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 .
Status of Claims
This office action is in response to the RCE filed on 8/11/2026.
Claims 1 have been amended.
Claims 1-9 and 11 are pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/11/2026 has been entered.
Priority
The present claims recite new matter which was not present in parent application 17/644,588.
The later-filed application, 19/108,158, must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/644,588, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
At least the limitations including “computing an optimal time of sending marketing engagement by capturing an hour of the day where the user were most active or when majority of users were active”, “analyzing, at the journey management system with the processor, the first set of data, the second set of data, the third set of data an the optimal time using an XGBoost machine learning model in real-time, wherein the analysis is performed based on training of the machine learning model, wherein the analysis is performed for predicting one or more patterns as a probability of user intent defined by at least one of: whether (Yu) the user will perform a predetermined goal; and a likelihood of success of the sent engagement of the user at each hour”, and “b) selecting an optimal content to be delivered via the personalized marketing engagement at the predicted optimal time, said selection performed via a multi-bandit algorithm based on the user intent and a campaign context” are not supported in accordance with 35 USC 112(a) in parent application 17/644,588.
See MPEP 2133.01:
“Any claim that only contains subject matter that is fully supported in compliance with the
statutory requirements of pre-AIA 35 U.S.C. 112, first paragraph, by the parent application of a CIP will
have the effective filing date of the parent application. On the other hand, any claim that contains a
limitation that is only supported as required by pre-AIA 35 U.S.C. 112, first paragraph, by the disclosure of
the CIP application will have the effective filing date of the CIP application. See, e.g., Santarus, Inc. v. Par
Pharmaceutical, Inc., 694 F.3d 1344, 104 USPQ2d 1641 (Fed. Cir. 2012)”
Since all of the claims include the new matter, the claims are afforded the filing date of the present application (1/13/2025).
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 1-9 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-9 and 11 are directed to a method. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
receiving, at a first entity, a first set of data associated with the plurality of users, wherein the plurality of users is associated with one or more communication devices, and the first set of data comprises a marketing engagement (C) sent to the plurality of users, and an intended user action (G) to be executed against the sent marketing engagement;
fetching, at the first entity, a second set of data associated with a plurality of past events of the plurality of users on a plurality of platforms,
obtaining, at the first entity, a third set of data associated with tracking a plurality of live events from the plurality of users on the plurality of platforms in response to the marketing engagement, where the third set of data corresponds to an actual user action (E);
computing an optimal time (H*u H*gobal) of sending the marketing engagement (C) by capturing an hour of the day when the plurality of users were most active or when the majority of the plurality of users were most active;
analyzing, at the first entity, the first set of data, the second set of data, the third set of data, and the optimal time using an model in real-time, wherein the analysis is performed based on training of the model using a combined loss function with jointly predicting a probability of user intent and a likelihood of engagement success at one or more engagement times, wherein the model is trained on the plurality of past events, the plurality of live events, and a plurality of features in real-time, wherein the analysis is performed for predicting one or more patterns as a probability of user intent defined by at least one of:
whether (Yu) the user will perform a predetermined goal; and
a likelihood of success of the sent engagement of the user at each hour;
generating, at the first entity, the plurality of journeys for engaging the plurality of users through a plurality of channels based on the one or more patterns of user intent by the steps of:
a) personalization of the marketing engagement (C) to be sent to a user of the plurality of users based on the patterns of user intent; and
b) selecting an optimal content to be delivered via the personalized marketing engagement at the computed optimal time, said selection performed via a multi-arm bandit algorithm based on the user intent and a campaign context; and
updating the multi-arm bandit algorithm for an updated selection of the optimal content based on a reward signal derived from the actual user action in response to previously delivered optimal content through the personalized engagement; and
creating, at the first entity, one or more goals for each of the plurality of journeys of the plurality of platforms,
wherein the one or more goals are an aim of the plurality of platforms for the plurality of journeys, wherein each of the one or more goals is tracked in real-time.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
2. wherein the first set of data corresponds to personal information of the plurality of users, wherein the first set of data comprises name data, age data, e-mail identity data, contact number data, gender data, geographic location data, angiographic data, demographic data, payment cards data, banking partners data, salary data, loan data, lifetime data on each of the plurality of platforms, and relationship status data, wherein the marketing engagement (C) comprises notification, email, in-app message, wireless communication messages, and wherein the intended user action comprises clicking on a link, viewing a product, purchasing an item.
3. further comprising identifying, at the first entity, an entry criterion for automated admittance of each of the plurality of users accessing the plurality of platforms in corresponding journey from the plurality of journeys in real-time.
4. further comprising identifying, at the first entity, the one or more patterns based on the training of the model on the plurality of past events, the plurality of live events, and the plurality of features in real-time.
5. further comprising training, at the first entity, the model for performing the analysis of the first set of data, the second set of data, and the third set of data, wherein the model is trained using a combined loss function associated with prediction of the probabilities of the user intent, and wherein the third set of data comprises clicking on a link, viewing a product, purchasing an item, device feature, operating system feature, and user interaction a device.
6. further comprising enabling, at the first entity, segmentation of the plurality of users in one or more segments based on the predicted one or more patterns of the user intent.
7. further comprising creating, at the first entity, a plurality of intent based micro-segments associated with each of the one or more segments based on the predicted one or more patterns of the user intent, wherein the plurality of intent based micro- segments is created in real-time, and are classified into High, Medium, and Low intent categories using the model by classifying the probability of user intent (?u) as High. Low or Medium based on a computed "F score".
8. further comprising computing, at the first entity, the optimal time of the engagement with the plurality of users in the plurality of journeys using user-specific historical data subject to availability or multiple user data.
9. further comprising identifying, at the first entity, an optimal channel from the plurality of channels for the plurality of journeys for the engagement with the plurality of users using one or more algorithms in real-time, wherein the plurality of channels comprising mobile channels, email channels, desktop channels, social channels, remarketing channels, and server channels.
11. wherein the selected content comprises reminders, recommendations, discounts based on the user intent and the content of campaign, and wherein the multi bandit algorithm for selected context is at least one of upper confidence bound and Thomson sampling.
The claims provide a manner of computing an optimal time, analyzing marketing engagement data, past events data, user action data, and the optimal time to determine performance of content, personalizing content, selecting optimal content, providing it and tracking performance. Thus, when considered individually and as an ordered combination, the claims embody certain methods of organizing human activity. Specifically, such activity is in the form of commercial interactions (in the form of advertising, marketing or sales activities or behaviors).
Additionally, but for the inclusion of generic computing device, the claims can be performed in the human mind or with pen and paper. Thus, the claims are also considered to be a mental process.
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements: journey management system with a processor (claim 1, 3, 4, 5, 6, 7, 8, 9); XGBoost machine learning (claim 1, 4, 5, 7); one or more machine learning algorithms (claim 9);
The journey management system with processor is recited at a high level of generality and merely acts to “apply it” using generic computing components (spec page 6). The computing device is merely used to send and receive data (receiving, fetching, obtaining) and process data (computing, analyzing, generating, creating, identifying, training, enabling, updating). Nothing in the claims improves computers themselves, technology, or a technical field (See MPEP 2106.05(f)).
The high level recitation of XGBoost machine learning and one or more machine learning algorithms does not go beyond the “apply it” level of implementation. Nothing in the claims improves upon machine learning itself, technology, or a technical field (See MPEP 2106.05(f)).
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea.
As a result, the claims are not patent eligible.
Examiner’s Comment Regarding Prior Art:
The examiner finds that while each limitation can be found in the prior art, the combination of references required to teach and every limitation in the context of the invention would not be obvious to one of ordinary skill in the art. The examiner’s search did not yield a reasonable combination of references that discloses each limitation in the context of the claimed invention.
Joseph et al (US 2023/01966393) is considered the closest prior art. Joseph teaches creating journey’s for a plurality of channels based on using machine learning to analyze various data of a plurality of users associated with communication devices. The data includes past events and live events. Further, an optimal time for sending content for engagement. Further, engagement with the content is then tracked to determine if the goals for that content have been achieved. Joseph does not expressly teach a first type of data is marketing engagements sent to users with an intended user action, tracking live responses to the marketing engagements, capturing an hour of the day where the users were most active, using an XGBoost algorithm to analyze the data for predicting one or more patterns as a probability of intent, personalizing marketing based on patterns of intent, or selecting optimal content based on a multi-arm bandit algorithm.
Paprocki (US 2014/0032265) teaches determining the best times of day, days of the week, etc for sending communications to a user in which they are more likely to respond. The best hour can be determined within a degree of confidence.
Shah et al (2019/0080348) teaches using a machine learning model to predict the likelihood of converting on particular targeted content. Further, the optimization for producing conversion rates includes minimizing a loss function.
Dorai et al (US 2012/0158412) teaches maximizing a probability of achieving a desired outcome utilizing a multi-arm bandit algorithm. Various purchase probabilities are calculated with regard to different advertisements of different products.
Shapiro (US 2019/0205931) teaches tracking conversions of advertisements and generating a predictive model for conversion.
Modarresi et al (US 2018/0137522) teaches segmenting users into groups based on their likelihood of conversion. A gradient boosted tree machine learning algorithm can be used and an F test score can be computed to determine the significance of various attributes toward the achievement of conversion.
Understanding Ads Click-Through Rate Prediction with Machine Learning by Varun Tyagi – 3/23/2024 teaches various techniques using machine learning for prediction of click through rates of advertisements. An XGBoost model is used for identifying influential features in making predictions. The model learns complex relationships between various features and the likelihood of ad clicks.
How to Use XGBoost for Time Series Forecasting by Jason Brownlee – 3/19/2021 teaches using XGBoost algorithms for classification and regression problems. Time series data can be transformed into supervised learning data and then implemented with an XGBoost model for time series forecasting.
Response to Arguments
The examiner has considered and finds persuasive applicant’s arguments that amendments to the claims have overcome previous objections. As a result, such objections have been withdrawn.
The examiner has considered but does not find persuasive applicant’s arguments regarding rejections under 35 USC 101.
With regard to the claims being directed to an abstract idea the examiner respectfully disagrees. It is quite clear that the aim of the invention is a process directed to commercial interactions. The claims analyze data regarding marketing engagements of users to determine journeys for users including personalized marketing and selecting optimal content to provide to users and tracking performance goals. This type of advertising optimization is the express purpose of the invention and is recited clearly in the claims. Therefore, the examiner fails to see how the claims do not fit within certain methods of organizing human activities related to commercial interactions in the form of advertising, marketing or sales activities or behaviors. The examiner has properly separated the limitations into those that make up the abstract idea and those that are additional elements. The specific limitations that make up the abstract idea are plainly recited by the examiner and the examiner has not abstracted away the specific technological operations. The “technological operations” are essentially what is considered with regard to the additional elements under step 2A prong 2 and step 2B. Most of applicant’s arguments in this section conflate the abstract idea with the additional elements. The fact that the steps are being done with a processor are evaluated with regard to the additional elements. Applicant does not appear to address the further characterization of mental process with regard to the claims. The examiner contends, but for the inclusion of generic computing components the process could be carried out by a human using pencil and paper or mentally.
The examiner clearly states that the additional elements have been viewed both individually and as a combination. Applicant has not invented nor improved upon machine learning techniques. The claims provide little more than applying machine learning using the applicant's chosen data. The steps are mostly data analysis and calculations with little more than saying "do it" with machine learning and “do it” with a processor. Thus, there is nothing in the claims that even remotely approaches anything that could be considered an improvement to machine learning, computers, or the technical field. Considering the claims only recite the use of a generic computer to implement the process and use machine learning, which is a process that runs on a computer, the examiner fails to see how the combination of using a computer to perform machine learning provides a practical application. All of the data analysis and calculations are merely part of the abstract idea. Nothing in the claims improves the process of machine learning nor does it improve the technical field. Applicant does little more than use machine learning with their particular choice of data. The addition of stating the model is trained by merely reciting another abstract idea of more calculations does not improve machine learning technology or a technical field. Claiming the data you are training the model with does not improve the actual technique itself. Again, this is little more than using machine learning with applicant’s particular choice of data. The claimed feedback is merely part of the abstract idea. The claims still merely rely on saying “do it” with a processor. They provide no improvement to technology or a technical field.
Further, the high level use of machine learning with applicants choice of data does not provide an inventive concept under step 2B. Applicant continues to conflate abstract ideas with additional elements. They list of “computational architecture” recited by the applicant is merely part of the abstract idea implemented using the generic computing device. Further, applicant is essentially saying exactly the issue the examiner points to as the problem. The examiner agrees that the inventive concept does not reside in XGBoost. It is little more than using this technique with applicants choice of data. Any alleged improvement is to the business concept itself and not underlying technology or a technical field. Regarding Berkheimer, applicant's assertions are moot as no additional elements were said to be well-understood, routine, or conventional and a lack of prior art does not in any way mean that all of the additional elements are not well-understood , routine, or conventional. Applicant consistently conflates abstract ideas with additional elements. Applicant is welcome to contact the examiner to discuss the rejection as the examiner finds the claims fall far short under 101.
As a result, such rejection has been maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
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, Waseem Ashraff can be reached at (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621
/CHRISTOPHER STROUD/ Primary Examiner, Art Unit 3621
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