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 § 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.
Claims 5, 9 and 10 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.
In regards to claim 5, it is unclear if the starting point in lines 3-4 and the ending point in lines 5-6 are feature points. If they are feature points then it is unclear how one would input the data into the graph trained model if the graph trained model has already processed the data to extract the starting points and ending points. For purposes of examination, the claim is being interpreted as inputting a section of a graph including start and end points into the graph trained model. The same issue is present in claim 9, in lines 3-6.
In claim 5, the sentence “pre-processing of extracting a section from the starting point to the ending point” in lines 7-8 is unclear. The recitation “pre-processing of extracting” is confusion since it is unclear if the extracting is the preprocessing, or if preprocessing is done to the extracted section. The same issue is present in claim 9, lines 7-8.
In regards to claim 10, it is unclear what is meant by “combining a result of the diagnosing with symptoms corresponding to the lower urinary tract symptoms” in lines 3-4. For purposes of examination, the claim is being interpreted as expressing the diagnosed lower urinary tract symptoms results as a binary or quaternary output.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 6 follows.
Regarding claim 6, the claim recites a series of steps or acts, including diagnosing whether the result sheet corresponds to the lower urinary tract symptoms by analyzing a correlation between the feature points and the lower urinary tract symptoms by the character trained model and the graph trained model. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of including diagnosing whether the result sheet corresponds to the lower urinary tract symptoms by analyzing a correlation between the feature points and the lower urinary tract symptoms by the character trained model and the graph trained model sets forth a judicial exception. This step describes a concept that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process, which is an Abstract Idea.
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. The diagnosis of the lower urinary tract symptoms does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the diagnosed lower urinary tract symptoms, nor does the method use a particular machine to perform the Abstract Idea.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. The claim, also includes mathematical concepts in the form of generating a character trained model using the character data as learning data to extract feature points having a correlation with a cause of lower urinary tract symptoms from the character data and generating a graph trained model using the graph data as learning data to extract feature points having a correlation with a cause of lower urinary tract symptoms from the graph data. Besides the Abstract Ideas, the claim recites additional steps of extracting character data from a result sheet obtained through a simple urine flow test; and extracting graph data from a result sheet obtained through a simple urine flow test. As well as receiving a simple urine flow test result sheet to be diagnosed by a lower urinary tract symptom diagnosis system; extracting the character data and the graph data from the result sheet; extracting a plurality of feature points having a correlation with a cause of lower urinary tract symptoms from the character data and the graph data, respectively, and integrating the feature points
Receiving and extracting data input into a machine learning model is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the receiving an extracting steps are each recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
The same rationale applies to claims 1 and 3.
Regarding claim 11, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The non-transitory computer-readable recording medium having recorded thereon a program is conf configured to perform the Abstract Idea and presolution activity. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to the type of feature points. The extraction steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent 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.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Belotserkovsky US 20160113562 A1 in view of Sobol US 20190209022 A1.
In regards to claim 1, Belotserkovsky teaches a method comprising
extracting character data from a result sheet obtained through a simple urine flow test ([0012] Primary parameters [0034] Reference database inherently contains simple urine flow test values);
and generating a model using the character data as learning data, and extracting feature points having a correlation with a cause of lower urinary tract symptoms from the character data ([0031-0034] model trained using reference database of parameters to analyze the patient's primary and secondary urine flow dynamic parameters and generates an assessment or prediction of the patient's urological condition, application calculates primary and secondary parameters).
Belotserkovsky fails to teach the model extracting feature points. Sobol teaches a deep learning model that extracts features and identity patterns ([0316] [0320]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the mathematical model of Belotserkovsky to be a deep learning machine learning model that perform the feature extraction of the primary references like the model of Sobol as well as and generating an assessment or prediction of the patient's urological condition. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of automating the extraction of features by using a deep learning model.
In regards to claim 2, modified Belotserkovsky teaches the method of claim 1, wherein the character data comprises at least one of a point (Qmax) having a maximum urine flow rate during urination, a voiding time, post-void residual (PVR), and a bladder filling volume (BFV) (Belotserkovsky [0012] Primary parameters include maximum urine flow rate (Qmax) and void time).
In regards to claim 3, Belotserkovsky teaches a method comprising
extracting graph data from a result sheet obtained through a simple urine flow test ([0027] secondary parameters [0034] Reference database inherently contains parameters graph data);
and generating a model using the graph data as learning data to, and extracting feature points having a correlation with a cause of lower urinary tract symptoms from the graph data ([0031-0034] model trained using reference database of parameters to analyze the patient's primary and secondary urine flow dynamic parameters and generates an assessment or prediction of the patient's urological condition, application calculates primary and secondary parameters).
Belotserkovsky fails to teach the model extracting feature points. Sobol teaches a deep learning model that extracts features and identity patterns ([0316] [0320]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the mathematical model of Belotserkovsky to be a deep learning machine learning model that perform the feature extraction of the secondary references like the model of Sobol as well as and generating an assessment or prediction of the patient's urological condition. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of automating the extraction of features by using a deep learning model.
In regards to claim 4, modified Belotserkovsky teaches the method of claim 3, wherein the graph data comprises a voided volume over time or a voided rate over time ([0027] void time/total time).
In regards to claim 5, modified Belotserkovsky teaches the method of claim 3, including inputting a section of a graph including a start and end point into a graph trained model (Fig. 2 graph 2 contains a start and end point of urine flow).
Claims 6-9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Belotserkovsky US 20160113562 A1 in view of Sobol US 20190209022 A1 in view of Bazhenov US 20200349414 A1.
In regards to claim 6, Belotserkovsky teaches a method comprising
extracting character data from a result sheet obtained through a simple urine flow test ([0012] Primary parameters [0034] Reference database inherently contains simple urine flow test values);
and generating a model using the character data as learning data, and extracting feature points having a correlation with a cause of lower urinary tract symptoms from the character data ([0031-0034] model trained using reference database of parameters to analyze the patient's primary and secondary urine flow dynamic parameters and generates an assessment or prediction of the patient's urological condition, application calculates primary and secondary parameters);
extracting graph data from a result sheet obtained through a simple urine flow test ([0027] secondary parameters [0034] Reference database inherently contains graph data);
and generating a model using the graph data as learning data, and extracting feature points having a correlation with a cause of lower urinary tract symptoms from the graph data ([0031-0034] model trained using reference database of parameters to analyze the patient's primary and secondary urine flow dynamic parameters and generates an assessment or prediction of the patient's urological condition, application calculates primary and secondary parameters);
receiving a simple urine flow test result sheet to be diagnosed by a lower urinary tract symptom diagnosis system ([0031] Fig 4 step 12);
extracting the character data and the graph data from the result sheet ([0031] Fig 4 steps 15-16.);
extracting a plurality of feature points having a correlation with a cause of lower urinary tract symptoms from the character data and the graph data, respectively ([0033]);
and diagnosing whether the result sheet corresponds to the lower urinary tract symptoms by analyzing a correlation between the feature points and the lower urinary tract symptoms by the character trained model and the graph trained model ([0029] LUTS, OAB or other urological condition).
Belotserkovsky fails to teach the model extracting feature points, and integrating the feature points. Sobol teaches a deep learning model that extracts features and identity patterns ([0316] [0320]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the mathematical model of Belotserkovsky to be a deep learning machine learning model that perform the feature extraction of the secondary references like the model of Sobol as well as and generating an assessment or prediction of the patient's urological condition. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of automating the extraction of features by using a deep learning model.
Belotserkovsky in view of Sobol fails to teach integrating the feature points. Bazhenov teaches integrating feature points into a cohesive representation to classify them ([0038]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the mathematical model of Belotserkovsky in view of Sobol to integrate the primary and secondary reference into a cohesive representation before generating an assessment of the patient's urological condition. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of assessing a user’s condition based on multiple combined features which would allow for a more thorough assessment of the user.
In regards to claim 7, modified Belotserkovsky teaches the method of claim 6, wherein the character data comprises at least one of a point (Qmax) having a maximum urine flow rate during urination, a voiding time, post-void residual (PVR), and a bladder filling volume (BFV) (Belotserkovsky [0012] Primary parameters include maximum urine flow rate (Qmax) and void time).
In regards to claim 8, modified Belotserkovsky teaches the method of claim 6, wherein the graph data comprises a voided volume over time or a voided rate over time (Belotserkovsky [0027] void time/total time).
In regards to claim 9, modified Belotserkovsky teaches the method of claim 6, including inputting a section of a graph including a start and end point into a graph trained model (Fig. 2 graph 2 contains a start and end point of urine flow).
In regards to claim 11, modified Belotserkovsky teaches non-transitory computer-readable recording medium having recorded thereon a program for executing the method of claim 6 (Belotserkovsky [0037]).
Claims 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20180256042 A1 in view of Sobol US 20190209022 A1 as applied to claim 6, further in view of Persidsky (US 20150342518 A1)
In regards to claim 10, modified Belotserkovsky teaches the method of claim 6, including indicating the likelihood of whether the patient has a urological disorder and if so the probability of whether the condition is LUTS, OAB or other urological condition ([Belotserkovsky 0029]). Modified Belotserkovsky fails to teach as expressing the diagnosed lower urinary tract symptoms results as a binary or quaternary output. Persidsky teaches outputting a red negative or green positive indication to a user in order to clearly indicate an evaluation to a user ([0342]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of modified Belotserkovsky to include a step of indicating if a patent has LUTS, OAB or other urological condition using a green indication or does not have a urological disorder using a red indication on a display like the device of Persidsky. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of clearly indicating to the user if they have a urological disorder.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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/LUCY EPPERT/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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