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 action is in response to the amendment filed on 2 February 2026. Claims 1 -7, 15, and 18-23 have been amended. Claims 1-27 are currently pending and have been exampled.
Claim Objections
Claims 22-24 and 26 are objected to because of the following informalities: the preamble of claims 21-27 directed to method, but the claims are depending on system claims. Appropriate correction is required.
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.
Step 1: The claims 1-17 are method and claims 18-27 are system. Thus, each independent claims, on its face, is directed to one of the statutory categories of 35 U.S.C 101. However, the claims 1-27 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: Independent claims (1 and 18) as a whole a method of organizing human interaction recite designating a based on the consumer activity data at the consumer level and the program level a plurality of consumer communication campaigns concerning consumer features each consumer commucation campaign corresponding to a different predetermined customer feature entering consumer activity data at the consumer and program levels as inputs for each determined consumer communication campaign, each configured to determine a campaign consumer activation profile based on the entered consumer activity data, assigning consumer communication campaigns for communication based on the determined campaign consumer activation profiles, and communicating designated consumer communications with each consumer according to the assigned consumer communication campaign, which is a method of managing interaction between people. The mere nominal recitation of a generic machine learing model and a processor does not take the claim out of the methods of organizing human interactions grouping. Thus, the claim recites an abstract idea.
Step 2A- Prong 2: the claims as a whole merely describe how to generally merely describes how to generally “apply” the concept of communicating consumer with assigned campaign in response to determine a campaign consumer activation profile based on entered consumer activity. The claims recite the additional limitation of obtaining consumer activity data concerning consumer transactions at the customer level including individual consumer transactions and program level including collective data of a group of consumer. The obtaining steps is recited at a high level of generality (i.e., a general means of gathering customer activity data of consumer transaction and activity level data for use in the designating, entering , assigning and communicating steps), and amounts to mere data gather , which is in a form of insignificant extra solution activity. The machine learing model is recited at a high level of generality and are merely invoked as tools to perform assigning consumer campaign in response to the obtained and determined data process. Simply implementing the abstract idea on a generic computer components is not a practical application of the abstract idea.
Step 2B: As noted previously, the claim as a whole merely describes how to generally “apply” the concept of updating medical records in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Dependent claims 2-17 and 19-27, these claims recite limitation that further define the abstract idea noted above. Therefore, they are considered patent ineligible for the reason given above.
The closest prior art to the Applicants claimed invention:
Rae (US Pub., No., 2018/0130091 A1) focused on methods and systems for determining marketing campaigns for customers based on customer transaction data are described. In one or more implementations, the customers are assigned to consumer classes based on respective customer values and activity levels. Appropriate marketing campaigns are generated and output for each user based on the assigned consumer class(abstract) , a method of allocating communication for consumer (Fig. 3, and paragraph [0040], discloses customers classes based on differing customer data.., ) , the method comprising: obtaining consumer activity concerning consumer transactions data at the consumer level including individual consumer transactions(Fig. 4, 402, discloses receive customer transaction data for a customer [customer activity], paragraph [0017], discloses collecting and/or generating the transaction data .., collecting data such as one or more of a consumer identification , a transaction date, value of the transaction or items and services purchased in the transaction and paragraph [0039], discloses a new customer transactions data is obtained, the marketing service may update consumer classes, customer value thresholds, activity level threshold, potential marketing campaign and associated with the customer classes, transaction with reward system, metric triggering condition, and so on) and program level including collective data of a group of consumers(paragraphs [0013], discloses a current activity level for the cusmter.., the activity level is indicative of whether behavior of th customer confirms to ., or varies from historical transaction pattern or predicted transaction patterns[program level].., and paragraph [0019], discloses analyze transaction data 112 including customer data 114, in order to determine a consumer class 118 for customer, the customer class maybe specific to the customer 106 or may be associated with other customer, depending on transaction data [program level] paragraph [0031] discloses determines an activity level for the customer paragraph [0033], discloses the consumer class module 210, the cusmter class indicates a value of the consumer 106 and whether a trend in behavior has been indicated, paragraph [0040], discloses customer A has been identified for example by customer evaluation module as falling within a top 15% [program level] and paragraph [0043], discloses an activity level is determined for the customer that is indicative of current behavior of the customer); and designating based on the consumer activity data at the consumer level and the program level of consumer communication campaigns concerning consumer features, each designated consumer communication campaign corresponding to a different predetermined consumer feature (paragraph [0035], discloses campaign manager module may select the campaign from a plurality of campaign based on consumer class, consumer class of customers is determined to respond to campaigns based on dollar amount discounts, then dollar amount discount campaigns will be associated with that consumer class [program level] …, appropriate marketing channels and forms of media that are specific to the respective customer and paragraphs [0040]-[0042], discloses customer with different customer classes based on differing customer data…, campaign A may comprise any number of messages or commucation including a single commucation (for example with a high discount representing the most aggressive state), camping A may comprise standard camping for top 15% and inactive consumer classes that .., customer B has been identified for example by the cusmter evolution model 116.., aggressive strategy has been selected for the 15-30% ..,, customer C has been identified for..), customer data 114, in order to determine a customer class for the customer 106, the consumer class 118 may be specific to the customer or may be associated with other customers, depending on the transaction data 112 and/or how many customer classes are being used (paragraph [0019]), selected from a plurlity of campaigns, may be any type of physical or electrical communication such as a postal advertismtns, email, text message, phone call, deposit of reward points etc., (channels) (paragraph [0020]) .
Sahasi et al (US Pub., No., 2023/0188792 A1) focused on methods, systems, and apparatuses for content recommendations based on user activity data are described herein. An analytics subsystem may receive first activity data. The first activity may be indicative of a plurality of first engagements with a first plurality of media assets. At least one machine learning model may be configured to receive an input of activity data, such as the first activity data, and to determine at least one content recommendation on that basis. The at least one content recommendation may comprise a recommendation for at least one media asset (abstract), entering the consumer activity data at the consumer level and the program level as inputs to each one of a plurality of machine learning models of a machine learning model system comprising one machine learning model for each separate (paragraph [0143], discloses each of plurality of machine learning models may be trained using training activity data/engagement .., training activity data and/or training recommendation data that corresponds to media asset consumption/interaction for one or more users..) directed content refers to digital media configured for a particular audience, or a particular outlet channel (such as a website, a streaming service, or a mobile application), or both (paragraph [0093], and the media provisioning unit 1260, in some cases, also can configure one or more platforms/ channels (web, mobile web, mobile app) to present the media asset(paragraph [0111]).
Cavander et al (US Pub., 2013/0035975 A1) focused on a software facility that analyzes consumer interactions with one or more marketing campaigns and the results of those interactions to generate a cross-media or cross-channel attribution model representing the true impact of marketing resource allocation decisions is provided. The facility collects, from a plurality of sources, information representing consumer interactions with marketing campaigns and any results of those interactions. The facility aggregates the information to assess or determine the behavior of consumers with respect to different marketing campaigns and marketing channels. The facility analyzes the information according to varying depths or levels of channel granularity to generate models representative of the true impact of resources allocated to each channel or sub-channel on the performance or effectiveness of the marketing campaign (abstract)
Weiss et al (US Pub., No., 2011/0099048 A1) discloses in embodiments, methods and systems for performing studies of consumer behavior are provided. The consumer behavior may have been determined using electronically-captured consumer location data for multiple consumers. The gathered data may be analyzed to determine behavior patterns or other characteristics of the multiple consumers. Further, inferences or predictions about consumers may be derived based on the characteristics.
Wu et al (US Pub., No., 2016/012546 A1) discloses various embodiments use contextual data to improve the targeting of advertising campaigns to consumers. Contextual data may include, e.g., data pertaining to products purchased or sold, places associated with a purchase or sale, and persons involved in the purchase or sale transaction. The collected contextual data may have a temporal component and a location component. The collected contextual data also has a location component, meaning that the data is associated with a particular coordinate, address, region, or other location. Time and location information may be used to recognize that sales patterns vary based on various factors.
Neither Rae nor Sahasi teaches the corresponding appropriate marketing channels of Rae and the corresponding one or more platforms/ channels (web, mobile web, mobile app) to present the media asset or the corresponding machine learing module of Sahasi entered one of the number of designated consumer communication campaigns such that each separate designated consumer communication campaign corresponds with a different predetermined consumer feature and with one distinct machine learning model of the machine learning model system, each one of the machine learning models of the machine learning model system being configured to determine one district campaign consumer activation profile for each individual consumer based on the entered consumer activity data ,assigning each of the plurality of consumer communication campaign for communication based on the determined campaign consumer activation profile for each individual customer from each one of the machine learning models and presenting each individual consumer with designated consumer communications with each consumer according to the assigned consumer communication campaign based on the determined campaign customer activation profiles for each individual consumer for responsive action from the individual consumer;
Response to Arguments
Applicant's arguments of 35 U.S.C 103 rejections with respect to claims 1-27 filed on 2 February 2026 fully considered but they are not persuasive. Applicants’ arguments of the claims are not “directed to” that judicial exception when considered as a whole by the recited interacting aspect and also as an improvement to computerized technology, implementing a practical application under prong 2. Each of these flows in the 101 rejection is futher emphasized by the present amendment is not persuasive. Under the Alice/Mayo framework claims for allocating consumer communication using machine learing is directed to an abstract idea and the 35 U.S.C 101 rejections is maintained.
The specific rejections typically center on the following issues:
A methods involving consumer data analysis as "certain functional mental processes" or "organizing human activity." Specifically, collecting consumer data and assigning marketing campaigns is viewed as a fundamental economic practice or a method of organizing human activity that can be (or historically was) performed by humans.
using "machine learning models" to determine "activation profiles" is merely a series of mathematical relationships or algorithms, which are excluded from patent eligibility unless they provide a specific technical improvement.
Lack of "Significantly More" Rejections state that:
Using a "machine learning model" or "computer" is merely using generic computer components to perform the abstract idea faster.
The steps of "obtaining," "entering," "assigning," and "presenting" are well-understood, routine, and conventional activities in the field of digital marketing and data processing.
With regard to applicants remark’s of Mr. Henry Jr, is not persuasive. Mr. Henry testimony focused on technical improvement rather that abstract idea, however, the claims at issue does not improves the functioning of a computer or another technology.
The 35 U.S.C 101 rejection with respect to claims 1-27 is maintained for reason above.
Conclusions
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/SABA DAGNEW/Primary Examiner, Art Unit 3621