DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claim(s) 21-40 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The Examine maintains the Double Patenting Rejections. The Terminal disclaimer was not approved.
The Examiner withdraws the 35 USC 112 Rejections and claim objections.
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.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-15 of U.S. Patent No. 11,494,907. Although the claims at issue are not identical, they are not patentably distinct from each other because they recite substantially the same limitations as outlined in the prior Office Action.
minimal residual disease1 - A term used to describe a very small number of cancer cells that remain in the body during or after treatment. Minimal residual disease can be found only by highly sensitive laboratory methods that are able to find one cancer cell among one million normal cells. Checking to see if there is minimal residual disease may help plan treatment, find out how well treatment is working or if cancer has come back, or make a prognosis. Minimal residual disease testing is used mostly for blood cancers such as lymphoma and leukemia. Also called measurable residual disease and MRD.
pathologic complete remission2 - The lack of all signs of cancer in tissue samples removed during surgery or biopsy after treatment with radiation or chemotherapy. To find out if there is a pathologic complete remission, a pathologist checks the tissue samples under a microscope to see if there are still cancer cells left after the anticancer treatment. Knowing if the cancer is in pathologic complete remission may help show how well treatment is working or if the cancer will come back. Also called pathologic complete response.
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.
Claim(s) 21-41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Parkinson (2010/0204973) in view of Schreier (PGPub 2020/0388371)
Parkinson discloses 21. (New) A computer-implemented method for processing electronic images, the method comprising:
receiving a digital image corresponding to a target specimen associated with a pathology category; (Parkinson, [240-241], “Confocal microscopy relies on the serial collection of light from spatially filtered individual specimen points, which is then electronically processed to render a magnified image of the specimen.”; “Once the cells are labeled, the plates can be scanned using an imager such as the Odyssey Imager (LiCor, Lincoln Nebr.) using techniques described in the Odyssey Operator's Manual v1.2., which is hereby incorporated in its entirety. Data obtained by scanning of the multiwell plate can be analyzed and activation profiles determined as described below.”; see also paragraph 14 , “Sample--A sample is a population of one or more cells. Samples can be derived, for example, from cells in culture or from patients.” And paragraph 21)
determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further output a cancer quantification; (Parkinson, [069], “The model generation module 316 generates statistical models based on node state data generated from samples associated with a known biological state. Example biological states for which models are built are discussed below in the section titled "Specific Embodiments".”; [288], “The classification of a test sample of one or more rare cells can comprise classifying the cell as being associated with a biological state of minimal residual disease or emerging resistance based on an association metric. See U.S. No. 61/048,886 which is incorporated by reference. The classification of a sample can comprise generating association metrics based on statistical models of patient response to a treatment, where the association metrics specify whether the patient the sample is derived from is likely to respond to treatment. ” and “In some embodiments, the models of patient response are generated from sets of samples from the group consisting of : complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction. The classification of a sample can comprise generating association metrics based on models generated from samples that have been treated according to different methods of treatment, which may include dosing and scheduling. Example of methods of treatments include, but are not limited to, chemotherapy, biological therapy, radiation therapy, bone marrow transplantation, peripheral stem cell transplantation, umbilical cord blood transplantation, autologous stem cell transplantation, allogeneic stem cell transplantation, syngeneic stem cell transplantation, surgery, induction therapy, maintenance therapy, watchful waiting, and other therapy.”; [289] “In some embodiments, statistical models are generated for samples (e.g. normal cells) other than samples associated with an aberrant or abnormal biological state (e.g. cancer samples) and a combination of these and other statistical models are to generate association metrics for a test sample and classify/diagnose the test sample based on the association metrics”)
“a base detection machine learning model trained from a first set of digital images corresponding to a first set of subjects that have not undergone treatment and/or whose tissue samples do not exhibit treatment effects”(Parkinson, paragraph 289, “[0289] In some embodiments, statistical models are generated for samples (e.g. normal cells) other than samples associated with an aberrant or abnormal biological state (e.g. cancer samples) and a combination of these and other statistical models are to generate association metrics for a test sample and classify/diagnose the test sample based on the association metrics,”)
providing the digital image as an input to the detection machine learning model; (Parkinson, [069], “In instances where the statistical model includes only one sample, a percentile or median node state metric may be specified as a characteristic of the sample. The model generation module 316 uses machine-learning methods to generate statistical models such as: logistic regression, random forest analysis, support vector machine (SVM) analysis, Bayesian analysis, neural network analysis, nearest-neighbor analysis, state transition models, boosting analysis and bagging analysis. Other machine-learning methods will be known to those skilled in the art. The model generation module 316 generates performance metrics that specify the accuracy of the statistical models such as confidence values and receiver operator curves (ROC). The model generation module 316 stores the statistical models in the biological state models dataset 350.”)
receiving a minimal residual disease (MRD) cancer qualification as an output from the detection machine learning model; receiving a confirmed cancer quantification comprising MRD as an output from the detection machine learning model (Parkinson, [288], “The classification of a test sample of one or more rare cells can comprise classifying the cell as being associated with a biological state of minimal residual disease or emerging resistance based on an association metric. See U.S. No. 61/048,886 which is incorporated by reference. The classification of a sample can comprise generating association metrics based on statistical models of patient response to a treatment, where the association metrics specify whether the patient the sample is derived from is likely to respond to treatment.” and “In some embodiments, the models of patient response are generated from sets of samples from the group consisting of : complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction.”; [289] “In some embodiments, statistical models are generated for samples (e.g. normal cells) other than samples associated with an aberrant or abnormal biological state (e.g. cancer samples) and a combination of these and other statistical models are to generate association metrics for a test sample and classify/diagnose the test sample based on the association metrics”; see also paragraph 287)
outputting the MRD cancer qualification based on the treatment effects machine learning model. (Parkinson, [288], “The classification of a test sample of one or more rare cells can comprise classifying the cell as being associated with a biological state of minimal residual disease or emerging resistance based on an association metric. See U.S. No. 61/048,886 which is incorporated by reference.”)
Parkinson discloses a detection model comprising a plurality of models. One of the models (paragraph 289) discloses a model that reads directly on a base detection model (tissue samples that do not exhibit treatment effects). Another model (paragraphs 287-288) discloses a model trained with image samples with response/non-response to drug treatment and disease/pre-disease state. Parkinson (paragraph 289) further states “a combination of these and other statistical models are to generate association metrics for a test sample and classify/diagnose the test sample based on the association metrics,” Thus Parkinson’s discloses a combination of these two models. Parkinson fails to disclose how they are combined.
Parkinson fails to disclose , in particular, “the treatment effect machine learning model having been generated using a base detection machine learning model ” “wherein the treatment effect machine learning model is generated using
a low shot or
transfer learning method initialized using the base detection machine learning model such that weights
Schreier discloses “the treatment effect machine learning model having been generated using a base detection machine learning model ” “wherein the treatment effect machine learning model is generated using
transfer learning method initialized using the base detection machine learning model such that weights (Schreier, “[0075] In practice, deep transfer learning techniques may be used to facilitate continuous learning of treatment planning engine 630 by respective local planning systems 611-614 in FIG. 6. Here, the term “deep transfer learning” may refer generally to technique(s) where one deep learning engine (see 630) is adapted or re-purposed (fully or partially) as a starting point for another deep learning engine (see 631-634). In one example, deep transfer learning represents an optimization strategy that facilitates faster progress or improved performance during the training process. This way, the knowledge learned by (global) treatment planning engine 630 may be leveraged by local planning systems 611-614 and transferred to respective (local) engines 631-634. As a variant of the example in FIG. 6, local planning systems 611-614 may have access to treatment planning engine 630, but not global data(A) used during training. In this case, continuous deep learning may be performed by re-training treatment planning engine 630 using respective local data(B1) to data(B4) to generate respective modified engines 631-634.”)
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 initialize a treatment effect models ([287-288] of Parkinson using transfer learning as shown by Schreier with the base detection model ([289]) .
The suggestion/motivation for doing so would have been faster training of the model and with less data.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Parkinson with Schreier to obtain the invention as specified in claim 21.
Parkinson in view of Schreier discloses 22. (New) The computer-implemented method of claim 21, further including receiving one of a pathological complete response (pCR) cancer qualification. (Parkinson, paragraph 288, “models of patient response are generated from sets of samples from the group consisting of: complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction”)
Parkinson in view of Schreier discloses 23. (New) The computer-implemented method of claim 21, wherein the MRD cancer qualification is protocol specific.(By definition of the term MRD is protocol specific; See definition above.)
Parkinson in view of Schreier discloses 24. (New) The computer-implemented method of claim 21, wherein the MRD cancer qualification corresponds to a number of cancer cells below a MRD threshold. (By definition of the term MRD compares the number of cancer cells to a threshold; See definition above)
Parkinson in view of Schreier discloses 25. (New) The computer-implemented method of claim 21,wherein the MRD cancer qualification identifies one or more diseases that remains occult within a subject, but may eventually lead to a relapse. (By definition of the term MRD compares the number of cancer cells to a threshold; See definition above)
Parkinson in view of Schreier discloses 26. (New) The computer-implemented method of claim 21, wherein the treatment effect machine learning model is trained based on tagged treatment effects in the plurality of training images. (Parkinson, paragraph 288, “models of patient response are generated from sets of samples from the group consisting of: complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction”)
Parkinson in view of Schreier discloses 27. (New) The computer-implemented method of claim 21, wherein receiving the confirmed cancer quantification also comprises receiving a type of cancer when the output of the detection machine learning model comprises a confirmed cancer quantification (Parkinson, [287] “As described above, a statistical model is generated based on node state data for a set of samples with a known biological state and used to generate an association metric for a sample ("test sample"), where the association metric classifies the test sample as being associated with a biological state. A biological state, as used herein, refers to any discrete, characterizable state of a cell such as a phenotype, a response to an modulator, a activation of an activatable element, an increase in expression, a morphological state, a response/non-response to drug treatment, a disease or pre-disease state.”. where the biological state reads on the type of cancer. Since the image is taken on a slide, properties of slide and sample (such as tissue characteristics, slide type, glass type, stain type) inform (affect) what is on the image, which is used to determine cancer qualifications/quantifications.)
Parkinson in view of Schreier discloses 28. (New) The computer-implemented method of claim 27, wherein the type of cancer is determined based on the digital image and one or more of a tissue characteristics, slide type, glass type, tissue type, tissue region, chemical used, or stain amount. (Parkinson, [287] “As described above, a statistical model is generated based on node state data for a set of samples with a known biological state and used to generate an association metric for a sample ("test sample"), where the association metric classifies the test sample as being associated with a biological state. A biological state, as used herein, refers to any discrete, characterizable state of a cell such as a phenotype, a response to an modulator, a activation of an activatable element, an increase in expression, a morphological state, a response/non-response to drug treatment, a disease or pre-disease state.”. where the biological state reads on the type of cancer. Since the image is taken on a slide, properties of slide and sample (such as tissue characteristics, slide type, glass type, stain type) inform (affect) what is on the image, which is used to determine cancer qualifications/quantifications.)
Parkinson in view of Schreier discloses 29. (New) The computer-implemented method of claim 1, wherein the digital image is from a pathology category, the pathology category selected from one or more of histology, cytology, frozen section, immunohistochemistry (IHC), immunofluorescence, hematoxylin and eosin (H&E), hematoxylin alone, molecular pathology, and/or 3D imaging. (Parkinson, [240-241], “Confocal microscopy relies on the serial collection of light from spatially filtered individual specimen points, which is then electronically processed to render a magnified image of the specimen.”; “Once the cells are labeled, the plates can be scanned using an imager such as the Odyssey Imager (LiCor, Lincoln Nebr.) using techniques described in the Odyssey Operator's Manual v1.2., which is hereby incorporated in its entirety. Data obtained by scanning of the multiwell plate can be analyzed and activation profiles determined as described below.”; see also paragraph 14 , “Sample--A sample is a population of one or more cells. Samples can be derived, for example, from cells in culture or from patients.” And paragraph 21)
Claims 30-37 are rejected under similar grounds as claims 21-25,27-29
Claims 38-40 are rejected under similar grounds as claims 21-23
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.
Claim(s) 21-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Parkinson (2010/0204973) in view of Kao(TW 108138497).
Parkinson discloses 21. (New) A computer-implemented method for processing electronic images, the method comprising:
receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen; (Parkinson, [240-241], “Confocal microscopy relies on the serial collection of light from spatially filtered individual specimen points, which is then electronically processed to render a magnified image of the specimen.”; “Once the cells are labeled, the plates can be scanned using an imager such as the Odyssey Imager (LiCor, Lincoln Nebr.) using techniques described in the Odyssey Operator's Manual v1.2., which is hereby incorporated in its entirety. Data obtained by scanning of the multiwell plate can be analyzed and activation profiles determined as described below.”; see also paragraph 14 , “Sample--A sample is a population of one or more cells. Samples can be derived, for example, from cells in culture or from patients.” And paragraph 21)
determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further output a cancer quantification if the cancer qualification is an confirmed cancer qualification; (Parkinson, [069], “The model generation module 316 generates statistical models based on node state data generated from samples associated with a known biological state. Example biological states for which models are built are discussed below in the section titled "Specific Embodiments".”; [288], “The classification of a test sample of one or more rare cells can comprise classifying the cell as being associated with a biological state of minimal residual disease or emerging resistance based on an association metric. See U.S. No. 61/048,886 which is incorporated by reference.” and “In some embodiments, the models of patient response are generated from sets of samples from the group consisting of : complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction.”; [289] “In some embodiments, statistical models are generated for samples (e.g. normal cells) other than samples associated with an aberrant or abnormal biological state (e.g. cancer samples) and a combination of these and other statistical models are to generate association metrics for a test sample and classify/diagnose the test sample based on the association metrics”)
providing the digital image as an input to the detection machine learning model; (Parkinson, [069], “In instances where the statistical model includes only one sample, a percentile or median node state metric may be specified as a characteristic of the sample. The model generation module 316 uses machine-learning methods to generate statistical models such as: logistic regression, random forest analysis, support vector machine (SVM) analysis, Bayesian analysis, neural network analysis, nearest-neighbor analysis, state transition models, boosting analysis and bagging analysis. Other machine-learning methods will be known to those skilled in the art. The model generation module 316 generates performance metrics that specify the accuracy of the statistical models such as confidence values and receiver operator curves (ROC). The model generation module 316 stores the statistical models in the biological state models dataset 350.”)
receiving a confirmed cancer quantification comprising of a minimal residual disease (MRD) as an output from the detection machine learning model, wherein the detection machine learning model comprises a treatment effect machine learning model, and (Parkinson, [288], “The classification of a test sample of one or more rare cells can comprise classifying the cell as being associated with a biological state of minimal residual disease or emerging resistance based on an association metric. See U.S. No. 61/048,886 which is incorporated by reference. The classification of a sample can comprise generating association metrics based on statistical models of patient response to a treatment, where the association metrics specify whether the patient the sample is derived from is likely to respond to treatment.” and “In some embodiments, the models of patient response are generated from sets of samples from the group consisting of : complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction.”; [289] “In some embodiments, statistical models are generated for samples (e.g. normal cells) other than samples associated with an aberrant or abnormal biological state (e.g. cancer samples) and a combination of these and other statistical models are to generate association metrics for a test sample and classify/diagnose the test sample based on the association metrics”)
outputting the MRD cancer qualification based on the treatment effects machine learning model. (Parkinson, [288], “The classification of a test sample of one or more rare cells can comprise classifying the cell as being associated with a biological state of minimal residual disease or emerging resistance based on an association metric. See U.S. No. 61/048,886 which is incorporated by reference.”)
Parkinson is silent on how the statistical models of patient response to a treatment is initialized, in particular does not expressly disclose “ wherein the detection machine learning model comprises a treatment effect machine learning model, the treatment effect machine learning model having been generated using aa base detection machine learning model trained from a first set of digital images corresponding to a first set of subjects that have not undergone and a second set of digital images from a second set of subjects that have undergone treatment, the first set of digital images comprising more images than the second set of digital images;;”
Kao discloses “wherein the detection machine learning model comprises a treatment effect machine learning model, the treatment effect machine learning model having been generated using a abase detection machine learning model trained from a first set of digital images corresponding to a first set of subjects that have not undergone and a second set of digital images from a second set of subjects that have undergone treatment, the first set of digital images comprising more images than the second set of digital images;;”;” (Kao, “The training performed by the training model 17 is formed by training the training model 17 multiple times using a plurality of training images of cervical cancer tumors (second training data). In one embodiment, each second training data may include a tumor image of a cervical cancer patient before treatment and a cervical cancer treatment response event of the patient after treatment (hereinafter defined as a second treatment response event) ). Preferably, the second training data can be expanded in advance through the small sample data expansion module 12 to generate multiple slice images.”; see also abstract “The basic structure of the pharyngeal cancer prognosis prediction model can be consistent with that of a uterine cervical cancer prognosis prediction model. The uterine cervical cancer prognosis prediction model is constructed by a deep learning technique and is trained to achieve excellent predictive results, and the pharyngeal cancer prognosis prediction model is transformed from the uterine cervical cancer prognosis prediction model by a transfer learning technique.”)
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 train the treatment effect models of Robinson using transfer learning from a similar cancer as shown by Kao.
The suggestion/motivation for doing so would have been faster training of the model and with less data.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Parkinson with Kao to obtain the invention as specified in claim 21.
Parkinson in view of Kao discloses 22. (New) The computer-implemented method of claim 21, further including receiving one of a pathological complete response (pCR) cancer qualification. (Parkinson, paragraph 288, “models of patient response are generated from sets of samples from the group consisting of: complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction”)
Parkinson in view of Kao discloses 23. (New) The computer-implemented method of claim 21, wherein the MRD cancer qualification is protocol specific.(By definition of the term MRD is protocol specific; See definition above.)
Parkinson in view of Kao discloses 24. (New) The computer-implemented method of claim 21, wherein the MRD cancer qualification corresponds to a number of cancer cells below a MRD threshold. (By definition of the term MRD compares the number of cancer cells to a threshold; See definition above)
Parkinson in view of Kao discloses 25. (New) The computer-implemented method of claim 21,wherein the MRD cancer qualification identifies one or more diseases that remains occult within the patient, but may eventually lead to a relapse. (By definition of the term MRD compares the number of cancer cells to a threshold; See definition above)
Parkinson in view of Kao discloses 26. (New) The computer-implemented method of claim 21, wherein the treatment effect machine learning model is trained based on tagged treatment effects in the plurality of training images. (Parkinson, paragraph 288, “models of patient response are generated from sets of samples from the group consisting of: complete response, partial response, nodular partial response, no response, progressive disease, stable disease and adverse reaction”)
Parkinson in view of Kao discloses 27. (New) The computer-implemented method of claim 21, wherein receiving the confirmed cancer quantification also comprises receiving a type of cancer when the output of the detection machine learning model comprises a confirmed cancer quantification (Parkinson, [287] “As described above, a statistical model is generated based on node state data for a set of samples with a known biological state and used to generate an association metric for a sample ("test sample"), where the association metric classifies the test sample as being associated with a biological state. A biological state, as used herein, refers to any discrete, characterizable state of a cell such as a phenotype, a response to an modulator, a activation of an activatable element, an increase in expression, a morphological state, a response/non-response to drug treatment, a disease or pre-disease state.”. where the biological state reads on the type of cancer. Since the image is taken on a slide, properties of slide and sample (such as tissue characteristics, slide type, glass type, stain type) inform (affect) what is on the image, which is used to determine cancer qualifications/quantifications.)
Parkinson in view of Kao discloses 28. (New) The computer-implemented method of claim 27, wherein the type of cancer is determined based on the digital image and one or more of a tissue characteristics, slide type, glass type, tissue type, tissue region, chemical used, or stain amount. (Parkinson, [287] “As described above, a statistical model is generated based on node state data for a set of samples with a known biological state and used to generate an association metric for a sample ("test sample"), where the association metric classifies the test sample as being associated with a biological state. A biological state, as used herein, refers to any discrete, characterizable state of a cell such as a phenotype, a response to an modulator, a activation of an activatable element, an increase in expression, a morphological state, a response/non-response to drug treatment, a disease or pre-disease state.”. where the biological state reads on the type of cancer. Since the image is taken on a slide, properties of slide and sample (such as tissue characteristics, slide type, glass type, stain type) inform (affect) what is on the image, which is used to determine cancer qualifications/quantifications.)
Parkinson in view of Kao discloses 29. (New) The computer-implemented method of claim 1, wherein the digital image is from a pathology category, the pathology category selected from one or more of histology, cytology, frozen section, immunohistochemistry (IHC), immunofluorescence, hematoxylin and eosin (H&E), hematoxylin alone, molecular pathology, and/or 3D imaging. (Parkinson, [240-241], “Confocal microscopy relies on the serial collection of light from spatially filtered individual specimen points, which is then electronically processed to render a magnified image of the specimen.”; “Once the cells are labeled, the plates can be scanned using an imager such as the Odyssey Imager (LiCor, Lincoln Nebr.) using techniques described in the Odyssey Operator's Manual v1.2., which is hereby incorporated in its entirety. Data obtained by scanning of the multiwell plate can be analyzed and activation profiles determined as described below.”; see also paragraph 14 , “Sample--A sample is a population of one or more cells. Samples can be derived, for example, from cells in culture or from patients.” And paragraph 21)
Claims 30-37 are rejected under similar grounds as claims 21-25,27-29
Claims 38-40 are rejected under similar grounds as claims 21-23
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.
Claim(s) 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over Parkinson in view of Schreier in further view of Georgakoudi(2010/0272651)
Parkinson in view of Schreier discloses 41. (New) The computer-implemented method of claim 21, But does not expressly disclose “wherein the cancer quantification of MRD includes an MRD ratio of cancer cells.”
Georgakoudi discloses “wherein the cancer quantification of MRD includes an MRD ratio of cancer cells.”(Georgakoudi, Fig. 1
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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 use the ratio of Georgakoudi as the cancer quantifier.
The suggestion/motivation for doing so would have been using a well-known quantifier used for cancer qualification.
Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Parkinson with Georgakoudi to obtain the invention as specified in claim 21.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cha KH, Hadjiiski L, Chan HP, Weizer AZ, Alva A, Cohan RH, Caoili EM, Paramagul C, Samala RK. Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning. Sci Rep. 2017 Aug 18;7(1):8738. Discloses “In this study, we explored the possibility that radiomics-based predictive models might be able to distinguish between bladder cancers that have fully responded to chemotherapy and those that have not, based upon analysis of pre- and post-treatment CT images. We evaluated three unique radiomics predictive models, which employ different fundamental design principles: (1) a pattern recognition method (DL-CNN), (2) a more deterministic radiomics feature based approach (RF-SL), and (3) a bridging method between the two, which extracts radiomics features from image patterns (RF-ROI). We studied both the properties of the different predictive models and the relationship between these different radiomics approaches. We also compared the performance of the models in predicting a complete response of bladder cancer to neoadjuvant chemotherapy with that of expert physicians”
Wang (WO2021108043A1) discloses “A method or system for assessing a patient response to a cancer treatment is provided. The method or system includes acquiring at least one base-line radiological image related to a patient immediately before a treatment, acquiring at least one follow-up radiological image during or after the treatment at a predetermined time interval, estimating a first number of specific tumor cells in a region of interest of the patient based on image features of the base-line radiological image using an algorithm or a model, estimating a second number of specific tumor cells in the region of interest of the patient based on image features of the follow-up radiological image using the algorithm or the model, obtaining a difference between the first number of specific tumor cells and the second number of specific tumor cells, and classifying a treatment response to a cancer based on the difference.”
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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/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
1 https://www.cancer.gov/publications/dictionaries/cancer-terms
2 https://www.cancer.gov/publications/dictionaries/cancer-terms