DETAILED ACTION
Status of Claims
This action is in reply to the amendment filed on 01/28/2026.
Claims 1-10 have been amended.
Claims 11-20 have been newly added.
Claims 1-20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-8 are directed to a method (i.e., a process), claims 9 and 11-15 are directed to non-transitory computer readable medium (i.e., a manufacture) and claims 10 and 16-20 are directed to a system (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 10 includes limitations that recite an abstract idea. Note that independent claim 10 is the system claim, while claim 1 covers a method claim and claim 9 covers the matching computer readable medium.
Specifically, independent claim 10 recites:
A system, comprising:
A user terminal comprising:
a communication interface;
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the user terminal to:
receive a plurality of optical datasets associated with a specific user detected by a plurality of photodiodes within a medical device at each of a plurality of time points, wherein the plurality of photodiodes are configured to detect light intensity associated with light irradiated to skin located above a bladder of the specific user;
estimate, based on the plurality of optical datasets, a bladder urine volume for each of the plurality of time points;
record the estimated bladder urine volume of the specific user for each of the plurality of time points; and
output, based on the estimated bladder urine volume of the specific user, a signal indicating the estimated bladder urine volume of the specific user.
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the user terminal to:
receive, from a plurality of photodiodes configured to detect light intensity associated with light irradiated to skin above a bladder of a specific user, a plurality of optical datasets that each correspond to a different time point of a plurality of time points;
provide, as input to a trained machine learning model, the plurality of optical datasets, wherein the trained machine learning model comprises a first machine learning model trained, using a learning dataset, to predict bladder urine volume, wherein the learning dataset comprises a plurality of pairs of actual urine volume data points and corresponding optical characteristic values of at least one of the plurality of photodiodes;
receive, as output from the trained machine learning model and based on the input, an estimated bladder urine volume for each time point of the plurality of time points;
record the estimated bladder urine volume of the specific user for each time point of the plurality of time points; and
train, based on the estimated bladder urine volume for each time point of the plurality of time points, a second machine learning model, for the specific user, configured to predict a urine volume of the specific user at a future time.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because estimating, predicting, recording and outputting bladder urine volume for a specific user are a part of a medical workflow, assessing a medical patient and implementing a medical procedure, which are managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “mathematical concepts” because estimating a bladder urine volume for a plurality of time points specific to a user and using a learning dataset, to predict bladder urine volume are a mathematical concept. The foregoing underlined limitations also relate to claim 10 (similarly to claims 1 and 9).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 2-8, these claims merely recite determining steps such as: claims 2 & 11 - outputting, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a total daily voiding volume of the specific user, claims 3 & 12– displaying, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a nocturnal voiding volume of the specific user with a second visual object, claims 4, 13, 16 & 18 – outputting, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, an average total daily voiding volume and an average daily voiding frequency of the specific user, claims 5 & 14 - outputting, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a number of daily nocturnal voidings or a daily nocturnal voiding volume ratio of the specific user, claims 6 & 15 – estimating, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a functional bladder capacity of the specific user and outputting the estimated functional bladder capacity of the specific user, claim 7 – estimating, using a urine volume estimation model based on the plurality of estimated optical characteristic value sets, the bladder urine volume for each of the plurality of time points, the urine volume estimation model is a deep learning-based model or a machine learning-based model that has learned a plurality of learning datasets, and the plurality of learning datasets comprise a pair of an actual urine volume of the specific user and an optical characteristic value set associated with the actual urine volume, claim 8 – the second actual urine volume is greater than the first actual urine volume, claim 7 – a deep learning-based model, claim 17 – display, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a waking urine volume of the specific user with a first visual object and display, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a nocturnal voiding volume of the specific user with a second visual object, claim 19 - output, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a number of daily nocturnal voidings or a daily nocturnal voiding volume ratio of the specific user and claim 20 - estimate, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a functional bladder capacity of the specific user; and output the estimated functional bladder capacity of the specific user.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 9 and 10, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations performed by humans mathematically but for the recitation of generic computer components. That is, other than reciting a user terminal, a communication interface, at least one processor, a memory, and a non-transitory, computer-readable medium storing instructions are recited at high levels of generality to perform the limitations, nothing in the claim elements precludes the steps from practically being performed by humans mathematically. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment performed by humans mathematically but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, reciting the user terminal, communication interface, the at least one processor, memory, and non-transitory, computer-readable medium storing instructions are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding claim 7, the additional limitations “the urine volume estimation model is a deep learning-based model or a machine learning-based model that has learned a plurality of learning datasets” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding claim 10, the additional limitation “receive a plurality of optical datasets associated with a specific user detected by a plurality of photodiodes within a medical device at each of a plurality of time points, …..” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 10, regarding the additional limitations of the user terminal, communication interface, the at least one processor, memory, and non-transitory, computer-readable medium storing instructions, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 10 and analogous independent claims 1 and 9 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 6-7, 9-13, 15-16 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kurzrock (US 2020/0022637 A1).
Claim 1:
Kurzrock discloses A digital voiding diary management method performed by at least one processor (See Fig. 22 a central processing unit (CPU) P0035-P0037 software analyzing catheterized or voided volumes. Also, see [P0127] target patient population can be routinely asked to keep CIC urine volumes diaries (to evaluate regimen changes, such as a new medication).), the method comprising:
receiving, from a plurality of photodiodes configured to detect light intensity associated with light irradiated to skin above a bladder of a specific user, a plurality of optical datasets that each correspond to a different time point of a plurality of time points, of a plurality of time points (See Fig.8, Fig. 11-Fig. 13, Fig. 16, P0078 photodiode to acquire diffuse optical signal, P0104 sensing on the skin where there’s more light absorption, monitoring a patient’s bladder using the optode patch on the skin of the patient in the lower abdominal area near the bladder in P0146 and [P0149-P0150] emit light photons from an LED aimed into the bladder region of a human subject and detect, through a set of photodiodes.);
providing, as input to a trained machine learning model, the plurality of optical datasets, wherein the trained machine learning model comprises a first machine learning model trained, using a learning dataset, to predict bladder urine volume, wherein the learning dataset comprises a plurality of pairs of actual urine volume data points (See [P0112] extensive trials can be performed on healthy subjects to collect data for building and training effective machine learning models for bladder volume prediction. Also, see fine tuning of the predictive model, support vector machine learning (SVM) model in P0116, P0127-P0128 and detected set of photodiodes in P0150, shown in Fig. 13.) and corresponding optical characteristic values of at least one of the plurality of photodiodes (With optical characteristics as cells, diffused and scattering characteristics, see Fig. 4B, Fig. 6, Fig. 8, P0030, P0053, P0067-P0070, P0081, P0086-P0088.);
receiving, as output from the trained machine learning model and based on the input, an estimated bladder urine volume for each time point of the plurality of time points (See recorded data as a function of time in P0072-P0073 and real-time recording in P0078.).
recording the estimated bladder urine volume of the specific user for each time point of the plurality of time points (monitoring a patient’s bladder using the optode patch on the skin of the patient in the lower abdominal area near the bladder in P0146.); and
training, based on the estimated bladder urine volume for each time point of the plurality of time points, a second machine learning model, for the specific user, configured to predict a urine volume of the specific user at a future time (See predicting urine production in P0038, urodynamic testing in P0047, P0052-P0053. Also, see a specific duration of time in P0119, P0155 and P0161.).
Regarding claim 6, Kurzrock discloses the voiding diary management method according to claim 1 mentioned above, and further comprising: estimating, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a functional bladder capacity of the specific user (Besides estimating bladder volume in P0006, P0031, see predicting urine production in P0038. Also, see a specific duration of time in P0119, P0155 and P0161.); and outputting the estimated functional bladder capacity of the specific user (See recorded data as a function of time in P0072-P0073 and real-time recording in P0078.).
Regarding claim 7, Kurzrock discloses the voiding diary management method according to claim 1, wherein: the estimating of the bladder urine volume comprises: estimating, based on the plurality of optical datasets, a plurality of optical characteristic value sets for at least a part of a body of the specific user (See Fig. 2, P0074-P0076 where detected patterns across collected measurements from all photodetectors serve as optical datasets with optical characteristic value sets.); and
estimating, using a urine volume estimation model based on the plurality of estimated optical characteristic value sets, the bladder urine volume for each of the plurality of time points (See Fig. 2, P0074-P0076, P0112 where the predictive models and personalizing the pattern recognition algorithms, via post-deployment fine-tuning of regression parameters based on user feedback construe a urine volume estimation model based on the plurality of estimated optical characteristic value sets for time points.),
the urine volume estimation model is a deep learning-based model or a machine learning-based model that has learned a plurality of learning datasets, and the plurality of learning datasets comprise a pair of an actual urine volume of the specific user and an optical characteristic value set associated with the actual urine volume (See P0112] extensive trials can be performed on healthy subjects to collect data for building and training effective machine learning models for bladder volume prediction. Also, see fine tuning of the predictive model and support vector machine learning (SVM) model in P0116, P0127-P0128.).
Claim 9:
Kurzrock discloses A non-transitory computer-readable medium storing instructions that, when executed (See [P0172] processing and memory implementations, including various computer readable storage media, may be used for storing and executing program instructions.), cause a computing device to:
receive, from a plurality of photodiodes configured to detect light intensity associated with light irradiated to skin above a bladder of a specific user, a plurality of optical datasets that each correspond to a different time point of a plurality of time points (See Fig.8, Fig. 11-Fig. 13, Fig. 16, P0078 photodiode to acquire diffuse optical signal, P0104 sensing on the skin where there’s more light absorption, monitoring a patient’s bladder using the optode patch on the skin of the patient in the lower abdominal area near the bladder in P0146 and [P0149-P0150] emit light photons from an LED aimed into the bladder region of a human subject and detect, through a set of photodiodes.);
provide, as input to a trained machine learning model estimate, based on the plurality of optical datasets, wherein the trained machine learning model comprises a first machine learning model trained, using a learning dataset, to predict bladder urine volume, wherein the learning dataset comprises a plurality of pairs of actual urine volume data points (See [P0112] extensive trials can be performed on healthy subjects to collect data for building and training effective machine learning models for bladder volume prediction. Also, see fine tuning of the predictive model, support vector machine learning (SVM) model in P0116, P0127-P0128 and detected set of photodiodes in P0150, shown in Fig. 13.) and corresponding optical characteristic values of at least one of the plurality of photodiodes (With optical characteristics as cells, diffused and scattering characteristics, see Fig. 4B, Fig. 6, Fig. 8, P0030, P0053, P0067-P0070, P0081, P0086-P0088.);
receive, as output from the trained machine learning model and based on the input, an
estimated bladder urine volume for each time point of the plurality of time points (See recorded data as a function of time in P0072-P0073 and real-time recording in P0078.).
record the estimated bladder urine volume of the specific user for each time point of the plurality of time points (monitoring a patient’s bladder using the optode patch on the skin of the patient in the lower abdominal area near the bladder in P0146.); and
train, based on the estimated bladder urine volume for each time point of the plurality of time points, a second machine learning model, for the specific user, configured to predict a urine volume of the specific user at a future time (See predicting urine production in P0038, urodynamic testing in P0047, P0052-P0053. Also, see a specific duration of time in P0119, P0155 and P0161.).
Claim 10:
Kurzrock discloses A user terminal comprising:
at least one processor (See a central processing unit (CPU) P0035-P0037 software analyzing catheterized or voided volumes.); and
a memory storing instructions that, when executed by the at least one processor, cause the user terminal (See Fig. 22 processors and memory mentioned in P0170.) to:
receive, from a plurality of photodiodes configured to detect light intensity associated with light irradiated to skin above a bladder of a specific user, a plurality of optical datasets that each correspond to a different time point of a plurality of time points (See Fig.8, Fig. 11-Fig. 13, Fig. 16, P0078 photodiode to acquire diffuse optical signal, P0104 sensing on the skin where there’s more light absorption, monitoring a patient’s bladder using the optode patch on the skin of the patient in the lower abdominal area near the bladder in P0146 and [P0149-P0150] emit light photons from an LED aimed into the bladder region of a human subject and detect, through a set of photodiodes.);
provide, as input to a trained machine learning model, the plurality of optical datasets, wherein the trained machine learning model comprises a first machine learning model trained, using a learning dataset, to predict bladder urine volume, wherein the learning dataset comprises a plurality of pairs of actual urine volume data points (See [P0112] extensive trials can be performed on healthy subjects to collect data for building and training effective machine learning models for bladder volume prediction. Also, see fine tuning of the predictive model, support vector machine learning (SVM) model in P0116, P0127-P0128 and detected set of photodiodes in P0150, shown in Fig. 13.) and corresponding optical characteristic values of at least one of the plurality of photodiodes (With optical characteristics as cells, diffused and scattering characteristics, see Fig. 4B, Fig. 6, Fig. 8, P0030, P0053, P0067-P0070, P0081, P0086-P0088.);
receive, as output from the trained machine learning model and based on the input, an estimated bladder urine volume for each time point of the plurality of time points (See recorded data as a function of time in P0072-P0073 and real-time recording in P0078.).
record the estimated bladder urine volume of the specific user for each time point
of the plurality of time points (monitoring a patient’s bladder using the optode patch on the skin of the patient in the lower abdominal area near the bladder in P0146.); and
train, based on the estimated bladder urine volume for each time point of the plurality of time points, a second machine learning model, for the specific user, configured to predict a urine volume of the specific user at a future time (See predicting urine production in P0038, urodynamic testing in P0047, P0052-P0053. Also, see a specific duration of time in P0119, P0155 and P0161.).
Regarding claim 11, Kurzrock discloses the non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, cause the computing device to: output, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a total daily voiding volume of the specific user (Besides estimating bladder volume in P0006, P0031, see predicting urine production in P0038. Also, see a specific duration of time in P0119, P0155 and P0161. See urine voiding process in P0052-P0053, P0072-P0073, P0107-P0109, P0136.).
Regarding claim 12, Kurzrock discloses the non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, cause the computing device to: display, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a waking urine volume of the specific user with a first visual object; and
display, based on the recorded bladder urine volume of the specific user for each time point
of the plurality of time points, a nocturnal voiding volume of the specific user with a second visual
object (Taught as triggered alerts during trip to bathroom mentioned in P0005, P0053-P0055.).
Regarding claim 13, Kurzrock discloses the non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, cause the computing device to: output, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, an average total daily voiding volume and an average daily voiding frequency of the specific user (Taught as before and after voiding bladder shown in Fig. 8, mentioned in P0103-P0104.).
Regarding claim 15, the non-transitory computer-readable medium of claim 9, wherein the instructions, when executed, cause the computing device to: estimate, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a functional bladder capacity of the specific user; and output the estimated functional bladder capacity of the specific user (See P0052-P0053, P0062-P0063 Near-infrared spectroscopy (NIRS) based systems measure light source-detector pair to determine full versus void states of the bladder by measuring the attenuation in light due to water.).
Regarding claim 16, Kurzrock discloses user terminal of claim 10, wherein the instructions, when executed by the at least one processor, cause the user terminal to: output, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a total daily voiding volume of the specific user (Besides estimating bladder volume in P0006, P0031, see predicting urine production in P0038. Also, see a specific duration of time in P0119, P0155 and P0161. See urine voiding process in P0052-P0053, P0072-P0073, P0107-P0109, P0136.).
Regarding claim 20, Kurzrock discloses the user terminal of claim 10, wherein the instructions, when executed by the at least one processor, cause the user terminal to: estimate, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a functional bladder capacity of the specific user; and output the estimated functional bladder capacity of the specific user (Besides estimating bladder volume in P0006, P0031, see predicting urine production in P0038. Also, see a specific duration of time in P0119, P0155 and P0161. See urine voiding process in P0052-P0053, P0072-P0073, P0107-P0109, P0136.).
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.
Claims 2-3, 5 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kurzrock (US 2020/0022637 A1) in view of Smyth (US 2019/0228867 A1).
Regarding claim 2, although Kurzrock discloses the method according to claim 1, Kurzrock does not explicitly teach outputting time points, a total daily voiding volume based on the recorded bladder urine volume of the specific user. Smyth teaches further comprises: outputting, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a total daily voiding volume of the specific user (See P0036-P0039 bladder diary 708 in Fig. 7A, diary entries according to 24 hours in Fig. 8 specific to a patient. Also, see Fig. 17A, 17B, P0049 track urination volume in milliliters and [P0059-P0060, P0062] If the patient has significant voiding symptoms on the LUTSS, the bladder diary can be checked for difficulty voiding episodes and what types of difficulties were mentioned.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical mobile app management before the effective filing date of the claimed invention to modify the method of Kurzrock to include outputting time points, a total daily voiding volume based on the recorded bladder urine volume of the specific user as taught by Smyth to help doctors save time and money while ultimately making more informed clinical decisions, mentioned in Smyth’s P0007.
Regarding claim 3, although Kurzrock discloses the method according to claim 1 mentioned above, Kurzrock does not explicitly teach displaying a waking urine volume and a nocturnal voiding volume based on the recorded bladder urine volume of the specific user. Smyth teaches:
displaying, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a waking urine volume of the specific user with a first visual object (See Fig. 15A, 15B, P0047 display screen to confirm time that the user woke to urinate. Also, see Fig. 17A, 17B, P0049 track urination volume in milliliters, average wake time in P0043 and diary with number of times a person awakes from sleep to urinate in P0062.); and
displaying, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a nocturnal voiding volume of the specific user with a second visual object (See diary entry information in [P0062] night-time total voided volume (e.g., NUV—nocturnal urine volume), number of night-time voids, primary nocturia voids—this is the number of times that a person is awakened from sleep by the urge to void, insomnia voids, nocturnal polyuria index and/or nocturia index.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical mobile app management before the effective filing date of the claimed invention to modify the method of Kurzrock to include displaying a waking urine volume and a nocturnal voiding volume based on the recorded bladder urine volume of the specific user as taught by Smyth to help doctors save time and money while ultimately making more informed clinical decisions, mentioned in Smyth’s P0007.
Regarding claim 5, although Kurzrock discloses the method according to claim 1 mentioned above, Kurzrock does not explicitly teach outputting a number of daily nocturnal voidings of the specific user based on the recorded bladder urine volume of the specific user. Smyth teaches:
outputting, based on the recorded bladder urine volume of the specific user for each of the plurality of time points, a number of daily nocturnal voidings or a daily nocturnal voiding volume ratio of the specific user (See Fig. 15A, 15B, P0047 display screen to confirm time that the user woke to urinate. Also, see Fig. 17A, 17B, P0049 track urination volume in milliliters, average wake time in P0043 and diary with number of times a person awakes from sleep to urinate in [P0062] night-time total voided volume (e.g., NUV—nocturnal urine volume), number of night-time voids, primary nocturia voids—this is the number of times that a person is awakened from sleep by the urge to void, insomnia voids, nocturnal polyuria index and/or nocturia index.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical mobile app management before the effective filing date of the claimed invention to modify the method of Kurzrock to include outputting a number of daily nocturnal voidings of the specific user based on the recorded bladder urine volume of the specific user as taught by Smyth to help doctors save time and money while ultimately making more informed clinical decisions, mentioned in Smyth’s P0007.
Regarding claim 17, although Kurzrock discloses the user terminal of claim 10 mentioned above, Kurzrock does not explicitly teach displaying a waking urine volume and a nocturnal voiding volume based on the recorded bladder urine volume of the specific user. Smyth teaches:
wherein the instructions, when executed by the at least one processor, cause the user terminal display, based on the recorded bladder urine volume of the specific user for each time point
of the plurality of time points, a waking urine volume of the specific user with a first visual object (See Fig. 15A, 15B, P0047 display screen to confirm time that the user woke to urinate. Also, see Fig. 17A, 17B, P0049 track urination volume in milliliters, average wake time in P0043 and diary with number of times a person awakes from sleep to urinate in P0062.); and
display, based on the recorded bladder urine volume of the specific user for each time point
of the plurality of time points, a nocturnal voiding volume of the specific user with a second visual
object (See diary entry information in [P0062] night-time total voided volume (e.g., NUV—nocturnal urine volume), number of night-time voids, primary nocturia voids—this is the number of times that a person is awakened from sleep by the urge to void, insomnia voids, nocturnal polyuria index and/or nocturia index.).
Therefore, it would have been obvious to one of ordinary skill in the art of medical mobile app management before the effective filing date of the claimed invention to modify the method of Kurzrock to include displaying a waking urine volume and a nocturnal voiding volume based on the recorded bladder urine volume of the specific user as taught by Smyth to help doctors save time and money while ultimately making more informed clinical decisions, mentioned in Smyth’s P0007.
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kurzrock (US 2020/0022637 A1) in view of Coats (US 2005/0070816 A1).
Regarding claim 4, although Kurzrock discloses the method according to claim 1 mentioned above, Kurzrock does not explicitly teach an average total daily voiding volume and an average daily voiding frequency of the specific user. Coats teaches, further comprising:
outputting, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, an average total daily voiding volume and an average daily voiding frequency of the specific user (See Fig. 1, Fig. 4, Fig. 6, Fig. 8, P0033, P0037, P0048 where total 24-hour volume construe an average total daily voiding volume and an average daily voiding frequency.).
Therefore, it would have been obvious to one of ordinary skill in the art of voiding bladder frequency before the effective filing date of the claimed invention to modify the method of Kurzrock to include an average total daily voiding volume and an average daily voiding frequency of the specific user as taught by Coats by increasing the ability of a clinician to relate measurements to clinical abnormalities of the urinary system for more accurate diagnosing, mentioned in Coats’ P0051.
Regarding claim 18, although Kurzrock discloses the user terminal of claim 10 mentioned above, Kurzrock does not explicitly teach an average total daily voiding volume and an average daily voiding frequency of the specific user. Coats teaches: wherein the instructions, when executed by the at least one processor, cause the user terminal to: output, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, an average total daily voiding volume and an average daily voiding frequency of the specific user (See Fig. 1, Fig. 4, Fig. 6, Fig. 8, P0033, P0037, P0048 where total 24-hour volume construe an average total daily voiding volume and an average daily voiding frequency.).
Therefore, it would have been obvious to one of ordinary skill in the art of voiding bladder frequency before the effective filing date of the claimed invention to modify the method of Kurzrock to include an average total daily voiding volume and an average daily voiding frequency of the specific user as taught by Coats by increasing the ability of a clinician to relate measurements to clinical abnormalities of the urinary system for more accurate diagnosing, mentioned in Coats’ P0051.
Claims 8, 14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kurzrock (US 2020/0022637 A1) in view of Ansell (US 2018/0214122 A1).
Regarding claim 8, although Kurzrock discloses the voiding diary management method according to claim 7 with machine learning datasets mentioned above, Kurzrock does not explicitly teach augmenting the learning dataset by adding learning datasets. Ansell teaches further comprising: augmenting the learning dataset by adding, to the learning dataset and before the trained machine learning model is trained, additional learning datasets (See [P0301-P0302] Once “trained up”, the status-mapping data is suitably augmented or replaced by the one or more trained classifiers.).
Therefore, it would have been obvious to one of ordinary skill in the art of estimating bladder status before the effective filing date of the claimed invention to modify the method of Kurzrock to include augmenting the learning dataset by adding learning datasets as taught by Ansell for treating urinary incontinence, suitably by providing pre-void alerts that allow a patient to void in a dignified manner, mentioned in Ansell’s P0001.
Regarding claim 14, although Kurzrock discloses the non-transitory computer-readable medium of claim 9 mentioned above, wherein the instructions, when executed, cause the computing device to: output, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, Kurzrock does not explicitly teach daily nocturnal voidings. Ansell teaches:
a number of daily nocturnal voidings or a daily nocturnal voiding volume ratio of the specific user (See nocturnal enuresis in P0136, P0142 [P0264] in this case 3 ultrasound transducers (e.g., the beams for one of which is illustrated in FIG. 18 in terms of the angles facing the walls of the bladder), allows for more effective self-recalibration if, for any reason, the bladder changes shape or location in a discontinuous or sudden manner (e.g. if a nocturnal enuresis patient rolls over during sleep, sits or stands).).
Therefore, it would have been obvious to one of ordinary skill in the art of estimating bladder status before the effective filing date of the claimed invention to modify the method of Kurzrock to include teach daily nocturnal voidings as taught by Ansell for treating urinary incontinence, suitably by providing pre-void alerts that allow a patient to void in a dignified manner, mentioned in Ansell’s P0001.
Regarding claim 19, although Kurzrock discloses the user terminal of claim 10, wherein the instructions, when executed by the at least one processor mentioned above, Kurzrock does not explicitly teach daily nocturnal voidings. Ansell teaches cause the user terminal to:
output, based on the recorded bladder urine volume of the specific user for each time point of the plurality of time points, a number of daily nocturnal voidings or a daily nocturnal voiding volume ratio of the specific user (See nocturnal enuresis in P0136, P0142 [P0264] in this case 3 ultrasound transducers (e.g., the beams for one of which is illustrated in FIG. 18 in terms of the angles facing the walls of the bladder), allows for more effective self-recalibration if, for any reason, the bladder changes shape or location in a discontinuous or sudden manner (e.g. if a nocturnal enuresis patient rolls over during sleep, sits or stands).).
Therefore, it would have been obvious to one of ordinary skill in the art of estimating bladder status before the effective filing date of the claimed invention to modify the method of Kurzrock to include teach daily nocturnal voidings as taught by Ansell for treating urinary incontinence, suitably by providing pre-void alerts that allow a patient to void in a dignified manner, mentioned in Ansell’s P0001.
Response to Arguments
Applicant alleges that amended claims are not directed to an abstract idea, involving user-specific machine learning model, see pgs. 9-10 of Remarks – Examiner disagrees.
Beside not explaining how the invention is applied in a meaningful way and merely indicating that the user-specific machine learning model might be trained based on use of photodiodes and specific optical characteristics, capable of predicting user-specific bladder volumes in the future, the claimed invention is not explained and are not solving a technological problem with a technological solution, but rather solves an already solved patient assessment problem in a non-technical manner. For example, it doesn’t appear optimization of future predictions or the computer’s operations are more efficient or improved upon based on the claimed functions of learning optical characteristic values observed from photodiodes, having steps of enrolling a medical professional in a computer system.
Applicant alleges that amended claims integrate the alleged abstract idea into a practical application, liken to McRo, see pgs. 10-12 of Remarks – Examiner disagrees.
With a photodiode as a semiconductor device used to convert incident light into an electrical current or voltage, the optical datasets detected by the photodiodes is merely insignificant pre-solution activity (data gathering; selecting data to be manipulated) and the user-specific machine learning model being trained based on use of photodiodes and specific optical characteristics are ways of merely using the computer as a tool to implement the abstract idea (saying “apply it”) and is merely using the computer in the manner in which it was designed to be used, i.e., performing generic computer functions. The claimed features (e.g., receiving, providing to a trained machine learning model, recording and training) Applicant has drawn attention to are not impressing upon software and hardware computer components that would optimize computer functionality such as speed, memory and/or processing, like McRo determined by the courts.
Applicant’s arguments that Kurzrock does not teach “an estimated bladder urine volume for each time point of the plurality of time points,” or “training, based on the estimated bladder urine volume for each time point of the plurality of time points, a second machine learning model, for the specific user configured to predict a urine volume of the specific user at a future time”. with respect to amended claims 1 and 9-10. Rather Kurzrock’s urodynamic testing, time volume and probing time-stamps, (P0047, P0052-P0053, P0055, P0072, P0082) are used as sources when predicting and recognizing patterns through machine learning (Abstract, P0037-P0038, P0092, P0112 and 1227-P0129).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/T.S.W./Examiner, Art Unit 3687 06/03/2026
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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