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 9 March 2026. Claim 1, 2, and 3 have been amended. Claims 1-11 are currently pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 11,615,441 art.
18/980,188
11,615,441
A multi-stage content analysis system that profiles users and selects promotions, comprising:
a computer comprising a memory and instructions within said memory, wherein said computer is configured to execute said instructions to receive a communication created by a user;
analyze said communication to determine whether said user is a potential target for one or more promotions,
wherein said analyze said communication comprises access a database comprising promotion information that comprises information that indicates how to determine which promotion of said one or more promotions is an appropriate response to said communication,
scan the communication and match said communication against said promotion information in said database, wherein when said communication matches said promotion information, a determination is made by said computer that said user is said potential target for said one or more promotions; based on and in response to said analyze said communication, when said computer determines that said user is said potential target for a promotion receive a communications history associated with said user, wherein said communications history comprises a plurality of communications created by said user; and,
when said computer receives said communication history based on when said computer determines that said user is said potential target for said one or more promotions, analyze said communications history to confirm or reject said determination that said user is said potential target for said one or more promotions,
from said analyze said communications history, classify said communications history to generate different categories, wherein said different categories comprise traditional demographic data user profile tags and enriched user profile tags, wherein said enriched user profile tags comprise information about the user that is more granular and descriptive than said traditional demographic data user profile tags, wherein said confirm or reject comprises assign one or more user profile tags to said user from one or both of said traditional demographic data user profile tags and said enriched user profile tags, wherein said one or more user profile tags describe characteristics of one or more of said user and said communication history of said user, wherein said analyze said communications history to assign said one or more user profile tags to said user comprises, extract word frequencies from the communications history, process said word frequencies using a probability table via a classification algorithm resulting in category probabilities for the communications history, wherein said one or more user profile tags are assigned to said user if a tag of said one or more tags comprises a category probability above a threshold value;
receive said one or more user profile tags; based on and in response to said analyze said communications history and in response to said receive said one or more user profile tags, analyze said one or more user profile tags and said communication to determine whether a specific promotion of said one or more promotions should be provided to the user based on whether said one or more user profile tags are relevant to any promotions of said one or more promotions, select said specific promotion from said one or more promotions; and, transmit said specific promotion to said user.
A multi-stage content analysis system that profiles users and selects promotions, comprising:
a computer comprising a memory and instructions within said memory, wherein said computer is configured to execute said instructions to receive a communication created by a user;
analyze said communication to determine whether said user is a potential target for one or more promotions, wherein said analyze said communication comprises access a database comprising promotion information that comprises information that indicates how to determine which promotion of said one or more promotions is an appropriate response to said communication,
scan the communication and match said communication against said promotion information in said database using one or more of text analysis, natural language processing, keyword matching or artificial intelligence, wherein said determine whether said user is said potential target for said one or more promotions depends on a type of said one or more promotions, wherein when said communication matches said promotion information, a determination is made by said computer that said user is said potential target for said one or more promotions; based on and in response to said analyze said communication, when said computer determines that said user is said potential target for a promotion receive a communications history associated with said user, wherein said communications history comprises a plurality of communications created by said user; and, when said computer receives said communication history based on when said computer determines that said user is said potential target for said one or more promotions analyze said communications history to confirm or reject said determination that said user is said potential target for said one or more promotions, wherein said confirm or reject comprises assign one or more user profile tags to said user, wherein said analyze said communications history to assign said one or more user profile tags to said user comprises access said database of promotion information comprising key words and phrases associated with each tag of a set of one or more tags;
calculate a frequency of each of said key words and phrases in said communications history;
calculate a tag relevance score for each tag of said set of one or more tags based on said frequency of each of said key words and phrases, wherein said calculate said tag relevance score for each tag of said set of one or more tags comprises
calculate a probability that said communications history is associated with each tag of said set of one or more tags using a naïve Bayes classifier, wherein said frequency of each of said key words and phrases in said communications history comprises a feature vector for said naïve Bayes classifier; and,
receive said one or more user profile tags; based on and in response to said analyze said communications history and in response to said receive said one or more user profile tags, analyze said one or more user profile tags and said communication to determine whether any promotion of said one or more promotions should be provided to the user, wherein when the one or more user profile tags do not contain tags relevant to any promotions, no promotion is selected, or a random promotion is selected to assist with machine learning, wherein when the one or more user profile tags do contain tags relevant to said any promotions, select a specific promotion from said one or more promotions; and, transmit said specific promotion to said user.
Although the claims at issue are not identical, they are not patentably distinct from each other because: though the wordings are different, the limitation carried are either inherently implied or would have been obvious to one of ordinary skill in the art. 18/980,188’s another major difference from the patent in language by the lacking the limitation of “using one or more text analysis, natural language processing, keyword matching or artificial intelligence and wherein said determine whether said user is said potential target one or mor promotion depend on a type of sid one or more promotion ” therefore implies such analysis must have occurred, as also obvious to one ordinary skill in the art that for scan to much the scanned promotion to potential target and nature of the limitation are different, however do not result in a patentable distinction, in either case. 18/188,434 differ from the patent by stating: “ form said analyze said communications history, classify said communications history to generate different categories, wherein said different categories comprise traditional demographic data user profile tags and enriched user profile tags, wherein said enriched user profile tags comprise information about the user that is more granular and descriptive than said…” however, the nature of the steps are different, however do not result in a patentable distinction, in either case, the commucation history must be classified to be processed in order to be assigned. 18/188,434’s another major difference in language by the lacking of the steps of “ access said database of promotion information comprising key words and phrase assocted with each tag of a set of one or more tags…,”, “ “calculate a frequency of each of said key words and phrases in said communications history;” “calculate a tag relevance score for each tag of said set of one or more tags based on said frequency of each of said key words and phrases, wherein said calculate said tag relevance score for each tag of said set of one or more tags comprises calculate a probability that said communications history is associated with each tag of said set of one or more tags using a naïve Bayes classifier, wherein said frequency of each of said key words and phrases in said communications history comprises a feature vector for said naïve Bayes classifier;” vs “extract words frequency form the commucation history” , “ processor said word frequency suing a probability table via a classification algorithm resulting in category probability for the commucation history” “ wherein said one or more profile tags are assigned to said user if a tag of said one or more tags comprise a category probability above a threshold value” which is merely a different way of wording to the patented application. Further, it is widely known in the art that, in order to effectively preserve record for future reference and/or assigning weight to feature or calculating the score is merely a routine work contemplatable by one of ordinary skill in the art.
Claims 1-11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of U.S. Patent No. 12,182,834.
18/980,188
12,182,834
A multi-stage content analysis system that profiles users and selects promotions, comprising:
a computer comprising a memory and instructions within said memory, wherein said computer is configured to execute said instructions to
receive a communication created by a user;
analyze said communication to determine whether said user is a potential target for one or more promotions, wherein said analyze said communication comprises
access a database comprising promotion information that comprises information that indicates how to determine which promotion of said one or more promotions is an appropriate response to said communication,
scan the communication and match said communication against said promotion information in said database, wherein when said communication matches said promotion information, a determination is made by said computer that said user is said potential target for said one or more promotions;
based on and in response to said analyze said communication, when said computer determines that said user is said potential target for a promotion receive a communications history associated with said user, wherein said communications history comprises a plurality of communications created by said user; and,
when said computer receives said communication history based on when said computer determines that said user is said potential target for said one or more promotions, analyze said communications history to confirm or reject said determination that said user is said potential target for said one or more promotions, from said analyze said communications history, classify said communications history to generate different categories, wherein said different categories comprise traditional demographic data user profile tags and enriched user profile tags, wherein said enriched user profile tags comprise information about the user that is more granular and descriptive than said traditional demographic data user profile tags, wherein said confirm or reject comprises assign one or more user profile tags to said user from one or both of said traditional demographic data user profile tags and said enriched user profile tags, wherein said one or more user profile tags describe characteristics of one or more of said user and said communication history of said user, wherein said analyze said communications history to assign said one or more user profile tags to said user comprises, extract word frequencies from the communications history, process said word frequencies using a probability table via a classification algorithm resulting in category probabilities for the communications history, wherein said one or more user profile tags are assigned to said user if a tag of said one or more tags comprises a category probability above a threshold value;
receive said one or more user profile tags; based on and in response to said analyze said communications history and in response to said receive said one or more user profile tags, analyze said one or more user profile tags and said communication to determine whether a specific promotion of said one or more promotions should be provided to the user based on whether said one or more user profile tags are relevant to any promotions of said one or more promotions, select said specific promotion from said one or more promotions; and, transmit said specific promotion to said user.
A multi-stage content analysis system that profiles users and selects promotions, comprising:
a computer comprising a memory and instructions within said memory, wherein said computer is configured to execute said instructions to
receive a communication created by a user via a message analyzer;
analyze said communication, via said message analyzer, to determine whether said user is a potential target for one or more promotions, wherein said analyze said communication comprises
access a database comprising promotion information that comprises information that indicates how to determine which promotion of said one or more promotions is an appropriate response to said communication,
scan the communication and match said communication against said promotion information in said database using one or more of text analysis, natural language processing, keyword matching or artificial intelligence, wherein said determine
whether said user is said potential target for said one or more promotions depends on a type of said one or more promotions, wherein when said communication matches said promotion information, a determination is made by said computer that said user is said potential target for said one or more promotions;
based on and in response to said analyze said communication, when said computer determines that said user is said potential target for a promotion receive a communications history associated with said user via a communication history analyzer, wherein said communications history comprises a plurality of communications created by said user; and, when said computer receives said communications history based on when said computer determines that said user is said potential target for said one or more promotions, analyze said communications history, via said communication history analyzer, to confirm or reject said determination that said user is said potential target for said one or more promotions, wherein said confirm or reject comprises assign one or more user profile tags to said user, wherein said one or more user profile tags describe characteristics of one or more of said user and said communications history of said user, wherein said analyze said communications history to assign said one or more user profile tags to said user comprises parse said communications history using a natural language processing classifier to derive features associated with said characteristics, wherein said features comprise n-grams, words or phrases across different messages within said communications history, assign different weights to said features, such that said features are weighted differently depending on one or more of a position of a message associated with said features within said communications history across said different messages, and a location of said features within said message; and, receive said one or more user profile tags; based on and in response to said analyze said communications history and in response to said receive said one or more user profile tags, analyze said one or more user profile tags and said communication to determine whether a specific promotion of said one or more promotions should be provided to the user, via a promotion selector, based on whether said one or more user profile tags are relevant to any promotions of said one or more promotions, wherein when the one or more user profile tags do not contain tags relevant to any promotions, via said promotion selector, no promotion is selected, or a random promotion is selected to assist with machine learning, wherein when the one or more user profile tags do contain tags relevant to said any promotions, via said promotion selector, select said specific promotion from said one or more promotions; transmit said specific promotion to said user; and, track said specific promotion, said communication, and statistics describing one or more of whether said user responded to said specific promotion that was transmitted to said user; how said user responded to said specific promotion; purchases, subscriptions, or enrollments made by said user; a response of said user to said specific promotion; incorporate said specific promotion, said communication and said data into a response record; and, assemble said response record into a training dataset; a machine learning engine configured to receive said response record including said training dataset; execute a machine learning algorithm on said training dataset to train said machine learning engine by learning or refining models that are applied, and update one or more of said message analyzer, said communication history analyzer and said promotion selector, wherein the training dataset indicates which of said one or more promotions are associated with positive responses from said user having certain user profile tags of said one or more user profile tags, to improve promotion selection in future communications.
Although the claims at issue are not identical, they are not patentably distinct from each other because: though the wordings are different, the limitation carried are either inherently implied or would have been obvious to one of ordinary skill in the art. 18/980,188’s differ from the patent by lucking the language of “message analyzer” though the wordings are different, the limitations carried are either inherently implied or would have been obvious to one of ordinary skill in the art, or otherwise described in language that do not carry patentable weight. 18/980,188’s another major difference from the patent in language by the lacking the limitation of “using one or more text analysis, natural language processing, keyword matching or artificial intelligence and wherein said determine whether said user is said potential target one or mor promotion depend on a type of sid one or more promotion ” therefore implies such analysis must have occurred, as also obvious to one ordinary skill in the art that for scan to much the scanned promotion to potential target and nature of the limitation are different, however do not result in a patentable distinction, in either case. 18/980,188’s another major difference in
language by the lacking of the step of said specific promotion, said communication, and
statistics describing one or more of whether said user responded to said specific promotion that was transmitted to said user: how said user responded to said specific promotion; purchases, subscriptions, or enrollments made by said user; a response of said user to said specific promotion; incorporate said specific promotion, said communication and said data into a
response record; and, assemble said response record into a training dataset; a machine learning engine configured to receive said response record including said training dataset; execute a machine learning algorithm on said training dataset to train said machine learning engine by learning or refining models that are applied, and update one or more of said message analyzer, said communication history analyzer and said promotion selector, wherein the training dataset indicates which of said one or more promotions are associated with positive responses from
said user having certain user profile tags of said one or more user profile tags, to improve promotion selection in future communications. A person of ordinary skill in the art would not have been motivated to modify the parent's claimed invention (by omitting the limitation) to arrive at the invention in the continuation application, the omission steps does not render the new claims unpatentable. Further, it is widely known in the art that, in order to effectively preserve record for future reference and/or problem diagnostics, logging of communication is merely a routine work contemplatable by one of ordinary skill in the art.
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 1-11 are a system. Thus, the independent claim, on its face, is directed to one of the statutory categories of 35 U.S.C. §101. However, the claims 1-11 are rejected under 5 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A-Prong 1: independent claim 1 recites receive a communication created by a user; analyze said communication to determine whether said user is a potential target for one or more promotions, wherein said analyze said communication comprises access comprising promotion information that comprises information that indicates how to determine which promotion of said one or more promotions is an appropriate response to said communication, match said communication against said promotion information in said database, wherein when said communication matches said promotion information, a determination is made by said computer that said user is said potential target for said one or more promotions; based on and in response to said analyze said communication, when said computer determines that said user is said potential target for a promotion receive a communications history associated with said user, wherein said communications history comprises a plurality of communications created by said user; and, when said computer receives said communication history based on when said computer determines that said user is said potential target for said one or more promotions, analyze said communications history to confirm or reject said determination that said user is said potential target for said one or more promotions, from said analyze said communications history, wherein said different categories comprise traditional demographic data user profile tags and enriched user profile tags, wherein said enriched user profile tags comprise information about the user that is more granular and descriptive than said traditional demographic data user profile tags, wherein said confirm or reject comprises assign one or more user profile tags to said user from one or both of said traditional demographic data user profile tags and said enriched user profile tags, wherein said one or more user profile tags describe characteristics of one or more of said user and said communication history of said user, wherein said analyze said communications history to assign said one or more user profile tags to said user comprises, extract word frequencies from the communications history, process said word frequencies using a probability table via a classification algorithm resulting in category probabilities for the communications history, wherein said one or more user profile tags are assigned to said user if a tag of said one or more tags comprises a category probability above a threshold value; receive said one or more user profile tags; based on and in response to said analyze said communications history and in response to said receive said one or more user profile tags, analyze said one or more user profile tags and said communication to determine whether a specific promotion of said one or more promotions should be provided to the user based on whether said one or more user profile tags are relevant to any promotions of said one or more promotions, select said specific promotion from said one or more promotions; and, transmit said specific promotion to said user, generate data describing whether said user responded to said specific promotion and how said user responded; and update probability assocted with said classification algorithm based on and modify subsequent category . Theses limitation as drafted is a process, under its broadest reasonable interpretation, covers a process of receiving a commucation, analyzing to determine a potential target for a promotion by analyzing the user’s commucation history for confirmation. These limitations fall within “Certain Methods Of Organizing Human Activity” for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) or a method of managing personal behavior or interaction between people, which falls into the category of an abstract idea. Simply put, theses limitation merely describe profiling users and selecting promotion or targeting advertisement based certain criteria which is clearly a business arrangement in its purest form.
Claims 2-11 merely provide additional abstract concepts and narrow the abstract idea of claim 1. Further, claims 1-11 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 select a promotion that matches a specified criteria, acknowledge an agreement to target promotion to potential target.
Step 2A-Prong 2: The only additional elements in independent claim 1 is some form of
multi-sage content analysis system. These multi-sage content analysis system are recited at a high-level of generality (i.e., as a generic computer comprises a memory and instructions performing a generic computer function of processing data and a generic memory and database storing data) such that it amounts no more adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Specifically, the claim recites (i) access database, (ii) scan commucation to match against the promotion (iii) classify commucation history to generate different categories, where category comprises a profile tag. Adding insignificant extra-solution active to the judicial exception (see eMPEP 2106(g). As recited in the claims mere data gathering in conjunction with the abstract idea and generally liking the user of judicial exception to a particular technological environment or field of use (see eMPEP 2106 (h).
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to integration of
the abstract idea into a practical application, the additional element of using the claimed
computer systems amount to no more than “apply” a selection of content on the systems. Furthermore, the claims recite that the process is performed by a “computer comprising a memory and instruction ” but does not specify a particular technological improvement to the computer’s functioning or unconventional technological method. Merely performing an abstract idea on generic computer is not enough to patent eligibility. The step described such as accessing a database, scanning, matching and classify data are like considered routine and convention computer functions as well as generally linking the claims to network environment that send and receive commucation. The claims are requiring a significantly more elements beyond the abstract idea itself to be patent-eligible, which appears to be missing in the presented claims. The "enriched user profile tags" are more granular data, but the processing itself is still an abstract data analysis method.
Therebefore, claims 1-11 are ineligible for patent under current U.S. patent law, based on Supreme Court precedent such as Alice Corp. v. CLS Bank and subsequent Federal Circuit cases. The claims are directed to an abstract idea performed with generic computer technology.
The closet prior arts to applicants claimed invention:
Peintner (US Pub., No 2014/0279906 A1) focused on a system for understanding social media(analyzing). The system may provide automated machine understanding of social media communication based on: social media assertion, social media statements and conversion, social connection, user profile info, crowed-sourced database, internet pages, and semantic networks (abstract), collects communications exchanged by internet users like posts, comments, or social media likes (see paragraphs 0001 and 0019) to extract user interests into a profile to target goods and services to them (see paragraphs 0001 and 0003), where the social network data is modeled against subculture models (see paragraph 0052) which include frequency or use of entities (keywords) and phrases (see paragraphs 0029-0035 and 0036-0037) to determine which subculture that user likely falls into (see paragraph 0003) to target goods or services to them (see paragraphs 0001 and 0127).
Allison et al. (US Pub. No.: US 2011/0082824 Al) focused on a framework for comparison and optimization of classifier and feature for classification of target including preparing training and testing sets, applying a classifier to the training set to achieve a distinctly trained classifier for each classifier applied, applying each resulting trained classifier to the testing data set, selecting an optimal classifier, and applying the optimal classifier to the target. The framework is used to optimally classify a physical representation of a target, such as a document, news article, or advertisement(abstract).
Wachtel (US Pub., No., 2001/0005847 A1) focused on a network information sharing model is described. The network described comprises a client-server model or client only model. There exists a shard information database, a shard category database, a shared internet profile database and shred client enhancement database each is continually and dynamically updated (abstract)
Pobbathi et al. ( US Pub., No., 2014/0172652 A1) focused on system and method for large-scale classification of product recommendations to consumers (see abstract) teaches the specific machine learning of native Bayes classifier or more specifically the recited limitation in the claims of using a native Bayes classifier, wherein said frequency of information in said history comprises a feature vector for said native Bayes classifier (see paragraphs 0012 and 0054).
Reid (US Pub., No., 2010/0312628 A1) focused on a method and system for delivering content to user over a computerized system based on an evaluation of sensitivity to social issues by collecting information from a user and preferable at least one other individual that know user regarding user’s sensitivity to social issue (abstract), and profile information comprises a religion (see paragraph 0021)
Lee ( US Pub., No., 2018/0197207 A1) focused on system, methods, and non-transitory computer-readable media can determine a plurlity of political engaged user based on political engagement criteria. A plurality of influential user is destemmed based on shared content influence criteria (abstract) and using machine learning to associate users with a particular political ideology for example very conservative, moderate, liberal, or very liberal (see paragraph 0033).
Calafiore et al. (US Pub., No., 2013/0204701 A1) focused on a system for marketing target product to users of an internet-based social media community. The system may include a recommendation, advertismetn and personalization engine for generating product recommendation (abstract) and user interaction with the production recommendations on the user interface are monitored by the engine and then used to refine the product offerings (see paragraphs 0071-0072).
Tseng et al. ( US Pub., No., 2013/0211915) focused on constructing one or more customized dictionaries for particular user, each customized dictionary comprising a different blending of one or more frequently used words collected from text submitted by one or more users (abstract) and advertising based on customized dictionaries which are created based on frequency of words used of users in social network channels (see abstract and paragraph 0053).
Bilenko et al. (US Pub., 2009/0271228 A1) focused on a system that facilitates targeted advertising. The system include a receiver component that receives user data that include historical searching and browsing activity of a user for predictive user profiles for advertising, which can be based at least in part upon recency or frequency of occurrence of the keyword in the user data (see abstract and paragraph 0030)
Bilenko et al. ( US 2011/0295687 A1) focused per-user profile data (e.g., maintained din browser cookie) as a factor in selecting advertisement to be presented to a user for current content and predictive profiles for personalized advertising where user profiles record keywords and frequencies (see abstract and paragraph 0019)
Zhang et al. ( US Pub., No., 2014/0136323 A1) focused on a system and method for advertising based on user intention detection, and discusses background of systems using keyword and the frequency of the keyword used by the user as an indicator or relevancy (see abstract and paragraph 0003)
None of the above reference either alone or in a combination teaches or suggests a multi-sage content analysis system as claimed with respect to one or more promotions that first analyzes whether the user is potential target for one or more promotions according to the claimed limitation, then based on the result of analysis determines whether to confirm or reject the user is a potential target for said or one or more promotion, and wherein said confirm or reject comprises assign one or more profile tags from or both of traditional demographic data user profile tags and enriched user profile tags determine and select a specific one or promotion and transmit to a user according to the claimed limitation and the finally based the results of analysis and claimed limitations continually analyzing the communication history to target a potential promotion and to build a detailed profile.
Response to Arguments
Applicant's arguments of 35 U.S.C 101 rejections with respect to claims 1-11 filed 9 March 2026 have been fully considered but they are not persuasive.
Applicants’ arguments of as amended, for example independent claim 1 is directed to a specific, staged, computer implemented probabilistic content classification architecture with adaptive probability updating, and not to a mere method of organizing human activity is not persuasive. The claimed elements described subject matter which involves analyzing commucation history to assign user tags for generating promotion is an abstract idea-specifically, the “gathering, analyzing and disply of data” which a method of organizing human activity (targeted marketing) implemented on generic computer .
Furthermore, the amended limitation is directed abstract idea such as:
Fundamental Economic Practice: Using automated analysis to target promotions (similar to intermediated settlement in Alice, which was found to be a "building block" of modern commerce). Additionally , the claim focuses on classifying users and providing promotions based on communication history. This limitation is viewed as a fundamental economic practice (marketing/advertising) that existed long before computers.
Method of Organizing Human Activity: Collecting user history, analyzing it, and triggering a promotion based on that analysis.
Mental Processes: The steps of analyzing text for word frequencies and identifying user profile tags can be likened to methods that could be performed by a human in their head or on paper, which is a key indicator of an abstract idea. The analysis of communication histories (even to create a vector) can be equated to a human analyzing a message to determine if a person is a potential customer, this limitation is also fails into mental process.
In addition, the claim fails to provide an “inventive concept” (significantly more that the abstract idea itself) because the steps of "receiving," "analyzing," "selecting," and "transmitting" data, even with weighted word frequencies and machine learning, may be viewed as conventional, generic computer activities. The weighted feature vector and category probabilities may be seen as mere mathematical calculations that do not add "significantly more" to the abstract idea.
Furthermore, see also the following:
Conventional Computer Activity: The claim uses generic computer functions: receiving, extracting word frequencies, and updating a classification algorithm.
Weighted Analysis is Insufficient: Merely weighting word frequencies based on message position or using a classification algorithm is often viewed as routine, conventional data processing, not a technological improvement.
No Technical Improvement: The claim focuses on what the data is (promotion tags) rather than how the computer works better (e.g., improved processor speed or reduced memory usage.
Applicant expressed that Multi-stage conditional processing architecture, Construction of weighted feature vectors across multiple communications, Weighting based on corpus position and feature location, Generation of category probabilities using a classification algorithm, Threshold-based assignment, and Adaptive updating of probabilities to modify future classification output. Applicant respectfully notes, for example, wherein these limitations define a specific machine-based probabilistic modeling framework. Such an architecture, for example, simply cannot be performed in the human mind and is not a fundamental economic practice, applicants’ arguments is not persuasive.
The provided a multi-sage content analysis system is classified an “abstract idea” (specifically, a mental process or a method of organizing human activity) that lacks an "inventive concept" to transform it into patent-eligible subject matter. Furthermore, data manipulation, classification, and analysis that can be performed by a human or on a generic computer. Furthermore, the system applies generic machine learning techniques to new data (communications) without improving the AI model itself. Thus, the claims are directed to an abstract idea, and therefore, the 35 U.S.C 101 rejections with respect to claim 1-11 is maintained.
In order to overcome the 35 U.S.C rejections, the Applicant should focus on Technical Solution to Technical Problem.
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.
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/SABA DAGNEW/Primary Examiner, Art Unit 3621
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