DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 16 January, 2026.
Claims 1, 16 and 18 have been amended.
Claims 1 - 13 and 16 - 20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The Claims are Directed to Non-Statutory Subject Matter
Claim 16 is rejected under 35 U.S.C. 101 because the claims are not directed to one of the statutory categories (process, machine, manufacture or composition). Claim 16 is directed to “a computer program product”; however, a computer program product may reasonably be construed to include signals or carrier waves, which are not directed to one of the statutory categories (process, machine, manufacture or composition) and is non-statutory. The specification discloses that machine-readable instructions may be stored using “any suitable computer readable storage device or media”. In order to overcome this rejection, applicant may amend Claim 16 to positively recite a “non-transitory” computer program product (as is recited in Claim 17).
The Claims are Directed to a Judicial Exception without Significantly More
Claims 1 - 13 and 16 - 20 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), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
Claim 1 is representative. Claim 1 recites:
A method comprising:
receiving input action frequency information associated with a user providing user input via a user input device;
applying a machine learning algorithm trained on training data correlating historical input action frequency information with historical blood oxygen saturation data to the received input action frequency information to predict a blood oxygen saturation level based at least in part on the received input action frequency information;
generating display information based at least in part on the predicted blood oxygen saturation level; and
presenting the display information.
Claim 16 recites a computer program product, and Claim 18 recites a system that executes the steps of the method recited in Claim 1.
STEP 1
The claims are directed to a system, and a method which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea including:
receiving input action frequency information associated with a user providing user input;
predict a blood oxygen saturation level based at least in part on the received input action frequency information.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion. The claims recite receiving input action frequency information for a user, for example a keystroke frequency, and predicting the user’s oxygen saturation level based on the information. The predicted oxygen saturation level may be determined as a quantitative value (i.e. 98%) or a qualitative value (low, medium, high, etc.). The specification discloses that the user’s oxygen saturation level “can be inferred from the key stroke frequency” (@ 0018 as published). The user’s oxygen saturation level may be predicted by “applying the input action frequency to a predetermined formula” . . . “or to a machine learning algorithm trained on training data.” (@ 0128) As an example, the specification discloses that a user with a low input action frequency may be indicative of a user’s lethargy, which can be an indicator of possibly low blood oxygen saturation. A user’s blood oxygen saturation level can be predicted by comparing the currently received input action frequency with a baseline minimum threshold established for different input action frequencies. (@ 0129) These are judgements and comparisons that can be made mentally. Analyzing input action frequency measures to predict blood oxygen saturation is a process that, except for generic computer implementation steps, can be performed in the human mind.
Collecting information, including when limited to particular content, is within the realm of abstract ideas, and analyzing information by steps people go through in their minds, or by mathematical algorithms, and displaying certain results, without more, are mental processes within the abstract idea category. Merely presenting the results of abstract processes is abstract as an ancillary part of such collection and analysis (Electric Power Group v. Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016).
As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims are directed to managing sleep-related disorders using oxygen saturation. This process is similar to “a mental process that a neurologist should follow when testing a patient for nervous system malfunctions”; and is process that merely organizes this human activity. This type of activity, i.e. managing sleep-related disorders, includes conduct that would normally occur when managing a patient’s particular sleep disorder. For example, it is routine in medicine for a patient to visit specialized caregivers to identify sleep-disorders. As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
The claims, as illustrated by Claim 1, also recite limitations that encompass an abstract idea within the mathematical formula or relationship grouping. The claims recite predicting a user’s blood oxygen saturation by applying an undisclosed, but predetermined formula, or generic machine learning algorithm to input action frequency information. As such, the claims recite a mathematical formula or relationship.
STEP 2A PRONG TWO
The claims recite limitations that include additional elements beyond those that encompass the abstract idea above including:
a user input device;
applying a machine learning algorithm trained on training data correlating historical input action frequency information with historical blood oxygen saturation data to the received input action frequency information to predict
generating display information based at least in part on the predicted blood oxygen saturation level; and presenting the display information.
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05)
The user input device and trained machine learning algorithm are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using a generic computer component – i.e. a keyboard or touch screen. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. Displaying the results of the abstract process using conventional devices is an insignificant extra-solution activity that does not improve the computer itself, or any other technology, nor does the display of results provide a meaningful limitation beyond generally linking the abstract idea to a particular technological environment.
In particular, the claims recite a machine learning algorithm trained on training data. The algorithm merely applies established methods of machine learning to an abstract diagnostic process in a new data environment – i.e. applying a trained model to the input action frequency information. The specification teaches that the algorithm may be trained to predict a blood oxygen saturation level using the input action frequency information. The trained algorithm correlates historical blood oxygen saturation levels with historical input action frequency information, at different input action frequency threshold levels. (@ 0128 – 0129)
Nonetheless, machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices does not provide a practical application of the abstract SpO2 prediction process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
As such, nothing in the claim recites specific limitations directed to an improved technology or technological process. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception by use of conventional computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a generic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claim do not integrate the abstract SpO2 prediction process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract SpO2 prediction process. Generating and displaying information is a conventional technique. For example, the specification discloses that information can be displayed in a visual format such as a number indicating the predicted saturation, on a generic user device. Displaying the results of the abstract process is an ancillary part of the abstract process itself as in Electric Power Group. The trained machine learning algorithm employed by the claims are disclosed at a high level of generality. Such algorithms of a generic kind are purely conventional as in Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025).
The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure (i.e. a user input device). Each of the above components are disclosed in the specification as being purely conventional and/or known in the industry. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting well-understood, routine and conventional computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not provide significantly more than the abstract SpO2 prediction process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
further limiting the input action to keystrokes (Claim 2);
further limiting the display information (Claim 5);
further limiting the period of time for measuring and predicting (Claims 9, 10);
those that recite additional abstract ideas such as:
determining input action frequency based on user input signals (Claim 3);
receiving and analyzing sensor data to determine input action frequency (Claim 4);
determining that the predicted SpO2 is below a threshold and generate a sleep apnea inference in response (Claim 6);
determining an effectiveness score based on received/monitored SpO2 sensor data (Claim 8);
determining a baseline for the user and comparing received information to the baseline (Claim 11);
selecting and generating a corrective action below the baseline; (Claim 12);
those that recite well-understood, routine and conventional activity or computer functions such as:
dynamic processing (Claim 7);
those that recite insignificant extra-solution activities such as:
generating a vibration using a tactile feedback device (Claim 13);
or those that are an ancillary part of the abstract idea.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. These elements merely narrow the abstract idea, recite additional abstract ideas, or append conventional activity to the abstract process. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 16 – 20 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
The Prior Art
The prior art does not teach or suggest predicting a blood oxygen saturation level based on a received input action frequency. For example, de Jong et al. teaches receiving a typing speed of a user and predicting mental fatigue of the user based on the typewriting dynamic changes over time. However, de Jong does not suggest predicting blood oxygen saturation based on typewriting speed changes over time.
Similarly, US PGPUB 2008/0195980 A1 to Morris teaches determining a user’s mood based on measuring typing speed, and other characteristics of the user, and determines a corrective action (i.e. therapy).
Response to Arguments
Applicant’s arguments filed 16 January, 2026, with respect to the U.S.C. §112 Rejection, and the amendment, have been fully considered and are persuasive. The rejection has been withdrawn.
Applicant's arguments filed 16 January, 2026, with respect to the U.S.C. §101 Rejection, have been fully considered but they are not persuasive. Applicant does not address the rejection of Claim 16.
Applicant argues that “applying a machine learning algorithm trained on training data . . .” is not an abstract idea. Examiner agrees. Similarly, Applicant asserts that the recited machine learning algorithm integrates, and recites significantly more than any abstract idea. Examiner disagrees.
Just as “applying an abstract idea on a programmed computer” does not render an abstract idea patent eligible; so to, merely applying an abstract idea on a trained machine learning algorithm – that is applying the machine learning algorithm in a new data environment - does not render the claim patent eligible (see Recentive Analytics, Inc.).
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPUB 2017/0265817 A1 to Takamatsu et al. discloses a system and method for predicting blood oxygen concentration based on biological information of a user and a machine learning algorithm.
US PGPUB 2021/0124427 A1 to Choate discloses a system and method for predicting health symptoms by correlating a user’s typing speed with health values such as heart rate and pulse using a machine learning model.
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 of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
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/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 13 February, 2026