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 .
Re-Open Prosecution
In view of the Appeal Brief filed on12 March 2026, PROSECUTION IS HEREBY REOPENED. New Ground of Rejection set forth below.
To avoid abandonment of the application, appellant must exercise one of the following two options:
(1) file a reply under 37 CFR 1.111 (if this Office action is non-final) or a reply under 37 CFR 1.113 (if this Office action is final); or,
(2) initiate a new appeal by filing a notice of appeal under 37 CFR 41.31 followed by an appeal brief under 37 CFR 41.37. The previously paid notice of appeal fee and appeal brief fee can be applied to the new appeal. If, however, the appeal fees set forth in 37 CFR 41.20 have been increased since they were previously paid, then appellant must pay the difference between the increased fees and the amount previously paid.
A Supervisory Patent Examiner (SPE) has approved of reopening prosecution by signing below:
/WASEEM ASHRAF/ Supervisory Patent Examiner, Art Unit 3621
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: In the instant case, claims 1-8, are directed to a method, claims 9-16, are directed to a system, and claims 17-20 are directed to a product, therefore the claims are directed to statutory categories of invention.
Step 2A- Prong 1: Independent claim 1 comprises steps of: extracting a set of features from a data storage of the subscription-based service for each user; classifying the plurality of users into a plurality of types of users based on their respective sets of features; determining, a favorite channel for each of the plurality of users based on a subset of their respective sets of features; displaying a targeted message on the determined favorite channel for each user of the plurality of users on a presentation device of the respective user.
The independent claims are directed to a method for providing targeted message to users of a subscription service. The messages are targeted to the channel preferences of the user(s). The claims apply a machine learning model to make user features determinations, and to determine channel preferences of the user(s). The claimed invention of providing a targeted message to users of a subscription service is directed to marketing and advertising. Accordingly, the claimed steps represent a method of organizing commercial interactions comprising advertising, marketing and sales activities, which falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping, wherein all the claim steps can be seen as being part of the abstract idea of for providing targeted message to users of a subscription service.
Claims 9 and 17, recite substantially similar subject matter and the same subsequent analysis should be applied thereto.
It is further noted that the above claimed steps are steps of collecting/tracking data (transmitting, receiving, storing, gathering), analyzing data, making determinations/correlations/comparisons, and displaying/presenting data. All these steps, but for the use of generic computer components that execute them, are abstract, generic functions performed by general-purpose computers, which relate to concepts that can be performed in the human mind
Because the claims include one or more abstract ideas, the analysis now proceeds to assess whether the elements of the claims, when taken both individually and/or as an ordered combination, integrate the abstract idea into a “practical application”.
Step 2A- Prong 2: Additional elements include: a system, comprising: one or more processors; and one or more memories coupled to the one or more processors and stored program instructions; feature extraction machine learning models; including user classification machine learning model; favorite channel determination machine learning model. Further additional elements recited in the claims include: a presentation device of the respective user, however this device is outside the scope of the claimed system (it merely receives data).
These additional elements are recited at a high level of generality and the steps that they execute represent generic functions which can be performed by a general-purpose computer without any novel programming or improvement in the operation of the computer itself. These additional elements are merely invoked as tools to perform an abstract idea (mere instructions to apply the exception) as discussed in MPEP 2106.05(f).
The method “uses” (emphasis added) machine learning models (ML) machine learning (ML) to generate outputs, however the invention only inputs data (i.e., user features) into a ML model from a database, therefore as claimed, this ML feature is simply a program applied to a general-purpose computer. In the claimed invention, conventional, generic computers are programmed with one or more machine learning algorithms, yet the technology of these computers, including the programming technology, is not improved in any way with this machine learning functionality. In the absence of a particular programmatic algorithm that may suggest a technology improvement, this recitation does not represent a practical application.
The machine learning features of the invention only represent a particular technological environment, merely a particular technical field of use to which the judicial exception is linked to, and this technological environment is used to merely transmit, receive, store, gather, analyze, make determinations/correlations with, and display data.
Accordingly, the additional elements when the claim elements are viewed individually and as a whole do not integrate the abstract idea into a practical application.
Step 2B: Based on the reasoning provided under Step 2A- Prong 2, the claims under Step 2B do not recite “significantly more” than the abstract idea. At this point, either under the “Certain Methods of Organizing Human Activity” grouping scenario where all the claim steps can be seen as being part of the abstract ideas, or even if viewed under the “Mental Processes” grouping scenario, the analysis is terminated because the same analysis with respect to Step 2A Prong Two applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
That is, these additional elements are recited at a high level of generality and the steps that they execute represent conventional functions which can be performed by a general-purpose computer without any improvement to the programming technique or improvement in the operation of the computer itself.
The dependent claims have been considered.
Dependent claims 2, 10, 18, further limit the targeted message (a particular type of message), thereby merely narrowing the abstract idea.
Dependent claims 3, 11, 19 and 5, 13, recite additional different types of features, thereby merely narrowing the abstract idea.
Dependent claims 4, 12, 20, recite additional different types of presentation devises which are only used to display data.
Dependent claims 6, 14, recite the additional element of a different type of model: a tree-based learning model, which is a math concept, thereby merely narrowing the step of using a model.
Dependent claims 7, 15, recite the additional element of a different type of model: distributed gradient-boosting framework for machine learning, which is a math concept, thereby merely narrowing the step of using a model.
Dependent claims 8, recite merely recites an old and well-known distributed processing technique to the workload completed, thereby merely narrowing the abstract idea.
When considered as a whole, the same analysis with respect to Step 2A Prong Two and step 2B, apply to these additional elements. They cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-7, 9-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Han et al (US Pub., 2017/0061344 A1) in view of Hoffman (US Pub., No., 2020/0267449 A1)
With respect to claim 1, Han teaches a method of delivering targeted messages to users of a subscription-based service(paragraph [0023], dislcies send and receive message with other entities, and paragraph [0024], dislcies the entities my include users that uses online professional networks)) , comprising:
extracting, using one or more feature extraction machine learning models, a set of features from a data storage of the subscription-based service for each user of a plurality of users of the subscription-based service(paragraph [0032], discloses identify customers by matching profile data, group membership [subscription-based service] , etc., paragraph [0033], discloses customers may include all entity’s in online professional network which may be targeted with product such as premium, subscription or membership with online professional network paragraph [0038], dislcies extract one or more features form the quired data , and paragraph [0039], discloses company features may include attributes and/or metrics assocted with a customer that is a company or other type of organization, company feature may include demographic attribute ).
Han teaches the above elements including displaying a targeted message on the for each user of the plurality of users on a presentation device of the respective user, wherein users in each type of the plurality of types of users share a same targeted message (fig. 4, 416, dislcies transmit commucation containing content for reducing churn risk to cusmter, paragraph [0048], discloses generate one or more emails, messages, alter and or other notification of high churn risk level and /or risk factor subset of the customer and transmit communication content, paragraph [0068], discloses higher discount can be offered when renews customer renews and paragraph [0070], discloses provide recommendation for mitigating one or more of the risk factor and paragraph [0085], discloses a recommendation for reducing the churn risk level based on the risk type ..).
Han failed to teach classifying, using a user classification machine learning model, the plurality of users into a plurality of types of users based on their respective sets of features, wherein each type of the plurality of types of users is associated with a range of probability values; determining, using a favorite channel determination machine learning model, a favorite channel for each of the plurality of users based on a subset of their respective sets of features; and displying targeted message based on determined favorite channel.
However, Hoffman teaches classifying, using a user classification machine learning model, the plurality of users into a plurality of types of users based on their respective sets of features, wherein each type of the plurality of types of users is associated with a range of probability values (Fig. 1, discloses content item consumption data see 106, 3, track users for churn , 114, not churned subset, 116 churned subset, Fig. 5 discloses gather content item consumption dta for a plurlity of users subscribed to a media server, gather consumption time slot data .., evaluate churn , 512 add user to subset 1, 514 add user to subset 2 [classifying the plurlity users into plurality of type of users], Fig. 6, discloses maintain a machine learing churn predication algorithm .., generate list of user who requested content item of identified type and identified time slots and 608 generate a list of user who did not request content time ) ) ; and
determining, using a favorite channel determination machine learning model, a favorite channel for each of the plurality of users based on a subset of their respective sets of features; and displying targeted message based on determined favorite channel (Fig. 1, identify time slot and content item types .., identify a user who is requesting content …., Fig. 6, 604, identify time slot and content item types predictive of churn, Fig. 8, 802 and 806 discloses identify a first and second set of to time slots, paragraph [0016], discloses record indicate that user 1 has consumed a drama series show “Game of Thrones” on Monday at 9:00 PM. Record 108 may indicate the user 1 has consumed sport program, NFL Game on Sunday [ determining channel], paragraph [0017], discloses the content recommendation application tracks or determine user churn.., and paragraph [0018], discloses identify that subset of users 120 ( e.g., subset 116) may be associated with certain types of media content ( e.g., sports and news) and with certain consumption time slots (e.g., weekend afternoons and evenings). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for customer appears at high-risk churn risk for customers using statistical models, converts the prediction into a, the system can send notifications or recommendations to help reduce that risk of Han modify by adding a future of identifying time slot and content item and media content source of Hoffman in order to maintain a machine learning content recommendation selection algorithm configured to identify a content recommendation designed to prevent chum based on the user profile data (e.g., media consumption data). (see, Hoffman, paragraph [0059])
With respect to claim 2 Han in view of Hoffman teaches elements of claim 1, furthermore, Han teaches the method wherein the targeted message for each type of users is a promotional message with a promotional value that is inversely proportional to an upper limit of the associated range of probability values(paragraph [0069], discloses cumber may receive 15% discount that a discount-based risk factor is present ... ).
With respect to claim 3 Han in view of Hoffman teaches elements of claim 1, furthermore, Han teaches the method wherein the set of features for each of the plurality of users includes one or more of: a user watching pattern; user watching pattern changes; a recording pattern; payment consistency; commitment nearness; signal strength; demographics; or recency, frequency, money (RFM) data (paragraph [0038], discloses generate a set of features for the customer, including one or more company features, one or more spending features, one or more usage features and one or mor account features) .
With respect to claim 4 Han in view of Hoffman teaches elements of claim 1, furthermore, Han teaches the method wherein the presentation device of the respective user is one of: a television, a personal computer, or a smart phone (Fig. 7 and paragraph [0090], discloses computer system 700) .
With respect to claim 5 Han in view of Hoffman teaches elements of claim 1, furthermore, Han teaches the method wherein the subset of features for each user of the plurality of users includes one or more of: a user watching pattern, user watching pattern changes, or a recording pattern(paragraph [0038], discloses generate a set of features for the customer, including one or more company features, one or more spending features, one or more usage features and one or mor account features) .
With respect to claim 6 Han in view of Hoffman teaches elements of claim 1, furthermore, Han teaches the method wherein each of the one or more feature extraction machine learning models, the user classification machine learning model, and the favorite channel determination machine learning model is a tree-based learning model(paragraph [0077], discloses statical model may use one or more decision trees to compare ..).
With respect to claim 7 Han in view of Hoffman teaches elements of claim 6, furthermore, Han teaches the method wherein the user classification machine learning model is a tree-based learning model that uses a distributed gradient-boosting framework for machine learning (paragraphs [0056], [0075] discloses statical model.., gradient tree boosting techniques ..).
With respect to claim 9, Han teaches a system (paragraph [0023], dislcies send and receive message with other entities, and paragraph [0024], dislcies the entities my include users that uses online professional networks)) , comprising:
one or more processors(paragraph [0022], discloses processor) ; and
one or more memories that are coupled to the one or more processors and storing program instruction for delivering target message to users of a subscription-based service which , when executed by the one or more processors, causes the system to perform operation(paragraph [0020], discloses memory and paragraph [0022], discloses processor that executed particular software module ..) comprising:
extracting, using one or more feature extraction machine learning models, a set of features from a data storage of the subscription-based service for each user of a plurality of users of the subscription-based service(paragraph [0032], discloses identify customers by matching profile data, group membership [subscription-based service] , etc., paragraph [0033], discloses customers may include all entity’s in online professional network which may be targeted with product such as premium, subscription or membership with online professional network paragraph [0038], dislcies extract one or more features form the quired data , and paragraph [0039], discloses company features may include attributes and/or metrics assocted with a customer that is a company or other type of organization, company feature may include demographic attribute ).
Han teaches the above elements including displaying a targeted message on the for each user of the plurality of users on a presentation device of the respective user, wherein users in each type of the plurality of types of users share a same targeted message (fig. 4, 416, dislcies transmit commucation containing content for reducing churn risk to cusmter, paragraph [0048], discloses generate one or more emails, messages, alter and or other notification of high churn risk level and /or risk factor subset of the customer and transmit communication content, paragraph [0068], discloses higher discount can be offered when renews customer renews and paragraph [0070], discloses provide recommendation for mitigating one or more of the risk factor and paragraph [0085], discloses a recommendation for reducing the churn risk level based on the risk type ..).
Han failed to teach classifying, using a user classification machine learning model, the plurality of users into a plurality of types of users based on their respective sets of features, wherein each type of the plurality of types of users is associated with a range of probability values; determining, using a favorite channel determination machine learning model, a favorite channel for each of the plurality of users based on a subset of their respective sets of features; and displying targeted message based on determined favorite channel.
However, Hoffman teaches classifying, using a user classification machine learning model, the plurality of users into a plurality of types of users based on their respective sets of features, wherein each type of the plurality of types of users is associated with a range of probability values (Fig. 1, discloses content item consumption data see 106, 3, track users for churn , 114, not churned subset, 116 churned subset, Fig. 5 discloses gather content item consumption dta for a plurlity of users subscribed to a media server, gather consumption time slot data .., evaluate churn , 512 add user to subset 1, 514 add user to subset 2 [classifying the plurlity users into plurality of type of users], Fig. 6, discloses maintain a machine learing churn predication algorithm .., generate list of user who requested content item of identified type and identified time slots and 608 generate a list of user who did not request content time ) ) ; and
determining, using a favorite channel determination machine learning model, a favorite channel for each of the plurality of users based on a subset of their respective sets of features; and displying targeted message based on determined favorite channel (Fig. 1, identify time slot and content item types .., identify a user who is requesting content …., Fig. 6, 604, identify time slot and content item types predictive of churn, Fig. 8, 802 and 806 discloses identify a first and second set of to time slots, paragraph [0016], discloses record indicate that user 1 has consumed a drama series show “Game of Thrones” on Monday at 9:00 PM. Record 108 may indicate the user 1 has consumed sport program, NFL Game on Sunday [ determining channel], paragraph [0017], discloses the content recommendation application tracks or determine user churn.., and paragraph [0018], discloses identify that subset of users 120 ( e.g., subset 116) may be associated with certain types of media content ( e.g., sports and news) and with certain consumption time slots (e.g., weekend afternoons and evenings). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for customer appears at high-risk churn risk for customers using statistical models, converts the prediction into a, the system can send notifications or recommendations to help reduce that risk of Han modify by adding a future of identifying time slot and content item and media content source of Hoffman in order to maintain a machine learning content recommendation selection algorithm configured to identify a content recommendation designed to prevent chum based on the user profile data (e.g., media consumption data). (see, Hoffman, paragraph [0059])
With respect to claim 10 Han in view of Hoffman teaches elements of claim 9, furthermore, Han teaches the system wherein the targeted message for each type of users is a promotional message with a promotional value that is inversely proportional to an upper limit of the associated range of probability values(paragraph [0069], discloses cumber may receive 15% discount that a discount-based risk factor is present ... ).
With respect to claim 11 Han in view of Hoffman teaches elements of claim 9, furthermore, Han teaches the system wherein the set of features for each of the plurality of users includes one or more of: a user watching pattern; user watching pattern changes; a recording pattern; payment consistency; commitment nearness; signal strength; demographics; or recency, frequency, money (RFM) data (paragraph [0038], discloses generate a set of features for the customer, including one or more company features, one or more spending features, one or more usage features and one or mor account features) .
With respect to claim 12 Han in view of Hoffman teaches elements of claim 9, furthermore, Han teaches the system wherein the presentation device of the respective user is one of: a television, a personal computer, or a smart phone (Fig. 7 and paragraph [0090], discloses computer system 700) .
With respect to claim 13 Han in view of Hoffman teaches elements of claim 9, furthermore, Han teaches the system wherein the subset of features for each user of the plurality of users includes one or more of: a user watching pattern, user watching pattern changes, or a recording pattern(paragraph [0038], discloses generate a set of features for the customer, including one or more company features, one or more spending features, one or more usage features and one or mor account features) .
With respect to claim 14 Han in view of Hoffman teaches elements of claim 9, furthermore, Han teaches the system wherein each of the one or more feature extraction machine learning models, the user classification machine learning model, and the favorite channel determination machine learning model is a tree-based learning model(paragraph [0077], discloses statical model may use one or more decision trees to compare ..).
With respect to claim 15 Han in view of Hoffman teaches elements of claim 14, furthermore, Han teaches the system wherein the user classification machine learning model is a tree-based learning model that uses a distributed gradient-boosting framework for machine learning (paragraphs [0056], [0075] discloses statical model.., gradient tree boosting techniques).
With respect to claim 17, Han teaches a non-transitory computer readable storage medium storing program instructions for delivering targeted message to user of a subscription-based server when executed by the one or more processors, causes the system to perform operation(paragraph [0020], discloses memory and paragraph [0022], discloses processor that executed particular software module .. paragraph [0023], dislcies send and receive message with other entities, and paragraph [0024], dislcies the entities my include users that uses online professional networks)) comprising:
extracting, using one or more feature extraction machine learning models, a set of features from a data storage of the subscription-based service for each user of a plurality of users of the subscription-based service(paragraph [0032], discloses identify customers by matching profile data, group membership [subscription-based service] , etc., paragraph [0033], discloses customers may include all entity’s in online professional network which may be targeted with product such as premium, subscription or membership with online professional network paragraph [0038], dislcies extract one or more features form the quired data , and paragraph [0039], discloses company features may include attributes and/or metrics assocted with a customer that is a company or other type of organization, company feature may include demographic attribute ).
Han teaches the above elements including displaying a targeted message on the for each user of the plurality of users on a presentation device of the respective user, wherein users in each type of the plurality of types of users share a same targeted message (fig. 4, 416, dislcies transmit commucation containing content for reducing churn risk to cusmter, paragraph [0048], discloses generate one or more emails, messages, alter and or other notification of high churn risk level and /or risk factor subset of the customer and transmit communication content, paragraph [0068], discloses higher discount can be offered when renews customer renews and paragraph [0070], discloses provide recommendation for mitigating one or more of the risk factor and paragraph [0085], discloses a recommendation for reducing the churn risk level based on the risk type ..). Han failed to teach classifying, using a user classification machine learning model, the plurality of users into a plurality of types of users based on their respective sets of features, wherein each type of the plurality of types of users is associated with a range of probability values; determining, using a favorite channel determination machine learning model, a favorite channel for each of the plurality of users based on a subset of their respective sets of features; and displying targeted message based on determined favorite channel.
However, Hoffman teaches classifying, using a user classification machine learning model, the plurality of users into a plurality of types of users based on their respective sets of features, wherein each type of the plurality of types of users is associated with a range of probability values (Fig. 1, discloses content item consumption data see 106, 3, track users for churn , 114, not churned subset, 116 churned subset, Fig. 5 discloses gather content item consumption dta for a plurlity of users subscribed to a media server, gather consumption time slot data .., evaluate churn , 512 add user to subset 1, 514 add user to subset 2 [classifying the plurlity users into plurality of type of users], Fig. 6, discloses maintain a machine learing churn predication algorithm .., generate list of user who requested content item of identified type and identified time slots and 608 generate a list of user who did not request content time ) ) ; and
determining, using a favorite channel determination machine learning model, a favorite channel for each of the plurality of users based on a subset of their respective sets of features; and displying targeted message based on determined favorite channel (Fig. 1, identify time slot and content item types .., identify a user who is requesting content …., Fig. 6, 604, identify time slot and content item types predictive of churn, Fig. 8, 802 and 806 discloses identify a first and second set of to time slots, paragraph [0016], discloses record indicate that user 1 has consumed a drama series show “Game of Thrones” on Monday at 9:00 PM. Record 108 may indicate the user 1 has consumed sport program, NFL Game on Sunday [ determining channel], paragraph [0017], discloses the content recommendation application tracks or determine user churn.., and paragraph [0018], discloses identify that subset of users 120 ( e.g., subset 116) may be associated with certain types of media content ( e.g., sports and news) and with certain consumption time slots (e.g., weekend afternoons and evenings). Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for customer appears at high-risk churn risk for customers using statistical models, converts the prediction into a, the system can send notifications or recommendations to help reduce that risk of Han modify by adding a future of identifying time slot and content item and media content source of Hoffman in order to maintain a machine learning content recommendation selection algorithm configured to identify a content recommendation designed to prevent chum based on the user profile data (e.g., media consumption data). (see, Hoffman, paragraph [0059])
With respect to claim 18 Han in view of Hoffman teaches elements of claim 17, furthermore, Han teaches the non-transitory computer-readable storage medium wherein the targeted message for each type of users is a promotional message with a promotional value that is inversely proportional to an upper limit of the associated range of probability values(paragraph [0069], discloses cumber may receive 15% discount that a discount-based risk factor is present ... ).
With respect to claim 19 Han in view of Hoffman teaches elements of claim 17, furthermore, Han teaches the on-transitory computer-readable storage medium wherein the set of features for each of the plurality of users includes one or more of: a user watching pattern; user watching pattern changes; a recording pattern; payment consistency; commitment nearness; signal strength; demographics; or recency, frequency, money (RFM) data (paragraph [0038], discloses generate a set of features for the customer, including one or more company features, one or more spending features, one or more usage features and one or mor account features) .
With respect to claim 20 Han in view of Hoffman teaches elements of claim 17, furthermore, Han teaches the on-transitory computer-readable storage medium wherein the presentation device of the respective user is one of: a television, a personal computer, or a smart phone (Fig. 7 and paragraph [0090], discloses computer system 700) .
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Han et al (US Pub., 2017/0061344 A1) in view of Hoffman (US Pub., No., 2020/0267449 A1) and further view of USHER et al (US Pub., No., 2023/0394739 A1)
With respect to claim 8 Han in view of Hoffman teaches elements of claims 1, furthermore, Han teaches the method wherein the extracting of the set of features from the data storage of the subscription-based service (paragraph [0038], discloses extract one or more features from queried data and/or aggregate the queried data into one or more features), processor may support parallel processing and/or multi-threaded operation with other processor in computer system (paragraph [0090]) and different nodes of a distributed system (paragraph [0094]) and Hoffman teaches extract churner history and non-churner history (Fig. 4, 420). Han and Hoffman failed to teach for each user of a plurality of users is performed by a plurality of parallel processing nodes, wherein each of the one or more feature extraction machine learning models runs one of the plurality of parallel processing nodes.
However, USHER teaches for each user of a plurality of users is performed by a plurality of parallel processing nodes, wherein each of the one or more feature extraction machine learning models runs one of the plurality of parallel processing nodes (paragraph [0361], discloses the parallel processing platform used from machine learing can be divide into training platforms, the training platforms are generally highly parallel and include optimization to accelerate multi-GPU single node taring and multi-node .., deployed machine learning platforms generally include, paragraph [0373], discloses a neural network implementation may also extract futures of each rendered scene ..) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for customer appears at high-risk churn risk for customers using statistical models, converts the prediction into a, the system can send notifications or recommendations to help reduce that risk of Han modify by adding a future of identifying time slot and content item and media content source of Hoffman with feature of operating in parallel nodes of USHER in order to increase performance (see, USHER , paragraph [00122]).
With respect to claim 16 Han in view of Hoffman teaches elements of claims 9, furthermore, Han teaches the system wherein the extracting of the set of features from the data storage of the subscription-based service (paragraph [0038], discloses extract one or more features from queried data and/or aggregate the queried data into one or more features), processor may support parallel processing and/or multi-threaded operation with other processor in computer system (paragraph [0090]) and different nodes of a distributed system (paragraph [0094]) and Hoffman teaches extract churner history and non-churner history (Fig. 4, 420). Han and Hoffman failed to teach for each user of a plurality of users is performed by a plurality of parallel processing nodes, wherein each of the one or more feature extraction machine learning models runs one of the plurality of parallel processing nodes.
However, USHER teaches for each user of a plurality of users is performed by a plurality of parallel processing nodes, wherein each of the one or more feature extraction machine learning models runs one of the plurality of parallel processing nodes (paragraph [0361], discloses the parallel processing platform used from machine learing can be divide into training platforms, the training platforms are generally highly parallel and include optimization to accelerate multi-GPU single node taring and multi-node .., deployed machine learning platforms generally include, and paragraph [0373], discloses a neural network implementation may also extract futures of each rendered scene ..) . Therefore, it would have been obvious to the one ordinary skill in the art before the effective filing date of the claimed invention for customer appears at high-risk churn risk for customers using statistical models, converts the prediction into a, the system can send notifications or recommendations to help reduce that risk of Han modify by adding a future of identifying time slot and content item and media content source of Hoffman with feature of operating in parallel nodes of USHER in order to increase performance (see, USHER, paragraph [00122]).
Prior arts:
Han et al (US Pub., 2017/0061344 A1) discloses the disclosed embodiments provide a system for processing data. During operation, the system obtains a set of data for a set of customers of a product, wherein the set of data comprises a set of churn risk levels for the customer. Next, the system uses the set of data to display a graphical user interface (GUI) comprising a chart of renewal opportunities with the set of customers, for the product, over an upcoming time interval.
Hoffman (US Pub., No., 2020/0267449 A1) discloses systems and associated methods are described for providing content recommendations. The system accesses content item consumption data for a plurality of users subscribed to a media service. Then, the system determines that a first subset of the plurality of users has unsubscribed from the media service and that a second subset of the plurality of users has not unsubscribed from the media service
USHER et al (US Pub., No., 2023/0394739 A1) discloses apparatus and method for piece-wise compositing. For example, one embodiment of an apparatus comprises: a plurality of nodes to operate in parallel to render an image associated with a data set; and an interconnect to couple the plurality of nodes; a first node of the plurality of nodes to: independently march a first one or more rays through local data comprising a first portion of the data set to determine first local moments; sum the first local moments to generate a first partial moment; combine the first partial moment with one or more additional partial moments received from other nodes of the plurality of nodes to generate a global moment.
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejections with respect to claims 1-20 filed on 12 March 2026 have been fully considered but they are not persuasive. Applicants’ arguments of claims 1-20 are directed to eligible subject matter and the examiner erred in the rejection claim 1-20 under 35 U.S.C 101 as allegedly being directed to a judicial exception without significantly more is not persuasive. Applicant is claiming ineligible subject matter. Specifically, the claims describe automating a mental process using generic, known technologies instead of providing a new, concrete technical solution.
Under step 1: the claims are directed to a process, but it encompasses abstract The claim is directed to a process, but it encompasses abstract concepts such as mathematical algorithms, data analysis, and human-like business methods.
Unser Step 2A: The claim focuses on an abstract idea. First, it recites mental processes and mathematical calculations (classifying users, determining favorite channels). Second, it covers fundamental economic or business practices (delivering targeted messages and advertising.
Under Step 2B: Lack of an Inventive Concept to be eligible, a claim must integrate the abstract idea into a practical application with "significantly more". This claim fails because:
Generic ML: It relies on generic, off-the-shelf "machine learning models" without defining any new technological improvements to the models themselves.
Routine Computer Use: It uses standard computer components (storage, presentation devices) merely as tools to automate a process
Thus, the 35 U.S.C 101 rejections with respect to claims 1-20 is maintained.
Applicant's arguments of 35 U.S.C 103 rejections with respect to claims 1-20 filed on 12 March 2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/SABA DAGNEW/Primary Examiner, Art Unit 3621
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