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 .
Formal Matters
Applicant's response, filed 07 April 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1-12 are currently pending and have been examined.
Claims 1-4, 7, and 11 have been amended.
Claims 1-12 have been rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for Application No. EP22176582.9 on 11/26/2024.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 5/31/2022 claiming benefit to EP22176582.9.
Objections
Examiner acknowledges that appropriate correction to the previous objections have been made and withdraws the objections accordingly.
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 1-12 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.
Step 1 – Statutory Categories of Invention:
Claims 1-12 are drawn to a method, which is a statutory category of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for tracking or predicting the progression of a neurological impairment or other disease in a subject in part performing the steps of extracting feature data from results of a digital test of neurological impairment performed by the subject by using an analytical model comprising an encoder configured to generate a latent representation comprising one or more latent variables; a latent distribution determination module configured to determine a plurality of probability distributions by determining, for each latent variable of the one or more latent variables, a respective probability distribution, the encoder being further configured to generate a sampled latent vector by sampling from the plurality of probability distributions; and a prediction head derived from the sampled latent vector and configured to generate a predicted clinical metric indicative of neurological impairment; and determining or predicting a status or progression of the neurological impairment based on the sampled latent vector and the predicted clinical metric generated by the prediction head wherein the results of the digital test of neurological impairment comprises a plurality of coordinates, each coordinate corresponding to a location of a user's finger on [a display] at a given time, as they attempt to trace a target shape.
Independent claim 3 recites a method for generating an analytical model for tracking or predicting the progression of a neurological impairment in part performing the steps of receiving training data comprising one or more results of a plurality of digital tests of neurological impairment; and training the analytical model using the received training data, thereby generating the analytical mode, wherein the analytical model comprises: an encoder configured to generate a latent representation comprising one or more latent variables; a latent distribution determination module configured to determine a plurality of probability distributions by determining, for each latent variable of the one or more latent variables, a respective probability distribution, the encoder being further configured to generate a sampled latent vector by sampling from the plurality of probability distributions; and a prediction head derived from the sampled latent vector and configured to generate a predicted clinical metric indicative of neurological impairment, and wherein the analytical model is configured to determine or predict a status or progression of the neurological impairment based on the sampled latent vector and the predicted clinical metric generated by the prediction head, wherein the training data comprising the results of the digital test of neurological impairment comprises a plurality of coordinates, each coordinate corresponding to a location of a user's finger on [a display] at a given time, as they attempt to trace a target shape.
These steps of receiving the output of a computerized finger tracing test to generate a predictive analytical model from historical data for forecasting a neurological disease prognosis amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping). Examiner notes that while the technological hardware of a display or a user input as a part of the abstract idea, the actions associated with displaying and receiving the “finger tracking” testing input are considered a part of the abstract idea — “We have recognized that "information as such is an intangible" and that collecting, analyzing, and displaying that information, without more, is an abstract idea. Elec. Power Grp. , 830 F.3d at 1353-54 ; see also id. at 1355 (noting claim requirement of " ‘displaying concurrent visualization” of two or more types of information" was insufficient to confer patent eligibility) Interval Licensing LLC v. AOL, Inc., 896 F.3d 1335, 1344 (Fed. Cir. 2018); see MPEP § 2106.04(a)(2)(I1)(C)).
Dependent claim 2 recites, in part, wherein determining or predicting the status or progression of the neurological impairment comprises comparing values of one or more latent variables of the sampled latent vector with one or more reference values, wherein the reference values are values of the latent variables obtained for one or more reference results of a digital test of neurological impairment.
Dependent claim 12 recites, in part, wherein the neurological impairment is multiple sclerosis.
Dependent claim 4 recites, in part, wherein the analytical model is a model and the encoder is configured to generate, from an input data set comprising a first number of variables, the latent representation of the input data set comprising a second number of latent variables, the second number being less than the first number
Dependent claim 5 recites, in part, wherein: the training data comprises a plurality of input data sets each comprising a first number of variables; and training the analytical model comprises training the encoder to learn a respective latent representation of the plurality of input data sets of the training data, wherein each respective latent representation comprises a second number of latent variables, the second number being less than the first number.
Dependent claim 7 recites, in part, each probability distribution corresponds to a respective dimension in the latent space.
Dependent claim 8 recites, in part, decoder configured to: generate, from the latent representation comprising the second number of latent variables, an output data set comprising a third number of variables, the third number being greater than the second number; or generate, from an input data set comprising the latent variables of the encoder, an output data set that reproduces the input data provided to the encoder.
Dependent claim 9 recites, in part, at least partially retraining the encoder previously trained using different training data and/or wherein training the encoder is performed by transfer learning.
Dependent claim 10 recites, in part, raining the encoder comprises training the encoder as part of an analytical model configured to predict one or more metrics indicative of the status or progression of neurological impairment; and/or training the encoder model comprises training the encoder model in a supervised manner using training data comprising the value of one or more metrics indicative of the status or progression of neurological impairment.
Dependent claim 11 recites, in part, wherein the neurological impairment is multiple sclerosis.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 3 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Examiner notes that while the machine learning autoencoder and the unsupervised training embodiment has been analyzed as an additional element, in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claims directed towards the training and structure of the autoencoder recite both a mental process and a mathematical concept and is not subject matter eligible. The use of a computer to train a model, including an autoencoder, utilizing the training embodiments and model structure offered in the instant specification (see at least ¶ 0124-133) amount to applying data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) and therefore are mere instructions to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014) consistent with Example 47 claim 2. The techniques outlined, and Examiner notes the known methods of training to one of ordinary skill in the art, are mathematical algorithms or mental processes of labeling and fitting data to a particular model representation.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claims 1 and 3 recite a computer with a touchscreen and digital display. The specification does not require specific computer hardware or architecture, but instead relies on generic embodiments (see the instant specification in ¶ 0026 and ¶ 0068). The use of a computer with a touchscreen and digital display, in this case to perform a digital test and process the results, only recites the computer with a touchscreen and digital display as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Claim 4 recites an analytical model is a machine-learning model. Claims 6 and 8 recite a machine-learning model is a variational autoencoder comprising the encoder; and the encoder has been trained or is trained in an unsupervised manner as part of the variational autoencoder. The specification provides that the machine learning model is a processor or computer system with a particular logic algorithm (here, a variational autoencoder) built on training data utilizing unsupervised learning methods (see the instant disclosure in ¶ 0036, ¶ 0124-128, and ¶ 0215-220). The use of an analytical model is a machine-learning model, in this case to , only recites the analytical model is a machine-learning model as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claims 1 and 3 recite a computer with a touchscreen and digital display. Claim 4 recites an analytical model is a machine-learning model. Claims 6 and 8 recite a machine-learning model is a variational autoencoder comprising the encoder; and the encoder has been trained or is trained in an unsupervised manner as part of the variational autoencoder.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1-12 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-12 are rejected under 35 U.S.C. 103 as being unpatentable over Creagh, The Development of Digital Biomarkers for Multiple Sclerosis from Remote Smartphone- and Smartwatch-Based Assessments, Doctorate Degree Thesis for St. Cross College University of Oxford (2020)[hereinafter Creagh] in view of Stettler, Optimize What You Can Predict: Model-Based Optimization Using Variational Auto-Encoders, Unit8 (July 2, 2021).
As per claim 1, Creagh teaches on the following limitations of the claim:
a computer-implemented method of tracking or predicting the progression of a neurological impairment or other disease in a subject, the computer-implemented method comprising the steps of is taught in the § 5.1 Introduction on p. 80-82, § 9.2.3 Improving Severity Outcome Estimates on p. 208, and (teaching on training and utilization of a machine learning model for analyzing Draw-A-Shape test data for determining a patient's multiple sclerosis prognosis)
extracting feature data from results of a digital test of neurological impairment performed by the subject is taught in the § 3.2 Building a modular, scalable and parallelizable feature extraction pipeline on p. 29-30 and § 5.3.2.5 Spatiotemporal Features on p. 87-88 (teaching on collecting spatiotemporal patient feature data from a Draw-A-Shape tracing test from a user device)
by using an analytical model comprising an encoder configured to generate a latent representation comprising one or more latent variables; and is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208 and § 5.3.3.4 Model Evaluation on p. 91 (teaching on a diagnostic or prognostic determination algorithm utilizing an encoder trained with a supervised technique and a proposed alternate embodiment including an autoencoder, both for determining the learned representations between the input variables in the embedded latent space)
configured to generate a predicted clinical metric indicative of neurological impairment is taught in the (teaching on diagnostic or prognostic determination algorithm utilizing an encoder wherein the feature selection is based on a predictive value comparisons (treated as comparing a variable to a reference value))
determining or predicting the status or progression of the neurological impairment is taught in the § 5.3.3.4 Model Evaluation on p. 91, Table 5.2: Comparison of top DaS features between dominant and non-dominant handed models on p. 94, and § 5.5 Discussion on p. 100 (teaching on diagnostic or prognostic determination algorithm utilizing an encoder wherein the feature selection is based on a predictive value comparisons (treated as comparing a variable to a reference value))
wherein the results of the digital test of neurological impairment comprises a plurality of coordinates, each coordinate corresponding to a location of a user's finger on the touchscreen display of an electronic device at a given time, as they attempt to trace a target shape is taught in the § 3.2 Building a modular, scalable and parallelizable feature extraction pipeline on p. 29-30, Figure 5.2: Example illustrations of figure-8-shapes drawn by HC, nPwMS, and aPwMS subjects on p. 86, and § 5.3.2.5 Spatiotemporal Features on p. 87-88 (teaching on collecting spatiotemporal patient feature data from a Draw-A-Shape tracing test from a user device wherein the test determines point locations as a patient traces a shape on the electronic device)
Creagh fails to teach the following limitation; Stettler however does disclose:
a latent distribution determination module configured to determine a plurality of probability distributions by determining, for each latent variable of the one or more latent variables, a respective probability distribution, the encoder being further configured to generate a sampled latent vector by sampling from the plurality of probability distributions; and is taught in the § (Variational) Auto-encoders And Manifold Learning (teaching on a multi-head variational encoder for image analysis wherein the model determined a probability distribution for each latent variable to generate a sampled latent vector)
a prediction head derived from the sampled latent vector is taught in the § Multi-Head Training (teaching on the multi-head variational encoder wherein each sampled latent vector head maps input data into multiple, varied predictive latent distributions)
based on the sampled latent vector and the predicted clinical metric generated by the prediction head is taught in the § Changing Digits and the Introduction (teaching on the multi-head variational encoder determining an outcome prediction classification)
One of ordinary skill in the art at the effective filing date of the invention would combine the autoencoder of Creagh with the multi-head variational encoder of Stettler with the motivation of easing the “information bottleneck effect that forces the auto-encoder to learn a meaningful, low-dimensional representation of the data space” (Stettler in the § (Variational) Auto-encoders And Manifold Learning).
As per claim 2, the combination of Creagh and Stettler discloses all of the limitations of claim 1. Creagh also discloses the following:
the computer-implemented method according to claim 1, wherein determining or predicting the status or progression of the neurological impairment comprises comparing values of one or more latent variables of the sampled latent vector with one or more reference values, wherein the reference values are values of the latent variables obtained for one or more reference results of a digital test of neurological impairment is taught in the § 5.3.3.4 Model Evaluation on p. 91, Table 5.2: Comparison of top DaS features between dominant and non-dominant handed models on p. 94, and § 5.5 Discussion on p. 100 (teaching on model feature selection based on a predictive value comparisons (treated as comparing a variable to a reference value) of a feature for predicting the patient's multiple sclerosis (treated as synonymous to a neurological impairment) disease state)
As per claim 12, the combination of Creagh and Stettler discloses all of the limitations of claim 1. Creagh also discloses the following:
the computer-implemented method according to claim 1, wherein the neurological impairment is multiple sclerosis is taught in the § 5.3.3.4 Model Evaluation on p. 91, Table 5.2: Comparison of top DaS features between dominant and non-dominant handed models on p. 94, and § 5.5 Discussion on p. 100 (teaching on model feature selection based on a predictive value comparisons (treated as comparing a variable to a reference value) of a feature for predicting the patient's multiple sclerosis disease state)
As per claim 3, Creagh teaches on the following limitations of the claim:
a computer-implemented method of generating an analytical model for tracking or predicting the progression of a neurological impairment, the computer-implemented method comprising is taught in the § 5.1 Introduction on p. 80-82, § 9.2.3 Improving Severity Outcome Estimates on p. 208, and (teaching on training and utilization of a machine learning model for analyzing Draw-A-Shape test data for determining a patient's multiple sclerosis prognosis)
receiving training data comprising the results of a plurality of digital tests of neurological impairment; and is taught in the § 3.2 Building a modular, scalable and parallelizable feature extraction pipeline on p. 29-30 and § 5.3.2.5 Spatiotemporal Features on p. 87-88 (teaching on collecting spatiotemporal patient feature data from a Draw-A-Shape tracing test from a user device as training data for a diagnostic or prognostic determination algorithm)
training the analytical model using the received training data, thereby generating the analytical model, wherein the analytical model comprises: is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208 and § 5.3.3.4 Model Evaluation on p. 91 (teaching on training the diagnostic or prognostic determination algorithm utilizing an encoder trained with a supervised technique and a proposed alternate embodiment including an autoencoder, both for determining the learned representations between the input variables in the embedded latent space)
an encoder configured to generate a latent representation comprising one or more latent variables is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208 and § 5.3.3.4 Model Evaluation on p. 91 (teaching on a diagnostic or prognostic determination algorithm utilizing an encoder trained with a supervised technique and a proposed alternate embodiment including an autoencoder, both for determining the learned representations between the input variables in the embedded latent space)
and configured to generate a predicted clinical metric indicative of neurological impairment, and is taught in the § 5.3.3.4 Model Evaluation on p. 91, Table 5.2: Comparison of top DaS features between dominant and non-dominant handed models on p. 94, and § 5.5 Discussion on p. 100 (teaching on diagnostic or prognostic determination algorithm utilizing an encoder wherein the feature selection is based on a predictive value comparisons (treated as comparing a variable to a reference value))
wherein the analytical model is configured to determine or predict a status or progression of the neurological impairment is taught in the § 5.3.3.4 Model Evaluation on p. 91, Table 5.2: Comparison of top DaS features between dominant and non-dominant handed models on p. 94, and § 5.5 Discussion on p. 100 (teaching on diagnostic or prognostic determination algorithm utilizing an encoder wherein the feature selection is based on a predictive value comparisons (treated as comparing a variable to a reference value))
wherein the training data comprising the results of the digital test of neurological impairment comprises a plurality of coordinates, each coordinate corresponding to a location of a user's finger on the touchscreen display of an electronic device at a given time, as they attempt to trace a target shape is taught in the § 3.2 Building a modular, scalable and parallelizable feature extraction pipeline on p. 29-30, Figure 5.2: Example illustrations of figure-8-shapes drawn by HC, nPwMS, and aPwMS subjects on p. 86, and § 5.3.2.5 Spatiotemporal Features on p. 87-88 (teaching on collecting spatiotemporal patient feature data from a Draw-A-Shape tracing test from a user device wherein the test determines point locations as a patient traces a shape on the electronic device)
Creagh fails to teach the following limitation; Stettler however does disclose:
a latent distribution determination module configured to determine a plurality of probability distributions by determining, for each latent variable of the one or more latent variables, a respective probability distribution, the encoder being further configured to generate a sampled latent vector by sampling from the plurality of probability distributions; and is taught in the § (Variational) Auto-encoders And Manifold Learning (teaching on a multi-head variational encoder for image analysis wherein the model determined a probability distribution for each latent variable to generate a sampled latent vector)
a prediction head derived from the sampled latent vector is taught in the § Multi-Head Training (teaching on the multi-head variational encoder wherein each sampled latent vector head maps input data into multiple, varied predictive latent distributions)
based on the sampled latent vector and the predicted clinical metric generated by the prediction head is taught in the § Changing Digits and the Introduction (teaching on the multi-head variational encoder determining an outcome prediction classification)
One of ordinary skill in the art at the effective filing date of the invention would combine the autoencoder of Creagh with the multi-head variational encoder of Stettler with the motivation of easing the “information bottleneck effect that forces the auto-encoder to learn a meaningful, low-dimensional representation of the data space” (Stettler in the § (Variational) Auto-encoders And Manifold Learning).
As per claim 4, the combination of Creagh and Stettler discloses all of the limitations of claim 3. Creagh also discloses the following:
the computer-implemented method according to claim 3, wherein the analytical model is a machine-learning model and the encoder is configured to generate, from an input data set comprising a first number of variables, the latent representation of the input data set comprising a second number of latent variables, the second number being less than the first number is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208 and § 5.3.3.4 Model Evaluation on p. 91 (teaching on training the diagnostic or prognostic determination algorithm utilizing an encoder trained with a supervised technique and a proposed alternate embodiment including an autoencoder, both for determining the learned representations between the input variables in the embedded latent space - Examiner notes that one of ordinary skill in the art would understand that latent variables in encoders serve to compressing complex data into a lower-dimensional (i.e. "less" number of variables) latent space - see § A.2.1 Principal Component Analysis (PCA) (a linear encoder) on p. 214-215 as evidence)
As per claim 5, the combination of Creagh and Stettler discloses all of the limitations of claim 4. Creagh also discloses the following:
the computer-implemented method according to claim 4, wherein: the training data comprises a plurality of input data sets each comprising a first number of variables; and training the analytical model comprises training the encoder to learn a respective latent representation of the plurality of input data sets of the training data, wherein each respective latent representation comprises a second number of latent variables, the second number being less than the first number is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208 and § 5.3.3.4 Model Evaluation on p. 91 (teaching on training the diagnostic or prognostic determination algorithm utilizing an encoder trained with a supervised technique and a proposed alternate embodiment including an autoencoder, both for determining the learned representations between the input variables in the embedded latent space - Examiner notes that one of ordinary skill in the art would understand that latent variables in encoders serve to compressing complex data into a lower-dimensional (i.e. "less" number of variables) latent space - see § A.2.1 Principal Component Analysis (PCA) (a linear encoder) on p. 214-215 as evidence)
As per claim 6, the combination of Creagh and Stettler discloses all of the limitations of claim 4. Creagh also discloses the following:
the computer-implemented method according to claim 4, wherein: the machine-learning model is a variational autoencoder comprising the encoder; and the encoder has been trained or is trained in an unsupervised manner as part of the variational autoencoder is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208 (teaching on a prognosis determination algorithm with a proposed alternate embodiment including an autoencoder for determining the learned representations between the input variables in the embedded latent space via an unsupervised training process)
As per claim 7, the combination of Creagh and Stettler discloses all of the limitations of claim 6. Creagh also discloses the following:
the computer-implemented method according to claim 6, wherein: each probability distribution corresponds to a respective dimension in the latent space is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208, § 5.3.3.2 Statistical Analysis on p. 89, and § 5.3.3.4 Model Evaluation on p. 91 (teaching on training the diagnostic or prognostic determination algorithm utilizing an encoder trained with a supervised technique and a proposed alternate embodiment including an autoencoder, both for determining the learned representations between the input variables in the embedded latent space via a probabilistic/Gaussian distribution encoding)
As per claim 8, the combination of Creagh and Stettler discloses all of the limitations of claim 6. Creagh also discloses the following:
the computer-implemented method according to claim 6, wherein the variational autoencoder further comprises a decoder configured to: generate, from the latent representation comprising the second number of latent variables, an output data set comprising a third number of variables, the third number being greater than the second number; or generate, from an input data set comprising the latent variables of the encoder, an output data set that reproduces the input data provided to the encoder is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208 (teaching on a prognosis determination algorithm with a proposed alternate embodiment including an autoencoder for determining the learned representations between the input variables in the embedded latent space wherein an autoencoder compress (encodes) input data into a compact representation (latent space) and then reconstruct (decodes) the input data provided to the encoder)
As per claim 9, the combination of Creagh and Stettler discloses all of the limitations of claim 3. Creagh also discloses the following:
the computer-implemented method according to claim 3, further comprising: at least partially retraining the encoder previously trained using different training data and/or wherein training the encoder is performed by transfer learning is taught in the § 9.2.3 Improving Severity Outcome Estimates on p. 208, § 5.3.3.4 Model Evaluation on p. 91, and 7.2.2 Deep Transfer Learning on p. 143-144 (teaching on training, via a deep transfer learning technique, the diagnostic or prognostic determination algorithm utilizing an encoder trained with a supervised technique and a proposed alternate embodiment including an autoencoder)
As per claim 10, the combination of Creagh and Stettler discloses all of the limitations of claim 3. Creagh also discloses the following:
the computer-implemented method according to claim 3, wherein: training the encoder comprises training the encoder as part of an analytical model configured to predict one or more metrics indicative of the status or progression of neurological impairment; and/or training the encoder model comprises training the encoder model in a supervised manner using training data comprising the value of one or more metrics indicative of the status or progression of neurological impairment is taught in the § 3.9 Deep Learning and Neural Networks on p. 56-58 (teaching on a prognosis determination algorithm utilizing an encoder trained with a supervised technique both for determining the learned representations between the input variables in the embedded latent space)
As per claim 11, the combination of Creagh and Stettler discloses all of the limitations of claim 10. Creagh also discloses the following:
the computer-implemented method according to claim 10, wherein the neurological impairment is multiple sclerosis is taught in the § 5.3.3.4 Model Evaluation on p. 91, Table 5.2: Comparison of top DaS features between dominant and non-dominant handed models on p. 94, and § 5.5 Discussion on p. 100 (teaching on model feature selection based on a predictive value comparisons (treated as comparing a variable to a reference value) of a feature for predicting the patient's multiple sclerosis disease state)
Response to Arguments
Applicant's arguments filed 07 April 2026 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant first asserts that the claims do not recite a mental process as it contains limitations that are not practically performed mentally. While Examiner agrees that there are mathematical representations that are challenging to perform on pen and paper, the claims still recite both the judicial exceptions of (1) MPEP § 2106.04(a)(2)(I)(A) citing the abstract idea grouping for mathematical concepts for mathematical relationships and (2) MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982). Merely reciting an AI or machine learning does not preclude the claim from reciting a judicial exception.
Next, Applicant asserts that the instant claims does not recite a method of organizing human activity as the claims do not contain limitations for a human to follow nor involve social activities, teaching, or interpersonal interactions. Examiner disagrees. Following a mathematical model applied to patient data for determining a progression or status of a neurological impairment by a doctor for a patient is the social activity of diagnostic medicine.
Next, Applicant asserts that under Step 2A Prong 2, the claims amount to a particular technical solution to the problem of conventional autoencoders. An improvement to the abstract idea of the design of a mathematical model of an encoder to improve the model accuracy does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”). There is no indication in the instant disclosure that the involvement of a computer assists in improving the technology for the outlined problem statement. Here, the improvement is to XXX. The instant application and claim language fail to detail how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient.
Applicant’s assertion that the instant claims are similar to Ex parte Desjardins is not persuasive. Desjardins was considered an improvement to the creation of data structures for preserving feature weights of a model, improving the performance of the computer. The purported improvement here is to the algorithmic structure of the model itself, and therefore does not result in improved performance of the computer hardware as required in MPEP § 2106.05(a)(II).
Applicant’s arguments filed 07 April 2026 with respect to 35 USC § 103 have been considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Stettler, as per the rejection above.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
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