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 .
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) 1-5, 8, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn (US20170211945A1) in view of Makowsky (US20210248656A1), further in view of Foster (US20040260621A1).
Regarding claim 1, 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 transaction data and image data associated with a user (¶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 transaction data, one or more first travel features (¶18, the transaction data can be search history, where each piece of the history can be a feature);
identify one or more second 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 for the user based on the one or more first travel features and the one or more second 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 the user will be interested 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 the user (¶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 second travel features from the image data.
Makowsky discloses the use of computer vision to identify one or more second 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 second features from the image data in order to create a robust recommendation model.
Kahn does not teach wherein the MLM is configured to rank the one or more first travel features and the one or more second travel features based on how each of the one or more first and second travel features may impact a first likelihood that the user will be interested in the one or more trip recommendations or where the threshold indication leads to a second likelihood the user will be interested in the first trip recommendation.
Foster teaches wherein an MLM is configured to rank one or more first features and one or more second features based on how each of the one or more first and second features may impact a first likelihood that a user will be interested in one or more recommendations (see Figure 5: 108) and where a second likelihood is determined via the first recommendation exceeding a predetermined threshold (Figure 5: 112-114, ¶25, “The term "popularity threshold" may include a minimum rank that a popular search term must hold in a category to be considered sufficiently popular to be used in a recommendation query.”) representing how likely it is a user will be interested in the first recommendation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize first and second liklihoods in Kahn such that the MLM is configured to rank the one or more first travel features and the one or more second travel features based on how each of the one or more first and second travel features may impact a first likelihood that the user will be interested in the one or more trip recommendations and where the threshold indication leads to a second likelihood the user will be interested in the first trip recommendation in order to provide accurate recommendations.
Regarding claim 2, Kahn as modified teaches all of the limitations of claim 1, wherein
the one or more first travel features comprise one or more of a merchant identifier, a location (¶18, GPS check-in data would comprise a location. Additionally or alternatively, the search query history may include locations), a date, a transaction amount, a reservation booking, rental information, insurance information, or combinations thereof.
Regarding claim 3, Kahn as modified teaches all of the limitations of claim 1, 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 4, Kahn as modified teaches all of the limitations of claim 1.
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 5, Kahn as modified teaches all of the limitations of claim 4, wherein
training the MLM to generate the one or more trip recommendations for the user is further based on the FER technology (see rejection of claim 4, 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 8, Kahn as modified teaches all of the limitations of claim 1, wherein training the MLM to generate the one or more trip recommendations for the user is further based on
social activity associated with a social media account, the social activity corresponding to the image data (¶18).
Regarding claim 22, Kahn as modified teaches all of the limitations of claim 1, wherein
The MLM is further configured to generate the one or more trip recommendations based on search history and cookies associated with the user (¶3, search history is utilized and stored search queries, which can be considered cookies, are used).
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 Foster (US20040260621A1), further in view of Jagtiani (US20140157295A1).
Regarding claim 9, Kahn as modified according to claim 5 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 associated with a user (¶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 for the user based on the one or more travel features and using a facial emotion recognition technology (¶18, a classifier may be trained to identify travel intent and provide a recommendation, see Kahn as modified according to claim 5 regarding the FER);
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 the user will be interested 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 the user (¶18, if the threshold is exceeded, travel intent is determined and recommendations are based on travel intent).
Kahn as modified does not teach updating the predetermined threshold based on a selection of the first trip recommendation by the user.
Jagtiani discloses updating the predetermined threshold based on a selection of a first recommendation by a user (¶54) in order to produce targeted recommendations (¶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 update the predetermined threshold based on a selection of the first trip recommendation by the user in order to produce targeted recommendations personalized to individual users.
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 associated with the user (¶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 for the user 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 Sewak (US20190355041A1).
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 associated with a user (¶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 for the user 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 the user will be interested 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 the user (¶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 trip recommendations are generated by tagging the one or more travel features based on trip themes, predicting a type of trip the user may be interested in based on the tagging, and generating the one or more trip recommendations based on the type of trip.
Sewak teaches wherein recommendations are generated by tagging features based on themes (¶53, metadata associated with images may include tags, i.e. images are tagged with features to create metadata), predicting a type of product the user may be interested in based on the tagging (¶53, products are recommended to users), and generating one or more product recommendations based on the type of product (¶53, the tagging is used as training data to provide a model to recommend products, which is a generation of a recommendation based on the product).
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 such that trip recommendations are generated by tagging the one or more travel features based on trip themes, predicting a type of trip the user may be interested in based on the tagging, and generating the one or more trip recommendations based on the type of trip in order to provide targeted recommendations to particular users.
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 for the user is further based on the FER technology (see rejection of claim 4, 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 associated with the user (¶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 for the user is further based on
social activity associated with the social media account, the social activity corresponding to the image data (¶18).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kahn (US20170211945A1) in view of Makowsky (US20210248656A1), further in view of Foster (US20040260621A1), further in view of Bai (CN108596735A).
Regarding claim 21, Kahn as modified teaches all of the limitations of claim 1, but does not teach wherein the one or more second travel features comprise individuals from the image data and wherein the MLM is configured to generate the one or more trip recommendations based on determined relationships between the individuals from the image data.
Bai teaches one or more second features comprise individuals from the image data and wherein the MLM is configured to generate the one or more recommendations based on determined relationships between the individuals from the image data (“In a second aspect, the invention further provides a kind of information pushing device, the apparatus comprising: an image obtaining module for obtaining the camera collecting the monitoring image, a group determining module for determining the monitoring image included in the target group; relationship identification module for to people relationship identifying the target group, to determine the target group of people relationship type, information pushing module to appoint the terminal based on the determined crowd relationship type, corresponding to the target group of pushing recommendation information.”)
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 such that the one or more second travel features comprise individuals from the image data and wherein the MLM is configured to generate the one or more trip recommendations based on determined relationships between the individuals from the image data in order to provide targeted recommendations to the user.
Response to Arguments
Applicant’s remarks filed 10/27/2025 have been fully considered.
Applicant’s arguments are directed to Kahn not teaching or rendering obvious the new limitations amended in the response filed 10/27/2025. Kahn is not purported to teach these features and therefore the arguments are moot.
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 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SCHYLER S SANKS/ Primary Examiner, Art Unit 2129
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