DETAILED ACTION
Acknowledgement
This non-final office action is in response to claims filed on 06/03/2025.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/29/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention, “Integrated Digital Marketing and Performance Analysis Platform”, is directed to an abstract idea, specifically Certain Methods of Organizing Human Activity, without significantly more. The claims as a whole do not include additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the abstract idea because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-20 are directed to a statutory category, namely a machine (claims 1-9), a process (claims 10-19), and a manufacture (claim 20).
Step 2A (1): Independent claims 1, 10, and 20 are directed to an abstract idea of Certain Methods of Organizing Human Activity, based on the following claim limitations: “receive a campaign parameter and a learning feedback from the user; provide an instructional content to the user based on campaign parameter; adjust the instructional content based on the learning feedback; generate a campaign for the user based on the campaign parameter; analyze the campaign based on an advertisement metric to produce a campaign analysis; generate a real-time performance report based on the campaign analysis of the campaign; adjust the campaign when the real-time performance report includes a determination for adjustment; and direct the user to view the instructional content and to provide another learning feedback when the real-time performance report includes a determination that adjusting the campaign requires an understanding of the user.”. These claims describe a process of assisting a user to generate and manage a marketing/advertisement campaign. Dependent claims 2-9, and 11-19 further describe the generation (e.g. digital asset), management (e.g. performance report, analytics, segment, financial accounting, adjusting budget), and assisting the user (e.g. instructional content) of the marketing/advertisement campaign. Assisting a user with a campaign through educational or instructional content reflect acts of managing personal behavior via teaching. Managing campaigns and advertisements reflect marketing or sales activities or behaviors. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract grouping of Certain Methods of Organizing Human Activity which encompasses fundamental economic principles or practices, commercial or legal interactions, and managing personal behavior, relationships or interactions between people. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping) (MPEP 2106.04(a)(2)). Therefore, claims 1-20 are directed to an abstract idea and are not patent eligible.
Step 2A (2): The claims as a whole do not integrate this abstract idea into a practical application. In particular, claims 1-7 and 9-20 recite additional elements of “A system…, comprising: a processor; a memory in communication with the processor, the memory including a user interface module, an education module, a campaign module, and an analytics module; wherein: the user interface module is configured to; the campaign module is configured to; the analytics module configured to; and the campaign module is further configured to (claims 1 and 10); digital asset (claims 2, 3, 11, and 12); wherein the campaign module is further configured to/includes (claims 2, 11, 18, 19); wherein the memory further includes a database configured to store (claims 3 and 12); wherein the memory further includes an artificial intelligence (AI) module configured to (claims 4 and 13), wherein the analytics module is configured to (claims 4, 5, 7, 15, 16, 17); wherein: the memory further includes a reporting module configured (claims 6 and 14); video content (claim 9); and a non-transitory computer-readable medium storing processor instructions for digital marketing analysis and management for a user that, when executed by a processor, cause the processor to (claim 20) ”. The Examiner evaluated the claims in light of the Applicant’s specification and determined that the additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing components that are used to perform the abstract process identified in Step 2A(1). Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Therefore, claims 1-20 as a whole do not include individual or a combination of additional elements that integrate the abstract idea into a practical application and thus are not patent eligible.
Step 2B: The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Claims 1-7 and 9-20 recite additional elements of “A system…, comprising: a processor; a memory in communication with the processor, the memory including a user interface module, an education module, a campaign module, and an analytics module; wherein: the user interface module is configured to; the campaign module is configured to; the analytics module configured to; and the campaign module is further configured to (claims 1 and 10); digital asset (claims 2, 3, 11, and 12); wherein the campaign module is further configured to/includes (claims 2, 11, 18, 19); wherein the memory further includes a database configured to store (claims 3 and 12); wherein the memory further includes an artificial intelligence (AI) module configured to (claims 4 and 13), wherein the analytics module is configured to (claims 4, 5, 7, 15, 16, 17); wherein: the memory further includes a reporting module configured (claims 6 and 14); video content (claim 9); and a non-transitory computer-readable medium storing processor instructions for digital marketing analysis and management for a user that, when executed by a processor, cause the processor to (claim 20)”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-20 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the abstract idea and thus are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-15, and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Miglani (US 2024/0370902 A1).
As per claim 1, Miglani teaches a system for digital marketing analysis and management for a user, comprising: a processor; a memory in communication with the processor, the memory including a user interface module, an education module, a campaign module, and an analytics module (Miglani e.g. The present invention relates to a personalized marketing and communication system for healthcare practices that incorporates artificial intelligence (AI) and machine learning (ML) technologies to optimize marketing campaigns, automate lead scoring and nurturing, perform predictive analytics on patient behavior and trends (Abstract, [0007], and Fig. 1). FIG. 9 illustrates an exemplary computing system 900 that may be used to implement an embodiment of the present invention [0072].); wherein:
Miglani teaches the user interface module is configured to: receive a campaign parameter and a learning feedback from the user; (Miglani e.g. Base Module 108 serves as the central hub for the Healthcare Marketing Automation System 106, coordinating various modules and functionalities to deliver a seamless user experience. The Base Module 108 receives user input from practitioners via the User Module 110, allowing them to manage patient data and customize marketing campaigns (Fig. 1 and [0021]). The User Module 110 allows healthcare professionals to manage marketing campaigns via the Marketing Automation Module 116 [0024]. For healthcare practitioners, the Chatbot Module 128 enables efficient management of patient data, streamlining the process of creating and customizing marketing campaigns, and generating content by offering AI-assisted suggestions. The chatbot can also analyze marketing campaign performance metrics by integrating with the Analytics Module 122, allowing practitioners to optimize their strategies accordingly [0035]. The chatbot could also provide real-time feedback and suggestions based on user preferences and past engagement data from the Lead Scoring Module 132, further enhancing the content customization experience for the user [0056]. The Chatbot Module 128 may assist the user during the content generation process by providing real-time feedback on their selections, answering questions, or offering suggestions based on the Correlation Module 130's analysis of previous successful content. This collaboration ensures that the content generated is not only tailored to the user's preferences but also optimized for the target audience's engagement and satisfactions [0057].)
Miglani teaches the education module is configured to: provide an instructional content to the user based on campaign parameter, and adjust the instructional content based on the learning feedback; (Miglani e.g. The Content Generation Module 120 is a component of the Healthcare Marketing Automation System 106, designed to facilitate the automated creation of customizable marketing and messaging content through the use of generative AI and machine learning tools [0029]. This module allows healthcare practitioners to develop a diverse range of content types, which may include, for example, images, voice memos, videos, surveys, articles, scripts, templates, clinical studies, presentations, and documents [0029]. These content types can be used individually or in any combination to create engaging and informative materials that resonate with patients and prospective patients [0029]. The Content Generation Module 120 also provides AI-generated Content Suggestions, offering ideas and inspiration for content creation based on the user's practice area, patient demographics, and other relevant factors. This feature ensures that the generated content is both relevant and appealing to the target audience [0029]. For example, a healthcare practitioner may choose a specific video style, background music, and on-screen text for a dental hygiene video aimed at children. By incorporating data from the Correlation Module 130, the Content Generation Module 120 might recommend video styles that have a higher engagement rate among similar target audiences [0056]. The Chatbot Module 128 may assist the user during the content generation process by providing real-time feedback on their selections, answering questions, or offering suggestions based on the Correlation Module 130's analysis of previous successful content. This collaboration ensures that the content generated is not only tailored to the user's preferences but also optimized for the target audience's engagement and satisfactions [0057]. In the case of a dental clinic, the Content Generation Module 120 may finalize an educational video about dental hygiene for a specific patient group. The module saves the video and sends it to the Messaging Module 118 for immediate distribution or schedules it for later delivery based on the clinic's marketing strategy [0059].)
Miglani teaches the campaign module is configured to: receive the campaign parameter from the user interface module, and generate a campaign for the user based on the campaign parameter; (Miglani e.g. The Base Module 108 connects with the Marketing Automation Module 116 to create automated, customizable marketing campaigns tailored to the patients' healthcare journey stages, utilizing AI algorithms for personalization (Fig. 1 and [0022]. The User Module 110 allows healthcare professionals to manage marketing campaigns via the Marketing Automation Module 116 [0024].).
Miglani teaches the analytics module configured to: analyze the campaign based on an advertisement metric to produce a campaign analysis, and generate a real-time performance report based on the campaign analysis of the campaign; and (Miglani e.g. The system tracks various performance metrics and optimize marketing channels and identifies correlations, patterns, and trends within the patient data using AI and machine learning techniques (Abstract and [0007]). The Analytics Module 122 is also executed by the Base Module 108 to analyze engagement and other metrics associated with campaigns and messages using AI techniques, providing valuable insights for healthcare professionals to optimize their marketing strategies [0022]. The Analytics Module 122 employs artificial intelligence or machine learning algorithms to generate reports and visualizations, presenting the analyzed data and insights to healthcare practitioners in a comprehensive and easily digestible format. In one example, the Analytics Module 122 generates a dashboard displaying the response rate, engagement, and conversion metrics for a myopia doctor's email campaign [0063].)
Miglani teaches the campaign module is further configured to: receive the real-time performance report from the analytics module, adjust the campaign when the real-time performance report includes a determination for adjustment, and direct the user to view the instructional content and to provide another learning feedback when the real-time performance report includes a determination that adjusting the campaign requires an understanding of the user (Miglani e.g. The Correlation Module 130 is a component of the Healthcare Marketing Automation System 106, designed to employ AI and ML techniques to establish relationships between business outcomes, patient outcomes, engagement metrics, and various aspects of marketing campaigns automated by the Marketing Automation Module 116, messages created by the Messaging Module 118, content generated by the Content Generation Module 120, analysis generated by the Analytics Module 122, and/or other patient and user data [0038]. Time series analysis can be utilized to understand and forecast the impact of marketing campaign components on patient outcomes and business performance over time, enabling practitioners to adjust their strategies proactively [0038]. AI algorithms can provide data-driven recommendations for campaign adjustments based on patient engagement patterns and other factors [0045]. The Chatbot Module 128 can also be leveraged to interact with users in real-time, gathering additional insights and feedback on the generated content, which can then be used to enhance future content creation [0053]. The chatbot could also provide real-time feedback and suggestions based on user preferences and past engagement data from the Lead Scoring Module 132, further enhancing the content customization experience for the user [0056]. The Chatbot Module 128 may assist the user during the content generation process by providing real-time feedback on their selections, answering questions, or offering suggestions based on the Correlation Module 130's analysis of previous successful content. This collaboration ensures that the content generated is not only tailored to the user's preferences but also optimized for the target audience's engagement and satisfactions [0057]. The Analytics Module 122 collects data concerning patient interactions, marketing campaigns, and messaging within the Healthcare Marketing Automation System 106 [0060]. For example, the module gathers information on message open rates, click-through rates, and patient responses for a myopia doctor's email campaign. For a dental clinic running a social media campaign, the Analytics Module 122 collects data related to user engagement, such as likes, shares, comments, and link clicks [0060]. The module also analyzes the effectiveness of the content and identifies trends to optimize future marketing efforts. The module collects information on content views, completion rates, and feedback from patients, helping the center evaluate the effectiveness of their communication and adjust their strategies accordingly [0060]. The Analytics Module 122 employs artificial intelligence or machine learning algorithms to generate reports and visualizations, presenting the analyzed data and insights to healthcare practitioners in a comprehensive and easily digestible format [0063].)
As per claim 2, Miglani teaches the system of Claim 1, Miglani also teaches wherein the campaign module is further configured to generate a digital asset for the user (Miglani e.g. The Marketing Automation Module 116 is a component of the Healthcare Marketing Automation System 106, designed to streamline and optimize the process of delivering digital media assets and communications to patients and prospective patients using AI algorithms (Fig. 1 and [0026])).
As per claim 3, Miglani teaches the system of Claim 2, Miglani also teaches wherein the memory further includes a database configured to store the campaign parameter, the learning feedback, the instructional content, the campaign, the real-time performance report, the another learning feedback, and the digital asset (Miglani e.g. Fig.1, Base Module 108 is responsible for storing and retrieving user data from the User Database 114, ensuring that healthcare professionals' preferences, settings, and other relevant information are readily accessible [0021]. The User Database 114 is a component of the Healthcare Marketing Automation System 106, responsible for storing and managing user data associated with healthcare practitioners. This data may include the practitioner's area of practice, location, phone number, whether they are accepting new patients, headshot or other photos, marketing campaign data, marketing content data, analytics data, and more [0025].).
As per claim 4, Miglani teaches the system of Claim 1, Miglani also teaches wherein the memory further includes an artificial intelligence (AI) module configured to: receive the real-time performance report from the analytics module, and generate an analytics forecast based on the real-time performance report (Miglani e.g. The system configured to utilize AI and ML algorithms for personalization of marketing campaigns, automated lead scoring and nurturing, and predictive analytics (Abstract). The system tracks various performance metrics and optimize marketing channels and identifies correlations, patterns, and trends within the patient data using AI and machine learning techniques (Abstract and [0007]). The generative AI techniques utilize machine learning models that takes in various inputs, such as patient demographics, medical history, history of engagement with digital media assets, interactions with chatbot, the content of digital media assets, practitioner data, etc. and process such inputs to parse elements in the digital media assets and patient data, and identify correlations, patterns, or trends in the data [0019]. The machine learning models may generate various outputs such as lead scores, personalized content for engagement, predictions in future engagements, or recommendations for content customization generation and delivery options [0019]. The Analytics Module 122 is also executed by the Base Module 108 to analyze engagement and other metrics associated with campaigns and messages using AI techniques, providing valuable insights for healthcare professionals to optimize their marketing strategies [0022]. The Chatbot Module 128 can also be leveraged to interact with users in real-time, gathering additional insights and feedback on the generated content, which can then be used to enhance future content creation [0053]. By connecting with the Chatbot Module 128, the module can also collect user preferences and feedback to refine content suggestions, providing a more tailored and engaging experience for both patients and healthcare practitioners [0054].).
As per claim 5, Miglani teaches the system of Claim 4, Miglani also teaches wherein the analytics module is further configured to segment the real-time performance report by a demographic group to produce a demographic report, and adjust the analytics forecast based on the demographic report. (Miglani e.g. The generative AI techniques utilize machine learning models that takes in various inputs, such as patient demographics, medical history, history of engagement with digital media assets, interactions with chatbot, the content of digital media assets, practitioner data, etc. and process such inputs to parse elements in the digital media assets and patient data, and identify correlations, patterns, or trends in the data [0019]. The Content Generation Module 120 also provides AI-generated Content Suggestions, offering ideas and inspiration for content creation based on the user's practice area, patient demographics, and other relevant factors [0029]. The Correlation Module 130 might identify patterns between patient demographics and preferences for communication channels [0032]. FIG. 7 illustrates a correlation module 130, according to an embodiment. The process begins at step 700, the Correlation Module 130 collects data from various sources, including the Patient Database 124, Analytics Module 122, Chatbot Module 128, Lead Scoring Module 132, and Predictive Analytics Module 134. For example, the module may gather patient demographics, clinical data, engagement metrics, and chatbot interaction data. In one example, the Correlation Module 130 uses clustering algorithms to segment patients based on similar characteristics, such as age, diagnosis, and communication preferences [0066].)
As per claim 6, Miglani teaches the system of Claim 4, Miglani also teaches wherein the memory further includes a reporting module configured to present the real-time performance report and the analytics forecast to the user via the user interface module (Miglani e.g. The User Module 110 allows users to access and view analytics generated by the Analytics Module 122, offering insights for data-driven decision-making based on algorithmic analysis [0024]. FIG. 6 illustrates an analytics module 122, according to an embodiment. The Analytics Module 122 collects data concerning patient interactions, marketing campaigns, and messaging within the Healthcare Marketing Automation System 106 [0060]. For example, the module gathers information on message open rates, click-through rates, and patient responses for a myopia doctor's email campaign. For a dental clinic running a social media campaign, the Analytics Module 122 collects data related to user engagement, such as likes, shares, comments, and link clicks. The module also analyzes the effectiveness of the content and identifies trends to optimize future marketing efforts [0060]. At step 606, the Analytics Module 122 employs artificial intelligence or machine learning algorithms to generate reports and visualizations, presenting the analyzed data and insights to healthcare practitioners in a comprehensive and easily digestible format [0063]. In one example, the Analytics Module 122 generates a dashboard displaying the response rate, engagement, and conversion metrics for a myopia doctor's email campaign [0063]. By utilizing data from the Chatbot Module 128, the Analytics Module 122 can also present insights into patient interactions and queries, helping the doctor identify common concerns or questions that can be addressed in future email campaigns [0063]. In another example, the Analytics Module 122 creates a report for a dermatology clinic's social media marketing campaign, presenting metrics such as reach, engagement, and conversion rates [0063].)
As per claim 8, Miglani teaches the system of Claim 1, Miglani also teaches wherein adjusting the campaign may include a member selected from a group consisting of a budget allocation, a target demographic, an increase in advertisement quantity, an increase in advertisement duration, and combinations thereof (Miglani e.g. FIG. 3 illustrates a user module 110, according to an embodiment [0047]. At step 308, the User Module 110 uses AI algorithms to provide more insightful and relevant user feedback, such as the results of a command, error messages, or data-driven recommendations for campaign improvements [0047]. For example, Dr. Smith can access the Predictive Analytics Module 134 to forecast the effectiveness of a flu vaccination campaign and make data-driven decisions on campaign adjustments and resource allocation. Dr. Smith can use the User Module 110 to switch between the different AI-driven modules, applying insights and recommendations to enhance marketing campaigns and overall patient care [0047]. The Marketing Automation Module 116 adjusts the marketing campaign or communication based on the analyzed data to optimize its effectiveness [0050]. In one example, the Analytics Module 122 identifies that a myopia doctor may improve patient engagement by adjusting the frequency or timing of their email campaign. In another example, the Analytics Module 122 pinpoints opportunities for a dental clinic to optimize their social media marketing campaign, such as adjusting post frequency or experimenting with different content formats [0064]. A recommendation may be generated regarding adjusting content of the templates based on the identified elements. The recommendation may be generated by the Content Generation Module 120 and provided to Practitioner Device 102 via User Module 110 or Display Module 112. The recommendation may further include a type of communication channel or a frequency of delivery of digital media asset that is associated with a high engagement with the group of users [0083].).
As per claim 9, Miglani teaches the system of Claim 1, Miglani also teaches wherein the instructional content includes a video content to assess an understanding of the user (Miglani e.g. Marketing Content Data may include data related to the marketing content created or used by the practitioner, such as articles, images, videos, and templates. For example, an article written by Dr. Smith on the importance of vaccinations and a series of educational videos she uses in her campaigns may be stored in User Database 114 [0025]. For example, a healthcare practitioner may choose a specific video style, background music, and on-screen text for a dental hygiene video aimed at children. By incorporating data from the Correlation Module 130, the Content Generation Module 120 might recommend video styles that have a higher engagement rate among similar target audiences [0056]. For instance, the healthcare practitioner could interact with the chatbot to quickly adjust content elements such as video style or background music, without having to navigate through complex menus or interfaces. The chatbot could also provide real-time feedback and suggestions based on user preferences and past engagement data from the Lead Scoring Module 132, further enhancing the content customization experience for the user [0056]. In the case of a dental clinic, the Content Generation Module 120 may finalize an educational video about dental hygiene for a specific patient group. The module saves the video and sends it to the Messaging Module 118 for immediate distribution or schedules it for later delivery based on the clinic's marketing strategy [0059].).
As per claim 10, Miglani teaches a method for digital marketing analysis and management for a user, comprising: providing a processor, a memory in communication with the processor, the memory including a user interface module, an education module, a campaign module, and an analytics module, wherein (Miglani e.g. Embodiments of the present invention include systems and methods for personalized marketing and communication in relation to healthcare practices (Figs. 1, 9, 10, & 11 and [0007]). FIG. 9 illustrates an exemplary computing system 900 that may be used to implement an embodiment of the present invention [0072]. FIG. 10 is a flow chart illustrating an exemplary method for delivery of personalized user content [0080]. FIG. 11 is a flow chart illustrating an exemplary method for delivery of personalized user content [0086].): Miglani teaches the user interface module is configured to: receive a campaign parameter and a learning feedback from the user, the education module is configured to: provide an instructional content to the user based on campaign parameter, and adjust the instructional content based on the learning feedback, the campaign module is configured to: receive the campaign parameter from the user interface module, and generate a campaign for the user based on the campaign parameter; the analytics module configured to: analyze the campaign based on an advertisement metric to produce a campaign analysis, and generate a real-time performance report based on the campaign analysis of the campaign, and the campaign module is further configured to: receive the real-time performance report from the analytics module, adjust the campaign when the real-time performance report includes a determination for adjustment, and direct the user to view the instructional content and to provide another learning feedback when the real-time performance report includes a determination that adjusting the campaign requires an understanding of the user; (See claim 1 response.)
Miglani teaches receiving the campaign parameter and the learning feedback from the user via the user interface module; providing an instructional content to the user based on campaign parameter via the education module; adjusting the instructional content based on the learning feedback via the education module; generating a campaign via the campaign module for the user based on the campaign parameter; analyzing the campaign via the analytics module based on an advertisement metric to produce a campaign analysis; generating a real-time performance report via the analytics module based on the campaign analysis of the campaign; adjusting the campaign via the campaign module when the real-time performance report includes a determination for adjustment; and directing the user to view the instructional content and to provide another learning feedback when the real-time performance report includes a determination that adjusting the campaign requires an understanding of the user (See claim 1 response.).
As per claim 11, Miglani teaches the method of Claim 10, wherein: the campaign module is further configured to generate a digital asset for the user; and the method further comprising: generating a digital asset for the user via the campaign module. (See claim 2 response.)
As per claim 12, Miglani teaches the method of Claim 11, wherein: the memory further includes a database configured to store the campaign parameter, the learning feedback, the instructional content, the campaign, the real-time performance report, the another learning feedback, and the digital asset; and the method further comprising: storing the campaign parameter, the learning feedback, the instructional content, the campaign, the real-time performance report, the another learning feedback, and the digital asset in the database. (See claim 3 response.)
As per claim 13, Miglani teaches the method of Claim 10, wherein: the memory further includes an artificial intelligence (AI) module configured to receive the real-time performance report from the analytics module, and generate an analytics forecast based on the real-time performance report; and the method further comprising: receiving the real-time performance report via the AI module from the analytics module; and generating the analytics forecast via the AI module based on the real-time performance report. (See claim 4 response.)
As per claim 14, Miglani teaches the method of Claim 13, wherein: the memory further includes a reporting module configured to present the real-time performance report and the analytics forecast to the user via the user interface module; and the method further comprising: presenting the real-time performance report by the reporting module to the user via the user interface module; and presenting the analytics forecast by the reporting module to the user via the user interface module. (See claim 6 response.)
As per claim 15, Miglani teaches the method of Claim 13, wherein: the analytics module is further configured to segment the real-time performance report by a demographic group to produce a demographic report, and adjust the analytics forecast based on the demographic report; and the method further comprising: segmenting the real-time performance report via the analytics module by a demographic group to produce a demographic report; and adjusting the analytics forecast via the analytics module based on the demographic report. (See claim 5 response.)
As per claim 18, Miglani teaches the method of Claim 10, Miglani also teaches wherein the step of adjusting the campaign via the campaign module is automated based on a key performance indicator (Miglani e.g. The Analytics Module 122 is a component of the Healthcare Marketing Automation System 106, designed to measure various aspects of the system in terms of patient and business metrics through analysis using a set of AI algorithms which may, for example, correlate content, messages, and other healthcare journey touchpoints with specific business outcomes, patient outcomes, and other metrics [0030]. This module provides valuable insights by evaluating key performance indicators, which may include, for example, engagement (clickthrough, view count, etc.), response rate (replies), conversion (scheduled appointments), retention (predicted vs. actual milestone on healthcare journey), payments, referrals, content types, and campaigns [0030]. By analyzing these metrics, the Analytics Module 122 helps healthcare practitioners identify trends, measure the effectiveness of their marketing efforts, and optimize their strategies accordingly using AI-generated recommendations [0030]. The Marketing Automation Module 116 adjusts the marketing campaign or communication based on the analyzed data to optimize its effectiveness [0050].)
As per claim 19, Miglani teaches the method of Claim 10, wherein: the campaign module includes a team feature configured to allow a plurality of users to collaborate on a campaign; and the method further comprising: allowing a plurality of users to collaborate on a campaign via the campaign module (Miglani e.g. The User Module 110 is a component of the Healthcare Marketing Automation System 106. It provides healthcare professionals with an interface for accessing and managing system functionalities that incorporate artificial intelligence algorithms. The User Module 110 allows healthcare professionals to manage marketing campaigns via the Marketing Automation Module 116 [0024]. The Analytics Module 122 stores the analyzed data and generated insights within the Healthcare Marketing Automation System 106 for future reference and use, allowing healthcare practitioners to review and compare the performance of future campaigns [0065].)
As per claim 20, Miglani teaches a non-transitory computer-readable medium storing processor instructions for digital marketing analysis and management for a user that, when executed by a processor, cause the processor to (Miglani e.g. Embodiments of the present invention include systems and methods for personalized marketing and communication in relation to healthcare practices (Figs. 1, 9, & 10 and [0007]). FIG. 9 illustrates an exemplary computing system 900 that may be used to implement an embodiment of the present invention [0072]. FIG. 10 is a flow chart illustrating an exemplary method for delivery of personalized user content [0080].):
Miglani teaches receive a campaign parameter and a learning feedback from the user; provide an instructional content to the user based on campaign parameter; adjust the instructional content based on the learning feedback; generate a campaign for the user based on the campaign parameter; analyze the campaign based on an advertisement metric to produce a campaign analysis; generate a real-time performance report based on the campaign analysis of the campaign; adjust the campaign when the real-time performance report includes a determination for adjustment; and direct the user to view the instructional content and to provide another learning feedback when the real-time performance report includes a determination that adjusting the campaign requires an understanding of the user. (See claim 1 response.)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Miglani (US 2024/0370902 A1) in view of Myers (US 2024/0346547 A1).
As per claim 7, Miglani teaches the system of Claim 1, Miglani does not explicitly teach, however, Myers teach wherein the analytics module is further configured to include in the real-time performance report a financial accounting of the user based on the campaign (Myers e.g. FIG. 1 is a system for advertising creative assessment [0097]. A performance module 118, which may use all of the linked data from the data linking module 116 as well as any unlinked but relevant data from the admin database 112 in order to assess several metrics relevant to advertising campaign performance such as ad revenue, ad spend/earning ratio, CPM, CTR, etc. This data is then stored in the performance database 120. A performance database 120 may store the results of the performance module so that the data can be displayed to the client [0098]. The client network 124 may also be able to receive data from the admin network 102, such as the evaluation results of a piece of creative or creatives, real-time analytics, promotional offers, etc. [0099]. FIG. 9 displays the functioning of the “Performance Module.” [0109]. The performance module 118 may calculate metrics from marketing data. These metrics may include key performance indicators as selected by the client provided intake information such as return on ad spend, cost to acquire, cost to retain, increase in traffic/visitors, increase in conversions, cost per click, cost per lead, click volume, lead volume, the total cost of engagement (COE), COE to sales ratio, or COE to profit ratio, at step 912 [0110].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Miglani’s Marketing Automation System to include financial tracking and optimization as taught by Myers in order enable advertisers to make better decision with their budgets to maximize ROI (Myers e.g. [0028]).
As per claim 16, Miglani teaches the method of Claim 13, wherein: the analytics module is further configured to adjust a budget allocation based on the analytics forecast; and the method further comprising: adjusting a budget allocation based on the analytics forecast.
Miglani teaches the analytics module is further configured to adjust resource allocations (Miglani e.g. Predictive Analytics Data may consist of predictions and forecasts generated by the Predictive Analytics Module 134, such as patient behavior predictions, campaign performance forecasts, and healthcare outcome predictions. For example, Dr. Smith's forecasted effectiveness of her flu vaccination campaign, along with patient behavior predictions and healthcare outcomes, may be stored in the User Database 114 for data-driven decision-making and resource allocation [0025].)
Miglani does not explicitly teach adjusting a budget allocation.
However, Myers teaches the analytics module is further configured to adjust a budget allocation based on the analytics forecast; and the method further comprising: adjusting a budget allocation based on the analytics forecast (Myers e.g. As the inclusion of AI, machine learning, and metadata becomes more widespread, advertisers can make better decisions with their budgets to maximize ROI. In one embodiment, this invention can integrate and use this AI to connect metadata tags to the efficacy of media spending [0028]. With the right data, AI-powered micro-targeted customer identification tools can detect patterns at scale then predict what changes to campaigns will improve performance against a specific key performance indicator (KPI). For each micro-audience, AI tools can choose the right creative message/imagery, right channels, right time, right pricing/promos, right budget mix, and right execution format. All designed to increase the return on ad spend, reduce staffing resources, identify ineffective budget items and improve ROI [0077]. Performance optimization is one of the key use cases for AI in advertising. Machine learning algorithms analyze ad performance across specific platforms then provide recommendations on performance improvement. AI can automatically manage ad performance and spend optimization, making decisions entirely on its own about how best to achieve advertising KPIs and recommending a fully optimized budget [0079]. FIG. 1 is a system for advertising creative assessment [0097]. A client intake module 106, which may prompt a client for campaign information and which metrics are most relevant to the client's goals. For example, a client may be more interested in ad revenue, CTR, and lead generation than in brand awareness, repeat customers, and ad cost optimization. A data collection module 108 may collect data from various sources, including the client network 124, one or more third-party networks 128, or even from databases within the admin network 102 such as the performance database 120 [0097]. The data tagging module 114 may tag data related to the client's chosen advertising medium or mediums as medium data. Medium data may be any data that relates to the choice and mix of which medium or channels the creative will be placed and the allocation of the marketing budget for each channel [0106].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Miglani’s Marketing Automation System to include budget and financial tracking and optimization as taught by Myers in order enable advertisers to make better decision with their budgets to maximize ROI (Myers e.g. [0028]).
As per claim 17, Miglani teaches the method of Claim 10, Miglani in view of Myers teach wherein: the analytics module is further configured to include in the real-time performance report a financial accounting of the user based on the campaign; and the method further comprising: generating a real-time performance report that includes a financial accounting of the user based on the campaign. (See claim 7 response.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure include: FOR: Splaine, Steven (AU-2014262739-A1) "Methods And Apparatus To Determine Impressions Using Distributed Demographic Information" and NPL: A. Todorova and D. Antonova, "Smart Marketing Solutions: Applications with Artificial Intelligence to Increase the Effectiveness of Marketing Operations," 2023 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkiye, 2023, pp. 1-6.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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