DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed February 04, 2026.
Claims 1-20 are pending.
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 .
Request for Continued Examination
Claims 1-20 remain pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 8, and 15:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent Claim 1 falls within the statutory category of method
Independent Claim 8 falls within the statutory category of machine
Independent Claim 15 falls within the statutory category of article of manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 8, and 15 (Claim 1 being representative) claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in claims 1, 8, and 15 (claim 1 being representative) are:
A method comprising: receiving stress-related measurements collected for a user, the stress-related measurements collected over a specified time period, the stress-related measurements representing one or more physiological responses of the user to stress and corresponding to heart rate variability (HRV)-based measurements, respiration-based measurements, skin temperature-based measurements, and sympathetic nervous system- parasympathetic nervous system (SNS-PNS) stress response characteristics;
determining stress profile features associated with the user based on the stress-related measurements;
selecting to cluster stress response characteristics into multiple candidate stress profiles,a stress profile from among the multiple candidate stress profiles for association with the user based on the stress profile features associated with the user;
receiving, context data for the user associated with the user, the context data including location information for the user, activity information for the user, movement information for the user, and time/date information;
providing the selected stress profile and the context data, based on the selected stress profile and the context data, a stress intervention activity to alleviate stress for the user relating to a detected stress event;
initiating at least part of the selected stress intervention activity according to a stress intervention recommendation that includes the at least part of the selected stress intervention activity;
and updating, the selected stress profile according to the stress profile features associated with the user and the context data associated with the user during the detected stress event.
The identified abstract idea falls within "mental processes" abstract idea grouping as it is an evaluation and judgement of stress intervention given to a user along with “certain methods of organizing human activity” such as managing personal behavior or relationships or interactions between people to recommend a stress intervention to a user. see MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1, 8, and 15 the judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional elements below:
an electronic device positioned in or on at least one of a smart watch or earbuds communicatively coupled to the electronic device
stress sensors
one or more context sensors
a trained stress profile identification machine learning model
a trained stress intervention recommendation machine learning model
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, an electronic device positioned in or on at least one of a smart watch or earbuds communicatively coupled to the electronic device, is merely executing the abstract idea and is “apply-it” or an equivalent as it’s a tool used to manipulate data as a general computer element (see spec. [0014])
The additional elements, stress sensors and one or more context sensors, is merely “apply-it” or an equivalent as it is using sensors as a tool to collect data
The additional elements, a trained stress profile identification machine learning model and a trained stress intervention recommendation machine learning model, is merely “apply-it” or an equivalent as it is using a general computer (see spec. [0014]) to apply mathematical machine learning modelling to analyze data
Independent claim 8 recites the additional elements below not already recited in claim 1:
An electronic device with at least one memory configured to store instructions and comprising at least one processing device
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, an electronic device with at least one memory storing instructions comprising at least one processing device, is performing the abstract idea and is stated with a high level of generality and is merely “apply-it” or an equivalent as it is using computers as a tool to execute the abstract idea
Independent claim 15 recites the additional elements below not already recited in claim 1 and 8:
An electronic device with at least one processor and a non-transitory machine-readable medium containing instructions
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, An electronic device with at least one processor and a non-transitory machine-readable medium containing instructions, is performing the abstract idea and is stated with a high level of generality and is merely “apply-it” or an equivalent as it is using computers as a tool to execute the abstract idea
Accordingly, independent claims 1, 8, and 15 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer elements as analyzed above in step 2A prong 2, are merely applying the abstract idea with general computer elements or machine learning therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2-7, 9-14, and 16-20
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 2-7 fall within the statutory category of method
The dependent claims 9-14 fall within the statutory category of machine
The dependent claims 16-20 fall within the statutory category of article of manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-7, 9-14, and 16-20 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1, 8, and 15 abstract idea by (1) further limiting the stress profile features, (2) further limiting the physiological responses, (3) the context information of the user, (4) the stress intervention recommendation, and (5) further limiting at least part of the selected stress intervention activity Therefore, the dependent claims inherit the same abstract idea which is "mental processes" abstract idea grouping as it is an evaluation and judgement of stress intervention given to a user along with “certain methods of organizing human activity” such as managing personal behavior or relationships or interactions between people to recommend a stress intervention to a user. see MPEP § 2106.04(a)(2).
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-7, 9-14, and 16-20 this judicial exception is not integrated into a practical application.
The dependent claims recite the below additional elements not already recited in the independent claims:
smart phone
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element (a), above are merely “apply-it” or an equivalent to apply the abstract idea by using wearable devices to collect data
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements which amount to significantly more as they are merely applying the abstract idea. The claims are patent ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-20 are rejected to under 35 U.S.C. 103 as being unpatentable over Wild et. al (hereinafter Wild) (US2021/0177347A1) in view of Neumann (US2022/0027783A1) and in further view of Causevic et. al (hereinafter Causevic) (US10885800B2)
As per claim 1, Wild teaches:
A method comprising: receiving, at an electronic device (stress profiler or system both denoted in fig. 1, 10 and see [0127] discloses, “the stress profiler 10 implemented in a computing device such as a smart phone , smart watch , tablet computer , desktop computer or laptop computer.”) stress-related measurements collected for a user by stress sensors positioned in or on at least one of a smart watch or earbuds communicatively coupled to the electronic device, ([0145] discloses, “heart rate monitor , such as chest - mounted or arm - mounted devices used in sports e.g. Catapult Sports performance monitoring device , PolarTM heart rate monitor , FitbitTM , or smart watch capable of detecting heart rate;”)the stress-related measurements collected over specified time period, the stress-related measurements representing one or more physiological responses of the user to stress; and corresponding to heart rate variability (HRV)-based measurements, respiration-based measurements, skin temperature-based measurements, and sympathetic nervous system- parasympathetic nervous system (SNS-PNS) stress response characteristics; ([0024] discloses, “An embodiment comprises the step of receiving the stress information.” And [0144] discloses, “The stress profiler 10 includes the ability to accept input from multiple physiological information collection tools ( 3 ) . Each physiological information collection tool measures an aspect of the user's physiology which is indicative of stress in the user . Examples of suitable physiological information collection tools which can be used in the stress profiler 10 include , but are not limited to :” and [0156] discloses, smart clothing with sensors used to collect these stress related measurements and [0037] discloses, “An embodiment comprises the step of generating an acute stress score indicative of a magnitude of acute stress for the individual” and see [0087] discloses, “The stress profiler 10 can include a learning function , which recognizes patterns of stress information associated with previous periods of stress . Over time , the learning function progressively improves the accuracy and speed of stress profiling for a user.” And see [0005] discloses, “As an example , if a person becomes acutely stressed exercising or giving a presentation at work — their stress indicators such as heart rate , heart rate variability , sweat ( skin conductivity ) and so on , would elevate . These stress measures can be detected and recorded.” And see [0029] discloses, “An embodiment comprises the step of generating the physiological information . The step of generating the physiological information may comprise the step of generating information for each of a plurality of physiological functions in the individual . The step of generating information indicative of stress in each of a plurality of physiological functions in the individual may comprise generating at least one of heart rate information , heart rate variability information , respiratory rate information , respiratory rate variability information , blood pressure information , physical movement information , cortisol level information , a skin conductivity information , skin temperature information , blood oxygen saturation information , surface electromyography information , electroencephalography information , blood information , blood information , saliva information , and urine information.”)
Determining, by the electronic device, (stress profiler or system both denoted in fig. 1, 10 and see [0127] discloses, “the stress profiler 10 implemented in a computing device such as a smart phone , smart watch , tablet computer , desktop computer or laptop computer.”) stress profile features associated with the user based on the stress-related measurements; ([0259] discloses, “Resilience to Stress Indicator” and [0260] discloses, “The processor can also measure a person's response to an acute stress event or an acute stress state , and generate a measure of stress resilience . This can be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combination , to rise in response to an acute stress ( speed of response to acute stress ) . It can also be a score , which is indicative of the level to which the individual acute stress elements and indicators , either singular or in combination , reach after an acute stress ( intensity of response to acute stress ) . It can also be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combination , to return to “ unstressed ' or baseline levels following any particular stressful event ( speed of resolution ).” / examiner notes the instant application states in [0047] “the stress profile features included in each stress profile feature set 211-213 include sympathetic nervous system-parasympathetic nervous system (SNS-PNS) balance, recovery speed, and heterogeneity of stress response.” And [0053] “The recovery speed is represented by the time it takes for all biomarker values to return to their baseline values and remain there for a predetermined amount of time.” Therefore examiner interprets resilience to stress indicator in the disclosed art as recovery speed a stress profile feature)
Selecting, by the electronic device, (stress profiler or system both denoted in fig. 1, 10 and see [0127] discloses, “the stress profiler 10 implemented in a computing device such as a smart phone , smart watch , tablet computer , desktop computer or laptop computer.”) …[…]…. a stress profile from among multiple candidate stress profiles for association with the user based on the stress profile features associated with the user; ([0078] discloses, “The system 10 , in this but not all embodiments , generates a stress profile indicative of the magnitude and form of stress experienced by the user at the time of testing” and [0084] discloses, “At a minimum the stress profiler 10 processes two of the types of stress information. In one embodiment ,the stress profiler 10 processes psychometric and physiological information. However , the accuracy and sensitivity of the stress profiler 10 generally increases when more of the types of stress information are processed . The stress profiler 10 may therefore process three of the four , or even all four of the four types of stress information.” And see [0088] discloses, “The stress profiler 10 can also include a predictive function which identifies patterns of stress information indicative of the early signs of stress and notify the user early . For example , the stress profiler 10 may correlate a pattern of eye movement with physiological or psychometric indicators of stress in the particular user , and notify the user when those eye movements are detected — before serious symptoms arise.”)
receiving, by the electronic device, context data for the user collected by one or more context sensors associated with the user, the context data including location information for the user, activity information for the user, movement information for the user, and time/date information;([0031] discloses, “An embodiment comprises the step of generating the behavioural information . The step of generating the behavioural information may comprise at least one of the steps of : generating eye movement information indicative of eye movement of the individual ; generating location information indicative of a plurality of locations the individual has been ; generating nearby device information indicative of the nearby presence a plurality of devices of a plurality of people to the individual ; generating internet browsing history information for the individual ; generating keystroke rate , cadence , typing style , pressure or .force ' detection information for the individual ; generating voice analysis , including tone , cadence , word and phrase detection information for the individual ; generating telephone usage analysis , including call time , numbers dialed and time of day calls placed information for the individual ; generating driving style , including steering inputs , acceleration , deceleration , braking , speed of driving , brake and accelerator force and data from door pressure sensor information for the individual ; generating movement , body temperature , television usage , including channels watched , time watched and eye movement whilst watching , refrigerator analytics , heating and cooling analytics information for the individual ; generating bicycle data , including pedal force , pedaling cadence , acceleration , speed , routes taken , GPS data , altimeter data , time on bicycle , pedometer data information for the individual ; generating pedometer data and gait analysis information for the individual ; generating application usage information indicative of application usage by the individual ; generating media consumption information indicative of media consumption by the individual ; generating spending behaviour information indicative of the individual's spending behaviour ; generating food choice information indicative of a plurality of food choices made by the individual ; generating social outing information indicative of the individual's social outing activity ; generating productivity information indicative of the individual's ability to work and be productive ; and generating leave information indicative of leave taken by the individual.” And see [0038] discloses, “An embodiment comprises the step of generating a stress resilience score indicative of a response to acute stress for the individual . Preferably , the stress resilience score is indicative of one or more of the time taken for the individual to respond to an acute stress event ,…[…]…” and see [0114] / examiner notes the / is considered a time or date )
Providing, by the electronic device, the selected stress profile and the context data ..[…]…based on the selected stress profile and the context data, …[…]…([0257] discloses, “Chronic Stress Score [ 0258 ] The processor also generates a chronic stress score which is indicative of the magnitude of chronic stress . This score is calculated from aspects of the stress information ( psychometric information , physiological information , behavioural information and cognitive function information ) which are indicative of chronic stress .” and see [0259] discloses, “Resilience to Stress Indicator” and see [0260] discloses, “The processor can also measure a person's response to an acute stress event or an acute stress state , and generate a measure of stress resilience . This can be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combi nation , to rise in response to an acute stress ( speed of response to acute stress ) . It can also be a score , which is indicative of the level to which the individual acute stress elements and indicators , either singular or in combination , reach after an acute stress ( intensity of response to acute stress ) . It can also be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combination , to return to “ unstressed ' or baseline levels following any particular stressful event ( speed of resolution ).” / examiner interprets under BRI that the users profile is updated with a score which is associated with the context and location type information. ; and see [0031] discloses, “An embodiment comprises the step of generating the behavioural information . The step of generating the behavioural information may comprise at least one of the steps of : generating eye movement information indicative of eye movement of the individual ; generating location information indicative of a plurality of locations the individual has been ; generating nearby device information indicative of the nearby presence a plurality of devices of a plurality of people to the individual ; generating internet browsing history information for the individual ; generating keystroke rate , cadence , typing style , pressure or .force ' detection information for the individual ; generating voice analysis , including tone , cadence , word and phrase detection information for the individual ; generating telephone usage analysis , including call time , numbers dialed and time of day calls placed information for the individual ; generating driving style , including steering inputs , acceleration , deceleration , braking , speed of driving , brake and accelerator force and data from door pressure sensor information for the individual ; generating movement , body temperature , television usage , including channels watched , time watched and eye movement whilst watching , refrigerator analytics , heating and cooling analytics information for the individual ; generating bicycle data , including pedal force , pedaling cadence , acceleration , speed , routes taken , GPS data , altimeter data , time on bicycle , pedometer data information for the individual ; generating pedometer data and gait analysis information for the individual ; generating application usage information indicative of application usage by the individual ; generating media consumption information indicative of media consumption by the individual ; generating spending behaviour information indicative of the individual's spending behaviour ; generating food choice information indicative of a plurality of food choices made by the individual ; generating social outing information indicative of the individual's social outing activity ; generating productivity information indicative of the individual's ability to work and be productive ; and generating leave information indicative of leave taken by the individual.” And see [0038] discloses, “An embodiment comprises the step of generating a stress resilience score indicative of a response to acute stress for the individual . Preferably , the stress resilience score is indicative of one or more of the time taken for the individual to respond to an acute stress event ,…[…]…” and see [0114] / examiner notes the / is considered a time or date )
…[…]…and updating by the electronic device…[…]…, the selected stress profile according to the stress profile features associated with the user and the context data associated with the user during the detected stress event ([0257] discloses, “Chronic Stress Score [ 0258 ] The processor also generates a chronic stress score which is indicative of the magnitude of chronic stress . This score is calculated from aspects of the stress information ( psychometric information , physiological information , behavioural information and cognitive function information ) which are indicative of chronic stress .” and see [0259] discloses, “Resilience to Stress Indicator” and see [0260] discloses, “The processor can also measure a person's response to an acute stress event or an acute stress state , and generate a measure of stress resilience . This can be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combi nation , to rise in response to an acute stress ( speed of response to acute stress ) . It can also be a score , which is indicative of the level to which the individual acute stress elements and indicators , either singular or in combination , reach after an acute stress ( intensity of response to acute stress ) . It can also be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combination , to return to “ unstressed ' or baseline levels following any particular stressful event ( speed of resolution ).” / examiner interprets under BRI that the users profile is updated with a score which is associated with the context and location type information. )
However, Wild does not teach the underlined portions:
selecting, by the electronic device, using stress profile identification machine learning model trained to cluster stress response characteristics into multiple candidate stress profiles, a stress profile from among multiple candidate stress profiles for association with the user based on the stress profile features associated with the user;
Providing, by the electronic device, the selected stress profile and the context data to a stress intervention recommendation machine learning model trained to select based on the selected stress profile and the context data, a stress intervention activity to alleviate stress for the user relating to a detected stress event;
Initiating, in the smart watch and the earbuds, at least part of the selected stress intervention activity according to a stress recommendation from the stress intervention recommendation machine learning model that includes the at least part of the selected stress intervention activity;
and updating by the electronic device using the stress profile identification machine learning model, the selected stress profile according to the stress profile features associated with the user and the context data associated with the user during the detected stress event
However, Neumann does teach the underlined portions:
selecting, by the electronic device, using stress profile identification machine learning model trained to cluster stress response characteristics into multiple candidate stress profiles, a stress profile from among multiple candidate stress profiles for association with the user based on the stress profile features associated with the user; (see Fig. 4 and Fig. 5 and [0003] discloses, “Computing device may calculate the stress score using a machine learning model trained as a function of stress score training data that delineates stress in a user“ and [0038] discloses, “Referring to FIG . 1 , computing device 104 may calculate , using the plurality of user metrics 128 generated by a stress machine - learning model 116 , stress score 120.” And abstract discloses, “. Calculating the stress score further comprises training a machine learning model as a function of stress score training data that numerically describes stress in a user” and see [0030] discloses, “A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together , found to be close under a distance metric as described below , and the like . Machine - learning module 300 may generate a classifier using a classification algorithm , defined as a process whereby a computing device and / or any module and / or component operating thereon derives a classifier from training data set 304. Classification may be performed using , without limitation , linear classifiers such as without limitation logistic regression and / or naïve Bayes classifiers , nearest neighbor classifiers such as k - nearest neighbors classifiers , support vector machines , least squares support vector machines , fisher's linear discriminant , quadratic classifiers , decision trees , boosted trees , random forest classifiers , learning vector quantization , and / or neural network - based classifiers . As a non - limiting example , training data classifier 316 may classify elements of training data to match one or more categories including elements of user data and / or constitutional data , such as without limitation a cohort of persons and / or other analyzed items and / or phenomena for which a subset of training data may be selected.” And see [0038] discloses, “Referring to FIG . 1 , computing device 104 may calculate , using the plurality of user metrics 128 generated by a stress machine - learning model 116 , stress score 120. A " user metric , ” as used in this disclosure , is a numerical value such as a metric , score , function , or the like , that mathematically delineates , describes , measures , summarizes , or otherwise captures at least an element of user data 108 of the plurality of user data 108 as it may relate to an affective response , user event , stress level , threshold , or the like , of a user in describing stress and / or stressors by quantifying and assigning numerical values to elements and / or variables identified in the data , extracting these elements and / or variables , and calculating by use of an equation , function , heuristic , or the like , as determined by a machine - learning model , a metric as it relates to stress in a user .”) / examiner has interpreted the overall stress score as a type of stress profile of the user under broadest reasonable interpretation)
Providing, by the electronic device, the selected stress profile and the context data to a stress intervention recommendation machine learning model trained to select based on the selected stress profile and the context data, a stress intervention activity to alleviate stress for the user relating to a detected stress event; (abstract discloses, “Computing device generates a stress balance instruction set by training a machine learning model , identifying a strategy , and generating an instruction set for implementing the strategy.” And see [0066] and see [0022])
Initiating in the smart watch and the earbuds, atleast part of the selected stress intervention activity according to a stress recommendation from the stress intervention recommendation machine learning model that includes the at least part of the selected stress intervention activity ([0060] discloses, “A user action datum may correspond to an action of implementing an instruction or may correspond to a user action that is not directed to an instruction …[…]… ; likewise the user action may correspond directly to an instruction , as described above . In non - limiting illustrative examples , a stress balance instruction set 148 may suggest a series of user actions for improving sleep quality , such as ‘ no elec tronic device use within 2 hours of sleep ' , ' taking a warm bath 1.5 hour before sleep ’ , “ meditating for 15 minutes 1 hour before sleep ' , and ' taking 10 mg of melatonin 45 minutes before sleep ’ , wherein completing these actions may be reflected indirectly via a wearable sleep monitoring device that shows an increase in the sleep quality over time , and that this may be attributed to a user indicating that the instructions were performed.” And see [ 0017 ] Referring now to FIG . 1 , an exemplary embodi ment of a system 100 for generating a stress balance instruction set for a user is illustrated . System 100 includes a computing device 104. Computing device 104 may include any computing device as described in this disclosure , includ ing without limitation a microcontroller , microprocessor , digital signal processor ( DSP ) and / or system on a chip ( SOC ) as described in this disclosure . Computing device may include , be included in , and / or communicate with a mobile device such as a mobile telephone or smartphone . Comput ing device 104 may include a single computing device operating independently , or may include two or more com puting device operating in concert , in parallel , sequentially and the like ; two or more computing devices may be included together in a single computing device or in two or more computing devices . Computing device 104 may inter face or communicate with one or more additional devices as described below in further detail via a network interface device . Network interface device may be utilized for connecting computing device 104 to one or more of a variety of networks , and one or more devices . Examples of a network interface device include , but are not limited to , a network interface card ( e.g. , a mobile network interface card , a LAN card ) , a modem , and any combination thereof .Examples of a network include , but are not limited to , a wide area network ( e.g. , the Internet , an enterprise network ) , a local area network ( e.g. , a network associated with an office , a building , a campus or other relatively small geographic space ) , a telephone network , a data network associated with a telephone / voice provider ( e.g. , a mobile communications provider data and / or voice network ) , a direct connection between two computing devices , and any combinations thereof . network may employ a wired and / or a wireless mode of communication . In general , any network topology may be used . Information ( e.g. , data , software etc. ) may be communicated to and / or from a computer and / or a computing device . Computing device 104 may include but is not limited to , for example , a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location . Computing device 104 may include one or more computing devices dedicated to data storage , security , distribution of traffic for load balancing , and the like . Computing device 104 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device , which may operate in parallel , in series , redundantly , or in any other manner used for distribution of tasks or memory between computing devices . Computing device 104 may be implemented using a “ shared nothing ” architecture in which data is cached at the worker , in an embodiment , this may enable scalability of system 100 and / or computing device.” And see abstract and [0066] and see [0022])
and updating by the electronic device using the stress profile identification machine learning model, the selected stress profile according to the stress profile features associated with the user and the context data associated with the user during the detected stress event (see Fig. 4 and Fig. 5 and [0003] discloses, “Computing device may calculate the stress score using a machine learning model trained as a function of stress score training data that delineates stress in a user“ and [0038] discloses, “Referring to FIG . 1 , computing device 104 may calculate , using the plurality of user metrics 128 generated by a stress machine - learning model 116 , stress score 120.” And abstract discloses, “. Calculating the stress score further comprises training a machine learning model as a function of stress score training data that numerically describes stress in a user” and see [0030] discloses, “A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together , found to be close under a distance metric as described below , and the like . Machine - learning module 300 may generate a classifier using a classification algorithm , defined as a process whereby a computing device and / or any module and / or component operating thereon derives a classifier from training data set 304. Classification may be performed using , without limitation , linear classifiers such as without limitation logistic regression and / or naïve Bayes classifiers , nearest neighbor classifiers such as k - nearest neighbors classifiers , support vector machines , least squares support vector machines , fisher's linear discriminant , quadratic classifiers , decision trees , boosted trees , random forest classifiers , learning vector quantization , and / or neural network - based classifiers . As a non - limiting example , training data classifier 316 may classify elements of training data to match one or more categories including elements of user data and / or constitutional data , such as without limitation a cohort of persons and / or other analyzed items and / or phenomena for which a subset of training data may be selected.” And see [0038] discloses, “Referring to FIG . 1 , computing device 104 may calculate , using the plurality of user metrics 128 generated by a stress machine - learning model 116 , stress score 120. A " user metric , ” as used in this disclosure , is a numerical value such as a metric , score , function , or the like , that mathematically delineates , describes , measures , summarizes , or otherwise captures at least an element of user data 108 of the plurality of user data 108 as it may relate to an affective response , user event , stress level , threshold , or the like , of a user in describing stress and / or stressors by quantifying and assigning numerical values to elements and / or variables identified in the data , extracting these elements and / or variables , and calculating by use of an equation , function , heuristic , or the like , as determined by a machine - learning model , a metric as it relates to stress in a user .”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wild’s teachings of a stress profiler and stress interventions with Neumann’s teachings of a trained machine learning model which selects the stress intervention and recommends the actual intervention, the motivation being that Wild teaches a learning function and a predictive function ([0087]-[0088] for improving accuracy and speed of profiling a user, and teaches the need for high precision in making stress recommendations to intervene see [0345]), therefore Neumann’s explicit teachings of machine learning models would not be unpredictable to use for learning and prediction and high precision decision making for stress recommendations as it would improve the accuracy and speed of the stress profiling and intervention recommendations.
However, Neumann also doe not teach:
Initiating in the smart watch and the earbuds, atleast part of the selected stress intervention activity according to a stress recommendation
However, Causevic does teach:
The method of Claim 1, wherein: the one or more context sensors are positioned in or on a smart phone and the at least part of the selected stress intervention activity comprises playing relaxation music and employing haptic feedback to notify the user of the stress intervention recommendation (Col. 3 lines 6-30 and see Col. 3 lines 48-65 and see Col. 7 and 8)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wild’s teachings of earbuds and smartphones as previously cited and Neumann’s explicit teachings of choosing a stress intervention based on effectiveness using machine learning with Causevic as previously cited, the motivation being its hit and miss outcomes would be further avoided by understanding the effectiveness of the intervention for the user as Wild teaches is trying to be achieved ([0344]) and implementing the intervention with wearable technology would improve the ease of use and response time to understand if an intervention is working.
As per claim 2, Wild does not teach:
The method of Claim 1, wherein the stress profile identification machine learning model is trained using unsupervised learning to cluster multiple prior users into multiple clusters based on stress profile features of the prior users, each cluster associated with at least one of the multiple candidate stress profiles.
However, Neumann does teach:
The method of Claim 1, wherein the stress profile identification machine learning model is trained using unsupervised learning to cluster multiple prior users into multiple clusters based on stress profile features of the prior users, each cluster associated with at least one of the multiple candidate stress profiles. ([0030] which discloses, “Further referring to FIG . 3 , training data may be filtered , sorted , and / or selected using one or more supervised and / or unsupervised machine - learning processes and / or models as described in further detail below ; such models may include without limitation a training data classifier 316….[…]… A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together , found to be close under a distance metric as described below , and the like…[…]… As a non - limiting example , training data classifier 316 may classify elements of training data to match one or more categories including elements of user data and / or constitutional data , such as without limitation a cohort of persons and / or other analyzed items and / or phenomena for which a subset of training data may be selected.” And in [0038] which discloses, “Referring to FIG . 1 , computing device 104 may calculate , using the plurality of user metrics 128 generated by a stress machine - learning model 116 , stress score 120. A " user metric , ” as used in this disclosure , is a numerical value such as a metric , score , function , or the like , that mathematically delineates , describes , measures , summarizes , or other wise captures at least an element of user data 108 of the plurality of user data 108 as it may relate to an affective response , user event , stress level , threshold , or the like , of a user in describing stress and / or stressors by quantifying and assigning numerical values to elements and / or variables identified in the data , extracting these elements and / or variables , and calculating by use of an equation , function , heuristic , or the like , as determined by a machine - learning model , a metric as it relates to stress in a user . For instance in non - limiting illustrative examples , a user metric 128 may relate to user stress levels of various physiological elements of data , for instance a number assigned to a user's ‘ stress from sleep quality ' wherein the stress machine - learning model trained with data that relates the user's current sleep schedule and rapid – eye movement…[…]…” and in [0041] which discloses, “For instance and without limitation , an imbalance machine - learning model 136 may calculate a normal stress range threshold by training with data retrieved from a database that relates user data 108 to ' normal stress range ' numerical values similar to how user data 108 cor responds to user metrics 128 , which may be represented by a range of values ; the trained imbalance machine - learning model 136 may then determine a numerical threshold asso ciated with the range , for instance the upper or lower value of the numerical range , above and / or below which may be considered a stress imbalance 132 , wherein the user is experiencing an amount of stress which may have deleteri ous effects on user physiology , emotion , lifestyle , and the like . In further non - limiting illustrative examples , an imbal ance machine learning model 136 may accept an input of a user stress score 120 and train using data relating current user stress scores 120 , for instance from one or more users , and determine a stress range as it relates to the scoring criteria , and ultimately calculate a stress score 120 threshold for determining if a stress imbalance exists . An imbalance machine learning model 136 may calculate a difference between a threshold and one or more individual user metrics 128 used to compute the user stress score 120 , to determine a stress imbalance 132. For instance and without limitation , an imbalance machine - learning model 136 may determine a numerical stress threshold associated with a normal stress range for quantifying a stress imbalance associated with sleep deprivation ; a user metric 128 with user data 108 regarding sleep quality may be used corresponding with the normal stress range values for sleep deprivation , and an imbalance machine - learning model 136 may determine a threshold value from this data , wherein comparing the user metric 128 to the threshold may reveal a stress imbalance 132. And in [0025] / Examiner notes the cited art teaches a classification machine learning model trained which clusters sets of data together which the data is stress data measurements for example and compared to a database of users information and/or other users to identify associated intervention instructions based on the users own profiling of stress characteristics therefore teaching the amended claim limitation as the stress profile under BRI as one of ordinary skill in the art would understand again is a way to assess and understand an individual's emotional responses to stress through self-report questionnaires or physiological measurements which help to identify ways to help a person with stress for example.)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wild’s teachings with Neumann’s teachings for the same reasons given for claim 1.
As per claim 3, Wild teaches:
The method of Claim 2, wherein the SNS-PNS stress response characteristics include balance, recovery speed, and heterogeneity of stress responses of the user. ([0259] discloses, “Resilience to Stress Indicator” and [0260] discloses, “The processor can also measure a person's response to an acute stress event or an acute stress state , and generate a measure of stress resilience . This can be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combination , to rise in response to an acute stress ( speed of response to acute stress ) . It can also be a score , which is indicative of the level to which the individual acute stress elements and indicators , either singular or in combination , reach after an acute stress ( intensity of response to acute stress ) . It can also be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combination , to return to “ unstressed ' or baseline levels following any particular stressful event ( speed of resolution ).” And [0299] discloses, “movement and physical balance” and ;/ examiner notes the instant application states in [0047] “the stress profile features included in each stress profile feature set 211-213 include sympathetic nervous system-parasympathetic nervous system (SNS-PNS) balance, recovery speed, and heterogeneity of stress response.” And [0053] “The recovery speed is represented by the time it takes for all biomarker values to return to their baseline values and remain there for a predetermined amount of time.” And [0056] “The heterogeneity of a user's stress response refers to how much the user's stress response changes over time for different occurrences of a stress event” and [0121] discloses, “Another example is an individual's physiological sleep measurements . For example , a sleep sensor might detect “ normal ' sleep patterns ( depth , timing of sleep cycles , and so on ) but a behavioural analysis of sleep might correlate and detect that a person tends to go to sleep later in the evening , wake later and take longer to “ get going in the morning when they are more stressed . The ‘ physiological sleep analysis ’ might suggest ‘ unstressed ' but a “ behavioural sleep analysis ' might detect ' stress behaviour”/ Therefore examiner interprets resilience to stress indicator in the disclosed art as recovery speed a stress profile feature, balance as a SNS-PNS feature, and change in stress response such as changes in normal sleep patterns as heterogeneity)
As per claim 4, Wild teaches the underlined portion:
The method of Claim 1, wherein the stress intervention recommendation machine learning model is configured to select the stress intervention activity for the user based on stress intervention activity characteristics and the context data, wherein the stress intervention activity characteristics include at least one of: an effectiveness of the stress intervention activity for the selected stress profile, and a user preference, and wherein the context data is employed to determine an appropriateness of the stress intervention activity in a current environment. ([0257] discloses, “Chronic Stress Score [ 0258 ] The processor also generates a chronic stress score which is indicative of the magnitude of chronic stress . This score is calculated from aspects of the stress information ( psychometric information , physiological information , behavioural information and cognitive function information ) which are indicative of chronic stress .” and see [0259] discloses, “Resilience to Stress Indicator” and see [0260] discloses, “The processor can also measure a person's response to an acute stress event or an acute stress state , and generate a measure of stress resilience . This can be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combi nation , to rise in response to an acute stress ( speed of response to acute stress ) . It can also be a score , which is indicative of the level to which the individual acute stress elements and indicators , either singular or in combination , reach after an acute stress ( intensity of response to acute stress ) . It can also be a score , which is indicative of the time taken for the individual acute stress elements and indicators , either singular or in combination , to return to “ unstressed ' or baseline levels following any particular stressful event ( speed of resolution ).”
However, Wild does not teach the underlined portions:
The method of Claim 1, wherein the stress intervention recommendation machine learning model is configured to select the stress intervention activity for the user based on stress intervention activity characteristics and the context data, wherein the stress intervention activity characteristics include at least one of: an effectiveness of the stress intervention activity for the selected stress profile, and a user preference, and wherein the context data is employed to determine an appropriateness of the stress intervention activity in a current environment.
However, Neumann teaches:
The method of Claim 1, wherein the trained stress intervention recommendation machine learning model is configured to select the stress intervention activity for the user based on stress intervention activity characteristics…[…]…, wherein the stress intervention characteristics include at least one of: an effectiveness of the stress intervention activity for the selected stress profile, (as disclosed effectiveness of an intervention and likelihood to implement is taken into consideration when giving instructions) and a user preference, and …[…]…employed to determine an appropriateness of the stress intervention activity in a current environment. ([0061] discloses, “Continuing in reference to FIG . 1 , computing device may calculate the likelihood to implement an instruction for a user by considering how past user events corresponds following an instruction of a stress balance instruction set 148. The “ likelihood ” to implement an instruction , as described in this disclosure , is a probabilistic quantitative metric that reflects a tendency for a user to follow an instruction , course of action , stress management strategy , or the like . An effectiveness machine learning process 164 may determine how user actions reflected in the updated user data 160 correspond to implementing the instructions in a stress balance instruction set 148 , and calculate the likelihood of implementing an instruction , wherein the likelihood changes over time with more user data.” And [0056] discloses, “The " effectiveness ” of an instruction , as used in this disclosure , is a qualitative and / or quantitative measure of efficacy of an instruction , set of instructions , and / or strategy that corresponds to a set of instructions in addressing a stress imbalance , altering a user stress score 120 , and / or impacting a user metric 128 ; effectiveness may be represented as a numerical value , function , matrix , vector , signifier , or any other suitable means by which a machine - learning process , computing device , or the like may refer to an effectiveness metric of an instruction . In non - limiting illustrative examples , effectiveness of an instruction may refer to recognizing user feedback in the updated user data 160 that relates to a user's reported effectiveness of a stress management strategy and / or instruction ; for instance , a user may directly report that they did not like a strategy, prefer a second strategy, or found an instruction effective.”
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wild’s teachings of interventions likely to be of assistance ([0343]) with Neumann’s explicit teachings of choosing a stress intervention based on effectiveness using machine learning, the motivation being hit and miss outcomes would be further avoided by understanding the effectiveness of the intervention for the user as Wild teaches is trying to be achieved ([0344]) and machine learning would only improve the speed and accuracy of this for the same reasons given for claim 1. Also the type of data used to use in the machine learning is choice data.
As per claim 5, Wild and Neumann do not teach:
The method of Claim 1, wherein: the one or more context sensors are positioned in or on a smart phone and the at least part of the selected stress intervention activity comprises playing relaxation music and employing haptic feedback to notify the user of the stress intervention recommendation
However, Causevic does teach:
The method of Claim 1, wherein: the one or more context sensors are positioned in or on a smart phone and the at least part of the selected stress intervention activity comprises playing relaxation music and employing haptic feedback to notify the user of the stress intervention recommendation (Col. 3 lines 6-30 and see Col. 3 lines 48-65 and see Col. 7 and 8)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Wild’s teachings of earbuds and smartphones as previously cited and Neumann’s explicit teachings of choosing a stress intervention based on effectiveness using machine learning with Causevic as previously cited, the motivation being its hit and miss outcomes would be further avoided by understanding the effectiveness of the intervention for the user as Wild teaches is trying to be achieved ([0344]) and implementing the intervention with wearable technology such as relaxation music and haptic feedback would improve the ease of use and response time to understand if an intervention is working.
As per claim 6, Wild teaches:
The method of Claim 1, wherein the one or more physiological responses of the user to the stress comprise a change in at least one of: blood pressure, heart rate, skin temperature, respiration, and heart rate variability. ([0029] discloses, “The step of generating information indicative of stress in each of a plurality of physiological functions in the individual may comprise generating at least one of heart rate information , heart rate variability information , respiratory rate information , respiratory rate variability information , blood pressure information , physical movement information , cortisol level information , a skin conductivity information , skin temperature information , blood oxygen saturation information , surface electromyography information , electroencephalography information , blood information , saliva information , and urine information.”)
As per claim 7, Wild teaches:
The method of Claim 1, wherein the movement information for the user comprises at least one of speed or direction, and wherein the : activity information for the user , corresponds to one of sitting, walking, running, reading, working, or sleeping. ([0121] discloses, “Another example is an individual's physiological sleep measurements . For example , a sleep sensor might detect “ normal ' sleep patterns ( depth , timing of sleep cycles , and so on ) but a behavioural analysis of sleep might corr late and detect that a person tends to go to sleep later in the evening , wake later and take longer to “ get going in the morning when they are more stressed . The ‘ physiological sleep analysis ’ might suggest ‘ unstressed ' but a “ behavioural sleep analysis ' might detect ' stress behaviour '.” and see [0292] discloses, “software which uses the camera to detect the direction and speed of eye movements , and determines the time spent on certain ' news articles and reading tasks ;” and see [0031] discloses, “gener ating bicycle data , including pedal force , pedaling cadence , acceleration , speed , routes taken , GPS data , altimeter data , time on bicycle , pedometer data information for the indi vidual ; generating pedometer data and gait analysis infor mation for the individual ;)”
As per claims 8-14, they are apparatus claims which repeat the same limitations of claims 1-7 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings and motivations to combine of Wild and Neumann disclose the underlying process steps that constitute the methods of claims 1-7, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well as cited below. As such, the limitations of claims 8-14 are rejected for the same reasons given above for claims 1-7.
An electronic device comprising: at least one memory configured to store instructions; ([0076] discloses, “The system 10 includes a suitable microprocessor 12 such as , or similar to , the INTEL XEON or AMD OPTERON microprocessor connected over a bus 16 to memory which includes a suitable form of random access memory 18 of around 1 GB , or generally any suitable alternative capacity , and a non - volatile memory 20 such as a hard disk drive or solid state non - volatile memory ( e.g. NAND - based FLASH memory ) having a capacity of around 500 Gb , or any alternative suitable capacity . Alternative logic devices may be used in place of the microprocessor 12 . Examples of suitable alternative logic devices include application - specific integrated circuits , field programmable gate arrays ( FPGAs ) , and digital signal processing units . Some of these embodiments may be entirely hardware based.”) and at least one processing device configured when executing the instructions to: ([0066] discloses, “Disclosed herein is non - transitory processor read able tangible media including program instructions which when executed by a processor causes the processor to perform a method disclosed above.”)
As per claims 15-20, it is an article of manufacture claim which repeats the same limitations of claims 1-7 the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings and motivations to combine of Wild and Neumann disclose the underlying process steps that constitute the method of claim 1-7 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well as cited below. As such, the limitations of claims 15-20 are rejected for the same reasons given above for claims 1-7.
A non-transitory machine-readable medium containing instructions that when executed cause at least one processor of an electronic device to: ([0066] discloses, “Disclosed herein is non - transitory processor readable tangible media including program instructions which when executed by a processor causes the processor to perform a method disclosed above.” and [0067] discloses, “Disclosed herein is a computer program for instructing a processor , which when executed by the processor causes the processor to perform a method disclosed above.”)
Response to Arguments Regarding 35 U.S.C § 101 Rejection
The applicant argues on pages 1-3 of the submitted remarks that the rejection of claims 1-20 under 35 U.S.C § 101 should be withdrawn in light of the below arguments.
The Applicant's claims have nothing to do with any methods of organizing human activity, such as fundamental economic principles or practices (like hedging, insurance, mitigating risk), commercial or legal interactions (like agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations), or managing personal behavior or relationships or interactions between people (like social activities, teaching, and following rules or instructions). MPEP @ 2106 specifically states that this grouping is limited to activity that falls within the enumerated sub- groupings of fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances (which do not apply here).
Moreover, Claim 1 recites "receiving ... stress-related measurements collected for a user by stress sensors positioned in or on at least one of a smart watch or earbuds communicatively coupled to the electronic device," "selecting ... a stress profile," "receiving ... context data for the user," and "initiating, in the smart watch and the earbuds, at least part of the selected stress intervention activity according to a stress intervention recommendation." The "at least part of the selected stress intervention activity" may include, for example, playing relaxation music and employing haptic feedback to notify the user of the stress intervention recommendation. Taken as a whole, these elements improve the functioning of a stress intervention mechanism. MPEP @@ 2106.04(d)(1), 2106.06(b). The claims therefore integrate the putative abstract idea into a practical application.
The Applicant therefore respectfully requests that the § 101 rejection be withdrawn.
Examiner appreciates applicant’s arguments but does not find them persuasive.
The MPEP § 2106.04(a) states, “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process. 654 F.3d at 1372-73, 99 USPQ2d at 1695. See also Flook, 437 U.S. at 586, 198 USPQ at 196 (claimed "computations can be made by pencil and paper calculations"); University of Florida Research Foundation, Inc. v. General Electric Co., 916 F.3d 1363, 1367, 129 USPQ2d 1409, 1411-12 (Fed. Cir. 2019) (relying on specification’s description of the claimed analysis and manipulation of data as being performed mentally "‘using pen and paper methodologies, such as flowsheets and patient charts’"); Symantec, 838 F.3d at 1318, 120 USPQ2d at 1360 (although claimed as computer-implemented, steps of screening messages can be "performed by a human, mentally or with pen and paper"
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process
The MPEP § 2106.04(a) The sub-grouping “managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions. An example of a claim reciting managing personal behavior is Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 115 USPQ2d 1636 (Fed. Cir. 2015). The patentee in this case claimed methods comprising storing user-selected pre-set limits on spending in a database, and when one of the limits is reached, communicating a notification to the user via a device. 792 F.3d. at 1367, 115 USPQ2d at 1639-40. The Federal Circuit determined that the claims were directed to the abstract idea of "tracking financial transactions to determine whether they exceed a pre-set spending limit (i.e., budgeting)", which "is not meaningfully different from the ideas found to be abstract in other cases before the Supreme Court and our court involving methods of organizing human activity." 792 F.3d. at 1367-68, 115 USPQ2d at 1640
MPEP 2106.04(a)(2) (II) states, “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping.”
MPEP 2106.05(a) states the abstract idea cannot provide the improvement but rather the additional elements must provide the improvement. It also recites. “It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer).” Therefore, examiner notes the amended claim limitations argued are abstract and thus cannot bring forth the improvement even with the recitation of a processor performing the steps.
The MPEP 2106.05(a) also recites, “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016). After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100. An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107.
The claims are directed to a mental process along with certain methods of organizing human activity as the claims recite gathering stress data about a user, analyzing it, and determining a recommendation or intervention. This happens today without the use of a computer by means of observation, judgement as well as by managing personal behaviors of human activity. The abstract idea cannot bring forth the practical application but rather the additional elements. The claims recite generic computer additional elements, wearable devices, as well as machine learning broadly and/or as “apply-it” to execute the abstract idea. The claims, even in light of the specification, do not reflect a solution to a technical problem caused by the technological environment to which the claims are confined, i.e., a well-known, computer (see appellant’s specification para. e.g. [0014]) and also do not reflect an improvement to machine learning. Further the playing of relaxation music and haptic feedback further limits the abstract idea as this can be executed by the human and is not limited to being controlled or performed by the machines in a tangible manner confined to the computer as currently constructed in the claims. Finally, stress intervention is not seen as a problem with a technical field or a technical improvement to the confined general computer environment reflected in the claims. The current amendments continue to be directed to the abstract idea as characterized in this office action and thus cannot integrate the claims into a practical application or significantly more.
The 35 U.S.C § 101 rejection is maintained.
Response to Arguments Regarding 35 U.S.C § 103 Rejections
Applicant argues on pages 3-5 of the remarks that claims 1-20 rejected under 35 U.S.C § 103 should be withdrawn. Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Examiner maintains the 35 U.S.C § 103 rejection.
Prior Art Cited but Not Relied Upon
US20230083418 – Mcduff et. al
Various methods and apparatus relating to estimating and mitigating a stress level of a user are disclosed herein. Methods can include collecting potential stress indicator data from the user interacting with a computing device. The potential stress indicator data can include one or more of environmental data and contextual data associated with the user. Methods can include estimating the stress level of the user based on the potential stress indicator data. Methods can include performing an evaluation of whether to mitigate the stress level of the user via one or more stress mitigation interventions. Methods can include presenting the one or more stress mitigation interventions to the user via a graphical user interface (GUI) when the evaluation indicates that the stress level should be mitigated.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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