Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Office action in response to amendment entered 2/28/2026. Claims 1-2, 5-10, and 13-18 remain pending. Claims 1, 9, and 17 are independent and are amended.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-2, 5-10 and 13-18 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.
Claim Priority
All pending claims have a filing date of 09/30/2022.
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) 1-3, 6-11 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180219759 A1 Brown; Dean T. et al. in view of US 11200807 B2 Beaurepaire; Jerome, and further in view of US 20240395419 A1 Jain; Praduman et al.
Consider Claims 1, 9, 17 and 18
Brown teaches A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor (Brown Figs. 1-3A system 100, 202, 300), cause the processor to perform steps (Fig. 2, [0068], “..data processing system 500 includes communications framework 502, which provides communications between processor unit 504, memory 506, persistent storage 508, communications unit 510, input/output (I/O) unit 512, and display 514..”) comprising:
accessing historical signal information received from at least a tracking device configured to scan for signals transmitted by local devices as the tracking device moves within a geographic area during each of a plurality of time intervals (FIG. 3a [0048] “…For example, devices d1, d2 and d3 are associated with User 1, and devices d4, d5 and d6 are with User 2. In accordance with this invention, it is envisioned that the devices (d1, d2, d3 and d4, d5, d6), at that particular time, location and workflow, define or identify the user..” [0049] “..In the FIG. 3a, Tasks 1 and 3 are being performed by two “device-defined” users; i.e., User1 and User 2 as shown by the arrow..” [0055] monitoring location devices and associations and analyzing data received from user definitions module);
generating a training dataset based on the historical signal information received from the tracking device (See Brown [0018] “…For example, the system would track historical route of travel to and from a day care center as data to be stored...” Figs. 3-4, [0052] “..system 202 tracks certain characteristics of the user 301 (e.g. User 1, User 2, . . . User n) using a user monitor module 310 and user definition module 320. As with the process of FIG. 3, the system 202 begins by collecting and monitoring data related to each user including a user monitoring module 410 that collects data through a location module 412, a device module 414 and an associations module 414..” where users 301 );
training a machine learning model using the training dataset (Brown [0026] Baseline user profile 125 can be generated in one of several ways… baseline user profile 125 may include an aggregate fitted model that is generated for the user. For example, a series of fittings may be generated by any of several techniques for role mining…In this example, a secondary model is built … to detect periodic user behavior. .. by clustering the individual fitting scores, using known techniques, such as k-means, Gaussian model, or a mixture of k-means and Gaussian model. In this example, a one-class classifier system, such as a support vector machine, is built to learn the samples…), the machine learning model configured to predict tracking device movement patterns (Brown [0027] “..if the k-means algorithm was applied to generate the secondary model then the distance to the nearest cluster centroid can be compared to the mean and standard deviation for all points belonging to that cluster. In this example, when the distance exceeds a threshold an alert is raised. Similarly, if a mixture of Gaussian model is used then the probability that the list of role fitness scores was drawn from the distribution can be calculated and alerted when the probability is statistically significant…”[0048] “…The system 202 learns or receives data regarding a particular workflow by observing historic interactions and associations so the system 202 can predict with certain amount of confidence future workflows and their associated devices and thus, users...” [0055] “..By compiling the location data from various users, the system 202 may identify and determine at step 434 a proximity distance between each device being tracked at step 432 over a time series as well as the user location at a given time by way of the location module 412. In this way, the system 202 is able to determine, for example, that multiple users and/or the user devices are together at a certain location…”);
accessing current signal information received from the tracking device as the tracking device moves within the geographic area (Brown [0018] “…The system would then monitor, via GPS or other location tracking, the current location or route of the user…. Then, the system would identify when a user has deviated from the historical route of travel to that same day care center..” [0049] “..In the FIG. 3a, Tasks 1 and 3 are being performed by two “device-defined” users; i.e., User1 and User 2 as shown by the arrows..”), the current signal information comprising enduring signals that attenuate and augment based on a distance between a source of the enduring signals and the tracking device (Brown [0018] “GPS or other location tracking” GPS signals may be accurately characterized as “enduring signals that attenuate and augment based on a distance between a source of the enduring signals and the tracking device” in the claim language);
applying the machine learning model to the current signal information to detect a variance from one or more predefined routines associated with the tracking device (Brown [0018] “…Then, the system would identify when a user has deviated from the historical route of travel to that same day care center..” [0049] “..In the FIG. 3a, Tasks 1 and 3 are being performed by two “device-defined” users; i.e., User1 and User 2 as shown by the arrows. If within that workflow and specific task, a device is missing, exchanged or misplaced, the system 202 would identify or detect that a variation from historical observation has occurred..”)
wherein the variance comprises a movement variance that exceeds a threshold movement variance, (Brown [0027], [0037], [0043] determining deviation Specifically [0027] “..In this example, when the distance exceeds a threshold an alert is raised... For example, when a one-class support vector machine (SVM) is used the anomaly score is the distance from the hyperplane. In this example, when the anomaly score exceeds a threshold an alert is generated…”);
and sending a notification to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device (Brown [0049] “..notify the user by issuing a notification or flag..”, [0051] “.. proactive notification of a user that certain deviations from historical data exist; e.g., notification that a user has forgotten an “expected device” or other personal effect like a wallet or driver's license..” [0062] “..The alert may be sent to the user's cell phone or other device in the possession of the user. Alternatively, the system 202 may send an alert to the user's car so that when the user starts the car the user will be notified that the user is without his or her wallet..”).
Brown does not teach wherein the threshold movement variance is predicted by the machine learning model.
Beaurepaire teaches wherein the threshold movement variance is predicted by the machine learning model (Claim 1: “..determining, by one or more processors of a platform, that the vehicle is engaged in a parking search behavior based on a deviation of the vehicle from an optimal route to a destination of a current user of the vehicle by a threshold value, wherein the threshold value is determined using a machine learning classifier on one or more of location sensor data or trajectory data associated with the vehicle..”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Brown, to include the noted teachings of Beaurepaire in order to compute a probability that the vehicle will become available for use by another user based on the parking search behavior. (Beaurepaire Abstract).
The combination does not teach current signal information also comprising transient signals from local devices within the geographic area; and applying the machine learning model to the current signal information to detect a variance from one or more predefined routines associated with the tracking device based on attenuation characteristics of the enduring signals relative to the transient signals.
Jain teaches current signal information also comprising transient signals from local devices within the geographic area (Jain ¶10 “. Location data for a device or person can be aggregated at the level of an individual user device” ¶435 “locations can be determined based on mobile phones or devices detecting radio-frequency emitting energy sources such as Bluetooth advertising agents (e.g., location beacons or other mobile devices), Wi-Fi access points, cellular base stations (e.g., cell towers), etc.” where bluetooth teaches transient signals while base stations teach enduring signals, wi-fi access points may be considered transient or enduring)
and applying the machine learning model to the current signal information (Jain ¶20 “predictions can be made using machine learning models that are generated using training data indicating monitoring data” ¶21 “including measurement of one or more physiological parameters of a user and location tracking.”) to detect a variance from one or more predefined routines (¶196 “From the variance of collected data and differences from the a user's baseline levels, the system makes predictions of a user's risk of contracting COVID-19; See also ¶244 “baseline measures can include data specifying a range of values over time (e.g., the resting heart rate range over the last day, over the last 7 days, etc.) and/or statistical measures (e.g., standard deviation, variance, etc.” ) associated with the tracking device based on attenuation characteristics of the enduring signals relative to the transient signals (¶435-436 “The strength of signals received and the identity of the transmitters can be used to characterize a location, e.g., to triangulate a position based on relative signal strength between different transmitters. Nearby mobile devices can show relative activity of nearby individuals and population densities present at different places at different times.”; ¶442; ¶638, ¶639 similar)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the combination, to include the noted teachings of Jain in order to provide contact tracking for Covid-19 (Jain ¶3).
Consider Claims 2 and 10.
Brown teaches The non-transitory computer-readable medium of claim 9,
wherein the instructions further cause the processor to:
receive a subset of the historical signal information from at least the tracking device over an initial period of time (See Brown [0039],[0040],[0055] teaching collecting historical data from tracking devices over time through tracking history repository and interaction patterns history);
apply the machine learning model to the subset of the historical information received from at least the tracking device over the initial period of time to predict one or more tracking device movement patterns associated with the tracking device (See Brown [0025]-[0027], [0055]-[0057] teaching analytics platforms and models to process historical user activity, generate patterns, and predict future movement behaviors using machine learning techniques);
map the predicted one or more tracking device movement patterns associated with the tracking device to a schedule (See Brown [0040]-[0043], [0059] integrating predicted patterns with workflows and schedules including external calendars to map user routines and identify scheduled tasks);
identify one or more candidate routines based on the mapping of the one or more movement patterns associated with the tracking device to the schedule (See Brown [0040], [0045], [0051] identifies candidate workflows by analyzing historical tracking data, comparing current activities with schedules, and determining likely routines);
and select one or more of the candidate routines as the one or more predefined routines associated with the tracking device (See Brown [0045], [0061], [0064] refining workflows by updating tracking histories, selecting predefined routines based on historical workflows, and incorporating learned patterns into future predictions).
Consider Claim 6 and 14.
Brown teaches The non-transitory computer-readable medium of claim 9,
wherein training the machine learning model using the training dataset comprises:
obtaining the training dataset (Brown [0027] “generating a baseline profile for a user..”);
determining a plurality of tracking device cohorts (See Brown Fig. 3a, [0047] “By observing and aggregating historic workflows of each device associated with a user, the system may define what devices will be together (as in defining one specific user), as well as the time and location for each device…”);
identifying a mapping between the plurality of tracking device cohorts and the training dataset (Brown [0048] “..The system 202 learns or receives data regarding a particular workflow by observing historic interactions and associations so the system 202 can predict with certain amount of confidence future workflows and their associated devices and thus, users..”);
segmenting the training dataset based on the mapping to generate a plurality of segments of the training dataset (See Brown [0029] “..another component of analytics platform 122 may generate a role model for a user using one or more of a discrete and probabilistic role mining, single and multi-clustering algorithms, generative models, such as latent Dirichlet allocation, and hidden topic Markov models…” using clustering to segment data for role-based models);
and training the machine learning model using a segment from the plurality of segments of training data (See Brown [0029] “..the role model generation process takes as input a set of user activity over a given time period and produces a model of roles defined by the set of user activity. In these illustrative examples, new user activity is then fit to the role model of the user to produce a vector of fitness functions indicating the degree to which the user as defined by the activity pattern matches to the role model of the user…” thus training into a segment).
Consider Claim 7 and 15.
Brown teaches The non-transitory computer-readable medium of claim 14,
wherein the plurality of tracking device cohorts comprises one or more of geography-based tracking device cohorts or activity-based tracking device cohorts (See Brown Fig. 3a, [0047] “By observing and aggregating historic workflows of each device associated with a user, the system may define what devices will be together (as in defining one specific user), as well as the time and location for each device…” where use of location and activity used in cohorts).
Consider Claim 8 and 16.
Brown teaches The non-transitory computer-readable medium of claim 9, wherein the instructions further cause the processor to:
predict a new tracking device movement pattern (See Brown [0056] “..the system 202 may compile and determine a movement pattern or movement patterns for multiple users at step 436 for the time series being analyzed and received at step 432..”);
identify the new tracking device movement pattern as relating to an unmapped routine (See Brown [0057] “At step 440, a workflow is generated based upon this user routine and stored..” where new workflow identifies unmapped routines);
receive a selection to add the unmapped routine to the one or more predefined routines associated with the tracking device (Brown [0059] “..At step 450, the system 202 interacts with the respective calendaring systems for each user to assist in the monitoring process.. This data may be used to confirm event sequences and variations from planned schedules.”;
add the unmapped routine to the one or more predefined routines associated with the tracking device (Brown [0017] “..The system uses the calendar information to update the tracking history and to notify the user(s) of any deviation on the expected devices or articles…”);
and map the unmapped routine to the new tracking device movement pattern (Brown [0060] “..the system 202 is able to track a user's devices, personal interactions, locations, personal items, appointments, routes of travel, points of contact, etc. At step 454, the system 202 compares the data collected related to current activities (step 452) to the workflow created at step 440 and the checklist created at step 445..”).
Claim(s) 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over US 20180219759 A1 Brown; Dean T. et al. and US 11200807 B2 Beaurepaire; Jerome. et al. and US 20240395419 A1 Jain; Praduman et al., in view of US 20240242591 A1 Wu; Shengzhi et al.
Consider Claim 5 and 13
Brown teaches The non-transitory computer-readable medium of claim 11, wherein the threshold geographic distance is determined by:
selecting an threshold (Brown [0027] threshold set by system distance function or threshold); sending a first notification to the monitoring device based on the initial threshold (Brown [0027], [0043] sending alert);
Brown does not teach receiving a dismissal of the first notification;
And updating the initial threshold based on the dismissal.
Wu teaches receiving a dismissal of the first notification; And updating the initial threshold based on the dismissal (Wu [0047] FIG. 3C illustrates an example request for user feedback regarding the CAF. Such feedback may be sought, for example, in response to a user declining to user the CAF, such as by dismissing the alert, ignoring the alert, updating alert settings such as to not be alerted in future instances, or the like. While these are merely some examples, feedback may be sought for any of a variety of reasons. According to some examples, such feedback may be used to automatically adjust alert settings.)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the combination to include the noted teachings of Wu in order for using contextual information to display templated bits of information on a user electronic device (Wu [0003]).
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 1-2 5-10, and 13-18 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 17957792 in view of US 20180219759 A1 Brown; Dean T. et al.
Although the claims at issue are not identical, they are not patentably distinct from each other because as shown in the table for the independent claim the scope and elements are substantially similar. The remaining claims are also similar although not included in the table for efficiency. Co-pending claim does not include sending a notification to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device as claimed in the instant application claim 1.
Brown teaches sending a notification to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device (Brown [0049] “..notify the user by issuing a notification or flag..”, [0051] “.. proactive notification of a user that certain deviations from historical data exist; e.g., notification that a user has forgotten an “expected device” or other personal effect like a wallet or driver's license..” [0062] “..The alert may be sent to the user's cell phone or other device in the possession of the user. Alternatively, the system 202 may send an alert to the user's car so that when the user starts the car the user will be notified that the user is without his or her wallet..”).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the instant claims to include the noted teachings of Brown in order for detecting abnormal behavior of users based on device analysis in real time. (Brown [0001]).
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application
Co-pending Application 17957804 Claim
Claim 1.
A method comprising:
accessing historical signal information received from at least one tracking device configured to scan for signals transmitted by local devices as the tracking device moves within a geographic area during each of a plurality of time intervals;
generating a training dataset based on the historical signal information received from at least the tracking device;
training a machine learning model using the training dataset, the machine learning model configured to predict tracking device movement patterns;
accessing current signal information received from the tracking device as the tracking device moves within the geographic area, the current signal information comprising both transient signals from local devices within the geographic area and enduring signals that attenuate and augment based on a distance between a source of the enduring signals and the tracking device;
applying the machine learning model to the current signal information to detect a variance from one or more predefined routines associated with the tracking device based on attenuation characteristics of the enduring signals relative to the transient signals;
and sending a notification to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device.
Claim 1.
A method comprising:
accessing historic movement information received from a tracking device and a monitoring device within a proximity of the tracking device;
generating a training set of data based on the accessed historic movement information;
training a machine-learned model using the training set of data, the machine-learned model configured to predict a location of the tracking device relative to a monitoring device within a proximity of the tracking device;
accessing current movement information representative of a movement of the tracking device and the monitoring device;
applying the machine-learned model to the current movement information to detect a variance in a location of the tracking device relative to the monitoring device;
and modifying a display of the monitoring device to include the location of the tracking device relative to the monitoring device.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/UMAIR AHSAN/Primary Examiner, Art Unit 2647
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