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
The IDS filed 9/20/2022, 10/14/2022, 10/01/2024, 10/23/2024, 1/29/2025 and 9/4/2025 have been considered by the Examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to JP2021-040829 filed 3/12/2021.
Claim Status
Amendments to the claims are acknowledged.
Claims 1-19, 21, and 29 are cancelled.
Claims 20, 22-28 and 30-37 are under examination.
Double Patenting
The instant rejection is maintained from the previous Office Action filed 8/27/2025.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 20, 22-28 and 30-37 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 17, 21-25 and 29-40 of copending Application No.17/691761. Although the conflicting claims are not identical, they are not patentably distinct from each other because the copending claims are either species of the instant claims and have only minor differences encompassed by the instant generic claims. Independent claim 17 of copending application US ‘761 recites assigning a specimen ID and generating a result data with the specimen ID. Instant independent claim 20 does not recite this limitation and is therefore broader. Instant dependent claims 21-37 are substantially identical to copending US ‘761 claims 21-25 and 29-40.
This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Claims 20, 22-28 and 30 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 5-7, 9, 11, 13 and 27-30 of copending Application No.17/691793. Although the conflicting claims are not identical, they are not patentably distinct from each other because the copending claims are either species of the instant claims and have only minor differences encompassed by the instant generic claims. Instant claims 20-22 encompass the process recited in copending US ‘793 claims 1 and 9. The instant claims are broader because they do not recite validating the result and transmitting the validation and removing classification information. Instant claims 23-25 substantially recite the same limitations as copending claims 5-7. Instant claim 26 substantially recites the same limitation as copending claim 11. Instant claims 27-30 substantially recite the same limitations as copending claims 27-30. Instant claim 31 is met by copending claim 13.
This is a provisional obviousness-type double patenting rejection because the conflicting claims have not in fact been patented.
Response to Arguments
Applicant's arguments filed 12/23/2025 have been fully considered but they are not persuasive.
Applicants state that a terminal disclaimer may be filed upon indication of allowable subject matter.
Claim Rejections - 35 USC § 103
The rejection of claims 20-37 under 35 U.S.C. 103(a) as being unpatentable over Ohsaka et al. (US 2019/0347467) are withdrawn in view of amendments filed 12/23/2025.
The following rejection is necessitated by Applicant’s amendments filed 12/23/2025.
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 20, 22-28 and 30-37 are rejected under 35 U.S.C. 103(a) as being unpatentable over Ohsaka et al. (US 2019/0347467) and further in view of Li et al. (cytometry Part A vol. 95A (2019) pages 499-509).
Ohsaka et al. teach (Abstract) an image analysis method combined with deep learning for classifying morphological features of a cell to determine if a cell belongs to a predetermined group.
Ohsaka et al. teach preparing a specimen with cells (par. 0049) including cell staining (par. 0059)(i.e. a sample preparator configured to prepare a measurement sample from the specimen and a reagent), as in claim 20.
Ohsaka et al. teach training images obtained with a known light microscope or image capture apparatus and trimming the image such that only the target cell is present in the image (par. 0060)(i.e. a detector configured to optically interrogate individual cells in the measurement sample), as in claim 20.
Ohsaka et al. teach image capturing and image analysis (par. 0061-0062) with which features of cells in cell images are determined (par. 0128)(i.e. obtain feature date of the individual cell by optical interrogation of individual cell by the detector), as in claim 20.
Ohsaka et al. teach obtaining image values of brightness and expressing them as a matrix (par. 0062 and Figure 2 items 72y, 72cb and 72cr)(i.e. the feature data includes a matrix of values each representing an intensity of the light sensed by the detector at respective time point), as in claim 20.
Ohsaka et al. teach using a classifier that calculates the probability that an analyzed target cell belongs to each predetermined morphological cell group of a plurality of cells (par. 0047); the classification is of the type of cell and classification of morphological features of the cell (par. 0048); morphology is analyzed with deep learning (par. 0021)(i.e. analyzing feature data by deep learning trained to output a classification of the individual cell in response to input of feature data of the cell, wherein the classification includes values corresponding to one cell type of multiple cell types, and the value represents a probability that the cell belongs to the corresponding cell type), as in claim 20.
Ohsaka et al. teach a first classification of cell features to determine the cell type (par. 0018)(i.e. determine a first cell type), and second morphology classification of a cell abnormality (par. 0018)(i.e. determine a second cell type) wherein a percentage of prediction is associated with a predicted cell type is determined, such as neutrophil has a higher percentage than metamyelocyte (Figure 17)(i.e. wherein the first cell type has the highest probability and the second cell type has a probability lower than the first cell type), as in claims 20.
Ohsaka et al. teach a classification of cell type and abnormality (par. 0018 and 0021-0023)(i.e. generate based on the first cell type a classification of cells in the specimen and a second analysis result), as in claim 20.
Ohsaka et al. do not teach a flow cell through which cells in a sample flow, a light source to irradiate cells, a light detector to sense light from interrogated cells and to generate an analog pulse waveform signal from each individual cell, as in claim 20.
Ohstaka et al. do not teach digitally sampling the analog pulse waveform signal from the individual cell passing through the flow cell at a plurality of time points, as in claim 20.
Li et al. teach real-time measurements through imaging flow cytometry (Abstract) and deep learning methods; Li et al. teach a flow cytometry where information is gathered from interaction of light and streaming cellular suspension wherein light is scattered or emitted from the suspension (page 2, par. 2); Li et al. teach a photodetector (page 7, par. 1)(i.e. a detector comprising a flow cell, light source, and light detector configured to sense light from interrogated cells), as in claim 1.
Li et al. teach features of the cells are encoded into the spectrum of optical pulses, representing one-dimensional frames, the pulses are stretched in a dispersive optical mapping their spectrum to time, and they are captured by a photodetector and converted to a digital waveform which can be analyzed (page 6-7, connecting par.)(i.e. light detector configured to sense light from interrogated cells to generate an analog pulse waveform signal for each individual cell.)
Li et al. teach mapping the spectrum of light emitted from cells to time where the optical output is sequentially captured and converted to a digital waveform (page 6-7, connecting par.); Li et al. teach time-stretch pulses are detected by a photodetector and converted to time series data by an analog-to-digital converter (page 9, lines 4-5 from bottom)(i.e. digitally sampling the analog pulse waveform signal from the individual cell passing through the flow cell at a plurality of time points), as in claims 20.
Li et al. teach that the waveform elements are reshaped to two dimensional arrays (page 3, par. 1); these waveform elements are one-dimensional timeseries data that are fit with the conventional convolutional neural network architectures, the waveform elements are reshaped into two dimensions: one dimension corresponds to the laser pulses in each element, the other dimension corresponds to the sampling points per pulse (page 3, Figure 1(a), caption)(i.e. the feature data includes a matrix of values each representing an intensity of the light sensed by the detector at respective time point). Therefore in an additional embodiment Li et al. also teach feature data of cells from light pulses organized into a matrix of values representing sensed light intensity, as in claim 20.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have combined the teachings of Ohsaka et al. for identifying cell types using machine learning with the teachings of Li et al. for cell sorting with waveform signals generated from flow cytometry and inputted into a deep learning model for cell classification. One of skill in the art would have motivation to use flow cytometry signal data taught by Li et al. because Li et al. teach using raw waveform files as input rather than image processing and manual feature extraction. One of skill in the art would have had a reasonable expectation of success at combining the teachings of Ohsaka et al. and Li et al. because both teach cell classification using neural network based models.
Regarding dependent claims 22-28 and 30-37
Ohsaka et al. teach that abnormality appears as a morphologically classified cell feature (par. 0031) and at a first and second algorithm are used (par. 0018), as in claim 22.
Ohsaka et al. teach grouping cells in a normal state (par. 0048 and 0051) and abnormal cells (par. 0051), as in claim 23.
Ohsaka et al. teach classification of the type of cell including lymphocyte, monocyte, eosinophils, neutrophils, basophils (par. 0015), as in claim 24.
Ohsaka et al. teach (par. 0050) a cell group that is blood cells including blast lymphocytes (i.e. blast cells) and atypical lymphocytes (i.e. abnormal lymphocytes), as in claim 25.
Ohsaka et al. teach training the deep learning algorithm with information regarding the cell (par. 0023) and cell features (par. 0024), as in claim 26
Ohsaka et al. teach training matrix data used to train the deed learning algorithm such as a neural network ( par. 0071 and Figure 2 and 4), as in claim 27.
Regarding claim 30, Ohsaka et al. teach converting captured pixels from images and expressing image pixels as a matrix of gradation values (par. 0062)(i.e. feature data is a matrix data digitally representing morphological features), as in claim 29. Ohsaka et al. do not explicitly teach an analog waveform signal at a plurality of time points, however Ohsaka et al. do teach a gradation of values (par. 0062 and Figure 2) which suggests that the image of pixels can be taken at a gradation of time points (as in claim 29) wherein images are taken a sampling rate, which would be well known to one of ordinary skill (claim 30).
Ohsaka et al. teach multiple processors and parallel arithmetic processing (par. 0084 and 0088), as in claim 31.
Regarding claim 32, Ohsaka et al. teach matrices divided into subsets (Figure 4, items 79) and a neural network divided into layers (Figure 4, item 60) wherein it would be obvious to one of ordinary skill to run a deep learning algorithm by processing the subsets of matrix data on various layers of the deep learning algorithm using parallel algorithm processing (par. 0084 and 0088). This is a combination of known elements that would yield a predictable result of parallel processing a neural network.
Ohsaka et al. teach a graph of cells with abnormalities and statistical sensitivity and specificity (Figure 18), as in claims 33-34.
Ohsaka et al. teach plotting a plurality of different cells (Figure 17) and the probability that the cell belong to that cell type and probable abnormality detected in the cell (Figure 18)(i.e. displays the second analysis result and statistical information based on the second analysis result), as in claims 35-36.
Ohsaka et al. teach that on the basis of training data, the first neural network extracts feature quantities (par. 0067) wherein the features quantities are extracted from analysis data (i.e. which suggests that the neural network received input of extraction conditions) and outputs a probability that the cell belongs to each of the morphological cell classifications(i.e. extract cells that meet the extraction conditions regarding the cell types); Ohsaka et al. teaches displaying the extracted cells in a distinguishable manner from other cells wherein the displaying can include labeling (par. 0075 and Figure 3) or segmentation (Figure 2 and par. 0064), as in claim 37.
Response to Arguments
Applicant's arguments filed 12/23/2025 have been fully considered but they are not persuasive.
Applicants argue (Remarks, page 8, par. 1-2) that Ohsaka et al. do not teach the newly amended limitation reciting a light detector configured to sense light from interrogated cells and generate an analogue pulse waveform signal for each individual cell. Applicants also argue (Remarks, page 9, par. 2) that the technical information contained in the “matrix” of Ohsaka is different from that of the claimed features wherein Ohsaka is directed to static images whereas the present invention’s waveform captures dynamic interaction between moving cells and light over a continuous period of time.
In response, the newly add prior art of Li et al.. as set forth above, teaches that features of the cells are encoded into the spectrum of optical pulses, representing one-dimensional frames, the pulses are stretched in a dispersive optical mapping their spectrum to time, and they are captured by a photodetector and converted to a digital waveform which can be analyzed (page 6-7, connecting par.). Li et al. teach time-stretch pulses are detected by a photodetector and converted to time series data by an analog-to-digital converter (page 9, lines 4-5 from bottom), which would read on a waveform over a continuous period of time. Li et al. also teach that the waveform elements are reshaped to two dimensional arrays (page 3, par. 1) which would read on the instantly recited matrix.
Applicants argue (Remarks, page 9, par. 2) that Ohsaka et al. do not teach determining a first cell type and a second cell type based on classification data wherein the first cell type has the highest probability and the second cell type has a probability lower than the first cell type. Applicants argue that Ohsaka’s classification of cell type and abnormality are not the same as “first analysis result” and “second analysis result” because Ohsaka does not teach first and second types based on probabilities.
In response, Ohsaka teach identification of “the type of cell’ in Figure 17. The classification accuracy is listed as a percentage. In Figure 17, Segmented Neutrophil is classified with 97.6% accuracy, meaning that the identified cell is 97.6% likely to be a Segmented Neutrophil (i.e. a first cell type) and inherently 2.4% as being a different cell type (i.e. a second cell type). The teaching of Ohsaka can also be understood that a Segmented Neutrophil (i.e. a first cell type) is identified with 97.6% accuracy and metamyelocyte (i.e. second cell type) is identified with 15.2% accuracy, which also reads on a first cell type having a higher probability than a second cell type.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Skibinsky whose telephone number is (571) 272-4373. The examiner can normally be reached on 12 pm - 8:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ram Shukla can be reached on (571) 272-7035. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Anna Skibinsky/
Primary Examiner, AU 1635
Prosecution Insights