherein determining an analysis parameter of the particles in the test sample based on the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range. Since both Weng and Zhou perform cell analysis using image detection. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Zhou’s system improves cell imaging culture on chip by using laser scanning cytometry that performs rapidly and automated enabling improved sensitivity and dynamic range, (see Zhou’s Abstract).
However, Weng in view of Zhou do not explicitly teach wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume
Kasdan teaches wherein the full volume image reflects an entire volume state of the test sample (Col. 3, lines 35-45, discloses “the user sets the maximum volume that the flow cell or the microscopic slide will analyze” wherein, the image obtain for analyzing is a max. volume image is analogous to the full volume image reflects an entire volume state of the test sample); wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume (Col. 5, lines 1-15, discloses “calculate the concentration of each type of the particle analyzed. The concentration calculation consists of simply taking the number of particles determined for each type of particle counted, divided by the volume of the fluid sample analyzed for each of the particular type of particle”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, and determining an analysis parameter of the particles in the test sample based on the full volume image with the teachings of Kasdan of having wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
Wherein having Weng’s method of having wherein the full volume image reflects an entire volume state of the test sample; wherein the analysis parameter of the particles includes a concentration of the particles which represents a count of the particles per unit volume.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve particle analysis in fluid flow image capturing by considering different types of particles in fluid sample in low concentration by having maximum volume set low, to encourage greater throughput. Since both Weng and Kasdan perform cell analysis using image detection based on liquid flow and chip image setting. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Kasdan’s system improves particle analysis in fluid flow image capturing by considering different types of particles in fluid sample in low concentration by having maximum volume set low, to encourage greater throughput, (see Kasdan’s Col. 1, lines 37-44).
However, Weng in view of Zhou and Kasdan donot explicitly teach and the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process including: iteratively training an initial image recognition model based on a plurality of training samples, wherein the plurality of training samples include sample images, labels for the plurality of training samples represent reference values of analysis parameters of the particles in the sample images.
Randolph teaches and the determining an analysis parameter of the particles (Abstract discloses “high-throughput flow imaging microscopy coupled with convolutional neural networks to analyze particles, such as aggregated biomolecules, and cells for use”) in the test sample based on the full volume image includes (Par. [0179] discloses “volume and analysis time requirement make this approach ideal for diagnosing neonatal sepsis. Larger blood samples may also be analyzed using this approach, increasing the analysis time due to the extra volume but yielding more reliable detection of trace concentrations of the pathogen” indicating a full volume image obtained): determining the analysis parameter of the particles by processing the full volume image based on an image recognition model (Par. [0125] discloses “whenever a parameter of the processing system is changed, the deep learning model is retrained”, Par. [0183] discloses “ConvNets for monitoring protein aggregates and other particles produced during the manufacture of therapeutic protein formulations may be detected and classified…monitor the particles in their product to ensure that the concentration and structure of particles present in each vial matches the product specification” wherein, the concentration and structure is analogous to the recited analysis parameter), wherein the image recognition model is a machine learning model generated through a training process including (Par. [0183] discloses “ConvNets for monitoring protein aggregates and other particles produced during the manufacture of therapeutic protein formulations may be detected and classified”, Par. [0184] discloses “the present inventors trained a ConvNet to identify aggregates of a polyclonal antibody”): iteratively training an initial image recognition model based on a plurality of training samples (Par. [0184] discloses “the network was then applied to synthetic FIM datasets containing mixtures of particles generated by stirring stress (a particle types the network was not shown during training)”), wherein the plurality of training samples include sample images (Par. [0184] discloses “the network was then applied to synthetic FIM datasets containing mixtures of particles generated by s tiring stress (a particle types the network was not shown during training)”; moreover, Par. [0185] discloses “color FIM images of these proteins were measured with a FlowCan VS device” and Par. [0189] discloses “the FIM images analyzed here were not contained in the training dataset”, wherein, the FIM dataset include color images), labels for the plurality of training samples (Par. [0186] discloses “C represents the net number of labeled classes in the training set”) represent reference values of analysis parameters of the particles in the sample images.
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou and Kasdan of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, and determining an analysis parameter of the particles in the test sample based on the full volume image with the teachings of Randolph of having the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process including: iteratively training an initial image recognition model based on a plurality of training samples, wherein the plurality of training samples include sample images, labels for the plurality of training samples represent reference values of analysis parameters of the particles in the sample images.
Wherein having Weng’s method of having the determining an analysis parameter of the particles in the test sample based on the full volume image includes: determining the analysis parameter of the particles by processing the full volume image based on an image recognition model, wherein the image recognition model is a machine learning model generated through a training process including: iteratively training an initial image recognition model based on a plurality of training samples, wherein the plurality of training samples include sample images, labels for the plurality of training samples represent reference values of analysis parameters of the particles in the sample images.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve assessment of imaged samples to detect particles by identifying of pathogens within samples to allow for effective treatment approach. Since both Weng and Randolph perform cell concentration determination through volume image. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Randolph’s system improves assessment of imaged samples to detect particles by identifying of pathogens within samples to allow for effective treatment approach, (see Randolph’s Par. [0004]).
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach wherein the image recognition model includes a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image.
Williamson teaches wherein the image recognition model includes: a feature extraction layer configured to extract at least one of the color or a shape feature of the particles in the full volume image (“or” indicates a selection, therefore, only one of the options is the instant scope of the claim, the examiner selects “shape” which is disclosed in Williamson’s page 3, 1st col., last par., including convolutional neural networks [according to page 2, 1st col., last par.]); outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image (Figure 4 illustrates the output of the neural network include peripheral region of the cells and the shape of the cell morphometric identity of the cell which is analogous to the analysis parameter as claimed, and analogous to the mapped morphometric and peripheral identity of the cell).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample using a machine learning model with the teachings of Williamson of having wherein the image recognition model includes a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image.
Wherein having Weng’s method of having wherein the image recognition model includes a feature extraction layer configured to extract at least one of a color or a shape feature of the particles in the full volume image; outputting the analysis parameter of the particle to be analyzed based on at least one of the color or the shape feature the full volume image.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve particle localization using machine learning approach to cluster analysis enabling fast and accurate results. Since both Weng and Williamson perform particle localization using machine learning model. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Williamson’s system improves particle localization using machine learning approach to cluster analysis enabling fast and accurate results, (see Williamson’s Abstract).
Claim 67 is rejected under 35 U.S.C. 103 as being unpatentable over Yi-Hsuan Weng et. al. (“US 2018/0144522 A1” hereinafter as “Weng”) in view of Jing Zhou et. al. (“High-Content Single-Cell Analysis On-Chip Using a Laser Microarray Scanner, August 2012, Lab on Chip Journal” hereinafter as “Zhou”) further in view of Harvey Lee Kasdan et. al. (“US 6,141,624” hereinafter as “Kasdan”) and Theodore W. Randolph et. al., (“US 2021/0303818 A1” hereinafter as “Randolph”) and David Michael Spriggs (“US 2016/0320284 A1” hereinafter as “Spriggs”).
Regarding claim 67, Weng in view of Zhou further in view of Kasdan and Randolph teach the method of claim 17.
However, Weng in view of Zhou further in view of Kasdan and Randolph do not explicitly teach wherein an angle is formed between a light source and each of the particles in a bright field of the bright field imaging.
Spriggs teaches wherein an angle is formed between a light source and each of the particles in a bright field of the bright field imaging (Par. [0007] discloses “scatter light at angles close to the axis of the illumination beam”).
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Weng in view of Zhou further in view of Kasdan and Randolph of having a method for analyzing particles in a test sample, comprising obtaining a full volume image of the test sample using an image acquisition device, with the teachings of Spriggs of having wherein an angle is formed between a light source and each of the particles in a bright field of the bright field imaging.
Wherein having Weng’s method of having wherein an angle is formed between a light source and each of the particles in a bright field of the bright field imaging.
The motivation behind the modification would have been to improve cell detection on chip image by more efficiently with less operating time and a simplified chip, and further to improve particle identification by improving fidelity of a measurement by relating light measured to a particle size enabling speed and reliability with which measurements happen. Since both Weng and Spriggs perform cell analysis using image detection. Wherein Weng’s system improves cell detection on chip image by more efficiently with less operating time and a simplified chip, (see Weng’s Par. [0005]) and Spriggs’ system improves particle identification by improving fidelity of a measurement by relating light measured to a particle size enabling speed and reliability with which measurements happen, (see Spriggs’ Par. [0041]).
Pertinent Prior Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
BACHALO, William D. et. al., “US 2020/0182766 A1”, discloses methods and apparatuses to image particles are described. A plurality of illuminating light beams propagating on multiple optical paths through a particle field are generated. The plurality of illuminating light beams converge at a measurement volume. A shadow image of a particle passing through a portion of the measurement volume at a focal plane of a digital camera is imaged. Shadow images of other particles in the particle field are removed using the plurality of illuminating light beams.
Glensbjerg, Martin et. al., “US 2016/0103058 A1”, discloses methods and systems for image cytometry analysis, typically at low optical magnification, where analysis is based on detection of biological particles using UV bright field, dark field or one or more sources of excitation light. The system comprises illumination means, a sample holder, a sample compartment, imaging means, collection means, light modulation means, and detection means with active detection elements.
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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/PHUONG HAU CAI/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
