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 .
1. Claims 1-32 have been presented for examination.
Information Disclosure Statement
2. Acknowledgement is made to the information disclosure statements (IDS) submitted on 7/2/2025, 8/7/2025, 9/26/2025, 10/21/2025, & 3/12/2026. The information disclosure statements are being considered by the examiner.
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.
3. Claims 1-32 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-32 of U.S. Patent No. 12,518,568, hereinafter US ‘568. Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are directed to the same subject matter and recite similar claim limitations. With respect to the independent claims, see the table below wherein similar claim limitations are in bold.
Instant application
US ‘568
1. A method comprising:
training, deploying, and updating a machine learning model, by at least one processor, with representative information,
wherein the representative information comprises: a plurality of reference indicator data sets obtained from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non-routine route relative to the routine route; and
deploying, by the at least one processor, the machine learning model to perform:
matching first travel information comprising a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created message comprising a reference indicator representative of a non-routine routed luggage item,
retrieving third travel information of the luggage item generated by a baggage handling system correlated to a time frame prior to or after the created message to locate the luggage item, and
generating delivery instructions, based on a baggage journey travel record associated with a master travel manifest for a current travel journey of the luggage item,
wherein the delivery instructions include information to reroute the luggage item from its found location to a location of a reservation of a registered passenger.
1. A system comprising:
at least one processor; and
at least one non-transitory, tangible memory communicatively coupled to the at least one processor, the at least one memory storing representative information and computer implemented instructions for training, deploying, and updating a machine learning model,
wherein the representative information comprises: a plurality of reference indicator data sets obtained from a first B-Type message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second B-Type message used to train the machine learning model to determine a non-routine route relative to the routine route;
wherein the at least one processor is configured to execute the at least one instruction and deploy the machine learning model to:
match first travel information comprising a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created B-Type message comprising a reference indicator representative of a non-routine routed luggage item;
retrieve third travel information of the luggage item generated by a baggage handling system correlated to a time frame prior to or after the created B-Type message to locate the luggage item; and
generate delivery instructions, based on a baggage journey travel record associated with the master travel manifest for a current travel journey of the luggage item, wherein the delivery instructions are configured to reroute the luggage item from it's found location to a location of a reservation of the registered passenger.
11. A method for utilizing a machine learning model for tracking and rerouting passenger luggage, comprising:
storing, by a cloud-based computing system having at least on processor and memory, a machine learning model;
interacting, by a local terminal, with the cloud-based computing system and deploying the machine learning model;
wherein the machine learning model is trained from representative information comprising a plurality of reference indicator data sets from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non- routine route relative to the routine route;
deploying, by the local terminal, the machine learning model to perform:
matching first travel information comprising a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created message comprising a reference indicator representative of a non-routine routed luggage item;
retrieving third travel information of the luggage item generated by a baggage handling system correlated to a time frame prior to or after the created message to locate the luggage item; and
generating delivery instructions, based on a baggage journey travel record associated with a master travel manifest for a current travel journey of the luggage item, wherein the delivery instructions include information to reroute the luggage item from its found location to a location of a reservation of a registered passenger.
11. A system for utilizing a machine learning model for tracking and rerouting passenger luggage, comprising:
a cloud-based computing system having at least on processor and memory for storing a machine learning model;
a local terminal for interacting with the cloud based computing system and deploying the machine learning model;
wherein the machine learning model is trained from representative information comprising a plurality of reference indicator data sets from a first B-Type message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second B-Type message used to train the machine learning model to determine a non-routine route relative to the routine route;
wherein machine learning model, when deployed by the local terminal, is configured to:
match first travel information comprising a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a manifest with second travel information from a created B-Type message comprising a reference indicator representative of a non-routine routed luggage item;
retrieve third travel information of the luggage item generated by a baggage handling system correlated to a time frame prior to or after the created B-Type message to locate the luggage item; and
generate delivery instructions, based on a baggage journey travel record associated with the master travel manifest for a current travel journey of the luggage item, wherein the delivery instructions are configured to reroute the luggage item from it's found location to a location of a reservation of the registered passenger.
22. A method comprising:
training, deploying, and updating, by at least one processor, a machine learning model with representative information,
wherein the representative information comprises: a plurality of reference indicator data sets obtained from a first message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second message used to train the machine learning model to determine a non-routine route relative to the routine route;
deploying, by the at least one processor, the machine learning model to perform:
matching first travel information including a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a flight manifest with second travel information from a created message that includes a reference indicator representative of a non-routine routed luggage item;
generating for the non-routine routed luggage item associated with the flight manifest;
retrieving third travel information of the non-routine routed luggage item generated by a baggage handling system prior to or after the created message; and
locating the non-routine routed the luggage item.
22. A system comprising:
at least one processor; and
at least one non-transitory, tangible memory communicatively coupled to the at least one processor, the at least one memory storing representative information and computer implemented instructions for training, deploying, and updating a machine learning model,
wherein the representative information comprises: a plurality of reference indicator data sets obtained from a first B-Type message used to train the machine learning model to determine a routine route and a plurality of reference indicator data sets from a second B-Type message used to train the machine learning model to determine a non-routine route relative to the routine route;
wherein the at least one processor is configured to execute the at least one instruction and deploy the machine learning model to:
match first travel information including a passenger name and an International Air Transport Association (IATA) license plate number for a luggage item of a passenger in a flight manifest with second travel information from a created B-Type message that includes a reference indicator representative of a non-routine routed luggage item;
generate for the non-routine routed luggage item associated with the flight manifest;
retrieve third travel information of the non-routine routed luggage item generated by a baggage handling system prior to or after the created B-Type message; and
locate the non-routine routed the luggage item.
With respect to the dependent claims, the limitations of claims 2-10, 12-21, & 23-32 of the instant application can be found in claims 2-10, 12-21, & 23-32 of US ‘568, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULTEP SAVUSDIPHOL whose telephone number is (571)270-1301. The examiner can normally be reached on M-F,7-3 EST. If the examiner cannot be reached by telephone, he can be reached through the following email address: paultep.savusdiphol@uspto.gov
If attempts to reach the examiner by telephone and email are unsuccessful, the examiner’s supervisor, Michael G. Lee can be reached on (571) 272-2398. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULTEP SAVUSDIPHOL/Primary Examiner, Art Unit 2876