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 .
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 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.
Status of Claims
Claims 6-16 remain pending, and are rejected.
Claims 21-29 have been added, and are rejected.
Claims 1-5 and 17-20 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 4/15/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 4/15/2026 with respect to the rejection under 35 U.S.C. 101 for claims directed to a judicial exception are not persuasive.
Notably, on pages 10-12 of the Applicant’s Remarks, arguments are made that the claims reflect an improvement to technology and thereby integrate the judicial exception into a practical application. The ARP decision of Ex Parte Desjardins is cited as comparison to the present application for eligibility. The Applicant argues that the machine learning models of the recommendation system are improved to select content items to promote the long-term objective, and the improvement is provided through use of mappings, which are used to generate a plurality of candidate content items having respective weights based on the determined mapping, as opposed to conventional recommendation systems that only consider short-term user behavior.
Examiner respectfully disagrees. The improvement of providing recommendations that promote long-term behavior over short-term behavior does not represent any technical undertaking, but is a commercial endeavor. This is an improvement to sales activity, and is an abstract idea. The machine learning models are merely applied to the abstract idea to provide an output of the abstract idea given an input of the abstract idea. The machine learning models are not recited with any particularity or functionality, merely being recited as being configured to perform the abstract idea of identifying a corpus of content items having respective weights based on the determined mapping. The weights and mappings also are part of the abstract idea as the algorithm of determining the content items to select to promote the long-term objective. Furthermore, the claims do not recite any machine learning models, but recite recommendation systems having one or more models, which can be any kind of model, including general recommendation algorithms. In Ex Parte Desjardins, specific technical abilities of the machine learning were addressed and improved, such as the problem of “catastrophic forgetting” encountered in continual learning systems, such as that the model can learn new tasks while protecting knowledge about previous tasks. As discussed above, the present claims do not address such technical matters, and the claims merely apply machine learning to the abstract idea to perform calculations as an output for use in the abstract idea.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 4/15/2026 with respect to the rejection under 35 U.S.C. 102 and 103 have been fully considered, but are moot in light of new grounds of rejection. Applicant’s amendments necessitated new grounds of rejection.
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 6-16 and 21-29 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 6-16 are directed to a method, which is a process. Claims 21-28 are directed to a system, which is an apparatus. Claim 29 is directed to a non-transitory computer storage media, which is an article of manufacture. Therefore, claims 6-16 and 21-29 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Claim 21 sets forth the following limitations reciting the abstract idea of determining and recommending content based on a long-term objective:
determining a long-term objective for recommending content to subscribers of a service, the long-term objective being associated with a defined time period;
determining a mapping between a plurality of content items and the long-term objective, wherein determining the mapping between the plurality of content items and the long-term objective includes determining a plurality of mappings between a plurality of interim metrics that relate the long-term objective to an aggregation of subscriber sessions, individual subscriber sessions to the aggregation of subscriber sessions, and content items to the individual subscriber sessions;
generating, using at least the mapping between the plurality of content items and the long-term objective, a recommendation system having one or more models configured to determine content items from a plurality of candidate content items, identified from a corpus of content items, that are responsive to a request for content items, wherein the one or more models generate the plurality of candidate content items having respective weights based on the determined mapping and wherein the content items are selected to promote the long-term objective.
The recited limitations above set forth the process for determining and recommending content based on a long-term objective. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors, etc.). The claims recite steps for mapping items to a long-term objective and generating a recommendation system for encouraging the long-term objective, which is a marketing activity.
Such concepts have been identified by the courts as abstract ideas (see: MPEP 2106.04(a)(2)).
Step 2A (Prong 2):
Returning to representative claim 21, Examiner acknowledges that claim 21 recites additional elements, such as:
one or more processors;
a memory storing program instructions;
Taken individually and as a whole, claim 21 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than apply the judicial exception on a general purpose computer.
Furthermore, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a processor and a memory, these elements are recited with a very high level of generality, and merely as a preamble to the claims as performing the steps of the abstract idea. Specification paragraph [0265] discloses the processors as being any of single-processor, multi-processor, single-core units, and multi-core units, which are well known in the art. The memory is disclosed in specification paragraph [0264], which discloses the memory can be any of ROM, solid-state memory devices, memory cards, etc. It is evident that the additional elements are any generic computing components that merely serve to implement the abstract idea on a computing device.
In view of the above, under Step 2A (prong 2), claim 21 does not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Returning to claim 21, taken individually or as a whole, the additional elements of claim 21 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in claim 20 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computing device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 21 do not add anything further than when they are considered individually.
In view of the above, claim 21 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding Claim 6 (method): Claim 6 recites at least substantially similar concepts and elements as recited in claim 21 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 6 is rejected under at least similar rationale as provided above regarding claim 21.
Regarding Claim 29 (non-transitory computer storage media): Claim 29 recites at least substantially similar concepts and elements as recited in claim 21 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 29 is rejected under at least similar rationale as provided above regarding claim 21.
Dependent claims 7-16 and 22-28 recite further complexity to the judicial exception (abstract idea) of claim 21, such as by further defining the algorithm of determining and recommending content based on a long-term objective. Thus, each of claims 7-16 and 22-28 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 7-16 and 22-28 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 7-16 and 22-28 rely on at least similar elements as recited in claim 21. Further additional elements (e.g., a trained model (claim 9)) are also acknowledged; however, the additional elements of claims 7-16 and 22-28 are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 7-16 and 22-28 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 7-16 and 22-28 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 21. Thus, dependent claims 7-16 and 22-28 do not add “significantly more” to the abstract idea.
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 6-7, 12, 21-22, 27, and 29 are rejected under 35 U.S.C. 103 as being unpatentable by Jogia (US 10,896,439 B1) in view of Laserson (US 11,900,395 B2).
Regarding Claim 6: Jogia discloses a method comprising:
determining a long-term objective for recommending content to subscribers of an online service, the long-term objective being associated with a defined time period; (Jogia: col. 8, ln. 4-23 – “a campaign goal for a content delivery campaign may be received. In some embodiments, a campaign goal may be recommended by embodiments of the disclosure rather than received from a user and/or user device. Campaign goals may be selected from preset options or may be input by a campaign manager or other user. For example, computer-executable instructions of one or more content delivery module(s) stored at a remote server may be executed to receive or otherwise determine a campaign goal. The campaign goal may be associated with a user account. The user account may be associated with a particular brand or company or other entity, and may be associated with one or more product identifiers of products made by or associated with the brand” Jogia: col. 2, ln. 51-55 – “campaign goals might be to increase brand awareness of a particular brand, increase sales, increase traffic, increase membership to a listserv, increase subscriptions, increase or improve customer engagement, and other goals”; Jogia: col. 8, ln. 25-27 – “Content delivery campaign parameters may include parameters such as budget values and allocations, start and/or end dates, flight time”).
determining a mapping between a plurality of content items and the long-term objective, wherein determining the mapping between the plurality of content items and the long-term objective includes determining a plurality of mappings between a plurality of interim metrics; (Jogia: col. 18, ln. 33-44 – “The datastore(s) 520 may include historical sales data 530 and historical campaign data 540. The product recommendation engine 510 may identify historical sales data for a particular brand or user account, a particular product, a competitor brand or account, or other relevant historical sales data that may be used to determine product recommendations. The product recommendation engine 510 may identify relevant historical campaign data, which may be used to determine previously targeted segments, previously promoted products, user interaction rates, previous campaign performance, and other information that may be used to determine product recommendations”; Jogia: col.
generating, using at least the mapping between the plurality of content items and the long-term objective, a recommendation system having one or more models configured to determine content items from a plurality of candidate content items, identified from a corpus of content items, that are responsive to a request for content items, wherein the one or more models generate the plurality of candidate content items having respective weights based on the determined mapping and wherein the content items are selected to promote the long-term objective. (Jogia: col. 10, ln. 38-66 – “The second user interface 330 may include product identifiers recommended for promotion 340. For example, product ID 1 and product ID 6 may be identified as products to include in a promotion associated with the content delivery campaign. The recommended products may be presented with any suitable identifier, and may be presented as hyperlinks in some embodiments. (47) Similarly, in FIG. 4, a content delivery campaign package 400 may be presented at a second user interface 440 that may be presented after the selection of a campaign goal and any other inputs made by the user. In some embodiments, the second user interface 440 may include a recommended campaign goal. The second user interface 440 may include products recommended to include in the campaign, such as product ID A and product ID V. Any suitable product identifier may be used. The user may be able to select one or more of the recommended products for inclusion in the campaign and/or for a promotion. In some instances, the user may not be able to modify or select individual product recommendations. (48) At block 208 of the process flow 200, a set of one or more recommended targeting segments may be generated. Targeting segments may include groups or types of users and/or particular users to which content impressions associated with the content delivery campaign are delivered. Targeting content impressions to certain users and/or certain platforms may increase a likelihood that users will consume the content impression and/or interact with the content impression”; Jogia: col. 14, ln. 35-40 – “One or more machine learning algorithms may be used to determine recommendations for the content delivery campaign, and may be trained using product information, related product information, competing product information, historical campaign results for respective products, and the like”; Jogia: col. 14, ln. 14-24 – “Budget allocation value recommendations may be determined based at least in part on the global budget value, or may be determined based at least in part on the available or forecasted supply of respective digital product types, or may be determined based at least in part on historical performance data associated with respective digital product types. For example, if a certain digital product type has performed very well in the past for the brand, a relatively high budget allocation value may be generated for that digital product type”).
Jogia does not explicitly teach relate the long-term objective to an aggregation of subscriber sessions, individual subscriber sessions to the aggregation of subscriber sessions, and content items to the individual subscriber sessions; Notably, however, Jogia does disclose historical sales data of content over time (Jogia: col. 18, ln. 33-44).
To that accord, Laserson does teach relate the long-term objective to an aggregation of subscriber sessions, individual subscriber sessions to the aggregation of subscriber sessions, and content items to the individual subscriber sessions; (Laserson: claim 11 – “identifying item codes associated with transaction items identified in the historical customer-specific transactions; assigning specific item vectors associated with the item codes per transaction; summing the specific item vectors per transaction creating a transaction vector for each transaction for the corresponding customer; normalizing the transaction vectors as normalized item vectors; and summing the normalized item vectors associated with all the historical customer-specific transactions from the transaction histories and producing an aggregate consumer-item vector for the corresponding customer, and plotting the aggregate consumer-item vector within the multidimensional space, wherein each aggregate consumer-item vector represents the corresponding customer's transaction vectors, each transaction vector for the corresponding customer represents given items purchased by the corresponding customer in a given transaction; processing a clustering algorithm against the aggregate consumer-item vectors based on plotted aggregated consumer-item vectors within the multidimensional space; receiving clusters of the customers as output from the clustering algorithm based on calculated distances within the multidimensional space between each aggregate consumer-item vector; and providing the clusters as customer segments to a promotion engine or a loyalty system”. In summary, individual item codes are vectorized from individual customer per transaction (content items to individual sessions), which are aggregating per customer, and then plotted with a plurality of customers (individual sessions to an aggregate of subscriber sessions) together to determine segments for a promotion (long-term objective to the aggregation).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Jogia disclosing the system for relating a long-term objective to an aggregation of subscriber sessions, individual sessions to an aggregation, and content items to individual sessions as taught by Laserson. One of ordinary skill in the art would have been motivated to do so in order to dynamically determine segments of customers for a marketer offer (Jogia: col. 1, ln. 25-38).
Regarding Claim 7: Jogia in view of Laserson discloses the limitations of claim 6 above.
Jogia further discloses wherein the plurality of interim metrics includes at least one of:
a plurality of parameters associated with an aggregation of subscriber sessions; or
a plurality of features associated with individual subscriber sessions.
Examiner notes that Applicant recites at least one of in the claim. (Jogia: col. 18, ln. 33-44 – “The datastore(s) 520 may include historical sales data 530 and historical campaign data 540. The product recommendation engine 510 may identify historical sales data for a particular brand or user account, a particular product, a competitor brand or account, or other relevant historical sales data that may be used to determine product recommendations. The product recommendation engine 510 may identify relevant historical campaign data, which may be used to determine previously targeted segments, previously promoted products, user interaction rates, previous campaign performance, and other information that may be used to determine product recommendations”).
Regarding Claim 12: Jogia in view of Laserson discloses the limitations of claim 6 above.
Jogia further discloses wherein the recommendation system includes a multi-stage recommendation system and at least one stage of the multi-staged recommendation system is configured to determine content items based at least in part on the long-term objective. (Jogia: col. 18, ln. 27-44 - “The product recommendation engine 510 may be stored at a user device or at one or more remote servers. The product recommendation engine 510 may be configured to generate product recommendations for inclusion in a campaign and/or promotion. One or more inputs may be received by or sent to the product recommendation engine 510. For example, in FIG. 5, the product recommendation engine 510 may be in communication with a datastore(s) 520. The datastore(s) 520 may include historical sales data 530 and historical campaign data 540. The product recommendation engine 510 may identify historical sales data for a particular brand or user account, a particular product, a competitor brand or account, or other relevant historical sales data that may be used to determine product recommendations. The product recommendation engine 510 may identify relevant historical campaign data, which may be used to determine previously targeted segments, previously promoted products, user interaction rates, previous campaign performance, and other information that may be used to determine product recommendations”; Jogia: col. 18, ln. 51-57 – “The product recommendation engine 510 may access the retail catalog database(s) 550 to determine products that are related to a selected product or a product that may be recommended for inclusion in a campaign. For example, the product recommendation engine 510 may determine parent, child, or sibling products by analyzing a browse node or other hierarchy of products”; Jogia: col. 19, ln. 8-10 – “The product recommendation engine 510 may implement one or more machine learning algorithms to generate product recommendations. The recommended products may be products that will result in optimized campaign performance if featured in or included in the campaign”).
Regarding Claims 21 and 29: Claims 21 and 29 recite substantially similar limitations as claim 6. Therefore, claims 21 and 29 are rejected under the same rationale as claim 6 above.
Regarding Claim 22: Claim 22 recites substantially similar limitations as claim 2. Therefore, claims 22 is rejected under the same rationale as claim 2 above.
Regarding Claim 27: Claim 27 recites substantially similar limitations as claim 12. Therefore, claims 27 is rejected under the same rationale as claim 12 above.
Claims 8, 10-11, 23, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Jogia (US 10,896,439 B1) and Laserson (US 11,900,395 B2), in view of Bajaj (US 20230244741 A1).
Regarding Claim 8: The combination of Jogia and Laserson discloses the limitations of claim 6 above.
The combination does not explicitly teach wherein determining the plurality of mappings between the plurality of interim metrics includes using a plurality of trained models configured to predict a respective first interim metric of the plurality of interim metrics based at least in part on an input of a respective second interim metric of the plurality of interim metrics. Notably, however, Jogia does disclose utilizing various metrics in determining products to include in the campaign (Jogia: col. 19, ln. 33-44).
To that accord, Bajaj does teach wherein determining the plurality of mappings between the plurality of interim metrics includes using a plurality of trained models configured to predict a respective first interim metric of the plurality of interim metrics based at least in part on an input of a respective second interim metric of the plurality of interim metrics. (Bajaj: [0060] – “predicting one or more product type intents of a user. Activity 405 can occur after activity 401 and also after one or more of activities 402, 403, or 404. In some embodiments, the product type intent of a user can be predicted using a first set of predictive algorithms. In these or other embodiments, an input for a predictive algorithm can comprise in-session activity and/or historical activity. In various embodiments, the in-session activity and/or the historical activity can be aggregated (e.g., selectively aggregated) as described above before being inputted into a predictive algorithm. In some embodiments, a first set of predictive algorithms can comprise one or more machine learning algorithms. In these or other embodiments, in-session activity and/or historical activity can be converted into vector format before being inputted into a predictive algorithm. In many embodiments, a vector can be constructed by incrementing a count in an activity database that tracks a specific action. For example, an activity database can include activity types such as views, purchases, add-to-carts, etc. and corresponding counts for in-session activity and historical activity. It will be understood that these narrative descriptions can be replaced by various identifiers (e.g., a key value) that can be understood by a computer system”).
It would have been obvious to one of ordinary skill in the art, before the filing date of the claimed invention, to modify the invention of the combination of Jogia and Laserson disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the predicting of interim metrics based on inputs of a second interim metric as taught by Bajaj. One of ordinary skill in the art would be motivated to do so in order to present information that is more likely to be used by the user (Bajaj: [0059]).
Regarding Claim 10: The combination of Jogia and Laserson in view of Bajaj discloses the limitations of claim 8 above.
Jogia does not explicitly teach wherein the plurality of parameters associated with the aggregation of subscriber sessions includes at least one of:
a frequency of session initiation over a period of time; or
a depth of session associated with the aggregation of subscriber sessions.
Notably, however, Jogia does disclose using metrics such as user interaction rates and previous campaign data (Jogia: col. 18, ln. 39-44).
To that accord, Bajaj does teach wherein the plurality of parameters associated with the aggregation of subscriber sessions includes at least one of:
a frequency of session initiation over a period of time; or
a depth of session associated with the aggregation of subscriber sessions.
Examiner notes that Applicant recites at least one of in the claim. (Bajaj: [0055] – “selectively aggregating in-session and/or historical activity can comprise sorting the in-session activity and/or the historical activity into groups. In these embodiments, the in-session and/or the historical activity can be grouped by, recency of interactions (e.g., interactions made during a previous month, previous week, previous day, previous hour, etc.), a categorization level in a hierarchical categorization scheme of an item that is the subject of an interaction (e.g., an item type, a sub-department, a department, a super-department, etc.) type of interaction (a click, a look, a selection, a grab, an add to cart, a view, a purchase, a bid, a swipe, a pinch, a reverse pinch, etc.), and/or a distribution of interaction counts”).
It would have been obvious to one of ordinary skill in the art, before the filing date of the claimed invention, to modify the invention of the combination of Jogia and Laserson disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the parameters of the subscriber sessions including a depth of session associated with the aggregation of subscriber sessions as taught by Bajaj. One of ordinary skill in the art would have been motivated to do so in order to determine weight and influence downstream predictive algorithms and target a specific intent (Bajaj: [0056]).
Regarding Claim 11: Jogia in view of Bajaj discloses the limitations of claim 8 above.
Jogia does not explicitly teach wherein the plurality of features associated with individual sessions includes at least one of:
a depth session associated with the individual subscriber sessions;
a plurality of actions performed by a subscriber within one or more individual subscriber sessions; or
an entropy associated with the individual subscriber sessions.
Notably, however, Jogia does disclose using metrics such as user interaction rates and previous campaign data (Jogia: col. 18, ln. 39-44).
To that accord, Bajaj does teach wherein the plurality of features associated with individual sessions includes at least one of:
a depth session associated with the individual subscriber sessions;
a plurality of actions performed by a subscriber within one or more individual subscriber sessions; or
an entropy associated with the individual subscriber sessions.
Examiner notes that Applicant recites at least one of in the claim. (Bajaj: [0055] – “selectively aggregating in-session and/or historical activity can comprise sorting the in-session activity and/or the historical activity into groups. In these embodiments, the in-session and/or the historical activity can be grouped by, recency of interactions (e.g., interactions made during a previous month, previous week, previous day, previous hour, etc.), a categorization level in a hierarchical categorization scheme of an item that is the subject of an interaction (e.g., an item type, a sub-department, a department, a super-department, etc.) type of interaction (a click, a look, a selection, a grab, an add to cart, a view, a purchase, a bid, a swipe, a pinch, a reverse pinch, etc.), and/or a distribution of interaction counts”).
It would have been obvious to one of ordinary skill in the art, before the filing date of the claimed invention, to modify the invention of the combination of Jogia disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the parameters of the subscriber sessions including a depth of session associated with the depth of session and plurality of actions as taught by Bajaj. One of ordinary skill in the art would have been motivated to do so in order to determine weight and influence downstream predictive algorithms and target a specific intent (Bajaj: [0056]).
Regarding Claim 23: Claim 23 recites substantially similar limitations as claim 8. Therefore, claims 23 is rejected under the same rationale as claim 8 above.
Regarding Claim 25: Claim 25 recites substantially similar limitations as claim 10. Therefore, claims 25 is rejected under the same rationale as claim 10 above.
Regarding Claim 26: Claim 26 recites substantially similar limitations as claim 11. Therefore, claims 26 is rejected under the same rationale as claim 11 above.
Claim 9 and 24 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Jogia (US 10,896,439 B1), Laserson (US 11,900,395 B2), and Bajaj (US 20230244741 A1), in view of Krystofik (US 20210357961 A1).
Regarding Claim 9: The combination of Jogia, Laserson, and Bajaj discloses the limitations of claim 8 above.
Jogia in view of Laserson does not explicitly teach wherein the plurality of trained models includes:
a first trained model to predict the long-term objective based at least in part on a first input of the plurality of parameters associated with the aggregation of subscriber sessions;
a second trained model configured to predict the plurality of parameters associated with the aggregation of subscriber sessions based at least in part on a second input of the plurality of features associated with individual subscriber sessions;
a third trained model to predict the plurality of features based at least in part on a third input of content items.
To that accord, Jogia does disclose using metrics such as user interaction rates and previous campaign data (Jogia: col. 18, ln. 39-44), and Laserson does disclose relating item codes with transactions of a customer, and mapping customer vectors together (Laserson: claim 11).
To that accord, Bajaj does teach wherein the plurality of trained models includes:
a second trained model configured to predict the plurality of parameters associated with the aggregation of subscriber sessions based at least in part on a second input of the plurality of features associated with individual subscriber sessions; (Bajaj: [0055] – “selectively aggregating in-session and/or historical activity can comprise sorting the in-session activity and/or the historical activity into groups. In these embodiments, the in-session and/or the historical activity can be grouped by, recency of interactions (e.g., interactions made during a previous month, previous week, previous day, previous hour, etc.), a categorization level in a hierarchical categorization scheme of an item that is the subject of an interaction (e.g., an item type, a sub-department, a department, a super-department, etc.) type of interaction (a click, a look, a selection, a grab, an add to cart, a view, a purchase, a bid, a swipe, a pinch, a reverse pinch, etc.), and/or a distribution of interaction counts”).
a third trained model to predict the plurality of features based at least in part on a third input of content items. (Bajaj: [0060] – “the product type intent of a user can be predicted using a first set of predictive algorithms. In these or other embodiments, an input for a predictive algorithm can comprise in-session activity and/or historical activity. In various embodiments, the in-session activity and/or the historical activity can be aggregated (e.g., selectively aggregated) as described above before being inputted into a predictive algorithm. In some embodiments, a first set of predictive algorithms can comprise one or more machine learning algorithms. In these or other embodiments, in-session activity and/or historical activity can be converted into vector format before being inputted into a predictive algorithm. In many embodiments, a vector can be constructed by incrementing a count in an activity database that tracks a specific action”).
Examiner’s Note: Examiner notes that the Applicant’s specification does not differentiate features, metrics, and parameters, always referring the three terms as “features, metrics, and/or parameters”, such as in specification paragraph [0187-0188], which discloses features, metrics, and/or parameters as being session depth, actions performed by the subscriber, session length, entropy associated with the sessions, and the like. As such, although the claims recite metrics in some limitations, and parameters and features in others, the Examiner will interpret metrics, parameters, and features as being the same, such as any of session depth, actions performed by the subscriber, session length, entropy associated with the sessions, and the like.
It would have been obvious to one of ordinary skill in the art, before the filing date of the claimed invention, to modify the invention of the combination of Jogia and Laserson disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the predicting of features of subscriber sessions based on an input of features and items as taught by Bajaj. One of ordinary skill in the art would be motivated to do so in order to present information that is more likely to be used by the user (Bajaj: [0059]).
The combination does not explicitly teach a first trained model to predict the long-term objective based at least in part on a first input of the plurality of parameters associated with the aggregation of subscriber sessions; Notably, however, Jogia does disclose where the campaign goal may be recommended to the user (Jogia: col. 8, ln. 5-10).
To that accord, Krystofik does teach a first trained model to predict the long-term objective based at least in part on a first input of the plurality of parameters associated with the aggregation of subscriber sessions; (Krystofik: [0080] – “updates are calculated on the historical data of the cluster templates and the industry templates. It will be appreciated by those of ordinary skill in the art, that as mentioned previously herein, any machine learning prediction modeling technique now known or in the future developed may be used within the scope of the invention. Thereafter, in Operation 1106, new campaign recommendations are implemented based on a new predictor campaign template 1110 for each individual business”; Krystofik: [0081] – “A predictor campaign template 1110 is essentially the guideline for the next marketing program iteration for a business 402. Predictor campaign templates 1110 are used to update the “running” campaign based on previous campaign performance. As previously described, each predictor campaign template 1120 is devised from the objective cluster and industry objective cluster performance data”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Jogia, Laserson, and Bajaj disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the predicting of the long-term objective based on parameters associated with the aggregation of sessions as taught by Krystofik. One of ordinary skill in the art would have been motivated to do so in order to improve performance of the business and the next campaign (Krystofik: [0082]).
Regarding Claim 24: Claim 24 recites substantially similar limitations as claim 9. Therefore, claim 24 is rejected under the same rationale as claim 9 above.
Claim 13 and 28 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Jogia (US 10,896,439 B1) and Laserson (US 11,900,395 B2), in view of Rajana (US 11,580,585 B1).
Regarding Claim 13: The combination of Jogia and Laserson discloses the limitations of claim 6 above.
Jogia further discloses identifying a subscriber interaction associated with a subscriber that corresponds to the long-term objective, wherein the subscriber interaction includes a first content item with which the subscriber interacted. (Jogia: col. 24, ln. 19-24 – “campaign performance metrics 1060 may include metrics such as a number of impressions delivered, a budget amount consumed, a number of attributed conversions or conversions attributed to the campaign, a ranking of products with the most conversions, and/or other performance metric”).
Jogia does not explicitly teach a method comprising:
identifying a first sequence of subscriber engagements preceding the subscriber interaction, wherein the first sequence of subscriber engagements includes a sequence of content items with which the subscriber engaged;
retrieving a plurality of candidate content items corresponding to the sequence of content items;
determining, for one or more content item of the sequence of content items, a respective similarity measure between the first content item and a respective content item of the sequence of content items, wherein the similarity measures represent a relevance of the respective content items to the subscriber interaction;
determining, based at least in part on the respective similarity measures, a plurality of weights for the plurality of candidate content items;
providing the plurality of weights to the recommendation system;
determining, based at least in part on the plurality of weights and using the recommendation system, at least one of a recommended content item from the plurality of candidate content items or a ranking of the plurality of candidate content items.
Notably, however, Jogia does disclose utilizing historical actions of user, including browsing histories, search histories, purchase histories, and the like (Jogia: col. 4, ln. 34-39).
To that accord, Rajana does teach a method comprising:
identifying a first sequence of subscriber engagements preceding the subscriber interaction, wherein the first sequence of subscriber engagements includes a sequence of content items with which the subscriber engaged; (Rajana: col. 9, ln. 62-col. 10, ln. 8 – the recommendation service 218 determines a current shopping mission 115 (FIG. 1) associated with the interactions. The current shopping mission 115 corresponds to an incomplete shopping mission 115 associated with the client device 206 currently interacting with electronic commerce application 215. The current shopping mission 115 is determined in response to detecting a sequence of interaction events (e.g., item views, item searches, item purchases, item clicks, etc.) that are clustered for a given item category. For example, the recommendation service 218 determines a current shopping mission 115 associated with “end tables” in response to determining that the interaction events associated with the interactions all correspond with the item category of “end tables.””).
retrieving a plurality of candidate content items corresponding to the sequence of content items; (Rajana: col. 11, ln. 17-35 – “the recommendation service 218 identifies items to recommend according to the user-preferred attributes 112 obtained from the attribute prediction model 118. In some examples, one or more items associated with particular interactions are obtained through one or more data sources. For example, assume that the user has entered a search query and the search results from the search query include one or more items. In another example, assume a user requests to view an item detail page for a given item and one or more items that are considered to be similar to the given item may be obtained. According to one or more embodiments, upon identifying the user-preferred attributes 112, these items are ranked or re-ranked according to whether the items include any one of the user-preferred attributes 112. As such, in some examples, the recommendation service 218 selects and/or otherwise identifies items determined to be presented to the user based at least in part on whether the item includes any item attributes 244 that correspond to any one of the user-preferred attributes”; Rajana: col. 10, ln. 15-24 – “the recommendation service 218 obtains an attribute prediction model 118 (FIG. 1) associated with the item category from the data store 212 (FIG. 2). The attribute prediction model 118 obtained from the data store 212 has been previously trained to detect user-preferred item attributes 112 based at least in part on the event features of one or more shopping missions 115. In some examples, the attribute prediction model 118 is only trained for the given item category. In other examples, the attribute prediction model 118 is trained for multiple item categories”).
determining, for one or more content item of the sequence of content items, a respective similarity measure between the first content item and a respective content item of the sequence of content items, wherein the similarity measures represent a relevance of the respective content items to the subscriber interaction; (Rajana: col. 15, ln. 42-51 - “the recommendation service 218 obtains an attribute prediction model 118 (FIG. 1) associated with the item category from the data store 212 (FIG. 2). The attribute prediction model 118 obtained from the data store 212 has been previously trained to detect user-preferred item attributes 112 based at least in part on the event features of one or more shopping missions 115. In some examples, the attribute prediction model 118 is only trained for the given item category. In other examples, the attribute prediction model 118 is trained for multiple item categories”; col. 16, ln. 4-14 – “the recommendation service 218 assigns a score to each item that is calculated according to a number of item attributes 244 included in the item. In some examples, a weight is assigned to each of the user-preferred item attributes 112 according to the ranking of the user-preferred item attribute 112 such that a higher-ranked user-preferred item attribute 112 is assigned a higher weight than a lower-ranked user-preferred item attribute 112. As such, the score is based on a sum of the weights for item attributes 244 included in a particular item that match the user-preferred item attributes 112. The listing of items is then ranked according to the score”). In summary, the weight is determined as a measure of how relevant that attribute is to the user, and items with those attributes would have more weight based on the intent of the user determined from interactions of the user in their shopping sessions (a similarity measure of items to the user interactions).
determining, based at least in part on the respective similarity measures, a plurality of weights for the plurality of candidate content items; (Rajana: col. 16, ln. 3-14 – “the recommendation service 218 assigns a score to each item that is calculated according to a number of item attributes 244 included in the item. In some examples, a weight is assigned to each of the user-preferred item attributes 112 according to the ranking of the user-preferred item attribute 112 such that a higher-ranked user-preferred item attribute 112 is assigned a higher weight than a lower-ranked user-preferred item attribute 112. As such, the score is based on a sum of the weights for item attributes 244 included in a particular item that match the user-preferred item attributes 112. The listing of items is then ranked according to the score”). While the claims recite a similarity to determine weights, Rajana discloses weights for the attributes to determine a score. It is interpreted as the weights of Rajana being equivalent as the similarity as recited in the claims, and the score of Rajana being equivalent of the weight as recited in the claims.
providing the plurality of weights to the recommendation system; (Rajana: col. 16, ln. 4-14 – “the recommendation service 218 assigns a score to each item that is calculated according to a number of item attributes 244 included in the item. In some examples, a weight is assigned to each of the user-preferred item attributes 112 according to the ranking of the user-preferred item attribute 112 such that a higher-ranked user-preferred item attribute 112 is assigned a higher weight than a lower-ranked user-preferred item attribute 112. As such, the score is based on a sum of the weights for item attributes 244 included in a particular item that match the user-preferred item attributes 112. The listing of items is then ranked according to the score”).
determining, based at least in part on the plurality of weights and using the recommendation system, at least one of a recommended content item from the plurality of candidate content items or a ranking of the plurality of candidate content items. (Rajana: col. 16, ln. 15-21 – “the recommendation service 218 determines an order of presentation of the user-preferred item attributes 112. In one or more embodiments, this order is based on the ranking associated with the user-preferred item attributes 112. At box 615, the order of presentation of the listing of items is according to the ranking associated the listing of items”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Jogia and Laserson disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the identifying a sequence of engagements to retrieve a plurality of candidate content items, and determining a similarity measure and weight, in order to ran the content items as taught by Rajana. One of ordinary skill in the art would have been motivated to do so in order to predict user preferences and goals to identify items resulting a purchase (Rajana: col. 2, ln. 1-19).
Regarding Claim 28: Claim 28 recites substantially similar limitations as claim 13. Therefore, claim 28 is rejected under the same rationale as claim 13 above.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Jogia (US 10,896,439 B1) and Laserson (US 11,900,395 B2), in view of Rajana (US 11,580,585 B1), and in further view of Qin (US 20240346566 A1).
Regarding Claim 14: The combination of Jogia and Laserson discloses the limitations of claim 6 above.
The combination does not explicitly teach a method comprising:
identifying a first sequence of subscriber engagements, wherein the first sequence of subscriber engagements includes a sequence of content items with which a subscriber engaged;
retrieving a plurality of candidate content items;
processing the sequence of content items to produce, for one or more first respective content item of the sequence of content items, a first respective content caption that is descriptive of the first respective content item;
processing the plurality of candidate content items to produce, for one or more second respective content item of the plurality of candidate content items, a second respective content item caption that is descriptive of the second respective content item;
determining, with a large language model and based at least in part on at least a portion of the first respective content item captions and at least a portion of the second respective content item captions, at least one content item of the plurality of candidate content items as a recommended content item for the subscriber.
Notably, however, Jogia does disclose utilizing historical actions of user, including browsing histories, search histories, purchase histories, and the like (Jogia: col. 4, ln. 34-39).
To that accord, Rajana does teach a method comprising:
identifying a first sequence of subscriber engagements, wherein the first sequence of subscriber engagements includes a sequence of content items with which a subscriber engaged; (Rajana: col. 9, ln. 62-col. 10, ln. 8 – “the recommendation service 218 determines a current shopping mission 115 (FIG. 1) associated with the interactions. The current shopping mission 115 corresponds to an incomplete shopping mission 115 associated with the client device 206 currently interacting with electronic commerce application 215. The current shopping mission 115 is determined in response to detecting a sequence of interaction events (e.g., item views, item searches, item purchases, item clicks, etc.) that are clustered for a given item category. For example, the recommendation service 218 determines a current shopping mission 115 associated with “end tables” in response to determining that the interaction events associated with the interactions all correspond with the item category of “end tables.””).
retrieving a plurality of candidate content items; (Rajana: col. 11, ln. 17-35 – “the recommendation service 218 identifies items to recommend according to the user-preferred attributes 112 obtained from the attribute prediction model 118. In some examples, one or more items associated with particular interactions are obtained through one or more data sources. For example, assume that the user has entered a search query and the search results from the search query include one or more items. In another example, assume a user requests to view an item detail page for a given item and one or more items that are considered to be similar to the given item may be obtained. According to one or more embodiments, upon identifying the user-preferred attributes 112, these items are ranked or re-ranked according to whether the items include any one of the user-preferred attributes 112. As such, in some examples, the recommendation service 218 selects and/or otherwise identifies items determined to be presented to the user based at least in part on whether the item includes any item attributes 244 that correspond to any one of the user-preferred attributes”; Rajana: col. 10, ln. 15-24 – “the recommendation service 218 obtains an attribute prediction model 118 (FIG. 1) associated with the item category from the data store 212 (FIG. 2). The attribute prediction model 118 obtained from the data store 212 has been previously trained to detect user-preferred item attributes 112 based at least in part on the event features of one or more shopping missions 115. In some examples, the attribute prediction model 118 is only trained for the given item category. In other examples, the attribute prediction model 118 is trained for multiple item categories”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Jogia and Laserson disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the identifying a sequence of engagements to retrieve a plurality of candidate content items as taught by Rajana. One of ordinary skill in the art would have been motivated to do so in order to predict user preferences and goals to identify items resulting a purchase (Rajana: col. 2, ln. 1-19).
The combination in view of Rajan does not explicitly teach a method comprising:
processing the sequence of content items to produce, for one or more first respective content item of the sequence of content items, a first respective content caption that is descriptive of the first respective content item;
processing the plurality of candidate content items to produce, for one or more second respective content item of the plurality of candidate content items, a second respective content item caption that is descriptive of the second respective content item;
determining, with a large language model and based at least in part on at least a portion of the first respective content item captions and at least a portion of the second respective content item captions, at least one content item of the plurality of candidate content items as a recommended content item for the subscriber.
Notably, however, Jogia does disclose retrieving product information, including brand identifiers, model numbers, pricing, ratings, family identifiers, inventory level, etc. (Jogia: col. 18, ln. 45-57), and Rajana does teach detecting a sequence of interaction events (Rajana: col. 9, ln. 62-col. 10, ln. 8).
To that accord, Qin does teach a method comprising:
processing the sequence of content items to produce, for each first respective content item of the sequence of content items, a first respective content caption that is descriptive of the first respective content item; (Qin: [0044] – “Prompt generator 212 may generate an augmented prompt for LLM 214 based on one or more of user contextual information 216, first feature vector 226, one or more of second feature vectors 228, indication(s) 230, and/or content item(s) 232. For example, prompt generator 212 may generate an augmented prompt 236 that includes contextual information (e.g., user contextual information), content information (e.g., recommendable content), and a question requesting a content recommendation based on the provided contextual information using the included content information”; Qin: [0045] – “LLM 214 may generate a summary of each recommended content item and include the generated summaries in recommendation(s) 238 along with a link to the recommended content items (e.g., a link to a webpage for a customer testimonial)”).
processing the plurality of candidate content items to produce, for each second respective content item of the plurality of candidate content items, a second respective content item caption that is descriptive of the second respective content item; (Qin: [0045] – “LLM 214 receives augmented prompt 236 from prompt generator 212 and generates one or more recommendations 238. For example, LLM 214 may process augmented prompt 236 to determine content item(s) 232 that are most relevant to the user based on the contextual information included in augmented prompt 236. In embodiments, LLM 214 may generate a summary of each recommended content item and include the generated summaries in recommendation(s) 238 along with a link to the recommended content items (e.g., a link to a webpage for a customer testimonial)”).
determining, with a large language model and based at least in part on at least a portion of the first respective content item captions and at least a portion of the second respective content item captions, at least one content item of the plurality of candidate content items as a recommended content item for the subscriber. (Qin: [0052] – “a recommendation is received from the large language model. For instance, GUI manager 108 may receive from content recommender 110 recommendation(s) 238 that are generated by LLM 214. As discussed above, LLM 214 may process augmented prompt 236 to determine content item(s) 232 that are most relevant to the user based on the contextual information included in augmented prompt 236. In embodiments, LLM 214 may generate a summary of each recommended content item and include the generated summary in recommendation(s) 238 along with a link to the recommended content items”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the invention of Jogia and Laserson, in view of Rajana, disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the processing of user interactions to determine content item captions for each content item using a large language model to recommend content to the user as taught by Qin. One of ordinary skill in the art would have been motivated to do so in order to provide the most relevant information to the context of the user (Qin: [0017]).
Regarding Claim 15: The combination ofJogia and Laserson, in view of Rajana and Qin, discloses the limitations of claim 14 above.
The combination does not explicitly teach wherein the first sequence of subscriber engagements includes engagements with content items across a plurality of subscriber sessions. Notably, however, Jogia does disclose utilizing historical sales data, including user interaction rates (Jogia: col. 18, ln. 33-41), but does not specifically disclose engagement with various items.
To that accord, Rajana does teach wherein the first sequence of subscriber engagements includes engagements with content items across a plurality of subscriber sessions. (Rajana: col. 10, ln. 25-38 – “the recommendation service 218 determines whether other shopping missions 115 are associated with the user and item category. For example, if a user has a user account and/or can be identified as having previously interacted with the electronic commerce application with respect to items included in the same item category, the interaction history data 236 (FIG. 2) and/or mission data 239 (FIG. 2) associated with the given user and/or user account indicates additional shopping missions 115. If there are other shopping missions 115, the recommendation service 218 proceeds to box 318 to obtain the other shopping missions 115 from the interaction history data 236 and/or mission data”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Jogia and Laserson disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the engagements of content items across a plurality of subscriber sessions as taught by Rajana. One of ordinary skill in the art would have been motivated to do so in order to predict user-preferred attributes likely to lead to further user engagement (Rajana: col. 10, ln. 39-67).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Jogia (US 10,896,439 B1) and Laserson (US 11,900395 B2), in view of Sarukkai (US 20130174045 A1).
Regarding Claim 16: The combination of Jogia and Laserson discloses the limitations of claim 6 above.
The combination does not explicitly teach a method comprising:
receiving, from a client device associated with a subscriber, a request for content items;
determining, using the content recommendation system, at least one content item from the corpus of content items that is responsive to the request for content items and is configured to encourage the long-term objective;
determining the at least one content item based at least in part on probabilities associated with transitions between subscriber states of the subscriber;
the probabilities are based at least in part on a current subscriber state, a subscriber history, and a subscriber context.
Notably, however, Jogia does disclose determining products to recommend to include in the campaign (Jogia: col. 10, ln. 6-25), the campaign having goals such as increasing or improving customer engagements (Jogia: col. 2, ln. 47-55).
To that accord, Sarukkai does teach a method comprising:
receiving, from a client device associated with a subscriber, a request for content items; (Sarukkai: [0037] – “receiving a request to present additional content to a user in association with the user viewing a video content item during a session, identifying candidate content formats, predicting a likelihood that the user will abandon the session for each candidate content format, selecting a format, determining when to present additional content to the user, and presenting additional content in accordance with the selected format”).
determining, using the content recommendation system, at least one content item from the corpus of content items that is responsive to the request for content items and is configured to encourage the long-term objective; (Sarukkai: [0040] – “predicting a likelihood that the user will abandon the session may include determining whether the likelihood is above a predetermined threshold, and presenting additional content only occurs if the likelihood is below the predetermined threshold. For example, the user behavior predictor 116 may determine that the likelihood that the user 104 will abandon his or her current session upon being presented with an additional content item of format 170 (e.g., "Format A", a pre-video format) is above the predetermined threshold 180 (e.g., above 10%, 20%, 30%, or another suitable percent chance). Thus, in the present example, the content presentation system 110 may elect to not present additional content of format”).
determining the at least one content item based at least in part on probabilities associated with transitions between subscriber states of the subscriber; (Sarukkai: [0053] – “The predictor, may return a continuous value between one (user stays and continues session) and zero (user abandons session), for example, indicative of a likelihood that a user will continue or abandon his or her current session when presented with a particular additional content item (e.g., an advertisement) of a particular content format”).
the probabilities are based at least in part on a current subscriber state, a subscriber history, and a subscriber context. (Sarukkai: [0053] – “Session features, for example, may include session-related data such as a number and type of video content items viewed, a number and type of additional content items presented, user interaction with the video content items and/or the additional content items, and other data related to a current video viewing session. Other features, for example, may include historical session features and user features, playback context (e.g., embedded player, mobile device, television, etc.), and external features such as current weather conditions, topics of interest currently tracking on various news feeds, the time of day, the day of week, etc. The predictor, may return a continuous value between one (user stays and continues session) and zero (user abandons session), for example, indicative of a likelihood that a user will continue or abandon his or her current session when presented with a particular additional content item (e.g., an advertisement) of a particular content format”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Jogia and Laserson disclosing a method for determining a content delivery campaign package to push item of a specific campaign goal with the request for content items and determining content items based on a probability of subscriber states using current state, history, and context as taught by Sarukkai. One of ordinary skill in the art would have been motivated to do so in order to run advertisement campaigns to optimize revenue generation (Sarukkai: [0002]).
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 TIMOTHY J KANG whose telephone number is (571)272-8069. The examiner can normally be reached Monday - Friday: 8:30am - 7:00pm EST.
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/T.J.K./Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/Primary Examiner, Art Unit 3689 7/10/2026
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