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 .
Priority
Applicant’s claim for the benefit of a prior-filed application 63/658,488, filed 06/11/2024, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
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.
D
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claims 1, 8, and 15 (claim 1 representative) recite collect sensor data from a subject user over a time period, the sensor data comprising at least motion data for the subject user; extract features from the sensor data; automatically assign at least one activity label of a plurality of activity labels to each feature based on at least one classification model; apply context filtering to the features based on the plurality of activity labels resulting in filtered feature data; and generate a hyperactivity risk score for the subject user based on the filtered feature data.
These limitations, as drafted, given the broadest reasonable interpretation, cover performance of the limitations in the mind of a user which constitute Mental Processes, but for the recitation of generic computer components. For example, these recitations encompasses observing, or writing with pen and paper, motion data of a subject from a time period, mentally determining features from the data, mentally, or with pen and paper, labeling the feature by applying a mental classification model, mentally, or with the pen and paper, filtering the labeled features, and mentally, or with the pen and paper, generating a score for the subject from the filtered data. These steps could be carried out by a doctor observing subject data and performing mental evaluations to derive a risk score. Therefore, these claims recite limitations that fall into the Mental Processes grouping of abstract ideas.
Claims 2-7, 9-14, and 16-20 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea. For example, claims 3, 10, and 17 further expand on determining the risk score. Claims 4, 11, 18 further define a visualization of the risk score across contexts and time segments. But for the recitation of a generic computer display, this output could be performed by a user with the aid of pen and paper. Claims 5, 12, and 19 further expand on the sensor data. As explained above, these steps encompass Mental Processes.
Step 2A Prong Two
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 along with adding elements similar to adding the words “apply it” to the abstract idea, and generally linking the abstract idea to a particular technological environment, along with insignificant, extra-solution data gathering activity.
Claims 20, directly or indirectly, recite the following additional elements at a high level of generality and merely utilized as tools to implement the abstract idea:
Claims 1-3:
at least one processor configured to.
Claim 4:
at least one processor is further configured to display at least one graphical user interface.
Claim 6:
the at least one processor comprises a processor of the wearable device.
Claim 7:
the at least one processor comprises a processor of a separate computing device.
Claim 13:
using a processor of the wearable device.
Claim 14:
using a processor of a separate computing device.
Claim 20:
the at least one processor comprises a processor of the wearable device.
Claim 11:
displaying at least one graphical user interface.
Claims 15-17:
A computer program product comprising at least one non-transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor.
Claim 18:
at least one processor is further caused to display at least one graphical user interface.
The written description discloses that the recited computer components encompass generic components including “’computing device’ may refer to one or more devices configured to process data. A computing device may, in some examples, include the necessary components to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface, and/or the like. A computing device may be a processor, such as a CPU or GPU, a mobile device, and/or other like devices. A computing device may also be a desktop computer, a server computer or other form of non-mobile computer. Reference to “a processor,” as used herein, may refer to a previously-recited processor that is recited as performing a previous step or function, a different processor, and/or a combination of processors.” (see paragraph 0041). As set forth in the MPEP 2106.04(d) “merely including 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.
Claims 1-20, directly or indirectly, recite the following additional elements at a high level of generality, involving no more that extra-solution data gathering and transmitting activity:
Claims 1, 8, 10:
collect/collecting sensor data from a wearable device.
These additional elements are recited at a high degree of generality and are merely involved in insignificant extra solution data gathering of sensor data. As set forth in MPEP 2106.05(g) insignificant, extra-solution activity, such as insignificant acquisition and data transmission, is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-20, directly or inderectly, recite the following additional elements at a high level of generality, generally linking the abstract idea to a particular technological environment:
Claims 1, 8, 15:
generate/generating…based on at least one machine learning model.
Claims 2, 9, 16:
train/training the machine-learning model based on the filtered feature data.
The machine-learning model and training are recited at a high degree of generality. The model is only used in the context of generating a score “based on” the model. The training of the model is only recited as being “based on the filtered feature data.” Therefore, there is no indication that the combination of the model and training with the other recited limitations provides any type of technical improvement or an improvement to another technical field. Accordingly, these recitations do not integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary 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.”).
Insignificant, extra solution, data gathering activity (e.g. collecting sensor data from a device) has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)).
Generally linking the abstract idea to a particular technological environment (e.g. a machine-learning model and training) does not amount to significantly more than the abstract idea (see MPEP 2016.05(h) and Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016)).
Additionally, the aforementioned additional elements, considered in combination, do not provide an improvement to a technical field or provide a technical improvement to a technical problem. These additional elements merely carry out the abstract idea through data collection, data processing, data communication, and data storage. Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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) 1-3, 5-10, 12-19, and 20 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Eleftheriou, US Patent Application Publication No. 2022/0061726.
As per claim 1, Eleftheriou teaches a system comprising: at least one processor configured to: collect sensor data from a wearable device worn by a subject user over a time period, the sensor data comprising at least motion data for the subject user (see paragraphs 0019-0020; wearable sensor devices collects sensor data including motion data of the user at a series of time increments); extract features from the sensor data (see paragraph 0020; system can access sensor data to record various parameters of the user); automatically assign at least one activity label of a plurality of activity labels to each feature based on at least one classification model (see paragraph 0025 biosignal data is labeled according to activities); apply context filtering to the features based on the plurality of activity labels resulting in filtered feature data (see paragraph 0034; biosignal data recorded during completion of a cognitive evaluation is accessed (biosignal data is filtered for an accessed timeseries)); and generate a hyperactivity risk score for the subject user based on at least one machine-learning model and the filtered feature data (see paragraphs 0034-0035; confidence score and intensity score for ADHD are encompassed by a hyperactivity risk score).
As per claim 2, Eleftheriou teaches the system of claim 1 as described above. Eleftheriou further teaches the at least one processor is further configured to train the machine-learning model based on the filtered feature data (see paragraph 0013; the machine-learning implemented system can learn biomarkers indicative of adverse cognitive state when detected in biosingal data).
As per claim 3, Eleftheriou teaches the system of claim 1 as described above. Eleftheriou further teaches the at least one processor is further configured to: determine the hyperactivity risk score for each time segment of a plurality of time segments of the time period, resulting in a plurality of risk scores (see paragraphs 0005 and 0007; determines a first confidence score for a first timeseries of biosignal data and a second confidence score for a second timeseries of biosignal data); and combine the plurality of risk scores to generate a daily hyperactivity risk score for the subject user (see paragraph 0007; scores are combined in the affirming of the positive ADHD diagnosis; additionally, paragraph 0059 describes estimating an updated confidence score by combining all information; paragraphs 0043-0044 describe daily trend identification).
As per claim 5, Eleftheriou teaches the system of claim 1 as described above. Eleftheriou further teaches he sensor data comprises the motion data and at least one of location data and heart rate data (see paragraph 0008; biosignal data can include heart-rate).
As per claim 6, Eleftheriou teaches the system of claim 1 as described above. Eleftheriou further teaches the at least one processor comprises a processor of the wearable device (see paragraph 0017).
As per claim 7, Eleftheriou teaches the system of claim 1 as described above. Eleftheriou further teaches the at least one processor comprises a processor of a separate computing device (see paragraph 0017).
Claims 8-10 and 12-14 recite substantially similar method limitations to system claims 1-3 and 5-7 and, as such, are rejected for similar reasons as given above.
Claims 15-17and 19-20 recite substantially similar computer medium limitations to system claims 1-3 and 12-13 and, as such, are rejected for similar reasons as given above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eleftheriou, US Patent Application Publication No. 2022/0061726 in view of Zhang, US Patent Application Publication No. 2015/0073294.
As per claim 4, Eleftheriou teaches the system of claim 1 as described above. Eleftheriou further teaches the at least one processor is further configured to display at least one graphical user interface configured to visualize adverse cognitive states, related to the hyperactivity risk score, across different contexts and time segments based on user interaction (see paragraph 0044; logs a series of adverse cognitive states throughout the day and displays the list to the user via the user’s mobile device). Eleftheriou does not explicitly teach displaying the hyperactivity risk score in the graphical user interface. Zhang teaches a graphical user interface configured to visualize a hyperactivity risk score across different contexts and time segments (see paragraph 0121; graphical representation of a change in an ADHD score over a period of time). It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to display the scores that are determined across different contexts and time segments on a graphical user interface with the motivation of improving the providing of objective assessments of ADHD severity (see paragraph 0007 of Zhang) because ADHD diagnosis accuracy is an objective of Eleftheriou (see paragraph 0034).
Claim 11 recites substantially similar method limitations to system claim 4 and, as such, is rejected for similar reasons as given above.
Claims 18 recite substantially similar computer medium limitations to system claim 4 and, as such, is rejected for similar reasons as given above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sharma, US Patent Application Publication No. 2024/0050006, discloses predicting ADHD and its level based on sensor data.
Gouailhardou, US Patent Application Publication No. 2024/0017064, discloses calculating a hyperactivity score based on sensor data.
Levy, US Patent Application Publication No. 2021/0196175, discloses calculating scores related to diagnosing and monitoring problems associated with ADHD.
Brancaccio, International Publication No. WO 2022/266447, discloses generating a hyperactivity score based on user motion data.
Thelagathoti et al., A Network Analysis Approach for the Classification of Psychiatric Disorders Using Multi-Modal Data, discloses identifying and distinguishing ADHD and other psychiatric disorders using wearable sensor data.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683
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