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 .
DETAILED ACTION
Claims 1-20 are pending in this office action.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on August 7, 2024, August 22, 2024, August 28, 2024, May 7, 2025, June 11, 2025, and September 26, 2025, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities: paragraph 0001 needs updated to reflect applications that have matured into patents. Appropriate correction is required.
Claim Objections
Claim 18 is objected to because of the following informalities: the claim is missing the ending period. Appropriate correction is required.
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.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,081,562. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim a method comprising: determining, by a second computing device, a relationship between the input data and an occurrence of one or more incidents in new incident data, representative of a plurality of incidents involving one or more of the assets with corresponding one or more assigned remediation actions, wherein each remediation action was assigned to mitigate reoccurrence of a corresponding incident; predicting, via a machine learning model trained to recognize one or more relationships between the occurrence of one or more incidents and second assets data, wherein the second assets data comprises data representative of second assets and data representative of relationships between the second assets, a relationship between the occurrence and the second assets data, based upon the input data from the machine learning model data store; and outputting a notification assigning one or more of the assigned remediation actions to at least one second asset.
The patent further claims compiling, by a first computing device, ownership data and metric data as input data to a machine learning model data store, wherein the ownership data comprises data representative of assets, involved in one or more incidents, of an entity and data representative of relationships between the assets, wherein the metric data comprises data representative of development operations tools metric data of the assets.
It would have been obvious to compile ownership data and metric data as input to a machine learning model. This input data helps train artificial intelligence to work on data relevant to the user.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sachan et al. (U.S. Patent Pub. No. 2020/0293946) in view of Anand et al. (U.S. Patent No. 9,317,829).
Regarding claims 1, 12, and 18, Sachan et al. teaches a method comprising: determining, by a computing device, a relationship between input data to a machine learning model data store and an occurrence of one or more incidents in new incident data, the new incident data representative of a plurality of incidents involving one or more first assets with corresponding one or more assigned remediation actions, wherein each remediation action was assigned to mitigate reoccurrence of a corresponding incident (paragraph 0034 and 0041 and fig. 2, ref. num 226 and 230); predicting, via a machine learning model trained to recognize one or more relationships between the occurrence of one or more incidents and assets data, wherein the assets data comprises data representative of second assets and data representative of relationships between the second assets, a relationship between the occurrence and the assets data, based upon the input data from the machine learning model data store (paragraph 0043 and fig. 8 and 15); and outputting a notification assigning one or more of the assigned remediation actions to at least one second asset (paragraph 0037 and fig. 9-10).
Sachan et al. does not teach providing relationships between incident occurrences and assets data to assign remediation action from previous incidents to different assets.
Anand et al. teaches predicting relationships between incidents and assets and using those predictions to assign preventive actions (col. 6, lines 25-35, col. 10, lines 50-65).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine predicting relationships to assign preventive actions, as taught by Anand et al., with the method of Sachan et al. It would have been obvious for such modifications because the combination improves incident prevention capabilities.
Regarding claims 2, 13, and 19, Sachan et al. teaches further comprising: generating, based on the predicted relationship, a score representative of risk of occurrence of an incident involving the at least one second asset, wherein the outputting is based on the score satisfying a threshold (fig. 6, ref. num 608).
Regarding claims 3 and 14, Sachan et al. teaches further comprising: generating, based on the predicted relationship, a second score representative of risk of occurrence of a second incident involving the at least one second asset, wherein the outputting is based on the second score satisfying a second threshold (fig. 7, ref. num 708).
Regarding claims 4 and 15, Sachan et al. as modified by Anand et al. teaches further comprising sending, to a second computing device, the second assets data (see col. 7, line 65 through col. 8, line 5 of Anand et al.).
Regarding claims 5 and 16, Sachan et al. teaches further comprising receiving refinement data to the machine learning model (0035 and 0063).
Regarding claims 6 and 17, Sachan et al. teaches wherein the refinement data updates the input data to the machine learning model data store based upon the new incident data (paragraph 0035 and fig. 12).
Regarding claim 7, Sachan et al. teaches wherein the predicting the relationship is based upon the updated input data from the machine learning model data store (paragraph 0069).
Regarding claim 8, Sachan et al. teaches further comprising receiving, by the computing device, the new incident data (fig. 15, ref. num 1500 and 1502).
Regarding claims 9 and 19, Sachan et al. teaches further comprising: generating, based on the predicted relationship, a first score representative of a risk of occurrence of a first incident involving the at least one second asset; and generating, based on the predicted relationship, a second score representative of a risk of occurrence of a second incident involving the at least one second asset, wherein the outputting is based on at least one of the first score or the second score (paragraph 0141).
Regarding claim 10, Sachan et al. teaches further comprising comparing the first score with the second score, wherein the outputting is based on the comparison (fig. 10).
Regarding claim 11, Sachan et al. as modified by Anand et al. teaches wherein the outputting is based on the first score being a higher score in comparison to the second score (see col. 8, lines 1-20 of Anand et al.).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON HOFFMAN whose telephone number is (571)272-3863. The examiner can normally be reached Monday-Friday 8:30AM-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Pwu can be reached at (571)272-6798. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON HOFFMAN/Primary Examiner, Art Unit 2433