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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/27/2026 has been entered.
Status of Claims
This action is in response to the RCE received 2/27/2026.
Claims 1, 4-9, 11-14 and 16-21 were canceled 2/27/2026.
Claims 22-36 were added 5/16/2025.
Claims 22-36 are currently pending and have been examined.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 22, 27 and 32 and therefore their dependent claims and claims 24, 29, and 34 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 22, 27, and 32 recite the limitation of “retrain the neural network using the augmented training data such that the neural network is continually updated based on an increasingly large training dataset over time”. The specification is silent on retraining the neural network. The specification recites that the neural network is training using patient data together with an external determination based on that patient data and that the neural network may be continually updated and improved based on an increasingly large training dataset over time (page 22). The neural network is being continually updated based on the training of the combination of patient data and external determinations and is not being retrained. The neural network is being updated based on the same training calculations of patient data added to external determinations. Retraining a neural network re-optimizes a model’s weights using a new dataset, while updating a neural network adds new data in continual learning. The specification recites the steps well-known in the art for updating, rather than retraining.
Claims 24, 29, and 34 recite the limitations of “the neural network generates the plurality of output activations: when provided with behavioural data in the absence of questionnaire data; when provided with questionnaire data in the absence of behavioural data; and when provided with both behavioural data and questionnaire data”. However, the specification is silent on behavioural data being absent and questionnaire data being absent. Further, the specification is silent on output activations. However, the specification does recite that neuron activation values are designated by question answers and/or behavioural data inputs which are pre-determined values based on their likelihood of indicating a condition (page 19). The “and/or” does not indicate that the system accounts for the presence or absence of a dataset in order to move forward with a calculation, but recites that the data inputs are pre-determined values that can be designated as neuron activation values based on two different types of data in combination or solitarily. The specification is silent that the neural network generates the plurality of output activations and suggests that the neurons in the neural network are assigned based on pre-determined values (page 19, Figure 5).
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 22-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 22-36 are drawn to a method, an apparatus, and non-transitory computer-readable medium which are statutory categories of invention (Step 1: YES).
Independent claims 22, 27, and 32 recite: determining a pelvic floor dysfunction condition of a patient, comprising: receive first questionnaire data and first behavioral data associated with a patient; apply to the first questionnaire data and first behavioral data at a first time to generate a first plurality of output activations, each output activation of the first plurality corresponding to a respective pelvic floor dysfunction condition and representing a likelihood of the respective condition, an input layer comprising a plurality of neurons corresponding to possible questionnaire answers and behavior data inputs, one or more hidden layers connected by weighted connections, and an output layer comprising a plurality of neurons corresponding to respective pelvic floor dysfunction conditions; determine a pelvic floor dysfunction condition based on the first plurality of output activations; during operation, with patient data received for additional patients together with associated external determinations or diagnoses for the additional patients.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the workload of a physician diagnosing a patient may be more efficient (page 25). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “efficient determination may also enable the apparatus to provide repeat determinations more easily and quickly at different points in time based on new, different or changing patient data.” (see: specification page 2). This problem is addressed That may enable a physician to more quickly determine whether an existing condition determination is correct and/or whether a current treatment plan is improving the patient's symptoms. That may reduce a time between a patient first presenting with symptoms and being correctly diagnosed, which may help to halt progression of the pelvic floor dysfunction condition or bladder condition and improve patient health.” (see: specification page 3). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “apparatus”, “processor”, and “memory”, “computer system”, and “neural network”, “train the neural network by adjusting weights and bias values of the neural network to reduce a cost function”, “augment training data for the neural network”, “retrain the neural network using the augmented training data such that the neural network is continually updated based on an increasingly large training dataset over time”, “non-transitory computer-readable medium”, are recited at a high level of generality (e.g., that the receiving and determining is performed using generic computer components with instructions are executed to perform the claimed limitations and the machine learning model is merely inputting and outputting data on nodes on a generic neural network). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
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 integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figures 1-3, 5 and
Page 6, “According to a third aspect, there is provided a non-transitory computer program comprising instructions which, when the program is executed by a processor, cause the processor carry out the method of second aspect. According to a fourth aspect, there is provided a computer-readable medium having the computer program of the third aspect stored thereon.”
Page 8, “The processor 105 may be configured to implement the determination of the one or more pelvic floor dysfunction conditions on a web application on a user electronic device (for example, a personal computer or smartphone), through a server back-end or in a native application on a user electronic device. The apparatus 100 may also be configured to generate a digital user interface for a user electronic device. The digital user interface may enable user input of patient data to the apparatus 100 (for example, via a keyboard or a touch screen) and/or may display an output of a determination of one or more pelvic floor dysfunction conditions by the processor 105.”
Pag 18, where “In the embodiment shown, the machine learning algorithm 315 is or comprises a neural network, although other machine learning algorithm structures may alternatively be used.”
Page 19, “An embodiment of the machine learning algorithm 315 is shown in more detail in Figure 5. In the embodiment shown, the machine learning algorithm 315 is a neural network comprising a plurality of layers, neurons and connections. The neural network comprises an input layer or primary layer 316 comprising a plurality of neurons 316a. Each possible answer to the questions that may be selected by the processor 305 and asked to a user is represented by a separate neuron 316a. The primary layer 316 further comprises additional neurons 316a which represent a behavioural data input (discussed further below), and may also comprise additional neurons 316a which represent a physiological data input (for example, a measured pelvic floor strength of the patient). The number of neurons in the primary layer 316 may vary depending on the number of possible questions and/or behavioural data inputs, possible branching of the questions during selection of a next question by the processor 305 etc.”
Page 19, “The neural network 315 is trained to provide a determination, by adjusting the weights and bias values in the neural network 315 in order to achieve accurate determinations. In the embodiment shown, the neural network 315 is trained using backpropagation, with gradient descent used to reduce a value of a cost function, although that is not essential.”
Page 19, “The training data used to train the network comprises patient questionnaire data, behavioural data and a corresponding determination or diagnosis for each of a plurality of patients. That may enable the neural network to be trained on a large data set (taking into account outcomes at a population level) in order to increase the accuracy of the determination provided for a new patient. In addition, the training data may be augmented with each new patient for which the apparatus 300 is used to provide a determination. By training the neural network using the patient data received for each new patient together with an external determination or diagnosis (for example, not determined by the apparatus 300) based on that patient data, the neural network may be continually updated and improved based on an increasingly large training dataset over time. In that way, the neural network may be configured to adjust and provide increasingly accurate determinations, rather than maintain a fixed approach as with conventional diagnostic approaches for pelvic floor dysfunction conditions or bladder conditions.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 23-26, 28-31, 33-36 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claims 23-26, 18-31 and 33-36 recite applying the generic neural network to different outputs corresponding to a healthcare condition in order to select, receive and apply activations related to the questionnaire and behavioral data.
Claims 25, 30, and 35 further recite “apply the neural network using activation corresponding to the selected subset of questionnaire items, thereby reducing a number of input-layer activations applied to the neural network” which are nominal or tangential addition to the abstract idea and amount to insignificant post-solution activity concerning an insignificant application. The addition of an insignificant extra-solution activity limitation does not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. In the claimed context, these claimed additional elements are incidental to the performance of correlating healthcare data as outlined in the recitations above. See: MPEP 2106.05(g).
Claims 25, 30, and 35 recite additional elements for extra-solution activity, as recited above, each of which amounts to mere post-solution activity concerning an insignificant application. The specification (e.g., pages 19, 20-21) does not indicate that the additional element(s) provide anything other than well‐understood, routine, and conventional functions when claimed in a merely generic manner (as they are here).
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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.
Claim(s) 22-36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ackerman (US 20220181027 A1) in view of Zhang (US 2022/0147818 A1).
CLAIM 22-
Ackerman teaches the limitations of:
An apparatus for determining a pelvic floor dysfunction condition of a patient, comprising: a processor; and ((Ackerman teaches determining the pelvic floor dysfunction condition of a patient using a computing device that includes a processor (para [0088-0089, 0100], Figure 4A))
a memory storing instructions that, when executed by the processor, cause the processor at least to: receive first questionnaire data (Ackerman teaches that questionnaire data is received from the patient and stored (para [0044, 0048])) and first behavioural data associated with a patient; (Ackerman teaches urinary incontinence data of frequency associated with a patient which is what behavioral data may be defined as a number of incontinence episodes (page 21 of specification) (para [0072, 0143]))
apply a neural network to the first questionnaire data and first behavioral data at a first time to generate a first plurality of output activations, each output activation of the first plurality corresponding to a respective pelvic floor dysfunction condition and representing a likelihood of the respective condition, (Ackerman teaches using a neural network to determine the diagnostic data based on the patient response data (i.e., questionnaire data) and the incontinence data (i.e., behavioral data) to correspond a diagnosis to a pelvic floor condition, including classifying the data that relate to whether the patient has the diagnosis of a pelvic floor condition (i.e., likelihood as defined on the specification is based on pre-determined values of the data page 19) by outputting multiple diagnostic clusters (i.e., output activations) (para [0097, 0081, 0077,0061, 0063]))
a pelvic floor dysfunction (Ackerman teaches that the machine learning system is used for pelvic floor dysfunction diagnosis by applying a neural network (para [0138, 0048]))
behavior data inputs (Ackerman teaches urinary incontinence data of frequency associated with a patient which is what behavioral data may be defined as a number of incontinence episodes (page 21 of specification) and is input into the neural network (para [0072, 0143]))
Ackerman teaches inputting questionnaire data and behavior data inputs into a neural network, however it does not explicitly teach how the neural network is performing, however Zhang teaches:
wherein the neural network comprises an input layer comprising a plurality of neurons corresponding to possible questionnaire answers and …data inputs, one or more hidden layers connected by weighted connections, and (Zhang teaches that a neural network comprises an input layer that comprises multiple input nodes, hidden layers and uses weights to connect the nodes and the inputs may be that of answers to a questionnaire and measured observed features (para [0050, 0047, 0158]))
an output layer comprising a plurality of neurons corresponding to respective …conditions; (Zhang teaches that a neural network has an output layer to determine diagnosis of a condition based on the inputted data (para [0050, 0197]))
determine… condition based on the first plurality of output activations; (Zhang teaches that a neural network has an output layer with output nodes (i.e., output activations) to determine diagnosis of a condition based on the inputted data (para [0050, 0197]))
train the neural network by adjusting weights and bias values of the neural network to reduce a cost function; and (Zhang teaches using an optimization function (i.e., adjusting weights and bias values) in the neural network to minimize cost function and trains the neural network (para [0148, 0103, 0127]))
during operation of the apparatus, augment training data for the neural network with patient data received for additional patients together with associated external determinations or diagnoses for the additional patients and retrain the neural network using the augmented training data such that the neural network is continually updated based on an increasingly large training dataset over time (Zhang teaches using a large scale dataset to generate new feature parameters, wherein the feature parameters include large swathes of patients data and contextual data (i.e., external determinations), to train the neural network over time and that the neural network is continually updated which reads on how the neural network is updated on page 19 of the specification (para [0127, 0196-0197, 0097-0098, 0220]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system apparatus that uses a neural network to determine pelvic floor dysfunction of Ackerman to integrate the application of combining with a neural network that augments existing neural networks with new features of Zhang with the motivation of making the system for treatment of a condition using improved machine learning techniques of a neural network. (see: Zhang, paragraphs 2-3, 7, 15).
CLAIM 23-
Ackerman in view of Zhang teach the limitations of claim 22. Regarding claim 23, Ackerman further teaches:
second behavioral data (Ackerman teaches urinary incontinence data of frequency (multiple episodes) associated with a patient which is what behavioral data may be defined as in the specification: a number of incontinence episodes (page 21 of specification) (para [0072, 0143]))
pelvic floor dysfunction (Ackerman teaches that the machine learning system is used for pelvic floor dysfunction diagnosis by applying a neural network (para [0138, 0048]))
Ackerman does not explicitly teach, however Zhang teaches:
store in the memory the first plurality of output activations; (Zhang teaches that the memory stores the code which includes outputs of the nodes (para [0048, 0042]))
at a second time after the first time, apply the neural network to second … data of the patient, collected after the first time, to generate a second plurality of output activations corresponding to the respective … conditions; and (Zhang teaches a second stage of training by applying the neural network following the first stage base model training of determining output node data based on the inputted patient questionnaire and measured observed features that correspond to health conditions (para [0092, 0184, 0157-0158, 0197]))
compare the first plurality of output activations to the second plurality of output activations to produce an updated or validated determination of … condition (Zhang teaches that the plurality of stages of the training the neural network are compared and used in determining an expected output and updating of a diagnosis of a health condition (para [0055, 0220, 0071, 0121]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system apparatus that uses a neural network to determine pelvic floor dysfunction of Ackerman to integrate the application of combining with a neural network that augments existing neural networks with new features of Zhang with the motivation of making the system for treatment of a condition using improved machine learning techniques of a neural network. (see: Zhang, paragraphs 2-3, 7, 15).
CLAIM 24-
Ackerman in view of Zhang teach the limitations of claim 22. Regarding claim 24, Ackerman further teaches:
behavioral data (Ackerman teaches urinary incontinence data of frequency (multiple episodes) associated with a patient which is what behavioral data may be defined as in the specification: a number of incontinence episodes (page 21 of specification) (para [0072, 0143]))
Ackerman does not explicitly teach, however Zhang teaches:
configure the neural network such that the neural network generates the plurality of output activations: when provided with …data in the absence of questionnaire data; when provided with questionnaire data in the absence of …data; and when provided with both …data and questionnaire data. (Zhang teaches that the neural network generates multiple output nodes and uses different feature data which can include sensor data and questionnaire data if one or the other feature has not been revealed for a prolonged period of time and can also include when multiple sets of data is included (i.e., both) (para [0192, 0157-0158, 0187, 0048, 0195, 0167, 0053]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system apparatus that uses a neural network to determine pelvic floor dysfunction of Ackerman to integrate the application of combining with a neural network that augments existing neural networks with new features of Zhang with the motivation of making the system for treatment of a condition using improved machine learning techniques of a neural network. (see: Zhang, paragraphs 2-3, 7, 15).
CLAIM 25-
Ackerman in view of Zhang teach the limitations of claim 22. Regarding claim 25, Zhang further teaches:
select a subset of questionnaire items from a plurality of questionnaire items based on prior responses received from the patient; receive responses to the selected subset of questionnaire items; (Zhang teaches that the system uses answers to questions presented to the user using different question content based on previous testing of patients most at risk of the condition which reads on the specification of using different subsets of data for each patient (page 22 of specification) (para [0195, 0119, 0053]))
and apply the neural network using activations corresponding to the selected subset of questionnaire items, thereby reducing a number of input-layer activations applied to the neural network (Zhang teaches that the new feature data (i.e., subset of questions) uses input-layer activations being optimized using a gradient descent procedure (as described in the specification page 21) (para [0081-0083, 0195, 0050, 0053, 0148-0149]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system apparatus that uses a neural network to determine pelvic floor dysfunction of Ackerman to integrate the application of combining with a neural network that augments existing neural networks with new features of Zhang with the motivation of making the system for treatment of a condition using improved machine learning techniques of a neural network. (see: Zhang, paragraphs 2-3, 7, 15).
CLAIM 26-
Ackerman in view of Zhang teach the limitations of claim 22. Regarding claim 26, Ackerman further teaches:
pelvic floor dysfunction (Ackerman teaches that the machine learning system is used for pelvic floor dysfunction diagnosis by applying a neural network (para [0138, 0048]))
Ackerman does not explicitly teach, however Zhang teaches:
store in the memory a ranking for the .. conditions in order of condition prevalence; and include in the subset of questionnaire items a first questionnaire item relating to a most prevalent condition; wherein selection of the subset of questionnaire items is based on a response to the first questionnaire item (Zhang teaches that the system uses answers to questions presented to the user using different question content based on previous testing of patients most at risk of the condition which reads on the specification of using different subsets of data for each patient (page 22 of specification) and classifies values of symptoms related to a condition for diagnosis (i.e., ranking) (para [0195, 0119, 0052-0053, 0223]))
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the system apparatus that uses a neural network to determine pelvic floor dysfunction of Ackerman to integrate the application of combining with a neural network that augments existing neural networks with new features of Zhang with the motivation of making the system for treatment of a condition using improved machine learning techniques of a neural network. (see: Zhang, paragraphs 2-3, 7, 15).
CLAIMS 27-31-
Claims 27-31 are significantly similar to claims 22-26 and are rejected upon the same prior art as claims 22-26 respectively.
CLAIMS 32-36-
Claims 32-36 are significantly similar to claims 22-26 and are rejected upon the same prior art as claims 22-26 respectively.
Response to Arguments
The arguments filed 2/27/2026 have been fully considered.
Regarding the arguments pertaining to the 103 rejection, these arguments are not persuasive. The new claims do not have the limitations cited in the Office Action dated 8/27/2025 that made the previous claims (that are now canceled) allowable over the prior art.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the claimed invention is directed towards an improvement to training of a neural network based on an increasingly large data set over time. Applicant further argues that the claimed invention is directed towards improvements in the application of the neural network to particular inputs based on applying the neural network a second time to generate a second plurality of output data for the determination of the patient’s condition. Examiner respectfully disagrees. The claimed invention uses a generic neural network without significantly more to the abstract idea. Applying a generic neural network multiple times to different data sets does not provide an improvement of technology nor an improvement on the neural network itself. A generic neural network is trained using increasingly large data sets over time and is well-known in the art to be able to handle large swathes of multiple data entries when computing outputs.
The functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
The dependent claims rely on the arguments of the independent claims and are rejected for the reasons stated above.
Conclusion
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/KIMBERLY A. SASS/Examiner, Art Unit 3686