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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/06/2026 has been entered.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn (US20170211945A1) in view of Makowsky (US20210248656A1), further in view of Gnanasekaran (US20220360637A1).
Regarding claim 9, Kahn teaches a system comprising:
one or more processors (¶37); and
a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to (¶37):
receive image data (¶18, user signals can include marked images (image data));
identify, from the image data via computer vision, one or more travel features (¶18, a marked image may be used and the information taken based on or from the image can be considered a feature. See rejection of claims 1 and 4-5, where Kahn as modified utilizes computer vision);
train a machine learning model (MLM) to generate one or more trip recommendations based on the one or more travel features (¶18, a classifier may be trained to identify travel intent and provide a recommendation);
determine, via the trained MLM, whether at least a first trip recommendation of the one or more trip recommendations exceeds a predetermined threshold indicating a likelihood of interest in the first trip recommendation (¶18, a travel score from the classifier is generated which is compared to a threshold which indicates an intent to travel); and
responsive to determining the first trip recommendation exceeds the predetermined threshold, provide the first trip recommendation to a device (¶18, if the threshold is exceeded, travel intent is determined and recommendations are based on travel intent).
Kahn does not teach wherein the MLM is configured to utilize facial emotion recognition (FER) technology.
Makowsky teaches wherein the MLM is configured to utilize facial emotion recognition (FER) technology (¶227-228).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize facial emotion recognition (FER) technology in the model in order to create a robust recommendation model.
Kahn does not teach evaluating, via the trained MLM, poses and facial recognitions of one or more persons in the image data to determine a likelihood of a positive or negative experience on a previous trip, wherein the one or more trip recommendations are further based on the determined likelihood.
Gnanasekaran teaches evaluating, via the trained MLM, poses and facial recognitions of one or more persons in the image data to determine a likelihood of a positive or negative experience on a previous trip, wherein the one or more trip recommendations are further based on the determined likelihood (¶66, “…User interactions monitoring 236 may include those user interactions with their device (e.g., inputs and other capturable data), as well as real-world interactions with other objects that may be captured by microphone, cameras, and the like. In this regard, facial expressions monitoring 238 may include data that similarly may be captured by a camera or other optical capture device, such as if the user is smiling, frowning, or has another facial expression indicating a positive, neutral, or negative user experience. [evaluating…facial recognitions of one or more persons in the image data]”, ¶69, “At step 404, user responses to the data loading events are determined based on the past user activities and interactions. These user responses may correspond to behavior of a user including breathing patterns, heart rate, eye movements and/or eye focus on a screen, hand or finger movements, leg or foot movements including tapping, vocal expressions, and the like. This behavior may indicate whether the user is happy, satisfied, neutral, annoyed or irritated, sad, or angry during the wait time. [evaluating…poses]”, ¶72, “If the user is having a positive experience (or at least not a negative experience and/or is not present), at step 414, the data loading event is continued without interruption. In this regard, the user may not want to view additional content, may not be present, or may be viewing preferred content. However, if the user is detected as having a negative user experience, at step 418, data to present to the user on the device during the data loading event is determined from the data presentation preferences. [determine a likelihood of a positive or negative experience…wherein the one or more trip recommendations are further based on the determined likelihood]) in order to remedy user dissatisfaction ¶2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kahn to include evaluating, via the trained MLM, poses and facial recognitions of one or more persons in the image data to determine a likelihood of a positive or negative experience on a previous trip, wherein the one or more trip recommendations are further based on the determined likelihood in order to attempt to remedy any user dissatisfaction with previous trips or recommendations.
Regarding claim 10, Kahn as modified teaches all of the limitations of claim 9, wherein the one or more second travel features comprise
one or more of a location, an object (¶18, the information, i.e. feature, associated with a marked image can be considered an object), a building, a landscape, a person, an animal, a frequency of an image, a size of an image, a scale of an image, or combinations thereof.
Regarding claim 11, Kahn as modified teaches all of the limitations of claim 9, wherein
the image data is stored locally and/or via cloud-based storage (Figure 9: 912, 920).
Regarding claim 12, Kahn as modified teaches all of the limitations of claim 9, wherein
the image data is stored via a social media account (¶18, the marked image is content marked through a social network).
Regarding claim 13, Kahn as modified teaches all of the limitations of claim 12, wherein training the MLM to generate the one or more trip recommendations is further based on
social activity associated with the social media account, the social activity corresponding to the image data (¶18).
Claim(s) 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn (US20170211945A1) in view of Makowsky (US20210248656A1), further in view of Bramnick (US20030233311A1).
Regarding claim 14, Kahn teaches
one or more processors (¶37); and
a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to (¶37):
receive transaction data and image data (¶18, user signals can include marked images (image data) and search history, GPS check-data, locational information provided to websites, etc. (transaction data));
identify, from the image data, one or more travel features (¶18, a marked image may be used and the information taken based on or from the image can be considered a feature);
train a machine learning model (MLM) to generate one or more trip recommendations based on the one or more travel features (¶18, a classifier may be trained to identify travel intent and provide a recommendation);
determine, via the trained MLM, whether at least a first trip recommendation of the one or more trip recommendations exceeds a predetermined threshold indicating a likelihood of interest in the first trip recommendation (¶18, a travel score from the classifier is generated which is compared to a threshold which indicates an intent to travel); and
responsive to determining the first trip recommendation exceeds the predetermined threshold, provide the first trip recommendation (¶18, if the threshold is exceeded, travel intent is determined and recommendations are based on travel intent).
Kahn does not explicitly disclose the use of computer vision to identify one or more travel features from the image data.
Makowsky discloses the use of computer vision to identify one or more features from the image data (¶175, ¶275) in order to make personalized recommendations to users (¶95).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize computer vision to identify the one or more features from the image data in order to create a robust recommendation model.
Kahn does not teach wherein, responsive to determining the first trip recommendation exceeds the predetermined threshold, access one or more travel reservation platforms to evaluate types of amenities available at a travel destination associated with the first trip recommendation; and provide the first trip recommendation with information regarding the evaluated amenities to a device.
It is known to access one or more travel reservation platforms to evaluate types of amenities available at a travel destination associated with the first trip recommendation (Bramnick, Figure 7, 220, “Breakfast included”, “Transfer between airport and hotel included” [evaluate types of amenities available at a travel destination]); and provide the first trip recommendation with information regarding the evaluated amenities to a device (Bramnick, Figure 7, 220, “Breakfast included”, “Transfer between airport and hotel included” [provide the first trip recommendation with information regarding the evaluated amenities to a device]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kahn such that, when the first trip recommendation is provided, the system further accesses one or more travel reservation platforms to evaluate types of amenities available at a travel destination associated with the first trip recommendation and provide the first trip recommendation with information regarding the evaluated amenities to a device in order to provide a user with a succinct description of the travel details for the first recommendation (See at least Figure 7 of Bramnick).
Regarding claim 15, Kahn as modified teaches all of the limitations of claim 14, wherein the one or more travel features comprise
one or more of a location, an object (¶18, the information, i.e. feature, associated with a marked image can be considered an object), a building, a landscape, a person, an animal, a frequency of an image, a size of an image, a scale of an image, or combinations thereof.
Regarding claim 16, Kahn as modified teaches all of the limitations of claim 14.
Kahn as modified does not teach wherein the MLM is configured to utilize facial emotion recognition (FER) technology.
Makowsky teaches wherein the MLM is configured to utilize facial emotion recognition (FER) technology (¶227-228).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize facial emotion recognition (FER) technology in the model in order to create a robust recommendation model.
Regarding claim 17, Kahn as modified teaches all of the limitations of claim 16, wherein
training the MLM to generate the one or more trip recommendations is further based on the FER technology (see rejection of claim 16, where Kahn as modified utilizes FER technology. Furthermore, the model of Kahn as modified generates trip recommendations with the recommendation model and thereby model training is based on the FER technology because the FER technology is utilized in curating training data).
Regarding claim 18, Kahn as modified teaches all of the limitations of claim 14, wherein
the image data is stored locally and/or via cloud-based storage (Figure 9: 912, 920).
Regarding claim 19, Kahn as modified teaches all of the limitations of claim 14, wherein
the image data is stored via a social media account (¶18, the marked image is content marked through a social network).
Regarding claim 20, Kahn as modified teaches all of the limitations of claim 19, wherein training the MLM to generate the one or more trip recommendations is further based on
social activity associated with the social media account, the social activity corresponding to the image data (¶18).
Response to Arguments
Applicant’s remarks filed 04/06/2026 have been fully considered.
Applicant’s arguments are directed to the prior art not teaching or rendering obvious the new limitations amended in the response filed 04/06/2026. None of the previously relied upon references are relied upon to teach these features and therefore the arguments are moot.
Allowable Subject Matter
Claims 1-4, 8 and 21-22 are allowed.
The following is an examiner’s statement of reasons for allowance:
The prior art does not anticipate or render obvious the tailoring of the trip recommendation to an individual within a group who is a trip organizer, where whether they are a trip organizer is determined via whether the individual is the one who tends to conduct the most transactions surrounding an upcoming trip.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gelda (US20200387988A1) discloses personalized travel recommendations generated via artificial intelligence.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCHYLER S SANKS whose telephone number is (571)272-6125. The examiner can normally be reached 06:30 - 15:30 Central Time, M-F.
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/SCHYLER S SANKS/ Primary Examiner, Art Unit 2129
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