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 .
Response to Amendment
This a Final Office Action in response to amendments filed on 10/5/2025. Claims 1-10 have been amended. Claim 13 is new. Claims 11 and 12 have been withdrawn. Examiner notes that since the claims were never restricted, claims 11 and 12 should not be labeled as withdrawn. As such, claims 11 and 12 will be interpreted as cancelled. Therefore, claims 1-10 and 13 are pending and addressed below.
Claim Objections
Claims 1 and 7 are objected to because of the following informalities:
Claim 1 recites, “access at least one one set of business rules”. It should recite, “access at least one set of business rules”.
Claims 1 and 7 recite “AI-generated content”. Acronyms must be defined in plain terminology within the claim before the acronym can be used. Per [0003] of the specification, “AI” stands for “Artificial Intelligence”. Therefore, “AI” will be interpreted as such.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6, 8, 9, and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites, “7) transmit the at least one set of user personalized content to at least one user content consumption device in accordance with the at least one distribution rule.” The limitation “the at least one distribution rule” is interpreted as one distribution rule. Step 6 of claim 1 recites, “6) access at least one set of distribution rules which is interpreted as at least two distribution rules. As such is it unclear to which “the at least one distribution rule” step 7 is referring. Therefore, claim 1 is indefinite. For purpose of examination, step 7 will be interpreted as “7) transmit the at least one set of user personalized content to at least one user content consumption device in accordance with the at least one set of distribution rules.” Examiner suggests amending as such.
Claim 2-6 and 13 are also rejected because of their dependencies on claim 1.
Claim 8 recites, “wherein the content distribution is determined in part”. There is insufficient antecedent basis for the underlined limitation. Therefore, claim 8 is indefinite. For purpose of examination, this limitation will be interpreted as “wherein content distribution is determined in part”. Examiner suggests amending as such.
Claim 9 recites, “wherein the user personalized content includes…” There is insufficient antecedent basis for the underlined limitation because claim 9 depends on claim 7 and claim 7 recites, “at least one set of user personalized content” and “the at least one set of user personalized content”. Since claim 7 recites “at least one set”, it is unclear to which “the user personalized content” claim 9 is referring. Therefore, Claim 9 is indefinite. For purpose of examination, this limitation will be interpreted as “wherein the at least one set of user personalized content includes…” Examiner suggest amending as such.
Claim 13 recites, “wherein the user personalized content is distributed…” There is insufficient antecedent basis for the underlined limitation because claim 13 depends on claim 1 and claim 1 recites, “at least one set of user personalized content” and “the at least one set of user personalized content”. Since claim 1 recites “at least one set”, it is unclear to which “the user personalized content” claim 13 is referring. Therefore, Claim 13 is indefinite. For purpose of examination, this limitation will be interpreted as “wherein the at least one set of user personalized content includes…” Examiner suggest amending as such.
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 1-10 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Under step 1, claim 1 is directed to a system and claim 7 is directed to a method. Thus, claims 1 and 7 are directed to statutory categories of patentable subject matter.
Step 2A, Prong 1: Independent claim 1 recites, “A system for artificial intelligence powered creation of personalized contextual commercial content, the system comprising: at least one processor communicatively coupled to at least one database, the at least one processor configured to utilize a context engine, a business rules and goals engine, and a content merge engine, each implemented as software instructions stored on a non-transitory medium and executed by the at least one processor, to: 1) analyze at least one set of context data related to at least one content user; 2) access at least one one set of business rules or goals related to at least one content set to be created; 3) access at least one set of user contextual data; 4) apply at least one set of business rules or goals and at least one set of user contextual data using artificial intelligence to create at least one set of user personalized content; 5) utilize the content merge engine to combine real content and AI-generated content in accordance with said business rules or goals and said user contextual data to generate the at least one set of user personalized content; 6) access at least one set of distribution rules related to the at least one set of user personalized content; 7) distribute the at least one set of user personalized content toward at least one content consumption device of the at least one content user in compliance with the at least one distribution rule; and 8) utilize real-time or near real-time feedback from consumption or use data including one or more of viewer behavior, reaction, performance metrics. device sensors. or physiological inputs such as eye tracking. heart rate. or neural feedback to dynamically adapt via a machine learning engine at least one of the business rules, contextual data, or content creation logic for subsequent generation of user personalized content; 9) wherein the at least one set of user personalized content is not limited to advertising and includes at least one of entertainment, education, training, security analysis, military strategy, or medical data content.” Independent claim 7 recites, “A method for artificial intelligence powered-creation of personalized contextual commercial content, the method comprising: utilizing at least one processor communicatively coupled to at least one database, the at least one processor executing software instructions stored on a non-transitory medium to utilize a context engine. a business rules and goals engine. and a content merge engine to: 1) analyze at least one set of user context data; 2) access at least one rule or goal set stored in at least one database related to content to be generated; 3) access at least one set of user contextual data; 4) apply artificial intelligence to create at least one set of user personalized content by combining said rule or goal set and said contextual data; 5) utilize a content merge engine to combine real content and Al-generated content to form the at least one set of user personalized content; 6) access at least one content distribution rule; 7) transmit the at least one set of user personalized content to at least one user content consumption device in accordance with the at least one distribution rule; 8) adapt future content creation by incorporating feedback from use data including user behavior, reaction, and performance metrics or physiological inputs such as eye tracking, heart rate, or neural feedback: 9) wherein the at least one set of user personalized content is not limited to advertising, and includes at least one of entertainment, education, training, security analysis, military strategy, or medical data content.” These limitations, except for the italicized portions, under their broadest reasonable interpretations, recite certain methods of organizing human activity. The claimed invention analyzes data, accesses data, applies business rules or goals, combines real content and AI generated content, distributes personalized content (i.e. advertisement), and utilizes real time feedback, which are advertising activities and behaviors since personalized content are advertisements. The Examiner notes that although the claim limitations are summarized, the analysis regarding subject matter eligibility considers the entirety of the claim and all of the claim elements individually, as a whole, and in ordered combination.
Step 2A, Prong 2: This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of “at least one processor”, “at least one database”, and “at least one user content consumption device”, These additional elements are generic computing elements performing generic computer functions such that it amounts to no more than mere instructions to apply the exception using a computer. The additional element of “artificial intelligence” is recited at a high level of generality. The additional elements of “a context engine”, “a business rules and goals engine”, and “a content merge engine” are generic engines that are software implemented per the claims. The additional elements of “7) distribute the at least one set of user personalized content toward at least one content consumption device of the at least one content user in compliance with the at least one distribution rule” of claim 1 and “7) transmit the at least one set of user personalized content to at least one user content consumption device in accordance with the at least one distribution rule” are insignificant extra solution activity. Accordingly, these additional elements when considered individually or as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The independent claims are directed to an abstract idea.
Step 2B: 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 elements of the additional elements of “at least one processor”, “at least one database”, and “at least one user content consumption device” are generic computing elements as supported by at least paragraphs 4, 40, and 8. The additional element of “artificial intelligence” is described in par. 4 of the specification as well-known in the art. The additional elements of “a context engine”, “a business rules and goals engine”, and “a content merge engine” are generic engines that are software implemented per the claims. As such these additional elements are generic computing elements performing generic computer functions and are not significantly more than the abstract idea. The additional elements of “7) distribute the at least one set of user personalized content toward at least one content consumption device of the at least one content user in compliance with the at least one distribution rule” of claim 1 and “7) transmit the at least one set of user personalized content to at least one user content consumption device in accordance with the at least one distribution rule” are is not significantly more than the abstract idea because per MPEP 2106.05(d) the courts have found receiving or transmitting data over a network and storing and retrieving information is not significantly more than the abstract idea. Therefore, the independent claims are not patent eligible.
Dependent claims 2-6, 8-10, and 13, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. §101 because the additional recited limitations fail to establish that the claims are not directed to the same abstract idea of Independent Claims 1 and 7 without significantly more.
As such, when claims 1-10 and 13 are considered individually, as a whole, or in combinations, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-10 and 13 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by McDevitt (US 2021/0185277).
Regarding claim 1, McDevitt teaches
A system for artificial intelligence powered creation of personalized contextual commercial content, the system comprising ([0023] "The system and method disclosed are in no way meant to be limited to any specific combination of hardware and software. As will be described below, the system and method disclosed herein relate to the creation of a CC set [personalized contextual commercial content] that is composed of multiple Content sets. (see also [0011].):
at least one processor communicatively coupled to at least one database, the at least one processor configured to ([0014] "at least one processor communicatively coupled to the at least one electronic database, the at least one processor configured to:"),
utilize a context engine, a business rules and goals engine, and a content merge engine, each implemented as software instructions stored on a non-transitory medium and executed by the at least one processor, to ([0008] "utilize in any given embodiment of the Combined Content (CC). Furthermore, these rules may act as logical engines that may organize, prioritize, include, exclude, change the likelihood, etc. of a given individual Content item (sub set of a Content item, or multiple Content items) to be used in the CC." [0057] "In various aspects, the systems and methods described herein may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the methods may be stored as one or more instructions or code on a non-transitory computer-readable medium."):
1) analyze at least one set of context data related to at least one content user ([0010] "This collected use data may be analyzed and interpreted by a user and or the larger system and provide data as the basis for a feedback loop that enables the system to dynamically learn and adjust the next generation of CC creation and distribution." [0043] "This usage data may be analyzed through a variety of human or ML/AI means to find correlations (causative or not) between various sets of CC, CC consumption behavior, and goals.");
2) access at least one one set of business rules or goals related to at least one content set to be created ([0031] "Furthermore, for example, the CCR may also include business rules dealing with how, when, where, and to whom, advertising and/or marketing materials may be added to the CC.");
3) access at least one set of user contextual data ([0010] "This collected use data may be analyzed and interpreted by a user and or the larger system and provide data as the basis for a feedback loop that enables the system to dynamically learn and adjust the next generation of CC creation and distribution." See also [0043].);
4) apply at least one set of business rules or goals and at least one set of user contextual data using artificial intelligence to create at least one set of user personalized content ([0014] "2) Access information stored in at least one database that contains a collection of content combination rules related to the combination of the at least more than one content sets. 3) Use, apply, and enforce the content combination rules and the at least more than one content sets to combine at least a portion of the content sets into at least one combined content set."([0010] "This collected use data[set of user contextual data] may be analyzed and interpreted by a user and or the larger system and provide data as the basis for a feedback loop that enables the system to dynamically learn and adjust the next generation of CC creation and distribution." See also [0043].);
5) utilize the content merge engine to combine real content and AI-generated content in accordance with said business rules or goals and said user contextual data to generate the at least one set of user personalized content ([0011] "The feedback loop may use various sets of information and machine learning/artificial intelligence (ML/ AI) analysis to improve the user experience by creating improved CC. The disclosed system may use ML/AI systems using traditional or quantum computing methodologies to aid in combining and coordinating the individual Content sets [real content], changing the rules, and even creating new computer generated Content [AI generated content] to better merge or fill gaps in the existing original individual Content sets such that the CC is optimized in accordance with the rules engine.");
6) access at least one set of distribution rules related to the at least one set of user personalized content ([0014] "5) Access information stored in at least one database that contains a collection of combined content distribution rules related to the distribution of the at least one combined content sets.");
7) distribute the at least one set of user personalized content toward at least one content consumption device of the at least one content user in compliance with the at least one distribution rule ([0014] "6) at least one server configured to transmit the at least one combined content set towards a display device in accordance with the combined content distribution rules."); and
8) utilize real-time or near real-time feedback from consumption or use data including one or more of viewer behavior, reaction, performance metrics. device sensors. or physiological inputs such as eye tracking. heart rate. or neural feedback to dynamically adapt via a machine learning engine at least one of the business rules. contextual data. or content creation logic for subsequent generation of user personalized content ([0011] "In some cases, the disclosed system provides for continuously [in real-time] or periodically changing and updating the CC such that over time the CC is different than the CC that is initially created ( or alternatively there may be multiple related, but unique CC sets created in parallel ( or near parallel)). These changes may be based on one or more of any relevant data such as additional individual Content sets, consumption rates, viewer reviews/feedback/"likes", viewer preferences, viewers paying for or subscription to content, sales performance (in commerce environments), resulting subsequent behavior, and any other consumption related results (both from the individual viewer and/or from a plurality of users-including up to the full population of CC consumers)...The feedback loop may use various sets of information and machine learning/artificial intelligence (ML/ AI) analysis to improve the user experience by creating improved CC. The disclosed system may use ML/AI systems using traditional or quantum computing methodologies to aid in combining and coordinating the individual Content sets, changing the rules, and even creating new computer generated Content to better merge or fill gaps in the existing original individual Content sets such that the CC is optimized in accordance with the rules engine. Furthermore, these ML/AI based approaches may be used specifically for improved interactive game play and/or VR/AR/MR experiences. Additionally, this system can be applied to recorded, live ( or near live) Content capture situations (e.g., as an event is occurring) and be applied to open-ended and non-predetermined storytelling (in which there are not pre-defined plots or endings to CC sets, but rather they develop through use over time and can be applied to any type of Content, including Content that is created by the user ( or sets of users) ( e.g., in game play)). This improvement process may be utilized for future CC consumption or also even as the CC is initially being consumed and the "end" of the CC that has not yet been consumed ( or created) and may be altered based on this dynamic learning methodology (and/or feedback loop) to improve the remaining CC to be consumed. Furthermore, this information may be directed to those individuals or systems that are capturing or creating Content such that they may adapt their capture or creation to the feedback information (a rapid and responsive feedback system)." See also [0008] and [0010].);
9) wherein the at least one set of user personalized content is not limited to advertising and includes at least one of entertainment, education, training, security analysis, military strategy, or medical data content ([0013] "Furthermore, it should be recognized that the resulting CC may be a collection of a wide variety of different Content, including but not limited to, entertainment, education, information, commerce, gamming, security analysis, police investigations, military strategy, emergency response, crowd analysis, medical imaging, remote surgery, medical data, health data, machine data, industrial data, and the like.").
Regarding claim 7, McDevitt teaches
7. (Amended) A method for artificial intelligence powered-creation of personalized contextual commercial content, the method comprising ([0023] "The system and method disclosed are in no way meant to be limited to any specific combination of hardware and software. As will be described below, the system and method disclosed herein relate to the creation of a CC set [personalized contextual commercial content] that is composed of multiple Content sets. (see also [0011].):
utilizing at least one processor communicatively coupled to at least one database, the at least one processor executing software instructions stored on a non-transitory medium ([0014] "at least one processor communicatively coupled to the at least one electronic database, the at least one processor configured to:" [0057] "In various aspects, the systems and methods described herein may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the methods may be stored as one or more instructions or code on a non-transitory computer-readable medium.")
to utilize a context engine. a business rules and goals engine. and a content merge engine to ([0008] "utilize in any given embodiment of the Combined Content (CC). Furthermore, these rules may act as logical engines that may organize, prioritize, include, exclude, change the likelihood, etc. of a given individual Content item (sub set of a Content item, or multiple Content items) to be used in the CC."):
1) analyze at least one set of user context data ([0010] "This collected use data may be analyzed and interpreted by a user and or the larger system and provide data as the basis for a feedback loop that enables the system to dynamically learn and adjust the next generation of CC creation and distribution." [0043] "This usage data may be analyzed through a variety of human or ML/AI means to find correlations (causative or not) between various sets of CC, CC consumption behavior, and goals.");
2) access at least one rule or goal set stored in at least one database related to content to be generated ([0031] "Furthermore, for example, the CCR may also include business rules dealing with how, when, where, and to whom, advertising and/or marketing materials may be added to the CC.");
3) access at least one set of user contextual data ([0010] "This collected use data may be analyzed and interpreted by a user and or the larger system and provide data as the basis for a feedback loop that enables the system to dynamically learn and adjust the next generation of CC creation and distribution." See also [0043].);
4) apply artificial intelligence to create at least one set of user personalized content by combining said rule or goal set and said contextual data ([0014] "2) Access information stored in at least one database that contains a collection of content combination rules related to the combination of the at least more than one content sets. 3) Use, apply, and enforce the content combination rules and the at least more than one content sets to combine at least a portion of the content sets into at least one combined content set."([0010] "This collected use data[set of user contextual data] may be analyzed and interpreted by a user and or the larger system and provide data as the basis for a feedback loop that enables the system to dynamically learn and adjust the next generation of CC creation and distribution." See also [0043].);
5) utilize a content merge engine to combine real content and Al-generated content to form the at least one set of user personalized content ([0011] "The feedback loop may use various sets of information and machine learning/artificial intelligence (ML/AI) analysis to improve the user experience by creating improved CC. The disclosed system may use ML/AI systems using traditional or quantum computing methodologies to aid in combining and coordinating the individual Content sets [real content], changing the rules, and even creating new computer generated Content [AI generated content] to better merge or fill gaps in the existing original individual Content sets such that the CC is optimized in accordance with the rules engine." See also [0040].);
6) access at least one content distribution rule ([0014] "5) Access information stored in at least one database that contains a collection of combined content distribution rules related to the distribution of the at least one combined content sets.");
7) transmit the at least one set of user personalized content to at least one user content consumption device in accordance with the at least one distribution rule ([0014] "6) at least one server configured to transmit the at least one combined content set towards a display device in accordance with the combined content distribution rules.");
8) adapt future content creation by incorporating feedback from use data including user behavior, reaction, and performance metrics or physiological inputs such as eye tracking, heart rate, or neural feedback ([0011] "In some cases, the disclosed system provides for continuously [in real-time] or periodically changing and updating the CC such that over time the CC is different than the CC that is initially created ( or alternatively there may be multiple related, but unique CC sets created in parallel ( or near parallel)). These changes may be based on one or more of any relevant data such as additional individual Content sets, consumption rates, viewer reviews/feedback/"likes" [user behavior and reaction], viewer preferences, viewers paying for or subscription to content, sales performance (in commerce environments), resulting subsequent behavior, and any other consumption related results [performance metrics] (both from the individual viewer and/or from a plurality of users-including up to the full population of CC consumers)...The feedback loop may use various sets of information and machine learning/artificial intelligence (ML/ AI) analysis to improve the user experience by creating improved CC. The disclosed system may use ML/AI systems using traditional or quantum computing methodologies to aid in combining and coordinating the individual Content sets, changing the rules, and even creating new computer generated Content to better merge or fill gaps in the existing original individual Content sets such that the CC is optimized in accordance with the rules engine. Furthermore, these ML/AI based approaches may be used specifically for improved interactive game play and/or VR/AR/MR experiences. Additionally, this system can be applied to recorded, live ( or near live) Content capture situations (e.g., as an event is occurring) and be applied to open-ended and non-predetermined storytelling (in which there are not pre-defined plots or endings to CC sets, but rather they develop through use over time and can be applied to any type of Content, including Content that is created by the user ( or sets of users) ( e.g., in game play)). This improvement process may be utilized for future CC consumption or also even as the CC is initially being consumed and the "end" of the CC that has not yet been consumed ( or created) and may be altered based on this dynamic learning methodology (and/or feedback loop) to
improve the remaining CC to be consumed. Furthermore, this information may be directed to those individuals or systems that are capturing or creating Content such that they may adapt their capture or creation to the feedback information (a rapid and responsive feedback system)." See also [0008] and [0010]. Examiner notes that in the limitation "and performance metrics or physiological inputs" the claim requires performance metrics or physiological inputs but not both. McDevitt teacher performance metrics in the above paragraphs.);
9) wherein the at least one set of user personalized content is not limited to advertising, and includes at least one of entertainment, education, training, security analysis, military strategy, or medical data content ([0013] "Furthermore, it should be recognized that the resulting CC may be a collection of a wide variety of different Content, including but not limited to, entertainment, education, information, commerce, gamming, security analysis, police investigations, military strategy, emergency response, crowd analysis, medical imaging, remote surgery, medical data, health data, machine data, industrial data, and the like.").
Regarding claim 2, McDevitt teaches
The system of claim 1, wherein video is part of the at least one set of user personalized content ([0025] "In this invention, Content includes but is not limited to, audio (in any digital format, e.g., aa, flac, mp3, way, wma, etc.), images (in any digital format, e.g., JPEG, TIFF, GIF, BMP, PNG, SVG, pdf, etc.), video (in any digital format.").
Regarding claim 3, McDevitt teaches
The system of claim 1, wherein audio is part of the at least one set of user personalized content ([0025] "In this invention, Content includes but is not limited to, audio (in any digital format, e.g., aa, flac, mp3, way, wma, etc.), images (in any digital format, e.g., JPEG, TIFF, GIF, BMP, PNG, SVG, pdf, etc.), video (in any digital format.").
Regarding claim 4, McDevitt teaches
The system of claim 1, wherein images are part of the at least one set of user personalized content ([0025] "In this invention, Content includes but is not limited to, audio (in any digital format, e.g., aa, flac, mp3, way, wma, etc.), images (in any digital format, e.g., JPEG, TIFF, GIF, BMP, PNG, SVG, pdf, etc.), video (in any digital format.").
Regarding claim 5, McDevitt teaches
The system of claim 1, wherein metadata is part of the at least one set of user personalized content ([0025] "and descriptive metadata related to or that describes any of the types of digital content.").
Regarding claim 6, McDevitt teaches
The system of claim 1, wherein text is part of the at least one set of user personalized content ([0025] "In this invention, Content includes but is not limited to, audio (in any digital format, e.g., aa, flac, mp3, way, wma, etc.), images (in any digital format, e.g., JPEG, TIFF, GIF, BMP, PNG, SVG, pdf, etc.), video (in any digital format.").
Regarding claim 8, McDevitt teaches
The method of claim 7, wherein the content distribution is determined in part by real-time sensor data or environmental inputs such as location, time of day, device orientation, or biometric response ([0029] "Content inclusions, exclusions, placements, prioritization, weighting based on; content of the Content, Content type, Content capturer, Content source, title, subject matter, MPAA or other agency rating, intellectual property restrictions or requirements, rights, licenses, time of creation, language, duration, rating, geographic location." See also [0040].).
Regarding claim 9, McDevitt teaches
The method of claim 7, wherein the user personalized content includes video, audio, images, metadata, or text ([0025] "In this invention, Content includes but is not limited to, audio (in any digital format, e.g., aa, flac, mp3, way, wma, etc.), images (in any digital format, e.g., JPEG, TIFF, GIF, BMP, PNG, SVG, pdf, etc.), video (in any digital format.").
Regarding claim 10, McDevitt teaches
The method of claim 7, wherein adapting future content creation includes retraining at least one artificial intelligence model or modifying at least one content rule set ([0043] "Through analysis of this information playlists that have a higher likelihood of achieving the goals may be created and these insights may be fed back into the system to update the CC creation process dynamically or periodically. Additionally, any of these types of analyses may be applied to original Content that is fed into the OCL to improve the creation of CC. Over time as new or additional Content is added and CC is consumed the process continues to improve creating CC experiences in compliance with the rules.").
Regarding claim 13, McDevitt teaches
The system of claim 1. wherein the user personalized content is distributed in real-time or near real-time based on dynamic context and rule updates ([0042] "Once the CC is in the CCDS, CCDRs are applied to ensure the proper CC can be broadcast or transmitted toward (the transfer or stream initiated via; a push command, a pull command, or a combination of both) and delivered to the proper user(s) (and user(s) CC consumption devices) (430). The CCDR can include rules, amongst others related bandwidth and bitrate related to the CC, the format of the CC, the rights required for consuming the CC, etc. Additionally, the CC may be distributed in part or in whole, periodically or continuously, streamed, or downloaded or any combination of these." See also [0010] and [0043].).
Response to Argument
On page 7, 2nd paragraph, Applicant makes certain statements about the claims that no longer apply since the claims were amended on 10/5/2025.
With regards to the 101 rejection, on pages 9-10, Applicant argues that the claims recite patent-eligible subject matter and states, “The claimed invention is not "merely organizing human activity" or applying business logic to a generic computer system. Rather, it recites a detailed software architecture for content creation that includes: • A context engine that analyzes user-specific contextual signals (par [0024]), • A business rules and goals engine that interprets dynamic objectives for content relevance (Pars [0022]-[0023]), • A content merge engine that combines real and AI-generated content under rule-driven logic (Pars [0025] & [0028]), • A machine learning engine that adapts this architecture in real-time or near real-time using feedback from physiological and environmental signals (Pars [0008] & [0032]-[0033]), • Output content that is not limited to advertising but encompasses education, military strategy, security analysis, and medical content (Par [0009]). These architectural components are not abstract mental processes or routine data operations. They form a non-generic system architecture that addresses a technical problem: how to scalably generate meaningful personalized content in real-time or near real-time from heterogeneous content sources and physiological input, a capability not supported by static or pre-generated media workflows.” Examiner respectfully disagrees that the claims are eligible. All of the engines to which Applicant points are all software per the claims and the specification and are not architecture or physical device or physical computing elements. Even if they were, they would still be considered generic based on the claims and specification. The machine learning is well-known per [0004] of the specification. There is nothing in the claims or the specification that suggests that the architecture is “non-generic”. Therefore, Examiner is not persuaded.
On page 11, Applicant points to Example 43 and states, “This structure is closely aligned with USPTO Example 43, which found patent-eligible a streaming content platform including: • A profile engine, • A delivery engine, • A feedback engine. Example 43 was found not directed to an abstract idea because it claimed a specific content delivery architecture that improved streaming content systems through feedback loops. Here, the AIC system is even more technically advanced than the Example 43 system because: • It creates (not merely filters) content using hybrid AI logic; •It incorporates biometric/physiological inputs, not just viewing patterns; • It adapts creation logic itself (not just selection logic); • It is not limited to commercial use cases.” Examiner respectfully disagrees. Example 43 is about treating kidney disease which has nothing to do with the instant claims. Furthermore, the claims do not create content using hybrid AI logic, or incorporate biometric/physiological inputs since those recitations are recited in the alternative and not required by the claims. Claim 7 recites “adapt future content creation” without providing any kind of details on how the creation of the future content is being adapted. Applicant is making arguments that are outside the scope of the claims. As such, Examiner is not persuaded.
On p. 11-12, Applicant points to Example 42 and states, “Likewise, the system reflects key principles of Example 42 (telemedicine feedback system), which was eligible because it: • Collected physiological signals in real time, • Adjusted output recommendations dynamically, • Addressed a technical medical need. The described invention similarly uses real-time and near real-time physiological signals (e.g., heart rate, eye tracking, neural data) to dynamically alter machine learning-based content generation, thereby addressing a technical limitation in prior content systems.” Examiner respectfully disagrees. The claims do not require the user of “real-time or near real-time physiological signals” or the adjusting of “output recommendations dynamically”. Applicant is making arguments that are outside the scope of the claims. As such, Examiner is not persuaded.
On p. 12, Applicant states, “These are not abstract concepts or mental processes. Rather, they are tied to a technological improvement: a system capable of learning from real-time and near real-time biometric and behavioral feedback and adapting future AI model behavior, which is not performed by generic computing components.” The claims do not require “real time biometric or behavior feedback or “adapting future AI model behavior”. Applicant is making arguments that are outside the scope of the claims. As such, Examiner is not persuaded.
On p. 12, Applicant states, “The present invention solves a specific technical problem: the inability of existing content generation systems to efficiently and dynamically produce personalized commercial content that adapts in real-time or near real-time to user context and biometric state. Traditional systems are static and limited to rules-based targeting or filtering, offering no capacity to modify content creation logic or output based on live feedback. The claimed system provides a dynamic solution through AI-driven personalization based on contextual and physiological data, enabling real-time or near real-time adaptive content generation.” Examiner respectfully disagrees. Applicant is making arguments that are outside the scope of the claims. As such, Examiner is not persuaded.
On p. 13, with regards to Step 2A, Prong 2, Applicant makes similar arguments that have already been addressed above.
On pages 14-15, with regards to Step 2B, Applicant makes similar arguments that have already been addressed above. The claims do not require “the user of biometric and sensor inputs” or “ability to modify its AI models” There is nothing in the claims that recites or suggests a technical improvement. Therefore, Examiner is not persuaded.
On p. 15, Applicant discusses the feedback mechanism and points to various paragraphs of the specification. Even though claims recite some kind of feedback, none of the details of the paragraphs listed are in the claims. Therefore, Examiner is not persuaded.
On p. 16, Applicant points to 2019 PEG and McRO and states, “The system also improves the functioning of the underlying computer system, not just a business process. The system improves the functionality of AI content generation platforms themselves by introducing a feedback-driven model updating mechanism that alters the structure and output of future content streams, thereby enabling a non-predefined and real-time adaptive content production pipeline-a result not achievable by conventional computing systems performing known functions.” Examiner respectfully disagrees. It is unclear how the functioning of the underlying computer system is actually being improved. The claims do not recite any limitation about altering the structure and output of future content streams. Therefore, Examiner is not persuaded.
With regards to the 102 rejection, Applicant’s arguments have been considered but they are moot since the Spivack reference is no longer used to reject the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/MARIE P BRADY/Primary Examiner, Art Unit 3622