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 .
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 – 24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 1, representative of claims 13, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to providing a feedback to the marketing team (or person) the estimated acceptance of the message by the targeted audience. When a request comprising a marketing message is received, a simulated profile directed to users limited to who are healthcare professionals (target audience) is generated and a simulated mock test is performed to determine simulated response from the target healthcare professionals and results of the mock presented as response to the received request.
These limitations describe marketing/sales/advertising activities. Subsequent to receiving a marketing message from a marketing team (or person), by referencing the plurality of healthcare professional profiles a simulated profile is generated which is used during a simulated mock campaign to determine how recipients of the message may respond to the message, and the results will be provided to the marketing team (or person).
In addition, a Large Language Model system is employed to simulate a first response based on the first prompt (comprising the provided candidate healthcare professional profile) and the provided marketing message. Large language model system provides simulated reaction of the candidate profile(s) associated healthcare professional to the one or more marketing messages from the candidate healthcare professional profile, updating the simulation profile and displaying the reaction to the marketing message based on the simulation profile.
Using a Large Language Model algorithm for simulating reactions of the received healthcare professional profile to the marketing message for which the simulation is being performed and causing display of the simulated reaction of the healthcare professionals would be presenting the final result, the simulated healthcare professional response information, to the marketing team (or person).
Represented claims 13, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a non-transitory storage.
The processor and storage are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, 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 an abstract idea.
The claims do 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 the processor and non-transitory storage amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 – 12 and 14 - 24, these claims recite limitations that further define the same abstract idea of using data frame in simulation profile; defining that the target profile characteristics comprise a plurality of profile attributes and a plurality of attribute distribution sets, corresponding to a particular profile attribute for the simulated healthcare professional profiles; defining that simulated healthcare professional profiles will be validated by comparing them to the plurality of attributes distribution sets; defining that a simulated reaction set for each marketing message by for each healthcare professional profile in the plurality of simulated healthcare professional profiles will be generated and displayed as a visualization to a display device, wherein the simulated reaction comprises a reaction indictor corresponding to one of a simulated ignore, an unchanged reaction, or a convinced reaction and a simulated rationale associated with the simulated reaction, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components. Accordingly, the claim recites an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3 – 5, 7 – 13, 15 – 17 and 19 – 24 are rejected under 35 U.S.C. 103 as being unpatentable over Khoury et al. US publication 2021/0042796 in view of Gharibshah et al. published article “User Response Prediction in Online Advertising” hereinafter referred to as Gharibshah.
Regarding claim 1 and representative claim 13, Khoury teaches a computer-implemented system and method for evaluating the effectiveness of a marketing message by simulating a reaction to the marketing message (Khoury, an intuitive, automated system that is capable of autonomous advertisement campaign generation, workflow implementation, and maintenance, especially with respect to the generation of media rich advertisements and the running and evaluating of an advertisement campaign form a centralized and/or decentralized platform) [Khoury, 0019], comprising:
a non-transitory storage memory storing a plurality of healthcare-professional profiles (Khoury, the system may generate and/or store one or more profiles for each person in an identified market and/or in a determined location of the organization for which the advertisements are to be produced.) [Khoury, 0115], comprising:
a processor in communication with the memory, the processor [Khoury, 0239, 0444]] configured for:
receiving, by a processor, a marketing message request comprising one or more marketing messages (Khoury, in initiating of the building of the advertisement, the system may generate a series of prompts or queries to be presented to the user at a generated dashboard interface, which prompts are designed to elicit from the user the appropriate information for determining the content for insertion into the advertisement template so as to build the template and/or generate the advertisement) [Khoury, 0105];
generating, by the processor, a simulation profile based on a plurality of healthcare-professional profiles (e.g., profiles of users limited to healthcare-professional) and the marketing message request (Khoury, the system may generate and/or store one or more profiles for each person in an identified market and/or in a determined location of the organization for which the advertisements are to be produced.) [Khoury, 0115];
Khoury does not explicitly teach simulating reaction to one or more marketing messages. However, Gharibshah teaches Predicting a click, as the first measurable user response, is an important step for many digital advertising and recommendation systems to capture the user propensity to following up actions, such as purchasing a product or subscribing a service. Based on this observed feedback, these systems are tailored for user preferences to decide about the order that ads should be served to them [Gharibshah, page 2]. Gharibshah further teaches user response prediction plays an essential role for online advertising and recommender systems [68], where the prediction is typically defined as the probability of users making a positive response on promoted item in a marketplace, ad, or news article in online platforms [Gharibshah, page 6]
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Khoury by adopting teachings of Gharibshah and probability as an indicator in bidding strategies to determine the potential revenue.
Khoury in view Gharibshah teaches system and method further comprising:
generating, at the processor, a simulated reaction to the one or more marketing messages for a candidate healthcare-professional profile of the plurality of healthcare-professional profiles in the simulation profile [Gharibshah, see atleast Fig.2 on page 7], the simulated reaction generated by:
sending, to a large language model system, a first prompt comprising the candidate healthcare-professional profile and the one or more marketing messages (Gharibshah, After the pre-processing and labeling steps, data samples are described with series of features (fields) along with label (class) values which are normally specified as binary user response value such as 1 for click, conversion, purchasing, etc. and 0 otherwise.) [Gharibshah, page 7]; and
receiving, from the large language model system, a first response based on the first prompt comprising the simulated reaction to the one or more marketing messages from the candidate healthcare-professional profile (Gharibshah, For the prediction task, it will output probability of users making an interaction (e.g. a click) on items in the list.) [Gharibshah, page 7],
updating the simulation profile with the simulated reaction (Gharibshah, Online environment is inherently dynamic that the stream of data are changing over time. It may gradual shift in user preferences which can affect the performance of predictive models. So recommendation systems generally need to apply online learning (retraining mechanisms) to update and tackle new user interactions.) [Gharibshah, page 48-49]; and
outputting, at a display device in communication with the processor, the reaction to the marketing message based on the simulation profile (Gharibshah, For the prediction task, it will output probability of users making an interaction (e.g. a click) on items in the list.) [Gharibshah, page 7].
Regarding claim 3 and representative claim 15, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method, wherein the plurality of healthcare-professional profiles comprises a plurality of simulated healthcare-professional profiles, wherein each simulated healthcare-professional profile is generated by a large language model in response to a profile creation prompt that specifies target profile characteristics, wherein the target profile characteristics comprise a plurality of profile attributes and a plurality of attribute distribution sets, wherein each attribute distribution set corresponds to a particular profile attribute in the plurality of profile attributes and each attribute distribution set defines a statistical distribution of attribute values that can be defined for the corresponding profile attribute for the simulated healthcare-professional profiles (Khoury, the system may generate and/or store one or more profiles for each person in an identified market and/or in a determined location of the organization for which the advertisements are to be produced.) [Khoury, 0115].
Regarding claim 4 and representative claim 16, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method further comprising generating the plurality of simulated healthcare-professional profiles by transmitting the profile creation prompt to the large language model and receiving the plurality of simulated healthcare-professional profiles as an output from the large language model (Khoury, the system may generate and/or store one or more profiles for each person in an identified market and/or in a determined location of the organization for which the advertisements are to be produced.) [Khoury, 0115].
Regarding claim 5 and representative claim 17, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method further comprising validating the simulated healthcare-professional profiles in the plurality of simulated healthcare-professional profiles by automatically comparing the profile attributes of each simulated healthcare-professional profile received from the large language model to the plurality of attributes distribution sets (Khoury, a second table, or other data structure, may be built whereby the subject consumer or a consumer group's current online use, with respect to a current communication campaign presently being performed, may be tracked and compared against the collective of current online users engaging with or otherwise responding to that campaign. The two data structures can be compared with one another so as to determine if the subject consumer's present interactions comport with their past interactions) [Khoury, 0404).
Regarding claim 7 and representative claim 19, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method further comprising:
generating, at the processor, a simulated reaction set for each marketing message by for each healthcare-professional profile in the plurality of simulated healthcare-professional profiles, generating a simulated reaction to that marketing message (Gharibshah, For the prediction task, it will output probability of users making an interaction (e.g. a click) on items in the list.) [Gharibshah, page 7].
Regarding claim 8 and representative claim 20, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method further comprising:
generating a visualization output for a given marketing message by, for that given marketing message, generating a visual representation of the simulated reaction set; and displaying the visualization output on a display device (Gharibshah, For the prediction task, it will output probability of users making an interaction (e.g. a click) on items in the list.) [Gharibshah, page 7].
Regarding claim 9 and representative claim 21, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method further comprising validating the simulated reaction set using a predefined validation script (Khoury, Once the request is made, a pre-processing step may take place, such as where the system may be configured for performing one or more data and/or parameter checks so as to validate the selected or derived campaign parameters. …. This data may then be collected, assessed, and/or validated by the system, such as for authenticity.) [Khoury, 0098].
Regarding claim 10 and representative claim 22, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method, wherein generating the simulation profile comprises:
sending, to the large language model system, at least one existing component of the candidate healthcare-professional profile (Gharibshah, After the pre-processing and labeling steps, data samples are described with series of features (fields) along with label (class) values which are normally specified as binary user response value such as 1 for click, conversion, purchasing, etc. and 0 otherwise.) [Gharibshah, page 7];
receiving, from the large language model system, a simulated component of the candidate healthcare-professional profile (Khoury, the system may generate and/or store one or more profiles for each person in an identified market and/or in a determined location of the organization for which the advertisements are to be produced.) [Khoury, 0434]; and
updating the candidate healthcare-professional profile with the simulated component prior to generating the simulated reaction for the candidate healthcare-professional profile (Gharibshah, Online environment is inherently dynamic that the stream of data are changing over time. It may gradual shift in user preferences which can affect the performance of predictive models. So recommendation systems generally need to apply online learning (retraining mechanisms) to update and tackle new user interactions.) [Gharibshah, page 48-49].
Regarding claim 11 and representative claim 23, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method wherein the simulated reaction comprises a reaction indictor corresponding to one of a simulated ignore, an unchanged reaction, or a convinced reaction (Khoury, The system may then receive response data back from the social media platform, such as where the response data pertains to the effectiveness of the advertisement to achieve a defined objective, such as reach, looks or views, clicks, impressions, engagements, transactions, conversions, shares, up votes, and the like.) [Khoury, 0125].
Regarding claim 12 and representative claim 24, as combined and under the same rationale as above, Khoury and Gharibshah teaches system and method wherein the simulated reaction comprises a simulated rationale associated with the simulated reaction, wherein the simulated rationale provides a qualitative description generated by the large language model of a rationale for the simulated reaction generated by the candidate healthcare-professional profile (Khoury, when various patterns are formed, the system may learn these patterns, breakdown and learn the factors leading to the pattern, thereby determine the existence of and the reason for the presence of a trend, e.g., in communications, and/or predict a likely manner in which the communication recipients will behave.) [Khoury, 0381].
Claims 2 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Khoury et al. US publication 2021/0042796 in view of Gharibshah et al. published article “User Response Prediction in Online Advertising” hereinafter referred to as Gharibshah and Ness-Intracity101 YouTube video “What is Data Frame” hereinafter referred to as Ness-Intracity.
Regarding claim 2 and representative claim 14, as combined and under the same rationale as above, Khoury in view of Gharibshah does not explicitly teach generating of data-frame. However, Ness-Intracity teaches a data frame is an in-memory snapshot that makes it much easier for engineers to design data processing programs for example if a data engineer is attempting to write a multi-step statistical process the steps in the process can themselves be milestones using a data frame this greatly reduces the amount of code that has to be written a data frame can Formats appear in many different kinds of formats depending on whether it's structured data semi-structured or unstructured [Ness-Intracity, page 11]. Ness-Intracity further teaches another interesting use of data frames came from snowflake in the form of a result scan command coupled with asynchronous queries in November of 2020 snowflake made it possible for their query compute pipe to be densely packed by allowing queries to be completely asynchronous by using python or java the juggling of these queries is managed by the programmer the amazing thing about asynchronous queries is that they can run as soon as they're compiled so in our tests we were issuing about a thousand queries per minute however such a large landscape of queries would obviously be daunting to capture in downstream logic however snowflake makes that process easy by generating query ids for each of the queries and by coupling that with the result scan command the output can behave like a data frame this means that you can run some amazing logic without hunting and pecking for the data or moving data in and out of the programming tools [Ness-Intracity, page 12-13].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Khoury in view of Gharibshah by adopting teachings of Ness-Intracity and use data-frames to run some amazing logic without hunting and pecking for the data or moving data in and out of the programming tools [Ness-Intracity, page 13]
Khoury and Gharibshah and Ness-Intracity teaches system and method further comprising:
generating, at the processor, a data-frame (as responded to above, Ness-Intracity, page 12-13], and
storing the data-frame in the simulation profile (as responded to above, Ness-Intracity, page 12-13].
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Khoury et al. US publication 2021/0042796 in view of Gharibshah et al. published article “User Response Prediction in Online Advertising” hereinafter referred to as Gharibshah and Kerry Coppinger published article “Do you have (bad) bots? 4 ways to spot malicious bot activity on your site” hereinafter referred to as Coppinger.
Regarding claim 6 and representative claim 18, as combined and under the same rationale as above, Khoury and Gharibshah does not teach identifying and omitted of simulated healthcare-profile. However, Coppinger teaches For any online business, distinguishing between malicious bots and human users is a challenge. Business leaders want to ensure the traffic coming to their websites is valid and is best positioned to convert into customers, but with Invalid Traffic becoming increasingly problematic for achieving business objectives [Copplinger, page 1] and teaches plurality of ways to spot malicious bots (e.g., simulated profiles) [Copplinger, page 2].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Khoury in view of Gharibshah by adopting teachings of Copplinger to control their online environment and prevent simulated profiles steal valuable information.
Khoury in view of Gharibshah and Copplinger teaches system and method further comprising identifying at least one invalid simulated healthcare-professional profile and omitting each simulated healthcare-professional profile in the at least one invalid simulated healthcare-professional profile from the plurality of healthcare-professional profiles included in the simulation profile (as responded to above) [Capplinger, page 1, 2].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tristan Nguyen published article “Building Generative AI into Marketing Strategies: A Primer”
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/NARESH VIG/Primary Examiner, Art Unit 3622
March 16, 2026