DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Amendment filed on 7/8/2026.
Claims 1-20 are pending. Claims 1, 10, and 19 are independent.
The previous rejection of claims 1-20 under 35 USC § 103 have been maintained in view of the amendment.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-6, 9-11, 13-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US2023/0206155) in view of Khodadadi et al. (“ChOracle: A Unified Statistical Framework for Churn Prediction”).
In regards to claim 1, Zhu et al. discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
obtaining a training dataset comprising a plurality of training examples, wherein each training example is generated by:
retrieving historical interactions by a user of a plurality of users with an online system, wherein the historical interactions for the user include a churn event indicating a loss of engagement of the user with the online system (Zhu et al. para[0013], uses statistical analysis of historical customer data to identify churn factors),
labeling an attribution of the churn event to one or more of the causal events; training a churn prediction model, wherein the churn prediction model is trained by:
applying the churn prediction model to each training example of the training dataset to generate an attribute score for each of the causal events, the attribute score for a causal event comprising a predicted probability of the attribution of the churn event the causal event (Zhu et al. para[0022], The feature vector is formed or generated with the resulting data. In some cases, a churn factor data may be transformed according to input constraints of the churn prediction model),
scoring the churn prediction model using a loss function and the label of the training example (Zhu et al. para[0026], The customer churn prediction system may select the customers with the top r predicted churn likelihoods and update a user interface and/or send a notification with the indications of the predicted churn likelihoods for those customers),
identifying a new churn event associated with a target user of the online system (Zhu et al. fig. 3 para[0031], identify a set of variables that are covariates with treatment selection and churn outcome and a set of variables that are observable customer attributes that correspond to customer heterogeneity);
applying the churn prediction model to a set user features and online system features from historical interactions by the target user with the online system, the churn prediction model outputting an attribution score for each of the one or more causal events (Zhu et al. fig. 3 para[0034], the retention recommendation system runs the treatment effectiveness estimator ensemble on the feature vector to obtain an estimated treatment effectiveness);
attributing a causal event to the new churn event based on the attribution scores (Zhu et al. fig. 3 para[0035], the retention recommendation system indicates the treatment effectiveness estimate produced by the ensemble in association with a treatment identifier and a treatment adjustment/rate); and
implementing a remedial action corresponding to the attributed causal event, wherein implementing the remedial action mitigates a reduction in a predicted probability of the target user interacting with the online system (Zhu et al. fig. 3 para[0038], provides a set of treatments to improve customer retention).
Zhu et al. does not explicitly disclose segmenting the historical interactions into at least a first time interval and a second time interval that is after the first time interval,
obtaining a set of user features from the historical interactions occurring in the first time interval,
obtaining a set of online system features from the historical interactions occurring in the second time interval, wherein the historical interactions occurring in the second time interval include one or more causal events,
updating one or more parameters of the churn prediction model by backpropagation based on the scoring until one or more criteria are satisfied.
However Khodadadi et al. discloses segmenting the historical interactions into at least a first time interval and a second time interval that is after the first time interval (Khodadadi et al. fig. 3 pg5, section 3.1 para1, segments interactions into multiple time intervals) ,
obtaining a set of user features from the historical interactions occurring in the first time interval (Khodadadi et al. fig. 3 pg5, section 3.1 para1, obtains historical interactions from di ),
obtaining a set of online system features from the historical interactions occurring in the second time interval, wherein the historical interactions occurring in the second time interval include one or more causal events (Khodadadi et al. fig. 3 pg5, section 3.1 para1, obtains interactions from interval di+1),
updating one or more parameters of the churn prediction model by backpropagation based on the scoring until one or more criteria are satisfied (Khodadadi et al. pg6 section 3.3 para2, uses backpropagation through time to maximize lower bound of corresponding parameters).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the Churn analysis method of Zhu et al. with the Churn prediction method of Khodadadi et al. in order to identify and prevent potential loss of customers (Khodadadi et al. pg1 section1 para1).
In regards to claim 2, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 1, wherein the new churn event comprises greater than a threshold time period lapsing between the target user interacting with the online system and the target user performing a specific action with the online system (Khodadadi et al. pg7 section 3.4 para1, alarms for churn prediction triggered if gap between sessions exceeds a threshold value).
In regards to claim 4, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 1, wherein attributing the causal event to the new churn event based on the attribution scores comprises:
selecting a causal event having a maximum attribution score (Zhu et al. para[0020], ranks possible impact factors with highest correlation to churn outcomes).
In regards to claim 5, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 1, wherein attributing the causal event to the new churn event based on the attribution scores comprises:
ranking the one or more causal events based on the attribution scores (Zhu et al. para[0020], ranks possible impact factors with highest correlation to churn outcomes); and
selecting one or more causal events having at least a threshold position in the ranking (Zhu et al para[0020], correlation coefficients obtained from the statistical analysis are used to rank the variables to allow for m of the variables to be chosen as impact factors.
In regards to claim 6, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 1, wherein identifying the set of user features comprises identifying an average time between performance of a specific action by the user (Zhu et al. para[0022], identifies average case age).
In regards to claim 9, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 1, wherein the new churn event occurs in a third time interval that is later than the second time interval (Khodadadi et al. pg5 section 3.1, segments observations into a collection of sequences).
Claims 10-11, 13-15, and 18 recite substantially similar limitations to claims claims 10-11, 13-15, and 18 are rejected along the same rationale as claims 1-2, 4-6, and 9.
Claims 19-20 recite substantially similar limitations to claims 1 and 4. Thus claims 19-20 are rejected along the same rationale as claims 1 and 4.
Claim(s) 3, 7-8, 12, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. in view of Khodadadi et al. and Ranjan et al. (US2023/0214869).
In regards to claim 3, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 2.
Zhu et al. does not explicitly disclose wherein the specific action comprises placing an order with the online system.
However Ranjan et al. discloses wherein the specific action comprises placing an order with the online system (Ranjan et al. para[0028], the website may allow the purchase of items).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the churn analysis method of Zhu et al. with the churn event prediction method of Ranjan et al. in order to tailor retention methods to customer (Ranjan et al. para[0003]).
In regards to claim 7, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 1.
Zhu et al. does not explicitly disclose wherein identifying the set of online system features comprises identifying one or more of: a rate at which pickers found items included in orders from the user, a number of orders fulfilled later than a time interval specified by a corresponding order, a rate at which orders were fulfilled later than time intervals specified by corresponding orders, or an average amount the online system charged the user for fulfilling orders.
However Ranjan et al. discloses wherein identifying the set of online system features comprises identifying one or more of: a rate at which pickers found items included in orders from the user, a number of orders fulfilled later than a time interval specified by a corresponding order, a rate at which orders were fulfilled later than time intervals specified by corresponding orders, or an average amount the online system charged the user for fulfilling orders (Ranjan et al. para[0037], fulfillment data may indicate the total and/or time-average amount spent on purchases by the particular customer and associated with a completed scheduled delivery).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the churn analysis method of Zhu et al. with the churn event prediction method of Ranjan et al. in order to tailor retention methods to customer (Ranjan et al. para[0003]).
In regards to claim 8, Zhu et al. as modified by Khodadadi et al. discloses the method of claim 1.
Zhu et al. does not explicitly disclose wherein the causal event is selected from a group consisting of:
timeliness with which pickers fulfill orders received by the online system, a number of items included an order obtained by a picker, an amount charged by the online system for fulfilling an order, and any combination thereof.
However Ranjan et al. discloses wherein the causal event is selected from a group consisting of:
timeliness with which pickers fulfill orders received by the online system, a number of items included an order obtained by a picker, an amount charged by the online system for fulfilling an order, and any combination thereof (Ranjan et al. para[0037], features or data that may be ingested by the trained and validated machine learning model includes fulfillment data).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the churn analysis method of Zhu et al. with the churn event prediction method of Ranjan et al. in order to tailor retention methods to customer (Ranjan et al. para[0003]).
Claims 12, and 16-17 recite substantially similar limitations to claims 3, and 7-8. Thus claims 12, and 16-17 are rejected along the same rationale as claims 3, and 7-8.
Response to Arguments
Applicant's arguments filed 7/8/26 have been fully considered but they are not persuasive. In regards to claim 1, Applicant argues on pg1 that Zhu et al. does not teach "segmenting the historical interactions into at least a first time interval and a second time interval that is after the first time interval" "obtaining a set of online system features from the historical interactions occurring in the first time interval" and "obtaining a set of online system features from the historical interactions occurring in the second time interval, wherein the historical interactions occurring in the second time interval include one or more causal events".
Applicant's specification [0006] defines causal events "includes timeliness within which pickers fulfill orders, a number of items in an order obtained by a picker, and amount charged by the online concierge for fulfilling an order, etc".
However Zhu et al. as modified by Khodadadi et al. substantially discloses segmenting the historical interactions into at least a first time interval and a second time interval that is after the first time interval (Khodadai et al. fig. 3 pg5 section3.1 para1, session is divided into interval di, gi+1 and di+1, di is a time interval starting at time ti for duration di and taking place before interval di+1), obtaining a set of online system features from the historical interactions occurring in the first time interval (Khodadadi et al. fig. 3 pg5, section 3.1 para1, obtains historical interactions(number of items in order) occurring in time interval di), obtaining a set of online system features from the historical interactions occurring in the second time interval, wherein the historical interactions occurring in the second time interval include one or more causal events (obtains historical interactions( Khodadadi et al. fig. 3 pg5, section 3.1 para1, number of items in order) occurring in time interval di+1).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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