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 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,077,193. Although the claims at issue are not identical, they are not patentably distinct from each other because:
The current claims in the current application is anticipated by the claims that are in the patented claims of 12,,077,193. Where the current application states: “A method for implementing a machine learning model to predict risk exposure, the method comprising: acquiring, via a sleep detecting device associated with a user, a set of user data including data collected by a sensor disposed in the sleep detecting device; predicting, by at least a trained ML model, a level of risk exposure for an other activity for the user, wherein input for the trained ML model is based on at least the set of user data, wherein the trained ML model is trained utilizing one or more sets of first data indicative of one or more sleep patterns collected by the sleep detecting device and one or more sets of second data indicative of the other activity to identify one or more relationships between the one or more sleep patterns and the level of risk exposure for the other activity; and generating a notification to alert the user of the level of risk exposure, as predicted, for the other activity.”
Where the patent claims, “A method for implementing a machine learning model to predict driving risk exposure based at least in part upon observed sleep patterns, the method comprising: receiving, by one or more processors, one or more sets of first data indicative of one or more sleep patterns collected by one or more sleep monitoring devices; receiving, by the one or more processors, one or more sets of second data indicative of one or more driving patterns collected by one or more driving monitor devices; acquiring, via a smart ring associated with a user, a set of user data including data collected by a physiological sensor disposed in the smart ring when the user sleeps; determining a user sleep pattern based on the set of user data; predicting, by at least a trained ML model, based on the user sleep pattern, a user driving pattern associated with the user, wherein the trained ML model is trained utilizing the one or more sets of first data indicative of the one or more sleep patterns collected by the one or more sleep monitoring devices and the one or more sets of second data indicative of the one or more driving patterns collected by the one or more driving monitor devices to discover one or more relationships between the one or more sleep patterns and the one or more driving patterns, wherein the one or more relationships includes a relationship representing a correlation between a given sleep pattern and a specific driving pattern, wherein the one or more driving monitor devices includes a vehicle computer or a dedicated electronic driving tracker device, and wherein the one or more sets of second data comprises the one or more sets of second data collected by the vehicle computer or the dedicated electronic driving tracker device; predicting, by at least the trained ML model, a level of risk exposure for the user during driving based on the user driving pattern; generating a notification to alert the user of a predicted level of risk exposure wherein the notification includes a suggested remediating action to reduce or eliminate the predicted level of risk exposure; and analyzing compliance of the user with the suggested remediating action.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BHAVESH V AMIN whose telephone number is (571)270-3255. The examiner can normally be reached M-Thur, 8-6:30, EST.
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BHAVESH V. AMIN
Primary Examiner
Art Unit 3657
/BHAVESH V AMIN/Primary Examiner, Art Unit 3657