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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number JP2024-042775 dated 3/18/2024. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDSs dated 3/06/2025, 5/15/2025, and 7/18/2025 have been considered and placed in the application file.
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.
Claims 10 is rejected under 35 U.S.C. § 112(b) as being indefinite for claiming both an apparatus and a process of using the apparatus. When both an apparatus and a method are claimed in the same claim it is unclear whether infringement occurs when the apparatus is constructed or when the apparatus is used. Therefore, the scope of the claim is indefinite. See MPEP 2173.05(p).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, 7, 8, 9, and 11 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by A modeling strategy for cell dynamic morphology classification based on local deformation patterns, (Li et al.).
Claim 1
[AltContent: textbox (Figure 1 shows the consecutive timing cell images and tracking.)]
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Regarding claim 1, Li et al. disclose a cell image analysis method comprising: an image acquisition step of acquiring a time-series cell image group obtained by collecting a plurality of cell images obtained at a plurality of consecutive different timings by any one of a bright-field observation method or a phase contrast observation method, in association with the plurality of consecutive different timings; ("which underwent back skin transplant surgery, and were observed with a phase contrast microscopy … the videos were recorded in the length of 20–30 s with 25 frames per second," sec. 2.1, par. 1) a region extraction step of extracting cell candidate regions from the plurality of cell images included in the time-series cell image group; ("an active contour model [14] is employed to track and segment the target cells," sec. 2.1, par. 4) a region tracking step of determining whether the cell candidate regions over the plurality of cell images associated with mutually different timings which are included in the time-series cell image group correspond to the same target, collecting the cell candidate regions determined to correspond to the same target in association with the timings, and acquiring the determined cell candidate regions as a time-series cell candidate region group; ("The lymphocytes were observed and recorded by phase contrast microscopy. Example frames of four cell videos are presented, and the target lymphocytes are marked with red boxes. Each row corresponds to a cell video, and the time interval between each column is 5 s," sec. 2.2, fig. 1) and an analysis step of analyzing information about a state of a cell based on information about the time-series cell candidate region group, ("Cell dynamic morphology happens in time series and has spatial heterogeneity. With intent to fully understand the deformation regularities, we decompose the dynamic morphology into local temporal deformation. The deformation in cell dynamic morphology is represented by a cell contour sequence," sec. 2.2.1, par. 1) wherein the analysis step includes using a trained model, and wherein the trained model comprises a machine learning model that has been trained, based on information about the time-series cell candidate region group and information about a state of a cell that had been acquired from cells for training, by using the information about the time-series cell candidate region group as input and the information about the state of the cell as output ("the frequency of the three local deformation patterns is calculated in the sample points of each cell, to summarize the dynamic morphology. And then the frequency is fed into SVM (Support Vector Machine) to train the classification model," sec. 2.2.2, par. 6).
Claim 6
Regarding claim 6, Li et al. disclose the cell image analysis method according to claim 1, wherein the information about the time-series cell candidate region group comprises feature value data included in the time-series cell image group, ("the arithmetic mean, the standard deviation and the maximum of both Gk and |f'k (t) |, are calculated to compose the temporal feature vector of cell local deformation. At this point, the temporal regularity of each contour point is represented by a local temporal feature vector with 6 dimensions," sec. 2.2.1, par. 6) wherein the feature value data includes one or more selected from the group consisting of: a luminance feature value in each of the cell candidate regions of the plurality of cell images; a morphological feature value of each of the cell candidate regions; information about a temporal change in the luminance feature value; and information about a temporal change in the morphological feature value, and wherein the feature value data comprises one of a scalar value or a vector ("the dynamic morphology of each cell is characterized by the feature vectors of these 180 points," sec. 2.2.2, par. 2).
Claim 7
Regarding claim 7, Li et al. disclose wherein the information about the time-series cell candidate region group comprises time-series feature value data obtained by collecting, in association with the timings, feature value data of the cell candidate regions included in the time-series cell image group, ("the arithmetic mean, the standard deviation and the maximum of both Gk and |f'k (t) |, are calculated to compose the temporal feature vector of cell local deformation. At this point, the temporal regularity of each contour point is represented by a local temporal feature vector with 6 dimensions," sec. 2.2.1, par. 6) the feature value data including one or more selected from the group consisting of: a luminance feature value in each of the cell candidate regions of the plurality of cell images; and a morphological feature value of each of the cell candidate regions ("the dynamic morphology of each cell is characterized by the feature vectors of these 180 points," sec. 2.2.2, par. 2).
Claim 8
Regarding claim 8, Li et al. disclose wherein the information about the time-series cell candidate region group comprises time-series image data ("The lymphocytes were observed and recorded by phase contrast microscopy. Example frames of four cell videos are presented, and the target lymphocytes are marked with red boxes. Each row corresponds to a cell video, and the time interval between each column is 5 s," sec. 2.2, fig. 1) obtained by collecting, in association with the timings, partial cell images corresponding to the cell candidate regions over the plurality of cell images included in the time-series cell image group ("Cell dynamic morphology happens in time series and has spatial heterogeneity. With intent to fully understand the deformation regularities, we decompose the dynamic morphology into local temporal deformation. The deformation in cell dynamic morphology is represented by a cell contour sequence," sec. 2.2.1, par. 1).
Claim 9
Regarding claim 9, Li et al. disclose a cell image analysis apparatus comprising: an image acquisition module configured to acquire a time-series cell image group obtained by collecting a plurality of cell images obtained at a plurality of consecutive different timings by any one of a bright-field observation method or a phase contrast observation method, in association with the plurality of consecutive different timings; ("which underwent back skin transplant surgery, and were observed with a phase contrast microscopy … the videos were recorded in the length of 20–30 s with 25 frames per second," sec. 2.1, par. 1) a region extraction module configured to extract cell candidate regions from the plurality of cell images included in the time-series cell image group; ("an active contour model [14] is employed to track and segment the target cells," sec. 2.1, par. 4) a region tracking module configured to determine whether the cell candidate regions over the plurality of cell images associated with mutually different timings which are included in the time-series cell image group correspond to the same target, (See fig. 1) collect the cell candidate regions determined to correspond to the same target in association with the timings, and to acquire the determined cell candidate regions as a time-series cell candidate region group; ("The lymphocytes were observed and recorded by phase contrast microscopy. Example frames of four cell videos are presented, and the target lymphocytes are marked with red boxes. Each row corresponds to a cell video, and the time interval between each column is 5 s," sec. 2.2, fig. 1) and an analysis module configured to analyze information about a state of a cell based on information about the time-series cell candidate region group, ("Cell dynamic morphology happens in time series and has spatial heterogeneity. With intent to fully understand the deformation regularities, we decompose the dynamic morphology into local temporal deformation. The deformation in cell dynamic morphology is represented by a cell contour sequence," sec. 2.2.1, par. 1) wherein the analysis module is configured to use a trained model, and wherein the trained model comprises a machine learning model that has been trained, based on information about the time-series cell candidate region group and information about a state of a cell that had been acquired from cells for training, by using the information about the time-series cell candidate region group as input and the information about the state of the cell as output ("the frequency of the three local deformation patterns is calculated in the sample points of each cell, to summarize the dynamic morphology. And then the frequency is fed into SVM (Support Vector Machine) to train the classification model," sec. 2.2.2, par. 6).
Claim 11
Regarding claim 11, Li et al. disclose a cell image analysis system comprising: an image acquisition device; ("which underwent back skin transplant surgery, and were observed with a phase contrast microscopy (Olympus BX51, 100×magnification, resolution 0.3 um)," sec. 2.1, par. 1) and an information processing device ("the frequency is fed into SVM (Support Vector Machine) to train the classification model," wherein the trained classification model is implemented on a computer system, sec. 2.2.2, par. 6), wherein the image acquisition device is configured to acquire cell images obtained at a plurality of consecutive different timings by any one of a bright-field observation method or a phase contrast observation method, wherein the information processing device includes: an image acquisition module configured to acquire the cell images from the image acquisition device, and acquire a time-series cell image group obtained by collecting the cell image in association with the plurality of consecutive different timings; ("which underwent back skin transplant surgery, and were observed with a phase contrast microscopy … the videos were recorded in the length of 20–30 s with 25 frames per second," sec. 2.1, par. 1) a region extraction module configured to extract cell candidate regions from the cell images included in the time-series cell image group; ("an active contour model [14] is employed to track and segment the target cells," sec. 2.1, par. 4) a region tracking module configured to determine whether the cell candidate regions over a plurality of the cell images associated with mutually different timings which are included in the time-series cell image group correspond to the same target, (See fig. 1) collect the cell candidate regions determined to correspond to the same target in association with the timings, and to acquire the determined cell candidate regions as a time-series cell candidate region group; ("The lymphocytes were observed and recorded by phase contrast microscopy. Example frames of four cell videos are presented, and the target lymphocytes are marked with red boxes. Each row corresponds to a cell video, and the time interval between each column is 5 s," sec. 2.2, fig. 1) and an analysis module configured to analyze information about a state of a cell based on information about the time-series cell candidate region group, ("Cell dynamic morphology happens in time series and has spatial heterogeneity. With intent to fully understand the deformation regularities, we decompose the dynamic morphology into local temporal deformation. The deformation in cell dynamic morphology is represented by a cell contour sequence," sec. 2.2.1, par. 1) and wherein the analysis module is configured to use a trained model that uses, for cells for training, the information about the time-series cell candidate region group as input and the information about the state of the cell as output ("the frequency of the three local deformation patterns is calculated in the sample points of each cell, to summarize the dynamic morphology. And then the frequency is fed into SVM (Support Vector Machine) to train the classification model," sec. 2.2.2, par. 6).
1st Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 3, 4, and 10 are rejected under 35 U.S.C. 103 as obvious over A modeling strategy for cell dynamic morphology classification based on local deformation patterns, (Li et al.) in view of The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death, (Vicar et al.).
Claim 2
Regarding claim 2, Li et al. teach the cell image analysis method according to claim 1 a noted above.
Li et al. do not explicitly teach all of wherein the trained model is configured to acquire the information about the state of the cell based on fluorescence luminance feature value data including at least information about a temporal change in a fluorescence luminance.
[AltContent: textbox (Figure 4 shows the range of cell death measured according to particular parameters.)]
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However, Vicar et al. teach wherein the trained model ("We trained linear Support Vector Machine (SVM) classifier for automatic classification of these cell death types. SVM classifier was trained for each cell line (one for all three treatments) because cell lines are morphologically different," sec. Cell death type identification, par. 1) is configured to acquire the information about the state of the cell based on fluorescence luminance feature value data including at least information about a temporal change in a fluorescence luminance ("we extracted several cell features including cell mass, area, mass density (average pixel brightness) … All these features were evaluated in all frames, where the result is a set of signals describing the cell behaviour in time," sec. Feature extraction, par. 1).
Therefore, taking the teachings of Li et al. and Vicar et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify tracking and cell classification methods as taught by Li et al. to use fluorescence feature value extraction methods as taught by Vicar et al. The suggestion/motivation for doing so would have been that, “All possible subsets of features were tested and classification accuracy was evaluated. Only density and CDS were finally used because accuracy does not increase significantly with the addition of other features (accuracy 75.4% and 76.2% for two and three features, respectively), where two features can be comprehensibly visualized. These two features have also a distinct biological meaning and its difference in an average aligned signal can be confirmed in Fig. 1” as noted by the Vicar et al. disclosure in sec. Cell death type identification, par. 1, which also motivates combination because the combination would predictably have a greater accuracy as there is a reasonable expectation that utilizing the luminescence derived density feature captures distinct biological meanings and changes in the aligned signal during cell death, thereby maximizing classification accuracy without unnecessary computational overhead; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 3
Regarding claim 3, Li et al. and Vicar et al. teach the cell image analysis method according to claim 2 as noted above.
Li et al. teach a trained model generation step of generating the trained model through training with the information about the state of the cell based on the label relating to the state of the cell being used as output and the information about the time-series cell candidate region group being used as input ("the frequency of the three local deformation patterns is calculated in the sample points of each cell, to summarize the dynamic morphology. And then the frequency is fed into SVM (Support Vector Machine) to train the classification model," sec. 2.2.2, par. 6).
Li et al. do not explicitly teach all of wherein the trained model is acquired by carrying out, for the cells for training: a fluorescent image acquisition step of acquiring a time-series fluorescent image group obtained by collecting, in association with the timings, fluorescent images corresponding to the plurality of cell images included in the time-series cell image group; a fluorescence luminance feature value calculation step of acquiring fluorescence luminance feature value data of the time-series cell candidate region group based on the time-series fluorescent image group; a labeling processing step of assigning a label relating to the state of the cell to the cell candidate region based on the fluorescence luminance feature value data; and wherein the fluorescence luminance feature value data includes the information about at least the temporal change in the fluorescence luminance.
[AltContent: textbox (Figure 6 shows the acquired fluorescent images in a time-series.)]
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However, Vicar et al. teach wherein the trained model is acquired by carrying out, for the cells for training: ("We trained linear Support Vector Machine (SVM) classifier for automatic classification of these cell death types. SVM classifier was trained for each cell line (one for all three treatments) because cell lines are morphologically different," sec. Cell death type identification, par. 1) a fluorescent image acquisition step of acquiring a time-series fluorescent image group obtained by collecting, in association with the timings, fluorescent images corresponding to the plurality of cell images included in the time-series cell image group; ("Characteristic quantitative phase and fluorescence images of lytic and apoptotic cell death … Apoptotic cells display a delay in PI signal onset compared to casp," fig. 6) a fluorescence luminance feature value calculation step of acquiring fluorescence luminance feature value data of the time-series cell candidate region group based on the time-series fluorescent image group; ("we extracted several cell features including cell mass, area, mass density (average pixel brightness) … All these features were evaluated in all frames, where the result is a set of signals describing the cell behaviour in time," sec. Feature extraction, par. 1) a labeling processing step of assigning a label relating to the state of the cell to the cell candidate region based on the fluorescence luminance feature value data; ("We observed two distinct dynamical patterns of cell mass manipulation during cellular death, where each cell was manually labelled as one of these types," sec. Cell death type identification, par. 1) and wherein the fluorescence luminance feature value data includes the information about at least the temporal change in the fluorescence luminance ("we extracted several cell features including cell mass, area, mass density (average pixel brightness) … All these features were evaluated in all frames, where the result is a set of signals describing the cell behaviour in time," sec. Feature extraction, par. 1).
Li et al. and Vicar et al. are combined as per claim 2.
Claim 4
Regarding claim 4, Li et al. teach the cell image analysis method according to claim 1 as noted above.
Li et al. do not explicitly teach all of wherein the information about the state of the cell includes at least information on a scalar value indicating one of a binary value or a possibility indicating whether the cell candidate region is a live cell region.
However, Vicar et al. teach wherein the information about the state of the cell includes at least information on a scalar value indicating one of a binary value or a possibility indicating whether the cell candidate region is a live cell region ("Quality of cell detection is evaluated in terms of accuracy, where detection is considered as correct if it detects a death in a ±5 h (±100 frames) window from ground truth cell death or if death is not detected and the cell is labelled as alive," sec. Label-free algorithm for cell death detection, par. 2).
Li et al. and Vicar et al. are combined as per claim 2.
Claim 10
Regarding claim 10, Li et al. teach the cell image analysis method according to claim 1 as noted above.
Li et al. do not explicitly teach all of a non-transitory storage medium having stored thereon a program for causing a computer to execute a cell image analysis in a computer-readable format.
However, Vicar et al. teach a non-transitory storage medium having stored thereon a program for causing a computer to execute a cell image analysis in a computer-readable format ("Complete quantitative phase image reconstruction and image processing were performed in Q-PHASE control software," sec. Correlative time-lapse quantitative phase-fluorescence imaging, par, 2).
Li et al. and Vicar et al. are combined as per claim 2.
2nd Claim Rejections - 35 USC § 103
Claim 5 is rejected under 35 U.S.C. 103 as obvious over A modeling strategy for cell dynamic morphology classification based on local deformation patterns, (Li et al.) in view of Deep convolutional and recurrent neural networks for cell motility discrimination and prediction, (Kimmel et al.).
Claim 5
Regarding claim 5, Li et al. teach the cell image analysis method according to claim 1 a noted above.
Li et al. do not explicitly teach all of wherein the information about the state of the cell includes at least information on a scalar value indicating one of a binary value or a possibility indicating whether the cell candidate region is a differentiated region.
However, Kimmel et al. teach wherein the information about the state of the cell includes at least information on a scalar value indicating one of a binary value or a possibility indicating whether the cell candidate region is a differentiated region.
Therefore, taking the teachings of Li et al. and Kimmel et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify tracking and cell classification methods as taught by Li et al. to use the cell differentiation classification methods as taught by Kimmel et al. The suggestion/motivation for doing so would have been that, “Results up to this point indicate that supervised classification of different cell motility phenotypes using RNN models is effective and comparable in performance to classification with hand-engineered features” as noted by the Kimmel et al. disclosure in sec. Autoencoders allow unsupervised learning of representations in motion feature space, par. 1, which also motivates combination because the combination would predictably have a greater utility as there is a reasonable expectation that would significantly reduce manual annotation labor and computational overhead by using feature extraction to map complex temporal changes in cell behavior; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent 10423819 B2 to Chang et al. discloses a method for analyzing cell movement by identifying cell nuclei, grouping them into clusters, and tracking both over time using fluorescence microscopic images.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KARSTEN F LANTZ whose telephone number is (571) 272-4564. The examiner can normally be reached Monday-Friday 8:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ms. Jennifer Mehmood can be reached on 571-272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Karsten F. Lantz/Examiner, Art Unit 2664
Date: 10/6/2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
