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 .
Prosecution History Summary
Claims 1, 8., 12, and 19 are amended.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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) without significantly more.
Subject Matter Eligibility Criteria – Step 1:
The claims recite subject matter within a statutory category as a process (claims 19-20), machine (claims 1-18). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria – Step 2A – Prong One:
Regarding Prong One of Step 2A of the Alice/Mayo test, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system for predicting illness onset, comprising:
-a wearable device comprising one or more sensors configured to acquire physiological data from a user, the physiological data comprising at least motion data associated with one or more arms of the user;
-a user device communicatively coupled with the wearable device; and
-one or more processors communicatively coupled with the wearable device and the user device, wherein the one or more processors are configured to:
-acquire baseline motion data associated with the one or more arms of the user, the baseline motion data acquired via the one or more sensors of the wearable device throughout a first time interval comprising a plurality of days;
-acquire additional motion data associated with the one or more arms of the user, the additional motion data acquired via the one or more sensors of the wearable device throughout a second time interval comprising at least one day that is subsequent to the plurality of days of the first time interval;
-input the baseline motion data and the additional motion data into one or more machine learning models, the one or more machine learning models trained to predict illness onset or recovery based at least in part on a plurality of features associated with movement of the one or more arms of the user within the additional motion data relative to the baseline motion data, the plurality of features comprising a first feature associated with a change in a hand swing movement range of the one or more arms, a second feature associated with a change in a shoulder angle range of the one or more arms, or both, wherein the one or more machine learning models are trained using training data comprising first motion data from a plurality of healthy users and second motion data from a plurality of users experiencing one or more illnesses;
-generate, using the one or more machine learning models, an illness prediction metric based at least in part on the plurality of features within the additional motion data, the illness prediction metric associated with a relative likelihood that the user is experiencing, will experience, or is recovering from the one or more illnesses; and
-transmit, to the wearable device, the user device, or both, an instruction configured to cause a graphical user interface (GUI) to display information associated with the illness prediction metric.
Examiner states submits that the foregoing underlined limitations constitute: “certain methods of organizing human activity” because managing a user’s movement to determine illness is managing people for chronic illness (i.e. managing behavior of people).
Furthermore, the foregoing underlined limitation constitute: a “mental process” because analyzing motion data to determine onset of illness can all be performed in the human mind.
Accordingly, the claim recites at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2A – Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether
the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP
§$2106.04(1D(A)(2), it must be determined whether any additional elements in the claim beyond
the abstract idea integrate the exception into a practical application in a manner that imposes a
meaningful limit on the judicial exception. The courts have indicated that additional elements
merely using a computer to implement an abstract idea, adding insignificant extra solution
activity, or generally linking use of a judicial exception to a particular technological environment
or field of use do not integrate a judicial exception into a “practical application.” MPEP
§2106.05(1(A).
In the present case, the additional limitations beyond the above-noted at least one abstract
idea recited in the claim are as follows (where the bolded portions are the “additional
limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system for predicting illness onset, comprising:
-a wearable device (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 16-17, generic ring, watch devices, etc.) comprising one or more sensors configured to acquire physiological data from a user, the physiological data comprising at least motion data associated with one or more arms of the user (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 20);
-a user device communicatively coupled with the wearable device (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 19); and
-one or more processors communicatively coupled with the wearable device and the user device, wherein the one or more processors (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 47-48) are configured to:
-acquire baseline motion data associated with the one or more arms of the user, the baseline motion data acquired via the one or more sensors of the wearable device throughout a first time interval comprising a plurality of days (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 20);
-acquire additional motion data associated with the one or more arms of the user, the additional motion data acquired via the one or more sensors of the wearable device throughout a second time interval comprising at least one day that is subsequent to the plurality of days of the first time interval (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 20);
-input the baseline motion data and the additional motion data into one or more machine learning models, the one or more machine learning models trained to predict illness onset or recovery based at least in part on a plurality of features associated with movement of the one or more arms of the user within the additional motion data relative to the baseline motion data, the plurality of features comprising a first feature associated with a change in a hand swing movement range of the one or more arms, a second feature associated with a change in a shoulder angle range of the one or more arms, or both, wherein the one or more machine learning models are trained using training data comprising first motion data from a plurality of healthy users and second motion data from a plurality of users experiencing one or more illnesses (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 103-104);
-generate, using the one or more machine learning models (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 103-104), an illness prediction metric based at least in part on the plurality of features within the additional motion data, the illness prediction metric associated with a relative likelihood that the user is experiencing, will experience, or is recovering from the one or more illnesses; and
-transmit, to the wearable device, the user device, or both, an instruction configured to cause a graphical user interface (GUI) to display information associated with the illness prediction metric (using computers as mere tools to perform the abstract idea, see MPEP 2106.05(f); para. 26).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the limitations reciting the at least one abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(IID(A)(2).
For these reasons, representative independent claim 19 and analogous independent claim
1 do not recite additional elements that integrate the judicial exception into a practical
application. Accordingly, representative independent claim 19 and analogous independent claim
1 are directed to at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the
abstract idea into a practical application as set forth below:
Claim 2: The claim specifies the processor to identify using machine learning models motion frequencies and classify using machine learning models the time intervals, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 3: The claim specifies time intervals classified by comparing motion frequencies to thresholds, which further narrows the abstract idea.
Claim 4: The claim specifies the processor to identify motion data associated with motion frequency and transmit instruction to the wearable device, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 5: The claim specifies the process to identify physiological data acquired and transmit instruction to the wearable device, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 6: The claim specifies the processor to determine change in shoulder angle range and generating illness prediction metric based on that, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 7: The claim specifies baseline motion data to be associated with baseline shoulder angle range, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 8: The claim specifies the processor to determining using machine learning models that change in hand swing movement and generating illness metric, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 9: The claim specifies wherein the baseline motion data comprises baseline hand swing movement, which further narrows the abstract idea.
Claim 10: The claim specifies the processor to determine using machine learning models symmetry metrics, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 11: The claim specifies plurality of features, which further narrows the abstract idea.
Claim 12: The claim specifies generating the illness prediction metric comprising determining using one or more machine learning models, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 13: The claim specifies processor to transmit to the device additional instruction, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 14: The claim specifies one or more illness, which further narrows the abstract idea.
Claim 15: The claim specifies the processor to determine user holding an object and input the indication into the machine learning model, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 16: The claim specifies a wearable device, which does no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the use of at least one abstract idea is performed (see MPEP 2106.05(h)).
Claim 17: The claim specifies sensors, which does no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the use of at least one abstract idea is performed (see MPEP 2106.05(h)).
Claim 18: The claim specifies the processor to determine physical activities engaged based on motion data, which uses the computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim 20: The claim specifies using machine learning models to identify motion frequencies and classify them associated with gait or tremor.
Thus, when the above additional limitations are considered as a whole along with the limitations directed to the at least one abstract idea, the at least one abstract idea is not integrated into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria – Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claims 1 and 19 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as acquire physiological data, acquire baseline motion data, transmitting instruction, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); input motion data into machine learning models based on features, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2, 4-5, 12-13, and 20, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 4, 5, 13 (transmit), 12 (display instruction), e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 2, 20 (classify), e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. 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.
Therefore, whether taken individually or as an ordered combination, claims 1-20
are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed for claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues that the claim is not directed to certain methods of organizing human data. Examiner disagrees. Managing a user’s physiological data to determine illness onset is managing a personal behavior and therefore is a certain method of organizing human activity.
Applicant argues that the claims are similar to CardioNet because the claims are directed to a technological improvement. The alleged improvement that the Applicant touts does not concern an improvement to computer capabilities but instead relates to an alleged improvement in receiving and processing information for which a computer is used as a tool in its ordinary capacity. Examiner states that gathering data over several days is not an improvement, but simply gathering information, which is a mental process.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sarker et al. – WO 2025/117801 – Teaches a system to assess physical movements of a patient using video or images to determine a medical disorder.
Sun et al. – CN 119498856 – Teaches a system for detecting arm movement for determining muscular tension disorder where the arm movement is captured through camera.
Abbasi et al. – WO 2025/030062 – Teaches a system for tracking using a video to determine movement disorders.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEETAL R. PAULSON whose telephone number is (571)270-1368. The examiner can normally be reached M-F 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marc Jimenez can be reached at (571) 272-4530. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHEETAL R PAULSON/Primary Examiner, Art Unit 3681
Prosecution Insights