DETAILED ACTION
Comments
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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.
Claims 17 and 22-40 are pending and examined in the instant Office action.
Even though the claims recite judicial exceptions, the claims recite a core algorithm involving deep learning that is too complex to be performed in the human mind.
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.
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.
The following rejection is reiterated:
Claim(s) 17 and 22-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ohsaka et al. [US PGPUB 2019/0347467 A1; on IDS] in view of Deka [US PGPUB 2016/0061711 A1].
Claim 17 is drawn to an analyzer configured to analyze a specimen containing cells. The analyzer comprises a sample preparator configured to prepare a measurement sample from the specimen and a reagent, wherein the specimen is assigned with a specimen ID. The analyzer comprises a detector configured to optically interrogate individual cells in the measurement sample. The analyzer comprises a signal processor configured to obtain feature data of the individual cells by processing a signal obtained by optical interrogation of the individual cells by the detector. The analyzer comprises a processor programmed to analyze the feature data by a deep learning algorithm. The deep learning algorithm is trained to output a classification data of the individual cell in response to an input of the feature data of the individual cell. The classification data includes a set of values each corresponding to one cell type of multiple cell types and the value represents a probability that the individual cell belongs to the individual cell type. The algorithm comprises determining, for the individual cell, a first cell type and a second cell type based on the classification data wherein the first cell type has the highest probability and the second cell type has a probability lower than that of the first cell type. The algorithm comprises generating a result data in association with the specimen ID. The result data includes a first analysis result including a count of cells and/or classification of cells in the specimen, wherein the first analysis result is generated based on the first cell type and a second analysis result generated based on the second cell type.
The document of Ohsaka et al. studies an image analysis method, apparatus, non-transitory computer readable medium, and deep learning algorithm generation method [title]. Figures 5 and 13 of Ohsaka et al. illustrate the computer for signal processing and computerized optical detector for optical analysis and optical interrogation of a sample. Figure 4 of Ohsaka et al. illustrates the cellular images with specimen IDs 70, 72Y, 72Cb, and 72Cr. Paragraphs 57 and 58 of Ohsaka et al. outline the deep learning and image classification algorithm of Ohsaka et al. Figure 17 of Ohsaka et al. teaches an example of output of the classification that results from the deep learning algorithm. Figure 17 of Ohsaka et al. teaches that deep learning successfully classifies a segmented neutrophil with a high probability of 97.6%. Figure 17 of Ohsaka et al. teaches that deep learning successfully classifies a bone marrow cell with a lower probability of 94%.
Ohsaka et al. does not teach all of the empirical limitations of the claim.
The document of Deka studies an apparatus and methods for cellular analysis [title]. Figures 5-6 and paragraphs 18-19 of Deka teach sample preparation and the optical detection device used to analyze the sample.
With regard to claim 22, Figure 17 of Ohsaka et al. teaches an example of output of the classification that results from the deep learning algorithm. For example, deep learning successfully classifies a segmented neutrophil with a success rate of 97.6% (i.e. the first analysis result). The statistic of Figure 17 of Ohsaka et al. teaches that the segmented neutrophil is classified as at least a second cell type with a rate of 2.4% (i.e. the second analysis result). Consequently, with a rate of 2.4%, at least one processor provides a different result from the first analysis result.
With regard to claim 23-28, Figures 17 and 18 of Ohsaka et al. teach normal cell types, abnormal cell types, lymphocytes, monocytes, eosinophils, neutrophils, basophils, blast cells, and abnormal lymphocytes. The tables in Figures 17 and 18 of Ohsaka et al. label/flag each cell type with an identifier. The tables in Figures 17 and 18 of Ohsaka et al. at least suggest results capable of being ranked.
With regard to claims 29-30, paragraphs 51-54 of Ohsaka et al. teach training based on features of the cells, including morphological features.
With regard to claims 31-33, Figures 5-6 of Deka teach the structure of the optical detection device with a flow cell for measuring cellular features, and signal/waveform/data matrix processing.
With regard to claims 34-35, paragraphs 84 and 88 of Ohsaka et al. teach parallel processors for simultaneous processing of portions of arithmetic/matrix signal data.
With regard to claims 36-40, Figure 17 of Ohsaka et al. teaches an example of output of the classification that results from the deep learning algorithm. For example, deep learning classifies a segmented neutrophil as a segmented neutrophil with a success rate of 97.6%. The statistic of Figure 17 of Ohsaka et al. teaches that the segmented neutrophil is classified as at least a second cell type with a rate of 2.4%. Consequently, with a rate of 2.4%, at least one processor provides a different result from the first analysis result. Even though the results and probabilities regarding cell type in Figures 17 and 18 of Ohsaka et al. are tabulated, it is obvious to convert the tabulated results into graphs. It is obvious to extract only results in Figures 17 and 18 of Ohsaka et al. that meet a threshold accuracy.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the deep learning and image analysis techniques of Ohsaka et al. by use of the empirical sample preparation and analysis of Deka wherein the motivation would have been that the sample preparation and analysis of Deka facilitates the image analysis of Ohsaka et al. [Figures 5-6 and paragraphs 18-19 of Deka]. There would have been a reasonable expectation of success in combining Ohsaka et al. and Deka because both studies are analogously applicable to optical analysis of cellular images.
Response to arguments:
Applicant's arguments filed 22 December 2025 have been fully considered but they are not persuasive.
Applicant argues that the Ohsaka et al. does not teach a first cell type with a higher probability than a second cell type. However, the amendment to claim 17 recites “the first cell type has the highest probability and the second cell type has a probability lower than that of the first cell type.” Since claim 17 does not define what the probability is measuring, claim 17 is broadly construed such that the probabilities measure the success rate of correct classification of cell type by the deep learning model.
Double Patenting
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined 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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
The following rejection is reiterated:
Double Patenting Rejection #1:
Claim [17, 30, 31, 34, or 35] is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim [26 or 34] of copending Application No. 18/185,814 (reference application) in view of Ohsaka et al. Although the claims at issue are not identical, they are not patentably distinct from each other because both studies are analogously applicable to using deep learning to analyze cellular features and images.
Application ‘814 does not teach sample IDs.
The document of Ohsaka et al. studies an image analysis method, apparatus, non-transitory computer readable medium, and deep learning algorithm generation method [title]. Figures 5 and 13 of Ohsaka et al. illustrates the computer for signal processing and computerized optical detector for optical analysis and optical interrogation of a sample. Figure 4 of Ohsaka et al. illustrates the cellular images with specimen IDs 70, 72Y, 72Cb, and 72Cr. Figure 17 of Ohsaka et al. teaches that deep learning successfully classifies a segmented neutrophil with a high probability of 97.6%. Figure 17 of Ohsaka et al. teaches that deep learning successfully classifies a bone marrow cell with a lower probability of 94%.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the deep learning and image analysis techniques of the claims of ‘814 by use of the sample IDs of Ohsaka et al. wherein the motivation would have been that identifying the sample facilitates data analysis [Figure 4 of Ohsaka et al.].
The claims in application ‘814 have been allowed.
Response to arguments:
Applicant has no arguments with regard to the double patenting rejections.
The following rejection is reiterated:
Double Patenting Rejection #2:
Claims 17, 23, 24, 25, 30, 31, 34, and 35 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims [1 or 12], [2 or 13], [3 or 14], [4 or 15], and 8-11, respectively, of U.S. Patent 12,437,563 [Application No. 17/691,793 (reference application) in the prior Office action that has since been issued] in view of Ohsaka et al. Although the claims at issue are not identical, they are not patentably distinct from each other because both studies are analogously applicable to using deep learning to analyze cellular features and images.
Application ‘793 does not teach sample IDs.
The document of Ohsaka et al. studies an image analysis method, apparatus, non-transitory computer readable medium, and deep learning algorithm generation method [title]. Figures 5 and 13 of Ohsaka et al. illustrates the computer for signal processing and computerized optical detector for optical analysis and optical interrogation of a sample. Figure 4 of Ohsaka et al. illustrates the cellular images with specimen IDs 70, 72Y, 72Cb, and 72Cr.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the deep learning and image analysis techniques of the claims of ‘793 by use of the sample IDs of Ohsaka et al. wherein the motivation would have been that identifying the sample facilitates data analysis [Figure 4 of Ohsaka et al.].
Response to arguments:
Applicant has no arguments with regard to the double patenting rejections.
The following rejection is reiterated:
Double Patenting Rejection #3:
Claims 17, 22-25, 29-31, and 33-40 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 20, 22-28, and 30-37, respectively, of copending Application No. 17/691,739 (reference application) in view of Ohsaka et al. Although the claims at issue are not identical, they are not patentably distinct from each other because both studies are analogously applicable to using deep learning to analyze cellular features and images.
Application ‘739 does not teach sample IDs.
The document of Ohsaka et al. studies an image analysis method, apparatus, non-transitory computer readable medium, and deep learning algorithm generation method [title]. Figures 5 and 13 of Ohsaka et al. illustrates the computer for signal processing and computerized optical detector for optical analysis and optical interrogation of a sample. Figure 4 of Ohsaka et al. illustrates the cellular images with specimen IDs 70, 72Y, 72Cb, and 72Cr.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the deep learning and image analysis techniques of the claims of ‘739 by use of the sample IDs of Ohsaka et al. wherein the motivation would have been that identifying the sample facilitates data analysis [Figure 4 of Ohsaka et al.].
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Response to arguments:
Applicant has no arguments with regard to the double patenting rejections.
E-mail Communications 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
No claim is allowed.
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Russell Negin, whose telephone number is (571) 272-1083. This Examiner can normally be reached from Monday through Thursday from 8 am to 3 pm and variable hours on Fridays.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Larry Riggs, Supervisory Patent Examiner, can be reached at (571) 270-3062.
/RUSSELL S NEGIN/ Primary Examiner, Art Unit 1686 6 March 2026