Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 6 is objected to because of the following informalities: hidden later. Appropriate correction is required. It should be hidden layer.
Drawings
Figures 4-8 are ineligible and replacement drawings are required. Some of the text present in the figures are not readable.
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 2, 9, and 15 are rejected as being directed to an abstract idea without significantly more.
Regarding Claim 2
Step 1: A machine.
Step 2A Prong 1: The claim limitation measuring interactions between the users in the predicated audience and the first piece of content; is a mental process that can be done with the aid of pen and paper.
Step 2A Prong 2: The additional limitations accessing an objective of a first entity is insignificant extra-solution activity” (or an equivalent) with the judicial exception, or activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim- see MPEP 2106.05(g) Examiner’s note- accessing an objective from an entity amounts to nothing more insignificant extra solution in this case since all that is being stated here is that an entity will be providing instructions.
feeding the objective into a first GAI model to generate a first piece of content based on the objective amounts to nothing more than “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea- see MPEP 2106.05(f) Examiner’s note high level recitation of feeding instructions into a GAI model to create content.
feeding the objective into an audience prediction model trained using interaction data between users and content, to produce a predicted audience for the first piece of content amounts to nothing more than “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) Examiner’s note- high level recitation providing a trained model data on users interactions with content.
and feeding the predicted audience and the first piece of content into a relevance and optimization model to predict relevance of the first piece of content to users in the predicted audience, wherein the audience prediction model and the relevance and optimization model are trained to optimize a unified machine learning model goal to provide end-to-end automation for causing presentation of the first piece of content for users in the predicted audience according to the objective amounts to nothing more than “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) Examiner’s note- high level recitation optimizing a model before presenting content to an audience.
Causing display of the first piece of content to users in the predicted audience is “insignificant extra-solution activity” (or an equivalent) with the judicial exception, or activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim-see MPEP 2106.05(g) Examiner’s note- displaying content to users amounts to nothing more than insignificant extra solution since all that is being stated is displaying content from a GAI model to an audience.
and retraining the audience prediction model based on the measured interactions amounts to nothing more than “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) Examiner’s note- high level recitation of training a model on user interactions.
Step 2B: The additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. Accessing an objective of a first entity; feeding an objective into a GAI model to generate content; feeding an objective and content into an audience prediction model trained with data to produce a predicated audience for the content; feeding the both the predicted audience and content into a relevance and optimization model to predict relevance of the content that would later be used to train and optimize the model; displaying that content to users in the predicted audience; and retraining the prediction model based on measured interactions does not add enough to the abstract idea to make it an inventive concept.
Regarding Claim 9
Step 1: A process.
Step 2A Prong 1: See the analysis of Claim 2.
Step 2A Prong 2: See the analysis of Claim 2.
Step 2B: See the analysis of Claim 2.
Regarding Claim 16
Step 1: A machine.
Step 2A Prong 1: See the analysis of Claim 2.
Step 2A Prong 2: See the analysis of Claim 2.
Step 2B: See the analysis of Claim 2.
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim’s 1- 4, 7, 8-11, 14, and 16-18 are rejected under 103 as being unpatentable over Thimmaiah et al (US 20200160373) (“Thimmaiah”), in view Tran et al (US 20230252224A1) (“Tran”).
Regarding Claim 1, Thimmaiah teaches a non-transitory computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to perform operations comprising ([0056] teaches that that their patent can be performed on several different computing devices that can include physical devices to implement their components which would cover the non-transitory computer readable medium, along with the processor mentioned in this limitation):
accessing an objective of a first entity ([0048] teaches accessing objectives through a drop-down menu);
feeding the objective and the first piece of content into an audience prediction model trained using interaction data between users and content, to produce a predicted audience for the first piece of content ([0035] teaches an optimization model that is trained on data from past campaigns to determine what to use for their next campaign before launching. Using BRI this can reasonably be interpreted as an audience prediction model trained on users’ interactions with content to produce a prediction model for content);
and feeding the predicted audience and the first piece of content into a relevance and optimization model to predict relevance of the first piece of content to users in the predicted audience ([0043] describes an example where after the advertisement content is launched to the audience those two things can be used to monitor the performance of the ad with the audience and used to adjust the optimization model based off of their performance dynamically. This could be reasonably interpreted as feeding the predicted audience and first piece of content into a relevance and optimization model to predict relevance of content to the audience);
wherein the audience prediction model and the relevance and optimization model are trained to optimize a unified machine learning model goal to provide end-to-end automation for causing presentation of the first piece of content for users in the predicted audience according to the objective ([0073] teaches a machine learning model that trained to find an appropriate target audience, a model that analyzes performance data, and a optimization model with the goal to use the campaign budget to launch content to the right audience. What teaches addresses all limitations).
Tim does not teach feeding the objective into a first GAI model to generate a first piece of content based on the objective
However, Tran teaches feeding the objective into a first GAI model to generate a first piece of content based on the objective ([0059] Teaches using a GAI model to generate content based on an objective. It can be reasonably interpreted that this teaches feeding an objective into a GAI model to create content based on that data).
Thimmaiah and Tran are analogous art because they both use GAI models and machine learning to create content.
It would have been obvious to a person skilled in the art before the effective filling date of the claimed invention to combine Tim with the GAI feeding of Tran. Doing so would allow for more personalized outputs from the machine learning model ([0003- Tran] “Generating natural language from machine representation systems is a common and increasingly important function. Existing natural language generation (NLG) systems, such as translators, summarizers, dialog generators, etc., while common, cannot produce variable output based on user-desired tunable specifications. Additionally, such existing systems cannot take input in the form of a variable form of text and a variable set of specifications and output a transformed version of the input text according to the specifications. Further, such existing systems are generally not readily extendable”).
Regarding Claim 2, Thimmaiah and Tran teaches all the limitations of Claim 1.
Thimmaiah also teaches wherein the operations further comprise: causing display of the first piece of content to users in the predicted audience ([0036] teaches displaying content to a target audience); measuring interactions between the users in the predicted audience and the first piece of content ([0037] teaches monitoring performance of the campaign);
and retraining the audience prediction model based on the measured interactions ([0037] teaches updating the trained optimization model based off of the performance of the campaign).
Regarding Claim 3, Thimmaiah and Tran teaches all the limitations of claim 2.
Thimmaiah also teaches wherein the operations further comprise: producing a new predicted audience for the first piece of content using the retrained audience prediction model ([0037] teaches after updating the trained model reallocating the budget to a new audience);
Thimmaiah does not teach and generating a second piece of content by feeding the objective and the new predicted audience into the first GAI model.
However, Tran teaches and generating a second piece of content by feeding the objective and the new predicted audience into the first GAI model ([0216] traches a 24/7 chatbot that answers users’ questions and is trained on user data’s interactions with the system. That data functioning as the predicted audience).
Regarding Claim 4, Thimmaiah and Tran teaches all the limitations of Claim 1.
Tran also teaches wherein the objective is obtained by access a text-based objective explicitly provided by an entity ([0059] Teaches using a GAI model to generate content based on an objective).
Regarding Claim 7, Thimmaiah and Tran teaches all the limitations of Claim 1
Thimmaiah also teaches the system of claim 1, wherein the first piece of content is a sponsored piece of content where an entity pays a price each time the first piece of content is displayed or interacted with ([0036] teaches allocating a budget for a campaign and then proceeding with that budget for the target audience),
and the relevance and optimization model include an automatic biding component that dynamically adjusts base bids for the price, based on the predicted relevance ([0037] teaches automizing the campaign dynamically which includes budget reallocation).
Regarding Claim 8, it is rejected according to the rejection of claim 1
Regarding Claim 9, it is rejected according to the rejection of claim 2.
Regarding Claim 10, it is rejected according to the rejection of claim 3
Regarding Claim 11, it is rejected according to the rejection of claim 4.
Regarding Claim 14, it is rejected according to the rejection of claim 7.
Regarding Claim 15, it is rejected according to the rejection of claim 1.
Regarding Claim 16, it is rejected according to the rejection of claim 2.
Regarding Claim 17, it is rejected according to the rejection of claim 3.
Regarding Claim 18, it is rejected according to the rejection of claim 4.
Claim 5, 12, 19 is rejected under 103 as being unpatentable over Thimmaiah et al (US 20200160373) (“Thimmaiah”), in view Tran et al (US 20230252224 A1) (“Tran”), and Cai et al (US US12141556B2) (“Cai”).
Regarding Claim 5, Tim and Tran teaches all the limitations of Claim 1.
Tim does not teach wherein the objective is obtained by feeding one or more pieces of content into a second GAI model to generate one or more embeddings.
However, Cai teaches wherein the objective is obtained by feeding one or more pieces of content into a second GAI model to generate one or more embeddings ([0010] teaches using a models output as input through chaining instantiations. objective is defined as a piece of information which would be the input for the model as well as teaching the use of a multiple LLMs which addresses the second GAI model part of the claim limitation. The input data also goes through embeddings [0132]).
Thimmaiah, Tran, and Cai are analogous arts because they all use machine learning centered around user interactions
It would have been obvious to a person skilled in the art before the effective filling date of the claimed invention to combine Tim with the GAI feeding of Tran and the machine learning embedding generation of Cai. Doing so would allow for the machine learning models to be more effective ([Abstract- Cai] “In response, the present disclosure introduces the concept of chaining instantiations of machine-learned language models (e.g., LLMs) together, where the output of one instantiation becomes the input for the next, and so on, thus aggregating the gains per step.”).
Regarding Claim 12, it is rejected according to the rejection of claim 5
Regarding Claim 19, it is rejected according to the rejection of claim 5
Claim 6, 13 and 20 are rejected under 103 as being unpatentable over Thimmaiah et al (US 20200160373) (“Thimmaiah”), in view of Tran et al (US 20230252224 A1) (“Tran”), and Sumant et al (US 20230140412) (“Sumant”).
Regarding Claim 6, Tim and Tran teaches all the limitations of Claim 1.
Time does not teach wherein the audience prediction model is a two-tower neural network machine learning model containing a first neural network having an embedding layer and a hidden layer and a second neural network having an embedding layer and a hidden later, the first neural network trained on user data and the second neural network trained on content data.
However, Suman teaches wherein the audience prediction model is a two-tower neural network machine learning model containing a first neural network having an embedding layer and a hidden layer and a second neural network having an embedding layer and a hidden later, the first neural network trained on user data and the second neural network trained on content data ([0025] defines a two-tower neural network including an embedding and a multiple-layer perceptron which will always include a hidden layer and figure 5 shows one of those neural networks using user embeddings and the other neural network using object data for training which would address the limitation on both neural networks being trained on user data and content data).
Thimmaiah, Tran, and Sumant are analogous art because they all use machine learning centered around user interactions.
It would have been obvious to a person skilled in the art before the effective filling date of the claimed invention to combine Tim with the GAI feeding of Verma and the Two tower neural network component of Suman. Doing so would lead to more effective advertising ([0003] - [0004] “While paid advertising may seem simple (e.g., paying a fee to run advertisements), effective online advertising has a technical component. Indeed, most online advertising is pay-per-click, meaning that it can quickly get very expensive if the people clicking on those ads are not qualified leads. Often, a marketing expert or dedicated team of advertising professionals is required to set up the ad campaigns to target qualified leads, and then continue to monitor those ads for effectiveness and make adjustments to achieve the desired result. The subject disclosure provides for systems and methods for lead generation based on predicted conversions for a social media program.”).
Regarding claim 13, it is rejected according to the rejection of claim 6
Regarding claim 20, it is rejected according to the rejection of claim 6
Conclusion
The prior art made of record and not relied upon is considered to applicant’s disclosure
Huiji et al US 20190370854 A1 (2018-05-31) (Abstract “Techniques for extracting features of entities and targets that can be applied in a set of applications, such as entity selection prediction, audience expansion, feed relevance, and job recommendation. In one technique, entity interaction data is stored that indicates, for each of multiple entities, one or more targets that are associated with items with which the entity interacted. Token association data is stored that indicates, for each of multiple tokens, one or more targets that are associated with the token. Then, using one or more machine learning techniques, entity embeddings and target embeddings are generated based on the entity interaction data and the token association data. Later, a request for content is received from a particular entity. Based on at least one entity embedding, a content item for the particular entity is identified. The content item is transferred over a computer network and presented to the particular entity.
Aslan et al US 20170132528 A1 (2016-06-28) (Abstract “Multiple machine learning models can be jointly trained in parallel. An example process for jointly training multiple machine learning models includes providing a set of machine learning models that are to learn a respective task, the set of machine learning models including a first machine learning model and a second machine learning model. The process can initiate training of the first machine learning model to learn a task using training data. During the training of the first machine learning model, information can be passed between the first machine learning model and the second machine learning model. Such passing of information (or “transfer of knowledge”) between the machine learning models can be accomplished via the formulation, and optimization, of an objective function that comprises model parameters that are based on the multiple machine learning models in the set.”)
Wang US 20230245651 A1 (2021-03-14) (Abstract “An approach is disclosed for enabling contextually relevant conversational interaction. Environment data is received by an AI System which detects a plurality of physical objects in a physical environment and forms a contextual understanding of the plurality of physical objects and the physical environment and identifies a user relevant to the contextual understanding. A most relevant contextual information to the user is predicted by the AI system and transformed into a textual form. A set of intents and objectives is predicted by the AI system for user-centered interaction. The AI system and the user interact iteratively through the user-centered interaction to determine an understanding of a most relevant intent and a most relevant objective which is validated by the AI system with the user until the user agrees. The validated most relevant intent and the most relevant objective is utilized to facilitate the user-centered and contextually relevant conversational interaction.”
Mohammed et al US 20230315999 A1 (2022-03-31) (Abstract “Systems and method are disclosed for processing unrecognized user queries. A received user query is classified via a first machine learning model. A first classification determination is made for the user query. In response to the first classification determination, features of the user query are identified via a second machine learning model. The user query is grouped into a cluster based on the features of the user query. Information about the cluster is displayed for prompting a user action. The user action may include identification of an intent for the user query.”
Wu et al (NPL- PromptChainer: Chaining Large Language Model Prompts through Visual Programming) (2022-04-28) (Abstract “While LLMs have made it possible to rapidly prototype new ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single run of an LLM. Recent work has found that chaining multiple LLM runs together (with the output of one step being the input to the next) can help users accomplish these more complex tasks, and in a way that is perceived to be more transparent and controllable. However, it remains unknown what users need when authoring their own LLM chains– a key step to lowering the barriers for non-AI-experts to prototype AI-infused applications. In this work, we explore the LLM chain authoring process. We find from pilot studies that users need support trans forming data between steps of a chain, as well as debugging the chain at multiple granularities. To address these needs, we designed PromptChainer, an interactive interface for visually programming chains. Through case studies with four designers and developers, we show that PromptChainer supports building prototypes for a range of applications, and conclude with open questions on scaling chains to even more complex tasks, as well as supporting low-fi chain prototyping.)
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/URIAH VENDELL MOORE/ Examiner, Art Unit 2142
/Mariela Reyes/ Supervisory Patent Examiner, Art Unit 2142
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