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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,285,255. Although the claims at issue are not identical, they are not patentably distinct from each other because the application is an obvious variant the patent.
Patent claim 1 recites every limitation of application claim 1 — obtaining visual/audio data via a camera/microphone associated with the user; detecting at least one trauma event of a defined trauma event type in the visual/audio data via at least one classification model; identifying a plurality of stressor indicators within the data; determining, responsive to the detecting, a stress score of the user based upon the plurality of stressor indicators in accordance with a stress prediction model; and generating an alert in response to the stress score exceeding a threshold — except that patent claim 1 recites the data as comprising calendar data (and the stressor indicators as including the calendar data), whereas application claim 1 recites the data as comprising environmental data of an environment of the user (and the stressor indicators as including the environmental data). However, patent claim 4 (depending from patent claim 1) expressly recites that the data associated with the user further comprises "environmental data of an environment of the user." It would have been obvious to one of ordinary skill in the art to identify and use the environmental data recited in patent claim 4 as one of the plurality of stressor indicators in the method of patent claim 1, because patent claim 1 already determines the stress score from a plurality of stressor indicators within the data associated with the user, and patent claim 4 places environmental data within that very data. The recitation of environmental data as the stressor indicator, in place of or in addition to the calendar data of patent claim 1, is an obvious variation in view of patent claim 4, which already claims environmental data as part of the data associated with the user, and does not patentably distinguish application claim 1 from the patent claims. Application claim 1 is therefore an obvious variant of, and not patentably distinct from, patent claims 1 and 4.
Application claims 17 (non-transitory computer-readable medium) and 18 (apparatus) recite operations corresponding to application claim 1 (claim 17 reciting both calendar data and environmental data; claim 18 reciting environmental data). Patent claims 17 and 18 recite the corresponding medium and apparatus performing operations corresponding to patent claim 1 (calendar data). For the same reasons given for claim 1, and further in view of the environmental data recited in patent claim 4, application claims 17 and 18 are not patentably distinct from patent claims 17 and 18. Application claim 17, which recites both calendar data and environmental data, is likewise an obvious variant, as patent claim 17 recites the calendar data and patent claim 4 recites the environmental data.
Application dependent claims 2-16 and 19-20 recite limitations corresponding to patent dependent claims 2-16 and 19-20, respectively, and are not patentably distinct therefrom (read together with patent claims 1 and 4 to supply the environmental-data base of the application's independent claims), as follows:
Application claim 2 is identical in scope to patent claim 2 (plurality of defined trauma event types).
Application claim 3 is identical in scope to patent claim 3 (classification model trained to detect the defined trauma event type).
Application claim 4 (location data, environmental data, or biometric data) corresponds to patent claim 4 (location, environmental, or biometric data), which encompasses the application claim 4 alternatives, including the environmental data alternative relied upon above.
Application claim 5 (calendar data including a personal event) corresponds to patent claims 1 and 5, patent claim 1 reciting the calendar data and patent claim 5 reciting the personal event.
Application claim 6 is identical in scope to patent claim 6 (quantity of time on screen within a designated time period).
Application claims 7-16 are identical in scope to patent claims 7-16, respectively.
Application claims 19 and 20 are identical in scope to patent claims 19 and 20, respectively (apparatus form).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5 and 7-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heywood (US 11,706,391 B1) in view of Jain (US 2012/0289788 A1).
Regarding claim 1, Heywood discloses a method comprising:
obtaining, by a processing system including at least one processor, data associated with a user, wherein the data associated with the user includes at least one of: visual data captured via at least one camera associated with the user or audio data captured via at least one microphone associated with the user (Heywood discloses a device that obtains sensor data from one or more sensors worn by a first responder (Abstract). The wearable device 102 is a body camera or like device worn by the first responder that captures sensor data 116 including video data (from camera(s) of the wearable device) and audio data (from one or more microphones of the wearable device) (col. 3 ll. 50–col. 4 ll. 60; FIG. 2, cameras 222 and microphone(s) 226). Heywood thus teaches obtaining visual data via a camera and audio data via a microphone associated with the user.);
detecting, by the processing system, at least one trauma event of at least one defined trauma event type in at least one of: the visual data or the audio data via at least one classification model (Heywood discloses a distress detection process 247 that analyzes the captured video/audio (sensor data 312) to detect defined events such as medical distress, a chokehold/headlock, a deployed weapon, and wounds (FIG. 3, analyzers 302/304/306 feeding alert generator 308). This detection is performed via classification models — Heywood expressly applies machine-learning-based classifiers, including CNN, MLP, and trained artificial neural network (ANN) classifiers, to the image/video and audio data (col. 7 ll. 55–col. 8 ll. 16; col. 11 ll. 12–24; col. 12 ll. 1–18; claim 9, "applying a machine learning-based classifier to image or video data"). The defined event types correspond to the application's "trauma event" types (e.g., discharged firearm, hand-to-hand combat, severe injury).); and
generating, by the processing system, an alert (Heywood discloses generating an alert based on the analyzer outputs — alert generator 308 issues alert 314, which is transmitted to a device of the user and/or to a designated recipient such as a supervisor (FIG. 3; Abstract; claims 3, 4). Heywood further discloses applying thresholds/heuristics and confidence-or-probability criteria to the analyzer outputs in deciding whether to issue the alert (col. 12 ll. 56–col. 13 ll. 50). Heywood thus teaches generating an alert, including alert generation gated by a threshold criterion.).
However, Heywood does not expressly disclose (i) wherein the data associated with the user further comprises environmental data of an environment of the user; (ii) identifying, by the processing system, a plurality of stressor indicators within the data associated with the user including at least the environmental data of the environment of the user; (iii) determining, by the processing system responsive to the detecting of the at least one trauma event, a stress score of the user based upon at least a portion of the data associated with the user in accordance with a stress prediction model, wherein the determining the stress score of the user comprises determining the stress score of the user based upon the plurality of stressor indicators in accordance with the stress prediction model; and (iv) generating, by the processing system, an alert in response to the stress score of the user exceeding a threshold.
Jain, in the same field of sensor-based monitoring and quantification of a person's stress, teaches the missing limitations. As to limitation (i), Jain discloses obtaining, as part of its multi-sensor data set for a person, environmental data of the person's environment, captured by environmental sensors and data feeds such as a weather sensor, thermometer, barometer, pollen counter, location sensor, or weather/news data feed, reflecting environmental conditions (e.g., temperature, humidity, weather) around the person (Jain [0043], [0060], [0267]; FIG. 13, step 1310). As to limitation (ii), Jain's analysis system identifies a plurality of stressor indicators within the data and correlates physiological, behavioral, and environmental state with stress, expressly including the environmental state among the inputs to its stress model ([0152], [0159], [0271]–[0279]). As to limitation (iii), Jain discloses determining a stress score of the user — the "stress index" — in accordance with a stress prediction model, namely a machine-learning-developed stress model that takes the plurality of stressor indicators as inputs (modeled as f.sub.sm=f(D.sub.env.sup.1, D.sub.mood.sup.2, D.sub.HR.sup.3, D.sub.BP.sup.4…)) and outputs the stress index on a defined scale (e.g., 0–100 or a 0-to-4 Likert scale) ([0159]–[0167], [0271]–[0283]; FIG. 13, step 1330); when combined with Heywood, the detection of the trauma event by Heywood's classifier serves as the predicate that triggers this stress score determination. As to limitation (iv), Jain discloses providing an alert or warning to the user or to a designated third party (e.g., the user's physician) based on the user's stress level, and transmitting such an alert when one or more threshold criteria are met ([0160]–[0161], [0395], [0403]). Jain therefore teaches generating an alert in response to the stress score exceeding a threshold.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Heywood's first responder monitoring system to incorporate Jain's environmental data informed stress prediction model and stress index threshold alerting, so that, upon Heywood detecting a trauma event via its classifier, the system determines the responder's own stress score from a plurality of stressor indicators including environmental data and issues an alert when that score exceeds a threshold; the combination is the use of a known technique (Jain's stress index computation and threshold alerting) to improve a similar device (Heywood's sensor based responder-monitoring system) in the same way it improves stress monitoring systems generally, yielding the predictable result of proactively quantifying and flagging responder stress following a traumatic event — a result expressly consistent with Heywood's recognition that first responders operate under stressful conditions that dull their senses and impair their function (Heywood col. 1 ll. 20–42; col. 19 ll. 45–67) — and one of ordinary skill would have had a reasonable expectation of success because both references operate on multimodal sensor data streams and apply conventional machine-learning modeling and classification techniques.
Regarding claim 2, Heywood in view of Jain discloses the method of claim 1, wherein the at least one defined trauma event type is one of a plurality of defined trauma event types (Heywood's distress detection process 247 is configured to detect events of multiple different defined types, and applies a plurality of classifiers/analyzers each directed to a respective defined event type — for example, an unconsciousness detector that classifies video as "unresponsive," "seizing," or "fainting," a wound detector trained to detect wounds (e.g., labeling data as "blood" or "bruise"), and a position analyzer that detects positions such as a chokehold/headlock or "laying prone" (FIG. 3, analyzers 304c, 306d, 306a; col. 10 ll. 44–col. 11 ll. 24; col. 12 ll. 1–18). Heywood expressly describes the alert generator as taking the form of an ensemble of classifiers/other machine-learning models that evaluates the outputs of these multiple analyzers to determine whether an alert should be issued (col. 12 ll. 30–46). Heywood thus discloses that the detected event is one of a plurality of defined trauma event types. The motivation to combine Heywood and Jain is the same as set forth in the rejection of claim 1.).
Regarding claim 3, Heywood in view of Jain discloses the method of claim 1, wherein the at least one classification model is trained to detect trauma events of the at least one defined trauma event type (Heywood discloses that its machine learning based detection models are trained using a training dataset of labeled samples exhibiting the event to be detected — for example, the wound detector 306d comprises one or more CNN-based or other classifiers "trained to detect the presence of wounds," using training datasets "depicting various wounds and other indicia, such as blood, scrapes, cuts, bruises, bone fractures, and the like, that have been labeled as such," together with labeled negative examples (col. 12 ll. 1–18). Heywood likewise describes supervised training of its classifiers on labeled sample images having labeled features (e.g., labeled postures/positions of a person) so that the trained model detects that defined condition in subsequently captured data (col. 7 ll. 33–col. 8 ll. 15; col. 14 ll. 45–62, position analyzer 306a trained on labeled video data; also see col. 18 ll. 60–col. 19 ll. 5). Heywood thus discloses that the at least one classification model is trained to detect trauma events of the at least one defined trauma event type. The motivation to combine Heywood and Jain is the same as set forth in the rejection of claim 1.).
Regarding claim 4, Heywood in view of Jain discloses the method of claim 1, wherein the data associated with the user further comprises at least one of: location data of the user; or biometric data of the user (Heywood teaches the sensor data 116 captured by the wearable device 102 of the first responder includes location data — the wearable device may generate and send data indicative of its location as part of sensor data 116, such as via cellular triangulation or GPS coordinates (col. 4 ll. 20–50, "Location data"; col. 6 ll. 41–52, GPS sensor of other sensors/interfaces 228). Heywood additionally teaches that the data comprises biometric data of the user — the wearable device may capture health data regarding the first responder, such as a measured pulse rate, temperature, pulse oximetry, or respiratory rate (col. 4 ll. 20–50, "Health data"; col. 6 ll. 41–52, pulse rate sensors and blood pressure sensors of other sensors/interfaces 228). Heywood thus discloses that the data associated with the user further comprises location data of the user and/or biometric data of the user, satisfying the "at least one of" limitation. The motivation to combine Heywood and Jain is the same as set forth in the rejection of claim 1.).
Regarding claim 5, Heywood in view of Jain discloses the method of claim 1, wherein the data associated with the user further comprises calendar data that includes at least one personal event of the user (Jain further teaches wherein the data associated with the user further comprises calendar data that includes at least one personal event of the user — namely, calendar data from an electronic calendar into which the user inputs activities "including appointments, social interactions, phone calls, meetings, work, tasks, chores, etc.," tagged with categories such as "personal" or "birthday" (Jain [0057]). It would have been obvious to one of ordinary skill in the art before the effective filing date to include Jain's calendar data as a further input to the stress prediction model of the combination, in order to supply an additional known stressor input and thereby improve the accuracy of the user's stress score determination, as Jain teaches correlating such calendar data with the user's other data to assess stress ([0057], [0073]).).
Regarding claim 7, Heywood in view of Jain discloses the method of claim 1, wherein the user is of a designated category of one of: a first responder; a medical professional; or a professional vehicle operator (Heywood discloses that the monitored user is a first responder — the device obtains sensor data from one or more sensors worn by a first responder (Abstract), and Heywood identifies the first responders as "police, firefighting, and emergency medical personnel" (col. 1 ll. 20–22). Heywood thus discloses that the user is of the designated category of a first responder (and, in the case of emergency medical personnel, a medical professional). The motivation to combine Heywood and Jain is the same as set forth in the rejection of claim 1.).
Regarding claim 8, Heywood in view of Jain discloses the method of claim 1, wherein the at least the portion of the data associated with the user is from at least one designated time period in relation to the at least one trauma event (Jain collects and analyzes a first data set "when the person is exposed to a particular stressor" and a second data set when not exposed (Jain [0215], [0292]), and analyzes the user's data immediately before and after a designated event ([0073]). It would have been obvious to one of ordinary skill in the art before the effective filing date to use, for the responsive stress score determination of the combination, a portion of the user's data from a designated time period defined in relation to the detected trauma event as taught by Jain, in order to capture the data most probative of the user's stress response to that event.).
Regarding claim 9, Heywood in view of Jain discloses the method of claim 8, wherein the at least one designated time period in relation to the at least one trauma event precedes the at least one trauma event, includes the at least one trauma event, or follows the at least one trauma event (Heywood teaches the "includes" option — the wearable device captures the user's sensor data contemporaneously with the detected event, such that the designated time period includes the trauma event (Heywood col. 3 ll. 50–col. 4 ll. 50). Jain teaches the "precedes" and "follows" options — Jain analyzes the user's data from time periods before and after a designated event, e.g., querying and analyzing the user's data "immediately before and after" the event (Jain [0073]; see also [0215], [0292], comparing data sets collected before/when exposed and after/when not exposed to a stressor). It would have been obvious to one of ordinary skill in the art before the effective filing date to define the designated time period of the combination to precede, include, or follow the detected trauma event, as taught by Heywood and Jain, in order to capture the user's data most probative of the user's stress response relative to that event.).
Regarding claim 10, Heywood in view of Jain discloses the method of claim 1, wherein the stress prediction model comprises a machine learning model trained on a plurality of sets of data associated with a plurality of users (Jain teaches that the stress model is developed using machine learning algorithms so that the user's stress index can be calculated (Jain [0159]), and that the stress model may be based on baseline data collected from a plurality of persons — e.g., a stress model "of one or more second persons" who are not currently the monitored subject, based on baseline data collected from those second persons (a patient group or cohort), which allows stress to be accurately measured in the monitored user without generating a personalized stress profile for that user (Jain [0212]; see also [0150], monitoring and analyzing data streams from a group of people). The machine learning stress model of Jain is thus trained on a plurality of sets of data associated with a plurality of users. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the stress prediction model of the combination as such a machine learning model trained on data from a plurality of users, as taught by Jain, in order to enable accurate stress-score determination for the user without having to first build an individualized stress profile for that user (Jain [0212]).).
Regarding claim 11, Heywood in view of Jain discloses the method of claim 10, wherein each of the plurality of sets of data is labeled with one of: a respective stress score; or a respective label of excess stress or non-excess stress (Jain teaches collecting data sets at known, labeled stress states — e.g., a first data set collected when the person is substantially stressed and a second data set collected when substantially unstressed (Jain [0215]; see also [0292]) — which constitutes labeling each data set with a respective label of excess stress or non-excess stress. Jain additionally teaches that the user's stress is quantified as a stress index on a defined scale (e.g., 0–100 or a 0-to-4 Likert scale) (Jain [0161]–[0167]), such that the collected data may be associated with a respective stress score. Jain thus teaches that each of the plurality of sets of data is labeled with a respective stress score and/or a respective label of excess stress or non-excess stress. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to label the training data sets of the combination's stress prediction model with a respective stress score or excess/non-excess stress label as taught by Jain, in order to provide labeled training data from which the machine-learning model learns to predict the user's stress, with a reasonable expectation of success because Jain already collects and labels stress data sets by stress state for use in modeling the user's stress.).
Regarding claim 12, Heywood in view of Jain discloses the method of claim 1, wherein the stress prediction model comprises a regression model (Jain teaches that the stress model may be a statistical model such as a "simple linear regression model," and that the analysis system fits the data to the model using techniques including regression (Jain [0128], [0129]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the stress prediction model of the combination as a regression model as taught by Jain, in order to model the relationship between the user's data and the user's stress using a known, conventional modeling technique already disclosed by Jain for that purpose.).
Regarding claim 13, Heywood in view of Jain discloses the method of claim 1, wherein the stress prediction model comprises a formula-based model that weights the plurality of stressor indicators (Jain teaches a stress model expressed as a formula/algorithm taking the plurality of stressor inputs (e.g., f.sub.sm=f(D.sub.env.sup.1, D.sub.mood.sup.2, D.sub.HR.sup.3, D.sub.BP.sup.4…)) (Jain [0270]–[0279]), and teaches weighting individual stressors within the stress index by assigning each stressor a respective stress factor — e.g., a minor stressor assigned a weight of (+5)/100 and a major stressor assigned a weight of (+20)/100 — which are combined via a mathematical function (including multiplication, exponentiation, or other operations) to calculate the user's stress index (Jain [0289]). Jain thus teaches a formula-based stress prediction model that weights the plurality of stressor indicators. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the stress prediction model of the combination as such a formula-based model that weights the plurality of stressor indicators as taught by Jain, in order to quantify the user's stress by accounting for the differing relative contributions of individual stressors using a known modeling technique disclosed by Jain for that purpose.).
Regarding claim 14, Heywood in view of Jain discloses the method of claim 1, wherein the at least one camera associated with the user comprises at least one of: a body-worn camera; or a camera of a vehicle operated by the user or in which the user is a passenger (Heywood discloses that the wearable device 102 worn by the first responder comprises a body camera (Heywood col. 4 ll. 13–15). Heywood additionally discloses that the sensor data may be captured by a camera of the first responder's vehicle — e.g., a dashcam on board the vehicle of the first responder (Heywood col. 11 ll. 28–31; see also col. 4 ll. 5-10, vehicle 108 capturing/relaying sensor data). Heywood thus discloses that the camera associated with the user comprises a body worn camera and/or a camera of a vehicle operated by the user. The motivation to combine Heywood and Jain is the same as set forth in the rejection of claim 1.).
Regarding claim 15, Heywood in view of Jain discloses the method of claim 1, wherein the processing system comprises at least one server, or an endpoint device of the user (Heywood discloses that the analysis (e.g., the distress detection process) may be performed remotely by a server — the supervisory service 110, which may comprise a cloud-hosted or datacenter-hosted service that receives and analyzes the sensor data (Heywood col. 5 ll. 1–15). Heywood additionally discloses that the analysis may be performed locally by an endpoint device of the user — the wearable device 102 worn by the first responder and/or the vehicle, the processing being "located locally (e.g., on the wearable device and/or the vehicle) or remotely (e.g., supervisory service), or in any combination thereof" (Heywood col. 8 ll. 50–60; see also FIG. 2, device 200 with processor 220 executing distress detection process 247). Heywood thus discloses that the processing system comprises at least one server and/or an endpoint device of the user. The motivation to combine Heywood and Jain is the same as set forth in the rejection of claim 1.).
Regarding claim 16, Heywood in view of Jain discloses the method of claim 1, wherein the alert is provided to at least one of: the user via an endpoint device of the user; or a designated authorized entity (Heywood discloses providing the alert to the user — the alert is an audible alert for the first responder, and may be provided to/conveyed via the wearable device of the first responder (Heywood claim 3; col. 6 ll. 41–52, conveying information to the user of the device via speakers/displays/vibration mechanisms). Heywood additionally discloses providing the alert to a designated authorized entity — the device provides the alert to a device of a supervisor of the first responder, and/or to a monitoring user/device of the supervisory service (Heywood claim 4; col. 5 ll. 50–67; col. 21 ll. 63–67). Heywood thus discloses that the alert is provided to the user via an endpoint device of the user and/or to a designated authorized entity. The motivation to combine Heywood and Jain is the same as set forth in the rejection of claim 1.).
Claim 17 is rejected under 35 U.S.C. § 103 as being unpatentable over Heywood (US 11,706,391 B1) in view of Jain (US 2012/0289788 A1). Claim 17 is rejected for the same reasons as set forth with respect to claims 1 and 5 above. Claim 17 is directed to a non-transitory computer-readable medium storing instructions for executing operations corresponding to the method steps/functions of claims 1 and 5 above, and the scope and content of the recited limitations are substantially the same. Heywood further teaches the recited non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform the operations — e.g., a memory 240 storing the distress detection process 247 as a software process comprising computer-executable instructions executed by hardware processor 220, embodied on a tangible, non-transitory computer-readable medium (Heywood FIG. 2, FIG. 4; col. 13 ll. 50–67; col. 6 ll. 53–col. 7 ll. 15). Accordingly, the teachings of Heywood in view of Jain that render claims 1 and 5 obvious likewise apply to claim 17 for the same reasons set forth above.
Claim 18 is rejected under 35 U.S.C. § 103 as being unpatentable over Heywood (US 11,706,391 B1) in view of Jain (US 2012/0289788 A1). Claim 18 is rejected for the same reasons as set forth with respect to claim 1 above. Claim 18 is directed to an apparatus configured to perform operations corresponding to the method steps/functions of claim 1 above, and the scope and content of the recited limitations are substantially the same. Heywood further teaches the recited apparatus comprising a processing system including at least one processor and a computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform the operations — e.g., an apparatus/device 200 comprising a hardware processor 220 and a memory 240 storing the distress detection process 247 as computer-executable instructions that, when executed by the processor, cause the device to perform the described functions (Heywood FIG. 2, FIG. 4; col. 13 ll. 50–67; col. 6 ll. 53–col. 7 ll. 15). Accordingly, the teachings of Heywood in view of Jain that render claim 1 obvious likewise apply to claim 18 for the same reasons set forth above.
Claim 19 recites limitations corresponding to those of claim 2 above, differing only in that they are recited in apparatus form (depending from apparatus claim 18) rather than method form, and the scope and content of the recited limitations are substantially the same. Accordingly, claim 19 is rejected for the same reasons as set forth with respect to claims 18 and 2 above.
Claim 20 recites limitations corresponding to those of claim 3 above, differing only in that they are recited in apparatus form (depending from apparatus claim 18) rather than method form, and the scope and content of the recited limitations are substantially the same. Accordingly, claim 20 is rejected for the same reasons as set forth with respect to claims 18 and 3 above.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heywood (US 11,706,391 B1) in view of Jain (US 2012/0289788 A1), further in view of Lin (US 2016/0352848 A1).
Regarding claim 6, Heywood in view of Jain discloses the method of claim 1, but does not expressly disclose wherein the data associated with the user further comprises a quantity of time on screen for the user within a designated time period. Jain teaches collecting behavioral data about the user and correlating it with the user's stress as a stressor input to the stress prediction model (Jain [0152], [0159]).
Lin, in the analogous field of monitoring a user's portable-device usage behavior, teaches this limitation. Lin discloses obtaining "screen information" regarding the user's use of a portable device, wherein the processor activates the display in response to a first trigger signal (a screen-on event) and deactivates the display in response to a second trigger signal (a screen-off event), and the time period during which the display is active between a screen-on event and a screen-off event is a "use epoch" (Lin [0032], [0042]). Lin further teaches calculating a usage duration for each such use of the device and a daily usage duration (T.sub.all) for each day "by adding the usage durations for all times of execution of the application in the one day," where this screen information is gathered over a predetermined time period (M) spanning at least one day, e.g., a month (Lin [0044], [0046]; FIG. 8). Lin's daily usage duration (T.sub.all) is thus a quantity of time the device screen is active/in use by the user (i.e., a quantity of time on screen), measured within a designated time period (the predetermined time period M). Lin further teaches that this device usage data is collected and evaluated in the medical field to assess a user's condition (Lin [0061], [0119]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further include, in the data associated with the user in the Heywood/Jain combination, a quantity of time on screen for the user within a designated time period as taught by Lin. Jain already teaches collecting behavioral data about the user and correlating it with the user's stress as a stressor input to the stress prediction model (Jain [0057], [0152]), and Lin teaches that a user's quantity of time on screen over a designated time period is a known, readily measurable behavioral metric obtained from the user's portable device. One of ordinary skill would have been motivated to add Lin's screen time quantity as a further behavioral data point so as to provide an additional known stressor input to the stress prediction model, thereby improving the completeness and accuracy of the user's stress score determination, with a reasonable expectation of success because Lin demonstrates that such screen-time data is conventionally collected and quantified from the same type of user portable device already present in the combination.
Conclusion
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/RAJSHEED O BLACK-CHILDRESS/Examiner, Art Unit 2685
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