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 .
Remarks
This action is in response to the amendments received on 7/18/25. Claims 1-20 are pending in the application. Applicants' arguments have been carefully and respectfully considered.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claim(s) 1, 2, 4-10, 12-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Piepgrass et al. (US 2015/0046528), and further in view of Evnine et al. (US 2019/0349323) and Friggeri et al. (US 2015/0347411).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
With respect to claims 1, 9, and 17, the limitation of “computing a score”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “computing” in the context of this claim encompasses the user mentally determining the likelihood of a user’s interaction with other users and the service.
Similarly, the limitation of “identifying a first set of inactive users”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “identifying” in the context of this claim encompasses the user mentally identifying users.
The limitation of “filtering out inactive users”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “filtering” in the context of this claim encompasses the user mentally eliminating users from consideration.
The limitations of “boosting the score”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “boosting” in the context of this claim encompasses the user mentally deciding that the previously identified set of users are better candidates. These “boosting” limitations also would fall under the “mathematical relationships” grouping because the claim explicitly states this is done with an algorithm or by applying a magnitude. This could be done mentally given the generality of the “optimization algorithm.”
The limitation of “selecting a subset of the plurality of destination user candidates”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “selecting” in the context of this claim encompasses the user mentally choosing ideal users.
The limitation of “detecting a computing device of the source user accessing an online system”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “detecting” in the context of this claim encompasses the user observing information that indicates that a particular source user is viewing the website.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. Claims 1, 9, and 17 recite a processor to execute the operations, a computing device, and an online system, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using generic computer components. Additionally, the claim recites “causing a recommendation for each one of the selected subset of the plurality of destination user candidates to be displayed.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “causing a recommendation for each one of the selected subset of the plurality of destination user candidates to be displayed”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
With respect to “a selectable graphical user interface (GUI) element”, there are several prior art references showing that providing a selectable GUI element was a well- understood, routine, and conventional feature. Verma et al. (US 2021/0081227), Fig. 4 element 414, Agarwal et al. (US 2015/0278962) Fig. 2, element 208 comment, and Evnine et al. (US 2019/0349323) Fig. 4, pa 0096-0097 & Fig. 5a-5b, pa 0105-0106.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 2-8, 10-16, and 18-20, the limitations do not recite any additional abstract ideas. The claims do further define the above identified abstract limitations but do not integrate the judicial exception into a practical application and do not amount to significantly more.
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.
Claim(s) 1, 2, 4-10, 12-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Piepgrass et al. (US 2015/0046528), and further in view of Evnine et al. (US 2019/0349323) and Friggeri et al. (US 2015/0347411).
With respect to claim 1, Piepgrass teaches a computer-implemented method performed by a computer system having a memory and at least one hardware processor, the computer-implemented method comprising:
for each one of a plurality of destination user candidates, computing a score (Piepgrass, pa 0048, Each generated objective value model 119 is then provided to the objective value model application module 320, which uses the objective value models 119 to generate an output score for an entity. & pa 0054, The output score is the expected increase in overall engagement of the candidate entity with the social networking system 130 that would result from the connection between the user 102A and the candidate entity) using a first objective function based on a probability…, a second objective function based on a probability …, and a third objective function based on a predicted measure of interaction by the destination user candidate with an online service to result from the particular destination user action being performed by the destination user candidate (Piepgrass, pa 0047, the page recommendation processing module 120 includes one or more objective value model generation modules 302A-302N configured to generate objective value models 119 that, along with a set of candidate entities 312 created by a candidate entity set generation module 304, is used by an objective value model module 320 to generate a set of ranked entities 314 that identify which of the candidate entities 312 should be shown to a user ( e.g. 102A) to maximize a predicted benefit (e.g., maximized entity engagement) for the social networking system 130. & pa 0048, Each objective value model generation module (e.g., 302A) generates an objective value model 119 based upon data of the social networking system 130 accessed from the data stores 101, which may include social graph data from the social graph store 140, historic data from the action log 148 representing actions of the users 102A-102N, etc. The generated objective value models 119 utilize one or more dimensions of objective data describing the entities (or actions of the entities) in the social networking system 130 and/or describing other interactions occurring with or related to the entities in the social networking system 130 as input values & pa 0062, several examples of configurable objective functions based on a level of a user interacting with the social networking system & pa 0063, The selection of which components of the configurable objective function 410, and the interrelationship between those components, is left to configuration, as operators of different social networking systems 130 may wish to focus upon increasing particular types of activity);
identifying a first set of inactive users from amongst the plurality of destination user candidates based on a determination that an amount of time that has passed since each user of the first set of inactive users has interacted with an online service satisfies a minimum threshold value (Piepgrass, pa 0062, configurable objective function 410 may be entity-centric and can be defined as a number of writes (e.g., posts, comments, pictures uploaded, videos uploaded, etc.), by an administrative user responsible for updating a page of the entity in the social networking system 130, to that page over a recent period of time. Examiner note: determining engagement for an entity that has interacted within a threshold of “a recent period of time”);
filtering out inactive users from amongst the first set of inactive users based on a determination that the amount of time that has passed since each inactive user of the first set of inactive users has interacted with the online service satisfies a maximum threshold value (Piepgrass, pa 0059, the objective function dimensional impact determination module 402 is configured to instruct the social networking system 130 to divide the some or all of the entities of the social networking system 130 into two primary groups-a control group, and a test group. The entities may be selected for this statistical analysis and/or divided into the two primary groups at random, or may be selected based on characteristics to ensure an appropriate sampling of the set of entities ( e.g., number of interactions within a certain period by each entity, number of users that "like" each entity, etc.).);
boosting the score for each unfiltered user of the first set of inactive users using an optimization algorithm to optimize the first objective function, the second objective function, and the third objective function (Piepgrass, pa 0065, After the experiment to collect data for determining the statistical value of recommending connections to entities, the objective function dimensional impact determination module 402 generates an output score for each of the test group and control group entities based upon a view of data at the time of the conclusion of the experiment. & pa 0087, At step 710, the flow 700 includes determining, for each of the plurality of candidate entities, a weight based upon an output score generated by an objective value model. The objective value model utilizes one or more input values describing the respective candidate entity that are objectively measurable based upon data stored by the social networking system. The objective value model generates larger output scores for those of the plurality of candidate entities that would create larger predicted increases in an objectively measurable amount of benefit (e.g., entity engagement) with the social networking system created by a potential connection being created between the user and the respective candidate entity.);
selecting a subset of the plurality of destination user candidates from the plurality of destination user candidates based on the scores of the plurality of destination user candidates, the selected subset including at least one user of the first set of inactive users or at least one user of the second set of inactive users (Piepgrass, pa 0048, only candidate entities with an output score greater than the specific threshold value are ranked. One or more of the candidate entities with the highest ranks (i.e., output scores) are then be displayed to the user as suggested entities that the user may want to connect with in the social networking system 130.); and
causing a recommendation for each one of the selected subset of the plurality of destination user candidates to be displayed on a computing device of the source user, the recommendation comprising a selectable graphical user interface (GUI) element to initiate the source user action for the one of the selected subset of destination user candidates to perform the particular source user action for the one of the selected subset of destination user candidates (Piepgrass, Fig. 5 & pa 0048, One or more of the candidate entities with the highest ranks (i.e., output scores) are then be displayed to the user as suggested entities that the user may want to connect with in the social networking system 130.).
Piepgrass does not expressly discuss an off-line system and an online system. However, Piepgrass does disclose that external systems may access information from the social networking system to predict the probability of users forming a connection within the social networking system. The information transmitted may include user profile information or the connection information of users (Piepgrass, paragraph 0031).
Piepgrass doesn't expressly discuss a probability of a source user performing a particular source user action directed towards the destination user candidate, … a probability of the destination user candidate performing a particular destination user action in response to the particular source user action, and detecting a computing device of the source user accessing and online system.
Evnine teaches for each one of a plurality of destination user candidates, … computing a score using … a probability of a source user performing a particular source user action directed towards the destination user candidate, … a probability of the destination user candidate performing a particular destination user action in response to the particular source user action (Evnine, pa 0069, the messaging system 100 can assign activity scores to each association (e.g., pairing) between the first user and the other users based on interactions related to each pairing. Each activity score can be based on interactions related to the pairings between the first user and the other users, and can represent a likelihood that the first user and a second user will engage in a highly active messaging thread.);
identifying… a first set of inactive users from amongst the plurality of destination user candidates based on a determination that an amount of time that has passed since each user of the first set of inactive users has interacted with an online service satisfies a minimum threshold value (Evnine, pa 0075, the activity score can represent a prediction of communications between the users for the next week. … the activity score can grow stale or irrelevant to the pair of users after a certain amount of time has passed, at which time the activity score can expire if the activity score is not updated based on new interactions. Examiner note: amount of time must satisfy threshold created by amount of time);
boosting … the score for each user of the first set of inactive users using an optimization algorithm to optimize the first objective function, the second objective function, and the third objective function (Evnine, pa 0072, The messaging system 100 can adjust the weight and/or effect that the presence and/or absence of certain interactions have on activity scores. Thus, some interactions can influence the activity score more than other interactions.);
selecting … a subset of the plurality of destination user candidates from the plurality of destination user candidates based on the scores of the plurality of destination user candidates, the selected subset including at least one user of the first set of inactive users or at least one user of the second set of inactive users (Evnine, pa 0085, the messaging system 100 ranks a plurality of users in the contact list 306 of the user of the client device 300 using the corresponding activity scores.);
detecting a computing device of the source user accessing an online system (Evnine, pa 0093, the messaging applications can send notifications or status updates to the messaging system 100 to indicate when the messaging applications are active or online. The messaging system 100 can then send the statuses of contacts associated with a given user to the client device(s) associated with the given user.); and
causing a recommendation for each one of the selected subset of the plurality of destination user candidates to be displayed on the computing device of the source user using the online system, the recommendation comprising a recommendation to perform the particular source user action for the one of the selected subset of destination user candidates (Evnine, pa 0085, the messaging system 100 can organize the contact list 306 for providing to the client device 300 based on the activity scores corresponding to each of the users in the contact list 306. In particular, the messaging system 100 ranks a plurality of users in the contact list 306 of the user of the client device 300 using the corresponding activity scores. Ranking the plurality of users allows the messaging system 100 to position the plurality of users in the contact list 306 in a way that allows the user to more easily find other users that are likely to engage with the user in highly active messaging threads. & pa 0093, the messaging applications can send notifications or status updates to the messaging system 100 to indicate when the messaging applications are active or online. The messaging system 100 can then send the statuses of contacts associated with a given user to the client device(s) associated with the given user.)
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Piepgrass with the teachings of Evnine because it increases the likelihood of high messaging activity between users of the system (Evnine, pa 0009).
Piepgrass in view of Evnine doesn't expressly discuss boosting, using the offline system, the score for a second set of inactive users from among the plurality of destination user candidates such that the score for the second set of inactive users that have user attributes of a user in the first set of inactive users is boosted by a same magnitude as the score for the user of the first set of inactive users.
Friggeri teaches boosting the score for a second set of inactive users from among the plurality of destination user candidates such that the score for the second set of inactive users that have user attributes of a user in the first set of inactive users is boosted by a same magnitude as the score for the user of the first set of inactive users (Friggeri, pa 0035, the recommendation module 235 may identify additional users of the social networking system 140 to a user. For example, the recommendation module 235 identifies additional users having at least a threshold number or percentage of attributes matching or similar to attributes of a user. & pa 0036, A user identifier associated with the selected additional user is presented along with the identified content items associated with the additional user).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified because it can provide users that share similar attributes with relevant recommendations (Friggeri, pa 0007).
With respect to claim 2, Piepgrass in view of Evnine and Friggeri teaches the computer-implemented method of claim 1, wherein the boosting of the score for each user of the first set of inactive users comprises boosting the score for the user using the optimization algorithm with a first constraint and a second constraint to optimize the first objective function, the second objective function, and the third objective function, the first constraint comprising a maximum threshold number of the first set of inactive users to display as recommendations to the source user (Piepgrass, pa 0077, Depending upon configuration, zero, one, or more of the ranked entities 314 are utilized by creating a recommendation module 345 for each ranked entity within an entity recommendation user interface 340 of the user 104A.), the second constraint comprising a minimum threshold number of the first set of inactive users for which the source user to perform the particular source user action (Piepgrass, pa 0064, When a configurable objective function 410 is configured, a criteria for the configurable objective function 410 is also configured to indicate what minimum (or maximum) output score of the configurable objective function 410 is the threshold for determining if the entity is sufficiently "engaged." For example, in an embodiment where the configurable objective function 410 is defined as a total number of user and administrator writes to an entity page within a seven-day range, the criteria may be set at "5 writes" (or 10 writes, 100 writes, etc.). Thus, any entity having 5 or more writes within the time period (i.e., any entity having a configurable objective function 410 output score satisfying the criteria value) is deemed as "engaged." The selection of the exact criteria value is also configurable and in many embodiments is dependent upon the configurable objective function 410 selected and the type (and activity level) of social networking system 130 itself.).
With respect to claim 4, Piepgrass in view of Evnine and Friggeri teaches the computer-implemented method of claim 1, wherein the identifying the first set of inactive users from amongst the plurality of destination user candidates is further based on a determination that the amount of time that has passed since each user of the first set of inactive users has interacted with the online service satisfies a maximum threshold value (Piepgrass, pa 0062, configurable objective function 410 may be entity-centric and can be defined as a number of writes (e.g., posts, comments, pictures uploaded, videos uploaded, etc.), by an administrative user responsible for updating a page of the entity in the social networking system 130, to that page over a recent period of time. Examiner note: determining engagement for an entity that has interacted within a threshold of “a recent period of time” & Evnine, pa 0075, the activity score can represent a prediction of communications between the users for the next week. … the activity score can grow stale or irrelevant to the pair of users after a certain amount of time has passed, at which time the activity score can expire if the activity score is not updated based on new interactions. Examiner note: amount of time is compared to threshold created by amount of time).
With respect to claim 5, Piepgrass in view of Evnine and Friggeri teaches the computer-implemented method of claim 1, wherein the particular source user action comprises submitting an invitation to connect via a social networking service, and the particular destination user action comprises accepting an invitation to connect via the social networking service (Piepgrass, pa 0042, a first user specifically selects a particular other user to be a friend. Connections in the social networking system 130 are usually in both directions, but need not be, so the terms "user," "friend" and "connection" depend on the frame of reference. Connections between users of the social networking system 130 are usually bilateral, or "mutual," but connections may also be unilateral, or "one-way." For example, if Bob and Joe are both users of the social networking system 130 and are connected to each other, Bob and Joe are each other's connections. If, on the other hand, Bob wishes to connect to Joe to view data communicated to the social networking system by Joe but Joe does not wish to form a mutual connection, a unilateral connection may be established. & Evnine, pa 0071, the interactions can include, but are not limited to, … interactions with notifications of activity relating to the users … number of social application requests between the users ( e.g., such as gaming requests or other application requests within the social networking system 206); amount of time since the first user or the second user has "liked" or commented on content by the other user in the messaging system 100 on one or more application platforms … number of times the users have interacted with each other in a third-party application; and/or number and times of messages exchanged between the users.).
With respect to claim 6, Piepgrass in view of Evnine and Friggeri teaches the computer-implemented method of claim 1, wherein the probability of the source user performing the particular source user action directed towards the destination user candidate is based on at least one of profile data of the source user,
interaction data indicating interactions of the source user with the online service (Evnine, pa 0071, After identifying the association between the first user and the second user, the messaging system 100 can identify the interactions by the first user and the second user relating to the association between the first user and the second user. … the interactions can include, but are not limited to, … interactions with notifications of activity relating to the users … number of social application requests between the users ( e.g., such as gaming requests or other application requests within the social networking system 206); amount of time since the first user or the second user has "liked" or commented on content by the other user in the messaging system 100 on one or more application platforms … number of times the users have interacted with each other in a third-party application; and/or number and times of messages exchanged between the users.),
or social graph data of the source user.
With respect to claim 7, Piepgrass in view of Evnine and Friggeri teaches the computer-implemented method of claim 1, wherein the probability of the destination user candidate performing the particular destination user action in response to the particular source user action is based on at least one of profile data of the destination user candidate,
interaction data indicating interactions of the destination user candidate with the online service (Evnine, pa 0071, the interactions can include, but are not limited to, … interactions with notifications of activity relating to the users … number of social application requests between the users ( e.g., such as gaming requests or other application requests within the social networking system 206); amount of time since the first user or the second user has "liked" or commented on content by the other user in the messaging system 100 on one or more application platforms … number and times of messages exchanged between the users.),
or social graph data of the destination user candidate.
With respect to claim 8, Piepgrass in view of Evnine and Friggeri teaches the computer-implemented method of claim 1, wherein the predicted measure of interaction by the destination user candidate with the online service to result from the particular destination user action being performed by the destination user candidate is based on a number of sessions between the destination user candidate and the online service within a specified period of time after the particular destination user action was performed by the destination user candidate (Piepgrass, pa 0062, several examples of configurable objective functions based on a level of a user interacting with the social networking system & pa 0063, The selection of which components of the configurable objective function 410, and the interrelationship between those components, is left to configuration, as operators of different social networking systems 130 may wish to focus upon increasing particular types of activity & Evnine, pa 0071, the interactions can include, but are not limited to, … interactions with notifications of activity relating to the users … number of social application requests between the users ( e.g., such as gaming requests or other application requests within the social networking system 206); amount of time since the first user or the second user has "liked" or commented on … number of times the users have interacted with each other in a third-party application; and/or number and times of messages exchanged between the users.).
With respect to claims 9, 10, and 12-16, the limitations are essentially the same as claim 1, 2, and 4-8, and are rejected for the same reasons.
With respect to claims 17, 18, and 20, the limitations are essentially the same as claim 1, 2, and 4, and are rejected for the same reasons.
Response to Arguments
35 U.S.C. 101
Applicant argues that the amended limitation “filtering out inactive users from amongst the first set of inactive users based on a determination that the amount of time that has passed since each inactive user of the first set of inactive users has interacted with the online service satisfies a maximum threshold value” further emphasizes the improvement of large-scale data searching systems as disclosed in the specification pa 0042. The Examiner respectfully disagrees. The “filtering” step is identified as part of the mental process since a person can mentally decide to exclude inactive users. An "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966). See MPEP 2106.05. “It is important to note, the judicial exception alone cannot provide the improvement.” See MPEP 2106.05(a). The “filtering” limitations are part of the recited judicial exception so any improvement is to the abstract idea alone, not an improvement to the functioning of the computer or technology.
Applicant argues that the claims are integrate the alleged exception into a practical application by reflecting the improvement described in the specification, similar to claim 3 of Example 47. The Examiner respectfully disagrees. As Applicant noted, Example 47 is directed towards improvements in the technical field of network intrusion. The consideration of whether the claim as a whole includes an improvement to a computer or to a technological field requires an evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. See MPEP 2106.04(d)(1). When determining whether claim 3 of Example 47 is eligible, the background is analyzed and determined to explain the improvement to enhance security by acting in real-time to proactively prevent network intrusions. Looking to the current specification, pa 0015 discusses biases in recommender systems as rich-get-richer behavior, recognized as the Matthew effect, where interactions of active users overwhelm the data used to train the ranking models, degrading the relevance of inactive users. The improvement provided by the current invention relates to preventing such biases. Therefore, this asserted improvement exists entirely outside of the functioning of the computer or technology. That is, “biases” in recommendations such as the “Matthew effect” exist in all forms of recommendations (i.e. the mental process). Specifically, the “Matthew effect” is a reference to the Bible, where Matthew 5:29 reads, “For whosoever hath, to him shall be given, and he shall have more abundance: but whosoever hath not, from him shall be taken away even that he hath” that is the problem is accumulated advantage. This describes the idea of “filtering out inactive users from amongst the first set of inactive users based on a determination that the amount of time that has passed since each inactive user of the first set of inactive users has interacted with the online service satisfies a maximum threshold value.” This step does not appear to be directed towards preventing biases because it filters out inactive users just as the background describes as prior art. Therefore, this limitation does not integrate the judicial exception into a practical application.
35 U.S.C. 103
Applicant argues that the references fail to teach “filtering out inactive users from amongst the first set of inactive users based on a determination that the amount of time that has passed since each inactive user of the first set of inactive users has interacted with the online service satisfies a maximum threshold value.” The Examiner respectfully disagrees. In Piepgrass, when determining the engagement of a user with the social networking system, different actions over a recent period of time are analyzed with a configurable objective function. It is noted that there are at least two times at which entities are filtered, when determining groups for analysis by the objective function and when determining the output score of the objective function. Since the claims specify that the score is boosted for each unfiltered user, the first filtering applies to the claimed filtering. Piepgrass describes that entities are divided into two primary groups, a control group and a test group. These entities can be selected according to a number of interactions within a certain period by each entity (pa 0059). This provides “filtering out inactive users from amongst the first set of inactive users based on a determination that the amount of time that has passed since each inactive user of the first set of inactive users has interacted with the online service satisfies a maximum threshold value.”
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 BRITTANY N ALLEN whose telephone number is (571)270-3566. The examiner can normally be reached M-F 9 am - 5:00 pm EST.
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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169