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 2-21 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 12,205,263 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the issued reference is directly related to utilizing machine-learning to gain insights about hazard vulnerability of a parcel/property from imaging data capturing the parcel/property (col. 1 lines 30-32).
Application No. 18/985,943
U.S. Patent No. 12,205,263 B2
2. (New) A computer-implemented method comprising: receiving a request for one or more damage propensity scores for a location responsive to a hazard event, the location including a plurality of parcels; receiving imaging data for the location including the plurality of parcels; extracting, by a machine-learned model comprising a plurality of classifiers, characteristics of a plurality of vulnerability features for each parcel of the plurality of parcels from the imaging data; determining, by the machine-learned model and from the characteristics of the plurality of vulnerability features, the one or more damage propensity scores for the location indicating a measure of risk to the location due to the hazard event; and providing a representation of the one or more damage propensity scores for the location including the plurality of parcels responsive to the hazard event for display.
1. A computer-implemented method comprising: receiving a request for a damage propensity score for a parcel for one or more hazard event scenarios; receiving imaging data for the parcel, the imaging data capturing an aspect of the parcel; extracting, by a trained machine-learned model comprising a plurality of classifiers, characteristics of a plurality of vulnerability features for the parcel from the imaging data; determining, by the trained machine-learned model and from the characteristics of the plurality of vulnerability features, the damage propensity score for the parcel for the one or more hazard event scenarios; determining, by the trained machine-learned model and from the characteristics of the plurality of vulnerability features for the parcel, a plurality of mitigation steps for reducing the damage propensity score for the one or more hazard event scenarios; selecting, by the trained machine-learned model and from the plurality of mitigation steps, a proposed subset of one or more mitigation steps, wherein the selecting comprises, for each subset of one or more mitigation steps: determining, by the trained machine-learned model and based on the selected subset of one or more mitigation steps, a corresponding updated damage propensity score; and selecting the proposed subset of one or more mitigation steps, wherein the proposed subset of one or more mitigation steps corresponds to the updated damage propensity score yielding a target reduction in the damage propensity score for the one or more hazard event scenarios; and providing a representation of the subset of one or more proposed mitigation steps and the corresponding updated damage propensity score for display.
13. (New) One or more non-transitory computer storage media encoded with computer program instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a request for one or more damage propensity scores for a location responsive to a hazard event, the location including a plurality of parcels; receiving imaging data for the location including the plurality of parcels; extracting, by a machine-learned model comprising a plurality of classifiers, characteristics of a plurality of vulnerability features for each parcel of the plurality of parcels from the imaging data; determining, by the machine-learned model and from the characteristics of the plurality of vulnerability features, the one or more damage propensity scores for the location indicating a measure of risk to the location due to the hazard event; and providing a representation of the one or more damage propensity scores for the location including the plurality of parcels responsive to the hazard event for display.
10. A non-transitory computer storage medium encoded with a computer program, the computer program comprising instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations comprising: receiving a request for a damage propensity score for a parcel for one or more hazard event scenarios; receiving imaging data for the parcel, wherein the imaging data comprises an aspect of the parcel; extracting, by a trained machine-learned model comprising a plurality of classifiers, characteristics of a plurality of vulnerability features for the parcel from the imaging data; determining, by the trained machine-learned model and from the characteristics of the plurality of vulnerability features, the damage propensity score for the parcel for the one or more hazard event scenarios; determining, by the trained machine-learned model and from the characteristics of the plurality of vulnerability features for the parcel, a plurality of mitigation steps for reducing the damage propensity score for the one or more hazard event scenarios; selecting, by the trained machine-learned model and from the plurality of mitigation steps, a proposed subset of one or more mitigation steps, wherein the selecting comprises, for each subset of one or more mitigation steps: determining, by the trained machine-learned model and based on the selected subset of one or more mitigation steps, a corresponding updated damage propensity score; and selecting the proposed subset of one or more mitigation steps, wherein the proposed subset of one or more mitigation steps corresponds to the updated damage propensity score yielding a target reduction in the damage propensity score for the one or more hazard event scenarios; and providing a representation of the subset of one or more proposed mitigation steps and the corresponding updated damage propensity score for display.
19. (New) A system comprising: one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a request for one or more damage propensity scores for a location responsive to a hazard event, the location including a plurality of parcels; receiving imaging data for the location including the plurality of parcels; extracting, by a machine-learned model comprising a plurality of classifiers, characteristics of a plurality of vulnerability features for each parcel of the plurality of parcels from the imaging data; determining, by the machine-learned model and from the characteristics of the plurality of vulnerability features, the one or more damage propensity scores for the location indicating a measure of risk to the location due to the hazard event; and providing a representation of the one or more damage propensity scores for the location including the plurality of parcels responsive to the hazard event for display.
17. A system comprising: a user device; and one or more computers operable to interact with the user device and to perform operations comprising: receiving a request for a damage propensity score for a parcel for one or more hazard event scenarios; receiving imaging data for the parcel, wherein the imaging data comprises an aspect of the parcel; extracting, by a trained machine-learned model comprising a plurality of classifiers, characteristics of a plurality of vulnerability features for the parcel from the imaging data; determining, by the trained machine-learned model and from the characteristics of the plurality of vulnerability features, the damage propensity score for the parcel for the one or more hazard event scenarios; determining, by the trained machine-learned model and from the characteristics of the plurality of vulnerability features for the parcel, a plurality of mitigation steps for reducing the damage propensity score for the one or more hazard event scenarios; selecting, by the trained machine-learned model and from the plurality of mitigation steps, a proposed subset of one or more mitigation steps, wherein the selecting comprises, for each subset of one or more mitigation steps: determining, by the trained machine-learned model and based on the selected subset of one or more mitigation steps, a corresponding updated damage propensity score; and selecting the proposed subset of one or more mitigation steps, wherein the proposed subset of one or more mitigation steps corresponds to the updated damage propensity score yielding a target reduction in the damage propensity score for the one or more hazard event scenarios; and providing a representation of the subset of one or more proposed mitigation steps and the corresponding updated damage propensity score for display.
Claims 3-12, 14-18, 20 and 21 of the current application corresponds to claims 2-9, 11-16, 18 and 19 of the issued reference, and therefore are rejected under nonstatutory double patenting.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYODEJI O AYOTUNDE whose telephone number is (571)270-7983. The examiner can normally be reached Monday - Friday, 7:00am-3:30pm.
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, Yuwen Pan can be reached at 571-272-7855. 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.
/AYODEJI O AYOTUNDE/Primary Examiner, Art Unit 2649
Prosecution Insights