DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/29/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim 1 have been considered but are moot because the arguments do not apply in view of newly found reference Sharma being used in the current rejection. See the new rejection below.
Double Patenting
Claims 1-4, 8-12, 16-20, 24-28, and 32-44 stand rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-7, 9, 17, and 25 of U.S. Patent No. 11,758,224. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application is an obvious variation, recited similarly to the patented subject matter as shown in the previous Office Action along with new rejection made below for claims 37-44.
**Applicant acknowledged the rejection and will take appropriate action upon the allowance of claims in the pending application**
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 1-4, 8-12, 16-20, 24-28, 32, and 41-44 are rejected under 35 U.S.C. 103 as being unpatentable over US PG Pub 2010/0287033 to Mathur (“Mathur”) in view of US PG Pub 2017/0228463 to Sharma (“Sharma”).
Regarding claim 1, “A method comprising: generating, by a computing device, a rank for a content item…” reads on the content item recommendations where the content item popularity is determined based on a recency factor (¶0048-¶0049) disclosed by Mathur and represented in Fig. 8.
As to “causing output of at least one of: a ranked list, based on the rank for the content item, of a plurality of content items comprising the content item, or the content item based on recommendation of the content item using the rank” Mathur discloses (¶0056) that the content items which have been modified based on their weights are displayed in different colors or with a particular indicator indicate the popularity of the content items; view option is used to access the content item as represented in Fig. 8.
Mathur meets all the limitations of the claim except “generating, by a computing device, a rank for a content item by weighting different interactions, of a plurality of interactions with the content item, proportional to differences in requirements for receiving the content item during the interactions.” However, Sharma discloses (¶0042) that the topic ranking module ranks the topics of interest associated with each time period and generates a ranked list of topics of interest for each time period; topics are ranked based on one or more suitable factors including a number of times the topic of interest was interacted by the user during the time period, how recently the topic of interest was interacted by the user during the time period, an amount of time spent by the user interacting with the topic of interest, a type of interaction with the topic, or a rate of interaction during the time period; (¶0034-¶0036, ¶0041) the system detects time based content interactions patterns of a user where based on the user's interactions with content over time periods, the system predicts likely topics of interest for a user for a given time in real time; the time segmentation module segments a time interval into multiple time periods that can later be used to predict topics of interest associated with each time period where this time segmentation is used in detecting time-based patterns of user interactions with content provided by the server; (¶0027) the page template associated with types of client devices, allowing content items to be presented in different relative locations and with different sizes when the content items are viewed using different client devices (differences in requirements for receiving content); (¶0032) the recommendation module ranks content for the user based on the match between the ranked content and the topics of interest for the user during the given time of day, and selects one or more content times based on the ranking for the user. Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the invention to modify Mathur’s system by generating ranking for a content item by weighting different interactions proportional to differences in requirements for receiving the content item as taught by Sharma in order to present users with meaningful content on topics likely to be of interest to users at the specific time the content is requested and to accommodate the dynamically changing interests of the user and the diverging topics in the digital content items (Sharma - ¶0002).
Regarding claim 2, “The method of claim 1, wherein the generating the rank for the content item comprises: determining an initial rank for the content item, wherein determining the initial rank is based on applying a first weight, associated with a first requirement for receiving the content item, to a first quantity of interactions of the plurality of interactions with the content item; and modifying the initial rank for the content item, wherein modifying the initial rank is based on applying a second weight, different from the first weight and associated with a second requirement for receiving the content item, to a second quantity of interactions of the plurality of interactions with the content item” combination of Mathur and Sharma is used to teach this limitation, where Mathur discloses (¶0048, ¶0050-¶0052, ¶0056-¶0057) that using the weights of the content items, the recommendation list is adjusted/re-ordered to display accurate ranking of the content items as represented in Fig. 6 (elements 615, 620) and Fig. 8; (¶0056) that the content items which have been modified based on their weights are displayed in different colors or with a particular indicators which indicate the popularity of the content items as represented in Fig. 8, and Sharma discloses (¶0042) that the topic ranking module ranks the topics of interest associated with each time period and generates a ranked list of topics of interest for each time period; topics are ranked based on one or more suitable factors including a number of times the topic of interest was interacted by the user during the time period, how recently the topic of interest was interacted by the user during the time period, an amount of time spent by the user interacting with the topic of interest, a type of interaction with the topic, or a rate of interaction during the time period; (¶0034-¶0036, ¶0041) the system detects time based content interactions patterns of a user where based on the user's interactions with content over time periods, the system predicts likely topics of interest for a user for a given time in real time; the time segmentation module segments a time interval into multiple time periods that can later be used to predict topics of interest associated with each time period where this time segmentation is used in detecting time-based patterns of user interactions with content provided by the server; (¶0027) the page template associated with types of client devices, allowing content items to be presented in different relative locations and with different sizes when the content items are viewed using different client devices (differences in requirements for receiving content); (¶0032) the recommendation module ranks content for the user based on the match between the ranked content and the topics of interest for the user during the given time of day, and selects one or more content times based on the ranking for the user.
Regarding claim 3, “The method of claim 1, wherein the generating the rank for the content item comprises: multiplying a first quantity of interactions, of the plurality of interactions and associated with a first requirement for receiving the content item, by a first weight; and multiplying a second quantity of interactions, of the plurality of interactions and associated with a second requirement for receiving the content item, by a second weight” combination of Mathur and Sharama teaches this limitation, where Mathur discloses (¶0027, ¶0042, ¶0057) that the activity module of the device calculates weights based on different actions on the plurality of content items; (¶0048, ¶0050-¶0052, ¶0056, ¶0057) using the weights of the content items, the recommendation list is adjusted/re-ordered to display accurate ranking of the content items, and Sharma discloses (¶0042) that the topic ranking module ranks the topics of interest associated with each time period and generates a ranked list of topics of interest for each time period; topics are ranked based on one or more suitable factors including a number of times the topic of interest was interacted by the user during the time period, how recently the topic of interest was interacted by the user during the time period, an amount of time spent by the user interacting with the topic of interest, a type of interaction with the topic, or a rate of interaction during the time period; (¶0034-¶0036, ¶0041) the system detects time based content interactions patterns of a user where based on the user's interactions with content over time periods, the system predicts likely topics of interest for a user for a given time in real time; the time segmentation module segments a time interval into multiple time periods that can later be used to predict topics of interest associated with each time period where this time segmentation is used in detecting time-based patterns of user interactions with content provided by the server; (¶0027) the page template associated with types of client devices, allowing content items to be presented in different relative locations and with different sizes when the content items are viewed using different client devices (differences in requirements for receiving content); (¶0032) the recommendation module ranks content for the user based on the match between the ranked content and the topics of interest for the user during the given time of day, and selects one or more content times based on the ranking for the user.
Regarding claim 4, “The method of claim 1, further comprising: determining, based on the ranked list, a rank for a second content item” Mathur discloses (¶0048, ¶0050-¶0052, ¶0056, ¶0057) that using the weights of the content items, the recommendation list is adjusted/re-ordered to display accurate ranking of the content items as represented in Fig. 6 (elements 615, 620) and Fig. 8; (¶0056) the content items which have been modified based on their weights are displayed in different colors or with a particular indicators which indicate the popularity of the content items as represented in Fig. 8.
Regarding claim 8, “The method of claim 1, further comprising: receiving interaction data associated with a user; adjusting, based on the interaction data, the rank for the content item; and sending, to a device associated with the user, the adjusted rank for the content item” Mathur discloses (¶0048, ¶0050-¶0052, ¶0056, ¶0057) that using the weights of the content items, the recommendation list is adjusted/re-ordered to display accurate ranking of the content items as represented in Fig. 6 (elements 615, 620) and Fig. 8; (¶0056) the content items which have been modified based on their weights are displayed in different colors or with particular indicators which indicate the popularity of the content items as represented in Fig. 8.
Regarding claim 9, see rejection similar to claim 1.
Regarding claim 10, see rejection similar to claim 2.
Regarding claim 11, see rejection similar to claim 3.
Regarding claim 12, see rejection similar to claim 4.
Regarding claim 16, see rejection similar to claim 8.
Regarding claim 17, see rejection similar to claim 1. Furthermore, Sharma discloses (¶0018) that the system environment includes a web server/digital magazine server (first device) connected with client devices (second device) as represented in Fig. 1.
Regarding claim 18, see rejection similar to claim 2.
Regarding claim 19, see rejection similar to claim 3.
Regarding claim 20, see rejection similar to claim 4.
Regarding claim 24, see rejection similar to claim 8.
Regarding claim 25, see rejection similar to claim 1.
Regarding claim 26, see rejection similar to claim 2.
Regarding claim 27, see rejection similar to claim 3.
Regarding claim 28, see rejection similar to claim 4.
Regarding claim 32, see rejection similar to claim 8.
Regarding claim 41, “The method of claim 1, wherein the generating the rank for the content item further comprises: weighting different interactions, of the plurality of interactions with the content item, differently based on different amounts of time spent interacting with the content item during the interactions” Sharma discloses (¶0041-¶0042) that the topics are ranked based on one or more suitable factors of the reading habits data of a user, including a number of times the topic of interest was interacted by the user during the time period, an amount of time spent by the user interacting with the topic of interest, a type of interaction with the topic; topics interacted upon by the user more than a threshold amount of times during a time period are considered topics of interest for the user for that time period.
Regarding claim 42, see rejection similar to claim 41.
Regarding claim 43, see rejection similar to claim 41.
Regarding claim 44, see rejection similar to claim 41.
Claims 33-36 are rejected under 35 U.S.C. 103 as being unpatentable over Mathur in view of Sharma, and further in view of US PG Pub 2016/0142774 to Sayyadi-Harikandehei (“Sayyadi”).
Regarding claim 33, combination of Mathur and Sharma meets all the limitations of the claim except “The method of claim 1, wherein the weighting the different interactions proportional to differences in requirements for receiving the content during the interactions comprises: weighting a first interaction proportional to a price of the content item at a time that the first interaction occurred; and weighting a second interaction proportional to a price of the content item at a time that the second interaction occurred.” However, Sayyadi discloses (¶0029) that the system uses consumption history of the user that includes user’s interaction associated with the interactive content, where (¶0044) the consumption history is analyzed with respect to content cost such as paid and free to reveal viewing pattern. Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the invention to modify Mathur and Sharma’s systems by using price points associated with interactions at different time as taught by Sayyadi in order to reveal alternative viewing patterns and the contributions of previously consumed content may be adjusted based on these alternative viewing patterns and alternative periodicities to improve other content recommendation techniques that are based on alternative characteristics (Sayyadi - ¶0044).
Regarding claim 34, see rejection similar to claim 33.
Regarding claim 35, see rejection similar to claim 33.
Regarding claim 36, see rejection similar to claim 33.
Claims 37-40 are rejected under 35 U.S.C. 103 as being unpatentable over Mathur in view of Sharma, and further in view of US Patent 8,752,099 to Riedl (“Riedl”).
Regarding claim 37, “The method of claim 1, wherein the weighting the different interactions proportional to differences in requirements for receiving the content during the interactions comprises: applying a first weight to a first subset of the plurality of interactions with the content item,…and applying a second weight different from the first weight to a second subset of the plurality of interactions with the content item” Mathur and Sharma is used to teach this limitation, where Mathur discloses (¶0048, ¶0050-¶0052, ¶0056-¶0057) that using the weights of the content items, the recommendation list is adjusted/re-ordered to display accurate ranking of the content items as represented in Fig. 6 (elements 615, 620) and Fig. 8; (¶0056) that the content items which have been modified based on their weights are displayed in different colors or with a particular indicators which indicate the popularity of the content items as represented in Fig. 8, and Sharma discloses (¶0042) that the topic ranking module ranks the topics of interest associated with each time period and generates a ranked list of topics of interest for each time period; topics are ranked based on one or more suitable factors including a number of times the topic of interest was interacted by the user during the time period, how recently the topic of interest was interacted by the user during the time period, an amount of time spent by the user interacting with the topic of interest, a type of interaction with the topic, or a rate of interaction during the time period; (¶0034-¶0036, ¶0041) the system detects time based content interactions patterns of a user where based on the user's interactions with content over time periods, the system predicts likely topics of interest for a user for a given time in real time; the time segmentation module segments a time interval into multiple time periods that can later be used to predict topics of interest associated with each time period where this time segmentation is used in detecting time-based patterns of user interactions with content provided by the server; (¶0027) the page template associated with types of client devices, allowing content items to be presented in different relative locations and with different sizes when the content items are viewed using different client devices (differences in requirements for receiving content); (¶0032) the recommendation module ranks content for the user based on the match between the ranked content and the topics of interest for the user during the given time of day, and selects one or more content times based on the ranking for the user.
Combination of Mathur and Sharma meets all the limitations of the claim except “wherein the first subset comprises one or more rentals of the content item; and wherein the second subset comprises one or more purchases of the content item.” However, Riedl discloses (7:20-44; claim 1) that the system provides content for purchase during the first time period and rental during the second time period. Therefore, it would have been obvious to one of the ordinary skills in the art before the effective filing date of the invention to modify Mathur and Sharma’s systems by making content available for purchase during first time period and rental during second time period as taught by Riedl in order to allow a subscriber to obtain copy of their desired content effortless manner using extant network infrastructure (5:43-47).
Regarding claim 38, see rejection similar to claim 37.
Regarding claim 39, see rejection similar to claim 37.
Regarding claim 40, see rejection similar to claim 37.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PINKAL R CHOKSHI whose telephone number is (571)270-3317. The examiner can normally be reached Monday - Friday, 8am-5pm.
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/PINKAL R CHOKSHI/Primary Examiner, Art Unit 2425