DETAILED ACTION
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 .
Notice to Applicant
This is the first Non-Final Office Action in response to Application Serial Number: 19/248,224, filed on June 24, 2025. Claims 1-40 are pending in this application and have been rejected below.
Priority
The Examiner has noted the Applicant is claiming priority from Provisional Application No. 63/663,687 filed June 24, 2024.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-20 are directed towards a system and claims 21-40 are directed towards a method, both of which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite generating output data associated with a scientific query.
Claim 1 recites limitations directed to an abstract idea based on mental processes. Specifically, receive a prompt input including a scientific query; generate a set of tasks for answering the scientific query; determine one or more tools to execute the set of tasks based on available memory resources; execute the set of tasks using the one or more tools to generate output data corresponding to the scientific query; determine, based on the output data, an observation associated with the scientific query or a ranked listing of the output data constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of processors and memories having stored thereon computer-executable instructions, the use of an artificial intelligence (Al) agent including a trained machine learning (ML) model and an output computing device does not take the claim out of the mental processes grouping. Thus the claim recites an abstract idea. Claim 21 recites mental processes for similar reasons as claim 1.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites cause at least one of (i) the output data, (ii) the observation, or (iii) the ranked listing to be displayed on an output computing device, which is considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites processors and memories having stored thereon computer-executable instructions and a output computing device at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites the use of an artificial intelligence (Al) agent including a trained machine learning (ML) model to generate a set of tasks, tools to execute the set of tasks, and an observation of the query or ranked listing of the output data. The general use of artificial intelligence techniques does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the AI agent disclosed in the claim is not technological in nature and merely limits the abstract idea to a particular environment and thus fails to add an inventive concept to the claims; see MPEP 2106.05(h) and is solely used as a tool to perform the instructions of the abstract idea. Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The method recited in claim 20 also amount to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). Thus, the additional elements recited in claim 21 does not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including processors and memories having stored thereon computer-executable instructions and a output computing device amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The artificial intelligence techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0029]; [0039]; [0044]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 3, 5, 6, 15, 17, 23, 25, 35 and 37 recite storing, output, which is considered an insignificant extra-solution activities of collecting and delivering data; see MPEP 2106.05(g) and does not integrate the abstract idea into practical application. Claims 20 and 40 recite an exascale computing environment at a high level of generality and merely recites an additional elements used as a tool to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claims 2, 4, 6, 7, 10, 11, 13, 14, 16, 22, 24, 26, 27, 30, 31, 33, 34 and 36 recites AI agent and machine learning model limitations respectively and are not technological in nature and merely limits the abstract idea to a particular environment and thus fails to add an inventive concept to the claims; see MPEP 2106.05(h) and is solely used as a tool to perform the instructions of the abstract idea. Additionally, claims 8, 9, 12, 18, 19, 28, 29, 32, 38 and 39 recite steps that further narrow the abstract idea. Therefore claims 2-20 and 22-40 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6-10, 12, 19, 21, 26-30, 32 and 39 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ramsundar et al., U.S. Publication No. 2024/0241701 [hereinafter Ramsundar].
Referring to Claim 1, Ramsundar teaches:
A system for scientific computing, the system comprising:
one or more processors; and one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors (Ramsundar, [0124]; [0068]; [0023]), cause the system to:
receive a prompt input including a scientific query (Ramsundar, [0110]), “users submit requests to perform machine learning tasks via a UI 402”; (Ramsundar, [0051]), “receive a request to perform a machine learning task”; (Ramsundar, [0061]);
generate, by processing the prompt input using an artificial intelligence (Al) agent including a trained machine learning (ML) model, a set of tasks for answering the scientific query (Ramsundar, [0051]), “an apparatus includes at least one memory and at least one processor coupled to the memory and configured to cause the apparatus to receive a request to perform a machine learning task, analyze the machine learning task to determine one or more functions for performing the machine learning task, generate a workflow for the one or more functions of the machine learning task, execute the generated workflow, and provide results of the executed workflow”; (Ramsundar, [0065]), “the computation apparatus 104 is configured to receive a request to perform a machine learning task using a dataset, analyze the machine learning task to determine the functions, e.g., operations, instructions, calls, or the like, that are needed to perform the machine learning tasks, and generate a workflow for the functions such that the functions are performed in an order using machine learning models and the dataset (e.g., inputting or providing the dataset to the machine learning models)”; (Ramsundar, [0110]), “The task module 202 may receive the request, at the cloud backend 401, including any data that is to be processed/analyzed as part of the request. The machine learning tasks may include featurization, training on datasets, large scale inference, or the like”; (Ramsundar, [0049]; [0117]);
determine, by the Al agent, one or more tools to execute the set of tasks based on available memory resources (Ramsundar, [0076]), “tasks may be run in a separate cloud cluster, such as AWS Batch or a Kubernetes cluster. When the task is launched, started, or the like (e.g., as a workflow described below), in one embodiment, a docker image is fetched from a cloud docker repository. The image may then be executed as a container in a cloud instance. The instance type (e.g., number of CPUs, GPUs, memory, or the like) can be configured using job configuration IDs that can be specified during submissions of tasks”; (Ramsundar, [0090]), “workflow module 206 is configured to determine one or more requirements for performing the one or more functions of the workflow. The requirements may include resources or capabilities (e.g., CPU, GPU, memory, network bandwidth, or the like), software, hardware, or other capabilities of a device. In such an embodiment, the workflow module 206 may identify and select one or more nodes for performing the one or more functions of the workflow based on the one or more requirements”; (Ramsundar, [0091]), “the workflow module 206 may execute a workflow on a dedicated or default node that is always available for running workflows, e.g., so that the user does not have to wait and/or pay to spin up a new node for a particular workflow. For example, if the machine learning task includes an analysis of a small data set that falls within the resources and capabilities available by the default node, then the workflow module 206 may run the workflow on the default node instead of using the resources, time, energy, and cost to identify, set up, spin up, and use a new node”; (Ramsundar, [0117]);
execute the set of tasks using the one or more tools to generate output data corresponding to the scientific query (Ramsundar, [0111]), “the workflow module 206 on the cloud backend 401 generates a workflow for the one or more functions of the machine learning task and determines one or more cloud nodes 404 for executing the workflow”; (Ramsundar, [0065]), “… executes the workflow and provides the results”; (Ramsundar, [0051]), “analyze the machine learning task to determine one or more functions for performing the machine learning task, generate a workflow for the one or more functions of the machine learning task, execute the generated workflow”; (Ramsundar, [0102]), “parses the functions/primitives of the workflow, generates an abstract syntax tree, and analyzes the rules for each operation. For each data point in the dataset, the budget module 208 may determine the processing needed and the associated cost (e.g., based on a linear function for each primitive). The costs may be allocated per data point and may be different for each data point depending on the processing/computation needed for each data point. The costs may also be impacted by the node that is used to execute the workflow. For instance, a node with more resources, with newer resources, and/or the like may cost more than a node with limited or older resources”; (Ramsundar, [0090]), “workflow module 206 is configured to determine one or more requirements for performing the one or more functions of the workflow. The requirements may include resources or capabilities (e.g., CPU, GPU, memory, network bandwidth, or the like), software, hardware, or other capabilities of a device. In such an embodiment, the workflow module 206 may identify and select one or more nodes for performing the one or more functions of the workflow based on the one or more requirements”;
determine, by the Al agent based on the output data, an observation associated with the scientific query or a ranked listing of the output data; and cause at least one of (i) the output data, (ii) the observation, or (iii) the ranked listing to be displayed on an output computing device (Ramsundar, [0095]), “the workflow module 206 provides or transmits the results of the executed workflow. For instance, the workflow module 206 may transmit the results directly to a user (e.g., via an API or other message), or may store the results in the datastore and provide a link or address to the user for accessing or viewing the results, and/or the like”; (Ramsundar, [0117]), “…The computation apparatus 104 may further provide for post-processing of the results including filter the results, eliminate outlier data, cluster data, and/or the like, as it relates to the molecular modeling”; (Ramsundar, [0051]; [0120]; [0123]).
Referring to Claim 6, Ramsundar teaches the system of claim 1. Ramsundar further teaches:
wherein the computer-executable instructions further cause the system to: receive context information corresponding to the prompt input; and inject the context information to the prompt input and/or to the Al agent (Ramsundar, [0079]), “the task module 202 receives a dataset as part of the request. The dataset may be provided to, input into, or the like the machine learning models that are used to perform the task. The dataset may be used for other purposes as well, including statistical analyses, or other processing. The dataset may include experimental data, simulation data, data describing physical laws, and/or the like. The request may further include a desired outcome or goal, constraints, limitations, or the like on the computations”; (Ramsundar, [0065]), “the computation apparatus 104 is configured to receive a request to perform a machine learning task using a dataset, analyze the machine learning task to determine the functions, e.g., operations, instructions, calls, or the like, that are needed to perform the machine learning tasks, and generate a workflow for the functions such that the functions are performed in an order using machine learning models and the dataset (e.g., inputting or providing the dataset to the machine learning models)”.
Referring to Claim 7, Ramsundar teaches the system of claim 1. Ramsundar further teaches:
wherein the computer-executable instructions further cause the system to determine, by the Al agent, one or more tools to execute the set of tasks based on available processing resources (Ramsundar, [0076]), “tasks may be run in a separate cloud cluster, such as AWS Batch or a Kubernetes cluster. When the task is launched, started, or the like (e.g., as a workflow described below), in one embodiment, a docker image is fetched from a cloud docker repository. The image may then be executed as a container in a cloud instance. The instance type (e.g., number of CPUs, GPUs, memory, or the like) can be configured using job configuration IDs that can be specified during submissions of tasks”; (Ramsundar, [0090]), “workflow module 206 is configured to determine one or more requirements for performing the one or more functions of the workflow. The requirements may include resources or capabilities (e.g., CPU, GPU, memory, network bandwidth, or the like), software, hardware, or other capabilities of a device. In such an embodiment, the workflow module 206 may identify and select one or more nodes for performing the one or more functions of the workflow based on the one or more requirements”; (Ramsundar, [0091]), “the workflow module 206 may execute a workflow on a dedicated or default node that is always available for running workflows, e.g., so that the user does not have to wait and/or pay to spin up a new node for a particular workflow. For example, if the machine learning task includes an analysis of a small data set that falls within the resources and capabilities available by the default node, then the workflow module 206 may run the workflow on the default node instead of using the resources, time, energy, and cost to identify, set up, spin up, and use a new node”; (Ramsundar, [0117]).
Referring to Claim 8, Ramsundar teaches the system of claim 1. Ramsundar further teaches:
wherein determining one or more tools to execute the set of tasks based on available memory resources includes determining an amount of memory required by a tool of the one or more tools to execute the set of tasks is less than a threshold amount of memory causing a memory failure (Ramsundar, [0052]-[0053]), “processor is configured to cause the apparatus to determine one or more requirements for performing the one or more functions of the workflow… identify one or more nodes for performing the one or more functions of the workflow based on the one or more requirements and transmit the workflow to the identified one or more nodes for performing the one or more functions of the workflow”; (Ramsundar, [0105]), “the checkpoint module 212 is configured to generate one or more checkpoints during execution of the workflow. For instance, if the estimated computation time for a workflow satisfies a threshold computation time, e.g., hours, days, weeks, or the like, the checkpoint module 212 may create one or more checkpoints during execution of the workflow. As used herein, a checkpoint may refer to the process of saving a snapshot of the workflow state, so that the workflow can restart from that point in case of failure or other interruption”; (Ramsundar, [0057]), “the at least one processor is configured to cause the apparatus to present a prompt for approval to proceed with execution of the workflow in response to the determined cost satisfying a threshold cost…”; (Ramsundar, [0090]; [0098]).
Referring to Claim 9, Ramsundar teaches the system of claim 1. Ramsundar further teaches:
wherein the computer-executable instructions further cause the system to: receive updated available memory resources; and execute the set of tasks based on the updated available memory resources (Ramsundar, [0057]), “at least one processor is configured to cause the apparatus to present a prompt for approval to proceed with execution of the workflow in response to the determined cost satisfying a threshold cost. In one embodiment, the at least one processor is configured to cause the apparatus to generate one or more visualizations associated with the workflow. In one embodiment, the one or more visualizations comprises an interactive molecular visualization that allows real-time editing of a molecule”; (Ramsundar, [0039]; [0094]; [0106]).
Referring to Claim 10, Ramsundar teaches the system of claim 7. Ramsundar further teaches:
wherein the computer-executable instructions further cause the system to: dynamically switch a current machine learning model used to execute the set of tasks to another machine learning model based on the updated available memory resources (Ramsundar, [0091]), “the workflow module 206 may execute a workflow on a dedicated or default node that is always available for running workflows, e.g., so that the user does not have to wait and/or pay to spin up a new node for a particular workflow. For example, if the machine learning task includes an analysis of a small data set that falls within the resources and capabilities available by the default node, then the workflow module 206 may run the workflow on the default node instead of using the resources, time, energy, and cost to identify, set up, spin up, and use a new node”; (Ramsundar, [0037]; [0093]).
Referring to Claim 12, Ramsundar teaches the system of claim 1. Ramsundar further teaches:
wherein the computer-executable instructions further cause the system to: determine, based on the scientific query and the output data, that additional processing of the output data is required (Ramsundar, [0117]), “The computation apparatus 104 may further provide for post-processing of the results including filter the results, eliminate outlier data, cluster data, and/or the like, as it relates to the molecular modeling”.
Referring to Claim 19, Ramsundar teaches the system of claim 1. Ramsundar further teaches:
wherein the set of tasks includes generating a set of executable code (Ramsundar, [0055]), “the containerized workflow comprises a command script comprising instructions for performing the one or more functions of the workflow. In one embodiment, the at least one processor is configured to cause the apparatus to store the dataset, the results, model inputs, model outputs, or a combination thereof, in dedicated storage associated with the workflow”; (Ramsundar, [0051]; [0075]; [0094]).
Referring to Claim 21, Ramsundar teaches:
A method for scientific computing, the method comprising:
Claim 21 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Claim 26 disclose substantially the same subject matter as claim 6, and is rejected using the same rationale as previously set forth.
Claim 27 disclose substantially the same subject matter as claim 7, and is rejected using the same rationale as previously set forth.
Claim 28 disclose substantially the same subject matter as claim 8, and is rejected using the same rationale as previously set forth.
Claim 29 disclose substantially the same subject matter as claim 9, and is rejected using the same rationale as previously set forth.
Claim 30 disclose substantially the same subject matter as claim 10, and is rejected using the same rationale as previously set forth.
Claim 32 disclose substantially the same subject matter as claim 12, and is rejected using the same rationale as previously set forth.
Claim 39 disclose substantially the same subject matter as claim 19, and is rejected using the same rationale as previously set forth.
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.
Claims 2 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsundar et al., U.S. Publication No. 2024/0241701 [hereinafter Ramsundar], and further in view of Anthony et al., U.S. Publication No. 2025/0005224 [hereinafter Anthony].
Referring to Claim 2, Ramsundar teaches the system of claim 1. Ramsundar teaches a cloud scientific machine learning programming environment that provides access to pretrained foundation models, datasets, and a rich scientific API, along with details of the organization/project collaboration structure, and a cloud storage datastore (see par. 0038), but Ramsundar does not explicitly teach:
wherein the trained machine learning model is a large language model (LLM).
However Anthony teaches:
wherein the trained machine learning model is a large language model (LLM) (Anthony, [0024]), “The prompt engineering interface service may use a variety of techniques such as… effective prompts for use with large language models, chatbots, virtual assistants, and the like… the prompt engineering interface service may generate natural language prompts to obtain relevant and accurate responses from large language models that can be incorporated into a data model, system configuration data, and the like”; (Anthony, [0045]; [0080]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified machine learning programming environment in Ramsundar to include the LLM limitation as taught by Anthony. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of utilizing large language models to assist with designing systems (see Anthony par. 0002).
Claim 22 disclose substantially the same subject matter as claim 2, and is rejected using the same rationale as previously set forth.
Claims 3-5 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsundar et al., U.S. Publication No. 2024/0241701 [hereinafter Ramsundar], and further in view of Li et al., U.S. Publication No. 2022/0329535 [hereinafter Li].
Referring to Claim 3, Ramsundar teaches the system of claim 1. Ramsundar teaches parses the functions/primitives of the workflow, generates an abstract syntax tree, and analyzes the rules for each operation (see par. 0102) and generating a workflow for the one or more functions of the machine learning task (see par. 0051), but Ramsundar does not explicitly teach:
wherein the computer-executable instructions further cause the system to store, in a database, a tuple for controlling a decision-making process of the agent for generating the set of tasks.
However Li teaches:
wherein the computer-executable instructions further cause the system to store, in a database, a tuple for controlling a decision-making process of the agent for generating the set of tasks (Li, [0071]), “The decision making module 403 determines, based on the output of the information analysis module 402, how to schedule a data flow. The scheduling operation includes scheduling a newly generated data flow and scheduling a data flow that is being transmitted…Therefore, the decision making module 403 may generate a plurality of types of instructions and send the plurality of types of instructions to the decision execution module 404. The plurality of types of instructions include at least one of the following: a card binding instruction for instructing to bind a data flow (that is, a 5-tuple) to a network interface card; a link disconnection instruction for instructing to kill a data flow, where the data flow is also indicated by a 5-tuple; and an IP address change instruction for changing a source IP address in a 5-tuple of a data flow”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified parsing and abstract syntax tree in Ramsundar to include the tuple limitation as taught by Li. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of effectively reducing a stalling rate of the service (see Li par. 0116).
Referring to Claim 4, Ramsundar in view of Li teaches the system of claim 3. Ramsundar teaches parses the functions/primitives of the workflow, generates an abstract syntax tree, and analyzes the rules for each operation (see par. 0102) and generating a workflow for the one or more functions of the machine learning task (see par. 0051), but Ramsundar does not explicitly teach:
wherein the computer-executable instructions further cause the system to: in response to receiving at least one of a new prompt input or a new scientific query, autonomously adapt the decision-making process of the agent by retraining the agent object with the stored tuple.
However Sequeira teaches:
wherein the computer-executable instructions further cause the system to: in response to receiving at least one of a new prompt input or a new scientific query, autonomously adapt the decision-making process of the agent by retraining the agent object with the stored tuple (Li, [0071]), “The decision making module 403 determines, based on the output of the information analysis module 402, how to schedule a data flow. The scheduling operation includes scheduling a newly generated data flow and scheduling a data flow that is being transmitted. For the newly generated data flow, a network interface card (that is, a network that is used) from which the newly generated data flow should be sent needs to be determined. For the data flow that is already being transmitted, whether the data flow needs to be adjusted needs to be determined. For example, as a network status changes, whether stalling occurs in a service carried in the currently transmitted data flow, whether the data flow needs to be killed, and whether a new data flow corresponding to a killed data flow needs to be created again through another network are determined. Therefore, the decision making module 403 may generate a plurality of types of instructions and send the plurality of types of instructions to the decision execution module 404. The plurality of types of instructions include at least one of the following: a card binding instruction for instructing to bind a data flow (that is, a 5-tuple) to a network interface card; a link disconnection instruction for instructing to kill a data flow, where the data flow is also indicated by a 5-tuple; and an IP address change instruction for changing a source IP address in a 5-tuple of a data flow”; (Li, [0083]), “data flow transmission process, when it is learned, through identification, that a packet received or sent by using a data flow includes a field indicating a type of service, that is, when the packet matches a preconfigured identification characteristic of the service type, it may be determined that the packet corresponds to the service type. For example, a URL including a string such as mp4, ts, or flv corresponds to video on demand. Alternatively, the data flow in which the packet is located is determined based on a 5-tuple of the packet, so that the service corresponding to the data flow can be determined. In this way, received or sent data flows can be distinguished based on services”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified parsing and abstract syntax tree in Ramsundar to include the tuple limitation as taught by Li. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of effectively reducing a stalling rate of the service (see Li par. 0116).
Referring to Claim 5, Ramsundar in view of Li teaches the system of claim 3. Ramsundar teaches parses the functions/primitives of the workflow, generates an abstract syntax tree, and analyzes the rules for each operation (see par. 0102) and generating a workflow for the one or more functions of the machine learning task (see par. 0051), but Ramsundar does not explicitly teach:
wherein the computer-executable instructions further cause the system to: load a current tuple for controlling a decision-making process of the agent for generating a current set of tasks into working memory; retrieve the stored tuple from a database, wherein a similarity score between the current tuple and a stored threshold is above a threshold; and update the current set of tasks with the set of tasks associated with the stored tuple.
However Li teaches:
wherein the computer-executable instructions further cause the system to: load a current tuple for controlling a decision-making process of the agent for generating a current set of tasks into working memory; retrieve the stored tuple from a database, wherein a similarity score between the current tuple and a stored threshold is above a threshold; and update the current set of tasks with the set of tasks associated with the stored tuple (Li, [0068]), “provides a description by using an example in which the information analysis module 402 includes the information sorting module and the information inference module. It should be understood that, in some implementations, functions of the information sorting module and the information inference module may be integrated into one module for implementation. The information sorting module is configured to analyze the information obtained from the information collection module 401, to obtain performance of transmitting the data flow. In an implementation, the module performs analysis by using a data table. For example, if a row in the data table represents a data flow (namely, a 5-tuple), a 5-tuple in an obtained data packet may be used to perform indexing and search in the data table. Further, the data table may be updated or edited in an analysis process”; (Li, [0015]), “recording a correspondence between at least one parameter in a 5-tuple of the first data flow and the first network interface. In this way, during subsequent creation of a new data flow, if some parameters are the same as those of the first data flow, reference may be made to the correspondence”; (Li, [0016]; [0037]; [0071]; [0087]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified parsing and abstract syntax tree in Ramsundar to include the tuple limitation as taught by Li. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of effectively reducing a stalling rate of the service (see Li par. 0116).
Claim 23 disclose substantially the same subject matter as claim 3, and is rejected using the same rationale as previously set forth.
Claim 24 disclose substantially the same subject matter as claim 4, and is rejected using the same rationale as previously set forth.
Claim 25 disclose substantially the same subject matter as claim 5, and is rejected using the same rationale as previously set forth.
Claims 11 and 31 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsundar et al., U.S. Publication No. 2024/0241701 [hereinafter Ramsundar], and further in view of Xu et al., U.S. Publication No. 2021/0312007 [hereinafter Xu]
Referring to Claim 11, Ramsundar teaches the system of claim 10. Ramsundar teaches identifying nodes for performing functions of a workflow based on requirements (see par. 0053), but Ramsundar does not explicitly teach:
wherein the another trained machine learning model is a quantized version of the current trained machine learning model, wherein the quantized version of the another trained machine learning model uses less memory resources than the current trained machine learning model.
However Xu teaches:
wherein the another trained machine learning model is a quantized version of the current trained machine learning model, wherein the quantized version of the another trained machine learning model uses less memory resources than the current trained machine learning model (Xu, [0033]), “Each quantized decision model 175 can correspond to a quantized version of a decision model 144. As a result, a quantized decision model 175 can take a smaller input resolution, make faster predictions, include a smaller memory footprint, and can take fewer single-pass floating point operations per second (FLOPs) to save compute and electrical power. The quantized decision model 175 can enable edge layer predictions 176 and actions 148 to be performed using the edge device 114 if network connectivity is temporarily unavailable, or when a decision is time sensitive…”; (Xu, [0031]), “A task profile 149 can include a profile of requirements for a quantized decision model 175… The edge layer decision threshold can refer to a minimum prediction confidence in percentage to delegate decision- making to the edge device 114, such that the edge device 114 can utilize the quantized decision model 175 without consulting the cloud decision manager 160 and the higher-confidence version or decision model 144…”; (Xu, [0040]; [0056]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified nodes for performing functions in Ramsundar to include the quantized limitation as taught by Xu. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of compressing decision models (see Xu par. 0011).
Claim 31 disclose substantially the same subject matter as claim 11, and is rejected using the same rationale as previously set forth.
Claims 13-17 and 33-37 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsundar et al., U.S. Publication No. 2024/0241701 [hereinafter Ramsundar], and further in view of Bonatti et al., U.S. Publication No. 2025/0383921 [hereinafter Bonatti]
Referring to Claim 13, Ramsundar teaches the system of claim 1. Ramsundar teaches analyzing the machine learning task to determine the functions, e.g., operations, instructions, calls, or the like, that are needed to perform the machine learning tasks, and generate a workflow for the functions such that the functions are performed in an order using machine learning models and the dataset (see par. 0065), but Ramsundar does not explicitly teach:
wherein the computer-executable instructions further cause the system to: select, by the Al agent and based on the available computing resources and prompt input, a second agent including a second trained machine learning model; and execute, by the second agent, one or more tasks in the set of tasks.
However Bonatti teaches:
wherein the computer-executable instructions further cause the system to: select, by the Al agent and based on the available computing resources and prompt input, a second agent including a second trained machine learning model; and execute, by the second agent, one or more tasks in the set of tasks (Bonatti, [0155]), “a computing system includes providing input to a first machine learning model, where the first machine learning model outputs a directed acyclic graph that includes nodes and edges based upon the input. The nodes represent steps of a multi-step task to be performed by a computer-executable agent and the edges represent relationships between the steps. The method also includes providing a step in the multi-step task to a second machine learning model, where the step is represented by a node in the acyclic graph. The second machine learning model outputs an action based upon the step, where the action is to be performed by the computer-executable agent to complete the step. The method additionally includes transforming the action into computer-executable code that is to be executed by the computer-executable agent…”; (Bonatti, [0157]-[0160]; [0170]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the generated workflows in Ramsundar to include the second agent limitations as taught by Bonatti. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of outputting an updated or new high-level plan to complete the task (see Bonatti par. 0045).
Referring to Claim 14, Ramsundar teaches the system of claim 12. Ramsundar teaches analyzing the machine learning task to determine the functions, e.g., operations, instructions, calls, or the like, that are needed to perform the machine learning tasks, and generate a workflow for the functions such that the functions are performed in an order using machine learning models and the dataset (see par. 0065), but Ramsundar does not explicitly teach:
wherein the second trained machine learning model is a multimodal machine learning model.
However Bonatti teaches:
wherein the second trained machine learning model is a multimodal machine learning model (Bonatti, [0038]-[0039]), “The prompt tools 502 facilitate in-context learning by a generative model as well as construction of chain of thoughts. The prompt tools 502 additionally facilitate multimodal prompting (e.g., where a prompt includes multi-modal content, such as text and image(s))… The planner module 206 also includes an orchestration module 504 that is configured to coordinate the use of multiple specialized machine learning models… the orchestration module 504 can provide a first prompt to a first machine learning model, receive output from the first machine learning model, construct a second prompt based upon output of the first machine learning model (and the prompt tools 502), and provide the second prompt to a second machine learning model, where the first and second machine learning models execute on different machines”; (Bonatti, [0032]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the generated workflows in Ramsundar to include the second machine learning model limitation as taught by Bonatti. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of outputting an updated or new high-level plan to complete the task (see Bonatti par. 0045).
Referring to Claim 15, Ramsundar in view of Bonatti teaches the system of claim 14. Ramsundar further teaches:
wherein the at least one of (i) the output data, (ii) the observation, or (iii) the ranked listing is a multimodal output (Ramsundar, [0103]), “the visualization module 210 is configured to generate one or more visualizations associated with the workflow. The visualizations may include graphical images, videos, graphs, charts, models, plots, plans, CAD models, and/or the like. The visualizations may be interactive such that the user can interact with the visualization using touch input, mouse input, keyboard input, voice input, motion input, and/or the like”.
Referring to Claim 16, Ramsundar in view of Bonatti teaches the system of claim 14. Ramsundar teaches visualization module is configured to generate one or more visualizations associated with the workflow (see par. 0103), but Ramsundar does not explicitly teach:
wherein the second trained machine learning model is trained to generate a graphical representation of the output data corresponding to the scientific query.
However Bonatti teaches:
wherein the second trained machine learning model is trained to generate a graphical representation of the output data corresponding to the scientific query (Bonatti, [0032]), “, the generative model can output an acyclic graph that is representative of the high-level plan. The generative model (or a different generative model) is then provided with a prompt that includes a step represented in the high-level plan, and the planner module 206 outputs a low-level plan for such step, where the low-level plan includes a series of actions that are to be performed by the agent 100 to complete the step”; (Bonatti, [0043]; [0053]; [0057]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the visualization in Ramsundar to include the graphical representation limitation as taught by Bonatti. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of outputting an updated or new high-level plan to complete the task (see Bonatti par. 0045).
Referring to Claim 17, Ramsundar in view of Bonatti teaches the system of claim 16. Ramsundar further teaches:
wherein the graphical representation of the output data is a plot of the output data (Ramsundar, [0103]), “The visualizations may include graphical images, videos, graphs, charts, models, plots, plans, CAD models, and/or the like”.
Claim 33 disclose substantially the same subject matter as claim 13, and is rejected using the same rationale as previously set forth.
Claim 34 disclose substantially the same subject matter as claim 14, and is rejected using the same rationale as previously set forth.
Claim 35 disclose substantially the same subject matter as claim 15, and is rejected using the same rationale as previously set forth.
Claim 36 disclose substantially the same subject matter as claim 16, and is rejected using the same rationale as previously set forth.
Claim 37 disclose substantially the same subject matter as claim 17, and is rejected using the same rationale as previously set forth.
Claims 18 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsundar et al., U.S. Publication No. 2024/0241701 [hereinafter Ramsundar], and further in view of Zheng et al., U.S. Publication No. 2025/0348740 [hereinafter Zheng].
Referring to Clam 18, Ramsundar teaches the system of claim 1. Ramsundar teaches providing results of an executed workflow (see par. 0051), but Ramsundar does not explicitly teach:
wherein the computer-executable instructions further cause the system to: determine a relevance of each observation of the output data to the prompt input; and rank the observations of the output data from most relevant to least relevant to the prompt input.
However Zheng teaches:
wherein the computer-executable instructions further cause the system to: determine a relevance of each observation of the output data to the prompt input; and rank the observations of the output data from most relevant to least relevant to the prompt input (Zheng, [0073]), “the top k content recommendations are ranked according to relevance”; (Zheng, [0185]), “the recommendation engine 766 can include a machine learning model trained to rank the digital content recommendations according to a relevance of the search query and/or a relevance of the search query given user information”; (Zheng, [0058]), “given an input of a list of items A, B, C and the search query 107, an output of a model executing listwise ranking is a ranking of the list of items ABC, e.g., a ranking score that reflects the relevance of the entire list A, B, C to the search query 107”; (Zheng, [0152]; [0181]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the workflow results in Ramsundar to include the rank and relevance limitations as taught by Zheng. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of identifying high-quality content recommendations (see Zheng par. 0030).
Claim 38 disclose substantially the same subject matter as claim 18, and is rejected using the same rationale as previously set forth.
Claims 20 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsundar et al., U.S. Publication No. 2024/0241701 [hereinafter Ramsundar], and further in view of Guyan et al., U.S. Publication No. 2022/0294865 [hereinafter Guyan].
Referring to Claim 20, Ramsundar teaches the system of claim 1. Ramsundar teaches a cloud scientific machine learning programming environment (see par. 0038), but Ramsundar does not explicitly teach:
wherein the set of tasks are executed in an exascale computing environment.
However Guyan teaches:
wherein the set of tasks are executed in an exascale computing environment (Guyan, [0010]-[0011]), “A cluster (e.g., an exascale high performance computing (HPC) cluster) may have a suite of management software, called a “cluster manager,” for purposes of providing management-related services for the cluster, such as set up and provisioning services, hardware monitoring services, system alert notification services, and so forth. For purposes of accessing and using these services, the cluster manager may provide a user interface, such as a command line interface and/or a graphical user interface (GUI)… For a cluster having a relatively large number of nodes (e.g., an exascale cluster having hundreds, if not thousands or tens of thousands of nodes), it may be challenging to identify and troubleshoot problems with the cluster using a command line-based interface or a GUI-based user interface…”; (Guyan, [0015]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the machine learning programming environment in Ramsundar to include the computing environment limitation as taught by Guyan. The motivation for doing this would have been to improve the method related to techniques for a cloud scientific machine learning programming environment in Ramsundar (see par. 0002) to efficiently include the results of efficiently process the information and recognize problematic trends with the cluster (see Guyan par. 0011).
Claim 40 disclose substantially the same subject matter as claim 20, and is rejected using the same rationale as previously set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen (US 20240311198 A1) – A first machine learning task having a first data size is executed via virtualized computing resource units in a research workspace. The first machine learning task is associated with the virtualized computing resource units and with an amount of execution time. A second machine learning task is executed in a production workspace having a plurality of physical computing resource units. The second machine learning task has a same algorithm as the first machine learning task and a second data size greater than the first data size. A subset of the physical computing resource units is allocated for the execution of the second machine learning task in the production workspace. The allocating is based on the virtualized computing resource units used during an execution of the first machine learning task in the research workspace and the amount of execution time of the first machine learning task in the research workspace.
Sriram et al. (Leveraging Local LLMs for Secure In-System Task Automation With Prompt-Based Agent Classification) – Recent progress in the field of intelligence has led to the creation of powerful large language models (LLMs). While these models show promise in improving personal computing experiences concerns surrounding data privacy and security have hindered their integration with sensitive personal information. In this study, a new framework is proposed to merge LLMs with personal file systems, enabling intelligent data interaction while maintaining strict privacy safeguards. The methodology organizes tasks based on LLM agents, which apply designated tags to the tasks before sending them to specific LLM modules. Every module is has its own function, including file search, document summarization, code interpretation, and general tasks, to make certain that all processing happens locally on the user’s device. Findings indicate high accuracy across agents: classification agent managed to get an accuracy rating of 86%, document summarization reached a BERT score of 0.9243. The key point of this framework is that it splits the LLM system into modules, which enables future development by integrating new task-specific modules as required. Findings suggest that integrating local LLMs can significantly improve interactions with file systems without compromising data privacy.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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