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 .
Information Disclosure Statement
The Examiner acknowledges receipt of the lengthy information disclosure
statement filed on 04/26/2025. There is no requirement that applicants explain the
materiality of English language references, however the cloaking of a clearly relevant
reference in a long list of references may not comply with applicants' duty to disclose,
see Penn Yan Boats, Inc. v. Sea Lark Boats, Inc., 359 F. Supp. 948, aff' d 479 F. 2d.
1338. There is no duty for the Examiner to consider these references to a greater
extent than those ordinarily looked at during a regular search by the Examiner.
Accordingly, the Examiner has considered these references in the same manner as
references encountered during a normal search of Office search files.
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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Claim 1 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 20 of U.S. Patent No. 11999296. Although the claims at issue are not identical, they are not patentably distinct from each other because of the same inventive entity or name at least one joint inventor in common and the instant application have a similar invention concept with above patent.
The instant application disclose an artificial intelligence decision making comprising an electronic, specifically programmed, communication computer system machine with artificial intelligence machine learning with expert system generation of motor vehicle danger driving and control signals; derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units; storing in memory said motor vehicle driving condition parameters and including parameters derived from monitoring the driver of said motor vehicle, drivers of said other motor vehicles, and/or said pedestrians; storing in memory artificial intelligence machine learning algorithms to assist in setting monitored driving condition parameter threshold levels used in derivation of said motor vehicle danger driving and control signals based on evaluation of degree of danger values; storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning; storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional parameter relationships between two or more of said motor vehicle driving condition parameters including said motor vehicle driver’s and pedestrian’s parameters; artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger values that may be different than degrees of danger values for individual parameters; and triggering generation of motor vehicle danger driving warning and control signals based on said artificial intelligence expert system analysis of said motor vehicle driving condition parameters.
The patent above disclose artificial intelligence decision making utilizing an electronic, specifically programmed, communication computer system for expert system generation of motor vehicle danger driving and control signals; derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1)communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units; storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional relationships between two or more of said motor vehicle driving condition parameters, and with expert defined individual parameter degree of danger value ranges; artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger value ranges that are different than degrees of danger values for individual parameters; derivation of integrated composite degree of danger warning indices including collision avoidance warning or vehicle control signals based on said artificial intelligence expert system analysis; and, generation of motor vehicle danger driving warning and control signals based on said artificial intelligence expert system analysis of said motor vehicle driving condition parameters.
The only differences between the instant application and the patent above are: the instant application disclose the machine learning and storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning and the patent above do not. However, one of ordinary skill in the art will understand the machine learning is obviously enhancement and driver driving habits, acumen or ability to react to particularly dangerous situations are obviously can be predictable subsets of driving condition parameters used in danger driving assessment systems. Therefore, it would be obviously for one of ordinary skill in the art to utilize the patent above to reject the instant application with non-statutory double patenting. Please see the claim mapping in the non-statutory double patenting table below.
Non-Statutory Double Patenting Table:
Instant Application No. 18648249
US Patent No. 11999296
1. An artificial intelligence motor vehicle danger driving warning and control method comprising: artificial intelligence decision making comprising an electronic, specifically programmed, communication computer system machine with artificial intelligence machine learning with expert system generation of motor vehicle danger driving and control signals;
1. An artificial intelligence motor vehicle danger driving warning and control method comprising: artificial intelligence decision making utilizing an electronic, specifically programmed, communication computer system for expert system generation of motor vehicle danger driving and control signals;
derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1)communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
storing in memory said motor vehicle driving condition parameters and including parameters derived from monitoring the driver of said motor vehicle, drivers of said other motor vehicles, and/or said pedestrians;
storing in memory artificial intelligence machine learning algorithms to assist in setting monitored driving condition parameter threshold levels used in derivation of said motor vehicle danger driving and control signals based on evaluation of degree of danger values; storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning;
storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional parameter relationships between two or more of said motor vehicle driving condition parameters including said motor vehicle driver’s and pedestrian’s parameters;
storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional relationships between two or more of said motor vehicle driving condition parameters, and with expert defined individual parameter degree of danger value ranges;
artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger values that may be different than degrees of danger values for individual parameters;
artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger value ranges that are different than degrees of danger values for individual parameters;
and triggering generation of motor vehicle danger driving warning and control signals based on said artificial intelligence expert system analysis of said motor vehicle driving condition parameters.
derivation of integrated composite degree of danger warning indices including collision avoidance warning or vehicle control signals based on said artificial intelligence expert system analysis; and, generation of motor vehicle danger driving warning and control signals based on said artificial intelligence expert system analysis of said motor vehicle driving condition parameters.
1. An artificial intelligence motor vehicle danger driving warning and control method comprising: artificial intelligence decision making comprising an electronic, specifically programmed, communication computer system machine with artificial intelligence machine learning with expert system generation of motor vehicle danger driving and control signals;
20. An artificial intelligence motor vehicle danger driving warning and control system comprising: an electronic, specifically programmed, communication computer system for artificial intelligence decision making utilizing for expert system generation of motor vehicle danger driving and control signals;
derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1)communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
storing in memory said motor vehicle driving condition parameters and including parameters derived from monitoring the driver of said motor vehicle, drivers of said other motor vehicles, and/or said pedestrians;
storing in memory artificial intelligence machine learning algorithms to assist in setting monitored driving condition parameter threshold levels used in derivation of said motor vehicle danger driving and control signals based on evaluation of degree of danger values; storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning;
storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional parameter relationships between two or more of said motor vehicle driving condition parameters including said motor vehicle driver’s and pedestrian’s parameters;
memory for storing expert defined propositional logic inference rules specifying multiple multidimensional conditional relationships between two or more of said motor vehicle driving condition parameters, and with expert defined individual parameter degree of danger value ranges;
artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger values that may be different than degrees of danger values for individual parameters;
artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger value ranges that are different than degrees of danger values for individual parameters;
and triggering generation of motor vehicle danger driving warning and control signals based on said artificial intelligence expert system analysis of said motor vehicle driving condition parameters.
derivation of integrated composite degree of danger warning indices with said electronic, specifically programmed, communication computer system including collision avoidance warning or vehicle control signals based on said artificial intelligence expert system analysis; and, generation of motor vehicle danger driving warning and control signals with said electronic, specifically programmed, communication computer system based on said artificial intelligence expert system analysis of said motor vehicle driving condition parameters.
Allowable Subject Matter
Claims 1-20 are allowed when the non-statutory double patent rejection is resolved.
Claims 2-20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 1, Aguirre De Carcer et al. US 20090079555; Dariush US 20140257659; Ellis US 20040145496; Virgilio US 20180056784; Wei US 20180211543; Kim et al. US 20180043901; Fields et al. US 20210166323; Park US 20160096473; Wingate et al. US 20160371977; Lawrie-Fussey et al. US 20170069144; Kobayashi et al. US 20180225963; Nespolo et al. US 20170158117; Schneider et al. US 20140218213 and Chuang et al. US 20110316702 are the closest art. They are teaching every limitation of claim 1 except for these limitation cite as a whole “artificial intelligence decision making comprising an electronic, specifically programmed, communication computer system machine with artificial intelligence machine learning with expert system generation of motor vehicle danger driving and control signals; derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;………. storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning; storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional parameter relationships between two or more of said motor vehicle driving condition parameters including said motor vehicle driver’s and pedestrian’s parameters; artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger values that may be different than degrees of danger values for individual parameters;”.
After update search, there are none of the prior arts of record singularly or combination, teaches or fairly suggest the features present in the claim 1 as a whole “artificial intelligence decision making comprising an electronic, specifically programmed, communication computer system machine with artificial intelligence machine learning with expert system generation of motor vehicle danger driving and control signals; derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;………. storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning; storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional parameter relationships between two or more of said motor vehicle driving condition parameters including said motor vehicle driver’s and pedestrian’s parameters; artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger values that may be different than degrees of danger values for individual parameters;”.
Prior arts of record fail to disclose “artificial intelligence decision making comprising an electronic, specifically programmed, communication computer system machine with artificial intelligence machine learning with expert system generation of motor vehicle danger driving and control signals; derivation of motor vehicle driving condition parameters for said motor vehicle based on monitoring operational status of said motor vehicle parameters and further based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;………. storing in memory database information recording motor vehicle driver driving habits, acumen, or ability to react to particularly dangerous situations, said database information based on artificial intelligence machine learning; storing in memory expert defined propositional logic inference rules specifying multiple multidimensional conditional parameter relationships between two or more of said motor vehicle driving condition parameters including said motor vehicle driver’s and pedestrian’s parameters; artificial intelligence expert system analysis with said electronic, specifically programmed, communication computer system of one or more of said multiple multidimensional conditional relationships, and wherein said multidimensional conditional relationships result in combined parameter degree of danger values that may be different than degrees of danger values for individual parameters;”. However, upon consideration of the claim invention, there is no reasoning to combine the applied references to arrive in the context of the claim invention.
Claims 2-20 depend on and further limit of independent claim 1, therefore claims 2-20 are considered allowable for the same reason.
Conclusion
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/THANG D TRAN/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686