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, see Pre-appeal brief, filed 06/29/26, with respect to claims 1-4, 7-14, 17-24, and 27-30 have been fully considered and are persuasive. The previous rejection of claims 1-4, 7-14, 17-24, and 27-30 been withdrawn, but a new 101 and 103 rejection has been made detailed below.
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.
Claims 1, 11, and 21 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to a judicial exception (an abstract idea) without reciting additional elements sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Step 1 – Statutory Category
Claim 1 recites a method and therefore falls within the statutory category of a process. Claim 11 recites a system comprising a display, one or more processors, and memory and therefore falls within the statutory category of a machine. Claim 21 recites a non-transitory computer-readable storage medium and therefore falls within a statutory category of manufacture.
Accordingly, claims 1, 11, and 21 satisfy Step 1 of the eligibility analysis.
Step 2A, Prong One – The claims recite a judicial exception
Claims 1, 11, and 21 recite substantially similar limitations directed to receiving time-series fluorescence data representing perfusion of tissue and classifying a plurality of regions in the time-series data based on the tissue-perfusion information, providing a numerical value representing a condition of the tissue, and providing a risk estimate for the plurality of regions.
The recited classification and determination of a tissue condition and risk constitute analyzing and evaluating information to reach a determination concerning the condition and risk associated with tissue regions. Such evaluation and judgment constitute a mental-process type of abstract idea, because mental processes include concepts performed in the human mind such as observations, evaluations, judgments, and opinions. See MPEP §2106.04(a)(2)(III).
The claims additionally recite that the plurality of regions are classified using one or more machine-learning models. The machine-learning limitation is considered as part of the claims as a whole; the judicial exception is not based merely upon labeling the machine-learning model itself as a mental process, but rather upon the broadly recited analysis and evaluation of tissue-perfusion information to determine tissue condition and risk. Current USPTO guidance cautions that a limitation should not be characterized as a mental process where it cannot practically be performed in the human mind.
Therefore, claims 1, 11, and 21 recite an abstract idea under Step 2A, Prong One.
Step 2A, Prong Two – The judicial exception is not integrated into a practical application
The claims recite additional elements beyond the identified abstract idea.
Claim 1 recites performing the method “at a computer system having one or more processors” and receiving time-series fluorescence data captured by an image capture system.
Claim 11 additionally recites a display, one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the processors.
Claim 21 recites a non-transitory computer-readable storage medium storing one or more programs for execution by a computing system having one or more processors and a display.
The recited computer system, processor(s), memory, programs, display, and storage medium merely provide the computing environment in which the recited information analysis is performed. As claimed, these elements do not require a particular improvement to the operation of the computer, processor, memory, display, or image capture system. Merely using a computer as a tool to perform an abstract idea does not integrate the judicial exception into a practical application. See MPEP §§2106.04(d) and 2106.05(f).
The claims also recite receiving time-series data of fluorescence from tissue of the subject, wherein the time-series data represents perfusion of the tissue and is or has been captured by an image capture system. This limitation obtains the information that is subsequently subjected to the claimed classification and risk analysis. Thus, the receiving limitation constitutes data gathering/insignificant extra-solution activity associated with the subsequent information analysis. MPEP §2106.05(g) recognizes necessary data gathering used as input to an abstract analysis as a consideration that does not integrate the exception into a practical application.
Further, claims 1, 11, and 21 do not require the resulting numerical tissue-condition value or risk estimate to be used to control or improve operation of the image capture system, modify an imaging procedure, administer a treatment to the subject, or otherwise effect a particular technological operation based upon the resulting risk estimate. Rather, the claimed analysis results in information comprising a numerical value representing tissue condition and a risk estimate.
Accordingly, considering the additional elements individually and in combination, the claims merely use computing and data-acquisition components as tools for carrying out the recited information analysis and do not integrate the judicial exception into a practical application.
Therefore, claims 1, 11, and 21 are directed to the judicial exception under Step 2A.
Step 2B – The claims do not recite significantly more
The claims are further analyzed to determine whether the additional elements, individually and as an ordered combination, amount to significantly more than the judicial exception.
The additional computer-related elements of claims 1, 11, and 21—the processor(s), memory, programs, display, computer-readable storage medium, and computer system—are recited at a high level of generality and perform their ordinary computer functions of storing and executing instructions and processing information. The receiving of fluorescence imaging data similarly supplies the information upon which the recited classification and risk analysis operate.
When considered as an ordered combination, the additional elements amount to implementing the recited analysis and evaluation of tissue-perfusion information in a computer environment and obtaining the information necessary to perform that analysis. The claims do not recite an additional element or combination of elements that transforms the claimed abstract analysis into a patent-eligible application.
Accordingly, the additional elements, considered individually and as an ordered combination, do not amount to significantly more than the judicial exception.
Therefore, claims 1, 11, and 21 are directed to the abstract idea of analyzing and evaluating tissue-perfusion information to classify tissue regions and determine tissue condition and risk, without additional elements that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Accordingly, claims 1, 11, and 21 are rejected under 35 U.S.C. 101.
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) 1-4, 7, 9-14, 17, 19-24, 27, 29, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over U. S. Publication No. 2012/0155735 to Friedman et al. in view of U. S. Publication No. 2013/0315465 to Cosatto et al. and further in view of U. S. Publication No. 2004/0127799 to Sorensen et al.
Regarding Claims, 1, 11, and 21, Friedman teaches a system, method and program comprising a computer system having one or more processors: receiving time series data of fluorescence from tissue of the subject, the time series data related to perfusion of the tissue and the time series data being or having been captured by an image capture system and classifying a plurality of regions using temporal analysis (abstract and para 0059-0063, 070-72 and 090-093 teaches time series data of fluorescence imaging representing perfusion for tissue diagnosis and classifying the plurality of regions based on time series analysis) .
Friedman does not expressly teach classifying a plurality of regions in the time series data, wherein the plurality of regions are classified using one or more machine learning models; and plurality of regions are classified by providing a numerical value representing a condition of the tissue and providing a risk estimate for the plurality of regions.
Cosatto teaches classifying a plurality of regions in the time series data, wherein the plurality of regions are classified using one or more machine learning models (abstract; para 025-030 teaches classifying and 035-038 machine learning models to classify images).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Friedman with a setup such that the plurality of regions are classified using machine learning models and providing a numerical value as taught by Cosatto, since such a setup would result in a more precise and fast analysis of the images.
Friedman and Cosatto teaches all of the above claimed limitations but does not expressly teach providing a risk estimate for the plurality of regions.
Sorensen teaches providing a risk estimate for the plurality of regions (para 025-027, 057, 064, 066 and 076 teaches continuous numerical risk value and risk estimate).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Friedman and Cosatto with a risk estimate representing a condition of tissue as taught by Sorensen, since such a setup would result in easy observation of different tissue conditions by the doctor. Furthermore, it would also speed up the time spent by doctors on evaluating images.
Regarding Claim 2, 12, and 22 Friedman teaches the regions are chosen based on one or more attributes of the data (para 070-072 and 087-089 teaches forming and clustering feature vectors from the images, which are attributes).
Regarding Claim 3, 13, and 23 Cosatto teaches the regions are classified based on a predicted tissue condition of the region (para 025-030 and 035-038).
Regarding Claim 4, 14, and 24 Cosatto teaches that the predicted tissue condition comprises inflammation, malignancy, abnormality or disease (para 035-038 teaches caner/tumor detection).
Regarding Claim 7, 17, and 27, Friedman teaches that the time series data comprises raw data, pre-processed data, or a combination thereof (abstract; para 059-063 and 070-072 teaches time series data is raw data).
Regarding Claim 9, 19, and 29, Friedman teaches that the plurality of regions are classified using an unsupervised clustering algorithm (para 087-089 teaches K-means clustering algorithm).
Regarding Claim 10, 20, and 30, Friedman teaches that the time series data of fluorescence from tissue of the subject comprises fluorescence intensity data (para 0062-063 and 070-072 teaches fluorescence intensity time curve data).
Claim(s) 8, 18, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over U. S. Publication No. 2012/0155735 to Friedman et al. in view of U. S. Publication No. 2013/0315465 to Cosatto et al. and further in view of U. S. Publication No. 2004/0127799 to Sorensen et al. further in view of U. S. Publication No. 2010/0142786 to Degani et al.
Regarding Claim 8, 18, and 28, Friedman, Cosatto and Sorensen teaches all of the above claimed limitations but does not expressly teach that the time series data comprises pre-processed data that has been pre-processed by applying data compression, principal component analysis, autoencoding, or a combination thereof.
Degani teaches that the time series data comprises pre-processed data that has been pre-processed by applying data compression, principal component analysis, autoencoding, or a combination thereof (para 051-053 teaches principal component analysis).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Friedman, Cosatto and Sorensen with a setup such that the time series data comprises pre-processed data that has been pre-processed by applying principal component analysis as taught by Degani, since such a setup would result in better filtered and processed resulting in better imaging results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANJAY CATTUNGAL whose telephone number is (571)272-1306. The examiner can normally be reached M-F 9-5 EST.
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/SANJAY CATTUNGAL/Primary Examiner, Art Unit 3793
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