DETAILED ACTION
This office action is based on the claim set submitted and filed on 06/02/2026.
Claims 1, 17-18 and 20 have been amended.
Claim 14 has been canceled.
Claim 21 is new.
Claims 1-13 and 15-21 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 § 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.
Claim(s) 12 and 15 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
In order to satisfy the written description requirement, the specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See MPEP 2161.01(1). However, generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed, and even original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function, See MPEP 2161.01(1) citing in part Ariad, 598 F.3d at 1349 ("[A]n adequate written description of a claimed genus requires more than a generic statement of an invention's boundaries.").
Specifically, with regard to computer-implemented functional claims, the specification must provide a disclosure of the computer and the algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, including how to program the disclosed computer to perform the claimed function. MPEP 2161.01(1).
Claim 12, recites “calculating a first biological rate of change between a previously determined third predicted age and the previously determined first predicted age;
comparing the first biological rate of change to the second biological rate of change;
based on the second biological rate of change being less than the first biological rate of change, providing a first notification to the user;
based on the second biological rate of change being greater than the first biological rate of change, providing a second notification to the user.”, for which the subject matter of the limitation 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, at the time the application was filed, had possession of the claimed invention. As best understood, it appears that there is no support for the underlined recitation in the original disclosure of the present application for this limitation. As described in applicant’s specification [0085] “if the biological age is above a recommended age for a preventative medical test, a recommendation may be provided to the user”, [0086-0088], the description discloses “a first age gap may be determined to be 2. If a second age gap determined a year later is 2.5, then the age gap rate of change is 0.5 per year. If the second age gap was determined to be 1.9, then the age gap rate of change is -0.1 per year”, “a biological rate of change can be calculated between a previously determined first predicted age and the second predicted age, and a comparison can be provided between the biological rate of change and a corresponding chronological rate of change. When the biological rate of change is greater than the chronological rate of change, this indicates a positive age gap rate of change, and a notification or recommendation can be provided”, where nothing is describing provide a notification when a second biological rate is less than first biological rate of change and the notification is generated based on comparison of biological rate of change being greater than chronological rate of change, and [0095] describes “if an age gap between the predicted age, e.g., the biological age, and the actual age, e.g., chronological age of the user, is above a threshold, then a trigger may occur to provide a health indicator”. There is not explicit disclosure as filed describing features underlined and as claimed.
Claim 15, recite “receiving candidate data having a first plurality of records;
forming a second plurality of records from the first plurality of records by including in the second plurality of records only those records corresponding to healthy individuals;
forming a third plurality of records from the second plurality of records by taking a random sampling from the second plurality of records;
training the machine learning model on a subset of the candidate data corresponding to the third plurality of records”, for which the subject matter of the limitation 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, at the time the application was filed, had possession of the claimed invention. As best understood, it appears that there is no support for the underlined recitation in the original disclosure of the present application for this limitation. As described in applicant’s specification [0020] describes “The subset of data from the study participants can be further limited into a training set and a validation set”, [0021] describes “the prefiltering is performed to indicate that the training set more likely than not constitutes healthy individuals. If one presumes that healthy individuals age biologically at the about same rate as their chronological age, then the trained model can be used to compare a user's physiological signal data to the trained model to determine a prediction of age as compared to a group of healthy individuals”, [0053] describes “the subset of data derived from the candidate data at block 202 may be randomly split into training data at block 218 and validation data at block 220”, and [0092], the description discloses “The machine learning model may be trained, for example, using a subset of data from participants in a health study”. There is not explicit disclosure as filed describing the underlined features as claimed.
The examiner takes the position that with respect to these limitations or features of the claims, the specification fails to provide an adequate written description of the invention to an extent that would sufficiently show that applicant was in possession of an invention that could operate as claimed. Simply disclosing a vague description, without actually explaining how to perform the function(s) claimed, results in a written description problem under 112(a). The examiner has no idea how applicant actually contemplated doing these steps because nothing is disclosed other than the broad disclosure of the specification as mentioned above.
Therefore, applicant has failed to show the actual subject matter in their possession at the time of the invention in a way sufficient to reasonably convey to one skilled in the relevant art that applicant had possession of the claimed invention at the time the application was filed. Therefore, these limitations of the claims are considered to be new matter. Appropriate correction is required.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1-17 are drawn to a method, Claim 18-19 are drawn to a system/device, and Claim 20 is drawn to an art of manufacturer, and each of which is within the four statutory categories (i.e., a machine and a process). Claims 1-20 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for identifying physiological state of an individual. Collecting physiological parameters to determine physiological or biological state of an individual is/are a step that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites the steps of:
“receiving, at a wearable device, data from a sensor of the wearable device, the data indicating physiological characteristics of a user of the wearable device;
applying the data to a machine learning model to predict an age of the user, the machine learning model having been trained based on physiological information from a subset of user data selected based on one or more characteristics indicating that the subset of user data aligns to a cohort of users that meets a desired characteristic or condition;
determining a predicted age of the user based on an output of the machine learning model;
triggering an indicator on the wearable device or a device in communicative contact with the wearable device, the triggering being based on the predicted age of the user or a difference between predicted age and a chronological age of the user”.
Independent Claims 18 and 20 recite similar steps as in Claim 1.
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper, reciting an abstract idea for Mental Process, but for the recitation of generic computer components. For example, the limitations encompass a user the ability to collect physiological characteristics of an individual, apply it to a physiological information model, and determine an estimated age of the individual based on the physiological model and compare it to a chronological age, which is a step that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper but other than the mere nominal recitation of "wearable device, sensor, machine learning", to implement the abstract idea which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “wearable device/device, sensor, machine learning, processor(s), memory, non-transitory computer-readable medium” that iteratively takes input data and analyzes said data to determine an output performing generic computer functions for estimating biological age and trigger[ing] an indicator on the device such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f) and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these 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. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). 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 and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-13, 15-17, 19 and 21, include all of the limitations of claim(s) 1 and 18, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 2, 5, 7-9, 13, and 21, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process. The claims are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible and cannot provide an inventive concept ("significantly more").
As for claims 3-4, 6, 15-17, and 19, the claim(s) recites limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept. This judicial exception is not integrated into a practical application. In particular, the claim(s) recite additional elements such as “wearable device/device, photoplethysmography sensor/electrocardiogram sensor, sensor, machine learning model” recited in the claim(s) at a high level to perform the claims steps (e.g., train[ing]). These additional elements have been interpreted to be computing components with a general - purpose processor that it amounts to no more than mere instructions to perform the steps of the claim(s), e.g., training the machine learning model, such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). Thus, the judicial exceptions recited in claims is/are not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claim 10, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process along with Mathematical Concepts “calculating...”. The claims are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 11, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process along with method of Organizing Human Activity “providing a notification...”. The claims are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claim 12, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human interaction, reciting an abstract idea for method of Organizing Human Activity along with Mathematical Concepts “calculating...”. The claims are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-9, 12, 15-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tavakoli et al. (US 2022/0031181 A1- “Tavakoli”)
Regarding Claim 1 (Currently Amended), Tavakoli teaches a method comprising:
receiving, at a wearable device, data from a sensor of the wearable device, the data indicating physiological characteristics of a user of the wearable device; Tavakoli discloses a system with a wearable physiological monitor comprising sensors to capture physiological data provide continuous collection and monitoring of physiological data providing a wearable health or fitness of a user (Tavakoli: [Fig. 1, 2], [0034-0035], [0055], [0153])
applying the data to a machine learning model to predict an age of the user, the machine learning model having been trained based on physiological information from a subset of user data selected based on one or more characteristics indicating that the subset of user data aligns to a cohort of users that meets a desired characteristic or condition Tavakoli discloses applying the physiological monitoring sample to trained machine learning models tested over a wide range of users such that data of objective data, heart pulses, may be acquired for a cohort of users and separating or filtering the data used to construct a predictive model for individual user objective measure such as predicting age of the individual user. Tavakoli describes the data used for training a predictive machine learning model for an individual measure filtered or extracted form a cohort data with the similar measures, For Example (Tavakoli: [0013]; Training the autoencoder may include labelling a representation of each one of the plurality of heart pulse samples with a corresponding objective measure of a subject associated with the one of the plurality of heart pulse samples, [0149]; The resulting models can improve on the static analysis techniques described above, and further facilitate dynamic analysis of changes in pulse shape, e.g., from pulse to pulse for a particular user or among pulses for different users, [0150] A machine learning model for analysis of PPG pulse waves may benefit significantly from large data sets. For example, the use of a large data set may facilitate the construction of accurate predictive models for an objective measure of an individual user such as cardiovascular fitness or age, [0151]; a plurality of heart pulse samples. PPG data of heart pulses may be acquired from individuals and separated into a training set and a testing set. As an example, the training and testing sets may be taken from a data set that includes millions of pulse shapes from thousands of individuals aged 18 to 70... The training and testing sets may be labelled with recorded objective measures of every individual represented in the training and testing sets [0154], [0174])
determining a predicted age of the user based on an output of the machine learning model Tavakoli discloses an objective measure using machine learning predictive models for the objective measure such as the user age (Tavakoli: [0150], [0155])
triggering an indicator on the wearable device or a device in communicative contact with the wearable device, the triggering being based on the predicted age of the user or a difference between predicted age and a chronological age of the user Tavakoli discloses the objective measure is displayed on the wearable system and alert to the user based on the objective measure information such that is the estimated measure is high and/or if the difference is above a predetermined threshold and the predicted age is older than the reported age, a warning may be displayed to the user (Tavakoli: [0157], [0161]).
Regarding Claim 2 (Previously presented), Tavakoli teaches the method of claim 1, further comprising:
comparing the predicted age of the user to the chronological age of the user to determine a health indicator; providing the health indicator to the user Tavakoli discloses comparing predicted age of the user to an actual age of the user and provide a recommendation to the user (Tavakoli: [0162]).
Regarding Claim 3 (Original), Tavakoli teaches the method of claim 2, further comprising:
obtaining health or activity information from the user or the wearable device; and correlating the health or activity information with the health indicator Tavakoli discloses obtaining physiological data and contextual data such as exercise and sleep including user input such as user’s emotion and associate the data with the health of the user compared to similar users age (Tavakoli: [0053-0054], [0162]).
Regarding Claim 4 (Original), Tavakoli teaches the method of claim 1, further comprising:
filtering the received data based on sensor data from a second sensor of the wearable device Tavakoli discloses the wearable devices measure the functional biomarkers of aging using Photoplethysmography (PPG) that measures changes of blood volume and provide biological data such as heart rate and in addition the wearable devices include one or more sensors such as an optical sensor, an accelerometer, a gyroscope, a temperature sensor, a galvanic skin response sensor, an environmental sensor, etc., for example, the accelerometer captures sedentary and activity data measuring biological aging and association with activity levels and age acceleration (Tavakoli: [0041], [0055-0056], [0181]).
Regarding Claim 5 (Original), Tavakoli teaches the method of claim 1, wherein the physiological characteristics include at least one of blood flow, heart rate, heart rhythm, heartbeat strength, or heartbeat timing Tavakoli discloses the physiological data collected includes heart rate, beat-to-beat intervals, heart rate variability (HRV), blood volume, etc. (Tavakoli: [0077]).
Regarding Claim 6 (Original), Tavakoli teaches the method of claim 1, wherein the sensor comprises a photoplethysmography sensor or an electrocardiogram sensor Tavakoli discloses the wearable device collects data from photoplethysmography (PPG) and electrocardiogram (ECG) (Tavakoli: [0077]).
Regarding Claim 7 (Original), Tavakoli teaches the method of claim 1, further comprising:
associating a difference in the predicted age and the chronological age of the user to a health condition or a user behavior Tavakoli discloses when predicted age exceeds chronological or actual age of an individual, it indicating the health and fitness level of the individual (Tavakoli: [0161-0162]).
Regarding Claim 8 (Original), Tavakoli teaches the method of claim 7, wherein the health condition or the user behavior comprises smoking, diabetes, or a heart condition Tavakoli discloses estimating an individual age using physiological information to include calorie burn and lifestyle information and information such as heart muscle condition (Tavakoli: [0054], [0147], [0155]).
Regarding Claim 9 (Original), Tavakoli teaches the method of claim 1, wherein the predicted age is a second predicted age, further comprising:
providing a comparison of the second predicted age to a previously determined first predicted age Tavakoli discloses the wearable physiological measurement system may access a user database to obtain previous information for the user where objective measure may be calculated based on prior user data may be used to calculate an age for the user (Tavakoli: [0159], [0162]).
Regarding Claim 12 (Original), Tavakoli teaches the method of claim 10, wherein the biological rate of change is a second biological rate of change, the method further comprising:
calculating a first biological rate of change between a previously determined third predicted age and the previously determined first predicted age; Tavakoli discloses the objective measure may be an age of the user via quantifies PPG pulse shape as one or more features can facilitate side-by-side comparison of different pulses in a manner that permits inferences of an objective measure based on a comparison of multiple pulses from a single user over short and/or long-time intervals, and/or based on measurements where a first, second, or third pulse occurring during this window (or more precisely, two separate instances of the window) may then be recorded and encoded into the latent space as such the second or third pulse may be for a history of the individual user (Tavakoli: [0161], [0167], [0175])
comparing the first biological rate of change to the second biological rate of change Tavakoli discloses comparing the objective measure or predicated age and comparison of multiple pulses from a single user over short and/or long-time intervals, and/or based on measurements where a first, second, or third pulse occurring during this window (or more precisely, two separate instances of the window) may then be recorded and encoded into the latent space as such the second or third pulse may be for a history of the individual user (Tavakoli: [0159], [0161], [0167], [0175])
based on the second biological rate of change being less than the first biological rate of change, providing a first notification to the user; Tavakoli discloses the objective measure is displayed on the wearable system and alert to the user based on the objective measure information such that is the estimated measure is different than a predetermined threshold, a warning may be displayed to the user (Tavakoli: [0157], [0161]).
based on the second biological rate of change being greater than the first biological rate of change, providing a second notification to the user Tavakoli discloses the objective measure is displayed on the wearable system and alert to the user based on the objective measure information such that is the estimated measure is high and/or if the difference is above a predetermined threshold, a warning may be displayed to the user (Tavakoli: [0157], [0161]).
Regarding Claim 15 (Original), Tavakoli teaches the method of claim 1, further comprising: training the machine learning model, comprising:
receiving candidate data having a first plurality of records; Tavakoli discloses receiving the PPG signals and collected over a period of time and determine a feature [first plurality of records] of the objective measure or age (Tavakoli: [Fig. 9, 10], [0147], [0151-0152], [0163])
forming a second plurality of records from the first plurality of records by including in the second plurality of records only those records corresponding to healthy individuals Tavakoli discloses the PPG signals is analyzing the shape of the signal such that indicating a healthy individuals and none healthy individuals PPG signals acquired form a large dataset and determining second feature(s) [second plurality of records] in association with the first feature (Tavakoli: [Fig. 9, 10], [0147-0148], [0151-0152], [0163])
forming a third plurality of records from the second plurality of records by taking a random sampling from the second plurality of records Tavakoli discloses determining third feature(s) [third plurality of records] randomly chosen in association with the second feature (Tavakoli: [Fig. 9, 10], [0147-0148], [0151-0152], [0156], [0163])
training the machine learning model on a subset of the candidate data corresponding to the third plurality of records Tavakoli discloses the features are separated into training sets and training a machine learning model autoencoder to encode the plurality of heart pulse samples into a latent space that differentiates among the plurality of heart samples according to pulse shapes such as using the third feature for training as such the autoencoder may be trained to map pulse data into a latent space with automatically generated features (Tavakoli: [Fig. 9, 10], [0163], [0173]).
Regarding Claim 16 (Original), El Saadawi teaches the method of claim 15, wherein the subset of the candidate data includes sensor data from a first time period for each of the third plurality of records, wherein the subset of the candidate data includes questionnaire response data, wherein at least one of the first time periods occurs before a corresponding date of the questionnaire response data Tavakoli discloses data collected from inputs by a user providing self-reporting and other sources where the user input of a provided questionnaire based on a physiological strain over a period before detecting the user input to the questionnaire (Tavakoli: [0053], [0107], [0159]).
Regarding Claim 17 (Currently Amended), Tavakoli teaches the method of claim 1, wherein the subset of user data corresponds to data of a plurality of users selected from a larger plurality of users, based on one or more common characteristics indicating that the plurality of users are healthy Tavakoli discloses using the PPG signal and analyzing the shape of the signal such that indicating a healthy individuals and none healthy individuals PPG signals acquired form the different individuals are separated into training sets and training a machine learning model autoencoder to encode the plurality of heart pulse samples into a latent space that differentiates among the plurality of heart samples according to pulse shapes (Tavakoli: [0147-0152]).
Regarding Claim 18 (Currently Amended), Tavakoli teaches a device comprising: a memory; and one or more processors configured to:
the claim recites substantially similar limitations to claim 1, as such, are rejected for similar reasons as given above
Regarding Claim 19 (Previously presented), the claim recites substantially similar limitations to claim 2, as such, are rejected for similar reasons as given above.
Regarding Claim 20 (Currently Amended), Tavakoli teaches a non-transitory computer-readable medium storing instructions thereon, which when executed cause one or more processors to perform a process including:
the claim recites substantially similar limitations to claim 1, as such, are rejected for similar reasons as given above.
Regarding Claim 21 (New), Tavakoli teaches the method of claim 1, wherein the desired characteristic or condition corresponds to a condition of being healthy Tavakoli discloses using the PPG signal and analyzing the shape of the signal, e.g., heart pulse samples and pulse shapes, [characteristic or condition] such that indicating a healthy (Tavakoli: [0147-0148], [0151-0152]).
Claim Rejections - 35 USC § 103
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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 10-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Tavakoli et al. (US 2022/0031181 A1- “Tavakoli”) in view of OLIVIER et al. (WO 2024/233664 A2– “Oliver”)
Regarding Claim 10 (Original), Tavakoli teaches the method of claim 1, wherein the predicted age is a second predicted age, further comprising:
calculating a biological rate of change between a previously determined first predicted age and the second predicted age; Tavakoli discloses quantifies PPG pulse shape as one or more features can facilitate side-by-side comparison of different pulses in a manner that permits inferences of an objective measure based on a comparison of multiple pulses from a single user over short and/or long-time intervals, and/or based on measurements where a first and second pulse occurring during this window (or more precisely, two separate instances of the window) may then be recorded and encoded into the latent space as such the second pulse may be for a history of the individual user (Tavakoli: [0167], [0175])
providing a comparison between the biological rate of change and a corresponding chronological rate of change Tavakoli discloses comparing the objective measure or predicated age to a reported objective measure or actual/chronological age for the user (Tavakoli: [0159], [0161]).
However, Tavakoli does not expressly disclose biological rate of change.
Oliver discloses predicting biological age acceleration level (Oliver: [Table 3], [0053], [0072], [0154], [0176])
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Tavakoli incorporate the acceleration level of biological aging feature, as taught by Oliver which help managing biological age acceleration and improve biological age (Oliver: [0073]).
Regarding Claim 11 (Original), the combination of Tavakoli and Oliver teaches the method of claim 10, wherein when the biological rate of change is greater than the corresponding chronological rate of change, providing a notification to the user Tavakoli discloses the objective measure is displayed on the wearable system and alert to the user based on the objective measure information such that is the estimated measure is high and/or above a predetermined threshold and the predicted age is older than the reported age, a warning may be displayed to the user (Tavakoli: [0157], [0161]). Oliver discloses when biological age exceeds chronological age, it indicates accelerated aging such that the biological age has a higher aging rate than then chronological age (Oliver: [0053]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 10, and incorporated herein.
Regarding Claim 13 (Original), the combination of Tavakoli and Oliver teaches the method of claim 10, further comprising:
determining an effectiveness of a previously provided intervention recommendation based on the difference between the biological rate of change and the corresponding chronological rate of change;
based on the difference between the biological rate of change being greater than the corresponding chronological rate of change, providing a different intervention recommendation than the previously provided intervention recommendation
Tavakoli disclose a recommendation to the user based on the objective measure information or an estimate of a cardiovascular age of the user so if the estimate is very high, a recommendation to the user to make a lifestyle change (Tavakoli: [0157]).
Oliver discloses determining the effects of an intervention by assessing the rate of acceleration of biological age over chronological age and displaying a recommended interventions to optimize effectiveness of an intervention for a particular individual (Oliver: [Fig. 2], [0023], [0074], [0076], [0078-0080], [0153]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Tavakoli incorporate the feature assessing interventions impact on the rate of the biological age acceleration, as taught by Oliver which help managing biological age acceleration and improve biological age (Oliver: [0073]).
Response to Amendment
Applicant's arguments filed 06/02/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 112(a) rejection on page 7-9.
On page 8 of the remarks, the Applicant argues “the Specification demonstrates possession of (i) determining multiple biological rates of change from successive predicted ages, (ii) comparing such rates, and (iii) adapting system outputs based on the comparison, as recited in claim 12” and citing Spec’s [0086-0089] supporting the argument. Examiner does not find this argument is persuasive. It is true that the specification may describe calculating multiple biological rates at later time however the claim limitation was specific for performing a calculation to specific sequence of predicated age(s), “between a previously determined third predicted age”.
Moreover, the claim recites “based on the second biological rate of change being less than the first biological rate of change, providing a first notification to the user”, however the specification [0085] clearly recites “if the biological age is above a recommended age for a preventative medical test, a recommendation may be provided to the user...”. In the specification [0086-0089] it describes “When the biological rate of change is greater than the chronological rate of change, this indicates a positive age gap rate of change, and a notification or recommendation can be provided as noted above”, but nothing is describing provide a notification when a second biological rate is less than first biological rate of change. The claim also recites “based on the second biological rate of change being greater than the first biological rate of change, providing a second notification to the user” but it is clearly the notification is generated based on comparison of biological rate of change being greater than chronological rate of change.
On page 9 of the remarks, the Applicant argues “The Specification provides clear support for each of these steps. Firstly, the Specification describes selective filtering of candidate data to isolate records corresponding to healthy individuals ... Secondly, the Specification discloses creating subsets of data through selection and partitioning. It explains that the filtered subset... Thirdly, the Specification discloses training the machine learning model using the resulting dataset... Taken together, these disclosures expressly or inherently describe the sequence recited in claim 15. The Specification therefore conveys possession of the claimed training pipeline,” and citing Spec’s [0020], [0045], [0048-0055], [FIGS. 2A, 2B, and 2C] supporting the argument. Examiner does not find this argument is persuasive. While the specification sections cited by the Applicant describes filtering candite data to isolate records to healthy individuals which similar to forming a second records (healthy records) from the first records (non-healthy records) as such filtering healthy records from the first records leaves the first records with the non-healthy records. Also as described in [0020] “The subset of data from the study participants can be further limited into a training set and a validation set”, and in [0049] “The disease conditions with which to filter may be selected from a list of recognized chronic or acute diseases, and may include...”, in [0050] any medications in use can be cross-referenced with a list of allowable or disallowable medications to determine if the participant should continue to be considered for inclusion in the training data, [0053] describes “the subset of data derived from the candidate data at block 202 may be randomly split into training data at block 218 and validation data at block 220”. However, none of the cited paragraphs specifically describe a further formation of records such as specifically a third record. Moreover, nowhere in the cited section by the Applicant describes that the training data specify a third training dataset for training the machine learning. Even as described in Fig. 2A-C, the drawings may indicate a plurality of training data similar to plurality of candidate data, subset data and validation data but does not point to a third plurality of records.
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 9-13.
On page 10-11 of the remarks, the Applicant argues “Independent claim I recites a method that is rooted in a specific technological environment involving physiological sensors and machine learning systems... This limitation is directed to a specific machine-learning training-data construction and conditioning process that relies on cohort-constrained physiological training data. The Specification explains that candidate data is prefiltered ... to train the model. Spec. [0020], [0045], [0055] ... These steps involve automated device functionality and are not capable of being practically performed mentally. The Specification describes that the system computes a biological age and age gap using learned relationships from physiological signals and triggers notifications on the device. Spec. [002 l], [0083]-[0085]. ... Accordingly, the claims do not recite a judicial exception”, Examiner respectfully disagree. Examiner respectfully asserts that the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. Claim 1, under BRI, recites receiving data from a sensor and applying the data to a machine learning model that have been trained based on physiological information. Nothing in Claim 1 describes a specialized sensor or steps that describes a training process of the machine learning model rather describing using a trained model from information provided. Moreover, no automation is recited in the claim nor the specification, at least the sections argued by Applicant. Similarly, the Applicant argues using a learned relationship which is similar to configuring a generic processor to perform the steps however nowhere in the claims nor the specification describes how the learning/training process other than feeding information to a machine learning model to has been configured to perform the steps.
The claim limitations, given their broadest reasonable interpretation, recite steps, i.e., receiving information of a user to determine age of the user based on the received information and may predict future age, which have been analyzed under Step 2A, Prong One reciting a process for obtaining/collecting, determining, comparing (analyzing) and predicting or provide an opinion, which are steps of observing, evaluating, judgment, and opinion that are citing a process for which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III), but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Mental Process.
On page 11-12 of the remarks, the Applicant argues “even if the claims were found to recite a judicial exception, the claims clearly integrate that exception into a practical application ... As disclosed in the Specification, wearable devices collect photoplethysmography and electrocardiogram signals that capture blood flow and cardiac activity, which are processed for downstream analysis. Spec. [0016], [0079]... The technical improvement achieved by this limitation lies in the transformation of raw physiological sensor signals into a normalized biological age representation based on cohort conditioned training... Accordingly, the claims improve both the functioning of the device and the underlying machine learning process”, Examiner respectfully disagree. As mentioned in the above rejection, the machine learning in the claims and specification is recited at a high level of generality and in an arbitrary form as a tool to perform the claim steps that is/are not configured in a manner other than what any off-the-shelf, commercially available processor is capable of being programmed to do. No where in the claims, specification, or drawings is disclosing a specific algorithm using available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function. The thrust of Applicant's invention is directed to an abstract idea of determining medical service(s) to be offered to a patient through leveraging computing technology and a trained machine learning in a well understood manner.
Similar to the machine learning recitation in the claim, the wearable device recited at a high level of generality to collect signals (i.e., photoplethysmography and electrocardiogram signals) which have been analyzed under Step 2A, Prong Two as an additional element cited as a tool for implementing claim steps that amounts to no more than mere instructions to implement “apply” the exception using a generic computer component and no more than adding the words "apply it" (or an equivalent) with the judicial exception. The claims as amended, under BRI, provide no technical implementation other than using a collected data to be analyzed using an algorithm that may be implemented on a generic computing program but does not describe a particular improvement of computer’s functionality or a technical field, rather using additional elements, “e.g., computer”, to perform the steps abstract idea such as collecting and analyzing data through leveraging computing technology in a well understood manner. Therefore, even when considering the claims additional elements, the claims as a whole, individually and in combination, provide no integration of the abstract ideas into a practical application that no meaningful limits on practicing the abstract idea are introduced, see MPEP 2106. The claims as a whole are therefore directed to an abstract idea.
On page 12 of the remarks, the Applicant argues “The claims are analogous to USPTO Eligibility Example 39 (AI-based anomaly detection) and Example 40 (neural network-based classification) ... Like those examples, the present claims use a specialized training constraint-namely”, Examiner respectfully disagree. Example 39 describes a process of training a neural network using a modified first training set to output a second training set and using the second training set to train the neural network and classify an input based upon a previous training process. Example 39 discloses expanded training set is developed by applying transformation functions on an acquired set of facial images and these transformations can include affine transformations to detect faces in distorted images while limiting the number of false positives used for retraining with an updated or second training set containing the false positives produced after face detection has been performed on a set of non-facial images. In contrast, the claim as amended provides no description of how a machine learning model is trained to provide an improved machine learning or wearable device but only disclosing the combination of applying a trained machine learning model that is trained based on user information to predict age of the user. While it is true that the claims recite a applying the data to machine learning model for carrying out the outcome function, the machine learning model is merely invoked as a tool to implement the steps of the claim.
Similarly, Example 40 is found eligible for solving a technical problem by monitoring network performance in order to analyze any obstruction that hinders the flow of data, resolving network issues, and optimizing network performance. The comparing step recites a specific manner of collecting additional data is used to solve network performance issue and resulting in improved network monitoring. In contrast, the applicant claimed invention is describing collection of a user information using a wearable device to apply it to a trained machine learning model to predict age of the user. Applying a process using algorithm to provide a likelihood output which is a mere mathematical calculation that can be performed by human actor as in Benson. see MPEP 2106.04(a)(2)(III)(c). Therefore, it is respectfully submitted that the claimed invention is not analogous to that of Example 39 nor Example 40.
On page 12-13 of the remarks, the Applicant argues “Even if the claims were deemed to recite an abstract idea and not integrate that idea into a practical application, the claims recite an inventive concept... The Specification describes a detailed and non-conventional data conditioning pipeline involving filtering candidate data based on disease conditions ... The claimed invention is analogous to Enfish, LLC v. Microsoft Corp., where the Federal Circuit held that a specific improvement to the way computers operate is patent eligible ... The invention is also consistent with McRO, Inc. v. Bandai Namco Games America Inc., where rule-based automation was found patent eligible ...”, Examiner respectfully disagree. The claims at issue do not require any nonconventional computer, network, or other components, or even a non-conventional and non-generic arrangement of known, conventional pieces but merely call for performance of the claimed functions on a set of generic computer components. In light of the Alice decision and the guidance provided in the 2019 PEG, the features listed in the claims, are not considered an improvement to another technology or technical field, or an improvement to the functioning of the computer itself.
Furthermore, in Enfish, the claim(s) provided an improvement to a computer function and/or technical field (self-pointing database) reciting a self-referential table for a computer database providing a particular improvement in the computer's functionality that improves the way a computer stores and retrieves data in memory whereas the instant claim(s) and specifications do not recite an improvement to technology, as in Enfish, but to performance of an abstract idea such as collecting information and using algorithm for analyzing data while using well-known computer system and components. Similarly, there is no evidence in Applicant's as-filed disclosure that Applicant's claimed invention is performing functions that previously only humans could perform as in McRO. The Examiner notes that in McRO the as-filed disclosure explicitly described that computers could not previously be programmed to perform the particular type of animation described and that only human animators were previously capable of such animation. Because the claimed invention solved this particular problem, the court found that the claimed invention was an improvement to computer technology. There is no such problem described in Applicant's disclosure.
Therefore, the elements of the instant process, when taken alone, each execute in a manner conventionally expected of these elements. The elements of the instant process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Hence, these additional elements do not add anything significantly more than an abstract idea.
Therefore, the Examiner has addressed the Applicant argument(s) and found this argument is not found to be persuasive. Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments.
Applicant's arguments with respect to the 35 U.S.C. §102/103 rejection page 13-15.
On page 14 of the remarks, the Applicant argues “Tavakoli does not disclose this limitation. Instead, Tavakoli describes training machine learning models using large datasets that broadly include...”, Examiner respectful disagree. Although the Applicant argument is directed to a newly added feature, Examiner has addressed the argued feature as descried in above rejection elaborating on Tavakoli [0149-0154] describing the argued feature.
Therefore, Examiner find the Applicant argument is/are unpersuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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