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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114 was filed in this application after appeal to the Patent Trial and Appeal Board, but prior to a decision on the appeal. Since this application is eligible for continued examination under 37 CFR 1.114 and the fee set forth in 37 CFR 1.17(e) has been timely paid, the appeal has been withdrawn pursuant to 37 CFR 1.114 and prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant’s submission filed on 01/16/2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 16-18, 20-23, and 26-35 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 16 contains a step of “generating, using a machine learning model that is provided with user data, a predicted cognitive activation score for a future time period based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user”. The specification fails to teach how a future cognitive activation score can be determined from the comparison between the first physiological pattern and a baseline physiological pattern of the user. Paragraph [0103] of the published specification teaches that a “Cognitive activation score of the user for different times of the day may be predicted based on machine learning techniques. For example, the user's data may be collected over a period of time to train a model, such that it can be predicted that a user is relaxed at certain times of the day or in response to certain events (e.g. massage, lunch, etc.), or that a user may be stressed at certain time of the day or in response to certain events (e.g. high workload, after work meeting, before doctor's appointment, etc.)”. It does not teach how a machine learning model can take a first physiological pattern and a baseline physiological pattern and use the difference to determine that a user will be stressed later in the day. The same issue is present in claims 28 and 33.
Claims not explicitly rejected above are rejected because they depend from claims rejected above as failing to comply with the written description requirement.
Claims 16-18, 20-23, and 26-35 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regards to claim 1 it is unclear how a machine learning model can take a first physiological pattern and a baseline physiological pattern and use the difference to determine that a user will be stressed later in the day. The claim appears to teach a machine learning model that receives an input of difference between a first physiological pattern and a baseline physiological pattern, wherein the first physiological pattern is from a past or current time period, and outputs a cognitive score for a time period that has not happened yet. It is unclear how a current physiological pattern difference can be used to determine that the user will have a certain cognitive score later in the day. Paragraph [0103] of the published specification teaches that “the user's data may be collected over a period of time to train a model, such that it can be predicted that a user is relaxed at certain times of the day or in response to certain events (e.g. massage, lunch, etc.), or that a user may be stressed at certain time of the day or in response to certain events (e.g. high workload, after work meeting, before doctor's appointment, etc.)”. This appears to be a separate model from the model that determines a cognitive score based on threshold differences as described in paragraph [0076]. For purposes of examination the claim is being interpreted as the cognitive score being determined based on historical data mapped to the event and/or time of day the historical data was gathered. The historical data including a cognitive score based on one or more threshold differences between the first physiological pattern and the baseline physiological pattern. The machine learning model takes in the event as an input and based on the historical data mapped to the event and/or time of day the historical data was gathered, it outputs a predicted cognitive score. For example, if the user’s cognitive score for driving is historically high, then the machine learning model can determine that the cognitive score will be high if the user drives in the future. The same issue is present in claims 22 and 28.
Claim 21 states that the apparatus obtains “the baseline physiological pattern by: obtaining historical physiological patterns of the user; determining boundaries in the historical physiological pattern based on a plurality of emotional or cognitive states; and applying labels for the plurality of emotional or cognitive states”. It is unclear how these steps establish a baseline physiological pattern. The physiological pattern comprises physiological data collected whole the user is in a relaxed state according to claim 20, but claim 21 says that this is determined by obtaining multiple patterns for various states. It is unclear how these steps produce a baseline physiological pattern. For purposes of examination the claim is being interpreted as “The apparatus as claimed in claim 20, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus to: obtain historical physiological patterns of the user…”
Claims not explicitly rejected above are rejected because they depend from claims rejected above as indefinite.
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) 16-18, 20, 22-23, and 26-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goyal (US 20180204439 A1) in view of Arechiga-Gonzalez (US 20220153302 A1) in view of Huang (US 20180249939 A1 – previously cited) in view of Lu (US 20210042680 A1).
In regards to claim 16 Goyal teaches an apparatus comprising:
at least one processor ([0069] one or more processors 16);
and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to ([0072] “memory 28 may include at least one program product having a set (e.g., at least one) of program processes that are configured to carry out the functions of embodiments of the invention”):
receive physiological measurements associated with a user, wherein the physiological measurements comprise heart beat measurements ([0027] “Examples of the cognitive state data may include, but not limited to, biometric and physiological data of the user 101, such as a heart rate”;
generate, using a machine learning model that is provided with user data, a predicted cognitive activation score for a future time period, based on historical cognitive states mapped to events ([0030] “In block 240, the intelligent alarm process 120 builds context-personality-cognitive state (CPC) mappings by associating the various cognitive states, the context, and the personality of the user 101, as identified from blocks 210 through 230. The intelligent alarm process 120 subsequently processes the CPC mappings by machine learning such that the updated CPC mapping knowledgebase (CKB) 170 may be utilized to predict cognitive state of the users in future event contexts. Then the intelligent alarm process 120 proceeds with block 250”);
provide an indication of the predicted cognitive activation score to the user or to one or more other users, wherein the indication comprises a representation of the predicted cognitive activation score, wherein the indication of the predicted cognitive activation score relates to one or more tasks to be performed by the user, and wherein the predicted cognitive activation score is predicted based, at least in part, on times of day or certain events;
and send, before the future time period, a trigger to the user indicating the predicted cognitive activation score, wherein the trigger is sent before the future time period ([0044] ”In block 360, the intelligent alarm process 120 generates a personalized alarm pursuant to the cognitive state of the user as predicted from block 350, and notifies the same to the user.”).
schedule at least one of the one or more tasks of the user based on the predicted cognitive activation score of the user ([0045] Alarms alert user to stay on schedule);
Goyal fails to teach determining, based on intervals between peaks of the physiological measurements, a first physiological pattern and determining a predicted cognitive activation score for a future time period based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user.
Arechiga-Gonzalez teaches determining based on intervals between peaks of the physiological measurements, a first physiological pattern ([0054-0055] “It has been determined that heart rate variability (HRV) is lower than a resting state when a person is in a decreased cognitive state. A fatigued state will likely have a slower heartbeat compared to baseline/norm. Another cognitive state may be characterized by a low HRV”, HRV is the interval between R peaks). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to determine the cognitive scores that are used to create the context-personality-cognitive state mappings of Goyal using HRV as the heart rate data like the process described in Arechiga-Gonzalez. Doing so would merely be substituting one cognitive state measuring method using heart rate data with another in order to obtain the predictable result of determining a cognitive activation score based on a heart rate pattern. Doing so would also make the predicted cognitive activation score for a future time period of Goyal be based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user.
Goyal/Arechiga-Gonzalez fails to teach scheduling at least one of the one or more tasks of the user based on the predicted cognitive activation score of the user. Huang teaches scheduling at least one of the one or more tasks of the user based on the a cognitive activation score of the user ([0072] “However, if the user has been determined to be under stress, as indicated by the stress detection classification described previously, the stress management tool may generate one or more suggested appointments 1010, 1011 to the user and place them in the user's column of appointments 1001. The suggested appointments include stress reduction activities such as meditation 1010 or a coffee break 1011. The suggested appointments 1010, 1011 may be a different color or font to alert the user to their suggested status”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the processor of Goyal/Arechiga-Gonzalez to schedule appointments when the user’s cognitive score indicates they will be stressed like the device of Huang in order to manage the user’s stress.
Goyal/Arechiga-Gonzalez/Huang fails to teach sending a trigger indicating the predicted cognitive activation score responsive to the predicted cognitive activation score satisfying a first threshold. Lu teaches sending a trigger to the user in response to a predicted cognitive activation score being greater than a first threshold (Lu [0054] “The caregiver alert system 120 determines whether the caregiver is able to perform tasks of a job based on comparing the current cognitive state and the current physical state with thresholds of care rules associated with the tasks of the job. In response to determining that the caregiver is not able to perform the tasks of the job, the caregiver alert system 120 provides one or more alerts and/or recommendations”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the processor of Goyal/Arechiga-Gonzalez/Huang to compare the cognitive state to event associated thresholds like the device of Lu in order for the user to be alerted to their cognitive state if the predicted cognitive state is too high for the time of day/event it is predicted for.
In regards to claim 17, modified Goyal teaches the apparatus as claimed in claim 16, wherein the cognitive activation score is determined based on one or more threshold differences between the first physiological pattern and the baseline physiological pattern (Arechiga-Gonzalez [0055] “It has been determined that heart rate variability (HRV) is lower than a resting state when a person is in a decreased cognitive state”, inherently a threshold of zero would indicate a decreased cognitive state).
In regards to claim 18, modified Goyal teaches the apparatus of claim 16. Modified Goyal fails to teach an apparatus wherein the heart beat measurements relate to electrocardiogram data of the user. Huang teaches an apparatus wherein the heart beat measurements relate to electrocardiogram data of the user (Huang [0022]). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the apparatus of modified Goyal to get its heart rate data from an electrocardiogram like the apparatus of Huang. Doing so would merely be substituting one type of heart rate acquisition method with another.
In regards to claim 20, modified Goyal teaches the apparatus as claimed in claim 16, wherein the baseline physiological pattern comprises physiological data collected while the user is in a relaxed state (Arechiga-Gonzalez [0054-0055] HRV in relaxed state is baseline).
In regards to claim 23, modified Goyal teaches the apparatus as claimed in claim 16, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus to: provide the indication on a calendar, wherein the indication is accompanied by one or more time stamps associated with the predicted cognitive activation score (FIG 10, shows time stamps next to the suggested appointments 1010, Huang [0072]).
In regards to claim 26, modified Goyal teaches the apparatus as claimed in claim 16, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus to: based on the cognitive activation score of the user, assign one or more tasks to at least one of the user or the one or more other users (Huang [0072] going to appointment is a task).
In regards to claim 27, modified Goyal teaches the apparatus as claimed in claim 16, wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus to: based on a respective cognitive state of one or more persons, comprised from at least one of the user or the one or more other users, assign a task to one of the one or more persons (Huang [0072]).
In regards to claim 28 Goyal teaches method comprising:
receiving physiological measurements associated with a user, wherein the physiological measurements comprise heart beat measurements ([0027] “Examples of the cognitive state data may include, but not limited to, biometric and physiological data of the user 101, such as a heart rate”;
generating, using a machine learning model that is provided with user data, a predicted cognitive activation score for a future time period, based on historical cognitive states mapped to events ([0030] “In block 240, the intelligent alarm process 120 builds context-personality-cognitive state (CPC) mappings by associating the various cognitive states, the context, and the personality of the user 101, as identified from blocks 210 through 230. The intelligent alarm process 120 subsequently processes the CPC mappings by machine learning such that the updated CPC mapping knowledgebase (CKB) 170 may be utilized to predict cognitive state of the users in future event contexts. Then the intelligent alarm process 120 proceeds with block 250”);
providing an indication of the predicted cognitive activation score to the user or to one or more other users, wherein the indication comprises a representation of the predicted cognitive activation score, wherein the indication of the predicted cognitive activation score relates to one or more tasks to be performed by the user, and wherein the predicted cognitive activation score is predicted based, at least in part, on times of day or certain events;
and sending, before the future time period, a trigger to the user indicating the predicted cognitive activation score, wherein the trigger is sent before the future time period ([0044] ”In block 360, the intelligent alarm process 120 generates a personalized alarm pursuant to the cognitive state of the user as predicted from block 350, and notifies the same to the user.”).
schedule at least one of the one or more tasks of the user based on the predicted cognitive activation score of the user ([0045] Alarms alert user to stay on schedule);
Goyal fails to teach determining, based on intervals between peaks of the physiological measurements, a first physiological pattern and determining a predicted cognitive activation score for a future time period based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user.
Arechiga-Gonzalez teaches determining based on intervals between peaks of the physiological measurements, a first physiological pattern ([0054-0055] “It has been determined that heart rate variability (HRV) is lower than a resting state when a person is in a decreased cognitive state. A fatigued state will likely have a slower heartbeat compared to baseline/norm. Another cognitive state may be characterized by a low HRV”, HRV is the interval between R peaks). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to determine the cognitive scores that are used to create the context-personality-cognitive state mappings of Goyal using HRV as the heart rate data like the process described in Arechiga-Gonzalez. Doing so would merely be substituting one cognitive state measuring method using heart rate data with another in order to obtain the predictable result of determining a cognitive activation score based on a heart rate pattern. Doing so would also make the predicted cognitive activation score for a future time period of Goyal be based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user.
Goyal/Arechiga-Gonzalez fails to teach scheduling at least one of the one or more tasks of the user based on the predicted cognitive activation score of the user. Huang teaches scheduling at least one of the one or more tasks of the user based on the a cognitive activation score of the user ([0072] “However, if the user has been determined to be under stress, as indicated by the stress detection classification described previously, the stress management tool may generate one or more suggested appointments 1010, 1011 to the user and place them in the user's column of appointments 1001. The suggested appointments include stress reduction activities such as meditation 1010 or a coffee break 1011. The suggested appointments 1010, 1011 may be a different color or font to alert the user to their suggested status”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Goyal/Arechiga-Gonzalez to schedule appointments when the user’s cognitive score indicates they will be stressed like the device of Huang in order to manage the user’s stress.
Goyal/Arechiga-Gonzalez/Huang fails to teach sending a trigger indicating the predicted cognitive activation score responsive to the predicted cognitive activation score satisfying a first threshold. Lu teaches sending a trigger to the user in response to a predicted cognitive activation score being greater than a first threshold (Lu [0054] “The caregiver alert system 120 determines whether the caregiver is able to perform tasks of a job based on comparing the current cognitive state and the current physical state with thresholds of care rules associated with the tasks of the job. In response to determining that the caregiver is not able to perform the tasks of the job, the caregiver alert system 120 provides one or more alerts and/or recommendations”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Goyal/Arechiga-Gonzalez/Huang to compare the cognitive state to event associated thresholds like the method of Lu in order for the user to be alerted to their cognitive state if the predicted cognitive state is too high for the time of day/event it is predicted for.
In regards to claim 29, modified Goyal teaches the method as claimed in claim 28, wherein the cognitive activation score is determined based on one or more threshold differences between the first physiological pattern and the baseline physiological pattern (Arechiga-Gonzalez [0055] “It has been determined that heart rate variability (HRV) is lower than a resting state when a person is in a decreased cognitive state”, inherently a threshold of zero would indicate a decreased cognitive state).
In regards to claim 30, modified Goyal teaches the method as claimed in claim 28, further comprising: providing the indication on a calendar, wherein the indication is accompanied by one or more time stamps associated with the predicted cognitive activation score (FIG 10, shows time stamps next to the suggested appointments 1010, Huang [0072]).
In regards to claim 31, modified Goyal teaches the method as claimed in claim 28, further comprising: based on the cognitive activation score of the user, assigning one or more tasks to at least one of the user or the one or more other users. (Huang [0072] going to appointment is a task).
In regards to claim 32, modified Goyal teaches the method as claimed in claim 28, based on a respective cognitive state of one or more persons, comprised from at least one of the user or the one or more other users, assign a task to one of the one or more persons (Huang [0072]).
In regards to claim 33 Goyal teaches a non-transitory computer readable medium comprising program instructions stored thereon for performing at least the following ([0072] “Computer system 12 may further include other removable/non-removable, volatile/non-volatile computer system storage media”):
receiving physiological measurements associated with a user, wherein the physiological measurements comprise heart beat measurements ([0027] “Examples of the cognitive state data may include, but not limited to, biometric and physiological data of the user 101, such as a heart rate”);
generating, using a machine learning model that is provided with user data, a predicted cognitive activation score for a future time period, based on historical cognitive states mapped to events ([0030] “In block 240, the intelligent alarm process 120 builds context-personality-cognitive state (CPC) mappings by associating the various cognitive states, the context, and the personality of the user 101, as identified from blocks 210 through 230. The intelligent alarm process 120 subsequently processes the CPC mappings by machine learning such that the updated CPC mapping knowledgebase (CKB) 170 may be utilized to predict cognitive state of the users in future event contexts. Then the intelligent alarm process 120 proceeds with block 250”);
providing an indication of the predicted cognitive activation score to the user or to one or more other users, wherein the indication comprises a representation of the predicted cognitive activation score, wherein the indication of the predicted cognitive activation score relates to one or more tasks to be performed by the user, and wherein the predicted cognitive activation score is predicted based, at least in part, on times of day or certain events;
and sending, before the future time period, a trigger to the user indicating the predicted cognitive activation score, wherein the trigger is sent before the future time period ([0044] ”In block 360, the intelligent alarm process 120 generates a personalized alarm pursuant to the cognitive state of the user as predicted from block 350, and notifies the same to the user.”).
schedule at least one of the one or more tasks of the user based on the predicted cognitive activation score of the user ([0045] Alarms alert user to stay on schedule);
Goyal fails to teach determining, based on intervals between peaks of the physiological measurements, a first physiological pattern and determining a predicted cognitive activation score for a future time period based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user.
Arechiga-Gonzalez teaches determining based on intervals between peaks of the physiological measurements, a first physiological pattern ([0054-0055] “It has been determined that heart rate variability (HRV) is lower than a resting state when a person is in a decreased cognitive state. A fatigued state will likely have a slower heartbeat compared to baseline/norm. Another cognitive state may be characterized by a low HRV”, HRV is the interval between R peaks). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to determine the cognitive scores that are used to create the context-personality-cognitive state mappings of Goyal using HRV as the heart rate data like the process described in Arechiga-Gonzalez. Doing so would merely be substituting one cognitive state measuring method using heart rate data with another in order to obtain the predictable result of determining a cognitive activation score based on a heart rate pattern. Doing so would also make the predicted cognitive activation score for a future time period of Goyal be based on a comparison between the first physiological pattern and a baseline physiological pattern of the user, wherein the baseline physiological pattern relates to a baseline state of the user and the first physiological pattern relates to a first emotional or cognitive state of the user.
Goyal/Arechiga-Gonzalez fails to teach scheduling at least one of the one or more tasks of the user based on the predicted cognitive activation score of the user. Huang teaches scheduling at least one of the one or more tasks of the user based on the a cognitive activation score of the user ([0072] “However, if the user has been determined to be under stress, as indicated by the stress detection classification described previously, the stress management tool may generate one or more suggested appointments 1010, 1011 to the user and place them in the user's column of appointments 1001. The suggested appointments include stress reduction activities such as meditation 1010 or a coffee break 1011. The suggested appointments 1010, 1011 may be a different color or font to alert the user to their suggested status”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Goyal/Arechiga-Gonzalez to schedule appointments when the user’s cognitive score indicates they will be stressed like the device of Huang in order to manage the user’s stress.
Goyal/Arechiga-Gonzalez/Huang fails to teach sending a trigger indicating the predicted cognitive activation score responsive to the predicted cognitive activation score satisfying a first threshold. Lu teaches sending a trigger to the user in response to a predicted cognitive activation score being greater than a first threshold (Lu [0054] “The caregiver alert system 120 determines whether the caregiver is able to perform tasks of a job based on comparing the current cognitive state and the current physical state with thresholds of care rules associated with the tasks of the job. In response to determining that the caregiver is not able to perform the tasks of the job, the caregiver alert system 120 provides one or more alerts and/or recommendations”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the method of Goyal/Arechiga-Gonzalez/Huang to compare the cognitive state to event associated thresholds like the method of Lu in order for the user to be alerted to their cognitive state if the predicted cognitive state is too high for the time of day/event it is predicted for.
In regards to claim 34, modified Goyal teaches the non-transitory computer readable medium of claim 33, wherein the program instructions are further configured to cause: providing the indication on a calendar, wherein the indication is accompanied by one or more time stamps associated with the predicted cognitive activation score. (Huang [0072] going to appointment is a task).
In regards to claim 35, modified Goyal teaches the non-transitory computer readable medium of claim 33, wherein the program instructions are further configured to cause: based on the cognitive activation score of the user, assigning one or more tasks to at least one of the user or the one or more other users (Huang [0072]).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goyal (US 20180204439 A1) in view of Arechiga-Gonzalez (US 20220153302 A1) in view of Huang (US 20180249939 A1 – previously cited) in view of Lu (US 20210042680 A1) as applied to claim 21, further in view of Derchak (US 20080221401 A1 -previously cited).
In regards to claim 21, modified Goyal teaches the apparatus as claimed in claim 20. Modified Goyal fails to teach an apparatus wherein the at least one memory and the computer program code are configured to, with the at least one processor, further cause the apparatus to: obtain historical physiological patterns of the user (Derchak [0055] “One approach uses a-priori data (already observed stimulus-response data) to train a selected algorithm”); determine boundaries in the historical physiological pattern based on a plurality of emotional or cognitive states (Derchak [0055] “Presentation of observed data along with the experienced emotional states to a classification training algorithm teaches the classifier (i.e., generates parameters that define a specific classifier) to recognize the distinctions between each of the classes that are present”; and apply labels for the plurality of emotional or cognitive states (Derchak [0056] “Step 1 preferably includes labeling the relevant classes (e.g., emotional states)”). It would have been prima facie obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the processor of modified Goyal to use the method of Derchak to train the cognitive state classifier of modified Goyal in order to define the cognitive states of a user.
Examiner’s Note
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Siefert (US 20140214335 A1 – previously cited) teaches method for determining a response to stimulus including a step of providing an indication of a measured response including a time-stamp of the response (“The system 200 can time-stamp or event stamp the measured responses along with the unique identifier of that participant”, Siefert [0132]).
Adeluyi (R-READER: A Lightweight Algorithm for Rapid Detection of ECG Signal R-peaks – previously cited) teaches:
computing one or more minimum points and maximum points of received physiological measurements for a first threshold time period (Page 2 “Using these varying slopes as a basis, we have developed an algorithm that can rapidly detect the QRS-complex region, especially the start-of-complex, R-peak and end-of-complex” The Q and R waves are minimum points, and R wave is a maximum point);
and determining one or more R peaks using a peak detection function, wherein the peak detection function comprises detecting a first peak by performing: storing information for a first peak from a starting point of an increasing slope (Page 2 Step i of R-READER process);
and determining R peaks based peak-adjacency-interval-distance (Page 2 Step v of R-READER process).
In regards to claim 22, none of the prior art teaches or suggests, either alone or in combination, an apparatus comprising a program code that determines an R-Peak by: storing information for a first peak from a starting point of an increasing slope; computing a density of the first peak; computing a first angle of the first peak after the start of a decreasing slope; computes an area of the first peak in response to determining that the decreasing slope ends, in response to the starting point being a distance away from a previous peak, immediately preceding the first peak, that satisfies a threshold distance: computing alignment values of the first peak in relation to other peaks; and determines whether the first peak is an R peak based on the alignment values in combination with the other claimed elements.
While no prior art rejections have been made against claim 22, the claim is not in condition for allowance due to the rejections under 35 U.S.C. 112(a) and 35 U.S.C. 112(b).
Response to Arguments
Applicant’s arguments, see remarks, filed 01/16/2026, with respect to the 35 U.S.C. 112(a) rejection(s) of claim(s) 16, 28, and 33 regarding the step of generating “a machine learning model based on historical physiological data collected from the user at one or more previous time periods” have been fully considered and are persuasive. The rejection has been withdrawn.
Applicant’s arguments, see remarks, filed 01/16/2026, with respect to the 35 U.S.C. 112(a) rejection(s) of claim(s) 16, 28, and 33, regarding the step of generating “using the machine learning model, a predicted cognitive activation score for a time period based on a comparison between the first physiological pattern and a baseline physiological pattern of the user”, and that the “predicted cognitive activation score is provided in relation to one or more tasks performed” have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in regards to the amendments made to claims 16, 28, and 33 regarding generating a predicted cognitive activation score for a future time period.
Applicant’s arguments, see remarks, filed 01/16/2026, with respect to the 35 U.S.C. 112(b) rejection(s) of claim(s) 16-24 and 26-35 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection(s) of claim(s) 16-24 and 26-35 have been withdrawn.
Applicant’s arguments, see remarks, filed 01/16/2026, with respect to the 35 U.S.C. 101 rejection(s) of claim(s) 16-24 and 26-35 have been fully considered and are persuasive. The 35 U.S.C.101 rejection(s) of claim(s) 16-24 and 26-35 have been withdrawn.
Applicant’s arguments, see remarks, filed 01/16/2026, with respect to the 35 U.S.C. 103 rejection(s) of claim(s) 16-21, 23-24, and 26-35 have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Goyal (US 20180204439 A1) in view of Arechiga-Gonzalez (US 20220153302 A1) in view of Huang (US 20180249939 A1 – previously cited) in view of Lu (US 20210042680 A1).
Conclusion
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/LUCY EPPERT/ Examiner, Art Unit 3791
/ADAM J EISEMAN/ Primary Examiner, Art Unit 3791