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, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/25/26 has been entered.
Response to Arguments
With regards to the 112b rejection for “Claims 1 and 12 recite “wherein calculating the stress score using the first feature data and the second feature data comprises normalizing the first feature data and the second feature data based on an average over a period of time and calculating the stress score as a weighted sum of at least one of sleep features, activity features, or heart features,”” the rejection is withdrawn in view of the amendment.
With regards to the 112b rejection for “Claim 21 recites “at least 30 days” however the scope of this is unclear” the rejection is withdrawn in view of the amendment.
Applicant's arguments filed 2/25/26 have been fully considered but they are not persuasive. With regards to the 101 rejection Applicants first argument the claim is integrated into a practical application. This is not persuasive.
Applicants point to the specifics of the EDA being on an outward side of the device opposite the wrist when worn, arguing that this unique hardware configured is designed to be easily accessible by a user’s palm or fingertip and that doing so provides an increase in Accuracy. Examiner notes that there is no statement in the specification supporting Applicants argument “by moving . . .” the spec discloses the configuration provides an accurate reading but it does not disclose that the better than the other alternative configurations. Examiner notes that Attorney arguments are not evidence on the record. Applicants also argue that the “unique configuration” incorporation “is tied to the practical application of accurately and automatically calculating stress of the user through the EDA sensor(s)”. However, as seen through the whole sentence it is not particular to the Applicant arguments stated “unique configuration”. To that end, Examiner notes that the part of the quote cut off is the reference number “154” which discloses sensors external to the housing including the in the figure provided 1B on the side toward the skin (see Fig. 1), thus contradicting the discussion of “unique hardware configuration”. The updated 101 rejection below provides evidentiary support for establishing that the EDA sensor configuration is well-understood, routine, conventional.
Furthermore, Applicants quote which is a subset of the full sentence “Accordingly, the present disclosure is tied to the practical application of accurately and automatically calculating stress of the user through the EDA sensor(s) 154.”. This portion and the rest of the specification discloses the practical application as being “calculating stress” which is itself the abstract idea.
To the extent the statement in the final paragraph is an argument directed to an improvement the improvement seemingly being pointed to is in the abstract idea itself and not in the additional elements. The claims are not directed to an improvement in technology like for example: a new type of sensor, a processor which can process more data with the same processor, or memory which can store more data with the same memory etc.
For the above reasons Applicants arguments are not persuasive.
Applicant’s arguments with respect to claim(s) 1 and 12 (and by extension the dependent claims) 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.
The remaining discussion depends on the arguments discussed above and are not persuasive and/or moot for the reasons discussed above.
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 1-2, 5, 7-13, 15, 17-21 are rejected under 101 as being directed to the abstract idea of a mental process and/or mathematical concept see the analysis below.
Step 1
The invention claimed in claims 1-20 the claims recite an apparatus or process.
Step 2A, Prong 1
Regarding Claims 1/12, the recited step of “calculating, via the processor of the wearable device, a stress score using the first feature data and the second feature data wherein calculating the stress score using the first feature data and the second feature data comprises normalizing the first feature data and the second feature data based on an average over a period of time and calculating the stress score as a weighted sum of at least one of sleep features, activity features, or heart features, wherein the weighted sum and the average is adapted to the user” is directed to a mental process of performing concepts in the human mind (including by a human using the aid of pen and paper) and/or the application of a mathematical relationships. For example, this limitation simply amounts to the mental process of a clinician reading a data printout and making a mental determination (such as by determining averages, normalizing/baselining/deviation to averages and multiplying a weight by the measured or normalized value) as to the stress score of the user; and/or, receiving the data and applying mathematical relationships (i.e. averages, normalizing, weighted sum) to the data to identify a stress score of the user.
Step 2A, Prong 2
Regarding Claims 1/12, the judicial exception is not integrated into a practical application. The claim includes the additional elements of “receiving, from one or more external sensors on the wearable device, first feature data corresponding to a state of the user of the wearable device, wherein the first feature data comprises electro-dermal activity (EDA) data captured using at least one external EDA sensor on a side of the wearable device away from a wrist of the user, the EDA data comprising a skin conductance level and a skin conductance response; obtaining, via a processor of the wearable device, second feature data corresponding to the state of the user, the second feature data provided by the user” and “performing, via the processor of the wearable device, at least one action based at least in part upon the calculated stress score, wherein the at least one action comprises generating an interface, the interface providing an option to receive additional stress data from the user; and receiving, from the interface, the additional stress data from the user.”. The steps of “receiving . . .” and “obtaining” amounts to insignificant, extra-solution activity in that the it is data gathering; while the steps of “performing . . .” amounts to insignificant, extra-solution activity in that the it is outputting a result. The processor (i.e., “processor”, “computer processor”, “cloud-computing device”, “mobile device”, “user device”) in computing steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining outputs from inputs) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
Regarding Claims 1/12, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As with step 2A, Prong 2 above, the additional elements of “receiving, from one or more external sensors on the wearable device, first feature data corresponding to a state of the user of the wearable device, wherein the first feature data comprises electro-dermal activity (EDA) data captured using at least one external EDA sensor on a side of the wearable device away from a wrist of the user, the EDA data comprising a skin conductance level and a skin conductance response; obtaining, via a processor of the wearable device, second feature data corresponding to the state of the user, the second feature data provided by the user” and “performing, via the processor of the wearable device, at least one action based at least in part upon the calculated stress score, wherein the at least one action comprises generating an interface, the interface providing an option to receive additional stress data from the user; and receiving, from the interface, the additional stress data from the user.”. The steps of “receiving . . .” and “obtaining” amounts to insignificant, extra-solution activity in that the it is data gathering; while the steps of “performing . . .” amounts to insignificant, extra-solution activity in that the it is outputting a result. The processor (i.e., “processor”, “computer processor”, “cloud-computing device”, “mobile device”, “user device”) in computing steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of determining outputs from inputs) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Additionally, per the Berkheimer requirement, the wearable with physiological sensing, processor/memory): (1) Ven see citations below; (2) White see recitations below; (3) US 20190290147 to Persen et al. [0003]-[0004], [0029], Figs. 5-7; (4) US 9711060 to Lusted et al. see Figs. 1-5B, Col 14:46-57; wearable with electrode/EDA/GSR sensor on front/outward side (away from skin): (1) US 20170000415 [0066], [0070]-[0071], ; (2) US 20180212449 see [0118], Fig. 9A; (3) US 20210052221 see [0085], Fig. 1; (4) US 20180220972 see [0078], [0085], [0088], [0114], [0122]. As such the elements are shown to be WRC.
The claim limitations when viewed individually and in combination therefore do not amount to significantly more than the abstract idea itself. The claims are therefore ineligible.
Claims 2, 5, 7-11, 13, 15, 17-21 only further define the data gathering (insignificant, extra-solution activity) or further define elements of the model (i.e., only further define the mental process/mathematical concept). Therefore, the claims do not include any additional elements that show integration into a practical application and do not include any additional elements that amount to significantly more than the abstract idea. The claims are 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 8, 10-13, 18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20140276119 to Subramaniam Venkatraman et al. (hereinafter Ven) in view of US 20170000415 to Lapetina et al. (hereinafter Lapetina) in further view of US 20120290215 to Jawahar Jain et al. (hereinafter Jain) in further view of US 20170053078 Lanzel et al. (hereinafter Lanzel) with evidentiary support from Yekta Said Can et al., Stress detection in daily life scenarios using smart phones and wearable sensors: A survey, Journal of Biomedical Informatics, Volume 92, 2019, 103139, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2019.103139. (hereinafter Can).
Regarding Claim 1, an interpretation of Ven discloses a method for accurately and automatically calculating a stress score for a user of a wearable device ([0122]), the method comprising:
receiving, from one or more external sensors on the wearable device ([0123]-[0124], [0226], [0233] including “the protrusion may contain other sensors that benefit from close proximity and/or secure contact to the user's skin.”, [0257] including “galvanic skin-response (GSR) circuitry to measure the response of the user's skin” see also [0479]), first feature data corresponding to a state of the user of the wearable device ([0122]-[0124], [0226], [0233], [0257] see also [0122], [0479]; recites various sensor data which “correspond” to a state of the user. related to sleep, heart or activity), wherein the first feature data comprises electro-dermal activity (EDA) data captured using at least one external EDA sensor ([0123] including Table, [0201]-[0202], [0233], [0257] including “The biometric monitoring devices of the present disclosure may also include galvanic skin-response (GSR)” see also [0479]), the EDA data comprising a skin conductance level and a skin conductance response ([0123] including Table, [0201]-[0202], [0233], [0257] including “The biometric monitoring devices of the present disclosure may also include galvanic skin-response (GSR)” see also [0479]; The Ven reference discloses gathering the GSR data. Per the evidentiary reference Can section 5.1.4. Electrodermal Activity (EDA) including “EDA, also known as Galvanic Skin Response (GSR) is the change of electrical properties of skin. . . . EDA can be computed by applying a small current and measure the resistance of skin between two placed electrodes. The EDA signal is composed of two components. The first one is the Skin Conductance Level (SCL). . . .The second part is the Skin Conductance Responses (SCR) . . .”, thus gathering the GSR data comprises gathering SCL and SCR data);
obtaining, via a processor of the wearable device ([0119], [0296] including “biometric monitoring devices may include one or more processors”, [0298]-[0299] see also [0479]), second feature data corresponding to the state of the user ([0038], [0123]-[0124], [0226], [0233], [0257] see also [0479]) the second feature data provided by the user ([0038] including “detecting a manually entered selection of activity type”, [0158] see also [0479]), the first feature data and the second feature data comprising feature data selected from sleep features, activity features, and heart features ([0038], [0122] including “the device or system may calculate the user's stress and/or relaxation levels through a combination of heart rate variability, skin conduction, noise pollution, and sleep quality.”, [0200]-[0201], [0226], [0233], [0257] see also [0123]-[0124], [0479]; recites various Features disclosed being gathered include features of sleep, heart and activity);
calculating, via the processor of the wearable device, a stress score using feature data ([0122] including “the biometric monitoring device or the system collating the data streams from the biometric monitoring device may calculate metrics derived from such data. For example, the device or system may calculate the user's stress”, [0201] including “determine the user's stress level, health state (e.g., risk, onset, or progression of fever or cold), and/or cardiac health using sensor data” see also [0380], [0383] see also [0479]).
While Ven discloses an EDA sensor, an interpretation of Ven may not explicitly disclose an EDA is on a side of the wearable device away from a wrist of the user.
However, in the same field of endeavor (medical diagnostic device), Lapetina teaches an EDA is on a side of the wearable device away from a wrist of the user ([0066], [0070]-[0071], Fig. 3A see also [0151]-[0152]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the device of Ven with the GSR sensors to include the placement of a GSR sensor on the outward (away from skin when worn side of the device) as recited by Lapetina because it is merely combining the device of Ven with the specifics of a placement of an electrode(s) on the front of the device away from which is combining prior art elements according to known methods to yield predictable results of gathering GSR sensor data using the sensors on the outward side of the device.
An interpretation of Ven may not explicitly disclose wherein calculating the stress score using the first feature data and the second feature data comprises normalizing the first feature data and the second feature data based on an average over a period of time wherein the average is adapted to the user; in response to calculating the stress score, performing at least one action based at least in part upon the calculated stress score wherein the at least one action comprises generating an interface, the interface providing an option to receive additional stress data from the user; and receiving, from the interface, the additional stress data from the user.
However, in the same field of endeavor (medical diagnostic device), Jain teaches obtaining second feature data corresponding to the state of the user, the second feature data provided by the user ([0056] including “user-input sensors”, [0063]-[0064] including “The user may input that he is “stressed” with an intensity of 2 on a 0-to-4 Likert scale.”, [0068] including “This “mood sensor” 400 is a type of user-input sensor that may receive psychological and behavioral input (i.e., stimulus) from a user.”, [0069]-[0070] including “the user may click, touch, speak, gesture, or otherwise interact with mood collection interface 420”, [0071] including “The user may touch one or more of the mood icons to input his current mood (i.e., psychological state). Mood intensity widget 440 is a row with numbered icons ranging from one to four that each correspond to a level of intensity of a psychological state.”);
calculating, a stress score using the first feature data and the second feature data ([0157] including “Any combination of two or more sensors may be used to generate a stress index value”, [0159] including “The stress level of a person may be quantified using a stress index, which may be any suitable scale for measuring or valuing stress . . . Further refinements could be developed, such as, for example, a transient stress index, a stress load, a stress-resilience coefficient, or other suitable stress measurements.”, [0165], [0238] see also [0069], [0155], [0166]-[0170], [0423]; assigning an index or value is a “score”), wherein calculating the stress score using the first feature data and the second feature data comprises normalizing the first feature data and the second feature data based on an average over a period of time wherein the average is adapted to the user ([0123] including “analysis system 180 may perform a variety of processes and calculations, including ranging, inspecting, cleaning, filtering, transforming, modeling, normalizing, averaging, annotating, correlating, or contextualizing data.”, [0127] including “The control period may be any suitable period. As an example, and not by way of limitation, a baseline model of a subject's blood pressure may simply be the subject's average blood pressure calculated from a series of blood-pressure measurements taken over the course of a week by a blood-pressure monitor.”, [0128] including “analysis system 180 may generate a model by normalizing or averaging one or more data streams. As an example and not by way of limitation, a model of a data stream from a single sensor 112 could simply be the average sensor measurement made by the sensor 112 over some initialization period.”, [0157]-[0159] See also [0305], [0328], [0423]; discloses normalizing each data stream by taking an average of it over period of time) wherein the first and second features are at least one of sleep features, activity features, or heart features ([0155], [0159] including “The stress level of a person may be quantified using a stress index, which may be any suitable scale for measuring or valuing stress.”, [0166]-[0170], [0195] including “access a data stream from a galvanic-skin-response sensor to measure and model stress in a person, wherein the data stream comprises galvanic-skin-response data of the person.”, [0223] See also [0043]-[0044], [0056]-[0057], [0071], [0423]; Recites data can be gathered from physiological sensors such as GSR/EDA, heart rate monitors etc. and from user input related to activity (exercise, sleeping etc.) which can be interpreted as different features); and
in response to calculating the stress score, performing, via the processor at least one action based at least in part upon the calculated stress score ([0157] including “analyze one or more of these data streams to determine the stress index of the user. Any combination of two or more sensors may be used to generate a stress index value.”, [0158]-[0159] including “Based on the comparison, analysis system 180 may then determine whether the user's stress level has changed over time.”, [0313]-[0314] including “a person may engage in a variety of stress-related therapies”, [0403] including “A therapy may be a recommended therapy for the user or a therapeutic feedback that provide a direct therapeutic benefit to the user.” See also [0074], [0392], [0423]); wherein the at least one action comprises generating an interface, the interface providing an option to receive additional stress data from the user ([0059], [0073] including “mood sensor 400 may query a user to input psychological or behavioral information. The user may input any suitable psychological or behavioral information into the mood sensor 400 . . . mood sensor 400 may query a user at a dynamic rate. The dynamic rate may be based on a variety of factors, including prior input into mood sensor 400, data streams from other sensors 112 or nodes 114 in sensor array 110, output from analysis system 180” see also [0157]-[0159], [0313]-[0314], [0403], [0423]; Recites prompting user input data after receiving an output of analysis unit as discussed above the analysis output include the output of the user stress index/score); and
receiving, from the interface, the additional stress data from the user ([0059], [0073] including “The dynamic rate may be based on a variety of factors, including prior input into mood sensor 400, data streams from other sensors 112 or nodes 114 in sensor array 110, output from analysis system 180” see also [0157]-[0158], [0313]-[0314], [0403], [0423]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina to include a calculation with additional features and providing an output based on the calculation as recited by Jain because the provided output includes providing a recommendation for management of the users stress ([0074], [0311]).
An interpretation of Ven may not explicitly disclose wherein calculating the score using the first feature data and the second feature data comprises calculating the score as a weighted sum of at least of features, wherein the weighted sum is adapted to the user.
However, in the same field of endeavor (medical diagnostic systems), Lanzel teaches wherein calculating the score using the first feature data and the second feature data comprises normalizing the respective feature data ([0085]-[0089], Fig. 6 see also [0034]-[0035]) and calculating the score as a weighted sum of the normalized first and second feature data ([0085]-[0089], Fig. 6 see also [0034]-[0035]) of at least one of sleep features, activity features, or heart features ([0033], [0037], [0081]-[0084), wherein the weighted sum is adapted to the user ([0085]-[0089], Fig. 6 see also [0034]-[0035]; The component scores (or scaled deviations/differentials) from the different physiological parameters are weighted and combined to generate a score).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score, outputting a result and gathering additional data as recited by Ven in view of Lapetina in further view of Jain to include a score determined as a weighted combination of various parameters as recited by Lanzel because the combination of subjective and objective inputs provides a more comprehensive view of the status ([0004). Additionally, It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to combine the analysis of Ven in view of Jain with the analysis as recited by Lanzel because it is combining prior art elements according to known methods to yield predictable results.
Regarding Claim 2, an interpretation of Ven discloses wherein the stress score represents at least one of a current stress level of the user of the wearable device ([0201] including “the biometric monitoring device may measure and/or determine the user's stress level”).
For purposes of compact prosecution, Examiner notes the stress score represents at least one of a current stress level is also disclosed by Jain ([0159] including “The stress level of a person may be quantified using a stress index, which may be any suitable scale for measuring or valuing stress.”, [0165] including “grade the stress level of a person on a scale of 0 to 100 . . . the stress index may quantify a person's transient stress (i.e., the change in stress caused by a stressor), baseline stress (i.e., the person's stress when in a normal state), and stress resilience (i.e., the rate at which a patient recovers from a stressor).”) or a stress resilience level ([0159], [0165] including “grade the stress level of a person on a scale of 0 to 100 . . . the stress index may quantify a person's transient stress (i.e., the change in stress caused by a stressor), baseline stress (i.e., the person's stress when in a normal state), and stress resilience (i.e., the rate at which a patient recovers from a stressor).”).
Regarding Claim 8, an interpretation of Ven further discloses determining activity level ([0124], [0291], [0384] including “the activity level being calculated from biometric monitoring device sensor data”; Examiner notes that per Applicants Specification (using PG Pub for paragraph numbers) [0033] including “It should be understood, however, that different numbers, selections, types, or variations can be used with different algorithms in accordance with various embodiments.” and [0040] including “In at least one embodiment, an algorithm can utilize at least some of these and/or other such features to generate a stress score that is representative of a current, past, or future stress state of a user.” The selection of “features” does not appear to have criticality), exercise or activity metrics ([0022] including “As a further example, detecting the intensity of user activity may be detecting running or walking.”, [0423] including “Such biometric monitoring devices may have sensors and run algorithms accordingly to measure and calculate biometric signals such as heart rate, heart rate variability, steps taken, calories burned, distance traveled, weight and body fat, activity intensity, activity duration and frequency, etc.” see also [0426]), exertion metrics ([0022], [0423] see also [0426]), activity type ([0046]), movement patterns ([0383] including “gait analysis”), or step count or movement ([0122] including “biometric monitoring device may calculate and store the user's step count using one or more biometric sensors.”).
While Ven recites gathering multiple of the recited features, an interpretation of Ven may not explicitly recite those features are features used in the determination of stress.
However, in the same field of endeavor (medical diagnostic systems), Jain teaches using activity features such as activity type as features used in the determination of stress ([0225] including “As an example and not by way of limitation, a user may report behavioral data by inputting that he is “driving” using activity input widget 450. Analysis system 180 may then analyze this behavioral data by inputting it into a stress model that correlates behavioral states with stress to calculate the user's stress index.” See also [0157], [0423])
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina to include a calculation with additional features and providing an output based on the calculation as recited by Jain because the provided output includes providing a recommendation for management of the users stress ([0074], [0311]).
Regarding Claim 10, an interpretation of Ven further discloses wherein the first feature data and the second feature data comprises feature data selected from at least one of (Examiner notes that per Applicants Specification (using PG Pub for paragraph numbers) [0033] including “It should be understood, however, that different numbers, selections, types, or variations can be used with different algorithms in accordance with various embodiments.” and [0040] including “In at least one embodiment, an algorithm can utilize at least some of these and/or other such features to generate a stress score that is representative of a current, past, or future stress state of a user.” The selection of “features” does not appear to have criticality): blood pressure ([0123]-[0124], [0383]), respiration rate ([0122]-[0124], [0383]), temperature ([0122]-[0124], [0201]), blood sugar level ([0424], [0468]), body weight or composition ([0122], [0383]).
While Ven recites gathering multiple of the recited first and second features including temperature for stress determination, an interpretation of Ven may not explicitly recite those features are features used in the determination of stress.
However, in the same field of endeavor (medical diagnostic systems), Jain teaches gathering features selected from at least one of: psychological state ([0063], [0140], [0157] including “transmit . . . psychological . . . data streams to analysis system 180. Analysis system 180 may analyze one or more of these data streams to determine the stress index of the user.” [0166], [0169] see also [0423]), perceived stress ([0140], [0166], [0169] including “Stress may also be measured by accessing self-reported stress information, such as, for example, from one or more user-input sensors that may receive information on a user's subjective experience of stress.” see also [0157], [0423]), blood cortisol/epinephrine/norepinephrine levels ([0155] including “The major catecholamines are dopamine, norepinephrine (noradrenaline), and epinephrine (adrenaline). Catecholamines are synthesized in the brain and other neural tissue.”, [0166], [0168] including “Stress may also be measured by accessing data from a biosensor and analyzing stress-related analyte levels, such as glucocorticoid and catecholamine levels.” See also [0157], [016], [0169]-[0170], [0423]), or mood log data ([0063] including “the user to input psychological or behavioral data into mood collection interface 420.”, [0140], [0157] including “transmit . . . psychological . . . data streams to analysis system 180. Analysis system 180 may analyze one or more of these data streams to determine the stress index of the user.” [0166], [0169] see also [0423]), as features used in the determination of stress ([0157]-[0158], [0166]-[0170] see also [0224], [0226], [0423])
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina to include a calculation with additional features and providing an output based on the calculation as recited by Jain because the provided output includes providing a recommendation for management of the users stress ([0074], [0311]).
Regarding Claim 11, an interpretation of Ven may not explicitly disclose wherein the at least one action includes at least one of generating an interface, providing a notification, modifying an operation of the wearable device, providing a recommendation for the user, or transmitting data for analysis.
However, in the same field of endeavor (medical diagnostic devices), Jain teaches wherein the at least one action includes providing a recommendation for the user ([0157] including “analyze one or more of these data streams to determine the stress index of the user. Any combination of two or more sensors may be used to generate a stress index value.”, [0158] including “Based on the comparison, analysis system 180 may then determine whether the user's stress level has changed over time.”, [0313] including “a person may engage in a variety of stress-related therapies”, [0403] including “A therapy may be a recommended therapy for the user or a therapeutic feedback that provide a direct therapeutic benefit to the user.” See also [0074], [0423]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina to include a calculation with additional features and providing an recommendation as output based on the calculation as recited by Jain because the provided output includes providing a recommendation for management of the users stress ([0074], [0311]).
Regarding Claim 12, an interpretation of Ven discloses wearable computing device (abstract), comprising:
one or more sensors ([0122]-[0124], Fig. 1);
at least one processor ([0119], [0296] including “biometric monitoring devices may include one or more processors”, [0298]-[0299], Fig. 1);
at least one memory device comprising instructions that, when executed by the at least one processor ([0012], [0119], [0298]-[0299] including “The executable code and data for the application may both reside on the application processor (or memory incorporated therein) or the data for the application may be stored and retrieved from an external memory.”, Fig. 1), cause the wearable computing device to:
receiving, from the one or more sensors ([0123]-[0124], [0226], [0233] including “the protrusion may contain other sensors that benefit from close proximity and/or secure contact to the user's skin.”, [0257] including “galvanic skin-response (GSR) circuitry to measure the response of the user's skin”), first feature data corresponding to a state of the user of the wearable device ([0123]-[0124], [0226], [0233], [0257]; recites various sensor data which “correspond” to a state of the user) wherein the first feature data includes electro-dermal activity (EDA) data captured using at least one external EDA sensor ([0123] including Table, [0201]-[0202], [0233], [0257] including “The biometric monitoring devices of the present disclosure may also include galvanic skin-response (GSR)” see also [0479]), the EDA data comprising a skin conductance level and a skin conductance response ([0123] including Table, [0201]-[0202], [0233], [0257] including “The biometric monitoring devices of the present disclosure may also include galvanic skin-response (GSR)” see also [0479]; The Ven reference discloses gathering the GSR data. Per the evidentiary reference Can section 5.1.4. Electrodermal Activity (EDA) including “EDA, also known as Galvanic Skin Response (GSR) is the change of electrical properties of skin. . . . EDA can be computed by applying a small current and measure the resistance of skin between two placed electrodes. The EDA signal is composed of two components. The first one is the Skin Conductance Level (SCL). . . .The second part is the Skin Conductance Responses (SCR) . . .”, thus in gathering the GSR data it has the SCL and SCR data);
obtain, second feature data corresponding to the state of the user ([0038], [0123]-[0124], [0226], [0233], [0257]) the second feature data provided by the user ([0038] including “detecting a manually entered selection of activity type”, [0158]), wherein the first feature data and the second feature data comprises feature data selected from sleep features, activity features, and heart features ([0038], [0122] including “the device or system may calculate the user's stress and/or relaxation levels through a combination of heart rate variability, skin conduction, noise pollution, and sleep quality.”, [0200]-[0201], [0226], [0233], [0257] see also [0123]-[0124], [0479]; recites various Features disclosed being gathered include features of sleep, heart and activity);;
calculate a stress score using feature data ([0122] including “the biometric monitoring device or the system collating the data streams from the biometric monitoring device may calculate metrics derived from such data. For example, the device or system may calculate the user's stress”, [0201] including “determine the user's stress level, health state (e.g., risk, onset, or progression of fever or cold), and/or cardiac health using sensor data” see also [0380], [0383]).
While Ven discloses an EDA sensor, an interpretation of Ven may not explicitly disclose an EDA is on a side of the wearable computing device away from a wrist of the user.
However, in the same field of endeavor (medical diagnostic device), Lapetina teaches an EDA is on a side of the wearable computing device away from a wrist of the user ([0066], [0070]-[0071], Fig. 3A see also [0151]-[0152]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the device of Ven with the GSR sensors to include the placement of a GSR sensor on the outward (away from skin when worn side of the device) as recited by Lapetina because it is merely combining the device of Ven with the specifics of a placement of an electrode(s) on the front of the device away from which is combining prior art elements according to known methods to yield predictable results of gathering GSR sensor data using the sensors on the outward side of the device.
An interpretation of Ven may not explicitly disclose calculate a stress score using the first feature data and the second feature data, wherein the stress score is calculated based on an average of the first feature data and the second feature data over a period of time of at least one of feature, wherein the average is adapted to the user; in response to the calculated stress score, performing at least one action based at least in part upon the calculated stress score; wherein the at least one action comprises generating an interface, the interface providing an option to receive additional stress data from the user; and receive, from the interface, the additional stress data from the user.
However, in the same field of endeavor (medical diagnostic device), Jain teaches obtaining second feature data corresponding to the state of the user, the second feature data provided by the user ([0056] including “user-input sensors”, [0063]-[0064] including “The user may input that he is “stressed” with an intensity of 2 on a 0-to-4 Likert scale.”, [0068] including “This “mood sensor” 400 is a type of user-input sensor that may receive psychological and behavioral input (i.e., stimulus) from a user.”, [0069]-[0070] including “the user may click, touch, speak, gesture, or otherwise interact with mood collection interface 420”, [0071] including “The user may touch one or more of the mood icons to input his current mood (i.e., psychological state). Mood intensity widget 440 is a row with numbered icons ranging from one to four that each correspond to a level of intensity of a psychological state.”);
calculate a stress score using the first feature data and the second feature data ([0157]-[0158] including “Any combination of two or more sensors may be used to generate a stress index value”, [0159] including “The stress level of a person may be quantified using a stress index, which may be any suitable scale for measuring or valuing stress . . . Further refinements could be developed, such as, for example, a transient stress index, a stress load, a stress-resilience coefficient, or other suitable stress measurements.”, [0165], [0238] see also [0069], [0166]-[0170], [0423]; assigning an index or value is a “score”), and wherein the stress score is calculated based on an average of the first feature data and the second feature data over a period of time of at least one of feature, wherein the average is adapted to the user ([0123] including “analysis system 180 may perform a variety of processes and calculations, including ranging, inspecting, cleaning, filtering, transforming, modeling, normalizing, averaging, annotating, correlating, or contextualizing data.”, [0127] including “The control period may be any suitable period. As an example, and not by way of limitation, a baseline model of a subject's blood pressure may simply be the subject's average blood pressure calculated from a series of blood-pressure measurements taken over the course of a week by a blood-pressure monitor.”, [0128] including “analysis system 180 may generate a model by normalizing or averaging one or more data streams. As an example and not by way of limitation, a model of a data stream from a single sensor 112 could simply be the average sensor measurement made by the sensor 112 over some initialization period.”, [0157]-[0159] See also [0305], [0328], [0423]; discloses normalizing each data stream by taking an average of it over period of time), wherein the features are at least one of sleep features, activity features, or heart features ([0155], [0159] including “The stress level of a person may be quantified using a stress index, which may be any suitable scale for measuring or valuing stress.”, [0166]-[0170], [0195] including “access a data stream from a galvanic-skin-response sensor to measure and model stress in a person, wherein the data stream comprises galvanic-skin-response data of the person.”, [0223] See also [0043]-[0044], [0056]-[0057], [0071], [0423]; Recites data can be gathered from physiological sensors such as GSR/EDA, heart rate monitors etc. and from user input related to activity (exercise, sleeping etc.) which can be interpreted as different features);
in response to the calculated stress score, performing, via the processor at least one action based at least in part upon the calculated stress score ([0157] including “analyze one or more of these data streams to determine the stress index of the user. Any combination of two or more sensors may be used to generate a stress index value.”, [0158]-[0159] including “Based on the comparison, analysis system 180 may then determine whether the user's stress level has changed over time.”, [0313] including “a person may engage in a variety of stress-related therapies”, [0403] including “A therapy may be a recommended therapy for the user or a therapeutic feedback that provide a direct therapeutic benefit to the user.” See also [0074], [0423]) wherein the at least one action comprises generating an interface, the interface providing an option to receive additional stress data from the user ([0059], [0073] including “mood sensor 400 may query a user to input psychological or behavioral information. The user may input any suitable psychological or behavioral information into the mood sensor 400 . . . mood sensor 400 may query a user at a dynamic rate. The dynamic rate may be based on a variety of factors, including prior input into mood sensor 400, data streams from other sensors 112 or nodes 114 in sensor array 110, output from analysis system 180” see also [0157]-[0159], [0313]-[0314], [0403], [0423]; Recites prompting user input data after receiving an output of analysis unit as discussed above the analysis output include the output of the user stress index/score); and
receive, from the interface, the additional stress data from the user ([0059], [0073] including “The dynamic rate may be based on a variety of factors, including prior input into mood sensor 400, data streams from other sensors 112 or nodes 114 in sensor array 110, output from analysis system 180” see also [0157]-[0158], [0313]-[0314], [0403], [0423]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina to include a calculation with additional features and providing an output based on the calculation as recited by Jain because the provided output includes providing a recommendation for management of the users stress ([0074], [0311]).
An interpretation of Ven may not explicitly disclose wherein calculating the score using the first feature data and the second feature data comprises calculating the score as a weighted sum of at least of features, wherein the weighted sum is adapted to the user.
However, in the same field of endeavor (medical diagnostic systems), Lanzel teaches wherein calculating the score using the first feature data and the second feature data comprises normalizing the features ([0085]-[0089], Fig. 6 see also [0034]-[0035]; normalizing such as using baselines) and calculating the score as a weighted sum of the baseline/normalized first feature data and second feature data ([0085]-[0089], Fig. 6 see also [0034]-[0035]) of at least one of sleep features, activity features, or heart features ([0033], [0037], [0081]-[0084), wherein the weighted sum is adapted to the user ([0085]-[0089], Fig. 6 see also [0034]-[0035] see also [0041]; The component scores (or scaled deviations/differentials) from the different physiological parameters are weighted and combined to generate a score).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score, outputting a result and gathering additional data as recited by Ven in view of Lapetina in further view of Jain to include a score determined as a weighted combination of various parameters as recited by Lanzel because the combination of subjective and objective inputs provides a more comprehensive view of the status ([0004). Additionally, It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to combine the analysis of Ven in view of Jain with the analysis as recited by Lanzel because it is combining prior art elements according to known methods to yield predictable results.
Regarding Claim 13, an interpretation of Ven discloses wherein the stress score represents at least one of a current stress level of the user of the wearable device ([0201] including “the biometric monitoring device may measure and/or determine the user's stress level”).
For purposes of compact prosecution, Examiner notes the stress score represents at least one of a current stress level is also disclosed by Jain ([0159] including “The stress level of a person may be quantified using a stress index, which may be any suitable scale for measuring or valuing stress.”, [0165] including “grade the stress level of a person on a scale of 0 to 100 . . . the stress index may quantify a person's transient stress (i.e., the change in stress caused by a stressor), baseline stress (i.e., the person's stress when in a normal state), and stress resilience (i.e., the rate at which a patient recovers from a stressor).”) or a stress resilience level ([0159], [0165] including “grade the stress level of a person on a scale of 0 to 100 . . . the stress index may quantify a person's transient stress (i.e., the change in stress caused by a stressor), baseline stress (i.e., the person's stress when in a normal state), and stress resilience (i.e., the rate at which a patient recovers from a stressor).”).
Regarding Claim 18, an interpretation of Ven further discloses determining activity level ([0124], [0291], [0384] including “the activity level being calculated from biometric monitoring device sensor data”; Examiner notes that per Applicants Specification (using PG Pub for paragraph numbers) [0033] including “It should be understood, however, that different numbers, selections, types, or variations can be used with different algorithms in accordance with various embodiments.” and [0040] including “In at least one embodiment, an algorithm can utilize at least some of these and/or other such features to generate a stress score that is representative of a current, past, or future stress state of a user.” The selection of “features” does not appear to have criticality), exercise or activity metrics ([0022] including “As a further example, detecting the intensity of user activity may be detecting running or walking.”, [0423] including “Such biometric monitoring devices may have sensors and run algorithms accordingly to measure and calculate biometric signals such as heart rate, heart rate variability, steps taken, calories burned, distance traveled, weight and body fat, activity intensity, activity duration and frequency, etc.” see also [0426]), exertion metrics ([0022], [0423] see also [0426]), activity type ([0046]), movement patterns ([0383] including “gait analysis”), or step count or movement ([0122] including “biometric monitoring device may calculate and store the user's step count using one or more biometric sensors.”).
While Ven recites gathering multiple of the recited features, an interpretation of Ven may not explicitly recite those features are features used in the determination of stress.
However, in the same field of endeavor (medical diagnostic systems), Jain teaches using activity features such as activity type as features used in the determination of stress ([0225] including “As an example and not by way of limitation, a user may report behavioral data by inputting that he is “driving” using activity input widget 450. Analysis system 180 may then analyze this behavioral data by inputting it into a stress model that correlates behavioral states with stress to calculate the user's stress index.” See also [0157], [0423])
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina to include a calculation with additional features including activity features and providing an output based on the calculation as recited by Jain because the provided output includes providing a recommendation for management of the users stress ([0074], [0311]).
Regarding Claim 20, an interpretation of Ven may not explicitly disclose wherein the at least one action includes at least one of generating an interface, providing a notification, modifying an operation of the wearable device, providing a recommendation for the user, or transmitting data for analysis.
However, in the same field of endeavor (medical diagnostic devices), Jain teaches wherein the at least one action includes providing a recommendation for the user ([0157] including “analyze one or more of these data streams to determine the stress index of the user. Any combination of two or more sensors may be used to generate a stress index value.”, [0158] including “Based on the comparison, analysis system 180 may then determine whether the user's stress level has changed over time.”, [0311], [0313] including “a person may engage in a variety of stress-related therapies”, [0403] including “A therapy may be a recommended therapy for the user or a therapeutic feedback that provide a direct therapeutic benefit to the user.” See also [0074], [0423]).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina to include a calculation with additional features and providing an recommendation as output based on the calculation as recited by Jain because the provided output includes providing a recommendation for management of the users stress ([0074], [0311]).
Claim Rejections - 35 USC § 103
Claim(s) 7, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ven in view of Lapetina in further view of Jain in further view of Lanzel in further view of US 20180107943 to White et al. (hereinafter White) with evidentiary support from Can.
Regarding Claim 7, an interpretation of Ven may not explicitly disclose the sleep features comprise at least one of restlessness at least one of restlessness, fragmentation, sleep reservoir level, deep/REM sleep duration, deep sleep latency, or nightmare occurrence.
However, in the same field of endeavor (medical diagnostic devices), White teaches the sleep features comprise at least deep/REM sleep duration ([0015], [0044] including “The sleep features 408 are based on the end-user's sleep behavior and includes . . . a deep-sleep duration,”; Examiner notes that per Applicants Specification (using PG Pub for paragraph numbers) [0033] and [0040]. The selection of “features” does not appear to have criticality).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina in further view of Jain to include a calculation with particular sleep features as recited by White because it is combining prior art elements (calculation using features recited by Ven with the particular feature as recited by White) according to known methods to yield predictable results, providing stress calculation using the features.
Regarding Claim 17, an interpretation of Ven may not explicitly disclose the sleep features comprise at least one of restlessness at least one of restlessness, fragmentation, sleep reservoir level, deep/REM sleep duration, deep sleep latency, or nightmare occurrence.
However, in the same field of endeavor (medical diagnostic devices), White teaches the sleep features comprise at least deep/REM sleep duration ([0015], [0044] including “The sleep features 408 are based on the end-user's sleep behavior and includes . . . a deep-sleep duration,”; Examiner notes that per Applicants Specification (using PG Pub for paragraph numbers) [0033] and [0040]. The selection of “features” does not appear to have criticality).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina in further view of Jain in further view of Wild to include a calculation with particular sleep features as recited by White because it is combining prior art elements (calculation using features recited by Ven with the particular feature as recited by White) according to known methods to yield predictable results, providing stress calculation using the features.
Claim Rejections - 35 USC § 103
Claim(s) 9, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ven in view of Lapetina in further view of Jain in further view of Lanzel in further view of US 20190059821 to Pekonen et al. (hereinafter Pekonen) with evidentiary support from Can.
Regarding Claim 9, an interpretation of Ven in view of Jain in further view of Lanzel discloses the above in claim 1 including using a plurality of features including heart features for determining the stress score.
An interpretation of Ven may not explicitly disclose the heart features comprise at least one of deep sleep heart rate variability (HRV) , elevated heart rate (HR) at rest, sleeping HR above resting heart rate (RHR).
However, in the same field of endeavor (medical diagnostic devices), Pekonen teaches the heart features comprise at least deep sleep heart rate variability (HRV) ([0103] including “That is, for example, the wearable device may be configured to detect that the user enters a certain sleep phase (e.g. Rapid eye movement sleep (REMS)) and to trigger the increased sampling frequency to perform some measurement (e.g. HRV). It may be beneficial to measure HRV when the user is sleeping as it may be used to determine stress of the user or quality of sleep,”; recites triggering HRV measurements when entering REM and that such measurements can be used for stress determination. Examiner notes that per Applicants Specification (using PG Pub for paragraph numbers) [0033] and [0040]. The selection of “features” does not appear to have criticality).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina in further view of Jain to include a calculation with particular heart features as recited by Pekonen because it is combining prior art elements (calculation using features recited by Ven with the particular feature as recited by Pek) according to known methods to yield predictable results, providing stress calculation using the features.
Regarding Claim 19, an interpretation of Ven in view of Jain in further view of Lanzel discloses the above in claim 12 including using a plurality of features including heart features for determining the stress score.
An interpretation of Ven may not explicitly disclose the heart features comprise at least one of deep sleep heart rate variability (HRV) , elevated heart rate (HR) at rest, sleeping HR above resting heart rate (RHR).
However, in the same field of endeavor (medical diagnostic devices), Pekonen teaches the heart features comprise at least deep sleep heart rate variability (HRV) ([0103] including “That is, for example, the wearable device may be configured to detect that the user enters a certain sleep phase (e.g. Rapid eye movement sleep (REMS)) and to trigger the increased sampling frequency to perform some measurement (e.g. HRV). It may be beneficial to measure HRV when the user is sleeping as it may be used to determine stress of the user or quality of sleep,”; recites triggering HRV measurements when entering REM and that such measurements can be used for stress determination. Examiner notes that per Applicants Specification (using PG Pub for paragraph numbers) [0033] and [0040]. The selection of “features” does not appear to have criticality).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score as recited by Ven in view of Lapetina in further view of Jain in further view of Wild to include a calculation with particular heart features as recited by Pekonen because it is combining prior art elements (calculation using features recited by Ven with the particular feature as recited by Pek) according to known methods to yield predictable results, providing stress calculation using the features.
Claim Rejections - 35 USC § 103
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ven in view of Lapetina in further view of Jain in further view of Lanzel in further view of US 20140316220 to Sheldon (hereinafter Sheldon) with evidentiary support from Can.
Regarding Claim 21, Ven in view of Jain in further view of Lanzel discloses claim 1, which included the recitation of the Jain reference disclosing the determination of baselines based on an average of the measurements over a period of time which may “may be any suitable period” (Jain [0127]).
An interpretation of Ven may not explicitly disclose wherein the period of time is 30-90 days.
However, in the same field of endeavor (medical diagnostic systems), Sheldon teaches wherein the period of time is 30-90 days ([0021] including “Using this data, normal baseline and normal range data that is unique for that person can be identified. The normal baseline for a particular attribute could be calculated as the average measurement . . . over a period of 30 days or any other medically acceptable range for a given attribute.”).
It would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score (including the calculation of an average over a period of time), outputting a result and gathering additional data as recited by Ven in view of Lapetina in further view of Jain in further view of Lanzel to include the period of time for calculating the average for the baseline/normalizing to be 30 days as recited by Sheldon because it is a suitable medically acceptable time period ([0021]). Additionally, it would have been prima facie obvious to one of skill in the art before the effective filing date of the claimed invention to have modified the wearable with various sensors sensing various features and processing the various features to calculate a stress score (including the calculation of an average over a period of time), outputting a result and gathering additional data as recited by Ven in view of Lapetina in further view of Jain in further view of Lanzel to include the period of time for calculating the average for the baseline/normalizing to be 30 days as recited by Sheldon because it is merely combining the concept of calculating an average based on data over a period time to specifically define suitable period of time to be 30 days.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170319122 to Wild et al. see [0279] including “For each stress score, the contributing measurements are each given a weighting depending on the relative impact on the stress score.”, [0280]-[0281] including “The variables a, b, c . . . below are weighting co-efficients”, [0283]-[0301] represents the equation written over multiple paragraphs of variables multiplied by “features” and summed see also [0088], [0103], [0182]
US 20220406453 to Applicants
US 20180107943 see Figs. 2-4, 6, 10A-B – see also the Written Opinion of the International Searching Authority of the PCT/US2021/044300 (parent of the present Application)
US 20140288448 see Figs. 1, 3-4
US 20180116607 see Figs. 1, 4-5, 8, 13
US 20190290147 see Figs. 1, 5-6E
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES R MOSS whose telephone number is (571)272-3506. The examiner can normally be reached Monday - Friday (9:30 am - 5:30 pm).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Unsu Jung can be reached at (571)272-8506. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/James Moss/ Examiner, Art Unit 3792
Prosecution Insights