Its DETAILED ACTION
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 .
Response to Amendment
Claims 1-16 are currently pending. Claims 1, 2, 6, 7, 9, 10, 14, and 15 have been amended. Claims 17-20 have been added.
Claim Objections
Claims 1 and 9 are objected to because of the following informalities:
In claim 1 on lines 4-5, “wherein the at least one data stream comprises electrodermal activity data from throughout a circadian cycle of the user” should read “wherein the at least one data stream comprises electrodermal activity data from
In claim 9 on lines 5-6, “receiving, by at least one processor, at least one data stream comprising wearable sensor data associated with a user from throughout a circadian cycle of the user” should read “receiving, by at least one processor, at least one data stream comprising wearable sensor data associated with a user from
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 therefore, 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. Each of Claims 1-20 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1-20 recites at least one step or instruction for predicting the onset of a seizure, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1-20 recites an abstract idea.
Specifically, Claims 1 and 9 recite the following abstract ideas, as evidenced by the claim language: receiving data from electrodermal activity; receiving at least one time span within the circadian cycle associated with at least one seizure of the user; extracting at least one circadian rhythm-dependent metric indicative of a relative behavior of the electrodermal activity data throughout the circadian cycle of the user to capture an autonomic behavior throughout the circadian cycle; predict a pre-ictal period associated with a time segment preceding a seizure based at least in part on training data comprising the at least one circadian rhythm-dependent metric indicative of the relative behavior of the electrodermal activity data throughout the circadian cycle and the at least one time space associated with the at least one seizure. These steps describe a concept performed in the human mind or by use of pen and paper (including an observation, evaluation, judgement, opinion). The claim is also drawn to a mental process and mathematical concept, which are also Abstract Ideas.
Step 2A, Prong 2
Regarding Claims 1 and 9 (and their respective dependent claims) meets Step 2A, Prong 2 because the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. For example, Claims 1 and 9 recites the outputting step where “at least one seizure prediction indicative of a likelihood of an impending seizure of the user,” which is merely adding extra-solution activity to the judicial exception (MPEP 2106.05(g). The outputting step does not provide an improvement to the following: function of a particular machine, manufacture or other technology; treatment or prophylaxis for a disease or medical condition; or transforming or reducing of a particular article to a different state or thing (MPEP 2106.04(d)).
Step 2B
Lastly, the claims as a whole are analyzed to determine whether any elements, or in combination, to ensure that they amount to significantly more than the judicial exception itself. However, these claims do not appear to recite additional elements that amount to significantly more than the judicial exception.
Regarding Claims 1 and 9 (and their respective dependent claims), it recites additional elements, more specifically wearable device, wearable sensor, biomarker sensor, at least one sensor, are not significantly more because US Reference 20140316229 A1 provides evidence within paragraph 0031 that they are well-known, routine, and conventional [Examiner’s note, the wearable sensor, biomarker sensor, and at least one sensor is an electrodermal activity sensor].
The above-identified additional elements, more specifically the processor, are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
The dependent claims also fail to add something more to the abstract independent claims. Claims 2, 10, and 11 recites a measuring step that is pre-solutional data gathering and a predicting step which is a mental process. Claims 4 and 12 recites additional elements (i.e., biomarker sensor) which does not add anything significantly more. Claims 5, 13, 17, and 19 recites mathematical processes which is a mental process. Claims 6-8, 14-16, and 20 do not add anything significantly more. The steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
Therefore, none of the Claims 1-20 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-20 are not patent eligible and rejected under 35 U.S.C. 101.
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.
The factual inquiries 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 1-5, 9-13, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nogueira et al. (US 20180206776 A1) in view of Poh et al (US 20120296175 A1).
Regarding Claim 1, Nogueira teaches a method ([0006]) comprising:
receiving, by at least one processor (processor – element 602; [0025], [0135]; [Examiner’s note, the computing device (600) is the wearable device. Paragraph 0135 explains “computing device 600 may, for example, be implemented as a wearable device, such as device 104 of FIG. 1.” Because of this, the computing device is the same embodiment as the wearable device (104).]), at least one data stream comprising wearable sensor data associated with a user (wearable device – element 104; [0024]);
wherein the at least one data stream comprises electrodermal activity data from throughout a circadian cycle of the user (Fig. 7 – more specifically blocks 704, 704.1, and 704.2; [0065]);
receiving, by the at least one processor (processor – element 602; [0025], [0135]), at least one time span within the circadian cycle associated ([0065]) with at least one seizure of the user (Fig. 5 – element 504; [0057]);
extracting, by the at least one processor, at least one circadian rhythm-dependent metric indicative of a relative behavior of the electrodermal activity data (electrodermal activity sensors – element 610; [0033], [0063-0064]; [0137-00138]) throughout the circadian cycle of the user to capture an autonomic behavior throughout the circadian cycle (temperature sensor – element 608; [0063-0065], [0137]; [Examiner’s note, the user’s temperature readings are simultaneously measured with the electrodermal activity. The relative behavior is the data measured by the electrodermal activity sensors and the autonomic behavior is the corresponding temperature data from the temperature sensor.]);
and training, by the at least one processor, a seizure machine learning model (seizure prediction facility – element 618; [0136 – processor 602 enables processing of data and execution of instructions. The data and instructions may be stored on the computer-readable storage media 612 and may, for example, enable communication between components of the computing device 600.]; [0138 – the media 612 may additionally store instructions for a seizure prediction facility 618, which may implement any of the techniques described above for predicting occurrence of a seizure during a time period.]) to predict a pre-ictal period associated with a time segment preceding a seizure ([0138]; [Examiner’s note, the pre-ictal period occurs prior to the onset of the seizure. The data measured by the wearable device, EDA and temperature, are provided to the seizure prediction facility. Where the seizure prediction facility predicts when the seizure may occur. As a result, the pre-ictal period is the time period when the seizure prediction facility sends out a warning about the possible onset of a seizure.]) based at least in part on training data comprising the at least one circadian rhythm-dependent metric indicative of the relative behavior of the electrodermal activity data throughout the circadian cycle and the at least one time space associated with the at least one seizure (Fig. 5, Fig. 8; [0020], [0057-0058], [0088]; [Examiner’s note, the biological characteristics is the user’s EDA, temperature, and motion data.]; and
inputting, by the at least one processor, electrodermal activity data (EDA sensors – element 610; [0137]) into the seizure machine learning model to output (seizure prediction facility – 618; [0137-0138]), based at least in part on the electrodermal activity data, at least one seizure prediction indicative of a likelihood of an impending seizure of the user ([0138]).
Nogueira teaches of measuring electrodermal activity data through the EDA sensors (EDA sensors – element 610; [0137]), but does not explicitly teach measuring subsequent electrodermal activity. Poh teaches subsequent electrodermal activity (Poh | Abstract; [0034]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of predicting a seizure from Nogueira to incorporate the teachings of subsequent data from Poh because it allows for observing the user’s sympathetic activity and parasympathetic activity before, during, and after a seizure, and the level of severity experienced by the user (Poh | Abstract).
Regarding Claim 2, Nogueira in view of Poh teaches the method as recited in claim 1. Nogueira teaches further comprising: receiving, by the at least one processor (processor – element 602; [0025], [0135]), the electrodermal activity data (Nogueira | electrodermal activity sensors – element 610; [0137]) from at least one data stream (Nogueira | [0137-0138]) comprising additional wearable sensor data ([0137]); utilizing, by the at least one processor (Nogueira | processor – element 602; [0025], [0135]), the seizure machine learning model (Nogueira | Seizure prediction facility – element 618; [0136 – processor 602 enables processing of data and execution of instructions. The data and instructions may be stored on the computer-readable storage media 612 and may, for example, enable communication between components of the computing device 600.]; [0138 – the media 612 may additionally store instructions for a seizure prediction facility 618, which may implement any of the techniques described above for predicting occurrence of a seizure during a time period.]) to identify the pre-ictal period based at least in part on the at least on data stream (Nogueira | [0138]; [Examiner’s note, the pre-ictal period occurs prior to the onset of the seizure. The data measured by the wearable device, EDA and temperature, are provided to the seizure prediction facility. Where the seizure prediction facility predicts when the seizure may occur. As a result, the pre-ictal period is the time period when the seizure prediction facility sends out a warning about the possible onset of a seizure.]); and generating, by the at least one processor (Nogueira | processor – element 602; [0025], [0135], a seizure alert on a computing device associated with the user to alert the user of an impending seizure (Nogueira | [0032], [0038-0039], [0135]).
Nogueira teaches of the electrodermal activity (Nogueira | electrodermal activity sensors – element 610; [0137]), the time when electrodermal activity is measured (Nogueira | [0137-0138]), and pre-ictal period (Nogueira | [0138]). However, Nogueira is silent in teaching that data being measured subsequently to the onset of a seizure. Poh teaches data measured subsequently to the onset of a seizure (Poh | Abstract; [0034]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of predicting a seizure from Nogueira to incorporate the teachings of subsequent data from Poh because it allows for observing the user’s sympathetic activity and parasympathetic activity before, during, and after a seizure, and the level of severity experienced by the user (Poh | Abstract).
Regarding Claim 3, Nogueira in view of Poh teaches the method as recited in claim 2, further comprising communicating, by the at least one processor, with a wearable device to receive at least one subsequent data stream in real-time (Nogueira | [0032-0034]).
Regarding Claim 4, Nogueira in view of Poh teaches the method as recited in claim 3, wherein the wearable device includes a biomarker sensor worn by the user (Nogueira | [0033 - one or more sensors may be monitoring biological characteristics of a patient. For example, a patient may be wearing a wearable device incorporating the sensor(s) and the sensor(s) may be monitoring the biological characteristics and generating data], [0137]).
Regarding Claim 5, Nogueira in view of Poh teaches the method as recited in claim 1, wherein the time segment used to calculate forecasts comprises twenty-four hours (Nogueira | [0057 - To analyze the biological characteristic data, sliding windows of different lengths may be used to segment the data, such as sliding windows of 10 seconds, 1 minute, 1 hour, 6 hours, 12 hours, and 24 hours. In addition, biological characteristic data for patients may be segmented based on whether patients experienced a seizure or experienced a seizure within a particular time.]).
Regarding Claim 9, Nogueira discloses a system ([0004]) comprising:
at least one sensor (Nogueira | electrodermal activity sensors – element 610; [0137]); and
at least one processor (processor – element 602; [0025], [0135]; [Examiner’s note, the computing device (600) is the wearable device. Paragraph 0135 explains “computing device 600 may, for example, be implemented as a wearable device, such as device 104 of FIG. 1.” Because of this, the computing device is the same embodiment as the wearable device (104).]) in communication with the at least one sensor ([0136]) and configured to perform steps of instructions stored in a non-transitory memory ([0136]), the steps comprising:
receiving, by at least one processor, at least one data stream comprising wearable sensor data associated with a user from throughout a circadian cycle of the user ([0065]); wherein the at least one data stream comprises electrodermal activity data (electrodermal activity sensors – element 610; [0033], [0063-0064]; [0137-00138]);
receiving, by the at least one processor, at least one time span within the circadian cycle associated ([0065]) with at least one seizure of the user (Fig. 5 – element 504; [0057]);
extracting, by the at least one processor, at least one circadian rhythm-dependent metric indicative of a relative behavior of the electrodermal activity data (electrodermal activity sensors – element 610; [0033], [0063-0064]; [0137-00138]) throughout the circadian cycle of the user to capture an autonomic behavior throughout the circadian cycle (temperature sensor – element 608; [0063-0065], [0137]; [Examiner’s note, the user’s temperature readings are simultaneously measured with the electrodermal activity. The relative behavior is the data measured by the electrodermal activity sensors and the autonomic behavior is the corresponding temperature data from the temperature sensor.]);
and training, by the at least one processor, a seizure machine learning model (seizure prediction facility – element 618; [0136 – processor 602 enables processing of data and execution of instructions. The data and instructions may be stored on the computer-readable storage media 612 and may, for example, enable communication between components of the computing device 600.]; [0138 – the media 612 may additionally store instructions for a seizure prediction facility 618, which may implement any of the techniques described above for predicting occurrence of a seizure during a time period.]) to predict a pre-ictal period associated with a time segment preceding a seizure ([0138]; [Examiner’s note, the pre-ictal period occurs prior to the onset of the seizure. The data measured by the wearable device, EDA and temperature, are provided to the seizure prediction facility. Where the seizure prediction facility predicts when the seizure may occur. As a result, the pre-ictal period is the time period when the seizure prediction facility sends out a warning about the possible onset of a seizure.]) based at least in part on training data comprising the at least one circadian rhythm-dependent metric indicative of the relative behavior of the electrodermal activity data throughout the circadian cycle and the at least one time space associated with the at least one seizure (Fig. 5, Fig. 8; [0020], [0057-0058], [0088]; [Examiner’s note, the biological characteristics is the user’s EDA, temperature, and motion data.]; and
inputting, by the at least one processor, electrodermal activity data (EDA sensors – element 610; [0137]) into the seizure machine learning model to output (seizure prediction facility – 618; [0137-0138]), based at least in part on the electrodermal activity data, at least one seizure prediction indicative of a likelihood of an impending seizure of the user ([0138]).
Nogueira teaches of measuring electrodermal activity data through the EDA sensors (EDA sensors – element 610; [0137]), but does not explicitly teach measuring subsequent electrodermal activity. Poh teaches subsequent electrodermal activity (Poh | Abstract; [0034]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of predicting a seizure from Nogueira to incorporate the teachings of subsequent data from Poh because it allows for observing the user’s sympathetic activity and parasympathetic activity before, during, and after a seizure, and the level of severity experienced by the user (Poh | Abstract).
Regarding Claim 10, Nogueira in view of Poh teaches the system as recited in claim 9. Nogueira teaches wherein the at least one processor (Nogueira | processor – element 602; [0025], [0135]) may be further configured to: receive the electrodermal activity data (Nogueira | electrodermal activity sensors – element 610; [0137]) from at least one subsequent data stream (Nogueira | [0137-0138]) comprising additional wearable sensor data ([0137]); utilize the seizure machine learning model (Nogueira | Seizure prediction facility – element 618; [0136 – processor 602 enables processing of data and execution of instructions. The data and instructions may be stored on the computer-readable storage media 612 and may, for example, enable communication between components of the computing device 600.]; [0138 – the media 612 may additionally store instructions for a seizure prediction facility 618, which may implement any of the techniques described above for predicting occurrence of a seizure during a time period.]) to identify the pre-ictal period based at least in part on the at least on data stream (Nogueira | [0138]; [Examiner’s note, the pre-ictal period occurs prior to the onset of the seizure. The data measured by the wearable device, EDA and temperature, are provided to the seizure prediction facility. Where the seizure prediction facility predicts when the seizure may occur. As a result, the pre-ictal period is the time period when the seizure prediction facility sends out a warning about the possible onset of a seizure.]); and generate a seizure alert on a computing device associated with the user to alert the user of an impending seizure (Nogueira | [0032], [0038-0039], [0135]).
Nogueira teaches of the electrodermal activity (Nogueira | electrodermal activity sensors – element 610; [0137]), the time when electrodermal activity is measured (Nogueira | [0137-0138]), and pre-ictal period (Nogueira | [0138]). However, Nogueira is silent in teaching that data being measured subsequently to the onset of a seizure. Poh teaches data measured subsequently to the onset of a seizure (Poh | Abstract; [0034]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of predicting a seizure from Nogueira to incorporate the teachings of subsequent data from Poh because it allows for observing the user’s sympathetic activity and parasympathetic activity before, during, and after a seizure, and the level of severity experienced by the user (Poh | Abstract).
Regarding Claim 11, Nogueira in view of Poh teaches the system as recited in claim 10, wherein the at least one processor may be further configured to communicate with a wearable device to receive the at least one subsequent data stream in real-time (Nogueira | [0032-0034]).
Regarding Claim 12, Nogueira in view of Poh teaches the system as recited in claim 11, wherein the wearable device includes a biomarker sensor worn by the user (Nogueira | [0033 - one or more sensors may be monitoring biological characteristics of a patient. For example, a patient may be wearing a wearable device incorporating the sensor(s) and the sensor(s) may be monitoring the biological characteristics and generating data], [0137]).
Regarding 13, Nogueira in view of Poh teaches the system as recited in claim 9, wherein the time segment used to calculate forecasts comprises twenty-four hours (Nogueira | [0057 - To analyze the biological characteristic data, sliding windows of different lengths may be used to segment the data, such as sliding windows of 10 seconds, 1 minute, 1 hour, 6 hours, 12 hours, and 24 hours. In addition, biological characteristic data for patients may be segmented based on whether patients experienced a seizure or experienced a seizure within a particular time.]).
Regarding Claim 17, Nogueira in view of Poh teaches the method as recited in claim 1, wherein the at least one circadian rhythm-dependent metric comprises a comparison between an average electrodermal activity amplitude within the second time segment (Nogueira | Fig. 4 – Block 402; [0024 - the wearable device 104 may additionally or alternatively include one or more sensors to detect an electrodermal activity (EDA) of the patient 102], [0050 - block 402, in which the seizure prediction facility calculates a statistical value for data relating to a biological characteristic over a first time period]; [Examiner’s note, the biological characteristics is the electrodermal activity. The average electrodermal activity within one time period is done by getting the average from multiple EDA sensors.]) and a circadian phase-matched baseline computed within the first time segment (Nogueira | Fig. 4 – Block 404; [0051-0052]; [Examiner’s note, the circadian phase-matched baseline is the biological characteristics that is phase-matched to the corresponding wake period to succeeding sleep period.]), the comparison comprising at least one of: a difference, a normalized score [Examiner’s note, the claim comprises multiple limitations; however, only one of the alternatives needs to be supported by the prior art.], or a ratio (Nogueira | Fig. 4 – Block 406) relative to the circadian phase-matched baseline (Nogueira | [0052]).
Regarding Claim 18, Nogueira in view of Poh teaches the method as recited in claim 6, wherein the pre-ictal probability threshold is dynamically varied as a function of circadian phase derived from the first time segment (Nogueira | [0008], [0054], [0056]; [Examiner’s note, the first time segment is the first wake period or first sleep period. The circadian phase corresponds to the time when the body temperature moves above or below a threshold. This threshold is the pre-ictal probability threshold, which tracks temperature changes right before a seizure starts.]).
Regarding Claim 19, Nogueira in view of Poh teaches the system as recited in claim 9, wherein the at least one circadian rhythm-dependent metric comprises a comparison between an average electrodermal activity amplitude within the second time segment (Nogueira | Fig. 4 – Block 402; [0024 - the wearable device 104 may additionally or alternatively include one or more sensors to detect an electrodermal activity (EDA) of the patient 102], [0050 - block 402, in which the seizure prediction facility calculates a statistical value for data relating to a biological characteristic over a first time period]; [Examiner’s note, the biological characteristics is the electrodermal activity. The average electrodermal activity within one time period is done by getting the average from multiple EDA sensors.]) and a circadian phase-matched baseline computed within the first time segment (Nogueira | Fig. 4 – Block 404; [0051-0052]; [Examiner’s note, the circadian phase-matched baseline is the biological characteristics that is phase-matched to the corresponding wake period to succeeding sleep period.]), the comparison comprising at least one of: a difference, a normalized score [Examiner’s note, the claim comprises multiple limitations; however, only one of the alternatives needs to be supported by the prior art.], or a ratio (Nogueira | Fig. 4 – Block 406) relative to the circadian phase-matched baseline (Nogueira | [0052]).
Regarding Claim 20, Nogueira in view of Poh teaches the system as recited in claim 14, wherein the pre-ictal probability threshold is dynamically varied as a function of circadian phase derived from the first time segment (Nogueira | [0008], [0054], [0056]; [Examiner’s note, the first time segment is the first wake period or first sleep period. The circadian phase corresponds to the time when the body temperature moves above or below a threshold. This threshold is the pre-ictal probability threshold, which tracks temperature changes right before a seizure starts.]).
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Nogueira in view of Poh and Sackellares et al. (US 20070213786 A1).
Regarding claim 6, Nogueira in view of Poh teaches the method as recited in claim 2. Nogueira in view of Poh is silent in teaching further comprising determining, by the at least one processor, an inter-ictal period upon the pre-ictal period probability falling below the pre-ictal probability threshold. Sackellares teaches further comprising determining, by the at least one processor, an inter-ictal period upon the pre-ictal period probability falling below the pre-ictal probability threshold (Sackellares | Figs. 18 and 21B; [0069], [0269-0270]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of Nogueira in view of Poh to incorporate the teachings of determining an inter-ictal period upon the pre-ictal period probability falling below the pre-ictal probability threshold from Sackellares because it allows for predicting the onset of a potential seizure and providing the appropriate care to the user (Sackellares | [0014]).
Regarding Claim 14, Nogueira in view of Poh teaches the system as recited in claim 10. Nogueira in view of Poh teaches is silent in teaching wherein the at least one processor may be further configured to determine an inter-ictal period upon a pre-ictal period probability falling below a pre-ictal probability threshold. Sackellares teaches wherein the at least one processor may be further configured to determine an inter-ictal period upon a pre-ictal period probability falling below a pre-ictal probability threshold (Sackellares | Figs. 18 and 21B; [0069], [0269-0270]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Nogueira in view of Poh to incorporate the teachings of determining an inter-ictal period upon the pre-ictal period probability falling below the pre-ictal probability threshold from Sackellares because it allows for predicting the onset of a potential seizure and providing the appropriate care to the user (Sackellares | [0014]).
Claims 7, 8, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nogueira in view of Poh, Sackellares, and Osorio et al. (US 20130096840 A1).
Regarding Claim 7, Nogueira in view of Poh and Sackellares teaches the method as recited in claim 6. Nogueira in view of Poh is silent in teaching further comprising maintaining, by the at least one processor, an alert status associated with a pre-ictal alert. Sackellares teaches maintaining, by the at least one processor, an alert status associated with the pre-ictal alert (Sackellares | Figure 3; Paragraphs 0074-0082). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of Nogueira in view of Poh to incorporate the teachings of an alert status associated with the pre-ictal alert from Sackellares because it allows for providing early detection to the user when there may be an onset of a potential seizure (Sackellares | [0013-0014]).
Nogueira in view of Poh and Sackellares is silent in teaching an alert status associated with the pre-ictal alert until a seizure occurrence period has passed. Osorio teaches an alert status associated with the pre-ictal alert until a seizure occurrence period has passed (Osorio | seizure termination determination module – element 570; Paragraphs 0112-0113 and 0118; ‘duration of warning’ can be controlled). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of Nogueira in view of Poh and Sackellares to incorporate the teachings of ending the seizure warning/alert once the seizure occurrence period has passed from Osorio because this action notifies the physicians when the patient’s seizure has ended and how to further assess in the aftermath of the event (Osorio | Paragraph 0088, 0108) and because Osorio teaches being able to adjust the duration of the warning as desired ( Osorio | Paragraph 0118).
Regarding Claim 8, Nogueira in view of Poh and Sackellares teaches the method as recited in claim 7. Nogueira in view of Poh is silent in teaching wherein the seizure occurrence period comprises one hour. Sackellares teaches wherein the seizure occurrence period comprises one hour (Sackellares | Figure 20; [0266 - FIG. 20 shows STLmax profiles over 3 hours including two seizures, and 1 hour after the second seizure]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the method of Nogueira in view of Poh to incorporate the teachings of the seizure occurrence period from Sackellares because it provides an overview of the number of seizures the user experienced over a specified period. For example, Fig. 20 shows a total of two seizures during an 180-minute window (Sackellares | Figure 20; [0266]).
Regarding Claim 15, Nogueira in view of Poh and Sackellares teaches the system as recited in claim 14. Nogueira in view of Poh is silent in teaching wherein the at least one processor may be further configured to maintain an alert status associated with a pre-ictal alert. Sackellares teaches wherein the at least one processor may be further configured to maintain an alert status associated with a pre-ictal alert (Sackellares | Figure 3; Paragraphs 0074-0082). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Nogueira in view of Poh to incorporate the teachings of an alert status associated with the pre-ictal alert from Sackellares because it allows for providing early detection to the user when there may be an onset of a potential seizure (Sackellares | [0013-0014]).
Nogueira in view of Poh and Sackellares is silent in teaching an alert status associated with the pre-ictal alert until a seizure occurrence period has passed. Osorio teaches an alert status associated with the pre-ictal alert until a seizure occurrence period has passed (Osorio | seizure termination determination module – element 570; Paragraphs 0112-0113 and 0118; ‘duration of warning’ can be controlled). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Nogueira in view of Poh and Sackellares to incorporate the teachings of ending the seizure warning/alert once the seizure occurrence period has passed from Osorio because this action notifies the physicians when the patient’s seizure has ended and how to further assess in the aftermath of the event (Osorio | Paragraph 0088, 0108) and because Osorio teaches being able to adjust the duration of the warning as desired ( Osorio | Paragraph 0118).
Regarding Claim 16, Nogueira in view of Poh and Sackellares teaches the system as recited in claim 15. Nogueira in view of Poh is silent in teaching wherein the seizure occurrence period comprises one hour. Sackellares teaches wherein the seizure occurrence period comprises one hour (Sackellares | Figure 20; [0266 - FIG. 20 shows STLmax profiles over 3 hours including two seizures, and 1 hour after the second seizure]). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Nogueira in view of Poh to incorporate the teachings of the seizure occurrence period from Sackellares because it provides an overview of the number of seizures the user experienced over a specified period. For example, Fig. 20 shows a total of two seizures during an 180-minute window (Sackellares | Figure 20; [0266]).
Response to Arguments
Applicant’s arguments and amendments filed 01/16/2026 have been fully considered.
The applicants amendments to the claims have overcome the 35 U.S.C. 112b Rejections.
Regarding the 35 U.S.C 101 Rejection, the Applicant argues “circadian rhythm dependent metric” with “a seizure machine learning model” demonstrates an improvement on computer or network performance (Last Paragraph of Page 12 to First Paragraph of Page 13 within Applicant’s Remarks), and that the claims as a whole integrates the exception into a practical application by improving the technical field (Page 9 within the Applicant’s Remarks) because the wearable device solves a problem within the technical field for monitoring seizure prediction through electro-dermal activity, measured by the wearable device, to predict impending seizures (Page 10 within Applicant’s Remarks).
However, the examiner respectfully disagrees, and the following analysis explains why the claims, as a whole, do not provide an exception nor provide details to improve computer components. The amendments to the claims do not overcome the rejection because the claims meet Step 2A Prong 1, Step 2B Prong 2, and Step 2B. The amended claims meet Step 2A Prong 1 because the claims discuss predicting the onset of a seizure; the claims are read as a mental process. The amended claims meet Step 2A Prong 2 because the abstract idea in each of independent claims are not integrated into a practical application, referring to Step 2A, Prong 2 for further clarification. Lastly, the amended claims meet Step 2B because the additional elements of wearable device, wearable sensor, biomarker sensor, and at least one sensor are well-known, routine, and conventional as taught in US Reference 20140316229 A1; the additional element of a processor are generically claimed computer components.
Regarding the 35 U.S.C. 103 Rejection, the examiner agrees that Sackellares in view of Picard does not teach the claim limitations found in the amended claims, more specifically the seizure machine learning model and at least one circadian rhythm-dependent metric. Due to the scope changes from the amended claims, further search and consideration was required. New references, Nogueira et al. (US 20180206776 A1) and Poh et al (US 20120296175 A1), are used to modify the method and system for predicting the onset of a seizure. Please refer to the 35 U.S.C 103 Rejection for further clarification.
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 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 SRISTI DIVINA GOMES whose telephone number is (571)272-1356. The examiner can normally be reached Monday-Friday: 9AM to 5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Chen can be reached at 571-272-3672. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SRISTI DIVINA GOMES/Examiner, Art Unit 3791
/AURELIE H TU/Primary Examiner, Art Unit 3791
Prosecution Insights