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 and amendment have persuasively overcome many of the 112 rejections.
The remaining issues are addressed below.
Objections
Applicant argues:
Applicant respectfully submits that the objection to the Title is rendered moot by the present amendment to the Title.
Examiner responds:
The examiner has proposed a new title.
Applicant argues:
Applicant respectfully submits that the objection to the Abstract is rendered moot by the present amendment to the Abstract.
Examiner responds:
The examiner has updated the objection to the abstract to focus on the problem being solved. If the problem being solved is that some images are too bright, the abstract should say so. If the problem being solved is something else, the abstract should specify what that is.
112a
Applicant argues:
In particular, Applicant submits that Claim 1 does not recite "unlimited functional claiming," but merely recites the steps in the flowchart shown in Figure 5, for example.
Examiner responds:
For example, ST52, from Fig. 5, is titled “Perform preprocessing associated with second feature amount.” This is a very broad statement. There are many, many things that the second feature amount could be, and for each of them, there are different ways to perform preprocessing. This is simply too broad to be a claim step.
112b
Applicant argues:
the training data used to train the model is very relevant to determining whether the model is well adapted to process a given X-ray image.
Examiner responds:
Claiming a machine learning model that was trained with certain data (i.e., a product by process), is expected to be definite. However, the literal language of the claim requires the presence of the training data. One could image a shelf with two hard drives. On the first hard drive is a trained model for X-ray images, and on the second hard drive is a collection of training data. Here, because the claim is directed to an X-ray diagnostic apparatus, (e.g., the first hard drive), it is not clear whether the second hard drive also needs to be present.
Applicant argues:
Moreover, Applicant respectfully submits that "feature amount" is a commonly used term in machine learning applications.
Examiner responds:
The office action stated that the problem was which feature amount was meant, not that the term feature amount is new. Specifying, for example, that the feature amount is the brightness is expected to overcome this rejection.
101
Applicant argues:
Applicant respectfully submits that Claim 1 is directed to an improvement to existing neural network technology of processing X-ray images
Examiner responds:
What is that improvement? Here, the claims have been amended to be directed to determining whether to preprocess an X-ray image. The examiner believes that the preprocessing is to correct for brightness (see the objection to the abstract). The examiner has not identified any claims to improved neural network technology.
Applicant argues:
applying image preprocessing to X-ray images cannot practically be performed in the human mind
Examiner responds:
Specification, [0003] and [0046] state that brightness is being corrected. Imagining an image as brighter (or less bright) can be performed in the human mind.
Applicant argues:
the Office incorrectly dismisses all of the functional features in the claims never to be considered again
Examiner responds:
Each of the features were carefully considered, and determined to be part of the abstract idea.
Applicant argues:
the Office provides no analysis of the claims as a whole
Examiner responds:
The office action states that the claims as a whole are a mental process
Applicant argues:
Moreover, the § 101 analysis in the Office Action is contrary to Enfish and Desjardins
Examiner responds:
The examiner does not understand why this case should be decided like those cases.
Applicant argues:
the present claims also provide an improvement to machine learning technology, in particular adjusting the X-ray image to fall within the scope of the training data so that the trained model can be applied. See paragraphs 3, 4, 46, and 65 in the published application.
Examiner responds:
Making an image brighter (or less bright) is not an improvement in machine learning technology. Rather, people have trouble reading text (or seeing images clearly) if the text/image is too bright/dark. If Applicant is intending a different improvement, this needs to be made explicit.
Applicant argues:
Thus, Applicant respectfully submits that such an improvement is equivalent to the improvement in the Ex parte Desjardins case of effectively learning new tasks while protecting knowledge of previous tasks. The present claims are configured to improve the performance of the machine learning model, by performing preprocessing under certain conditions before applying the image to the model.
Examiner responds:
If Applicant submits evidence of improvement to the machine learning model (such as test results), and limits the claim accordingly, this is expected to be persuasive. However, the present claims are too broad to capture an improvement (e.g., the feature amount to be considered is not specified in the claims).
Applicant argues:
See also the recent Board decision of Ex parte Holtmann-Rice
Examiner responds:
Please provide an application number so that the examiner can locate the referenced decision.
As to the prior art arguments, see the updated claim mapping below.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: X-Ray Diagnostic Apparatus to Determine Whether or Not a X-Ray Image Requires Preprocessing for a Particular Trained Machine Learning Model.
The abstract of the disclosure is objected to because it does not “enable the Office and the public generally to determine quickly from a cursory inspection the nature and gist of the technical disclosure.” 37 CFR 1.72(b). Specifically, the abstract does not convey the problem being solved. The examiner believes that the problem is that certain x-ray images are too bright. See, e.g., specification [0003] and [0046].
A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims1, 3-5, and 7-17 (all claims) 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 16, and 17 recite “perform a determination as to whether or not to apply image preprocessing to the X-ray image … based on a degree of deviation of the second feature amount … .” This is unlimited functional claiming because it recites a result rather than the steps to achieve that result. MPEP 2173.05(g). For example, there are many, many possible feature amounts, and these many, many, feature amount in turn have different preprocessing techniques. Specifying that the determination is based on brightness is expected to overcome this rejection.
Claims 1, 16, and 17 recite “wherein the first trained model … applies noise reduction,” but this is also unlimited functional claiming because of the wide variety of noise reduction techniques. MPEP 2173.05(g). Specifying a particular architecture (such as convolutional neural network, specification [0048]) is expected to overcome this rejection.
Claims 1, 16, and 17 recite applying preprocessing to the image, but this conflicts with specification [0004] “A trained model is therefore required to be adapted to X-ray images to be input.” In other words, the claimed trained model reads on “a machine learning-based trained model has been known in recent years” (Specification, [0003]), and thus is not the required adapted trained model.
Claim 10 recites “alternative processing that differs from the image preprocessing and which does not use the first trained model,” but this is also unlimited functional claiming because it describes the result rather than the steps. MPEP 2173.05(g).
Claim 11 recites “modify the first trained model,” but this is unlimited functional claiming. MPEP 2173.05(g). In particular, the claim does not even specify what the modification is.
Dependent claims are likewise rejected.
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, 3-5, and 7-17 (all claims) 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.
Claims, 1, 16, and 17 recite “before inputting the X-ray image to a first trained model,” but this conflicts with the later part of the claim that specifies that the preprocessed image is the version that is input.
Claims 1, 3-5, 8, and 14-17 recite “feature amount,” but this subjective because different people can have different opinions as to which feature amount is at issue. MPEP 2173.05(b)(IV). Specifying brightness is expected to overcome this rejection.
Claims 7, 8 and 14 recite “estimate a collection condition,” but parent claim 1 recites that there is a determination as to whether the model is adapted to the image. Specification, [0072] and [0074] suggests that the estimation and the determination are the same decision, but this makes it unclear what it means to estimate something that has been determined.
Claims 8, 14, and 15 recite features of training data. However, the training of the model is outside the scope of the present claims, and thus it is unclear what weight to give to recitations regarding training data that is outside of the claimed apparatus. Even if this were interpreted as a product by process claim (MPEP 2113), the specification provides insufficient guidance on how to identify the structure implied by the recited steps.
Claims 8 and 14 recite various “conditions,” but these are each subjective because different people can have different opinions as to which condition is at issue. MPEP 2173.05(b)(IV).
Claims 8 and 14 recite replacing an estimated collection condition, but it is unclear if the intent is to change an estimate that is never applied or if the intent is to change a criteria that was used to train the model.
Dependent claims are likewise rejected.
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, 3-5, and 7-17 (all claims) are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Step 1: Claim 17 recites a method, and processes are eligible subject matter.
Claims 1 and 16 (and their dependents) recite apparatuses, and machines are eligible subject matter.
Step 2A, prong one: All of the elements of the claims are a mental process because a person can look at an x-ray image and envision it looking differently. In particular, TLI communications found that compressing images was part of a mental process, and that compression meets the present claims, whereas DDR Holdings considered compression that was improved and particular. MPEP 2106. Further, the various models are also mental processes, see example 47, claim 2, element (d) (from the July 2024 AI subject matter eligibility examples). MPEP 2106.04(a)(2)(III)(C) explains that use of a generic computer or in a computer environment is still a mental process. In particular, this section begins by citing Gottschalk v. Benson, 409 US 63 (1972). “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea.” In Benson the Supreme Court did not separately analyze the computer hardware at issue; the specifics of what hardware was claimed is only included in an appendix to the decision.
Because there are no additional elements, no further analysis is required for Step 2A, prong two or Step 2B.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1, 3-5, and 7-17 (all claims) are rejected under 35 U.S.C. 103 as being unpatentable over US20210201482A1 (“Hattori”) and US20220180514A1 (“Vlasimsky”).
1. An X-ray diagnostic apparatus comprising processing circuitry configured to:
acquire an X-ray image; and (Hattori, Fig. 6, ST202. [0055] discloses an x-ray CT image (e.g., X-ray CT apparatus 511) which teaches the claimed x-ray.)
perform a determination as to whether or not to apply image preprocessing to the X-ray image before inputting the X-ray image to a first trained model, (Hattori, Fig. 6, ST205)
in response to determining to apply the image preprocessing, (Hattori, Fig. 6, ST205 “no” branch. As per the below combination, Vlasimsky teaches preprocessing in this situation.)
apply the preprocessed X-ray image or the acquired X-ray image to the first trained model; (Hattori, Fig. 6)
Hattori is not relied on for the below claim language.
However, Vlasimsky teaches to apply the image preprocessing to the X-ray image to generate a preprocessed X-ray image; and (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast”)
wherein training data for the first trained model comprises first feature amounts and the X-ray image comprises a second feature amount, (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast.” Vlasimsky’s standard brightness teaches the claimed first feature amounts and the brightness of the image teaches the claimed second feature amount.)
the processing circuitry is further configured to perform the determination based on a degree of deviation of the second feature amount from an average of a distribution of the first feature amounts, and (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast” Vlasimsky’s standardizing teaches the claimed based on a degree of deviation.)
wherein the first trained model is a model that, in response to an input of the X-ray image, applies noise reduction to the input X-ray image and outputs a noise-reduced X-ray image. (Vlasimsky, [0011] “produce a first image at a … up-sampled resolution”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Vlasimsky to the teachings of Hattori such that Vlasimsky’s training and processing serve as one of Hattori’s alternative processing options (e.g., Fig. 6, ST209) because Vlasimsky’s techniques are more advanced than Hattori’s computer aided diagnosis (Hattori, [0077]). See, e.g., Vlasimsky, [0058] “thereby detect potential features that could otherwise not be detected by a human technician.”
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
3. The X-ray diagnostic apparatus according to claim 1, further comprising a memory for storing the average and a standard deviation of the distribution of the first feature amounts, (Hattori, Fig. 2, memory 30)
wherein the processing circuitry is further configured to calculate the degree of deviation by dividing a difference between the second feature amount and the average by the standard deviation, and to perform the determination according to whether or not the degree of deviation is equal to or below a predetermined threshold. (Hattori, Figs 7A-7C.)
4. The X-ray diagnostic apparatus according to claim 1, wherein the processing circuitry is further configured to, in response to determining to apply the image preprocessing, select, from a plurality of second feature amounts, the second feature amount, which has the degree of deviation greater than a predetermined threshold, and conduct the image preprocessing associated with the selected second feature amount. (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast)
5. The X-ray diagnostic apparatus according to claim 4, wherein the processing circuitry is further configured to set an average value of a distribution of image feature amounts among the first feature amounts to be a target value of the image processing, and (Hattori, Fig. 6, ST205)
to apply the image preprocessing to the X-ray image based on the target value. (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast)
7. The X-ray diagnostic apparatus according to claim 1, wherein the processing circuitry is further configured to estimate a collection condition for the X-ray image to be acquired (Hattori, Fig. 6, ST201)
8. The X-ray diagnostic apparatus according to claim 7, wherein the processing circuitry is further configured to, in response to determining to apply the image preprocessing, (Hattori, Fig. 6, ST205)
Select, from a plurality of second feature amounts, the second feature amount, which has the degree of deviation greater than a predetermined threshold, and preconduct the image processing associated with the selected second feature amount, (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast)
the first feature amounts comprise a first X-ray condition and a first imaging condition as a collection condition for an input image used in the training data, (Hattori, Fig. 6, ST201)
the second feature amount comprises a second X-ray condition and a second imaging condition as a collection condition for the X-ray image, and (Hattori, Fig. 6, ST203)
the processing circuitry is further configured to, when the second X-ray condition deviates from the first X-ray condition at a first deviation degree and the second imaging condition deviates from the first imaging condition at a second deviation degree, estimate a collection condition for an X-ray image to be acquired in a subsequent operation, wherein either the second X-ray condition with the first deviation degree or the second imaging condition with the second deviation degree, whichever has a larger deviation degree, is replaced with the collection condition for the input image that corresponds to the condition with the larger deviation degree. (Hattori, Fig. 6, ST201. The “set” of attribute data teaches the claimed first and second conditions.)
9. The X-ray diagnostic apparatus according to claim 1, wherein the processing circuitry is further configured to conduct one or more loop processes of repeating the determination after the image preprocessing for a predetermined number of times, or a predetermined length of time, equal to or below a predetermined threshold. (Hattori, Fig. 6. The claim reads on a single pass.)
10. The X-ray diagnostic apparatus according to claim 9, wherein the processing circuitry is further configured to, if the determination performed a last time in the one or more loop processes is a determination to apply the image preprocessing, subject the X-ray image to alternative processing that differs from the image preprocessing and which does not use the first trained model. (Hattori, Fig. 6, ST208)
11. The X-ray diagnostic apparatus according to claim 1, wherein the processing circuitry is further configured to in response to determining to apply the image preprocessing, modify the first trained model. (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast)
12. The X-ray diagnostic apparatus according to claim 11, wherein the processing circuitry is further configured to modify the first trained model by retraining a machine learning model in parallel with performing the image preprocessing, and upon finishing the retraining, update the first trained model to a new first trained model obtained by the retraining. (Vlasimsky, [0052] “Distributed ML subsystems can be emplaced at these various locations and trained using local data.” Vlasimsky’s “various sources separated by time and/or geography” teaches that the training is ongoing, and thus the claimed parallel.)
13. The X-ray diagnostic apparatus according to claim 11, wherein the first trained model is more than one, and the processing circuitry is further configured to: determine whether or not it is necessary to apply the image preprocessing to the X-ray image before inputting the X-ray image to each of the more than one first trained model, and in response to determining not to apply the image preprocessing for one or more of the more than one first trained model, select one of the more than one first trained model. (Vlasimsky, Fig. 1, Ensemble Classifier 115)
14. The X-ray diagnostic apparatus according to claim 7, wherein the processing circuitry is further configured to, in response to determining to apply the image preprocessing, select, from a plurality of second amounts, the second feature amount, which has the degree of deviation greater than a predetermined threshold, and conduct the image preprocessing associated with the selected second feature amount, the first feature amounts comprise a first X-ray condition and a first imaging condition as a collection condition for an input image used in the training data, the second feature amount comprises a second X-ray condition and a second imaging condition as a collection condition for the X-ray image which is to be acquired, and the processing circuitry is further configured to, when the second X-ray condition deviates from the first X-ray condition at a first deviation degree and the second imaging condition deviates from the first imaging condition at a second deviation degree, estimate a collection condition for the X-ray image to be initially acquired, wherein either the second X-ray condition with the first deviation degree or the second imaging condition with the second deviation degree, whichever has a larger deviation degree, is replaced with the collection condition for the input image that corresponds to the condition with the larger deviation degree. (Hattori, Fig. 6, ST205, “no” branch. The “set” of attribute data at ST201 teaches the claimed first and second conditions. The claim does not require use of the estimated collection condition, and thus is unable to patently distinguish the claimed invention. MPEP 2144.04(I).)
15. The X-ray diagnostic apparatus according to claim 1, wherein the processing circuitry is further configured to, in response to determining to apply the image preprocessing, input the X-ray image to a second trained model, and conduct image processing to generate an output X-ray image corresponding to the input X-ray image with a distribution of image feature amounts that matches that of input images used in training data for the first trained model, and the second trained model was trained to generate an input image used in the training data for the first trained model based on another image of which distribution of image feature amounts different from that of the input image. (Vlasimsky, [0011] “the pre-processing module … (ii) standardizes image brightness and contrast)
Claims 16 and 17 are rejected as per claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Applicant Admitted Prior Art – “For example, supposing that an input X-ray image involves an X-ray condition or a patient condition that falls outside the scope of the training data, the trained model could output an X-ray image with a brightness changed and artifacts created.” Thus it is obvious to correct the brightness to be within the scope of the training data before submitting the image.
US 20220414832 A1 – titled “X-ray imaging restoration using deep learning algorithms”
US 11348226 B2 – abstract “in which the noise of the medical image or the noise of the intermediate image is reduced”
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 DAVID ORANGE whose telephone number is (571)270-1799. The examiner can normally be reached Mon-Fri, 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID ORANGE/ Primary Examiner, Art Unit 2663
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