DETAILED ACTION
This Action is responsive to Claims filed 06/09/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/09/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/10/2026 was filed after the mailing date of the Final Rejection on 03/11/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of the Claims
Claims 1, 3, 6-7, 9, 11, 13, 16-17, and 19 have been amended. Claims 2 and 12 have been cancelled. Claims 1, 3-11, and 13-20 are currently pending.
Response to Arguments
The amendments to Claim 1 has overcome the Objections to informalities.
Applicant's arguments, see Pages 10-16, filed 06/09/2026, regarding the 35 U.S.C. 101 Rejection of Claims 1-20 have been fully considered but they are not persuasive.
The Applicant argues on Page 11, that the claimed limitations are not merely highly general steps of determining distances and selecting a model. The Applicant proceeds to argue, on Page 12, the claimed steps are not merely directed toward improved prediction accuracy or the application of a model on a generic computer. The Applicant proceeds to describe the steps, as recited in the instant Specification Paragraphs [0037]-[0049], as being performed by a “processor,” which performs the generating, normalizing, and clustering of data, as well as the selecting of a model. The Applicant essentially argues the claimed steps are not abstract idea mental process steps performed by generic computer components to be performed on generic models in order to realize improvements to model accuracy, while citing the portions of the instant Specification that directly indicate said interpretation.
The Examiner submits the claim limitations, as presently drafted, when viewed as a whole, represent a set of data manipulation steps, interpretable as abstract idea mental process steps, performed by a generic processor. These steps are applied in order to generically train multiple generic models. The generic process or selects a model, also interpretable as an abstract idea mental process step. The selection is then applied again by acquiring a generic prediction result from the selected generic model. The Examiner submits the training optimization and/or prediction accuracy improvements are results of the aforementioned data manipulation and subsequent selection of the optimal model, rathe than an additional element reciting specific structure or implementation linking the additional element to a specific improvement.
Similarly, analyzing the claims under Step 2B, the additional elements recited in the claims are recited at such a high level of generality that the ordered combination of the steps represents an algorithmic set of data manipulation steps followed by generic “training” and “predicting” steps without significantly more. See the updated 101 Rejection below.
Applicant's arguments, see Pages 16-18, filed 06/09/2026, regarding the 35 U.S.C. 103 Rejection(s) of Claims 1-20 have been fully considered but they are not persuasive.
The Applicant appears to argue against the references individually, in that reference is made to the deficiencies of both cited references Ma and Sturlaugson, without addressing how the prior art as interpreted was applied to Claim 2, which was the source of the amended limitations.
Regarding the combination of the references, the Examiner reiterates the combination as obvious to one of ordinary skill in the art at the time of the Applicant’s , and points to the References not Cited below.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-11, and 13-20 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; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1:
Claims 1 and 3-10 recite a data predicting method, which falls under the statutory category of a process. Claims 11 and 13-20 recite a data predicting apparatus, which falls under the statutory category of a machine.
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “executing a dimensionality reduction analysis on a plurality of feature sets to obtain an analysis result, wherein each of the feature sets comprises a plurality of features;”, “normalizing the feature sets according to the analysis result to generate a plurality of normalized feature sets;”, ”generating a distance relationship of the normalized feature sets, wherein the distance relationship comprises a distance between two of the normalized feature sets;”, “clustering the feature sets according to the distance relationship to generate the data groups, wherein each of the data groups comprises the feature set;”, “determining a plurality of distances between predicting data and the data groups;” and “selecting a first machine learning model corresponding to one of the data groups having a shortest distance with the predicting data from the machine learning models respectively trained through the data groups;” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. These limitations therefore fall within the mental process group.
Executing a dimensionality reduction analysis, normalizing feature data, generating distance relationship, and clustering data are a set of highly generic algorithmic steps practically performed within the human mind or with the aid of pen and paper. Determining a plurality of distances is practically performed within the human mind or with the aid of pen and paper. Selecting a machine learning model is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “a plurality of data groups” are recognized as generic computer components recited at a high level of generality (the Specification does not indicate these elements are different from a typical processing unit). Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitations “A data predicting method”, “a first machine learning model”, and “a prediction result” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional element “a sleep event” is found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional elements “respectively training machine learning models through the data groups;” and “predicting a prediction result corresponding to the predicting data through the selected first machine learning model, wherein the prediction result comprises a sleep event” are found to be mere instructions to apply the abstract idea steps of determining and selecting (See MPEP 2106.05(f)).
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “a plurality of data groups” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements recited in the limitations “A data predicting method”, “a first machine learning model”, and “a prediction result” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional element “a sleep event” is found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional elements “respectively training machine learning models through the data groups;” and “predicting a prediction result corresponding to the predicting data through the selected first machine learning model, wherein the prediction result comprises a sleep event” are found to be mere instructions to apply the abstract idea steps of determining and selecting (See MPEP 2106.05(f)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claim 11.
Claim 11 recites similar limitations to claim 1, with the exception of “A data predicting apparatus, comprising: a memory, storing program code; and a processor, loading the program code for executing:” (generic computer components); therefore, both claims are similarly rejected.
Dependent Claims:
Claim 3 (claim 13) recites refinements to abstract idea mental process steps of claim 2 and abstract ide amental process step “selecting a first principal component from the principal components, and normalizing the feature sets according to the first principal component.”
Claim 4 (claim 14) recites refinements to the data types of claims 2 and 3.
Claim 5 (claim 15) recites refinements to the data types of claims 2 and 3.
Claim 6 (claim 16) recites refinements to the data types of claims 2 and 3.
Claim 7 (claim 17) recites abstract idea mental process step “clustering the feature sets with the smallest distance relationship into one of the data groups according to the distance relationship through a hierarchical clustering.”
Claim 8 (claim 18) recites abstract idea mental process steps “determining a default number of the data groups; determining a cluster distance according to the default number; and clustering the feature sets according to the cluster distance.”
Claim 9 (claim 19) recites pre- or post-solution/WURC activity “transforming a plurality of sensing data into the feature sets, wherein the sensing data is time- dependent data;” (See MPEP 2106.05(g) and MPEP 2106.05(d)(II)) and instructions to apply the abstract idea “training a corresponding machine learning model basing on the feature sets or the sensing data corresponding to each of the data groups.”
Claim 10 (claim 20) recites additional elements generally linking the abstract idea to a specific data type or field of use.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3, 9-11, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (Combined unsupervised‑supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea, 2021), hereinafter Ma and Sturlaugson et al. (US 2016/0358099 A1), hereinafter Sturlaugson.
In regards to Claim 1: The present invention claims: “A data predicting method, the data predicting method comprising: executing a dimensionality reduction analysis on a plurality of feature sets to obtain an analysis result, wherein each of the feature sets comprises a plurality of features;” Ma teaches “a multimetric phenotyping framework by combining supervised and unsupervised machine learning” (Abstract). Ma performs dimensionality reduction “The PSG data is very high dimensional with some highly correlated features. Therefore, we extracted a smaller set of new uncorrelated variables from the original PSG data and used it for the cluster analysis (Fig. 7). For this, we used principal component analysis, which is a dimension reduction technique that linearly transforms a number of possibly correlated features into a small number of uncorrelated variables called principal components.” (Page 12).
“generating a distance relationship of the normalized feature sets, wherein the distance relationship comprises a distance between two of the normalized feature sets; clustering the feature sets according to the distance relationship to generate the data groups, wherein each of the data groups comprises the feature set;” Ma teaches “The DPGMM was used to cluster the patients, where each cluster was identified as a distinct phenotype. The DPGMM is a Bayesian nonparametric clustering model that is an extension of the Gaussian mixture model using the Dirichlet process prior on the mixing proportions. While clustering methods previously used for PSG-based phenotyping such as K-Means clustering require the number of clusters to be set in advance, the DPGMM infers the number of clusters that best fits the training dataset within a Bayesian statistical framework.” (Page 12, mapping the clustering of data to necessitate a distance relation between data points or feature sets).
“determining a plurality of distances between predicting data and the data groups” Ma teaches “a multimetric phenotyping framework by combining supervised and unsupervised machine learning” (Abstract) that “calculates the cluster assignment probabilities for new patients based on their 43 PSG features (left) by using the trained DPGMM model…” (Page 9, Figure 6). See Ma Table 1 (Page 4) for a list of the extracted features from sensed sleep data, including numerous time-dependent features.
“selecting a first machine learning model corresponding to one of the data groups having a shortest distance with the predicting data…” Ma teaches a random search forest (RSF) and “To overcome this limitation of clustering analysis, we additionally performed prediction analysis which utilizes labels in the training process and thus provides the relationship between the PSG data and comorbidity outcomes. Specifically, we performed survival prediction analysis on the full patient cohort by using the RSF: 43 PSG features (Table 1) were used as the input and the cardio-neuro-metabolic comorbidity outcomes were used as the label” (Page 5).
“and predicting a prediction result corresponding to the predicting data through the selected first machine learning model, wherein the prediction result comprises a sleep event…” Ma teaches “To overcome this limitation of clustering analysis, we additionally performed prediction analysis which utilizes labels in the training process and thus provides the relationship between the PSG data and comorbidity outcomes. Specifically, we performed survival prediction analysis on the full patient cohort by using the RSF: 43 PSG features (Table 1) were used as the input and the cardio-neuro-metabolic comorbidity outcomes were used as the label. The RSF provides the importance of each feature (Fig. 4) in predicting comorbidity risks (fivefold cross-validation concordance index = 0.65, integrated Brier score = 0.13), where features with greater importance can be considered more relevant to the comorbidity outcomes for our patient cohort. Among 43 PSG features, 18 features accounted for 95% of the total importance in predicting comorbidity outcomes (Fig. 4 and Supplementary Table S8). They included features regarding demographic and anthropometric characteristics (age, waist-hip ratio), sleep architecture and quality (the proportion of N3 sleep, REM latency, the Pittsburgh sleep quality index), oxygen desaturation (sleep time spent below 90% oxygen saturation, average oxygen saturation, oxygen desaturation event index, lowest oxygen saturation), respiratory events (supine AHI, hypopnea index, lateral AHI, mixed apnea, REM AHI, AHI, NREM AHI, central apnea), and snoring (number of snoring episodes).” (Page 5).
Ma fails to explicitly teach: “and respectively training the machine learning models through the data groups.” And “…from a plurality of machine learning models;” However, Sturlaugson, in a similar field of endeavor of machine learning classification, teaches evaluating multiple machine learning models in “The same training dataset and evaluation dataset may be used for one or more, optionally all, of the machine learning models 32. Additionally or alternatively, each machine learning model 32 may be tested ( optionally exclusively) with an independent division of the dataset (which may or may not be a unique division for each machine learning model). The experiment module 30 may be configured to train the machine learning model(s) 32 with the respective training dataset(s) (to produce a trained model) and to evaluate the machine learning model(s) 32 with the respective evaluation dataset(s).” ([0036]).
Sturlaugson highlights the difficulty in choosing an optimal machine learning model for a given dataset or output, and a need to directly compare a given set of options ([0004]-[0005]). A cursory search also indicates using multiple machine learning models for different datasets or datatypes would have been known in the art at the time of Ma’s writing (See References not Cited below). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to combine the known methods similar to Sturlaugson’s in implementing multiple models for each data cluster present in Ma in order to determine which model best addresses a given specific environment or problem.
“normalizing the feature sets according to the analysis result to generate a plurality of normalized feature sets;” While neither Ma nor Sturlaugson teach normalization explicitly, Sturlaugson does teach “Machine learning systems 10 may include data preprocessor 24, also referred to as an initial data preprocessor and a global preprocessor. Data preprocessor 24 is configured to prepare the input dataset for processing by the experiment module 30.” The Examiner interprets this broadly given the pervasiveness of normalization in machine learning data processing. It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to apply some kind of normalization in data preparation.
In regards to claim 3: The present invention claims: “wherein the dimensionality reduction analysis is principal components analysis (PCA) or principal co-ordinates analysis (PCoA), the analysis result comprises proportions of a plurality of principal components, and normalizing the feature sets according to the analysis result comprises: selecting a first principal component from the principal components, and normalizing the feature sets according to the first principal component.” Ma teaches “For this, we used principal component analysis, which is a dimension reduction technique that linearly transforms a number of possibly correlated features into a small number of uncorrelated variables called principal components.” (Page 12). See above how normalizing would have been an obvious step in a combination of Ma and Sturlaugson. A cursory search also indicates PCA and normalization should be used in conjunction.
In regards to claim 9: The present invention claims: “transforming a plurality of sensing data into the feature sets, wherein the sensing data is time-dependent data; and training a corresponding machine learning model basing on the feature sets or the sensing data corresponding to each of the data groups.” Sturlaugson teaches “Data analysis problems may be classification problems or regression problems. Data analysis problems may relate to time-dependent data, which may be called sequence data, time-series data, temporal data, and/or time-stamped data. Time-dependent data relate to the progression of an observable (also called a quantity, an attribute, a property, or a feature) in a sequence and/or through time (e.g., measured in successive periods of time).” ([0018]). See subsequent paragraphs and the above rejection of claim 1 for the models being trained on the input data.
In regards to claim 10: The present invention claims: “wherein each of the sensing data is a sensing result of a radar.” Sturlaugson teaches “For example, time-dependent data may relate to the operational health of equipment such as aircraft and their subsystems (e.g., propulsion system, flight control system, environmental control system, electrical system, etc.). Related observables may be measurements of the state of, the inputs to, and/or the outputs of electrical, optical, mechanical, hydraulic, fluidic, pneumatic, and/or aerodynamic components.” (The Examiner contends this disclosure may reasonably include radar output in an aircraft, far example).
In regards to Claims 11-13: Claim 11-13 recite similar limitations to Claims 1-3, with the exception of “A data predicting apparatus, comprising: a memory, storing program code; and a processor, loading the program code for executing:” of Claim 11; therefore, both sets of claims are similarly rejected.
Claim(s) 4-5 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma and Sturlaugson as applied to claim 1 and 11 above, and further in view of Abdi et al. (Principal Component Analysis, 2010), hereinafter Abdi.
While Ma uses PCA, resulting in “Overall, eight principal components explaining up to 70% of the total data variance were used as the input features for the cluster analysis.” (Page 12). The combination of Ma and Sturlaugson fails to explicitly teach the limitations of claims 4-5 (and 14-15):
Claim 4: “wherein the first principal component is a principal component with highest proportion among the principal components.”
Claim 5: “wherein the first principal component is the principal component with the highest proportion or a principal component with second highest proportion among the principal components, a difference between the principal component with the highest proportion and the principal component with the second highest proportion is less than a threshold value.”
However, Abdi, in describing PCA, teaches methods of determining a number of components in Sections 5.3 and 5.3.1. The description of scree or elbow test reasonably reads on a generic recitation of a “highest proportion” among principal components (Claim 4). The description of the Q and W values of Section 5.3.1 reasonably reads on a generic recitation of “a threshold value” when determining when to add more components (Claim 5).
Ma utilizes PCA in their disclosure. It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing, and at the time of Ma’s writing, to use known methods from Abdi in the use of PCA in Ma’s implementation.
In regards to claims 14 and 15: Claims 14 and 15 recite similar limitations to Claims 4 and 5, with the exception of “A data predicting apparatus, comprising: a memory, storing program code; and a processor, loading the program code for executing:” of Claim 11 (presumably, see 112(b) Rejection above); therefore, both sets of claims are similarly rejected.
Claim(s) 6-8 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma and Sturlaugson as applied to claim 1 and 11 above, and further in view of Lu, Xin (Information Mandala: Statistical Distance Matrix With Clustering, 2021), hereinafter Lu.
While a combination of Ma and Sturlaugson reads on a dimensionality reduction of normalized data using PCA, they fail to explicitly teach the limitations of claims 6-7 (and 16-17):
Claim 6: “wherein the distance relationship is a distance matrix, and each element in the distance matrix is a distance between the features in two of the normalized feature sets.”
Claim 7: “clustering the feature sets with the smallest distance relationship into one of the data groups according to the distance relationship through a hierarchical clustering.”
However, Lu, in a similar field of endeavor of data clustering for machine learning, teaches “In machine learning, observation features are measured in a metric space to obtain their distance function for optimization. Given similar features that are statistically sufficient as a population, a statistical distance between two probability distributions can be calculated for more precise learning. Provided the observed features are multi-valued, the statistical distance function is still efficient. However, due to its scalar output, it cannot be applied to represent detailed distances between feature elements. To resolve this problem, this paper extends the traditional statistical distance to a matrix form, called a statistical distance matrix. (Claim 6) The proposed approach performs well in object recognition tasks and clearly and intuitively represents the dissimilarities between cat and dog images in the CIFAR dataset, even when directly calculated using the image pixels. By using the hierarchical clustering of the statistical distance matrix, (Claim 7) the image pixels can be separated into several clusters that are geometrically arranged around a center like a Mandala pattern. The statistical distance matrix with clustering is called the Information Mandala.” (Abstract).
Lu highlights the shortcomings of calculating a distance function on scalar data such as the comparison of two distributions (Abstract). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to use methods known from Lu in comparing feature space distances between clusters to improve the precision of learning.
In regards to claim 8: The present invention claims: “determining a default number of the data groups; determining a cluster distance according to the default number; and clustering the feature sets according to the cluster distance.” Ma teaches “The clustering algorithms used in previous OSA phenotyping studies15–18 required the number of clusters to be manually and potentially subjectively determined. On the other hand, we used DPGMM to cluster OSA patients such that the number of clusters can be inferred from the observed data instead of predetermining it. However, the number of clusters learned from data may change depending on the concentration parameter (a larger concentration parameter more likely yields a higher number of clusters). Although the clustering results were robust to changes in the concentration parameter in our study (Supplementary Table S3), there may be situations where the clustering results may not be as robust. In such cases, the concentration parameter may also be inferred from data by placing a hyperprior on the concentration parameter41.” (Page 9, Ma’s algorithm determines cluster count based on concentration parameter, which affects cluster distance (and therefore counts), and clusters data around said concentration parameter).
In regards to claims 16-20: Claims 16-20 recite similar limitations to Claims 6-10, with the exception of “A data predicting apparatus, comprising: a memory, storing program code; and a processor, loading the program code for executing:” of Claim 11; therefore, both sets of claims are similarly rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rahman et al. (Cluster-based ensemble of classifiers, 2012), teaches that an ensemble of classifiers, trained on individual portions of a dataset, where a result is chosen at the end is known in the art “This paper presents cluster-based ensemble classifier – an approach toward generating ensemble of classifiers using multiple clusters within classified data. Clustering is incorporated to partition data set into multiple clusters of highly correlated data that are difficult to separate otherwise and different base classifiers are used to learn class boundaries within the clusters. As the different base classifiers engage on different difficult-to-classify subsets of the data, the learning of the base classifiers is more focussed and accurate. A selection rather than fusion approach achieves the final verdict on patterns of unknown classes.” (Abstract)
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/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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