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 1-20 were subject to restriction.
Claims 6-16 have been elected without traverse.
Claims 1-5 and 17-20 are non-elected, and have been withdrawn.
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 hare rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claim 6 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 an online service;
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;
generating, using at least the mapping between the plurality of content items and the long-term objective, a recommendation system configured to determine content items from a corpus of content items that are responsive to a request for content items and are configured to encourage 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):
Claim 6 does not recite any additional elements. As such, the claims do not integrate the recited judicial exception into a practical application of the exception.
The claims integrate the judicial exception into a practical application when the claims (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement a judicial exception with a particular machine, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
However, as claim 6 does not recite any additional elements, the claims are directed to the judicial exception and do not integrate the recited exception into a practical application (see: MPEP 2106.04(d)).
Step 2B:
Claim 6 does not recite any additional elements, and as such, does not amount to significantly more than the exception itself.
In view of the above, claim 6 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 6-7 and 12 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jogia (US 10,896,439 B1).
Regarding Claim 6: Jogia discloses a method comprising:
determining a long-term objective for recommending content to subscribers of an online service; Jogia discloses determining a campaign goal of a content delivery campaign, such as increasing subscriptions (Jogia: col. 8, ln. 4-23; see also: col. 2, ln. 47-55).
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 discloses historical sales data of the content is used to identify various metrics over time to determine products for the campaign (Jogia: col. 18, ln. 33-44; see also: col. 3, ln. 36-42; col. 10, ln. 11-25; col. 15, ln. 37-41; col. 19, ln. 33-45).
generating, using at least the mapping between the plurality of content items and the long-term objective, a recommendation system configured to determine content items from a corpus of content items that are responsive to a request for content items and are configured to encourage the long-term objective. Jogia discloses generating the content delivery campaign package to recommend content to users for the particular campaign goal with the items that were selected to be included in the campaign (Jogia: col. 10, ln. 38-66; see also: col. 4, ln. 19-22; col. 18, ln. 7-19).
Regarding Claim 7: Jogia 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 discloses historical sales data of the content is used to identify various metrics over time, which would involve the history (or aggregation) of subscriber sessions (Jogia: col. 18, ln. 33-44; see also: col. 19, ln. 33-45 col. 15, ln. 37-41).
Regarding Claim 12: Jogia 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 discloses the recommendation stages of accessing a catalog data and datastore of sales data and historical campaign data, determine products to include in the campaign, and then determining other related products of hierarchy of products, the products being those determined to result in optimized campaign performance (Jogia: col. 18, ln. 27-col. 19, ln. 13; see also: col. 24, ln. 27-43).
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 8 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable by Jogia (US 10,896,439 B1) in view of Bajaj (US 20230244741 A1).
Regarding Claim 8: Jogia discloses the limitations of claim 6 above.
Jogia does not explicitly teach wherein determining the plurality of mappings between the plurality of interim metrics includes using a plurality of trained models and each of the plurality of trained models is 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 and each of the plurality of trained models is 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 teaches a set of predictive algorithms to predict one or more product type intents, such as to predict the type of purchase (new purchase or repurchase) (first metric) based on other metrics of user activity based on in-session and historical data (second metric) (Bajaj: [0060]; see also: [0055]).
It would have been obvious to one of ordinary skill in the art, before the filing date of the invention, to modify the invention of Jogia 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: Jogia 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 teaches aggregating historical activity (aggregation of sessions) for depth of in-session activity, such as recency and types of interactions, including views, purchases, add to cart, etc. (Bajaj: [0055]).
It would have been obvious to one of ordinary skill in the art, before the filing date of the invention, to modify the invention 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 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 each 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 each 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 teaches in-session activity such as interactions, including views, purchases, add to cart, etc. (Bajaj: [0055]).
It would have been obvious to one of ordinary skill in the art, before the filing date of the invention, to modify the invention 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]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable by the combination of Jogia (US 10,896,439 B1) and Bajaj (US 20230244741 A1), in view of Krystofik (US 20210357961 A1).
Regarding Claim 9: The combination of Jogia and Bajaj discloses the limitations of claim 8 above.
Jogia 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).
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 teaches in-session activity such as interactions, including views, purchases, add to cart, etc., and a set of predictive algorithms to predict one or more product type intents, such as to predict the type of purchase (new purchase or repurchase) (first parameter) based on other data of user activity based on in-session and historical data (second parameter) (Bajaj: [0055]; see also: [0060]).
a third trained model to predict the plurality of features based at least in part on a third input of content items. Bajaj teaches a set of predictive algorithms to predict intents of the user, and predicting weights to associate with items based on inputs associated with the items, such as decreasing a count for a recently purchased item as it is unlikely to be weighed heavily by the user (Bajaj: [0060]).
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 invention, to modify the invention of Jogia 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]).
Jogia in view of Bajaj 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 teaches generating predictor campaign templates, using machine learning techniques, to predict new campaigns to achieve a desired performance based off of cluster performance data (historical performance for multiple businesses, an aggregation of various sessions) (Krystofik: [0080-0081]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of the combination of Jogia 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]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable by Jogia (US 10,896,439 B1) in view of Rajana (US 11,580,585 B1).
Regarding Claim 13: Jogia 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 discloses data of previous campaigns, such as user interactions with content that result in conversions attributed to the campaign (Jogia: col. 24, ln. 19-24; see also: col. 11, ln. 8-11).
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 each 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 teaches detecting a sequence of interaction events clustered for a given item category, including the items interacted with (Rajana: col. 9, ln. 62-col. 10, ln. 8).
retrieving a plurality of candidate content items corresponding to the sequence of content items; Rajana teaches items to recommend to the user associated with the particular interaction sequence of the user based on user-preferred attributes determined from the sequence of interactions (Rajana: col. 11, ln.17-35; col. 10, ln. 15-24; see also: Fig. 3, #310).
determining, for each 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 teaches predicting user-preferred attributes using features from the current and prior shopping sessions to determine a weight for each user-preferred attribute that measures the probability the user will interact with an item including that attribute (Rajana: col. 15, ln. 42-col. 16, ln. 14; see also: Fig. 6, #606,609). 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 teaches assigning a score to each item based on the user-preferred attributes and their weights (Rajana: col. 16, ln. 3-14). 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 teaches the weights and scores being used by the recommendation service (Rajana: col. 15, ln. 64-col. 16, ln. 14).
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 teaches ranking the items according to the score, and displaying an order of presentation of the ranked items (Rajana: col. 16, ln. 15-21; see also: col. 11, ln. 27-31; Fig. 6, #615).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of Jogia 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).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable by Jogia (US 10,896,439 B1) in view of Rajana (US 11,580,585 B1), and in further view of Qin (US 20240346566 A1).
Regarding Claim 14: Jogia discloses the limitations of claim 6 above.
Jogia 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 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;
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;
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 teaches detecting a sequence of interaction events clustered for a given item category (Rajana: col. 9, ln. 62-col. 10, ln. 8).
retrieving a plurality of candidate content items; Rajana teaches items to recommend to the user associated with the particular interaction sequence of the user based on user-preferred attributes determined from the sequence of interactions (Rajana: col. 11, ln.17-35; col. 10, ln. 15-24; see also: Fig. 3, #310).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of Jogia 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).
Jogia in view of Rajan does not explicitly 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;
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;
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 teaches generating summaries to display with the recommended content item based on the contextual information of the user (Qin: [0044-0045]; see also: [0052]).
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 teaches generating a summary for each recommended item of the recommended items (Qin: [0044-0045]; see also: [0052]).
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 teaches using a large language model to generate the product recommendations and their summaries, and determining which content items are most relevant to the user (Qin: [0052]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of Jogia 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: Jogia in view of Rajana and Qin discloses the limitations of claim 14 above.
Jogia 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 teaches determining previous shopping missions of the user and the interaction history data of the shopping missions (Rajana: col. 10, ln. 25-38; see also: col. 3, ln. 33-45; col. 13, ln. 52-64).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of Jogia 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 Jogia (US 10,896,439 B1) in view of Sarukkai (US 20130174045 A1).
Regarding Claim 16: Jogia discloses the limitations of claim 6 above.
Jogia 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 teaches receiving a request to present additional content to a user (Sarukkai: [0037-0038]).
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 teaches determining content items that are likely to keep the user from abandoning their session (Sarukkai: [0039-0040]).
determining the at least one content item based at least in part on probabilities associated with transitions between subscriber states of the subscriber; Sarukkai teaches determining the likelihood of the user changing states, such as staying and continuing their session or abandoning the session when presented with a particular content (Sarukkai: [0053]).
the probabilities are based at least in part on a current subscriber state, a subscriber history, and a subscriber context. Sarukkai teaches determining the likelihood based on the current state of the user, historical session features of the user, and various contextual information, including number of content items viewed, the time of day, etc. (Sarukkai: [0053]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the invention of Jogia 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. PTO-892 Reference U discloses a system for profit-based metrics in order to offer the correct assortment of products in marketing campaigns, such as to determine identifying what customers to target, what products to offer, what time to contact customers, and when to offer promotions.
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: 7:30 - 5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.J.K./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689