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 Arguments
Claim Rejections - 35 USC § 101
Applicants’ arguments filed 07/27/2026 have been fully considered but they are not persuasive.
Applicant argues that their claim limitation does not recite a mental process, stating that “dependent claim 2 does not describe a mental process. The Revised Guidance defines the "mental processes" abstract idea grouping as concepts performed in the human mind and explains that claims recite a mental process when they contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions. In contrast, USPTO guidance explains that claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. The Kim Memo specifically provides reminders on this point, explicitly stating: Mental process grouping: The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper," to be an abstract idea. The USPTO subject matter eligibility analysis follows this precedent and instructs examiners to determine that a claim recites a mental process when it contains limitation(s) that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions. On the other hand, a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s). The mental process grouping is not without limits. Examiners are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind. The MPEP and the AI-SME Update provide examples of claim limitations that cannot be practically performed in the human mind. Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping. As set forth in the Revised Guidance and the Kim Memo, the mental processes grouping is not without limits, and as such, claim limitations that cannot practically be performed in the human mind do not fall within this grouping. In the instant case, the measuring step of claim 2 contains limitations that cannot practically be performed in the human mind. An example of the measuring step is given in paragraph 123, which states: [0123] The I/O components 1050 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1050 that are included in a particular machine 1000 will depend on the type of machine 1000. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1050 may include many other components that are not shown in FIG. 10. The I/O components 1050 are grouped according to functionality merely for simplifying the following discussion, and the grouping is in no way limiting. In various example embodiments, the I/O components 1050 may include output components 1052 and input components 1054. The output components 1052 may include visual components (e.g., a display such as a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The input components 1054 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. As indicated in paragraph 123, "The I/O components 1050 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on." For example, the measurements or made using I/O components 1050, including output components 1052 and input components 1054, which are electronic components of an electronic device. Hence, the claim does not recite a mental process, such as comparing or categorizing information that can be performed in the human mind, or by a human using a pen and paper. Rather, the claims describe measuring operations using I/O components of an electronic device such as a computing device. Therefore, dependent claim 2 (and corresponding claims 9 and 16) do not recite a mental process.
The representative claim 1, from which representative claim 2 depends, recites an improvement in the functioning of a computer or an improvement to other technology or technical field. In particular, the representative claim is directed to an improvement in a functioning of a computer, or an improvement to other technology or technical field, and is therefore patent eligible. See MPEP §§ 2106.04(d)(1) and 2106.05(a) (citing Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016)). Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that "[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes." 822 F.3d at 1339. Moreover, because "[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can," the Federal Circuit held that the eligibility determination should turn on whether "the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Id. at 1336. Similar to Enfish, the representative claim reflects an improvement to machine learning models as described in the instant application. For example, paragraphs 18-22 of the Specification, identifies a technical problem where different components of an online network may utilize different machine learning models, each model trained independently of each other to optimize a different goal. For example, a relevance model may be optimized for conversions, an audience model optimized for audience expansion, and a creative model optimized for engagement. However, optimizing for one goal may be at the expense of another goal. To solve this technical problem, a technical solution is provided that performs end-to-end optimization from input to output, rather than for separate components. Spec. 23-24. At least the following limitations of the representative claim reflects the improvement, such as "adjust a first parameter of the audience prediction model based on a second parameter determined by the relevance and16 optimization model." This constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified abstract idea (e.g., a mental process). Paragraph 25 of the Specification provides an example of improvements to machine learning models, stating: [0025] More particularly, in an example embodiment, a GAI model may be used to generate one or more embeddings for content, such as text content. These embeddings may then be fed into a separate audience prediction model that produces a predicted audience for the content. This predicted audience may then be passed to a separate relevance and optimization model that is trained to predict relevance of a piece of content to users in the predicted audience. The audience prediction model and the relevance and optimization model may be trained to optimize a unified goal, and may act to essentially continuously "retrain" each other to optimize that goal. Thus, while each individual model may be trained to optimize their own goal (e.g., the audience prediction model being trained to identify an audience most likely to engage with a piece of content and the relevance and optimization model trained to identify a piece of content to display to users in an audience), by optimizing both together on a unified goal (e.g., maximizing value to an online platform in which the piece of content is being considered for display), then the system as a whole is able to function more effectively than if each model were trained and retrained separately. This improves the functioning of a computer since it provides more precise inferencing operations by the machine learning models. Additionally, this makes a computer operate more efficiently because it improves use of limited computer resources, such as compute, memory, and bandwidth. The improvement also provides a technical solution of end-to- end automation and joint optimization that solves a technical problem of optimizing individual machine learning models on independent objectives that may conflict with each other. Therefore, the improvement "enables a computer ... to do things it could not do before." Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1305 (Fed. Cir. 2018). The July 2024 Subject Matter Eligibility Examples ("2024 AI Guidance") include Example 47 directed to an artificial neural network (ANN) to identify or detect anomalies. Example 47 of the 2024 AI Guidance supports Applicant's position above that amended claim 1 is eligible under 35 U.S.C. § 101 because it integrates the alleged abstract idea into a practical application thereof or includes something substantially more than the alleged abstract idea itself. Claim 3 of Example 47 recites a method of using an artificial neural network (ANN) to detect malicious network packets. The method includes training the ANN based on input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm. Then, one or more anomalies are detected in network traffic using the trained ANN. Next, at least one detected anomaly is determined to be associated with one or more malicious network packets. The source address of the malicious network packets is determined, one or more malicious network packets are dropped, and then future traffic from the source address is blocked. Though Claim 3 of Example 47 is determined to recite the abstract idea of mathematical concepts and mental processes, the claim is nonetheless deemed to be eligible under 35 U.S.C. § 101 because the claim as a whole integrates the alleged judicial exception into a practical application. In coming to this conclusion, the Example highlights that "one way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth and improvement in technology and the claim itself must reflect the disclosed improvement." Claim 3 of Example 47 included the operations of "detecting a source address associated with the one or more malicious network packets," "dropping the one or more malicious network packets," and "blocking future traffic from the source address." The specification of Example 47 disclosed that existing systems use various detection techniques for detecting potentially malicious network packets and can alert a network administrator to potential problems. The disclosed system detects network intrusions and takes real-time remedial actions, including dropping the suspicious packets and blocking traffic. The Example explains that this is an improvement to network security and claim 3 reflects this improvement in the recited steps and provide for improved network security. As such, claim 3 is deemed to be eligible. The representative claim is similar to claim 3 of Example 47 in that it recites all of the features required to achieve the improvement in end-to-end optimization of different components using different machine learning models trained on separated objectives. As discussed above, paragraph 76 of the pending application makes clear that end-to-end optimization of different components using different machine learning models allow for cross-component optimization. The recited features therefore allow the method to improve upon current systems. As detailed in the Kim Memo, an improvement in computer-related technologies integrate a recited judicial exception into a practical application of the exception. Notably, the Kim Memo states: Improvements consideration: In computer-related technologies, examiners can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception. This consideration has also been referred to as the search for a technological solution to a technological problem. An important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. The examiner is reminded to consult the specification to determine whether the disclosed invention improves technology or a technical field, and evaluate the claim to ensure it reflects the disclosed improvement. The specification does not need to explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. The claim itself does not need to explicitly recite the improvement described in the specification. Kim Memo 4. In the instant case, the representative claim improves technology or a technical field in ML models for improved quality and accuracy of predictions for networking services provided by a connections networking system. For example, a technical problem is described in paragraph 18 of the application, which states: [0018] An online network may contain various different components, each programmed to perform a different task. Some of these components may utilize one or more machine learning models in the furtherance of those tasks. These machine learning models, however, are optimized independently of one another. In other words, each model is trained independently to one another to optimize a different goal. While there have been some efforts made to train co-existing machine learning models to optimize a single goal by, for example, training those machine learning models together, such efforts have been limited to machine learning models within the same component of an online network and thus have more traditionally overlapping goals. This technical problem is solved by a technical solution of end-to-end optimization, as discussed in paragraph 24 of the application, which states: [0024] This acts to allow components to interact with each other in what may be called a "flywheel", with each component relying on some aspect of at least one other component for joint optimization. This integrated approach allows for a seamless and efficient process to optimize various online network activities, such as content and/or advertising display, geared towards a unified goal. It additionally creates a closed optimization cycle where each component potentially interacts with each other, with the flywheel connecting and sharing knowledge among previously isolated optimization components to improve outcomes. For example, insights from measuring qualified leads and audience signals can be used to continuously improve model performance and drive further outcome optimization. The technical problem described in paragraph 18 is solved by the technical solution of paragraph 24 of the application. The technical solution is fully recited in claim 1 along with specific architecture details for a neural network that implements the technical solution. For example, claim 1 explicitly recites "adjust a first parameter of the audience prediction model based on a second parameter determined by the relevance and optimization model." An example of this language is described in paragraph 76 of the application: [0076] In order to facilitate end-to-end optimization of all of these components in FIG. 2, connections between the components may be established in a manner that allows for such cross-component optimization. Specifically, connections between certain components can themselves be trained to optimize over some goal. For example, connection 232 in FIG. 2 is between relevance and optimization component 200 and audience component 202. A goal here may be to ensure that bidding on content does not get too high for a narrow audience, which would impair conversions. Thus, if the system as a whole determines that such a goal is not being met or might not be met if certain criteria related to bidding are strictly enforced, those criteria may be relaxed to find the nearest relevant audience to expand the audience for that particular content delivery to a wide enough level to ensure that the goal is met. This is why this connection 232 is labeled "delivery20 controlled audience", because the audience itself (which is determined in the audience component 202) can be adjusted based on the delivery parameters (which are determined in the relevance and optimization component 200). Training of a model for this connection 232 uses tracking data to track the delivery parameters and the audience segments being served by the delivery. Therefore, claim 1 recites an improvement in computer-related technologies that integrates any recited judicial exception into a practical application of the exception. Applicant respectfully submits that the representative claim is analogous to claim 3 of Example 47 of the 2024 AI Guidance and integrates the alleged abstract idea into a practical application thereof (Step 2A, Prong Two: YES)”.
Examiner respectfully disagrees. Regarding the 101 rejection on Claim 2, the arguments for the measuring being done using I/O components is not reflected in the claim language, the claim language for the limitation specifically states “measure interactions between the users in the predicted audience and the first piece of content” recites a mental process that can be accomplished with the aid of pen and paper.
Applicant points towards claim 3 of Example 47 to support their argument for the following limitation, “adjust a first parameter of the audience prediction model based on a second parameter determined by the relevance and optimization model.” pointing towards an improvement in how the machine learning model operates. The examiner disagrees that the cited limitation points towards an improvement in how a machine learning model operates, instead the cited limitation recites a mental process that can be done with the aid of pen and paper.
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under U.S.C. 101 as being directed to an abstract idea without adding significantly more
Regarding Claim 1
Step 1: “A system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: and adjust a first parameter of the audience prediction model based on a second parameter determined by the relevance and optimization model is a mental process that can be done with the aid of pen and paper, a person can adjust parameters of a model based on another parameter.
Step 2A Prong 2: The additional limitations access an objective of a first entity; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) - examiners note: accessing an objective from an entity amounts to nothing more than insignificant extra solution in this case since all that is being stated here is that an entity will be providing instructions
feed the objective into a first GAI model to generate a first piece of content based on the objective are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of feeding instructions into a GAI model to create content
feed 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 are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of providing a trained model data on user’s interactions with content
feed 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 are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – high level recitation of optimizing a model before presenting content to an audience
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation access an objective of a first entity; is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore the additional element is directed to transmitting data over a network - examiners note: accessing an objective from an entity amounts to nothing more than insignificant extra solution in this case since all that is being stated here is that an entity will be providing instructions
feed the objective into a first GAI model to generate a first piece of content based on the objective are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of feeding instructions into a GAI model to create content
feed 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 are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of providing a trained model data on user’s interactions with content
feed 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 are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – high level recitation of optimizing a model before presenting content to an audience
Regarding Claim 2
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: measure interactions between the users in the predicted audience and the first piece of content is a mental process that can be done with the aid of pen and paper, a person can measure interactions between users and content
Step 2A Prong 2: The additional limitations cause display of the first piece of content to users in the predicted audience is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners 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 retrain the audience prediction model based on the measured interactions. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of retraining a model based on data
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation cause display of the first piece of content to users in the predicted audience is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to transmitting data over a network – examiners 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 retrain the audience prediction model based on the measured interactions. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of retraining a model based on data
Regarding Claim 3
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 2
Step 2A Prong 2: The additional limitations produce a new predicted audience for the first piece of content using the retrained audience prediction model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting a prediction using a retrained model
and generate a second piece of content by feeding the objective and the new predicted audience into the first GAI model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting producing content by providing instructions to a model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation produce a new predicted audience for the first piece of content using the retrained audience prediction model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting a prediction using a retrained model
and generate a second piece of content by feeding the objective and the new predicted audience into the first GAI model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting producing content by providing instructions to a model
Regarding Claim 4
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: the additional limitation wherein the objective is obtained by accessing a text-based objective explicitly provided by an entity is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of providing instructions through user input
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the objective is obtained by accessing a text-based objective explicitly provided by an entity is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2) – examiners note: high level recitation of providing instructions through user input
Regarding Claim 5
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitation wherein the objective is obtained by feeding one or more pieces of content into a second GAI model to generate one or more embeddings are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating embeddings using an AI model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the objective is obtained by feeding one or more pieces of content into a second GAI model to generate one or more embeddings are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating embeddings using an AI model
Regarding Claim 6
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitation 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 layer, the first neural network trained on user data and the second neural network trained on content data is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation 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 layer, the first neural network trained on user data and the second neural network trained on content data is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Regarding Claim 7
Step 1: “The system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitation 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, and the relevance and optimization model includes an automatic bidding component that dynamically adjusts base bids for the price, based on the predicted relevance. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note of having a model adjust values based on a predicted relevance
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation 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, and the relevance and optimization model includes an automatic bidding component that dynamically adjusts base bids for the price, based on the predicted relevance. are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note of having a model adjust values based on a predicted relevance
Regarding Claim 8
Step 1: “A method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Regarding Claim 9
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 10
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 3
Step 2A Prong 2: See the analysis of Claim 3
Step 2B: See the analysis of Claim 3
Regarding Claim 11
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 4
Step 2A Prong 2: See the analysis of Claim 4
Step 2B: See the analysis of Claim 4
Regarding Claim 12
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 5
Step 2A Prong 2: See the analysis of Claim 5
Step 2B: See the analysis of Claim 5
Regarding Claim 13
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 6
Step 2A Prong 2: See the analysis of Claim 6
Step 2B: See the analysis of Claim 6
Regarding Claim 14
Step 1: “The method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 7
Step 2A Prong 2: See the analysis of Claim 7
Step 2B: See the analysis of Claim 7
Regarding Claim 15
Step 1: “A non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Regarding Claim 16
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
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 17
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 3
Step 2A Prong 2: See the analysis of Claim 3
Step 2B: See the analysis of Claim 3
Regarding Claim 18
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 4
Step 2A Prong 2: See the analysis of Claim 4
Step 2B: See the analysis of Claim 4
Regarding Claim 19
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 5
Step 2A Prong 2: See the analysis of Claim 5
Step 2B: See the analysis of Claim 5
Regarding Claim 20
Step 1: “The non-transitory machine-readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: recites the abstract ideas of Claim 15
Step 2A Prong 2: The additional limitation wherein the audience prediction model is a two-tower neural network machine learning model is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the audience prediction model is a two-tower neural network machine learning model is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to claim(s) Claim's 1-15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
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”), and Gil David Allouche (US10607252B2), (“Allouche”)
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):
access an objective of a first entity ([0048] teaches accessing objectives through a drop-down menu);
feed 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);
feed 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 feed the objective into a first GAI model to generate a first piece of content based on the objective
and adjust a first parameter of the audience prediction model based on a second parameter determined by the relevance and optimization model
However, Tran teaches and 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])
The combination of Thimmaiah and Tran does not teach and adjust a first parameter of the audience prediction model based on a second parameter determined by the relevance and optimization model.
However, Allouche does teach and adjust a first parameter of the audience prediction model based on a second parameter determined by the relevance and optimization model ([Column 3: Lines 19 to 29] teaches training parameters model based on one or more parameters and the goal metric. The relationship between the goal metric and the experimental parameters is done through a prediction engine which is being interpreted as another model. The training of the parameters model here is being interpreted as parameter adjustment in response to a different set of parameters teaching this limitation)
Thimmaiah, Tran, and Allouche are analogous art because they both use machine learning models in response to 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 parameter adjustment of Allouche. Doing so would allow for more personalized outputs from the machine learning model ([0003- Tran])
Regarding Claim 2, Thimmaiah, Tran, and Allouche teaches all the limitations of Claim 1.
Thimmaiah also teaches wherein the operations further comprise: cause display of the first piece of content to users in the predicted audience ([0036] teaches displaying content to a target audience); measure interactions between the users in the predicted audience and the first piece of content ([0037] teaches monitoring performance of the campaign);
and retrain 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, Tran, and Allouche teaches all the limitations of claim 2.
Thimmaiah also teaches wherein the operations further comprise: produce 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);
Tran also teaches and generate a second piece of content by feeding the objective and the new predicted audience into the first GAI model ([0216] teaches 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, Tran, and Allouche 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, Tran, and Allouche 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 20230252224A1) (“Tran”), Gil David Allouche (US10607252B2), (“Allouche”), and Cai et al (US US12141556B2) (“Cai”).
Regarding Claim 5, Thimmaiah, Tran, and Allouche 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 model’s 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, Allouche, 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, the parameter adjustment of Allouche and the machine learning embedding generation of Cai. Doing so would allow for the machine learning models to be more effective ([Abstract- Cai]).
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 Tran et al (US 20230252224A1) (“Tran”), Gil David Allouche (US10607252B2), (“Allouche”), and Sumant et al (US 20230140412) (“Sumant”).
Regarding Claim 6, Thimmaiah, Tran, and Allouche teaches all the limitations of Claim 1.
Thimmaiah 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 layer, 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, Allouche, 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 ([Sumant: 0003 - 0004])
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 transforming 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.)
THIS ACTION IS MADE FINAL. 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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to URIAH V MOORE whose telephone number is (571)384-8341. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mariela Reyes can be reached at (571)270-1006. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/URIAH VENDELL MOORE/Examiner, Art Unit 2142 /Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
