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 .
Status of Claims
Claims 1-15 are pending.
This communication is in response to the communication filed December 29, 2023.
Claim Objections
Claim 1 is objected to for the following informalities: The last limitation of the claim recites “when a new fundus image is input, predict the incidence probability of the moyamoya disease by inputting the new fundus image into the learned incidence prediction model”. The repetition of the inputting adds confusion. Appropriate correction is required.
Claim 9 is objected to for the following informalities: the last line of the claim recites “new fundus image thus input into the learned incidence prediction model”. The “thus” adds confusion. Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 4-8 and 13-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
“While there is a presumption that an adequate written description of the claimed invention is present in the specification as filed, In re Wertheim, 541 F.2d 257, 262, 191 USPQ 90, 96 (CCPA 1976), a question as to whether a specification provides an adequate written description may arise in the context of an original claim. An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) en banc. The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement." Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002).” (MPEP 2163.03).
Claims 4 and 13 recite the limitation “split the fundus images labeled as the normal patient into a fold dataset at a ratio of 1:n” and claims 5 and 14 recite the limitation “learning a plurality of incidence prediction models as many as ‘n’ data types” for which the subject matter of the limitation was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention. The specification states generally that n is an integer greater than 1, and does not set a limit as to what n may be. The prediction models that may learn an infinite number of data types is not sufficiently described, and may not exist. The specification does not specifically teach any algorithms or models that would split data into an infinite ratio or learn an infinite number of data types.
As shown, the originally filed specification provide a vague disclosure as to what is being done as far as the end result but fail to provide a clear disclosure as to how the claimed invention perform the features claimed. The disclosure is informing the reader of what happens as far as end results is concerned but fails to disclose the process that results in the claimed function. The broad disclosure of the result that is desired is not providing an adequate written description of the invention, because this is not actually showing that applicant had developed any specific method to do what is claimed.
This is different from the enablement issue, which is a separate part of 112(a). For the examiner to be able to search this aspect of the invention, the examiner must ascertain how applicant is accomplishing the disclosed result of the method steps being claimed. All that the examiner has to work from is the broad disclosure, with no further comments or guidance as to how this would be done or what it could be. The examiner takes the position that with respect to these limitations or features of the claims, the specification fails to provide an adequate written description of the invention to an extent that would sufficiently show that applicant was in possession of an invention that could operate as claimed. Simply disclosing a vague description, without actually explaining how the system perform the functions claimed, results in a written description problem under 112(a). The examiner has no idea how applicant actually contemplated doing these steps because nothing is disclosed other than the broad disclosure of the specification. This does not satisfy the written description part of 112(a) in the opinion of the examiner. The examiner is left guessing as to how this is accomplished.
As the courts have made clear, 35 U.S.C §112(a) contains a written description requirement that is separate and distinctive from the enablement requirement. See Ariad, 598 F.3d 1336, 1340 (Fed. Cir. 2010).
As the Federal Circuit has stated in Ariad:
a generic claim may define the boundaries of a vast genus of chemical compounds, and yet the question may still remain whether the specification, including original claim language, demonstrates that the applicant has invented the species sufficient to support a claim to a genus. The problem is especially acute with genus claims that use functional language to define the boundaries of a claimed genus. In such a case, the functional claim may simply claim a desired result, and may do so without describing species that achieve that result. But the specification must demonstrate that the applicant has made a generic invention that achieves the claimed result and do so by showing that the applicant has invented species sufficient to support a claim to the functionally-defined genus.
Ariad, 598 F.3d at 1349 (emphasis added). While Ariad relates to chemical compounds, the legal principles are the same. Since Applicant has failed in providing throughout the specification examples as to how the invention is accomplishing the results, Applicant has failed to provide a disclosure and therefore possession of any species of the genus of what is being claim.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite systems and methods for predicting incidence of moyamoya disease through learning of fundus image, which are statutory categories of inventions.
Specifically, the independent claims, taking claim 9 as exemplary, recite labeling input fundus images as a moyamoya disease patient with the moyamoya disease and a normal patient without the moyamoya disease; learning the incidence prediction model by recognizing fundus images labeled as the moyamoya disease patient and fundus images labeled as the normal patient into the incidence prediction model; and predicting the incidence probability of the moyamoya disease by inputting a new fundus image.
The claims limitations are interpreted as being grouped within the “certain methods of organizing human activity” grouping of abstract ideas, because the claims involve a series of steps for learning a model to make predictions. See MPEP 2106.04. The claims are interpreted to recite concepts relating to tracking or organizing disease information. Accordingly, the claims recite an abstract idea.
The dependent claims recite the same abstract idea as a above. The dependent claims further explain removing data, changing images, splitting labelled images, generating datasets, learning data, determining diseases, and stages of diseases. The limitations are interpreted as organizing human activity, because they involve managing human interactions related to disease prediction.
This judicial exception is not integrated into a practical application. Integration into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Here, the additional elements of the claims include memory, server, processor, and prediction model.
The claims merely use the additional elements as tools to perform abstract ideas and generally link the use of a judicial exception to a particular technological environment. The use of the additional elements as tools to implement the abstract idea and generally to link the use of the abstract idea to a particular technological environment does not render the claim patent eligible, because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. Specifically, the server includes a memory and processor performing data receiving, processing, analyzing, outputting, and inputting functions. A server device may be a server that processes information by communicating with an external device and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, a web server, processor, memory, display, input, display, interface unit (par. 44, 52). The processor 220 may perform an algorithm for controlling operations of components within the server 200. The processor may control the memory, which stores data for a program that implements the algorithm. The processor may perform the above-described operations by using data stored in the memory. In this case, each of the memory and the processor may be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip (par. 55). The prediction model is recited at a high level of generality and may be any learned model used to make predictions.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. See Apple v. Ameranth, 842 F.3d 1229, 1240 (Fed. Cir. 2016). The additional elements do not use the exception to affect a particular treatment or prophylaxis for a disease, do not apply the exception using particular machines, and do not effect a transformation or reduction of a particular article to a different state or thing, rather the computer elements are generally stated as to their structure and function and are only used to make predictions instead of directly providing specific treatment or prophylaxis. Therefore, the additional elements do not impose any meaningful limits on practicing the abstract idea and the additional limitations are not indicative of materializing into a practical application. Accordingly, the claim is directed to an abstract idea.
Generic computer elements recited as performing generic computer functions that are well-understood, routine, or conventional activities amount to no more than implementing the abstract idea with a computerized system (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network and performing repetitive calculations); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); See MPEP 2106.05(d) and July 2015 Update: Section IV). Here, the claim limitations of performing repetitive calculations are similar to predicting probability of diseases and limitations of receiving and sending information are similar to inputting images, labeling images, splitting labelled images, and removing data.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a memory, server, processor, and prediction model to perform the steps of the claims amount to no more than using computer related devices to automate or implement the abstract idea of predicting incidence of moyamoya disease through learning fundus image.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible.
In conclusion, the claims are directed to the abstract idea of predicting incidence of moyamoya disease through learning fundus image. The claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Yukinori (IDS NPL reference) in view of Rom et al. US20230281816.
As per claim 1, Yukinori teaches
a server comprising: a memory configured to store a deep learning-based incidence prediction model; and (Yukinori p. 3 col 1 teaches data processing using computers, processing unit, and memory and deep learning)
a processor configured to predict an incidence probability of a moyamoya disease of a patient from a fundus image by using the incidence prediction model, wherein the processor is configured to: (Yukinori p. 3 col 1, lines 20-29 teaches Image processing was performed separately for the training and validation image sets because it is necessary to prevent over learning in this method of deep learning. Data augmentation of training data sets from 136 to 7400, 72 to 5100, and 148 to 7600 was performed for moyamoya disease, atherosclerotic disease, and controls, respectively. Data augmentation of validation data sets from 32 to 2700, 12 to 1100, and 20 to 1700 was carried out for moyamoya disease, atherosclerotic disease, and controls, respectively. Fig. 1 teaches blocks and prediction.)
label input fundus images as a moyamoya disease patient with the moyamoya disease and a normal patient without the moyamoya disease; (Yukinori p. 3 col 1, lines 20-29 teaches images of patients with moyamoya and controls, interpreted as patients without moyamoya disease. Table 2 includes labeling of patient data.)
learn the incidence prediction model by recognizing fundus images labeled as the moyamoya disease patient and fundus images labeled as the normal patient into the incidence prediction model; and (Yukinori p. 2 col 2, lines 14-33, Table 1 teaches using learning data and validation data for normal patients, moyamoya patients, and atherosclerotic disease patients)
Yukinori teaches moyamoya disease related models, but does not specifically teach the following limitations met by Rom, when a new fundus image is input, predict the incidence probability of the…disease by inputting the new fundus image into the learned incidence prediction model (Rom par. 102 teaches making a prediction, regarding the new retinal image, using each of the models to return a respective value. The new retinal image is run against each of the specialized models to receive a prediction. In embodiments, each of the predictions comes with a score or percentage of likelihood that the prediction is correct. A value reflecting the level of certainty that the prediction is correct. The system may take these resulting values or scores and combine all the values together and provide a calculated score or prediction for the retina from the various models.).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Yukinori to predict incidence probability of retinal disease using fundus images as taught by Rom with the motivation to predict the manifestation and development of a retinal malady related to an existing ailment in the body (Rom par. 1-2). Moreover, the substitution of using a retinal malady probability, it is well within the capabilities of one of ordinary skill in the art to use a retinal malady probability as a moyamoya disease probability.
As per claim 9, see claim 1 rejection above.
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Yukinori (IDS NPL reference) in view of Rom et al. US20230281816 in view of Virmani et al. US20220375610 in view of He et al. US20220358791 in further view of Choi et al. US20220301709.
As per claim 2, Yukinori and Rom teach all the limitations of claim 1, but do not teach the following limitations disclosed by Virmani, He, and Choi.
wherein the processor is configured to: remove metadata and text data; (Virmani par. teaches metadata extraction, which may be interpreted as removing metadata)
use only a fundus image including an optic disk for learning; (He par. 98, 117 teaches training using fundus images including optic disc, macula, and retina)
perform gray scaling on an image; and (Virmani par. 63 teaches converting images to grayscale)
preprocess the input fundus images by enhancing a contrast of an image (Choi par. 590 teaches processing regions of an image by changing color, brightness, and histogram of a region of a fundus image, interpreted as enhancing a contrast, in which blood vessels are distributed or a blood vessel image extracted from the fundus image).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Yukinori and Rom to remove metadata and text data; use only a fundus image including an optic disk for learning; perform gray scaling on an image; and preprocess the input fundus images by enhancing a contrast of an image as taught by Virmani, He, and Choi with the motivation to improve analysis of fundus images and diagnosis of retinal diseases.
As per claim 10, see claim 2 rejection above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H. Choi can be reached on (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAY M. PATEL/Primary Examiner, Art Unit 3686
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