Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Office Action is in response to the filing with the office dated 03/20/2024.
Claims 1, 17 and 19 are independent claims. Claims 1-20 are presented in this office action.
Priority
3. Applicant’s claim for the benefit of a prior-filed provisional Application No. 63/248,354 filed on 09/24/2021 is acknowledged by the examiner.
4. Applicant’s claim for the benefit of a prior-filed PCT Application No. PCT/US22/44761 filed on 09/26/2022 is acknowledged by the examiner.
Information Disclosure Statement
5. The information disclosure statement (IDS) submitted on 05/07/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
Regarding independent claims 1, 17 and 19 the claim limitations recite in part “ accessing plurality of files…”, “generating metadata for each file….”, “performing cross-validation….”, “generating a report….”
under its broadest reasonable interpretation, covers performance of the limitation in the mind. and/or There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. under its broadest reasonable interpretation, covers performance of the limitation in the mind. and/or There is, nothing in the claim element precludes the steps from practically being performed by a human mentally or with pen and paper. These limitations, at the high level of generality as drafted, would encompass a user to access files, generate metadata, perform cross validation and generate a report which is mentally performable as an evaluation or judgement. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites “system”, “computer-readable non-transitory storage media”, “processor”, memory” are recited at a high level of generality as generic computer components and additional elements “providing instructions for display” which is an insignificant extra-solution activity of a data gathering process. These additional elements amount to nothing more than mere instructions to apply the recited abstract idea on a computer, under MPEP 2106.05(f). The additional elements of “providing instructions for display” amount to mere data outputting which are insignificant extra-solution activity. Combination of these additional elements is no more than mere instructions to apply the exception using series of steps and outputting the result of the mental process. Accordingly, even in combination, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the recitation of generic computing components is still mere instructions to apply the exception under MPEP 2106.05(f) and does not provide significantly more. The “providing instructions for display” element that was identified as insignificant extra-solution activity as mere data gathering when re-evaluated still does not provide significantly more, Considering the additional elements in combination and the claim as a whole does not change the analysis, and does not amount to significantly more. Thus the claims are abstract.
The remaining dependent claims 2-16, 18 and 20 which impose additional limitations explained above also fail to claim patent-eligible subject matter because the limitations cannot be considered statutory. In reference to claim 1, these dependent claims have also been reviewed with the same analysis as independent claim 1. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
Claim Rejections - 35 U.S.C. § 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 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.
7. Claims 1, 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Geneslaw; Luke (US 20240242816 A1) in view of GORTON; Danielle (US 20230351599 A1).
Regarding independent claim 1, Geneslaw; Luke (US 20240242816 A1) teaches, a method comprising, by a data quality control system: accessing a plurality of slide files of a plurality of tissue samples, respectively, wherein each of the plurality of slide files is associated with vendor metadata, respectively (Paragraphs [0024]- [0027] discloses, plurality of files corresponding to a tissue sample are associated with different vendor);
generating, for each of the plurality of slide files by one or more machine-learning models, label metadata, image content metadata, and technical metadata associated with the slide file (Paragraph [0026] discloses, each slide file/ record is associated with label data, image data and technical data (in specification technical metadata is also known as data source) machine-learning models is taught by GORTON et al (Paragraph [0006];
performing metadata cross-validation on each of the plurality of slide files based on a comparison of the respective vendor metadata with the respective label metadata, image content metadata, and technical metadata associated with the slide file (Paragraph [0030] discloses, identifying the respective vendor data based on the record unpacker).
Geneslaw et al fails to explicitly teach, generating a report summarizing the plurality of slide files based on the metadata cross-validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and providing instructions for displaying, via a user interface, the report to a user, wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files.
GORTON; Danielle (US 20230351599 A1) teaches, generating a report summarizing the plurality of slide files based on the metadata cross-validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and providing instructions for displaying, via a user interface, the report to a user (Paragraph [0008] report with information about the collection of suspicious slides (and/or nonsuspicious slides), and outputting the report to a user (examiner interprets matches and mismatches as suspicious slides and nonsuspicious slides);
wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files (Paragraphs [0008], [0011] discloses, viewing the report includes label data and image data and technical data/ data source. Vendor data is taught by Geneslaw et al Paragraphs [0024]- [0027]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al by generating a report summarizing the plurality of slide files based on the metadata cross- validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and provide instructions for displaying, via a user interface, the report to a user, wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files, as taught by GORTON et al Paragraphs [0008], [0011]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would instantly alerts users to urgent anomalies that require immediate review or downstream testing as taught by GORTON et al).
Regarding dependent claim 11, Geneslaw et al and GORTON et al teach, the method of Claim 1.
Geneslaw et al further teaches, wherein two or more of the plurality of slide files are based on different file formats (Paragraph [0027] the files created may be in a variety of formats depending on the scanning hardware used. Also see [0105]).
Regarding dependent claim 12, Geneslaw et al and GORTON et al teach, the method of Claim 1.
Geneslaw et al further teaches, further comprising: generating a synthetic metadata file by aggregating the label metadata, image metadata, and technical metadata associated with each of the plurality of slide files, wherein the comparison is based on the synthetic metadata file (Paragraph [0030] discloses, identifying the respective vendor data based on the record unpacker);
Regarding dependent claim 13, Geneslaw et al and GORTON et al teach, the method of Claim 1.
GORTON et al further teaches, wherein the data quality control system is based on a plurality of modules comprising a module for automatic label detection and recognition (Paragraph [0006] discloses, automatic label categorization), a module for classification of staining, and a module for tissue identification (Paragraph [0045] discloses, classifying the images of staining and tissue identification), wherein the report comprises content specific to each module, and wherein the user interface is operable for the user to view the content specific to each module separately (Paragraphs [0008], [0011] discloses, viewing the report includes label data and image data and technical data/ data source).
Regarding dependent claim 14, Geneslaw et al and GORTON et al teach, the method of Claim 1.
Geneslaw et al further teaches, wherein each of the vendor metadata, label metadata, and technical metadata is based on a tabular structure comprising one or more metadata fields (Paragraph [0027] discloses, formatting the label and data source/ technical data includes label and image data and also identifies the vendor data based on store data structure such as tables. Also see [0037]),
and wherein the matches and mismatches are determined based on comparisons between the metadata fields of the vendor metadata and the corresponding metadata fields of the label metadata and technical metadata, respectively (Paragraph [0030] discloses, identifying the respective vendor data based on the record unpacker).
Regarding dependent claim 15, Geneslaw et al and GORTON et al teach, the method of Claim 14.
GORTON et al further teaches, wherein the user interface displays the vendor metadata, label metadata, and technical metadata in the respective tabular structure (Paragraph [0008] report with information about the collection of suspicious slides (and/or nonsuspicious slides), and outputting the report to a user (examiner interprets matches and mismatches as suspicious slides and nonsuspicious slides);
Regarding dependent claim 16, Geneslaw et al and GORTON et al teach, the method of Claim 1.
GORTON et al further teaches, further comprising: detecting one or more artifacts associated with one or more of the plurality of slide files, wherein the report further comprises information associated with the detected artifacts (Paragraph [0008] report with information about the collection of suspicious slides (and/or nonsuspicious slides), and outputting the report to a user (examiner interprets matches and mismatches as suspicious slides and nonsuspicious slides);
Regarding independent claim 17, Geneslaw; Luke (US 20240242816 A1) teaches, One or more computer-readable non-transitory storage media embodying software that is operable when executed to: access a plurality of slide files of a plurality of tissue samples, respectively, wherein each of the plurality of slide files is associated with vendor metadata, respectively (Paragraphs [0024]- [0027] discloses, plurality of files corresponding to a tissue sample are associated with different vendor);
generate, for each of the plurality of slide files by one or more machine-learning models, label metadata, image content metadata, and technical metadata associated with the slide file(Paragraph [0026] discloses, each slide file/ record is associated with label data, image data and technical data (in specification technical metadata is also known as data source) machine-learning models is taught by GORTON et al (Paragraph [0006];
perform metadata cross-validation on each of the plurality of slide files based on a comparison of the respective vendor metadata with the respective label metadata, image content metadata, and technical metadata associated with the slide file(Paragraph [0030] discloses, identifying the respective vendor data based on the record unpacker);
Geneslaw et al fails to explicitly teach, generate a report summarizing the plurality of slide files based on the metadata cross- validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and provide instructions for displaying, via a user interface, the report to a user, wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files.
GORTON; Danielle (US 20230351599 A1) teaches, generate a report summarizing the plurality of slide files based on the metadata cross- validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and provide instructions for displaying, via a user interface, the report to a user(Paragraph [0008] report with information about the collection of suspicious slides (and/or nonsuspicious slides), and outputting the report to a user (examiner interprets matches and mismatches as suspicious slides and nonsuspicious slides), wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files (Paragraphs [0008], [0011] discloses, viewing the report includes label data and image data and technical data/ data source. Vendor data is taught by Geneslaw et al Paragraphs [0024]- [0027]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al by generating a report summarizing the plurality of slide files based on the metadata cross- validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and provide instructions for displaying, via a user interface, the report to a user, wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files, as taught by GORTON et al Paragraphs [0008], [0011]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would instantly alerts users to urgent anomalies that require immediate review or downstream testing as taught by GORTON et al).
Regarding dependent claim 18, Geneslaw et al and GORTON et al teach, the media of Claim 17.
GORTON et al further teaches, wherein the data quality control system is based on a plurality of modules comprising a module for automatic label detection and recognition (Paragraph [0006] discloses, automatic label categorization), a module for classification of staining, and a module for tissue identification (Paragraph [0045] discloses, classifying the images of staining and tissue identification), wherein the report comprises content specific to each module, and wherein the user interface is operable for the user to view the content specific to each module separately (Paragraph [0045] discloses, classifying the images of staining and tissue identification).
Regarding independent claim 19, Geneslaw; Luke (US 20240242816 A1) teaches, a system comprising: one or more processors; and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: access a plurality of slide files of a plurality of tissue samples, respectively, wherein each of the plurality of slide files is associated with vendor metadata, respectively (Paragraphs [0024]- [0027] discloses, plurality of files corresponding to a tissue sample are associated with different vendor);
generate, for each of the plurality of slide files by one or more machine-learning models, label metadata, image content metadata, and technical metadata associated with the slide file (Paragraph [0026] discloses, each slide file/ record is associated with label data, image data and technical data (in specification technical metadata is also known as data source) machine-learning models is taught by GORTON et al (Paragraph [0006];
perform metadata cross-validation on each of the plurality of slide files based on a comparison of the respective vendor metadata with the respective label metadata, image content metadata, and technical metadata associated with the slide file (Paragraph [0030] discloses, identifying the respective vendor data based on the record unpacker).
Geneslaw et al fails to explicitly teach, generate a report summarizing the plurality of slide files based on the metadata cross- validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and provide instructions for displaying, via a user interface, the report to a user, wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files.
GORTON; Danielle (US 20230351599 A1) teaches, generate a report summarizing the plurality of slide files based on the metadata cross- validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and provide instructions for displaying, via a user interface, the report to a user(Paragraph [0008] report with information about the collection of suspicious slides (and/or nonsuspicious slides), and outputting the report to a user (examiner interprets matches and mismatches as suspicious slides and nonsuspicious slides), wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files (Paragraphs [0008], [0011] discloses, viewing the report includes label data and image data and technical data/ data source. Vendor data is taught by Geneslaw et al Paragraphs [0024]- [0027]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al by generating a report summarizing the plurality of slide files based on the metadata cross- validation, wherein the report indicates a number of matches and a number of mismatches from the metadata cross-validation for the plurality of slide files; and provide instructions for displaying, via a user interface, the report to a user, wherein the user interface is operable for the user to view the vendor metadata, label metadata, image content metadata, and technical metadata associated with each of the plurality of slide files, as taught by GORTON et al Paragraphs [0008], [0011]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would instantly alerts users to urgent anomalies that require immediate review or downstream testing as taught by GORTON et al).
Regarding dependent claim 20, Geneslaw et al and GORTON et al teach, the system of Claim 19.
GORTON et al further teaches, wherein the data quality control system is based on a plurality of modules comprising a module for automatic label detection and recognition Paragraph [0006] discloses, automatic label categorization), a module for classification of staining, and a module for tissue identification (Paragraph [0045] discloses, classifying the images of staining and tissue identification), wherein the report comprises content specific to each module, and wherein the user interface is operable for the user to view the content specific to each module separately (Paragraph [0045] discloses, classifying the images of staining and tissue identification).
8. Claims 2-5 are rejected under 35 U.S.C. 103 as being unpatentable over Geneslaw; Luke (US 20240242816 A1) in view of GORTON; Danielle (US 20230351599 A1) and in further view of D'COSTA; Maya (US 20200400930 A1).
Regarding dependent claim 2, Geneslaw et al and GORTON et al teach, the method of Claim 1.
Geneslaw et al and GORTON et al fails to explicitly teach, wherein the image content metadata comprises one or more of a type of staining used for the slide file or a type of tissue of the slide file
D'COSTA; Maya (US 20200400930 A1) teaches, wherein the image content metadata comprises one or more of a type of staining used for the slide file or a type of tissue of the slide file (Paragraph [0073] barcode info can indicate a certain type of slide (e.g., type of cancer and type of stain) and the scanning device can recognize that type of slide and set scanning settings associated with that specific type of slide).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al and GORTON et al by providing wherein the image content metadata comprises one or more of a type of staining used for the slide file or a type of tissue of the slide file, as taught by D'COSTA et al (Paragraph [0073]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would facilitates efficient and accurate scanning of specimen bearing slides; facilitate increased scanning speeds and increased efficiencies in the acquisition of image data. Applicants further submit that the system and methods disclosed herein enable increased accuracy in the scanning of microscope slides as taught by D'COSTA et al (Paragraph [0017]).
Regarding dependent claim 3, Geneslaw et al and GORTON et al teach, the method of Claim 1.
Geneslaw et al and GORTON et al fails to explicitly teach, wherein the label metadata comprises one or more of label encoded metadata or label textual metadata.
D'COSTA; Maya (US 20200400930 A1) teaches, wherein the label metadata comprises one or more of label encoded metadata or label textual metadata (Paragraph [0013] he system further comprises instructions for automatically recognizing slide label information within the scanned images and populating the slide label information into one or more metadata fields. Also see [0127]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al and GORTON et al by providing wherein the label metadata comprises one or more of label encoded metadata or label textual metadata, as taught by D'COSTA et al (Paragraph [0073]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would facilitates efficient and accurate scanning of specimen bearing slides; facilitate increased scanning speeds and increased efficiencies in the acquisition of image data. Applicants further submit that the system and methods disclosed herein enable increased accuracy in the scanning of microscope slides as taught by D'COSTA et al (Paragraph [0017]).
Regarding dependent claim 4, Geneslaw et al and GORTON et al teach, the method of Claim 1.
Geneslaw et al and GORTON et al fails to explicitly teach, wherein each slide file of the plurality of slide files comprises a plurality of layers, wherein the plurality of layers comprise at least a thumbnail image, and wherein the thumbnail image comprises one or more of an assay content or a label associated with the corresponding slide file, wherein the label comprises one or more of text or a digital code.
D'COSTA; Maya (US 20200400930 A1) teaches, wherein each slide file of the plurality of slide files comprises a plurality of layers, wherein the plurality of layers comprise at least a thumbnail image, and wherein the thumbnail image comprises one or more of an assay content or a label associated with the corresponding slide file, wherein the label comprises one or more of text or a digital code (Paragraph [0033] discloses, slide files include thumbnail image scans and the thumbnail image includes label portions of each scanned slide, the label portions including slide label information such as barcodes and/or alphanumeric information. Also see [0092]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al and GORTON et al by providing wherein each slide file of the plurality of slide files comprises a plurality of layers, wherein the plurality of layers comprise at least a thumbnail image, and wherein the thumbnail image comprises one or more of an assay content or a label associated with the corresponding slide file, wherein the label comprises one or more of text or a digital code, as taught by D'COSTA et al (Paragraph [0033]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, would facilitates efficient and accurate scanning of specimen bearing slides; facilitate increased scanning speeds and increased efficiencies in the acquisition of image data. Applicants further submit that the system and methods disclosed herein enable increased accuracy in the scanning of microscope slides as taught by D'COSTA et al (Paragraph [0017]).
Regarding dependent claim 5, Geneslaw et al, GORTON et al and D'COSTA et al teach, the method of Claim 4.
Geneslaw et al further teaches, wherein the label metadata comprises label encoded metadata, wherein the method further comprises: for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises a label associated with the corresponding slide file; identifying boundaries of the label within the thumbnail image; generating a label image by cropping out the label based on the boundaries of the label (Paragraph [0004], [0005] discloses, extracting the thumbnail image associated with label and Edge detection may be used to recognize the boundary or a perimeter of a label from the remainder of the image. Also see [0075]);
D'COSTA et al further teaches, detecting a presence of a digital code in the label image; and generating the label encoded metadata based on decoding the digital code (Paragraph [0073] discloses, detecting a digital code in the image, generating/ populating the label based on decoding the digital code), wherein the label encoded metadata comprises one or more of a filename, a study identifier, a block identifier, or a database identifier (Paragraph [0074] discloses, the label metadata includes study ID. The label metadata fields are DICOM attributes [0009])).
9. Claims 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Geneslaw; Luke (US 20240242816 A1) in view of GORTON; Danielle (US 20230351599 A1), D'COSTA; Maya (US 20200400930 A1) and in further view of Barnes; Michael (US 20200143542 A1).
Regarding dependent claim 6, Geneslaw et al, GORTON et al and D'COSTA et al teach, the method of Claim 5.
Geneslaw et al, GORTON et al and D'COSTA et al fails to explicitly teach, further comprising: detecting an error of an orientation of the label in the label image; and fixing the error by rotating the label image based on a correct orientation of the label.
Barnes; Michael (US 20200143542 A1) teaches, further comprising: detecting an error of an orientation of the label in the label image; and fixing the error by rotating the label image based on a correct orientation of the label (Paragraph [0041] The present invention further accommodates images that are derived from consecutive microtome slices, where they may require rotation in addition to translation to align common features of interest. Also, the present invention may involve tagging images with metadata to describe their location in a tissue section, and this this information is used for construction of affine transforms to adjust the images to a common reference frame for display. Additionally, the present invention allows for simultaneous zooming in magnification of all images at the same scale.
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al, GORTON et al and D'COSTA et al by providing further comprising: detecting an error of an orientation of the label in the label image; and fixing the error by rotating the label image based on a correct orientation of the label., as taught by Barnes et al (Paragraph [0041]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it is advantageous as a user, such as a pathologist, can readily view and manipulate images obtained from tissue slices of a tissue region in an intuitive way that facilitates the task of performing a diagnosis as taught by Barnes et al (Paragraph [0026]).
Regarding dependent claim 7, Geneslaw et al, GORTON et al and D'COSTA et al teach, the method of Claim 4.
Geneslaw et al further teaches, wherein the label metadata comprises label textual metadata, wherein the method further comprises: for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises a label associated with the corresponding slide file; identifying boundaries of the label within the thumbnail image; generating a label image by cropping out the label based on the boundaries of the label (Paragraph [0004], [0005] discloses, extracting the thumbnail image associated with label and Edge detection may be used to recognize the boundary or a perimeter of a label from the remainder of the image. Also see [0075]);
detecting text in the preprocessed label image; and generating the label textual metadata based on optical character recognition on the detected text (Paragraph [0073] discloses, detecting a digital code in the image, generating/ populating the label based on decoding the digital code).
Geneslaw et al, GORTON et al and D'COSTA et al fails to explicitly teach, preprocessing the label image, wherein the preprocessing comprises one or more of image blurring, illumination correction, or thresholding.
Barnes; Michael (US 20200143542 A1) teaches, preprocessing the label image, wherein the preprocessing comprises one or more of image blurring, illumination correction, or thresholding (Paragraph [0103] the operation of converting the plurality of preprocessed images (2120) may perform nonlinear corrections on the plurality of preprocessed images to remove optical distortions. Exemplary nonlinear corrections may include removal of pincushion or barrel distortion, defocus, coma, or astigmatism).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al, GORTON et al and D'COSTA et al by providing preprocessing the label image, wherein the preprocessing comprises one or more of image blurring, illumination correction, or thresholding, as taught by Barnes et al (Paragraph [0103]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it is advantageous as a user, such as a pathologist, can readily view and manipulate images obtained from tissue slices of a tissue region in an intuitive way that facilitates the task of performing a diagnosis as taught by Barnes et al (Paragraph [0026]).
Regarding dependent claim 8, Geneslaw et al, GORTON et al, D'COSTA et al and Barnes et al teach, the method of Claim 7.
Geneslaw et al further teaches, further comprising: formatting, based on a template-based pattern matching, the text to into one or more metadata fields in a tabular structure, wherein the template is determined based on the vendor metadata (Paragraph [0027] discloses, formatting the label based on the vendor metadata).
Regarding dependent claim 9, Geneslaw et al, GORTON et al and D'COSTA et al teach, the method of Claim 4.
D'COSTA et al further teaches, wherein the image content metadata comprises a type of staining used for the slide file (Paragraph [0073] barcode info can indicate a certain type of slide (e.g., type of cancer and type of stain) and the scanning device can recognize that type of slide and set scanning settings associated with that specific type of slide):
Geneslaw et al, GORTON et al and D'COSTA et al fails to explicitly teach, wherein the method further comprises: for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises an assay associated with the corresponding slide file; identifying boundaries of the assay within the thumbnail image; generating an assay image by cropping out the assay based on the boundaries of the assay; and determining, based on the assay image, the type of staining, wherein the determining is further based on one or more of an amount of chemical used for staining or the one or more machine-learning models.
Barnes; Michael (US 20200143542 A1) teaches, wherein the image content metadata comprises a type of staining used for the slide file; wherein the method further comprises: for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises an assay associated with the corresponding slide file; identifying boundaries of the assay within the thumbnail image; generating an assay image by cropping out the assay based on the boundaries of the assay; (Paragraph [0003] discloses, image comprises an assay associated with the file. By identifying the biomarkers as boundaries in the image, creating/ generating an assay);
and determining, based on the assay image, the type of staining, wherein the determining is further based on one or more of an amount of chemical used for staining or the one or more machine-learning models (Paragraph [0128] discloses, based on the assay image, the type of staining is identified based on the concentration/ amount of chemicals used for staining).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al, GORTON et al and D'COSTA et al by providing wherein the method further comprises: for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises an assay associated with the corresponding slide file; identifying boundaries of the assay within the thumbnail image; generating an assay image by cropping out the assay based on the boundaries of the assay; and determining, based on the assay image, the type of staining, wherein the determining is further based on one or more of an amount of chemical used for staining or the one or more machine-learning models, as taught by Barnes et al (Paragraphs [0003], [0128]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it is advantageous as a user, such as a pathologist, can readily view and manipulate images obtained from tissue slices of a tissue region in an intuitive way that facilitates the task of performing a diagnosis as taught by Barnes et al (Paragraph [0026]).
10. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Geneslaw; Luke (US 20240242816 A1) in view of GORTON; Danielle (US 20230351599 A1) and in further view of Barnes; Michael (US 20200143542 A1).
Regarding dependent claim 10, Geneslaw et al and GORTON et al teach, the method of Claim 1.
Geneslaw et al and GORTON et al fails to explicitly teach, wherein the image content metadata comprises one or more types of tissue of the slide file, wherein the method further comprises, for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises an assay associated with the corresponding slide file; identifying boundaries of the assay within the thumbnail image; generating an assay image by cropping out the assay based on the boundaries of the assay; detecting one or more assay pieces within the assay image; segmenting the one or more assay pieces; and determining, based on the segmented one or more assay pieces, the one or more types of tissue by the one or more machine-learning models.
Barnes; Michael (US 20200143542 A1) teaches, wherein the image content metadata comprises one or more types of tissue of the slide file, wherein the method further comprises, for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises an assay associated with the corresponding slide file; identifying boundaries of the assay within the thumbnail image; generating an assay image by cropping out the assay based on the boundaries of the assay; detecting one or more assay pieces within the assay image; segmenting the one or more assay pieces (Paragraph [0003] discloses, image comprises an assay associated with the file. By identifying the biomarkers as boundaries in the image, creating/ generating an assay);
and determining, based on the segmented one or more assay pieces, the one or more types of tissue by the one or more machine-learning models (Paragraph [0128] discloses, based on the assay image, the type of staining is identified based on the concentration/ amount of chemicals used for staining. machine-learning models is taught by GORTON et al (Paragraph [0006]).
Therefore it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention, to have modified the teachings of Geneslaw et al, GORTON et al by providing w wherein the image content metadata comprises one or more types of tissue of the slide file, wherein the method further comprises, for each of the plurality of slide files: extracting the thumbnail image of the slide file, wherein the thumbnail image comprises an assay associated with the corresponding slide file; identifying boundaries of the assay within the thumbnail image; generating an assay image by cropping out the assay based on the boundaries of the assay; detecting one or more assay pieces within the assay image; segmenting the one or more assay pieces; and determining, based on the segmented one or more assay pieces, the one or more types of tissue by the one or more machine-learning models, as taught by Barnes et al (Paragraph [0003]).
One of the ordinary skill in the art would have been motivated to make this modification, by doing so, it is advantageous as a user, such as a pathologist, can readily view and manipulate images obtained from tissue slices of a tissue region in an intuitive way that facilitates the task of performing a diagnosis as taught by Barnes et al (Paragraph [0026]).
Closest Prior Art
11. The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Fuchs; Thomas (US 20230143593 A1) teaches, [0038] At each data source 110, the data service 130 may maintain and manage the record database 135 in storing one or more digital pathology records 165 (hereinafter generally referred to as record 165). Each data source 110 may be operated and administered by a respective vendor of bioinformatics data for histopathology, and may have a particular format (e.g., a proprietary protocol or standard) to package and maintain the bioinformatics data on the database 135. For example, the format used by the vendor of the first data source 110A may differ from the format used by the vendor of the second data source 110B. Each record 165 on the database 135 may be generated, maintained, stored, and indexed in accordance with the format of the data source 110 to which the record database 135 belong. Generally, across different vendors (and by extension the associated data sources 110 and databases 135), each record 165 may include at least one biomedical image 170 and metadata 175 associated with the biomedical image 170.
12. Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968))).
Conclusion
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/S. R./
Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
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