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 .
DETAILED ACTION
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve model prediction for urine flow rate to medical analysis supported by the specification, and reflected by the claims e.g. in spec which describes operations to improve the accuracy by overlapping the consecutive windows in a urine model, as well as the data processing speed, efficiency, and performance. In other words, the claims enable the invention to utilize learning models to more accurately classify urine sounds and rate thereof for more accurate measurements for medical institutions.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Step 2B: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT AMOUNT TO SIGNIFICANTLY MORE THAN THE JUDICIAL EXCEPTION?
If Yes at step 2A.1 and step 2A.2 fails, the interpretation in the context of 35 USC 101 amounts to models that handled physical human data measured at multiple windows e.g. urine sounds into a microphone transduced. While windowing in itself can be mathematical, when combined as recited in combination with such models using urine data or a person, the process as a whole is significantly more than applied mathematical concepts. Further, such an amendment/claim-language provides significance beyond mere data manipulation or mental decision making, and is also analogous to interim amendment examples. For instance, a person can listen for urine or no-urine but cannot identify this based on the sound-alone per se, and certainly not the flow rate of urine based on sound of urine or generally speaking. While some humans are more perceptive than others to determine how fast urine flows and whether the sound is actually urine, such anomalies in perception, or gifted humans in aural perception, are not common and when combined with the models and separate sequenced windows of applied to the model, reasonable analogies of mental processes are no longer warranted. Such claim language provides significance beyond mere data manipulation or mental decision making and is also analogous to interim amendment examples. Additionally, if one or ordinary skill in the art fails to properly consider any specified hardware or arranged components in the claims, considers such claim limitations non-generic, or simply disregards Enfish, the claims still demonstrate that there exists improvements to the functioning of a computer (or technology), e.g., a modification of conventional Internet hyperlink protocol to dynamically produce a dual-source hybrid webpage, as discussed in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258-59, 113 USPQ2d 1097, 1106-07 (Fed. Cir. 2014) (see MPEP § 2106.05(a));. Therefore, in light of the above as a whole, it is not warranted to give a rejection under 35 USC 101.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1, 19, and 20 with dependent claims thereof, are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1 and 15 and any dependent claims thereof of U.S. Patent No. 12198679. Although the conflicting claims are not identical, they are not patentably distinct from each other because said claims of the instant application includes all of the features of said claims of U.S. Patent No. 12198679. It would have been obvious to one of ordinary skill in the art to omit the step of clarifying that a start and stop point is present in a window length, otherwise an inherent operation, In re Karlson 136 USPQ 184 (1963): "Omission of an element and its function is an obvious expedient if the remaining elements perform the same functions as before"
Present invention US Patent 12198679
1. A method of obtaining urination information, the method comprising: obtaining, by a processor, sound data which urination sound is recorded; obtaining, by the processor, a first plurality of segmented target data for a first plurality of windows, wherein the first plurality of windows is determined from the sound data and each of the first plurality of windows includes a first length; obtaining, by the processor, a second plurality of segmented target data for a second plurality of windows, wherein the second plurality of windows is determined from the sound data and each of the second plurality of windows includes a second length; obtaining, by the processor, a plurality of segmented classification data by using the first plurality of segmented data and a classification model, wherein the classification model is configured to output data comprising at least a value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtaining, by the processor, a plurality of segmented urine flow rate data by using the second plurality of segmented data and a prediction model, wherein the prediction model is configured to output data comprising at least a value for urine flow rate when data related to urination sound are inputted; and obtaining, by the processor, urination data using at least the plurality of segmented classification data and the plurality of segmented urine flow rate data.
2. The method of claim 1, wherein the first plurality of segmented target data includes first to m-th segmented target data and the first plurality of windows includes m windows, wherein the first to m-th segmented target data corresponds to the m windows respectively, wherein the m is a natural number greater than or equal to 2, wherein the second plurality of segmented target data includes first to n-th segmented target data and the second plurality of windows includes n windows, wherein the first to n-th segmented target data corresponds to the n windows respectively, and wherein the n is a natural number greater than or equal to 2.
3. The method of claim 2, wherein consecutive windows among the n windows partially overlap each other.
4. The method of claim 3, wherein the obtaining the urination data further comprises: calculating a urine flow rate for a first time period by using a first urine flow rate and a second urine flow rate, wherein the plurality of segmented urine flow rate data includes at least first segmented urine flow rate data including the first urine flow rate for the first time period and second segmented urine flow rate data including the second urine flow rate for the first time period, and wherein the first segmented urine flow rate data and the second segmented urine flow rate data are consecutive segmented urine flow rate data.
5. The method of claim 3, wherein consecutive windows among the m windows partially overlap each other, and wherein an overlapping degree of the consecutive windows among the m windows is different from an overlapping degree of consecutive windows among the n windows.
6. The method of claim 3, wherein consecutive windows among the m windows partially overlap each other, and wherein an overlapping degree of the consecutive windows among the m windows is same as an overlapping degree of consecutive windows among the n windows.
7. The method of claim 3, wherein consecutive windows among the m windows do not overlap each other.
8. The method of claim 2, wherein each of the m windows are same as each of the n windows.
9. The method of claim 2, wherein each of the m windows are different from each of the n windows.
10. The method of claim 1, wherein the obtaining the first plurality of segmented target data comprises: transforming the sound data to spectrogram data; and obtaining first to m-th segmented target data corresponding to m windows of the first plurality of windows from the spectrogram data.
11. The method of claim 2, wherein the obtaining the first plurality of segmented target data comprises: obtaining first to m-th segmented sound data corresponding to m windows of the first plurality of windows, and transforming each of the first to m-th segmented sound data to spectrogram data to obtain first to m-th segmented target data.
12. The method of claim 1, wherein the first length is same as the second length.
13. The method of claim 1, wherein the first length is different from the second length.
14. The method of claim 1, wherein the obtaining the urination data comprises: obtaining urination classification data using the plurality of segmented classification data; obtaining candidate urine flow rate data using the plurality of segmented urine flow rate data; and processing the candidate urine flow rate data using the urination classification data.
15. The method of claim 14, wherein the urination data are obtained through convolution operation of the urination classification data and the candidate urine flow rate data.
16. The method of claim 14, wherein the urination data are obtained through multiplication of at least part of the urination classification data and the candidate urine flow rate data.
17. The method of claim 1, further comprising: correcting the urination data by using a compensation value.
18. The method of claim 17, wherein the compensation value is obtained by: obtaining sample sound data including sound of urination, obtaining a predictive value by using the sample sound data, the classification model, and the prediction model, wherein the predictive value represents voiding volume, obtaining a measured value by measuring voiding volume corresponding to the sample sound data, and obtaining the compensation value by using at least the predictive value and the measured value.
19. A server comprising a processor, the processor is configured to: obtain sound data which urination sound is recorded; obtain a first plurality of segmented target data for a first plurality of windows, wherein the first plurality of windows is determined from the sound data and each of the first plurality of windows includes a first length; obtain a second plurality of segmented target data for a second plurality of windows, wherein the second plurality of windows is determined from the sound data and each of the second plurality of windows includes a second length; obtain a plurality of segmented classification data by using the first plurality of segmented data and a classification model, wherein the classification model is configured to output data comprising at least a value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtain a plurality of segmented urine flow rate data by using the second plurality of segmented data and a prediction model, wherein the prediction model is configured to output data comprising at least a value for urine flow rate when data related to urination sound are inputted; and obtain urination data using at least the plurality of segmented classification data and the plurality of segmented urine flow rate data.
20. A non-transitory computer-readable recording medium having recorded thereon one or more instructions which, when executed by at least one processor of an electronic device, cause the electronic device to perform operations comprising: obtaining, by a processor, sound data which urination sound is recorded; obtaining, by the processor, a first plurality of segmented target data for a first plurality of windows, wherein the first plurality of windows is determined from the sound data and each of the first plurality of windows includes a first length; obtaining, by the processor, a second plurality of segmented target data for a second plurality of windows, wherein the second plurality of windows is determined from the sound data and the ending point of the sound data and each of the second plurality of windows includes a second length; obtaining, by the processor, a plurality of segmented classification data by using the first plurality of segmented data and a classification model, wherein the classification model is configured to output data comprising at least a value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtaining, by the processor, a plurality of segmented urine flow rate data by using the second plurality of segmented data and a prediction model, wherein the prediction model is configured to output data comprising at least a value for urine flow rate when data related to urination sound are inputted; and obtaining, by the processor, urination data using at least the plurality of segmented classification data and the plurality of segmented urine flow rate data.
1. (Currently amended) A method of obtaining urination information, the method comprising: obtaining sound data by using a sound sensor; obtaining, by a processor, a first plurality of segmented target data corresponding to a first plurality of windows selected from the sound data, wherein each of the first plurality of windows includes a first length and is sequentially determined between a starting point and an ending point of the sound data; obtaining, by the processor, a second plurality of segmented target data corresponding to a second plurality of windows selected from the sound data, wherein each of the second plurality of windows includes a second length and is sequentially determined between the starting point and the ending point of the sound data; obtaining, by the processor, a plurality of segmented classification data by sequentially inputting using the first plurality of segmented data intoand a classification model, wherein the classification model is configured to output data comprising at least a value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtaining, by the processor, a plurality of segmented urine flow rate data by sequentially inputting using the second plurality of segmented data into and a prediction model, wherein the prediction model is configured to output data comprising at least a value for urine flow rate when data related to urination sound are inputted; and obtaining urination data using at least the plurality of segmented classification data and the plurality of segmented urine flow rate data.
2. (Currently amended) The method of The method of wherein the first plurality of segmented target data includes first to m-th segmented target data and the first plurality of windows includes corresponding to m windows, wherein the first to m-th segmented target data corresponds to the m windows, from the sound data, each of the m windows has a first length and is sequentially determined between a starting point and an ending point of the sound data, and wherein the m is a natural number greater than or equal to 2, and wherein the second plurality of segmented target data includes first to n-th segmented target data andthesecondplurality of windowsincludescorresponding to n windows, wherein the first to n-th segmented target data corresponds to the n windows, from the sound data, each of the n windows has a second length and is sequentially determined between the starting point and the ending point of the sound data, and wherein the n is a natural number greater than or equal to 2.
3. (Original) The method of claim 2, wherein consecutive windows among the n windows partially overlap each other.
4. (Original) The method of The method of wherein consecutive windows among the m windows partially overlap each other, and wherein an overlapping degree of the consecutive windows among the m windows is different from an overlapping degree of consecutive windows among the n windows.
5. (Original) The method of The method of wherein consecutive windows among the n windows partially overlap each other, and wherein an overlapping degree of the consecutive windows among the m windows is same as an overlapping degree of consecutive windows among the n windows.
6. (Original) The method of claim 2, wherein consecutive windows among the m windows do not overlap each other.
7. (Original) The method of claim 2, wherein the each of m windows are same as each of the n windows.
8. (Original) The method of claim 2, wherein the obtaining of the first plurality of segmented target data comprises:transforming the sound data to spectrogram data; and obtaining the first to m-th segmented target data corresponding to the m windows from the spectrogram data.
9. (Original) The method of The method of wherein the obtaining of the first plurality of segmented target data comprises: obtaining first to m-th segmented sound data corresponding to the m windows, and transforming each of the first to m-th segmented sound data to spectrogram data to obtain the first to m-th segmented target data.
10. (Original) The method of claim 1, wherein the obtaining of the urination data comprises:obtaining urination classification data using the plurality of segmented classification data; obtaining candidate urine flow rate data using the plurality of segmented urine flow rate data; and processing the candidate urine flow rate data using the urination classification data.
11. (Original) The method of claim 10, wherein the urination data are obtained through convolution operating of the urination classification data and the candidate urine flow rate data.
12. (Original) The method of claim 10, wherein the urination data are obtained through multiple operating of at least part of the urination classification data and the candidate urine flow rate data.
13. (Original) The method of claim 1, further comprising:correcting the urination data by using a compensation value.
14. (Original) The method of claim 13, wherein the compensation value is obtained by:obtaining sample sound data including sound of urination, obtaining a predictive value by using the sample sound data, the classification model, and the prediction model, wherein the predictive value represents voiding volume, obtaining a measured value by measuring voiding volume corresponding to the sample sound data, and obtaining the compensation value by using at least the predictive value and the measured value.
15. (Currently amended) A non-transitory computer-readable recording medium having recorded thereon one or more computer readable instructions which, when executed by at least one processor of an electronic device, cause the electronic device to perform operations comprising:obtaining obtain sound data recorded by a sound sensor; obtaining obtain, by the at least one processor, a first plurality of segmented target data corresponding to a first plurality of windows selected from the sound data, wherein each of the first plurality of windows has a first length and is sequentially determined between a starting point and an ending point of the sound data; obtaining obtain, by the at least one processor, a second plurality of segmented target data corresponding to a second plurality of windows selected from the sound data, wherein each of the second plurality of windows has a second length and is sequentially determined between the starting point and the ending point of the sound data; obtaining obtain, by the at least one processor, a plurality of segmented classification data by sequentially inputting using the first plurality of segmented data intoand a classification model, wherein the classification model is configured to output data comprising at least a value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtaining obtain, by the at least one processor, a plurality of segmented urine flow rate data by sequentially inputting using the second plurality of segmented data intoand a prediction model, wherein the prediction model is configured to output data comprising at least a value for urine flow rate when data related to urination sound are inputted; and obtaining obtain urination data using at least the plurality of segmented classification data and the plurality of segmented urine flow rate data.
16. (New) The method of claim 1, wherein the first length is same as the second length.
17. (New) The method of claim 2, wherein the first length is different from the second length.
Allowable Subject Matter
Claims 1-20 are allowed.
The following is an examiner’s statement of reasons for allowance:
After a full review of the prior arguments, and after careful review of the complex claims as a whole, the examiner believes that the prior art taken alone or in combination fails to teach the claims as a whole such as: obtaining, by a processor, sound data which urination sound is recorded; obtaining, by the processor, a first plurality of segmented target data for a first plurality of windows, wherein the first plurality of windows is determined from the sound data and each of the first plurality of windows includes a first length; obtaining, by the processor, a second plurality of segmented target data for a second plurality of windows, wherein the second plurality of windows is determined from the sound data and each of the second plurality of windows includes a second length; obtaining, by the processor, a plurality of segmented classification data by using the first plurality of segmented data and a classification model, wherein the classification model is configured to output data comprising at least a value for classifying a urination section or a non-urination section when data related to urination sound are inputted; obtaining, by the processor, a plurality of segmented urine flow rate data by using the second plurality of segmented data and a prediction model, wherein the prediction model is configured to output data comprising at least a value for urine flow rate when data related to urination sound are inputted; and obtaining, by the processor, urination data using at least the plurality of segmented classification data and the plurality of segmented urine flow rate data.
The above claims are deemed allowable given the complex nature of determining urination data and flow rate using different combinations tied flow and data models as precisely claimed. The closest prior art fails to address the concept of two windows for two distinct models. While rate and urine vs. non-urine are taught, the specificity of the windows as precisely claimed under BRI does not appear to be suggested in the art. Additionally, closest prior art teaches general windowing which show inherent start and end times for window segments of interest such as speech or no speech, or a target sound versus silence. However, such instances are not tied to distinct models. Other prior art teaches urine flow rate, urination data such as sound, overlap of windows, and convolutional analysis. Under BRI the claims minimally show variations with respect to the window lengths, the prior does show that lengths cannot be the same. Additionally, lengths cannot equal each other where the numbers of variables are extracted independently and not algorithmically set per se as L1=L1(machine learning urine data flow rate predication model). Further, such concepts under BRI are applicable when only one window is used initially or there is no overlap. The claims as a whole produce step which the prior art cannot reasonably suggest particularly when tying in prediction models and classification models with the rules of first and second window lengths for distinct models as precisely claimed as a whole. Therefore, the prior art fails to teach or suggest the complex claims as a whole.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210275073 A1 Korkor, II; Bishara Charles et al.
Urine frames for flow and yes/no urine categorization
US 20180163388 A1 STATON; FIELDING B. et al.
Smart urinals flow rate
US 20160058412 A1 YOSHIMURA; Yasuo et al.
Urine measuring device
US 20140018702 A1 Belotserkovsky; Edward
Urine flow measurement
US 20090062644 A1 McMorrow; Gerald et al.
Urine image analysis
US 20200394781 A1 Hall; David R. et al.
Urine duration
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov
Prosecution Insights