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 .
DETAILED ACTION
2. This action is in response to the Application filed February 20, 2026.
3. Claims 1-20 have been examined and are pending with this action.
4. The Information Disclosure Statement filed May 14, 2026 has been considered.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) 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.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Tu et al. (US 2020/0311745 A1).
INDEPENDENT:
As per claim 1, Tu teaches a method for content delivery (see Tu, Abstract: “Technologies for optimizing content delivery to end-users are provided.”), comprising:
receiving a request to deliver a target content to a target object (see Tu, [0022]: “The content notification system 130 may receive a request, from the content management platform 110, that contains one or more pending content items that are available for consumption by a target user. For example, if the target user has 8 new content items in their content item feed, then the content management platform 110 may send a request to generate a notification to the content notification system 130.”);
for a content delivery strategy of a plurality of content delivery strategies (see Tu, [0011]: “Content presentation optimization is improved by adding technology that implements a particular approach of performing online experiments to test various content delivery strategies in order to identify distinct subsets of users based upon the online experiments.”; [0020]: “For example, the content management platform 110 may adjust which content items are presented in a user's feed and may adjust how content items are ranked for presentation to users based upon optimization strategies provided by the user optimization system 120. For example, if the user optimization system 120 optimizes content presentation for maximizing the average duration of user sessions, then the content management platform 110 may adjust which content items are presented within a user's feed and/or how content items are ordered for presentation within the user's feed.”; and [0024]: “The online experiments may be experiments tailored to apply different content presentation strategies to a set of users and capture user interactions in order to determine whether the content presentation strategies result in better optimization for a particular objective. The user optimization system 120 may analyze the output of the online experiments to determine distinct subsets of users based upon user interactions within the online experiments and user profile properties of users participating in the online experiments. Upon determining the distinct subsets of users, the user optimization system 120 may optimize content presentation strategies specific to each of the distinct subsets of users based upon desired objectives.”),
determining at least one group of observed samples based on target features of the target content and the target object, an observed sample comprising reference features of a reference content and a reference object, an indication of whether the content delivery strategy is applied to deliver the reference content to the reference object and an object response of the reference object to the reference content (see Tu, [0003]: “Therefore optimization strategies that determine groups of users based on historical user interaction behavior may be preferred to determine user groups and how to optimize content delivery for the groups of users.”; [0021]: “the data store 105 may represent data storage that stores user profile data related to users, user session metrics, and aggregated user session metrics. User profile data may include, but is not limited to, user job titles, user experience, a user's network connections, education data, and geo-location history. User session metrics may include user interaction history of user sessions. The stored data may be accessed by the content management platform 110 and the user optimization system 120.”; [0025]: “An A/B test is a randomized experiment with two variants, A and B. A/B testing is one way of comparing applied values of a single variable and observing responses from users. For example, if an A/B test is configured to test whether sending notifications at a frequency of every hour versus every two hours, then the A/B test may apply one hour notification treatment to a set of users and apply the two hour notification treatment to another set of users. Outcomes of whether the one hour or two hour notification treatment resulted in a higher number of new user sessions may be observed.”; [0027]: “In an embodiment, the treatment model generation service 122 may generate nodes of the causal tree using based upon historical user properties and historical interaction data of users. The historical interaction data may refer to captured user interactions and statistics derived from the user interactions, where the user interactions are observed interactions prior to the A/B tests. The observed interactions may be stored within the data store 105, as training data. FIG. 2 depicts an example embodiment of a portion of a generated causal tree with leaf nodes representing distinct subsets of users.”; and [0030]: “Having many small subsets of users may result in erroneous optimization strategies based upon small sample sizes for the small subsets of users. In an embodiment, the treatment model generation service 122 may use one or more hyperparameters to define minimum sizes of nodes within the causal tree.”);
determining an effect metric for the content delivery strategy based on the at least one group of observed samples, the effect metric indicating an impact degree of the content delivery strategy to object responses (see Tu, Abstract: “Users within each of the plurality of distinct subsets may be identified based on metric impacts of the online experiment.”; [0055]: “Upon completing operation 330, process 300 may return to decision diamond 320 to determine whether there are remaining model parameters for the particular subset of users to analyze. If there are remaining model parameters to analyze for the particular subset of users, then process 300 may select the next model parameter and proceed to operation 325. If at decision diamond 320, process 300 determines that there are no more remaining model parameters to analyze, then process 300 may proceed back to decision diamond 315 to determine whether there are remaining subsets of users to process.”; and [0064]: “In an embodiment, if multiple model parameter values are assigned to a user, then the content management platform 110 may be configured to select one of the multiple model parameters for the user. For example, the content management platform 110 may randomly select one of the multiple model parameters using the associated probability value as a weighted factor in determining which model parameter value to select.”); and
selecting, from the plurality of content delivery strategies, a target content delivery strategy for delivering the target content to the target object based on respective effect metrics determined for the plurality of content delivery strategies (see Tu, [0011]: “Upon determining the distinct subsets of users, output from the online experiments may be analyzed to determine optimal content presentation strategies for users based upon their assignment to a distinct subset of users.”; and [0022]: “The content notification system 130 may receive a request, from the content management platform 110, that contains one or more pending content items that are available for consumption by a target user. For example, if the target user has 8 new content items in their content item feed, then the content management platform 110 may send a request to generate a notification to the content notification system 130.”).
As per claim 9, Tu teaches an electronic device, comprising:
at least one processor (see Tu, [0047]: “Process 300 may be performed by a single program of multiple programs. The operations of the process as shown in FIGS. 3A and 3B may be implemented using processor-executable instructions that are stored in computer memory”); and
at least one memory coupled to the at least one processor and storing instructions executable by the at least one processor, the instructions, upon execution by the at least one processor, causing the electronic device to perform acts (see Tu, [0047]: “Process 300 may be performed by a single program of multiple programs. The operations of the process as shown in FIGS. 3A and 3B may be implemented using processor-executable instructions that are stored in computer memory”) comprising:
receiving a request to deliver a target content to a target object (see Claim 1 rejection above);
for a content delivery strategy of a plurality of content delivery strategies (see Claim 1 rejection above),
determining at least one group of observed samples based on target features of the target content and the target object, an observed sample comprising reference features of a reference content and a reference object, an indication of whether the content delivery strategy is applied to deliver the reference content to the reference object and an object response of the reference object to the reference content (see Claim 1 rejection above);
determining an effect metric for the content delivery strategy based on the at least one group of observed samples, the effect metric indicating an impact degree of the content delivery strategy to object responses (see Claim 1 rejection above); and
selecting, from the plurality of content delivery strategies, a target content delivery strategy for delivering the target content to the target object based on respective effect metrics determined for the plurality of content delivery strategies (see Claim 1 rejection above).
As per claim 17, Tu teaches a non-transitory computer readable storage medium having computer executable instructions stored thereon, the computer executable instructions, when executed by an electronic device, causing the electronic device to perform acts (see Tu, [0068]: “Main memory 406 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Such instructions, when stored in non-transitory storage media accessible to processor 404, render computer system 400 into a special-purpose machine that is customized to perform the operations specified in the instructions.”) comprising:
receiving a request to deliver a target content to a target object (see Claim 1 rejection above);
for a content delivery strategy of a plurality of content delivery strategies (see Claim 1 rejection above),
determining at least one group of observed samples based on target features of the target content and the target object, an observed sample comprising reference features of a reference content and a reference object, an indication of whether the content delivery strategy is applied to deliver the reference content to the reference object and an object response of the reference object to the reference content (see Claim 1 rejection above);
determining an effect metric for the content delivery strategy based on the at least one group of observed samples, the effect metric indicating an impact degree of the content delivery strategy to object responses (see Claim 1 rejection above); and
selecting, from the plurality of content delivery strategies, a target content delivery strategy for delivering the target content to the target object based on respective effect metrics determined for the plurality of content delivery strategies (see Claim 1 rejection above).
DEPENDENT:
As per claims 2, 10, and 18, which respectively depend on claims 1, 9, and 17, Tu further teaches wherein the at least one group of observed samples comprises a plurality of groups of observed samples (see Claim 1 rejection above), and determining the effect metric for the content delivery strategy comprises: for a group of observed samples in the plurality of groups of observed samples, determining a group effect metric indicating an impact degree of the content delivery strategy to objects in the group of observed samples; and determining the effect metric for the content delivery strategy based on respective group effect metrics determined for the plurality of groups of observed samples (see Tu, [0003]: “Therefore optimization strategies that determine groups of users based on historical user interaction behavior may be preferred to determine user groups and how to optimize content delivery for the groups of users.”; and Claim 1 rejection above).
As per claims 3, 11, and 19, which respectively depend on claims 2, 10, and 18, Tu further teaches wherein a plurality of causal trees is constructed for the content delivery strategy, and each group of the plurality of groups of observed samples corresponds to a leaf node in a causal tree of the plurality of causal trees (see Tu, FIG. 2; [0026]: “In an embodiment, the treatment model generation service 122 implements a causal tree algorithm, as described by Ye Tu, et al., https://arxiv.org/pdf/1901.10550.pdf, to estimate heterogeneity in causal effects of applying different treatments to a set of users. A causal tree is a type of decision tree where distinct groups of users, represented by leaf nodes, are grouped based upon user profile properties and historical user behavior.”; and [0027]: “In an embodiment, the treatment model generation service 122 may generate nodes of the causal tree using based upon historical user properties and historical interaction data of users. The historical interaction data may refer to captured user interactions and statistics derived from the user interactions, where the user interactions are observed interactions prior to the A/B tests. The observed interactions may be stored within the data store 105, as training data. FIG. 2 depicts an example embodiment of a portion of a generated causal tree with leaf nodes representing distinct subsets of users.”).
As per claims 4, 12, and 20, which respectively depend on claims 2, 10, and 18, Tu further teaches wherein determining the group effect metric comprises: determining, from the group of observed samples, a first subgroup of observed samples with the content delivery strategy applied and a second subgroup of observed samples without the content deliver strategy applied; and determining the group effect metric based on object responses in the first subgroup of observed samples and object responses in the second subgroup of observed samples (see Tu, FIG. 3A & 3B; and [0011]: “Content presentation optimization is improved by adding technology that implements a particular approach of performing online experiments to test various content delivery strategies in order to identify distinct subsets of users based upon the online experiments.”).
As per claims 5 and 13, which respectively depend on claims 1 and 9, Tu further teaches wherein determining the at least one group of observed samples comprises:
obtaining a causal tree constructed for the content delivery strategy, an internal node in the causal tree being split according to a feature dimension (see Tu, [0026]: “A causal tree is a type of decision tree where distinct groups of users, represented by leaf nodes, are grouped based upon user profile properties and historical user behavior.”);
searching, by comparing the target features with one or more internal nodes in the causal tree, the causal tree until a leaf node is reached (see Tu, [0025]: “An A/B test is a randomized experiment with two variants, A and B. A/B testing is one way of comparing applied values of a single variable and observing responses from users. For example, if an A/B test is configured to test whether sending notifications at a frequency of every hour versus every two hours, then the A/B test may apply one hour notification treatment to a set of users and apply the two hour notification treatment to another set of users. Outcomes of whether the one hour or two hour notification treatment resulted in a higher number of new user sessions may be observed.”; and [0059]: “In operation 345, process 300 identifies a particular distinct subset of users from the plurality of distinct subsets of users to which the second user belongs. In an embodiment, the treatment optimization service 124 may analyze and compare user profile properties and historical user interactions within the content management platform 110 of the particular second user to associate model parameters and user profile properties of each of the distinct subsets of users to determine which distinct subset of users aligns with the properties of the second user.”); and
determining a group of observed samples corresponding to the reached leaf node as one of the at least one group of observed samples (see Tu, [0026]: “A causal tree is a type of decision tree where distinct groups of users, represented by leaf nodes, are grouped based upon user profile properties and historical user behavior. Users are grouped into distinct groups based upon observable properties and user reactions and/or effects of the A/B tests .”; and [0027]: “In an embodiment, the treatment model generation service 122 may generate nodes of the causal tree using based upon historical user properties and historical interaction data of users. The historical interaction data may refer to captured user interactions and statistics derived from the user interactions, where the user interactions are observed interactions prior to the A/B tests. The observed interactions may be stored within the data store 105, as training data. FIG. 2 depicts an example embodiment of a portion of a generated causal tree with leaf nodes representing distinct subsets of users.”).
As per claims 6 and 14, which respectively depend on claims 1 and 9, Tu further teaches wherein a group of observed samples of the at least one group of observed samples is determined based on a causal tree for the content delivery strategy, and the causal tree is constructed by:
for a first node in the causal tree, determining a target feature dimension and a threshold feature in the target feature dimension from a plurality of candidate feature dimensions by maximizing a splitting gain (see Tu, [0027]: “In an embodiment, the treatment model generation service 122 may generate nodes of the causal tree using based upon historical user properties and historical interaction data of users. The historical interaction data may refer to captured user interactions and statistics derived from the user interactions, where the user interactions are observed interactions prior to the A/B tests. The observed interactions may be stored within the data store 105, as training data. FIG. 2 depicts an example embodiment of a portion of a generated causal tree with leaf nodes representing distinct subsets of users.”; [0032]: “In an embodiment, the treatment model generation service 122 may analyze the leaf nodes of the causal tree to determine an output of distinct subsets of users, where each leaf node represents one distinct subset of users. Each distinct subset of users may have one or more associated user properties based upon user profile information and historical interaction data and one or more model parameters. The associated model parameters may refer to system parameters used during the online experiments, such as model thresholds or one or more treatment values for one or more applied treatments of the online experiments and/or weighted parameters in a multi-objective optimization function.”; and [0034]: “A model parameter value represents an amount of treatment applied to users, for instance the model parameter value may specify thresholds for sending notifications to users or any other treatment type threshold”); and
allocating observed samples in the first node to a second node and a third node by comparing respective features of the observed samples in the target feature dimensions and the threshold feature, wherein the second and third nodes are child nodes of the first node (see Tu, FIG. 2; [0027]: “The historical interaction data may refer to captured user interactions and statistics derived from the user interactions, where the user interactions are observed interactions prior to the A/B tests. The observed interactions may be stored within the data store 105, as training data. FIG. 2 depicts an example embodiment of a portion of a generated causal tree with leaf nodes representing distinct subsets of users. Each of the nodes may represent one or more user properties based on past user activity prior to performing the online experiment. Node 205 may represent a set of users that have been identified by their past activity as having initiated greater than 4 user sessions in a week. Nodes 210 and 215 are child nodes of node 205… ”; and [0028]: “Each heterogeneous subset of users from the users of their parent node may comprise a set of users for each child node generated.”).
As per claims 7 and 15, which respectively depend on claims 6 and 14, Tu further teaches wherein the causal tree is one of a plurality of causal trees for the content delivery strategy, and each causal tree of the plurality of causal trees is constructed based on a plurality of observed samples selected from an observed sample set (see Tu. [0026]: “In an embodiment, the treatment model generation service 122 implements a causal tree algorithm, as described by Ye Tu, et al., https://arxiv.org/pdf/1901.10550.pdf, to estimate heterogeneity in causal effects of applying different treatments to a set of users. A causal tree is a type of decision tree where distinct groups of users, represented by leaf nodes, are grouped based upon user profile properties and historical user behavior.”; and [0027]: “In an embodiment, the treatment model generation service 122 may generate nodes of the causal tree using based upon historical user properties and historical interaction data of users. The historical interaction data may refer to captured user interactions and statistics derived from the user interactions, where the user interactions are observed interactions prior to the A/B tests. The observed interactions may be stored within the data store 105, as training data. FIG. 2 depicts an example embodiment of a portion of a generated causal tree with leaf nodes representing distinct subsets of users.”).
As per claims 8 and 16, which respectively depend on claims 1 and 9, Tu further teaches wherein the content delivery strategy comprises at least one of: a content presentation pattern, a content presentation timing, or a page to be presented in response to a content interaction (see Tu, [0002]: “Content delivery strategies may include the frequency in which notifications of new content are sent to users, what time of day sent notifications of new content result in user engagement, and determining which content should be displayed within a user's feed in order to promote a desired outcome.”; [0011]: “For instance, different frequencies of notification delivery may be applied to the set of users, where a random group users may receive notifications at one particular frequency, another random group of users may receive notifications at another particular frequency, and yet another random group of users may receive notifications at a control frequency. Different configurations of content delivery and properties associated with users receiving various treatment approaches may be represented by a plurality of model parameters.”; and [0077]: “Computer system 400 can send messages and receive data, including program code, through the network(s), network link 420 and communication interface 418. In the Internet example, a server 430 might transmit a requested code for an application program through Internet 428, ISP 426, local network 422 and communication interface 418.”).
Conclusion
6. For the reasons above, claims 1-20 have been rejected and remain pending.
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL Y WON whose telephone number is (571)272-3993. The examiner can normally be reached on Wk.1: M-F: 8-5 PST & Wk.2: M-Th: 8-7 PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nicholas R Taylor can be reached on 571-272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael Won/Primary Examiner, Art Unit 2443
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