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.
Claim(s) 1-3 and 8-11 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Claim 1 recites:
A method for predicting a stenosis of a dialysis access by using a convolutional neural network (CNN), comprising:
learning a stenosis prediction model on a basis of a learning data set including first audio data with respect to the dialysis access acquired before an angioplasty procedure and second audio data with respect to the dialysis access acquired after the angioplasty procedure;
acquiring third audio data with respect to a dialysis access of an object; and
predicting a degree of stenosis corresponding to the third audio data on a basis of the stenosis prediction model including a previously learned CNN,
wherein the stenosis prediction model has a spectrogram as an input and a degree of stenosis as an output,
wherein a learning of the stenosis prediction model is configured by preprocessing a learning data set and learning the stenosis prediction model on a basis of a learning data set preprocessed with first audio data as a first correct answer label which represents a state in which a degree of stenosis of the dialysis access is 50% or higher and second audio data as a second correct answer label which represents a state in which a degree of stenosis of the dialysis access is less than 50%, and
wherein the preprocessing the learning data set is configured by, with respect to audio data included in the learning data set, acquiring audio data in a predetermined interval from the audio data included in the learning data set, acquiring a spectrogram on a basis of the audio data in the predetermined interval, normalizing the acquired spectrogram, horizontally shifting the normalized spectrogram to increase a number of spectrograms, and reducing a size of the spectrogram to a predetermined size.
Step 1:
The claim as a whole falls within at least one statutory category, i.e. a process, machine, manufacture, or composition of matter.
Step 2A Prong One:
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mathematical concepts” because the various processing steps (e.g. “normalizing” data, shifting) are directed towards mathematical algorithms, i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations. MPEP § 2106.04(a)(2)(I)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Certain methods of organizing human activity” because the step of diagnosing a patient for stenosis is traditionally performed by a physician when treating a patient, i.e. managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). MPEP 2106.04(a)(2)(II)
The highlighted portion, as drafted, is a process that, under its broadest reasonable interpretation, falls under “Mental processes”.
But for a generic computer recited with a high level of generality in a post hoc manner in its known and existing capacity to implement the abstract idea, the step of predicting a stenosis degree may be performed in the human mind either mentally or with pen and paper.
Accordingly, these limitations have been found to be directed towards concepts performed in the human mind (including an observation, evaluation, judgment, opinion). MPEP 2106.04(a)(2)(III)
Dependent claim(s) recite(s) additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim(s) 2-3, 8-9 reciting limitations further defining the abstract idea, which may be performed in the mind but for recitation of generic computer components, and/or may be a method of managing relationship or interactions between people).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s), if any:
acquiring third audio data with respect to a dialysis access of an object; and
including a previously learned CNN.
The additional element(s) do(es) not integrate the abstract idea into a practical application, other than the abstract idea per se.
Regarding the CNN, this limitation has been invoked with a high level of generality to implement the abstract idea, and amount(s) to mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea). MPEP 2106.05(f))
Regarding the step of acquiring audio data, this step merely add(s) insignificant extra-solution activity to the abstract idea (mere data gathering). MPEP 2106.05(g))
Dependent claim(s) recite(s) additional subject matter which amount to limitation(s) consistent with the additional element(s) in the independent claims.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, the additional elements do not integrate the judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, the claim recites an abstract idea.
Step 2B:
The claim does 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 elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use.
The additional elements of, as discussed above and incorporated herein, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use, as discussed above and incorporated herein.
Mere instructions to apply an exception, insignificant extra-solution activity, and linking to a particular technological environment using a generic computer component cannot provide an inventive concept.
Regarding the step of acquiring audio data, this step amount(s) to element(s) that have been recognized as well-understood, routine, and conventional (WURC) activity in particular fields (e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i)). MPEP 2106.05(d)(II)(ii))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. MPEP 2106.05(d)(II)(ii))
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The claim is not patent eligible.
Claim(s) 10-11 recite(s) substantially similar limitations as those of claim(s) 1-3, 8-9 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
In particular, the memory and processor, when read in light of the Specification as originally filed by one of ordinary skill in the art, would include generic computer and associated generic computer functions, i.e. mere instructions to apply an exception (invoking computers as a tool to perform the abstract idea, “apply it”).
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.
The factual inquiries 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.
Claim(s) 1-3, 8-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Muchhala (10602940) in view of case law
Claim 1: Muchhala discloses:
A method (Abstract illustrating a method) for predicting a stenosis of a dialysis access (column 32 line 60 to column 33 line 8 illustrating reading stenosis) by using a convolutional neural network (CNN) (column 6 line 10-13 illustrating a CNN), comprising:
learning a stenosis prediction model on a basis of a learning data set including first audio data
acquiring third audio data with respect to a dialysis access of an object (column 14 line 49-53 illustrating receiving an audio signal of the patient); and
predicting a degree of stenosis corresponding to the third audio data on a basis of the stenosis prediction model (column 22 line 34-35 illustrating determining a degree of vessel stenosis) including a previously learned CNN (column 6 line 10-13 illustrating a CNN),
Muchhala does not disclose:
with respect to the dialysis access acquired before an angioplasty procedure and with respect to the dialysis access acquired after the angioplasty procedure;
wherein the stenosis prediction model has a spectrogram as an input and a degree of stenosis as an output,
wherein a learning of the stenosis prediction model is configured by preprocessing a learning data set and learning the stenosis prediction model on a basis of a learning data set preprocessed with first audio data as a first correct answer label which represents a state in which a degree of stenosis of the dialysis access is 50% or higher and second audio data as a second correct answer label which represents a state in which a degree of stenosis of the dialysis access is less than 50%, and
wherein the preprocessing the learning data set is configured by, with respect to audio data included in the learning data set, acquiring audio data in a predetermined interval from the audio data included in the learning data set, acquiring a spectrogram on a basis of the audio data in the predetermined interval, normalizing the acquired spectrogram, horizontally shifting the normalized spectrogram to increase a number of spectrograms, and reducing a size of the spectrogram to a predetermined size.
As a preliminary matter, the recited limitations do not impart any functionality on the recited method steps in a “manipulative” sense. Furthermore, Examiner submits that nothing in the applied art would preclude the data processing from being configured in the recited manner.
The limitation appears to merely describe the nature of the information that constitutes the type of data used to train the CNN. Such mere descriptions of data are not entitled to patentable weight unless the information functionally affects, or otherwise alters, the manner in which the claimed method is performed. See Ex parte Nehls, 88 USPQ2d 1883, 1888 (BPAI 2008) (precedential).
Here, the training data as recited does not affect nor in any way alter the manner in which the claimed method is performed. Furthermore, Examiner submits that nothing in the applied art would preclude the disclosed CNN from being configured in the manner claimed.
As such, the claimed limitations constitute non-functional descriptive material that may not be relied on to distinguish the claimed invention from the prior art in terms of patentability. See In re Ngai, 367 F.3d 1336, 1339 (Fed. Cir. 2004); cf In re Gulack, 703 F.2d 1381, 1385 (Fed. Cir. 1983) (when descriptive material is not functionally related to the substrate, the descriptive material will not distinguish the invention from the prior art in terms of patentability).
Claim 2: Muchhala in view of case law discloses:
The method for predicting a stenosis of a dialysis access of claim 1, as discussed above and incorporated herein.
Muchhala further discloses:
wherein the acquiring of third audio data is configured by preprocessing the third audio data (column 14 line 54-67 illustrating encoding audio data [considered to be a form of “preprocessing”]) and the predicting of a degree of stenosis is configured by inputting the preprocessed third audio data to the stenosis prediction model and predicting a degree of stenosis corresponding to the third audio data on a basis of an output value of the stenosis prediction model (column 15 line 18-24 illustrating inputting the encoded audio data into the CNN to predict stenosis).
Claim 3: Muchhala in view of case law discloses:
The method for predicting a stenosis of a dialysis access of claim 2, as discussed above and incorporated herein.
Muchhala further discloses:
wherein the acquiring of third audio data is configured by acquiring third audio data in a predetermined interval of the third audio data column 10 line 31-39 illustrating 10-second segments of audio [considered to be a form of “predetermined interval”]), acquiring a spectrogram on a basis of the third audio data in the predetermined interval (column 13 line 46 illustrating an electrocardiogram), normalizing the acquired spectrogram (column 30 line 50-51 illustrating averaging the audio data), and adjusting a size of the normalized spectrogram (column 30 line 52-53 illustrating providing equivalence audio data [considered to be a form of “adjusting a size”]).
Claim 8: Muchhala in view of case law discloses:
The method for predicting a stenosis of a dialysis access of claim 1, as discussed above and incorporated herein.
Muchhala further discloses:
wherein the learning of the stenosis prediction model is configured by dividing the preprocessed learning data set into a training data set, a tuning data set, and a validation data set according to a predetermined criteria, learning the stenosis prediction model by using the training data set, tuning the learned stenosis prediction model using the tuning data set, and validating the tuned stenosis prediction model using the validation data set (column 7 line 28-54 illustrating dividing data into training set, test set [considered to be a form of “tuning data set”], and a validation set for use in training the machine learning module as claimed).
Claim 9: Muchhala in view of case law discloses:
The method for predicting a stenosis of a dialysis access of claim 1, as discussed above and incorporated herein.
Muchhala further discloses:
A computer program stored in a non-transitory computer readable storage medium (column 34 line 17-18 illustrating a CRM) to allow a computer to execute the method for predicting a stenosis of a dialysis access by using a convolutional neural network of claim 1, as discussed above and incorporated herein.
Claim(s) 10, 11 recite(s) substantially similar limitations as those of claim(s) 1, 2 above, and are therefore rejected for substantially similar rationale as applied above, and incorporated herein.
In particular, Muchhala discloses a memory and associated computer processor (column 14 line 13-27).
Response to Arguments
In the Remarks filed on 29 December 2025, Applicant makes numerous arguments. Examiner will address these arguments in the order presented.
On page 6-7 Applicant argues that the claims are eligible under Step 1 of the Alice/Mayo two-part framework.
Examiner agrees, and has withdrawn the rejection of claim 9 under nonstatutory subject matter.
On page 8-9 Applicant argues that the claims are not directed towards an abstract idea.
While Applicant’s arguments have been carefully considered, they are not found persuasive for the reasons stated in the section above, and incorporated herein.
Similarly, Applicant’s arguments on page 9-10 are also not found persuasive for similar rationale.
Applicant’s arguments with respect to claim(s) 1, 10 on page 10-11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Based on the evidence presented above, Applicant’s arguments are not found persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guo (20180107791) discloses using machine learning to diagnose a patient (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
Nguyen (10692602) discloses medical diagnosis by machine learning (Abstract) in a manner similar to those disclosed in the instant pending Specification as originally filed.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRAN N NGUYEN whose telephone number is (571)272-0259. The examiner can normally be reached Monday-Friday 9AM-5PM Eastern.
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, KAMBIZ ABDI can be reached on (571)272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.N.N./ Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685