DETAILED ACTION
This communication is in response to the Applicant Arguments/Remarks filed 11/13/2025. Claims 51, 53-58, 60-65, 67-68 and 70-75 are pending in the application.
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 . 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 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.
Response to Arguments
Applicant Arguments/Remarks dated 11/13/2025 have been considered.
Regarding the applicant arguments on page 9 of the Remarks that Sood fails to teach "determining to display a recommendation for a media asset corresponding to the location" based on determining that "(a) the number of times the user has visited during the current time period exceeds the number of times the user has visited the location during the previous time period," as recited in claims 51 and 63, examiner respectfully disagree.
Sood teaches at para. 13-14: media captured by the client device may include metadata such as the timestamp or geographical location at which the client device captured the corresponding photo, image, video, etc.;
para. 34: the online system may use information from the third-party tracker along with information from the user profile store and/or action log to build a travel user profile for a user including travel-related actions performed by the user. The actions indicate travel history information, for example, cities, regions, states, or countries visited based on GPS data or internet protocol (IP) addresses of users' client devices, time period of travel (e.g., day or month of the year), frequency/number of trips for a duration of time (e.g., the past certain number of years);
para. 47: training data for the model indicates time periods during which users of the online system visited geographical locations, determine temporal patterns or trends in users' travel-preferences.
Thus, Sood does teach tracking shifts in user travel preferences (based on the frequency/number of trips in time periods visited to geographical locations). Therefore, an upward trend shows user increased interest is typically used for personalized recommendations – See para. 17: the online system is a social networking system. The social networking system may recommend travel-related recommendations.
Yang also teaches at para. 31-32: ranked lists may be customizable based on any number of factors including user preference (or user predicted preference), location, time period, type of venues, output a ranked generated list that provides summation of current or past popularity/trendiness related to evaluated venue data. The number of venues displayed in the ranked list may vary based on a threshold determination of a predicted popularity rank/score. Thus, if the user visits or check-ins increased at a venue at the current time period, the popularity trendiness is upward.
Regarding the applicant argument that “The Office Action does not rely on Yang or Calman to teach tracking the number of times a user has visited a location, and that Yang, Calman, and the other cited references fail to cure this deficiency of Sood”, examiner respectfully disagree.
It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. "The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claims above for the convenience of the Applicants. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claims, typically other passages and figures will apply as well. Please see the cited response above.
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.
Claims 51, 55-58, 60-65, 67-68, 71-72 are rejected under 35 U.S.C. 103 as being unpatentable over Sood (US 20190139089) in view of Yang (US 20170124465) and further in view of Calman et al. (US 20120230540).
As per claims 51, 63, Sood teaches
identifying a visual asset captured by an electronic mobile device; determining, based on metadata associated with the captured visual asset, a geographical location associated with the captured visual asset (para. 13-14: media captured by the client device may include metadata such as the timestamp or geographical location at which the client device captured the corresponding photo, image, video, etc.; para. 39, 48);
determining a user visit frequency based on a number of times a user of the electronic mobile device has visited the geographical location during a) a current time period and b) a previous time period (para. 34: the online system may use information from the third party tracker along with information from the user profile store and/or action log to build a travel user profile for a user including travel-related actions performed by the user. The actions indicate travel history information, for example, cities, regions, states, or countries visited based on GPS data or internet protocol (IP) addresses of users' client devices, time period of travel (e.g., day or month of the year), frequency of trips for a duration of time (e.g., the past certain number of years). Booking information for lodging may include a name of a hotel, geographical location of a hotel (e.g., neighborhood or zip code), number of rooms, type of room or related services, dates of stay; para. 47-48: training data for the model indicates time periods during which users of the online system visited geographical locations);
in response to determining that both: (a) the number of times the user has visited during the current time period exceeds the number of times the user has visited the location during the previous time period (para. 13-14: media captured by the client device may include metadata such as the timestamp or geographical location at which the client device captured the corresponding photo, image, video, etc.; para. 34: the online system may use information from the third-party tracker along with information from the user profile store and/or action log to build a travel user profile for a user including travel-related actions performed by the user. The actions indicate travel history information, for example, cities, regions, states, or countries visited based on GPS data or internet protocol (IP) addresses of users' client devices, time period of travel (e.g., day or month of the year), frequency/number of trips for a duration of time (e.g., the past certain number of years); para. 47: training data for the model indicates time periods during which users of the online system visited geographical locations, determine temporal patterns or trends in users' travel-preferences. Thus, Sood does teach tracking shifts in user travel preferences (based on the frequency/number of trips in time periods visited to geographical locations).
(b) and the current popularity associated with the geographical location exceeds a previous popularity: increasing a weight of a popularity score associated with the popularity of the geographical location, wherein the increased weight corresponds to an increase in the user visit frequency (para. 19: responsive to determining that the affinity is greater than a threshold value, the online system may determine that the particular user may be likely to travel to the geographical location, e.g., for a vacation; para. 34: the online system may use information from the third-party tracker along with information from the user profile store and/or action log to build a travel user profile for a user including travel-related actions performed by the user. The actions indicate travel history information, for example, cities, regions, states, or countries visited based on GPS data or internet protocol (IP) addresses of users' client devices, time period of travel (e.g., day or month of the year), frequency/number of trips for a duration of time (e.g., the past certain number of years). Booking information for lodging may include a name of a hotel, geographical location of a hotel (e.g., neighborhood or zip code), dates of stay; para. 47-48: training data for the model indicates time periods during which users of the online system visited geographical locations); para 44: a check-in at a geographical location has a greater weight than visiting a website including information about the geographical location);
determining to display a recommendation for a media asset corresponding to the location (para 3: an online system uses a model to determine affinities of users for geographical locations. Using the affinities, which may indicate travel-related preferences of the users, the online system may customize content items to include content captured by client devices of other users of the online system. For example, the online system presents to a particular user a content item including a photo or video of a geographical location captured by a camera of a client device of another user who is connected to the particular user; para. 17: the social networking system may recommend travel-related recommendations, social groups, events, other social networking objects, and potential connections (e.g., friends) to a user; para. 19; fig. 3: generate a content item including the content associated with the second geographical location and provide the content item for presentation);
determining that the visual asset captured by the electronic mobile device (para. 3: the online system presents to a particular user a content item including a photo or video of a geographical location captured by a camera of a client device of another user who is connected to the particular user; para. 14: media captured by the client device may include metadata such as the timestamp or geographical location at which the client device captured the corresponding photo, image, video, etc.);
receiving selection of the particular object, from the plurality of objects from the visual asset captured by the electronic mobile device (fig. 4: display the image object 140 and prompt the user an indication to select to “book” a vacation “Flights on sale to Hawaii!”; para. 39-43);
displaying the recommendation for a media asset that corresponds to the selected particular object from the visual asset captured by the electronic mobile device (para. 3: the online system presents to a particular user a content item including a photo or video of a geographical location captured by a camera of a client device of another user who is connected to the particular user; para.17-18: the social networking system may recommend travel-related recommendations, social groups, events, other social networking objects, and potential connections (e.g., friends) to a user; para. 28-29: the edge store stores information describing connections between users and other objects on the online system as edges. Edges may be defined by users, allowing users to specify their relationships with other users, e.g., that parallel the users' real-life relationships such as friends, co-workers, family members, etc.)
Sood does not explicitly teach determining a current popularity associated with the geographical location.
Yang teaches
determining a current popularity associated with the geographical location (para. 25: an exemplary ranking signal is an example of an indication of trendiness of a particular venue based on analysis performed by the venue popularity prediction component 110. Such types of features are used to provide metrics about various user behaviors such as inferred visits, explicit check-ins, likes and dislikes, saves to a list, and tips, etc., which in aggregate provide indication of a trendiness of a particular venue); para. 31-32: ranked lists may be customizable based on any number of factors including user preference (or user predicted preference), location, time period, type of venues, output a ranked generated list that provides summation of current or past popularity/trendiness related to evaluated venue data. The number of venues displayed in the ranked list may vary based on a threshold determination of a predicted popularity rank/score. Thus, if the user visits or check-ins increased at a venue at the current time period, the popularity trendiness is up also.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood and Yang to effectively determine users’ interests over time and to display recommendations and/or advertisements regarding users’ most popular venues for selections.
Sood and Yang do not explicitly teach the captured visual asset includes a plurality of objects.
Calman et al. teaches
determining that the visual asset captured by the electronic mobile device includes a plurality of objects (para. 81: determining which objects from the real-time video stream are associated with individuals meeting a user defined criteria 120. As represented by block 510, an individual is identified from the image captured in the real time video stream. The object recognition application 225 then collects additional information regarding the identified individual. The object recognition application 225 or a different application accessible to the mobile device 200 and its processor 210 may then group the individuals that have similar (or compatible) interests and assign such individuals to the same tables at the wedding reception. This grouping may be presented to the user on the display of the mobile device);
transmitting a notification to electronic mobile device for selecting a particular object, from the plurality of objects in the visual asset, wherein the notification comprises a list of objects in the plurality of objects, each listed object being represented by an indicator (para. 67: the mobile device 200 is configured to utilize markers 330 to identify objects 320, for example individuals from the user's hometown, and indicate to the user 310 identified objects 320 by displaying a virtual image 400 on the mobile device display; para. 87-88: so for instance, if the identified individual's profile page on the social networking site from which the image was collected indicates that one of the individual's hobbies is wakeboarding, the AR application 221 will present an indicator on the display 230 of the mobile device; the object recognition application 225 may then determine which images from the real-time video stream are associated with a business contact that may have capacity to conduct additional business with the user. As the business traveler approaches her seat, she receives a notification from the mobile device 200 and looks to the mobile device display 230 and selects the virtual image 400 presented on the display. Selecting the virtual image 400 presents the user with display page 800 wherein a picture of an individual, in this case the individual seated in the row behind the business traveler, is displayed along with her name, employer, position and additional information about the contact indicating that this individual has done business with the user in the past and may be in a position to do additional business now).
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood and Yang with the captured visual asset includes a plurality of objects of Calman to effectively determine users’ interested object(s) in order to send an indicator or recommendations to users for selections.
As per claim 55, Sood teaches
wherein the current time period and the previous time period are identified by the user of the electronic mobile device (para. 34: store and/or action log to build a travel user profile for a user including travel-related actions performed by the user. The actions indicate travel history information, for example, cities, regions, states, or countries visited based on GPS data or internet protocol (IP) addresses of users' client devices 110, time period of travel (e.g., day or month of the year), frequency of trips for a duration of time (e.g., the past certain number of years)).
As per claims 56, 65, Sood teaches
identifying one or more objects from the captured visual asset, wherein the captured visual asset includes a popular object and an ordinary object; separating the popular object from the ordinary object; and selecting the popular object as the identified object (para. 46-47: content associated with beaches may include photos of the ocean or keywords such as “sun” or “surf,” while content associated with mountains may include photos of snow or keywords such as “hiking” or “ski.” The model may determine levels of affinity based on the classifications. Referring to the above example, the model may determine that the first and second geographical locations are both classified as beaches. Thus, the model may determine that the first and second users are interested in visiting beaches and that the second user is more likely to have affinity for the beach in Hawaii because the second user (as well as the first user) already visited another beach in Los Angeles; determine temporal patterns or trends in users' travel-preferences. In particular, the model 260 learns that a geographical location is a more popular destination during a certain time range of the year (e.g., users typically travel to beaches during the summer and to mountain resorts in the winter). Thus, determine popular locations beaches/mountains etc.)
As per claim 57, Sood does not explicitly teach claim 57.
Yang teaches
wherein, the object is determined to be popular based on a number of social media postings generated exceeding a threshold number within a time period, wherein the social media postings are related to the object (para. 31-32: the number of venues displayed in a ranked list may vary depending on a predetermined program conditions (e.g., output the top N number of ranked venues). In another example, the number of venues displayed in the ranked list may vary based on a threshold determination of a predicted popularity rank /score, where a number of venues above the threshold determination may be displayed. Calculation of a predicted popularity rank /score is described above in the description of system; provide the ability to select or navigate through user interface elements associated with a ranked list to access data for venues, information on ranking/scoring, change geographic locations, change time periods for viewing other generated lists or trigger generation of a new ranked list, as well as use the list to interact in with other portions of the application/service such as user profiles (like/dislike, postings, tips/recommendations, check-ins,) and/or directed information. In further examples, selecting an entry from the ranked list may provide additional information about a selected venue). Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood and Yang in order to effectively allow users to better view, interact and/or manipulate available data.
As per claims 58, 67, Sood does not explicitly teach claim 58.
Yang teaches
wherein the popularity score is based on the amount of metadata created within the current time period (para. 31-32: the generated ranked list shown in processing device view 200 is a predictive list of venues that are estimated to be the most popular/trendy for an upcoming time period. For instance, the ranked list is titled "Hot This Week" highlighting venues that the application/service predicts may be most popular in the upcoming week. The number of venues displayed in the ranked list may vary based on a threshold determination of a predicted popularity rank /score (shown as "Hotness Score" in FIG. 2 and described in processing operations of system 100 as ranking signal), where a number of venues above the threshold determination may be displayed. Calculation of a predicted popularity rank /score is described above in the description of system 100. In some examples, display of trending venues in a ranked list highlight new venues in a geographic region. As previously described, new venues may have venue data that exists for a period of time less than or equal to a predetermined time threshold value. For instance, a new venue of "Hot Cross Buns" is displayed as a trending new venue; figs. 2-3). Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood and Yang in order to effectively allow users to better view, interact and/or manipulate available data.
As per claims 60-61, 68, Sood do not explicitly teach claims 60-61.
Yang teaches
wherein a higher popularity score is generated if the metadata created within the current time period exceeds the amount of metadata created for the same popular object for a same amount of time period prior to the current time period; wherein a lower popularity score is generated if the metadata created within the current time period does not exceed the amount of metadata created for the same popular object for a same amount of time period prior to the current time period (para. 22: inferential postings associated with a user profile that may be collected for analysis; para. 32: generation of a new ranked list, as well as use the list to interact in with other portions of the application/service such as user profiles (like/dislike, postings, tips/recommendations, check-ins,) and/or directed information. In further examples, selecting an entry from the ranked list may provide additional information about a selected venue; figs. 2-3: where popularity scores are display from high to low). Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood and Yang in order to effectively allow users to better view, interact and/or manipulate available data.
As per claim 64, Sood teaches
wherein determining the number of times the user of the electronic mobile device has visited the geographical location during the previous time period comprises: accessing a database of previously captured visual assets; identifying a previous object from each of the previously captured visual assets; and determining the number of times the identified previous object matches the object of the visual asset, wherein the number of times is used for reporting the number of times the user of the electronic mobile device has previously visited the geographical location (para. 32: the third party tracker receives third party information from third party systems and stores the received information in the user profile store, action log, content store, or other databases; para. 34: frequency of trips for a duration of time; para. 37, 46: determine that certain user(s) have visited certain beaches, mountains, time periods etc.)
As per claim 71, Sood teaches
wherein the recommendation for the media asset corresponding to the location is displayed if a relevancy score exceeds a predetermined threshold, wherein the relevancy score is based on any one of a) user travel score associated with the location, b) a user frequency score associated with the location, or c) a popularity score associated with the location (para. 34, 43: the model determines from training data that each user of a sample group of users visited both the first and second geographical locations (e.g., users who visit the beach in Los Angeles tend to also visit the beach in Hawaii, or vice versa). Predictions of a user's travel preferences or travel intent such as preferred destination types, dates or time of travel, transportation modes, frequency of travel, type of flight class, type of hotel etc.; para. 47: a geographical location is a more popular destination during a certain time range of the year (e.g., users typically travel to beaches during the summer and to mountain resorts in the winter).
Even if Sood does not explicitly teach score, Yang teaches at para. 32: the number of venues displayed in the ranked list may vary based on a threshold determination of a predicted popularity rank/score (shown as “Hotness Score” in FIG. 2 and described in processing operations of system 100 as ranking signal), where a number of venues above the threshold determination may be displayed.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood and Yang to effectively determine users’ interests at certain venues in order to display relevant recommendations and/or advertisements to the users.
As per claim 72, Sood teaches
determining a frequency score based on the number of times the user has visited during the current time compared to the number of times the user has visited the location during the previous time period; determining a popularity score based on the current popularity compared to the previous popularity; assigning a different weight to the frequency score than the popularity score (para. 34, 43-44: the model determines from training data that each user of a sample group of users visited both the first and second geographical locations (e.g., users who visit the beach in Los Angeles tend to also visit the beach in Hawaii, or vice versa). Predictions of a user's travel preferences or travel intent such as preferred destination types, dates or time of travel, frequency of travel, etc. The weights may vary based on a type of the signal, e.g., a check-in at a geographical location has a greater weight than visiting a website including information about the geographical location; para. 47: a geographical location is a more popular destination during a certain time range of the year (e.g., users typically travel to beaches during the summer and to mountain resorts in the winter).
Even if Sood does not explicitly teach score, Yang teaches at para. 25-26: an exemplary ranking signal is an example of an indication of trendiness of a particular venue based on analysis performed by the venue popularity prediction component. Such types of feature are used to provide metrics about various user behaviors such as inferred visits, explicit check-ins etc. The applied machine learning processes may assign weights (e.g., probabilities, scaling factors, etc.) based on certain types of venue data, where some types of venue data may be given greater weight than others in determining an overall predicted popularity for the venue; para. 31-32: ranked lists may be customizable based on any number of factors including user preference (or user predicted preference), location, time period, type of venues, output a ranked generated list that provides summation of current or past popularity/trendiness related to evaluated venue data. The number of venues displayed in the ranked list may vary based on a threshold determination of a predicted popularity rank/score where a number of venues above the threshold determination may be displayed; para. 37: the scored venues may be filtered based on ranking scores, geographic location of the venues, type of the venues, or any other filtering category.)
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood and Yang to effectively determine users’ interests at certain venues in order to display relevant recommendations and/or advertisements to the users.
Specification, para. 25-29 describes objects; para. 123 describes “indication”.
As per claim 74, Sood teaches
causing to display on the electronic mobile device, an indication of each object of the plurality of objects in the visual asset ();
displaying a prompt to select the particular object based on the displayed indication of each object of the plurality of objects, wherein the selected particular object is associated with the user's object of interest (fig. 4: display the image object 140 and prompt the user an indication to select to “book” a vacation “Flights on sale to Hawaii!”; para. 39-43).
Claims 53 is rejected under 35 U.S.C. 103 as being unpatentable over Sood (US 20190139089) in view of Yang (US 20170124465) and further in view of Calman et al. (US 20120230540) and Ramer (US 20120215602).
As per claim 53, Sood does not teach claim 53. Calman teaches mobile televisions, gaming devices, tablet computers at para. 48. Sood, Yang and Calman do not explicitly teach said claim.
Ramer teaches
wherein the displayed and recommended media asset that corresponds to the object from the visual asset captured is selected from a group consisting of television programming, on-demand programs, Internet content, video clips, audio clips, pictures, documents, music playlists, books, electronic books, blogs, chat sessions, social media posts, and software applications (para. 172, 191, 204, 236: chat sessions; para. 257, 306, 487, 2077: tv shows, programs; para. 1158, 1986: a collection of songs; para. 2075-2077).
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood, Yang, Calman and Ramer to effectively display relevant recommendations and/or advertisements to the users based on the user interests over time in relating to visual assets.
Claim 54 is rejected under 35 U.S.C. 103 as being unpatentable over Sood (US 20190139089) in view of Yang (US 20170124465) and further in view of Calman et al. (US 20120230540) and Ngo (US 20150094083).
As per claim 54, Sood teaches
wherein determining the number of times the user of the electronic mobile device has visited the geographical location during the previous time period comprises: identifying a list of visual assets previously captured by the electronic mobile device, wherein each asset in the list is associated with the geographical location; and calculating a number of distinct dates on which assets from the list of assets were captured in the predetermined time period (para. 14: media captured by the client device may include metadata such as the timestamp or geographical location at which the client device captured the corresponding photo, image, video, etc.; para. 47: training data for the model indicates time periods during which users of the online system visited geographical locations and may learn that users from a certain geographical region tend to travel to other geographical regions having different average temperatures during a certain time range (e.g., users from areas that are colder in the winter travel to warmer destinations. Thus, periods of time or time periods, e.g., winter, summer etc. have distinct time frames or dates).
Yang also teaches at para. 32: provide the ability to select or navigate through user interface elements associated with a ranked list to access data for venues, information on ranking/scoring, change geographic locations, change time periods for viewing other generated lists or trigger generation of a new ranked list, as well as use the list to interact in with other portions of the application/service such as user profiles (like/dislike, postings, tips/recommendations, check-ins,) and/or directed information.
Even if Sood, Yang, Calman do not explicitly disclose a number of distinct dates on which assets from the list of assets were captured in the predetermined time period.
Ngo teaches
a number of distinct dates on which assets from the list of assets were captured in the predetermined time period (para. 40: locations, times, activities; para. 44: taking of more than a predetermined number of photos within a predetermined period of time or within a predetermined radius. Alternatively, the implicit trigger may be a photo taken in a geographical location that the user has not visited within a prescribed period of time. The device may recognize that the user is traveling in Italy for the first time. If the user stops at a destination for more than a predetermined period of time (e.g., more than 5 minutes, 10 minutes, 30 minutes, or other user-configurable amount of time; para. 50: a travel application or a calendar application may be mined to obtain destination information which can be another trigger for a place.) Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood, Yang, Calman and Ngo in order to effectively allow users to better view, interact, and/or analyze user activities.
Claims 62, 70 are rejected under 35 U.S.C. 103 as being unpatentable over Sood (US 20190139089) in view of Yang (US 20170124465) and further in view of Calman et al. (US 20120230540) and Fisher (US 9020832).
As per claims 62, 70, Sood teaches
retrieving, from the metadata associated with the visual asset, a first set of Global Positioning System (GPS) coordinates for the object in the visual asset (para. 14, 39: use metadata of the photo (e.g., GPS information) or a comment (e.g., “relaxing at the beach in Hawaii) provided with the photo by the first user to determine that the photo was captured at the beach; para. 51: the online system receives the sponsored content from a third party system for promoting an asset of a third party system. For example, the online system receives a catalog of flights on sale to Hawaii (e.g., from an origin airport nearby the residence of the second user).
Sood, Yang, Calman do not explicitly teach retrieving from a media asset source, a second set of Global Positioning System (GPS) coordinates of the object; comparing the first set of GPS coordinates with the second set of GPS coordinates; and determining a match if the first set of GPS coordinates are within a threshold distance of the second set of GPS coordinates.
Fisher teaches said limitations at col. 26:48-59: The location of the event can be specified using GPS coordinates of the actual location, such as for a small building, or coordinates of the boundaries of a geographically dispersed region, such as a fair ground or large convention center. These data fields are provided to the remote device operated by the first party photographer to tag each captured image with the metadata necessary to link the transmitted image to the particular event. The location of the first party image provider can also be established based on the GPS coordinates of the remote device or image capturing device, and used to determine the image provider's actual location or travel distance to the event or location; para. 30: allows image providers to receive only requests for capturing images of events or locations that are within a reasonable distance from their position, and allows the organizer to manage the number or providers contacted for each such event or request.
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood, Yang, Calman and Fisher to effectively determine nearby location(s) that the users may be interested in and to display relevant recommendations and/or advertisements to the users.
Claim 73 is rejected under 35 U.S.C. 103 as being unpatentable over Sood (US 20190139089) in view of Yang (US 20170124465) and further in view of Calman et al. (US 20120230540) and Mishne (US 9836461).
As per claim 73, Sood teaches at para. 44: the weights may vary based on a type of the signal, e.g., a check-in at a geographical location has a greater weight than visiting a website including information about the geographical location; para. 47: a geographical location is a more popular destination during a certain time range of the year (e.g., users typically travel to beaches during the summer and to mountain resorts in the winter
Sood, Yang, Calman seem not teach doubling the popularity score.
Mishne teaches
doubling the popularity score in response to determining that the number of times the user has visited during the current time period exceeds the number of times the user has visited the location during the previous time period (col. 11:12-32: the popularity of the message is represented by a number of rebroadcasts of the source message. If the number of rebroadcasts exceeds a predefined threshold of 10, the aggregation module (145) assigns a weight of 2 to relevant terms identified in the message. The search module (150) then doubles the impact of those weighted terms (i.e., based on the weight of 2) in the message relevance scoring algorithm; col. 12:8-19).
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood, Yang, Calman and Mishne to effectively determine location(s) that the users may be most interested in and to display relevant recommendations and/or advertisements to the users.
Claims 75 is rejected under 35 U.S.C. 103 as being unpatentable over Sood (US 20190139089) in view of Yang (US 20170124465) and further in view of Calman et al. (US 20120230540) and Aultman et al. (US 20150294323).
As per claim 75, Sood, Yang, Calman do not explicitly teach said claim.
Aultman et al. teaches
identifying a second plurality of objects from preexisting visual assets stored on the electronic mobile device (para. 15-16: a computing device may be a mobile computing device such as, for example, but not limited to, a smart phone, a cell phone, a pager, a personal digital assistant (PDA), a mobile computer with a smart phone client, or the like; para. 22: the list of pre-identified attributes may include people, objects or events; para. 35: the data feeds may be provided by devices such as surveillance cameras, drones, microphones, etc.);
determining whether at least one object of the first plurality matches at least one object of the second plurality; and based at least in part on determining at least one object of the first plurality matches at least one object of the second plurality: tagging the at least one object for exclusion from the notification; and adding the at least one tagged object to an exclusion list (para. 26-27: as an example, a sensor event may occur when a match between a behavior, person or object matches a metadata classifier tag applied to the data feed. Detecting the sensor event may include artificial intelligence based algorithms for entity resolution or data matching, tracking and motion prediction combined with a workflow of key requirements gathered by analysts to enable event detection via actionable intelligence actions. Actions may include, but are not limited to, metadata tagging, alert generation, content management services (CMS) integration or other scriptable activities. Metadata classifier tagging creates a notification which may alert the method or an analyst of a match or possible match between the metadata classifier tags and data analyzed from the data feed; para. 32-34: in fig. 3A, the system may use a function, such as, object training 302 to identify an object, a person, a place, a time or some other event to track from a list of pre-identified attributes. It may also be desired to exclude particular objects, people, places, times or other events using a function, such as, object exclusion, for example.)
Thus, it would have been obvious to one or ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Sood, Yang, Calman and an exclusion list of Aultman to ensure that certain assets do not trigger unnecessary alerts/notifications in order to save costs and/or focus on actionable/critical data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Trivelpiece et al. (US 20180308040) teaches at para. 9: the second information is communicated from the tag reader continuously or at every pre-defined interval of time. A second portion of the local list includes exclusion information identifying tags that are not to be counted during a tag counting cycle.
Huang et al. (US 20160266258) teaches at para. 38: images captured by the image capture device 224 may be used to generate a location signal indicative of a range of locations of the mobile computing device 102 and/or a velocity signal indicative of a speed and direction in which the mobile computing device 102 is moving. For example, images captured by the image capture device 224 of the environment of the mobile computing device 102 may be transmitted to the location logic 204 (discussed below), and the location logic 204 may compare the captured images to images stored in the storage device 236 to identify recognized landmarks, in accordance with known techniques. When landmarks in the environment are identified in the captured images.
Carpenter et al. (US 8255411) teaches at col. 28:32-67: popular locations.
Singh (US 20180197197) teaches at para. 104: frequency of visits, popularity score.
Klassen et al. (US 20140089223) teaches at para. 50: a popularity score of the establishment can be generated from the determined one or more indicators of popularity or from the determined patterns of popularity of the establishment.
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.
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/LINH BLACK/Examiner, Art Unit 2163 3/9/2026
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163