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 .
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, 4-8, 11-15, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites (additional limitations crossed out):
A pretreatment prediction system comprising:
receive at least one image of a pre-treatment biopsy from a patient;
segment the at least one image into a plurality of grid regions and discard any grid regions comprising predominantly white space;
process remining grid regions from the at least one image
display,
The above limitations, as drafted, is a process that, under its broadest reasonable interpretation covers managing personal behavior or relationships or interactions between people, and mental processes. That is, other than reciting the steps as being performed by a “display device”, a “computing device comprising a processor and a memory”, and a “trained CNN”, nothing in the claim precludes the steps as being described as managing personal behavior or relationships or interactions between people, or mental processes. For example, but for the “display device”, a “computing device comprising a processor and a memory”, and a “trained CNN” language, the limitations describe the obtaining of data (i.e., image of a pre-treatment biopsy) related to a patient, removing excess data (i.e., discarding white space), processing the remaining data, and displaying a prediction of the likelihood of a complete clinical response to treatment based on the processing, which describes both managing personal behavior or relationships or interactions between people, and actions that may be performed mentally and/or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, describes managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, describes steps that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of a “display device”, and a “computing device comprising a processor and a memory” to perform the steps. These additional elements are recited at a high level of generality (see at least Para. [00110]) such that they amount to no more than mere instructions to apply the exception using generic computing components. Moreover, the functionality intended to be performed by the “trained CNN” appears to be based on very rudimentary constraints (e.g., image of a pre-treatment biopsy). Without some prohibition in the claims regarding scalability, computation load, etc., the abstract idea could reasonably be considered as falling in the “mental process” category, but for which the “trained CNN” is merely applied (i.e., “apply it”).
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 additional element of using a “display device”, a “computing device comprising a processor and a memory” and “trained CNN” to perform the steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component, or generally linking the judicial exception to a particular environment cannot provide an inventive concept. Therefore, the claim is not found to be patent eligible.
Claims 8 and 15 feature limitations similar to those of claim 1, and are therefore also found to be directed to an abstract idea without significantly more.
Claims 4-7 are dependent on claim 1, and include all the limitations of claim 1. Claims 11-14 are dependent on claim 8, and includes all the limitations of claim 8. Claims 18-20 are dependent on claim 15, and includes all the limitations of claim 15. Therefore, they are also directed to the same abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4-8, 11-15, and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “predominantly” in claims 1, 8, and 15 is a relative term which renders the claim indefinite. The term “predominantly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Dependent claims are rejected as well since they inherit the limitations of the independent claims.
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, 4-6, 8, 11-13, 15, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vladimirova (US 2020/0105413) in view of Goldman (US 2019/0361006) and Wang (US 2020/0381121).
Regarding claim 1, Vladimirova discloses A pretreatment prediction system comprising:
a display device; (See at least Para. [0100])
a computing device comprising a processor and a memory, the memory storing instructions that, when executed by the processor, cause the processor to: (See at least Para. [0099])
Vladimirova also discloses:
receive at least one image of a pre-treatment biopsy from a patient;
process remaining grid regions from the at least one image using a trained convolutional neural network (CNN);
display, on the display device, the prediction of the likelihood of the complete clinical response of the patient to the treatment.
See at least Abstract – “In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.”, Para. [0005] –“The machine learning model may include, for example, a Naive Bayes (NB) model, a logistic regression (LR) model, a random forest (RF) model, a support vector machine (SVM) model, an artificial neural network model, a multilayer perceptron (MLP) model, a convolutional neural network (CNN), other machine learning or deep leaning models, etc. The machine learning model can be updated/trained using a supervised learning technique, an unsupervised learning technique, etc.”, and Para. [0038] – “In some embodiments, prediction engine 330 can generate an output ( e.g., for a display, for an audio speaker, etc.) to indicate a prediction result, to enable the patient and/or a health care provider to make a decision on whether to receive/administer the platinum drug treatment.”
Vladimirova does not explicitly disclose segment the at least one image into a plurality of grid regions and discard any grid regions comprising predominantly white space (See at least Wang, Para. [0063] – “In step 104, the image from FIG. 4 is segmented into a grid of fixed size, where each square in the grid (referred to interchangeably herein as a “tile”) is configured for use as an input to a neural network.”, and Para. [0066] – “An exemplary background removal process includes the steps of setting a pixel threshold mask where the average pixel intensity value across RGB channels is above a threshold, for example 217. Where a pixel has an average value above the threshold, that pixel is set to (255,255,255) (white). Where a pixel has an average value below the threshold, that pixel may be set to (0,0,0) (black). An example of a binary mask is shown in FIG. 13. Suitable methods for determining which tiles to discard include use of a threshold, for example discarding tiles having more than 50% or 60% or 65% of raw image pixels as white space background (pixel intensity value above a threshold, for example 200 or 215 or 217); or above a threshold of segmented objects within the tile area classified as other, for example above 60% or above 70% or above 80% or above 85%.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Vladimirova to utilize the teachings of Wang since it would make the system more efficient by evaluating only those regions of the image which are relevant to the outcome (Para. [0066] of Wang).)
Vladimirova does not fully disclose processing the image using the trained CNN to generate a prediction of a likelihood that the patient will experience a complete clinical response to a treatment selected from one or more of: radiation, chemotherapy, or immune therapy treatment. (The treatment of Vladimirova are selected from radiation, chemotherapy, or immune therapy treatment. See Para. [0027] – “The treatment response prediction can be performed before starting a medical treatment A ( e.g., a platinum treatment, an immunotherapy, a chemotherapy, etc.) at time Tl. Based on the prediction, it can be determined whether medical treatment A is recommended.” However, while Vladimirova discloses determining a likelihood that a patient may be resistant or sensitive to a drug treatment (Para. [0039]), this slightly differs from a complete clinical response. See Goldman, Para. [0009] – “In some embodiments, according to any of the methods described above, the output predicts complete clinical response, partial clinical response, or no clinical response of the individual to administration of the anticancer drug regimen.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Vladimirova to utilize the teachings of Goldman since they are both in the same field of endeavor (i.e., using machine learning techniques to predict efficacy of cancer treatments) and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claims 8 and 15 feature limitations similar to those of claim 1, and are therefore rejected using the same rationale.
Regarding claim 4, Vladimirov does not explicitly disclose The pretreatment prediction system of claim 1,wherein the at least one image of the pretreatment biopsy from the patient comprises at least one stained pre-treatment rectal adenocarcinoma biopsy. (Vladimirov discloses the image being a stained biopsy. See at least Para. [005] – “The biopsy image data may comprise biopsy image data of primary tumor, such as hematoxylin- and eosin-stained (H&E) histopathology data.” Also see at least Para. [0021] – “ The score can represent, for example, a percentage value indicating a likelihood that the patient is sensitive to the platinum drug treatment for ovarian cancer, or other treatments for other cancers.” However, Vladimirov does not disclose that said biopsy image is of a rectal adenocarcinoma. The Examiner asserts that the particular biopsy in the image is simply a label for the image and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the particular matter shown in the biopsy image) which does not explicitly alter or impact the steps of the method (i.e., receiving and processing an image of the biopsy) does not patentably distinguish the claimed invention from the prior art in terms of patentability. Therefore, it would have been obvious to a person of ordinary skill in the art at the time of invention to biopsy image of Vladimirov feature a rectal adenocarcinoma because the matter featured in the biopsy image does not functionally alter or relate to the steps of the method and merely labeling the biopsy image differently from that of the prior art does not patentably distinguish the claimed invention.
Claims 11 and 18 feature limitations similar to those of claim 4, and are therefore rejected using the same rationale.
Regarding claim 5, Vladimirova discloses The pretreatment prediction system of claim 1, wherein the memory stores the trained CNN. (See at least Para. [0038] – “Prediction engine 330 may include a processor 350 and a memory 335. Molecular data 322 and H&E histopathology images data 324 may be stored locally in prediction engine 330 in memory 335, or externally in an external memory 340 or a storage device 345. Prediction engine 330 may also include a set of instructions stored in memory 335 to be executed by processor 350 to perform the clinical prediction.”
Claims 12 and 19 feature limitations similar to those of claim 5, and are therefore rejected using the same rationale.
Regarding claim 6, Vladimirova discloses The pretreatment prediction system of claim 1,wherein the trained CNN is stored remotely from the computing device, and the instructions stores in the memory cause the processor to process the at least one image using the trained CNN by transmitting the at least one image for input to the remotely stored CNN. (See at least Para. [0101] – “A computer system can include a plurality of the same components or subsystems, e.g., connected together by external interface 81 or by an internal interface. In some embodiments, computer systems, subsystem, or apparatuses can communicate over a network. In such instances, one computer can be considered a client and another computer a server, where each can be part of a same computer system. A client and a server can each include multiple systems, subsystems, or components.”)
Claims 13 and 20 feature limitations similar to those of claim 6, and are therefore rejected using the same rationale.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vladimirova in view of Goldman (US 2019/0361006) and Wang (US 2020/0381121), and in further view of Yener (US 2006/0036372).
Regarding claim 7, Vladimirova, Goldman, and Wang do not explicitly disclose The pretreatment prediction system of claim 1,further comprising selecting a grid size. (See Yener, Para. [0170] – “In the generation of cell-graphs, the following four control parameters were used: (1) the value of K for the K-means clustering algorithm; (2) the grid size (i.e., number of pixels per grid entry; (3) the node-threshold; and (4) the value of .alpha.. The value of K in the K-means algorithm should be large enough to represent all of the different tissue parts in the biopsy sample. The value of K was set to 16, since the greater values of K do not significantly improve the quantization results. In identification of the nodes, the grid size was selected to be 6 and the node-threshold was selected to be 0.25. The grid size of 6 matches the size of a typical cell in the magnification of 100.times.. The node-threshold value of 0.25 eliminates the noise that arises from staining without resulting in significant information lost on the cells for the selected grid size. The value of .alpha. range between 2.0 and 4.8 in increments of 0.4.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Vladimirova, Goldman, and Wang to utilize the teachings of Yener since it may be used to classify segments in the biopsy images of Vladimirova.)
Claim 14 features limitations similar to those of claim 7, and is therefore rejected using the same rationale.
Response to Arguments
Applicant's arguments filed regarding claims rejected under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that the claims constitute a “specific technological improvement in computer-assisted pretreatment prediction and analysis rather than an abstract idea”. This is not persuasive as technological improvements are not considered in determining if the claims recite an abstract idea (Step 2A Prong 1).
Applicant argues that the claims integrate any abstract idea into a practical application by “improv[ing] the technological field of treatment and pretreatment prediction. This is not persuasive since the functionality of any involved computing elements (i.e., display device, computing device, trained CNN) remain unchanged. The claims merely utilize a trained CNN to analyze image data (i.e., apply it).
For at least the reasons above, the 101 rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 EST.
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/KYLE G ROBINSON/Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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