CTNF 18/860,315 CTNF 94153 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejection – 35 U.S.C. 102 07-07-aia AIA 07-07 2. 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 – 07-08-aia AIA (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. 07-12-aia AIA (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. 07-06 AIA 15-10-15 3. 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 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. 07-15 AIA Claim s 1 – 10, 18, 19 and 20 are rejected under 35 U.S.C. 102( a)(1)/(a)(2 ) as being anticipated by Saraee et al. (United States Patent Publication Number 2025/0078453 ), hereinafter Saraee . Regarding claim 1 Saraee teaches a computer-implemented method, (ABS., method) (Fig. 3 – 9, 11, 12, 14 - 18, 20, 22 – 27, 40 – 43 and 46 – 49 method [0007] – [0014], [0016], [0017], [0019] – [0023], [0025], [0027] – [0032], [0046] – [0049] and [0053] – [0056]) comprising: obtaining, (obtaining [1347], [1354], [1361], [1366], [13762], [1397], [1402], [1405] – [1407]) by an artificial intelligence (Al) system (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) and prior to receiving a query, (prior to receipt of the selection of a potential request. [0175]) input data; (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) generating, by the Al system (generating, by the one or more processors using the content scoring machine learning model [1353]) and based on the input data, (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) one or more candidate digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) using a machine learning model; (search engine machine learning model [0955]) SEE ALSO [1353] obtaining, (obtaining [1347], [1354], [1361], [1366], [13762], [1397], [1402], [1405] – [1407]) by the Al system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) user preference data (user preference data [0252]) limiting usage of at least one candidate digital component (In some implementations, the user preference data can include audience data. So, instead of mounting costs, wasted time and energy, high uncertainty with A/B testing, and overall sub-optimal content, the systems and methods described below can help content producers to move towards only having to do "A Testing" and being confident that the image, text, video, audio, or other creative assets they are publishing are maximizing the likelihood that their message will resonate with the target audience and drive the best possible business outcome or goal. [0252]) of the one or more candidate digital components; (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) receiving, by the Al system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) the query; (receive the query [0957]) identifying, (ABS., identifying) (identifying [0789]) by the Al system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) one or more digital component regulations associated with the query; identifying, (ABS., identifying) (identifying [0789]) by the Al system (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) based on the one or more digital component regulations (The aforementioned search and segmentation process can take place on a purely anonymous or personally identifiable basis, or any combination thereof, in accordance with accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media who possess the user data [0099]) and the user preference data, (user preference data [0252]) at least one particular candidate digital component (The user can select one of the set of images 3202 and select an insert image button 3206 to insert the selected image at the location of the image 2902, such as by replacing the image 2902 with the selected image. [1143]) of the one or more candidate digital components; (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) and generating, (generating [1156]) by the Al system (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) and based on (based on [1148]) the at least one particular candidate digital component (The user can select one of the set of images 3202 and select an insert image button 3206 to insert the selected image at the location of the image 2902, such as by replacing the image 2902 with the selected image. [1143]) of the one or more candidate digital components, (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207] an output digital component (output images [1029]) Claim 18 corresponds to claim 1 and is rejected accordingly Regarding claim 2 Saraee teaches the computer-implemented method of claim 1, Saraee further teaches wherein generating, (generating, by the one or more processors using the content scoring machine learning model [1353]) by the Al system (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) and based on the input data, (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) the one or more candidate digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) using the machine learning model (generative machine learning model [1109]) comprises: generating, (generating [1156]) by the Al system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) a prompt comprising (( e.g., a prompt) input ( e.g., a user input or an input from another computing device) [1113]) the input data; (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) inputting, (inputting [1148]) by the Al system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) the prompt (( e.g., a prompt) input ( e.g., a user input or an input from another computing device) [1113]) into the machine learning model; (generative machine learning model [1109]) and generating, (generating, by the one or more processors using the content scoring machine learning model [1353]) by the machine learning model, (generative machine learning model [1109]) the one or more candidate digital components. (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) Claim 19 corresponds to claim 2 and is rejected accordingly Regarding claim 3 Saraee teaches the computer-implemented method of claim 2, Saraee further teaches wherein generating, (generating, by the one or more processors using the content scoring machine learning model [1353]) by the Al system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) the prompt (( e.g., a prompt) input ( e.g., a user input or an input from another computing device) [1113]) comprising the input data (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) comprises: obtaining, by the Al system, additional input data (in accordance with accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) comprising data different from the input data, (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) wherein the additional input data limits (in accordance with accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing [0207]) generated by the machine learning model; and generating, (generating, by the one or more processors using the content scoring machine learning model [1353]) by the Al system, the prompt (( e.g., a prompt) input ( e.g., a user input or an input from another computing device) [1113]) comprising the input data (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) and the additional input data. (in accordance with accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) Claim 20 corresponds to claim 3 and is rejected accordingly Regarding claim 4 Saraee teaches the computer-implemented method of claim 1, Saraee further teaches wherein the user preference data (user preference data [0252]) indicates at least one of a serving time period, a geographical location, or an event (user preferences, which may include, for example, creative restrictions, brand guidelines, or marketing requirements [0254]) for using the at least one candidate digital component (The user can select one of the set of images 3202 and select an insert image button 3206 to insert the selected image at the location of the image 2902, such as by replacing the image 2902 with the selected image. [1143]) of the one or more candidate digital components. (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) Regarding claim 5 Saraee teaches the computer-implemented method of claim 1, Saraee further teaches comprising, before receiving the query: obtaining, (obtaining [1347], [1354], [1361], [1366], [13762], [1397], [1402], [1405] – [1407]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) one or more basic regulation review results (requests that violate the limits. [1038]) associated with the one or more candidate digital components, (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) wherein each basic regulation review result (requests that violate the limits. [1038]) indicates whether a corresponding candidate digital component violates (requests that violate the limits. [1038]) a basic digital component regulation (a maximum number of images that can be selected for any single request and/or a maximum number of generated images that can be selected. [1038]) Regarding claim 6 Saraee teaches the computer-implemented method of claim 5, Saraee further teaches wherein obtaining, (obtaining [1347], [1354], [1361], [1366], [13762], [1397], [1402], [1405] – [1407]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) the one or more basic regulation review results (requests that violate the limits. [1038]) associated with the one or more candidate digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) comprises: inputting (inputting [1148]) a candidate digital component (any one of (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) and one or more basic digital component regulations (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) into an additional machine learning model (a neural network ( or any other machine learning model) [0798]) to determine whether the candidate digital component violates (requests that violate the limits. [1038]) at least one of the one or more basic digital component regulations. (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099] Regarding claim 7 Saraee teaches the computer-implemented method of claim 5, Saraee further teaches comprising: determining, by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) that a basic regulation review result (requests that violate the limits. [1038]) indicates that a candidate digital component (For example, if the audience has been shown an ad of a red apple four times per day, the system 1000 may determine that the audience has been fatigued by red apples due to the high frequency with which red apples appeared in other content items displayed to the audience. [0261]) violates (requests that violate the limits. [1038]) a basic digital component regulation; (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) and in response to determining (determining [0415]) that the basic regulation review result (requests that violate the limits. [1038]) indicates that the candidate digital component (For example, if the audience has been shown an ad of a red apple four times per day, the system 1000 may determine that the audience has been fatigued by red apples due to the high frequency with which red apples appeared in other content items displayed to the audience. [0261]) violates (requests that violate the limits. [1038]) violates (requests that violate the limits. [1038]) the basic digital component regulation, (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) removing the candidate digital component from the one or more candidate digital components (As a result, the system 1000 may determine that the next transformation of this content item should de-emphasize or eliminate the prominent red attribute. Thus, the transformation may produce, for example, an image of a green apple rather than a red apple. Alternatively, the system 1000 may remove or replace the apple within the content item for this audience. [0261]) Regarding claim 8 Saraee teaches the computer-implemented method of claim 1, Saraee further teaches wherein identifying, (ABS., identifying) (identifying [0789]) by the AI system (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) based on the one or more digital component regulations (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) and the user preference data, (user preference data [0252]) the at least one particular candidate digital component (a particular content item [0257]) of the one or more candidate digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) comprises: determining whether a candidate digital component (a particular content item [0257]) complies (The user may also indicate whether a system-provided transformation is acceptable or preferable with his or her creative vision or business goal. [0307]) with the one or more digital component regulations (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) and the user preference data. (user preference data [0252]) Regarding claim 9 Saraee teaches the computer-implemented method of claim 8, Saraee further teaches wherein determining whether the candidate digital component (a particular content item [0257]) complies (The user may also indicate whether a system-provided transformation is acceptable or preferable with his or her creative vision or business goal. [0307]) with the one or more digital component regulations (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) and the user preference data (user preference data [0252]) comprises: inputting (inputting [1148]) the candidate digital component, (a particular content item [0257]) the one or more digital component regulations, (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) and the user preference data (user preference data [0252]) into an additional machine learning model (a neural network ( or any other machine learning model) [0798] to determine whether the candidate digital component (a particular content item [0257] complies (The user may also indicate whether a system-provided transformation is acceptable or preferable with his or her creative vision or business goal. [0307] with the one or more digital component regulations (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) and the user preference data. (user preference data [0252]) Regarding claim 10 Saraee teaches the computer-implemented method of claim 1, Saraee further teaches comprising: obtaining, (obtaining [1347], [1354], [1361], [1366], [13762], [1397], [1402], [1405] – [1407]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) performance data (performance data [0261], [0266], [0267], [0307]) indicating an acceptance level (ABS., performance scores for content items) (performance scores for content items [1353]) of each candidate digital component (a particular content item [0257]) of the one or more candidate digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) wherein identifying, (ABS., identifying) (identifying [0789]) by the Al system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) based on the one or more digital component regulations (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) and the user preference data, (user preference data [0252]) the at least one particular candidate digital component (a particular content item [0257]) of the one or more candidate digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) comprises: identifying, (ABS., identifying) (identifying [0789]) by the AI system (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) based on the one or more digital component regulations, (accepted privacy regulations and standards, privacy measures taken by individual users, and the policies of websites, social networking platforms, and other marketing media [0099]) the user preference data, (user preference data [0252]) and the performance data, (performance data [0261], [0266], [0267], [0307]) the at least one particular candidate digital component (a particular content item [0257]) of the one or more candidate digital components (digital video, digital video ad spending, mobile video, mobile video ad spending, video marketing, [0207]) Claim Rejections – 35 U.S.C. §103 07-20-aia AIA 4. 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. 07-23-aia AIA 5. The factual inquiries set forth in Graham v John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: a. Determining the scope and contents of the prior art b. Ascertaining the differences between the prior art and the claims at issue c. Resolving the level of ordinary skill in the pertinent art d. Considering objective evidence present in the application indicating obviousness or nonobviousness 07-21-aia AIA Claim s 11, 13 - 17 are rejected under 35 U.S.C. 103 as being unpatentable over Saraee et al. (United States Patent Publication Number 2025/0078453 ), hereinafter Saraee, in view of Quatro et al. (United States Patent Publication Number 20250045304 ), hereinafter referred to as Quatro . Regarding claim 11 Saraee teaches a computer-implemented method, (ABS., method) (Fig. 3 – 9, 11, 12, 14 - 18, 20, 22 – 27, 40 – 43 and 46 – 49 method [0007] – [0014], [0016], [0017], [0019] – [0023], [0025], [0027] – [0032], [0046] – [0049] and [0053] – [0056]) comprising: providing, by an artificial intelligence (AI) system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) Saraee does not fully disclose a plurality of levels of automation comprising an automatic mode; receiving, by the AI system, a first query indicating the automatic mode, the first query comprising a first objective; generating, by the AI system using a generative model and based on the first query, one or more first tasks for achieving the first objective; and automatically executing, by the AI system, the one or more first tasks. Quattro teaches a plurality of levels of automation (generative automation [0614], robotic process automation [0708], automation systems to execute predefined actions, such as scheduling maintenance, ordering replacement parts, or adjusting machine parameters to prevent failure. [0709], marketing automation platforms to execute the targeted campaigns, monitor their performance, and adjust strategies based on real-time feedback and analytics. [0710], industrial automation [0771]) comprising an automatic mode; (automatic execution on datasets [0371]) receiving, (At runtime, a worker will query the model storage with a package identifier for a version of the model and execute it. [0108]) by the AI system, (artificial intelligence models [0623]) a first query (the query [0674]) indicating the automatic mode, (automatic execution on datasets [0371]) the first query (the query [0674]) comprising a first objective; (Optimization techniques refers to algorithms used to find the best solution from a set of feasible solutions. [0620]) generating, by the AI system using a generative model (generative artificial intelligence [0700]) and based on the first query, (the query [0674]) one or more first tasks (optimizing one or more intelligence modules [0753]) for achieving the first objective; (Optimization techniques refers to algorithms used to find the best solution from a set of feasible solutions. [0620]) and automatically executing, (automatic execution on datasets [0371]) by the AI system, (artificial intelligence models [0623]) the one or more first tasks. (optimizing one or more intelligence modules [0753]) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Saraee to incorporate the teachings of Quattro whereby a plurality of levels of automation comprising an automatic mode; receiving, by the AI system, a first query indicating the automatic mode, the first query comprising a first objective; generating, by the AI system using a generative model and based on the first query, one or more first tasks for achieving the first objective; and automatically executing, by the AI system, the one or more first tasks. By doing so optimizing its actions to maximize cumulative rewards over time. Quattro [0618] Regarding claim 13 Saraee in view of Quattro teaches the computer-implemented method of claim 11, Saraee as modified further teaches wherein generating, by the AI system (generating, by the one or more processors using the content scoring machine learning model [1353]) using the generative model (a neural network ( or any other machine learning model) [0798]) : generating, by the AI system, a prompt (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) inputting, by the AI system, the prompt (inputs to initiate the training and/or finetuning of the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) into the generative model; (a neural network ( or any other machine learning model) [0798]) and generating, by the generative model, (a neural network ( or any other machine learning model) [0798]) Saraee does not fully disclose and based on the first query, the one or more first tasks for achieving the first objective comprises comprising the first query; the one or more first tasks. Quattro teaches and based on the first query, (the query [0674]) the one or more first tasks (optimizing one or more intelligence modules [0753]) for achieving the first objective (Optimization techniques refers to algorithms used to find the best solution from a set of feasible solutions. [0620]) comprises comprising the first query; (the query [0674] the one or more first tasks. (optimizing one or more intelligence modules [0753]) 53]) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Saraee to incorporate the teachings of Quattro wherein based on the first query, the one or more first tasks for achieving the first objective comprises comprising the first query; the one or more first tasks. By doing so the advantage is minimal resource (processors, memory, etc.) usage to enable fastest inclusion of feedback for future model executions. Quattro [0242] Regarding claim 14 Saraee in view of Quattro teaches the computer-implemented method of claim 11, Saraee as modified further teaches comprising: generating, by the AI system, a prioritized task list indicating a sequence of executing the one or more first tasks, ) wherein automatically executing, by the AI system, the one or more first tasks comprises: automatically executing, by the AI system, the one or more first tasks based on the sequence of executing the one or more first tasks. Quattro teaches comprising: generating, (generate [0614]) by the AI system, (generative artificial intelligence (Al) [0614]) a prioritized task list indicating a sequence of executing (sequence of operation [0032]) the one or more first tasks, (tasks such as classification, regression, feature extraction, language translation, text completion, summarization, question-answering, and more [0619]) wherein automatically executing, (automatically executed [0371]) by the AI system, (generative artificial intelligence (Al) [0614]) the one or more first tasks (tasks such as classification, regression, feature extraction, language translation, text completion, summarization, question-answering, and more [0619]) comprises: automatically executing, (automatically executed [0371]) by the AI system, (generative artificial intelligence (Al) [0614]) the one or more first tasks (tasks such as classification, regression, feature extraction, language translation, text completion, summarization, question-answering, and more [0619]) based on the sequence of executing (sequence of operation [0032]) the one or more first tasks. (tasks such as classification, regression, feature extraction, language translation, text completion, summarization, question-answering, and more [0619]) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Saraee to incorporate the teachings of Quattro wherein generating, by the AI system, a prioritized task list indicating a sequence of executing the one or more first tasks,) wherein automatically executing, by the AI system, the one or more first tasks comprises: automatically executing, by the AI system, the one or more first tasks based on the sequence of executing the one or more first tasks. By doing so automated decision making in real-time with a variety of information can be achieved. Quattro [0615] Regarding claim 15 Saraee in view of Quattro teaches the computer-implemented method of claim 11, Saraee as modified further teaches comprising: identifying, (ABS., identifying) (identifying [0789]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) performance data (performance data [0261], [0266], [0267], [0307]) associated with executing (executing [1049]) the one or more first tasks; (certain tasks and recommendations. [0201]) generating, (generating, by the one or more processors using the content scoring machine learning model [1353]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) training data (training data [0870]) based on the performance data; (performance data [0261], [0266], [0267], [0307]) and refining, (refine and manually adjust results [0102]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) the generative model (generative model [1030]) using the training data. (training data [0870]) Regarding claim 16 Saraee in view of Quattro teaches the computer-implemented method of claim 15, Saraee as modified further teaches wherein the performance data comprises at least one of clickthrough rate (CTR), (clicks [0428]) conversion rate (CVR), (conversions [0440]) or cost per day (CPD) (Cost per lead, Cost per click and Cost of customer acquisition [0441] – [0443]) Regarding claim 17 Saraee in view of Quattro teaches the computer-implemented method of claim 11, Saraee as modified further teaches , comprising: generating, (generating, by the one or more processors using the content scoring machine learning model [1353]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) a summary based on execution results (the system may display an activity summary for a certain time period, which would give the user a digest about past conversations with this target contact list and relevant outreach and success metrics [0160]) associated with the one or more first tasks; (certain tasks and recommendations. [0201]) and transmitting, (transmitting [0907]) by the AI system, (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) the summary (the system may display an activity summary for a certain time period, which would give the user a digest about past conversations with this target contact list and relevant outreach and success metrics [0160]) to a client device (client device [0957]) 07-21-aia AIA Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Saraee et al. (United States Patent Publication Number 2025/0078453 ), hereinafter Saraee, in view of Quatro et al. (United States Patent Publication Number 20250045304 ), hereinafter referred to as Quatro and in further view of Plankey et al, (United States Patent Number11,922,974) hereinafter Plankey Regarding claim 12 Saraee in view of Quattro teaches the computer-implemented method of claim 11, Saraee as modified teaches generating, by the AI system (various algorithms for example … for example, algorithms utilizing machine learning, network analysis, predictive analytics, descriptive statistics, natural language processing, graph algorithms, sequencing algorithms, numerical algorithms, optimization algorithms, database algorithms, signal processing, deep learning, artificial intelligence, etc [0222]) using the generative model (the generative machine learning model may include relevant images to a given target audience, a list of relevant text prompts, or both [1030]) Saraee as modified does not fully disclose wherein the plurality of levels of automation comprise a semi-automatic mode, and wherein the computer- implemented method comprises: receiving, by the AI system, a second query indicating the semi-automatic mode, the second query comprising a second objective; and based on the second query, one or more second tasks for achieving the second objective; transmitting, by the AI system to a client device, the one or more second tasks; receiving, by the AI system from the client device, an approval of the one or more second tasks; and executing, by the AI system, the one or more second tasks. Plankey teaches wherein the plurality of levels of automation (Fig. 2A automated functions Col 9 ln 20 – 25) comprise a semi-automatic mode, (semi- automatic mode of operation Col 4 ln 30 – 35) and wherein the computer- implemented method (Figs. 1A, 1B and 1C Col 7 ln 35 – 40) comprises: receiving, by the AI system, (artificial intelligence Col 17 ln 40 – 45) a second query (any one of service requests Col 24 ln 50 – 55) indicating the semi-automatic mode, (semi-automatic mode of operation Col 4 ln 30 – 35) the second query (any one of service requests Col 24 ln 50 – 55) comprising a second objective; (to enable the sales representative with the business 132 to select multimedia segments from one or more base videos in the 35 custom video library 138 with the multimedia dashboard application and without having to open or otherwise run any other application Col 25 ln 34 – 38) and based on the second query (any one of service requests Col 24 ln 50 – 55) , one or more second tasks (integration and connectivity of the separate tasks of recording multimedia segments, editing the segments into multimedia promotions, and distributing the multimedia promotions within the multimedia dashboard Col 25 ln 39 - 45) for achieving the second objective; (to enable the sales representative with the business 132 to select multimedia segments from one or more base videos in the 35 custom video library 138 with the multimedia dashboard application and without having to open or otherwise run any other application Col 25 ln 34 – 38) transmitting, by the AI system (artificial intelligence Col 17 ln 40 – 45) to a client device, (client computer systems Col 27 ln 67 - l Col 28 ln 1) the one or more second tasks; (integration and connectivity of the separate tasks of recording multimedia segments, editing the segments into multimedia promotions, and distributing the multimedia promotions within the multimedia dashboard Col 25 ln 39 - 45) receiving, by the AI system (artificial intelligence Col 17 ln 40 – 45) from the client device, (client computer systems Col 27 ln 67 - l Col 28 ln 1) an approval of the one or more second tasks; (integration and connectivity of the separate tasks of recording multimedia segments, editing the segments into multimedia promotions, and distributing the multimedia promotions within the multimedia dashboard Col 25 ln 39 - 45) and executing, (executed Col 17 ln 39) by the AI system, (artificial intelligence Col 17 ln 40 – 45) the one or more second tasks. (integration and connectivity of the separate tasks of recording multimedia segments, editing the segments into multimedia promotions, and distributing the multimedia promotions within the multimedia dashboard Col 25 ln 39 - 45) It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Saraee in view of Quattro to incorporate the teachings of Plankey wherein the plurality of levels of automation comprise a semi-automatic mode, and wherein the computer- implemented method comprises: receiving, by the AI system, a second query indicating the semi-automatic mode, the second query comprising a second objective; and based on the second query, one or more second tasks for achieving the second objective; transmitting, by the AI system to a client device, the one or more second tasks; receiving, by the AI system from the client device, an approval of the one or more second tasks; and executing, by the AI system, the one or more second tasks. By doing so allows a user to select the multiple tools displayed on the multimedia dashboard including video recording, photography, photo and video editing, adding music and voiceover to the created multimedia content. Plankey Col 5 ln 55 – 60 Conclusion 07-96 6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wong et al., (United States Patent Publication Number2009/0228914) teaches “Intelligent Control Module (2) 1s capable of multidirectional communication with Ad Center (1) and/or TV Service providers, has multiple connectors, through which users can follow-up and search for ad information [0014]” 7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kweku Halm whose telephone number is (469) 295- 9144. The examiner can normally be reached on 7:30AM - 5:30PM Mon - Thur. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sanjiv Shah can be reached on (571) 272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273- 8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /KWEKU WILLIAM HALM/Examiner, Art Unit 2166 /SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166 Application/Control Number: 18/860,315 Page 2 Art Unit: 2166 Application/Control Number: 18/860,315 Page 3 Art Unit: 2166 Application/Control Number: 18/860,315 Page 4 Art Unit: 2166 Application/Control Number: 18/860,315 Page 5 Art Unit: 2166 Application/Control Number: 18/860,315 Page 6 Art Unit: 2166 Application/Control Number: 18/860,315 Page 7 Art Unit: 2166 Application/Control Number: 18/860,315 Page 8 Art Unit: 2166 Application/Control Number: 18/860,315 Page 9 Art Unit: 2166 Application/Control Number: 18/860,315 Page 10 Art Unit: 2166 Application/Control Number: 18/860,315 Page 11 Art Unit: 2166 Application/Control Number: 18/860,315 Page 12 Art Unit: 2166 Application/Control Number: 18/860,315 Page 13 Art Unit: 2166 Application/Control Number: 18/860,315 Page 14 Art Unit: 2166 Application/Control Number: 18/860,315 Page 15 Art Unit: 2166 Application/Control Number: 18/860,315 Page 16 Art Unit: 2166 Application/Control Number: 18/860,315 Page 17 Art Unit: 2166 Application/Control Number: 18/860,315 Page 18 Art Unit: 2166 Application/Control Number: 18/860,315 Page 19 Art Unit: 2166 Application/Control Number: 18/860,315 Page 20 Art Unit: 2166 Application/Control Number: 18/860,315 Page 21 Art Unit: 2166 Application/Control Number: 18/860,315 Page 22 Art Unit: 2166 Application/Control Number: 18/860,315 Page 23 Art Unit: 2166 Application/Control Number: 18/860,315 Page 24 Art Unit: 2166 Application/Control Number: 18/860,315 Page 25 Art Unit: 2166 Application/Control Number: 18/860,315 Page 26 Art Unit: 2166 Application/Control Number: 18/860,315 Page 27 Art Unit: 2166 Application/Control Number: 18/860,315 Page 28 Art Unit: 2166 Application/Control Number: 18/860,315 Page 29 Art Unit: 2166 Application/Control Number: 18/860,315 Page 30 Art Unit: 2166 Application/Control Number: 18/860,315 Page 31 Art Unit: 2166 Application/Control Number: 18/860,315 Page 32 Art Unit: 2166
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