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 amendment filed on 14 March 2026. Claims 1, 7, and 13 have been amended. Claims 6, 12 and 18 have been cancelled. Claims 1-5, 7-11, 13-17 and 19-23 are currently pending and have been examined.
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 1-5, 19, 22, 24 and 25 are a method and claims 7-11, 20 and 23 are a system and claims 13-17 and 21 are medium . Thus, each independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-5, 7-11, 13-17 and 19-25 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, 7 and 13) recite storing first and second dataset an input dataset…, augmenting the input dataset by taking a join of the first set of individual IDs that uniquely identify but maintain conditionally of the individual in the selected of the audience with a second set of individual IDs in a set of reference audiences stored within a cloud system, wherein the join adds in the input dataset a set of individual characteristics data of each matched individual of the first set of individual IDs of the audience with the second set of individual IDs in the set of reference audiences wherein augmenting the input dataset adds features to the input dataset adds feature …,
This limitation as drafted, is a process, under its broadest reasonable interpretation, the claimed limitation covers categorizing as a method of organizing human activity, a fundamental economic practice (e.g., audience selection for marketing), or a data processing method that can arguably be performed in the human mind (albeit less efficiently);
predicting an engagement score for each individual in the first set of individual IDs by processing the input dataset with augmented input data wherein the engagement score is a probability of engagement of the individual with one or more targeted content items and wherein a machine learning model is trained using the information about engagement for each matched individual form the second individual characteristics data of the second set of individual IDs in the set of reference audiences and is based on earned correlations between features amount the resulting characterstics data with information above engagement by individuals from the second individual characterstics data. This limitation as drafted, is a process, under its broadest reasonable interpretation, it covers the performance of the human behavior aro analyzing data for commercial purposes, which fails into a mathematical concept or a method of organizing human activity, thus recite an abstract idea. Classifying, for each individual in the first set of individual IDs, a tier category from a set of tier categories by using a threshold value on the engagement score generated; identifying a target audience, wherein the target audience comprises of a subset of the first set of individual IDs belonging to a particular tier category of the set of tier categories; computing an individual similarity score using a similarity model for each individual belonging to the target audience with the second set of individual IDs in the set of reference audiences, using the resulting individual characteristics data aft er the augmenting adds features to the input dataset; wherein similarity model uses item based to find individual who are similar based on purchasing behavior for similar items, and wherein the uses user based to find individual who are similar based on similar content consumption patterns; wherein different individuals of the target audience are determined to be similar to individual of the set of reference audiences based on different characterstics. These limitations as drafted are a process, under its broadest reasonable intepration, the claimed limitation covers calculating scores, applying thresholds, identifying a target audience based on criteria to the organized human activity which falls within mathematical concepts and methods of organizing human activity. Additionally, the claims focus on using mathematical steps and mental processes (e.g., calculating scores, applying thresholds, identifying a target audience based on criteria, using collaborative filtering algorithms) to organize a human activity (marketing/audience targeting). These are common categories of abstract ideas identified by the courts. based at least in part on the one or more specified constraints, generating [[an]]the expanded audience based on the individual similarity scores while meeting the one or more specified constraints, wherein the expanded audience comprises individuals belonging to the target audience and the subset of individuals from the set of reference audiences, wherein the subset of individuals from the set of reference audiences are selected as having above a threshold value on the individual similarity score; wherein the expanded audience does not include at least some individuals of the set of reference audiences that do not belong to the target audience;
determining an increase in audience size and an increase in engagement from the input audience to the expanded audience even though the at least some individuals are not included in the expanded audience; wherein the increase in audience size is constrained by the one or more specified constraints: and based at least in part on the increase in audience size, determining an increase in cost for the expanded audience compared to the target audience; wherein the increase in cost meets the one or more specified constraints; wherein the displayed report further describes the expanded audience, the increase in audience size and the increase in cost; and receiving a selection, via the user interface, to send one or more targeted content items to the expanded audience at the increased cost. These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover the audience expansion and cost/engagement determination steps, especially the use of similarity scores and constraints, would likely be categorized as mathematical concepts, mental processes, or certain methods of organizing human activity (specifically, a business method for marketing/advertising), all of which are considered abstract ideas. Simply put, these limitation merely describe expanding audience based on computed similarity score in order to reduce and expand audience based on cost constraint which is clearly a business arrangement in its purest form. Further, claims 1-5, 7-11, 13-17 and 19-25 are recited at such a high level that the claimed steps amount to no more than a mental processes, such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because a human can identify content that meets a specified criteria to the expended audiences based on computed similarity score.
Step 2A-Prong 2: The claims recite a combination of additional elements client devices and its components used to perform the generic computer functions, receiving an input data set comprising of a set of individual identider correspond to the audience. The generic computer compoints is recited at a high level of generality (i.e., i.e., machine learning, collaborative filtering, similarity model, generic processor, client devices) for performing the generic computer function of processing data ( computing engagement score for target audience). The receiving steps recited at a high level of generality (i.e., as a general means of data gathering collecting and storing activity for generating the engagement score) and amounts to mere data gathering, which is a form of insignificant extra solution activity. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the generic processor, client devices). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (the generic processor, client devices). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The claims appear to apply generic ML techniques (trained ML model, item/user-based collaborative filtering) to a new field (audience targeting) without detailing specific, non-generic improvements to the underlying ML models or methods themselves. The Federal Circuit has held that merely applying known ML to new data environments is patent-ineligible
The act of "maintaining confidentiality" is an intended outcome or a business practice/compliance goal, not a specific technical process that would inherently be non-abstract. Merely stating a goal of confidentiality does not make the underlying data storage a patent-eligible invention.
The process can likely be performed mentally or using well-understood, routine, and conventional computer functions (e.g., using a database and access controls).
Futher, the claims, as phrased, does not appear to include any "additional elements" that amount to "significantly more" than the abstract idea itself. Relying on a generic computing device for performing these functions does not add an inventive concept; the functions must provide a technical improvement to the computer or a technical solution to a technical problem beyond the abstract idea.
Dependent claims 2-5, 8-12 , 14-17 and 19-25, these claims recite limitation that further define the same abstract noted in claims 1, 7 and 13. The additional elements of the computer components (i.e., machine learning model, a deep neural network model, a support vector machine, etc.) are recited at a high level of generally and merely automates the generic computer function. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the machine learning model, a deep neural network model, a support vector machine, etc. ). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (the generic processor, client devices). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
The Closes prior art to the applicants’ claimed invention:
Jordan et al (US Pub., No., 2014/0180804 A1) focused on tunable algorithmic segment techniques are described. In one or more implementations, a target audience definition is obtained that is input to initiate creation of a look-alike model. The target audience definition indicates traits associated with a baseline group of consumers who have interacted with online resources in a designated manner, such as by buying a product, visiting a website, using a service, and so forth (abstract), collect, access, and/or make use of audience data 126 regarding consumer traits including characteristics (e.g., age, sex, location, affiliations, etc.) and behaviors (e.g., browsing habits, favorites, purchase history, preferences, account activity, etc.) from the various data sources 104. The data sources may include first party databases of a particular marketer, data collected by the service provider 106, and/or third-party data services provided by other entities (paragraph [0025]).
Gupta et al (US Pub., No., 2016/0140623 A1) focused on target audience content interaction techniques are described. In one or more implementations, a plurality of content is quantified by one or more computing devices as a content feature representation for each of the plurality of content. A plurality of content feature clusters are generated by the one or more computing devices based at least in part on similarity of the content feature representations, one to another (abstract), calculate a plurlity of user interaction clusters from the content cluster interaction data by the one or more computing devices based at least in part on similarity of the content cluster interaction data of the plurlity of users one to another and employ the calculated plurality of user interaction clusters by the one or more computing devices to determine content preferences of a target audience (Fig. 2).
Huang et al. (Pub. No.: US 2023/0177543 Al) discloses methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using machine learning models to expand user groups while preserving user privacy and data security are described. In one aspect, a method includes receiving, for a web-based resource, a set of user group identifiers for a set of user interest groups that each include, as members, one or more users that requested content from the web-based resource over a given time period
Kelman et al. (Pub. No.: US 2017/0111461 Al) A first plurality of data points related to visitors to at least one website is received. The data points comprise at least an identification of the visitor and an interaction of the visitor with the website. A target audience comprising at least some of the visitors having a known, desired interaction and a plurality of selection rules defining tolerances for a similarity audience are received.
Jackson et al (US Pub., 2019/0259041 A1) discloses a system or method for identifying a plurality of entities in a first dataset that satisfy a predetermined target attribute by deploying on the first dataset a relationship model generated from a second dataset having a plurality of entities not in the first dataset.
Tuschman et al (US Pub., No., 2019/0102802 A1) discloses a method and system provides for: training at least one machine-learning method of predicting psychometric profiles of individual users in an online population based on automatically collected records of their online behavior; using the resulting predicted psychometric profiles and engagement data on users to learn an engagement model of likelihood of engaging with a stimulus based on psychometric dimensions; and using the engagement model on a population to determine audiences for the stimulus ranked according to predicted likelihood of engagement
None of the above reference either alone or in combination teaches, suggests or teaches a cost-sensitive audience expansion analysis includes "receiving, via the user interface, an indication of one or more specified constraints for determining an expanded audience; wherein the one or more specified constraints are based at least in part on a cost of the expanded audience," and "based at least in part on the one or more specified constraints, generating the expanded audience based on the individual similarity scores while meeting the one or more specified constraints to the increase in audience size is constrained by the one or more specified constraints,". The claims further recite "receiving a selection, via the user interface, to send one or more targeted content items to the expanded audience at the increased cost." At least these features of cost-sensitive audience expansion analysis reducing, and expanding of the audience in order to take advantage of the reduced target audience while also constraining the expansion of the audience based on cost constraint(s) specified in the user interface.
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejections with respect to claims 1-5, 7-11, 13-17 and 19-23 filed on 14 2026 have been fully considered but they are not persuasive. Applicants’ arguments of amended claims 1, 7 and 13 recite “wherein the similarity model computes the individual similarity scores using a representative individual for each audience in the set of reference audiences to reduce computational cost of computing similarity scores across the reference audiences;”. This feature reflect a specific improvement in the way similarity modeling is performed on large databases in not persuasive. The amended limitation directed to ana abstract idea specifically, mathematical algorithm (similarity models, collaborative filtering) and methods of organizing human activity (target audience analysis)- without providing a sufficient “inventive” concept to transform it into patent-eligible subject matter.
A "similarity model" using item-based and user-based collaborative filtering as a "mathematical concept" or "method of organizing human activity". The steps of augmenting data, calculating similarity scores, and using representative individuals to reduce cost are considered routine algorithmic techniques or abstract calculations rather than tangible physical transformations. Furthermore, claim lacks "significantly more" than the abstract idea itself. The elements—collecting data, computing scores, and using a "representative individual" sampling/clustering)—are considered well-understood, routine, and conventional computer functions. The goal to "reduce computational cost" is often treated as a conventional desire to make algorithms more efficient rather than a technical improvement to the computer's operation. Thus, the amended limitation does not overcome the 35 U.S.C 101 rejections and the rejections is maintained.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SABA DAGNEW whose telephone number is (571)270-3271. The examiner can normally be reached 9-6:45.
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 Ashraf can be reached on (571) 270 -3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SABA DAGNEW/Primary Examiner, Art Unit 3621