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 .
Status of Claims
This office action for the 18/786328 application is in response to the communications filed December 08, 2025.
Claims 1, 9 and 15 were amended December 08, 2025.
Claims 1-20 are currently pending and considered below.
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.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method comprising: obtaining sensor data associated with a subject; extracting, for each time period of one or more past or current time periods, one or more features based on the sensor data; generating, using the extracted one or more features in a specific time frame, a predicted symptom-intensity score for one or more symptoms of the subject using a symptom-forecasting model, the symptom-forecasting model uses power at each of one or more frequency bands as a proxy to calculate a symptom-intensity score, the power being identified by transforming the sensor data into frequency-space data, wherein the predicted symptom-intensity score represents a predicted intensity of the one or more symptoms during a future time period; and outputting a result based on the predicted symptom-intensity score, wherein the result provides a basis for a recommendation or includes the recommendation to perform an intervening action to avoid symptoms in accordance with the predicted symptom-intensity score. These steps, as drafted, under the broadest reasonable interpretation recite:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. For example, but for the additional element(s) of “computer-implemented”, the identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person is able to perform in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“computer-implemented” and “being a machine learning model that” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0011] of the as-filed specification describes that the hardware that implements the abstract idea is at a level of a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that 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/or generally link the abstract idea to a particular technological environment or field of use.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 2 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“further comprising: determining, for each time period of the one or more past or current time periods, a symptom-intensity score based on at least a portion of the sensor data that corresponds to the time period, wherein the one or more features for the time period are determined using the symptom-intensity score.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the predicted symptom-intensity score corresponds to a predicted intensity of the one or more symptoms of Parkinson's disease.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the predicted symptom-intensity score corresponds to a predicted intensity of tremors or a predicted intensity of dyskinesia.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the sensor data comprises data collected by an accelerometer or a gyroscope.” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 6 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the predicted symptom-intensity score is further generated based on medical information comprising dosage information associated with a treatment of the subject, medication time delta information, and information regarding effect of the treatment on the subject.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 7,
Claim 7 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein generating the predicted symptom-intensity score includes generating a trend using symptom-intensity scores corresponding to the one or more past or current time periods, and wherein the predicted symptom-intensity score is based on the trend.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 8 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 8 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the predicted symptom-intensity score is further generated based on multi-modal data comprising the sensor data, medical information, and mobility metrics of the subject.” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 9 is substantially similar to claim 1. Accordingly, claim 9 is rejected for the same reasons as claim 1.
Claim 9 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of operations including:” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 10 is substantially similar to claim 3. Accordingly, claim 10 is rejected for the same reasons as claim 3.
As per claim 11,
Claim 11 is substantially similar to claim 4. Accordingly, claim 11 is rejected for the same reasons as claim 4.
As per claim 12,
Claim 12 is substantially similar to claim 5. Accordingly, claim 12 is rejected for the same reasons as claim 5.
As per claim 13,
Claim 13 is substantially similar to claim 6. Accordingly, claim 13 is rejected for the same reasons as claim 6.
As per claim 14,
Claim 14 is substantially similar to claim 7. Accordingly, claim 14 is rejected for the same reasons as claim 7.
As per claim 15,
Claim 15 is substantially similar to claim 1. Accordingly, claim 15 is rejected for the same reasons as claim 1.
Claim 15 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of operations comprising” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim 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 recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 16,
Claim 16 is substantially similar to claim 2. Accordingly, claim 16 is rejected for the same reasons as claim 2.
As per claim 17,
Claim 17 is substantially similar to claim 3. Accordingly, claim 17 is rejected for the same reasons as claim 3.
As per claim 18,
Claim 18 is substantially similar to claim 6. Accordingly, claim 18 is rejected for the same reasons as claim 6.
As per claim 19,
Claim 19 is substantially similar to claim 7. Accordingly, claim 19 is rejected for the same reasons as claim 7.
As per claim 20,
Claim 20 is substantially similar to claim 8. Accordingly, claim 20 is rejected for the same reasons as claim 8.
Claim Rejections - 35 USC § 102
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)(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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kapur et al. (US 2024/0185997; herein referred to as Kapur).
As per claim 1,
Kapur discloses computer-implemented method comprising: obtaining sensor data associated with a subject:
(Paragraph [0003] of Kapur. The teaching describes a computer-implemented method that includes receiving, at a first time during a clinical exam and from a wearable sensor system, first sensor data indicative of a first patient activity.)
Kapur further discloses extracting, for each time period of one or more past or current time periods, one or more features based on the sensor data:
(Paragraph [0006] of Kapur. The teaching describes identifying an annotating non-exam activities during monitoring of a patient in a free-living setting. The computer-implemented method includes receiving, at an input device of a wearable user device, a first user input identifying a beginning of a first time period in which a virtual motor exam (VME) is conducted and receiving, at the input device of the wearable user device, a second user input identifying an end of the first time period. The computer-implemented method also includes accessing, by the wearable user device and based on the VME, first signal data output by a first sensor of the wearable user device during the first time period.)
Kapur further discloses generating, using the extracted one or more features in a specific time frame, a predicted symptom-intensity score for one or more symptoms of the subject using a symptom-forecasting model, the symptom-forecasting model being a machine learning model that uses power at each of one or more frequency bands as a proxy to calculate a symptom intensity score, the power being identified by transforming the sensor data into frequency-space data, wherein the predicted symptom-intensity score represents a predicted intensity of the one or more symptoms during a future time period:
(Paragraphs [0071], [0121] and [0131] of Kapur. The teaching describes training a second machine-learning algorithm using the second sensor data and the second annotation. The second machine-learning algorithm may be of a similar or identical type to the first machine-learning algorithm described above, and with the additional training data from the second sensor data and the second annotations, the second machine-learning algorithm may produce additional annotations, more accurate annotations, and further be capable of identifying activities associated with the VME, or other tasks, without input from the user indicating the start of a task. The second machine-learning algorithm may receive inputs of the sensor data, time, activity data, or any other suitable data corresponding to actions, activities, and free living environments. The second machine-learning algorithm may be trained using the second annotations, the sensor data, and any additional data, such as the time of day, location data, and activity data, such as to recognize correlations between the sensor data and other aspects of their daily lives. For example, the second machine learning algorithm may receive sensor data and then annotations from the first machine-learning algorithm, along with time information. The second machine-learning algorithm may encode such data into a latent space, which may enable it to populate the latent space over time and develop the ability to predict user activity based on the encoded data. For example, over time, the second machine-learning algorithm may develop a latent space that indicates that the user has more significant tremors in the morning, but that they abate over the course of the day, or that the tremors are associated with particular movements. In this manner, the second machine-learning algorithm may be trained to identify patterns and long-term trends in symptoms and conditions for the user. A task requiring the user to hold hands still in their lap may have varying results over time and receive different ratings, the machine-learning algorithm may identify that sensor data indicating higher amplitude or frequency of tremors may receive a lower rating while more steady sensor data (with respect to accelerometer data) may receive a higher rating. The user device 102 includes one or more processor units 106 that are configured to access a memory 108 having instructions stored thereon. The processor units 106 of FIG. 1 may be implemented as any electronic device capable of processing, receiving, or transmitting data or instructions. For example, the processor units 106 may include one or more of: a microprocessor, a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), or combinations of such devices.)
Kapur further discloses outputting a result based on the predicted symptom-intensity score, wherein the result provides a basis for a recommendation or includes the recommendation to perform an intervening action to avoid symptoms in accordance with the predicted symptom-intensity score:
(Paragraph [0132] of Kapur. The teaching describes generating, using the second machine-learning algorithm, a third annotation associated with an activity. The second machine-learning algorithm may enable identification of longer term patterns, trends, and correlations between certain activities, times of days, and other triggers for symptoms or conditions. The second machine-learning algorithm may generate third annotations corresponding to sensor data gathered while the user wears the user device 102 but outside of a clinical or VME setting and may therefore provide insights into longer term trends, triggers, or other patterns associated with various symptoms of a user. This is enabled by identifying longer terms trends and triggers by identifying portions of sensor data identified by similarity with previously tagged sensor data and subsequently identifying further data over a longer period of time to identify additional triggers for particular symptoms or times of day when a condition may be especially difficult for a user. The third annotations may be more expansive than the first and second annotations and may annotate the sensor data outside of the indicated times when a user performed a VME task. For instance, in the exemplary illustration above, a user may sit with their hands in their lap in a manner similar to a VME task without intentionally performing a VME task. The second machine-learning algorithm may first identify periods of activities similar to VME or clinical tasks. The second machine-learning algorithm, or an additional machine-learning algorithm, may then generate the third annotations corresponding to contexts, performance, and other information related to the tasks to append to the sensor data. This output of a third annotation provides a basis for a recommendation.)
As per claim 2,
Kapur discloses the limitations of claim 1.
Kapur further discloses further comprising: determining, for each time period of the one or more past or current time periods, a symptom-intensity score based on at least a portion of the sensor data that corresponds to the time period, wherein the one or more features for the time period are determined using the symptom-intensity score:
(Paragraph [0131] of Kapur. The teaching describes training a second machine-learning algorithm using the second sensor data and the second annotation. The second machine-learning algorithm may be of a similar or identical type to the first machine-learning algorithm described above, and with the additional training data from the second sensor data and the second annotations, the second machine-learning algorithm may produce additional annotations, more accurate annotations, and further be capable of identifying activities associated with the VME, or other tasks, without input from the user indicating the start of a task. The second machine-learning algorithm may receive inputs of the sensor data, time, activity data, or any other suitable data corresponding to actions, activities, and free living environments. The second machine-learning algorithm may be trained using the second annotations, the sensor data, and any additional data, such as the time of day, location data, and activity data, such as to recognize correlations between the sensor data and other aspects of their daily lives. For example, the second machine learning algorithm may receive sensor data and then annotations from the first machine-learning algorithm, along with time information. The second machine-learning algorithm may encode such data into a latent space, which may enable it to populate the latent space over time and develop the ability to predict user activity based on the encoded data. For example, over time, the second machine-learning algorithm may develop a latent space that indicates that the user has more significant tremors in the morning, but that they abate over the course of the day, or that the tremors are associated with particular movements. In this manner, the second machine-learning algorithm may be trained to identify patterns and long-term trends in symptoms and conditions for the user.)
As per claim 3,
Kapur discloses the limitations of claim 1.
Kapur further discloses wherein the predicted symptom-intensity score corresponds to a predicted intensity of the one or more symptoms of Parkinson's disease:
(Paragraph [0032] of Kapur. The teaching describes a sensor-based remote monitoring may help health care professionals better track disease progression such as in Parkinson's disease (PD), and measure users' response to putative disease-modifying therapeutic interventions)
As per claim 4,
Kapur discloses the limitations of claim 1.
Kapur further discloses wherein the predicted symptom-intensity score corresponds to a predicted intensity of tremors or a predicted intensity of dyskinesia:
(Paragraph [0131] of Kapur. The teaching describes training a second machine-learning algorithm using the second sensor data and the second annotation. The second machine-learning algorithm may be of a similar or identical type to the first machine-learning algorithm described above, and with the additional training data from the second sensor data and the second annotations, the second machine-learning algorithm may produce additional annotations, more accurate annotations, and further be capable of identifying activities associated with the VME, or other tasks, without input from the user indicating the start of a task. The second machine-learning algorithm may receive inputs of the sensor data, time, activity data, or any other suitable data corresponding to actions, activities, and free living environments. The second machine-learning algorithm may be trained using the second annotations, the sensor data, and any additional data, such as the time of day, location data, and activity data, such as to recognize correlations between the sensor data and other aspects of their daily lives. For example, the second machine learning algorithm may receive sensor data and then annotations from the first machine-learning algorithm, along with time information. The second machine-learning algorithm may encode such data into a latent space, which may enable it to populate the latent space over time and develop the ability to predict user activity based on the encoded data. For example, over time, the second machine-learning algorithm may develop a latent space that indicates that the user has more significant tremors in the morning, but that they abate over the course of the day, or that the tremors are associated with particular movements. In this manner, the second machine-learning algorithm may be trained to identify patterns and long-term trends in symptoms and conditions for the user.)
As per claim 5,
Kapur discloses the limitations of claim 1.
Kapur further discloses wherein the sensor data comprises data collected by an accelerometer or a gyroscope:
(Paragraph [0044] of Kapur. The teaching describes that during execution of PD-VME tasks, tri-axial accelerometer and gyroscope data was collected at a sample rate of 200 Hz.)
As per claim 6,
Kapur discloses the limitations of claim 1.
Kapur further discloses wherein the predicted symptom-intensity score is further generated based on medical information comprising dosage information associated with a treatment of the subject, medication time delta information, and information regarding effect of the treatment on the subject:
(Paragraph [0066] of Kapur. The teaching describes that dopaminergic medication can considerably improve severity of motor signs over short time frames. This “on-off” difference is well-accepted as a clinically meaningful change, and when coupled with wearable sensors and user-reported tagging of daily medication regimen, creates multiple “natural experiments” in the course of users' daily lives. These may allow testing of the clinical validity of the PD-VME measures as pharmacodynamic/response biomarkers for people with PD in the remote setting. Indeed, digital measures for tremor, upper-extremity bradykinesia and gait may be able to detect significant change in users' motor signs before and after medication intake. These pharmacodynamic/response biomarkers are construed to include dosages, time deltas and effect of the treatment.)
As per claim 7,
Kapur discloses the limitations of claim 1.
Kapur further discloses wherein generating the predicted symptom-intensity score includes generating a trend using symptom-intensity scores corresponding to the one or more past or current time periods, and wherein the predicted symptom-intensity score is based on the trend:
(Paragraph [0131] of Kapur. The teaching describes training a second machine-learning algorithm using the second sensor data and the second annotation. The second machine-learning algorithm may be of a similar or identical type to the first machine-learning algorithm described above, and with the additional training data from the second sensor data and the second annotations, the second machine-learning algorithm may produce additional annotations, more accurate annotations, and further be capable of identifying activities associated with the VME, or other tasks, without input from the user indicating the start of a task. The second machine-learning algorithm may receive inputs of the sensor data, time, activity data, or any other suitable data corresponding to actions, activities, and free living environments. The second machine-learning algorithm may be trained using the second annotations, the sensor data, and any additional data, such as the time of day, location data, and activity data, such as to recognize correlations between the sensor data and other aspects of their daily lives. For example, the second machine learning algorithm may receive sensor data and then annotations from the first machine-learning algorithm, along with time information. The second machine-learning algorithm may encode such data into a latent space, which may enable it to populate the latent space over time and develop the ability to predict user activity based on the encoded data. For example, over time, the second machine-learning algorithm may develop a latent space that indicates that the user has more significant tremors in the morning, but that they abate over the course of the day, or that the tremors are associated with particular movements. In this manner, the second machine-learning algorithm may be trained to identify patterns and long-term trends in symptoms and conditions for the user.)
As per claim 8,
Kapur discloses the limitations of claim 1.
Kapur further discloses wherein the predicted symptom-intensity score is further generated based on multi-modal data comprising the sensor data, medical information, and mobility metrics of the subject:
(Paragraph [0044] of Kapur. The teaching describes that during execution of PD-VME tasks, tri-axial accelerometer and gyroscope [multi-modal] data was collected at a sample rate of 200 Hz.)
(Paragraph [0066] of Kapur. The teaching describes that dopaminergic medication can considerably improve severity of motor signs over short time frames. This “on-off” difference is well-accepted as a clinically meaningful change, and when coupled with wearable sensors and user-reported tagging of daily medication regimen, creates multiple “natural experiments” in the course of users' daily lives. These may allow testing of the clinical validity of the PD-VME measures as pharmacodynamic/response biomarkers for people with PD in the remote setting. Indeed, digital measures for tremor, upper-extremity bradykinesia and gait may be able to detect significant change in users' motor signs before and after medication intake [medical information].)
(Paragraph [0131] of Kapur. The teaching describes training a second machine-learning algorithm using the second sensor data and the second annotation. The second machine-learning algorithm may be of a similar or identical type to the first machine-learning algorithm described above, and with the additional training data from the second sensor data and the second annotations, the second machine-learning algorithm may produce additional annotations, more accurate annotations, and further be capable of identifying activities associated with the VME, or other tasks, without input from the user indicating the start of a task. The second machine-learning algorithm may receive inputs of the sensor data, time, activity data, or any other suitable data corresponding to actions, activities [mobility metrics], and free living environments. The second machine-learning algorithm may be trained using the second annotations, the sensor data, and any additional data, such as the time of day, location data, and activity data, such as to recognize correlations between the sensor data and other aspects of their daily lives. For example, the second machine learning algorithm may receive sensor data and then annotations from the first machine-learning algorithm, along with time information. The second machine-learning algorithm may encode such data into a latent space, which may enable it to populate the latent space over time and develop the ability to predict user activity based on the encoded data. For example, over time, the second machine-learning algorithm may develop a latent space that indicates that the user has more significant tremors in the morning, but that they abate over the course of the day, or that the tremors are associated with particular movements. In this manner, the second machine-learning algorithm may be trained to identify patterns and long-term trends in symptoms and conditions for the user.)
As per claim 9,
Claim 9 is substantially similar to claim 1. Accordingly, claim 9 is rejected for the same reasons as claim 1.
As per claim 10,
Claim 10 is substantially similar to claim 3. Accordingly, claim 10 is rejected for the same reasons as claim 3.
As per claim 11,
Claim 11 is substantially similar to claim 4. Accordingly, claim 11 is rejected for the same reasons as claim 4.
As per claim 12,
Claim 12 is substantially similar to claim 5. Accordingly, claim 12 is rejected for the same reasons as claim 5.
As per claim 13,
Claim 13 is substantially similar to claim 6. Accordingly, claim 13 is rejected for the same reasons as claim 6.
As per claim 14,
Claim 14 is substantially similar to claim 7. Accordingly, claim 14 is rejected for the same reasons as claim 7.
As per claim 15,
Claim 15 is substantially similar to claim 1. Accordingly, claim 15 is rejected for the same reasons as claim 1.
As per claim 16,
Claim 16 is substantially similar to claim 2. Accordingly, claim 16 is rejected for the same reasons as claim 2.
As per claim 17,
Claim 17 is substantially similar to claim 3. Accordingly, claim 17 is rejected for the same reasons as claim 3.
As per claim 18,
Claim 18 is substantially similar to claim 6. Accordingly, claim 18 is rejected for the same reasons as claim 6.
As per claim 19,
Claim 19 is substantially similar to claim 7. Accordingly, claim 19 is rejected for the same reasons as claim 7.
As per claim 20,
Claim 20 is substantially similar to claim 8. Accordingly, claim 20 is rejected for the same reasons as claim 8.
Response to Arguments
Applicant's arguments filed December 08, 2025 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that the claimed forecasting step involves computational analysis of multi-dimensional features derived from multiple time periods. Such forecasting cannot reasonably be performed mentally. Therefore, the pending claims do not recite a mental process or another category of abstract idea.
The Examiner respectfully disagrees. A limitation that does not recite a mental process does not preclude that same limitation from reciting another category of abstract idea. More to the point, the basis of rejection is not placed upon mental processes. The Applicant is arguing against a position that the Examiner has not made. Accordingly, this argument is not relevant to the standing rejection. This rejection is based upon “Certain Methods of Organizing Human Activity” which contains completely different criteria than for mental processes. A human person is capable of performing the steps of the identified abstract idea above in the course of their personal behavior. Accordingly, the pending claims recite an abstract idea.
The Applicant further argues that the pending claims provide a practical technological application of any alleged abstract idea. These limitations are not generic data-processing steps, but specific technological transformations and model inputs that configure how the machine learning model operates.
The Examiner respectfully disagrees. It is not clear to which relevant consideration of practical application that the Applicant drawing from. A specific technological implementation in a claim does not necessitate a particular application of an abstract idea. Relevant considerations for additional elements that provide for a practical application of a judicial exception can be found at MPEP 2106.04(d)(I). As for the specific implementation of transforming data from the time-domain or analog domain, to the frequency-domain is fundamentally a generic process that is the basis of all Digital Signal Processing applications. This particular application for generating a predicted symptom-intensity score for a future time period is merely using known mathematical calculations in the medical field.
The Applicant further argues that the record has not shown that using frequency-domain power as a physiological proxy in a forecasting model is conventional. Accordingly, the pending claims recites “significantly more”.
The Examiner respectfully disagrees. Only additional elements to an abstract idea can qualify as something significantly more than the abstract idea. The element of using frequency-domain power as a physiological proxy in a forecasting model is abstract as this element amounts to nothing more than mathematical functions that a human person can perform in the course of their personal behavior. This element, being abstract, cannot also be an additional element to this same abstract idea.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 102 are not persuasive.
The Applicant argues that Kapur does not disclose the symptom-forecasting model being a machine learning model that uses power at each of one or more frequency bands as a proxy to calculate a symptom-intensity score, the power being identified by transforming the sensor data into frequency-space data, as recited by claim 1.
The Examiner respectfully disagrees. Paragraphs [0071] and [0121] of Kapur teaches these limitations as can be seen in the updated rejection above.
Conclusion
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 CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681