DETAILED ACTION
This action is responsive to the request for continued examination filed 10/16/2025.
Claims 1-20 are pending. Claims 1-3, 6, 7, 9-13, 16, 17, 19 and 20 are currently amended.
All prior rejections under 35 U.S.C. § 103 are withdrawn as necessitated by amendment.
Claim Rejections - 35 USC § 103
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.
Claims 1, 4, 5, 7, 9-11, 14, 15, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, et al., U.S. PGPUB No. 2017/0111458 (“Huang”), in view of Stathacopoulos, et al., U.S. PGPUB No. 2016/0255163 (“Stathacopoulos”), and in view of Trencher, et al., U.S. PGPUB No. 2017/0061022 (“Trencher”).
With regard to Claim 1, Huang teaches a computer-implemented method comprising:
receiving, from a connection database of a first user, connection data for each of one or more other users; determining a degree of connection of the one or more other users to the first user based on the connection data ([0041]-[0042] describes that a user can be provided with a list of the user’s connections on a social networking site, where the connections can be ranked at least in part based on a “friendship coefficient” that represents an affinity between the users determined from interaction data retrieved from the social networking site);
receiving query information from one or more electronic devices associated with the first user ([0046] describes a destination search module that receives a user query);
receiving authentication information for each of the one or more other users ([0051] describes that the system can provide user identifiers and message contact information to the other user);
determining a destination of the first user based on the query information ([0046] describes that the system receives a search query about a destination and provides relevant information about the particular destination);
determining one or more other user destinations for the one or more other users, based on the authentication information for the one or more other users ([0048] describes that the system is able to identify which of the user’s connections have previously lived in or traveled to the queried destination);
identifying one or more correlated other users from the one or more other users by comparing the destination of the first user with the one or more other user destinations of the one or more other users and further based on the degree of connection of the one or more other users ([0048] describes that connections are ranked by the connection ranking module when it is identified through the social media platform that the user’s connections have the destination in common as a place they have lived in or traveled to. [0041]-[0043] describe that the other users identified as having the destination in common are then ranked according to both the friendship coefficient and the level of familiarity with the destination); and
providing the identified one or more correlated other users to the first user ([0051] describes that the ranked list of connections can be presented to the user).
Huang, in view of Stathacopoulos teaches based on the degree of connection, classifying each of the one or more other users as a first degree connection or a second degree connection to determine one or more other first degree users and one or more other second degree users, respectively; and carrying out the above steps for first degree users.
Stathacopoulos teaches at [0038] that an application can generate a closeness metric quantifying how close a user is to a social media network contact (analogous to the “friendship coefficient” described in Huang). [0044]-[0045] describe that closeness metrics can be stored corresponding to social media network contacts, and compared to a threshold. If a contact has a closeness metric above a threshold value, that contact is determined to be a sufficiently close friend to the user to generate recommendations therefrom. If the closeness metric is below a threshold, recommendations are not generated for the user from the social media contact. Therefore, steps carried out in Huang are carried out for contacts that are “first degree” in the sense that they are above the specified closeness threshold.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to modify Huang to classify users above and below a threshold friendship or closeness level for the purpose of receiving recommendations therefrom, as described in Stathacopoulos. Stathacopoulos describes at [0002] that users may only want to receive recommendations from close friends. One of skill in the art would have therefore sought the modification, to improve user experience by ensuring that users are only shown contacts for receiving travel recommendations that are sufficiently close to the requesting user.
Huang, in view of Trencher teaches receiving (i) transaction data of the first user from a connection database of the first user based on the query information and (ii) transaction data of the one or more other users; and identifying one or more correlated other users from the one or more other users by comparing: the transaction data of the first user and the transaction data of the one or more other first degree users.
Huang teaches retrieving information about other users and ranking according to common destinations in response to a search query, as described above. Trencher describes at [0053]-[0055] that a system can access user information about users, including transaction data related to transactions, where this data is accessed in order to calculate a similarity between a first user and each other user. The calculation uses a weighted formula that calculates a similarity using the transaction data, or can calculate a separate similarity measure for transactions. The system can then determine if the similarity is above a threshold.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to modify Huang and Stathacopoulos, to use transaction data for identifying similar users as described in Trencher. One of skill in the art would have sought the modification, to improve system functioning by incorporating additional types of data in identifying similar users, thereby increasing the accuracy of similarity determinations.
With regard to Claim 4, Huang teaches that the destination of the first user may comprise a one or more of a geographic location, an accommodation, an establishment, or an attraction. [0045] describes a geographic location of a city as a potential travel destination.
With regard to Claim 5, Huang teaches that the degree of connection of the one or more other users is based on one or more of a purchase history overlap, an interaction, or an indicated level of trust with the first user. [0042] describes that the friendship coefficient can take into account a quantity and quality of interactions between the users.
With regard to Claim 7, Huang teaches receiving, from the first user via a graphical user interface (GUI), a selection of a second user of the one or more correlated other first degree users; and upon selecting the second user from the one or more correlated other first degree users, automatically triggering a communication system to initiate communication between the first user and the second user. [0051] describes that the list of other users in the interface includes selectable elements, which the user can select in order to initiate a messaging session with the selected user.
With regard to Claim 9, Huang teaches requesting authorization from the identified one or more correlated other first degree users to share authentication information with the first user. [0083] describes that an authorization server enforces privacy settings that govern what of a user’s information is accessible by other users.
With regard to Claim 10, Huang teaches concealing identification of the one or more correlated other first degree users from the first user. [0083] describes that a user can determine privacy settings that specify with whom user information can be shared, thereby allowing users to conceal their information from other users.
With regard to Claim 11, Huang teaches a computer-implemented method, the method comprising:
a data storage device storing instructions for identifying correlated users in an electronic storage medium; and a processor configured to execute the instructions (Fig. 8) to perform a method including:
receiving query information from one or more electronic devices associated with a first user; determining one or more other users associated with the first user by accessing a connection database ([0041]-[0042] describes that a user can be provided with a list of the user’s connections on a social networking site; [0046] describes a query.);
transmitting, to the one or more other users, an opt-in preference for opting in to communicating and sharing authentication information ([0083] describes that users can define preferences including whether they wish to share their information with other users);
receiving authentication information for each of the one or more other users, upon receiving indicia of opting in by the one or more other users ([0073] describes users providing profile information to the system);
receiving, from the connection database of the first user, connection data for each of one or more other users based on receiving the authentication information for the one or more other users, wherein the connection data includes digital context data; determining a degree of connection of the one or more other users to the first user, based on the connection data ([0041]-[0042] describes that a user can be provided with a list of the user’s connections on a social networking site, where the connections can be ranked at least in part based on a “friendship coefficient” that represents an affinity between the users determined from interaction data retrieved from the social networking site);
determining a destination of the first user based on the query information ([0046] describes that the system receives a search query about a destination and provides relevant information about the particular destination);
determining one or more other user destinations for the one or more other users, based on the authentication information for the one or more other users ([0048] describes that the system is able to identify which of the user’s connections have previously lived in or traveled to the queried destination);
identifying one or more correlated other users from the one or more other users by comparing the destination of the first user with the one or more other user destinations of the one or more other users and further based on the degree of connection of the one or more other users ([0048] describes that connections are ranked by the connection ranking module when it is identified through the social media platform that the user’s connections have the destination in common as a place they have lived in or traveled to. [0041]-[0043] describe that the other users identified as having the destination in common are then ranked according to both the friendship coefficient and the level of familiarity with the destination); and
transmitting, using a graphical user interface (GUI), the identified one or more correlated other users to the first user ([0051] describes that the ranked list of connections can be presented to the user).
Huang, in view of Stathacopoulos teaches based on the degree of connection, classifying each of the one or more other users as a first degree connection or a second degree connection to determine one or more other first degree users and one or more other second degree users, respectively; and carrying out the above steps for first degree users.
Stathacopoulos teaches at [0038] that an application can generate a closeness metric quantifying how close a user is to a social media network contact (analogous to the “friendship coefficient” described in Huang). [0044]-[0045] describe that closeness metrics can be stored corresponding to social media network contacts, and compared to a threshold. If a contact has a closeness metric above a threshold value, that contact is determined to be a sufficiently close friend to the user to generate recommendations therefrom. If the closeness metric is below a threshold, recommendations are not generated for the user from the social media contact. Therefore, steps carried out in Huang are carried out for contacts that are “first degree” in the sense that they are above the specified closeness threshold.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to modify Huang to classify users above and below a threshold friendship or closeness level for the purpose of receiving recommendations therefrom, as described in Stathacopoulos. Stathacopoulos describes at [0002] that users may only want to receive recommendations from close friends. One of skill in the art would have therefore sought the modification, to improve user experience by ensuring that users are only shown contacts for receiving travel recommendations that are sufficiently close to the requesting user.
Huang, in view of Trencher teaches receiving (i) transaction data of the first user from a connection database of the first user based on the query information and (ii) transaction data of the one or more other users; and identifying one or more correlated other users from the one or more other users by comparing: the transaction data of the first user and the transaction data of the one or more other first degree users.
Huang teaches retrieving information about other users and ranking according to common destinations in response to a search query, as described above. Trencher describes at [0053]-[0055] that a system can access user information about users, including transaction data related to transactions, where this data is accessed in order to calculate a similarity between a first user and each other user. The calculation uses a weighted formula that calculates a similarity using the transaction data, or can calculate a separate similarity measure for transactions. The system can then determine if the similarity is above a threshold.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to modify Huang and Stathacopoulos, to use transaction data for identifying similar users as described in Trencher. One of skill in the art would have sought the modification, to improve system functioning by incorporating additional types of data in identifying similar users, thereby increasing the accuracy of similarity determinations.
Claim 20 recites a system which carries out the method of Claim 11, and the claim is similarly rejected.
With regard to Claim 14, Huang teaches that the destination of the first user may comprise a one or more of a geographic location, an accommodation, an establishment, or an attraction. [0045] describes a geographic location of a city as a potential travel destination.
With regard to Claim 15, Huang teaches that the degree of connection of the one or more other users is based on one or more of a purchase history overlap, an interaction, or an indicated level of trust with the first user. [0042] describes that the friendship coefficient can take into account a quantity and quality of interactions between the users.
With regard to Claim 17, Huang teaches receiving, from the first user via a graphical user interface (GUI), a selection of a second user of the one or more correlated other first degree users; and upon selecting the second user from the one or more correlated other first degree users, automatically triggering a communication system to initiate communication between the first user and the second user. [0051] describes that the list of other users in the interface includes selectable elements, which the user can select in order to initiate a messaging session with the selected user.
With regard to Claim 19, Huang teaches requesting authorization from the identified one or more correlated other users to share authentication information with the first user. [0083] describes that an authorization server enforces privacy settings that govern what of a user’s information is accessible by other users.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Stathacopoulos, in view of Trencher, and in view of Olmstead, et al., U.S. PGPUB No. 2018/0082678 (“Olmstead”).
With regard to Claim 2, Huang does not teach that identifying the one or more correlated other users is performed using a machine-learning algorithm trained based on historical one or more correlated other users. Olmstead teaches at [0060] describes that a machine learning server can use relationship factors to score a relationship between users by analyzing communications, where the communications between users have been used to train the machine learning server.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Huang, Stathacopoulos and Trencher with Olmstead. One of skill in the art would have sought the combination, to improve system functioning by leveraging machine learning to provide better, more thorough processes for scoring or ranking relationships between a user and other users.
Claim 12 recites additional subject matter substantially the same as that of Claim 2, and is similarly rejected.
Claims 3, 6, 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Trencher, and in view of Serad, U.S. PGPUB No. 2014/0344036 (“Serad”).
With regard to Claim 3, Huang does not teach that the identifying the one or more correlated other users is based on a Global Position Service (GPS) location of the first user and a GPS location of the one or more other users. Serad teaches at [0002] that another user’s GPS location can be used as a destination within the system. [0016] describes that the current user location can also be used to plan activities at a location.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Serad with Huang, Stathacopoulos and Trencher. One of skill in the art would have sought the combination, to improve user experience by incorporating additional features aimed at facilitating communication and interaction between users regarding a common location.
Claim 13 recites additional subject matter substantially the same as that of Claim 3, and is similarly rejected.
With regard to Claim 6, Huang does not teach using a GPS location of the first user to update the identified one or more correlated other users. [0016] describes that a users’ location can be used to populate a map display, which can also identify other friends’ locations which are close by, for display to the user.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Serad with Huang, Stathacopoulos and Trencher. One of skill in the art would have sought the combination, to improve user experience by incorporating additional features aimed at facilitating communication and interaction between users regarding a common location.
Claim 16 recites additional subject matter substantially the same as that of Claim 6, and is similarly rejected.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Stathacopoulos, in view of Trencher, and in view of Delong, et al., U.S. PGPUB No. 2015/0262435 (“Delong”).
With regard to Claim 8, Huang does not teach determining, using GPS data of the first user, whether the determined destination of the first user is visited by the first user; and prompting the first user to indicate a review for the destination visited by the first user. Delong teaches at [0014]-[0015] that a GPS can determine a user location at an event; the user location being detected as leaving or arriving home can prompt the user to provide a review of the location or event.
It would have been obvious to one of ordinary skill in the art at the time this application was filed to combine Delong with Huang, Stathacopoulos and Trencher. One of skill in the art would have sought the combination, to improve user experience by encouraging users to review places they visit, thereby increasing the amount of information users have about various destinations.
Claim 18 recites additional subject matter substantially the same as that of Claim 8, and is similarly rejected.
Response to Arguments
Applicant’s arguments have been considered but are moot, as the newly cited Stathacopoulos reference teaches or suggests the subject matter related to classifying users as a first or second degree connection using a determined degree of connection. As the prior art teaches or suggests the elements of the claims, including those added by amendment, the claims remain obvious in view of the prior art.
Conclusion
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/KEITH D BLOOMQUIST/Primary Examiner, Art Unit 2171
2/5/2026