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. Acknowledgement is made to the amendment, filed 12/4/2025. Claims 3, 7, 14, 18, 25, & 29 have been canceled. Claims 1, 2, 4-6, 8-13, 15-17, 19-24, 26-28, & 30-33 are pending.
Information Disclosure Statement
2. Acknowledgement is made to the information disclosure statement (IDS) submitted on 12/5/2025. The information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
3. Claims 1, 2, 4-6, 8-13, 15-17, 19-24, 26-28, & 30-33 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan (US 2023/0186509 A1), Krasko et al. (US 2017/0004444 A1), hereinafter Krasko, and Wang (US 2012/0089639 A1).
Regarding claim 1, Ryan discloses a method comprising:
capturing, by at least one security screening imaging machine a security screening image (SSI) of a checked-in luggage item of a passenger [0070, 0072, 0093, & 0165-0166];
generating, by at least one of at least one processor, a checked-in luggage item link by linking a first unique baggage identifier to the SSI [0070, 0076, 0093, & 0165];
repeating, by at least one of the at least one processor, the capturing and the generating for each of a plurality of checked-in luggage items [0070 & 0165-0167];
generating, by at least one of the at least one processor, a first database comprising the checked-in luggage item link for each of the plurality of checked-in luggage items [0076 & 0165-0166];
based on receiving a message identifying a checked-in luggage item among the plurality of checked-in luggage items as a lost or delayed luggage item, retrieving, by at least one of the at least one processor, baggage handling system scanning data associated with the lost or delayed luggage item based on the checked-in luggage item link associated with the lost or delayed luggage item and data in the message [0196-0198 & 0230-0233]; and
locating, by at least one of the at least one processor, the lost or delayed luggage item based on a machine learning algorithm configured to process International Air Transportation Association (IATA) data messages [0196-0198 & 0230-0233].
Regarding claim 12, Ryan discloses a system comprising:
at least one memory storing one or more instructions; and at least one processor configured to execute the one or more instructions, wherein the one or more instructions, when executed by the at least one processor, cause the system to:
capture, by at least one security screening imaging machine of the system, a security screening image (SSI) of a checked-in luggage item of a passenger [0070, 0072, 0093, & 0165-0166],
generate a checked-in luggage item link by linking a first unique baggage identifier to the SSI [0070, 0076, 0093, & 0165],
repeat the capturing and the generating for each of a plurality of checked-in luggage items [0070 & 0165-0167],
generate a first database comprising the checked-in luggage item link for each of the plurality of checked-in luggage items [0076 & 0165-0166],
based on receiving a message identifying a checked-in luggage item among the plurality of checked-in luggage items as a lost or delayed luggage item, retrieve baggage handling system scanning data associated with the lost or delayed luggage item based on the checked-in luggage item link associated with the lost or delayed luggage item and data in the message [0196-0198 & 0230-0233], and
locate the lost or delayed luggage item based on a machine learning algorithm configured to process International Air Transportation Association (IATA) data messages [0196-0198 & 0230-0233].
Regarding claim 23, Ryan discloses a non-transitory computer readable medium having instructions stored therein, which when executed by at least one processor, cause the at least one processor to execute a method comprising:
capturing, by at least one security screening imaging machine a security screening image (SSI) of a checked-in luggage item of a passenger [0070, 0072, 0093, & 0165-0166];
generating, by at least one of at least one processor, a checked-in luggage item link by linking a first unique baggage identifier to the SSI [0070, 0076, 0093, & 0165];
repeating, by at least one of the at least one processor, the capturing and the generating for each of a plurality of checked-in luggage items [0070 & 0165-0167];
generating, by at least one of the at least one processor, a first database comprising the checked-in luggage item link for each of the plurality of checked-in luggage items [0076 & 0165-0166];
based on receiving a message identifying a checked-in luggage item among the plurality of checked-in luggage items as a lost or delayed luggage item, retrieving, by at least one of the at least one processor, baggage handling system scanning data associated with the lost or delayed luggage item based on the checked-in luggage item link associated with the lost or delayed luggage item and data in the message [0196-0198 & 0230-0233]; and
locating, by at least one of the at least one processor, the lost or delayed luggage item based on a machine learning algorithm configured to process International Air Transportation Association (IATA) data messages [0196-0198 & 0230-0233].
With respect to claims 1, 12, & 23, the teachings of Ryan have been discussed above.
Ryan is silent with respect to explicitly disclosing communicating to an electronic communication device of the passenger, through a network interface coupled to at least one of the at least one processor and a communication network, a found luggage item message identifying a location of the lost or delayed luggage item, as recited in claims 1, 12, & 23.
Krasko teaches, regarding claims 1, 12, & 23, communicating to an electronic communication device of the passenger, through a network interface coupled to at least one of the at least one processor and a communication network, a found luggage item message identifying a location of the lost or delayed luggage item [0070, 0123, & 0127].
It would have been obvious to one of ordinary skill in the art at the time the invention was made to further employ the communicating features of Krasko within the system of Ryan for at least the benefit of improving the speed and accuracy for which baggage is identified while enhancing the convenience for passengers [0004].
With respect to claims 1, 12, & 23, the teachings of the combination of Ryan and Krasko have been discussed above.
This combination is silent with respect to explicitly disclosing that the images are of the contents within a luggage item, as recited in claims 1, 12, & 23.
Wang teaches, regarding claims 1, 12, & 23, capturing images that are of the contents within a luggage item [0019 & 0030].
It would have been obvious to one of ordinary skill in the art at the time the invention was made to further employ the content imaging features of Wang within the combination of Ryan and Krasko for at least the benefit of reducing the costs and work load for the travel carrier as well as improving the success rate of finding the baggage owner [0003, 0009, & 0020].
Regarding claim 2, Ryan, as modified above, discloses the method of claim 1, further comprising: time stamping, by the at least one security screening imaging machine, a time and a date the SSI is captured [0034, 0089, & 0165].
Regarding claim 4, Ryan, as modified above, discloses the method of claim 1, further comprising: storing, in a secure database, the security screening image (SSI) of contents within the checked-in luggage item; receiving, by at least one of the at least one processor, a contents list from the passenger; generating, by at least one of the at least one processor, a list of the contents in the SSI using a machine learning algorithm; determining, by at least one of the at least one processor, a match between one or more items listed in the contents list from the passenger and the list of the contents in the SSI; and based on determining one or more matches, validating, by at least one of the at least one processor, the passenger as an owner of the lost or delayed luggage item [0165-0167, 0196-0198 & 0230-0233].
Regarding claim 5, Krasko, as modified above, discloses the method of claim 1, wherein the message comprises the first unique baggage identifier printed on a bag tag affixed to the checked-in luggage item, a passenger name of the passenger, or a picture of the lost or delayed luggage item provided by the passenger [0048, 0123, & 0127].
Regarding claim 6, Ryan, as modified above, discloses the method of claim 5, wherein locating, by at least one of the at least one processor, the lost or delayed luggage item further comprises: extracting from the picture of the checked-in luggage item, by at least one of the at least one processor, features of the checked-in luggage item using a feature extraction machine learning algorithm; and locating the lost or delayed luggage item by matching the extracted features with extracted features in a current picture of a candidate luggage item [0103, 0106, 0166, 0196-0198 & 0230-0233].
Regarding claim 8, Ryan, as modified above, discloses the method of claim 1, further comprising: training a model, by least one of the at least one processor, with one or more reference indicators of one or more non-routine routed luggage items associated with one or more IATA data messages, wherein locating the lost or delayed luggage item further comprises: inputting, by least one of the at least one processor, into the model, data representative of information associated with a routine route; inputting, by least one of the at least one processor, into the model, data from one or more current baggage information messages related to transport of the checked-in luggage item to determine a current route; and outputting, by least one of the at least one processor, the current route, wherein the model uses the machine learning algorithm to detect that the checked-in luggage item is a non-routine routed luggage item based on a difference between the current route and the routine route being greater than a threshold, and wherein the method further comprises generating, by least one of the at least one processor, the message identifying the checked-in luggage item as the lost or delayed luggage item based on the checked-in luggage item being detected as the non-routine routed luggage item [0103, 0116-0120, & 0177-0187].
Regarding claim 9, Ryan, as modified above, discloses the method of claim 1, further comprising: training a model, by least one of the at least one processor, with handling and processing data for each baggage handling system predicted to handle the checked-in luggage item; inputting, by least one of the at least one processor, into the model, data representative of actual handling and processing data of one or more scanning devices handling the checked-in luggage item in real time to determine a current route; and outputting, by least one of the at least one processor, the current route, wherein the model uses the machine learning algorithm to detect that the checked-in luggage item is a non-routine routed luggage item based on a difference between the current route and a routine route being greater than a threshold, and wherein the method further comprises generating, by least one of the at least one processor, the message identifying the checked-in luggage item as the lost or delayed luggage item based on the checked-in luggage item being detected as the non-routine routed luggage item [0103, 0116-0120, & 0177-0187].
Regarding claim 10, Ryan, as modified above, discloses the method of claim 8, further comprising: determining, by at least one of the at least one processor, whether the one or more reference indicators represent a deviation in time or distance greater than a predetermined threshold between the routine route of the checked-in luggage item and the current route of the checked-in luggage item [0178-0188].
Regarding claim 11, Ryan, as modified above, discloses the method of claim 8, further comprising: electronically communicating location data associated with the lost or delayed luggage item to the electronic communication device of the passenger, wherein the location data is updated based on locations associated with at least one of the one or more reference indicators, a location of an imaging device capturing a picture of the lost or delayed luggage item, or a machine address of a scanning machine associated with the baggage handling system on the current route [0089, 0097, & 0188].
Regarding claim 13, Ryan, as modified above, discloses the system of claim 12, wherein the one or more instructions, when executed by the at least one processor, cause the system to: cause the at least one security screening imaging machine to stamp a time and a date the SSI is captured [0034, 0089, & 0165].
Regarding claim 15, Ryan, as modified above, discloses the system of claim 12, wherein the one or more instructions, when executed by the at least one processor, cause the system to: store, in a secure database, the security screening image (SSI) of contents within the checked-in luggage item, receive a contents list from the passenger, generate a list of the contents in the SSI using a machine learning algorithm, determine a match between one or more items listed in the contents list from the passenger and the list of the contents in the SSI, and based on determining one or more matches, validate the passenger as an owner of the lost or delayed luggage item [0165-0167, 0196-0198 & 0230-0233].
Regarding claim 16, Krasko, as modified above, discloses the system of claim 12, wherein the message comprises the first unique baggage identifier printed on a bag tag affixed to the checked-in luggage item, a passenger name of the passenger, or a picture of the lost or delayed luggage item provided by the passenger [0048, 0123, & 0127].
Regarding claim 17, Ryan, as modified above, discloses the system of claim 16, wherein the one or more instructions, when executed by the at least one processor, cause the system to locate the lost or delayed luggage item by: extracting from the picture of the checked-in luggage item features of the checked-in luggage item using a feature extraction machine learning algorithm, and locating the lost or delayed luggage item by matching the extracted features with extracted features in a current picture of a candidate luggage item [0103, 0106, 0166, 0196-0198 & 0230-0233].
Regarding claim 19, Ryan, as modified above, discloses the system of claim 12, wherein the one or more instructions, when executed by the at least one processor, cause the system to: train a model with one or more reference indicators of one or more non-routine routed luggage items associated with one or more IATA data messages, locate the lost or delayed luggage item further by: inputting into the model, data representative of information associated with a routine route, inputting into the model, data from one or more current baggage information messages related to transport of the checked-in luggage item to determine a current route, and outputting the current route, wherein the model uses the machine learning algorithm to detect that the checked-in luggage item is a non-routine routed luggage item based on a difference between the current route and the routine route being greater than a threshold, and wherein the one or more instructions, when executed by the at least one processor, cause the system to generate the message identifying the checked-in luggage item as the lost or delayed luggage item based on the checked-in luggage item being detected as the non-routine routed luggage item [0103, 0116-0120, & 0177-0187].
Regarding claim 20, Ryan, as modified above, discloses the system of claim 12, wherein the one or more instructions, when executed by the at least one processor, cause the system to: train a model with handling and processing data for each baggage handling system predicted to handle the checked-in luggage item, input, into the model, data representative of actual handling and processing data of one or more scanning devices handling the checked-in luggage item in real time to determine a current route, and output the current route, wherein the model uses the machine learning algorithm to detect that the checked-in luggage item is a non-routine routed luggage item based on a difference between the current route and a routine route being greater than a threshold, and wherein the one or more instructions, when executed by the at least one processor, cause the system to generate the message identifying the checked-in luggage item as the lost or delayed luggage item based on the checked-in luggage item being detected as the non-routine routed luggage item [0103, 0116-0120, & 0177-0187].
Regarding claim 21, Ryan, as modified above, discloses the system of claim 19, wherein the one or more instructions, when executed by the at least one processor, cause the system to: determine whether the one or more reference indicators represent a deviation in time or distance greater than a predetermined threshold between the routine route of the checked-in luggage item and the current route of the checked-in luggage item [0178-0188].
Regarding claim 22, Ryan, as modified above, discloses the system of claim 19, wherein the one or more instructions, when executed by the at least one processor, cause the system to: electronically communicate location data associated with the lost or delayed luggage item to the electronic communication device of the passenger, wherein the location data is updated based on locations associated with at least one of the one or more reference indicators, a location of an imaging device capturing a picture of the lost or delayed luggage item, or a machine address of a scanning machine associated with the baggage handling system on the current route [0089, 0097, & 0188].
Regarding claim 24, Ryan, as modified above, discloses the non-transitory computer readable medium of claim 23, wherein the method further comprises: time stamping, by the at least one security screening imaging machine, a time and a date the SSI is captured [0034, 0089, & 0165].
Regarding claim 26, Ryan, as modified above, discloses the non-transitory computer readable medium of claim 23, wherein the method further comprises: storing, in a secure database, the security screening image (SSI) of contents within the checked-in luggage item; receiving, by at least one of the at least one processor, a contents list from the passenger; generating, by at least one of the at least one processor, a list of the contents in the SSI using a machine learning algorithm; determining, by at least one of the at least one processor, a match between one or more items listed in the contents list from the passenger and the list of the contents in the SSI; and based on determining one or more matches, validating, by at least one of the at least one processor, the passenger as an owner of the lost or delayed luggage item [0165-0167, 0196-0198 & 0230-0233].
Regarding claim 27, Krasko, as modified above, discloses the non-transitory computer readable medium of claim 23, wherein the message comprises the first unique baggage identifier printed on a bag tag affixed to the checked-in luggage item, a passenger name of the passenger, or a picture of the lost or delayed luggage item provided by the passenger [0048, 0123, & 0127].
Regarding claim 28, Ryan, as modified above, discloses the non-transitory computer readable medium of claim 27, wherein locating, by at least one of the at least one processor, the lost or delayed luggage item further comprises: extracting from the picture of the checked-in luggage item, by at least one of the at least one processor, features of the checked-in luggage item using a feature extraction machine learning algorithm; and locating the lost or delayed luggage item by matching the extracted features with extracted features in a current picture of a candidate luggage item [0103, 0106, 0166, 0196-0198 & 0230-0233].
Regarding claim 30, Ryan, as modified above, discloses the non-transitory computer readable medium of claim 23, wherein the method further comprises: training a model, by least one of the at least one processor, with one or more reference indicators of one or more non-routine routed luggage items associated with one or more IATA data messages, wherein locating the lost or delayed luggage item further comprises: inputting, by least one of the at least one processor, into the model, data representative of information associated with a routine route; inputting, by least one of the at least one processor, into the model, data from one or more current baggage information messages related to transport of the checked-in luggage item to determine a current route; and outputting, by least one of the at least one processor, the current route, wherein the model uses the machine learning algorithm to detect that the checked-in luggage item is a non-routine routed luggage item based on a difference between the current route and the routine route being greater than a threshold, and wherein the method further comprises generating, by least one of the at least one processor, the message identifying the checked-in luggage item as the lost or delayed luggage item based on the checked-in luggage item being detected as the non-routine routed luggage item [0103, 0116-0120, & 0177-0187].
Regarding claim 31, Ryan, as modified above, discloses the non-transitory computer readable medium of claim 23, wherein the method further comprises: training a model, by least one of the at least one processor, with handling and processing data for each baggage handling system predicted to handle the checked-in luggage item; inputting, by least one of the at least one processor, into the model, data representative of actual handling and processing data of one or more scanning devices handling the checked-in luggage item in real time to determine a current route; and outputting, by least one of the at least one processor, the current route, wherein the model uses the machine learning algorithm to detect that the checked-in luggage item is a non-routine routed luggage item based on a difference between the current route and a routine route being greater than a threshold, and wherein the method further comprises generating, by least one of the at least one processor, the message identifying the checked-in luggage item as the lost or delayed luggage item based on the checked-in luggage item being detected as the non-routine routed luggage item [0103, 0116-0120, & 0177-0187].
Regarding claim 32, Ryan, as modified above, discloses the non-transitory computer readable medium of claim 30, wherein the method further comprises: determining, by at least one of the at least one processor, whether the one or more reference indicators represent a deviation in time or distance greater than a predetermined threshold between the routine route of the checked-in luggage item and the current route of the checked-in luggage item [0178-0188].
Regarding claim 33, Ryan, as modified above, discloses the non-transitory computer readable medium of claim 30, wherein the method further comprises: electronically communicating location data associated with the lost or delayed luggage item to the electronic communication device of the passenger, wherein the location data is updated based on locations associated with at least one of the one or more reference indicators, a location of an imaging device capturing a picture of the lost or delayed luggage item, or a machine address of a scanning machine associated with the baggage handling system on the current route [0089, 0097, & 0188].
Response to Arguments
4. Applicant’s arguments with respect to claims 1, 12, & 23 have been considered but are moot in view of the new grounds of rejection. The claims have been amended to further limit the type of image or picture that is captured of the luggage item and have therefore necessitated the new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/PAULTEP SAVUSDIPHOL/Primary Examiner, Art Unit 2876