DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Application filed on 4/1/2024.
Claims 1-20 are pending. Claims 1, 11, and 19 are independent.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
Claim 1 recites:
causing one or more models to be trained during a training process;
determining, during the training process and by one or more processing units, one or more attributes associated with the one or more models; and
storing the one or more attributes as metadata in association with the one or more models.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human can determine attributes of a model mentally or with a pen and paper.
Step 2A, Prong 1 (Yes)
Claim 11 recites:
receive, from an endpoint, a request to execute a model using one or more devices associated with the endpoint;
obtain metadata corresponding to the model, the metadata indicating at least one or more attributes associated with the model;
evaluate the one or more attributes with respect to at least one of a policy associated with the endpoint or one or more capabilities associated with the one or more devices; and
provide, to the endpoint, at least one of:
at least a portion of data for executing the model using the one or more devices; or
an indication that the model is unavailable for use with the one or more devices.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind or by a human using pen and paper. A human can evaluate attributes of a model mentally or with a pen and paper.
Step 2A prong 1 (Yes)
Step 2A, Prong 2
The additional elements in claim 1 are “causing one or more models to be trained” and “storing the one or more attributes”. These elements are recited at a high level of generality and thus is a generic computer component performing computer functions. These are mere instructions to apply the exception using a generic computer component. See MPEP 2106.5(f).
Even when viewed in combination the additional element does not integrate the recited judicial exception into a practical application.
Step 2A, Prong 2 (No).
The additional elements in claim 11 are “receive a request” and “obtain metadata”, and “provide to the endpoint”. These elements are understood to be about receiving information, which is understood to be insignificant extra-solution activity such as data gathering. See MPEP 2106.5(g).
Even when viewed in combination the additional element does not integrate the recited judicial exception into a practical application.
Step 2A, Prong 2 (No).
Step 2B
As explained with respect to Step 2A, the only additional elements of claim 1 are “causing one or more models to be trained” and “storing the one or more attributes” which at best is mere instruction to apply the abstract ideas and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(f).
Step 2B (No).
Claim 1 is ineligible.
As explained with respect to Step 2A, the only additional elements of claim 11 are “receive a request” and “obtain metadata”, and “provide to the endpoint” which at best is insignificant extra-solution activity and cannot provide an inventive concept, even when considered in combination. See MPEP 2106.05(g).
Step 2B (No).
Claim 11 is ineligible.
With respect to claim 19,
This claim is similar in scope to claim 1 and is rejected under a similar rationale. The processor recited in this claim are also generic computing components
Claim 19 is ineligible
Dependent Claims:
Claims 2-10, 12-18, and 20: These claims only recite further abstract ideas (mental processes) and thus are ineligible.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-8, 10-12, and 14-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Yao et al. (US2023/0368077).
In regards to claim 1, Yao et al. discloses a method comprising:
causing one or more models to be trained during a training process (Yao et al. para[0061], ML training management involves allowing the MnS consumer to request and/or manage the model training/retraining);
determining, during the training process and by one or more processing units, one or more attributes associated with the one or more models (Yao et al. para[0097], attribute represents the status of the ML model training and includes information the consumer can use to monitor progress and results); and
storing the one or more attributes as metadata in association with the one or more models (Yao et al. para[0188], model repository stores models, model parameters, and model metadata).
In regards to claim 2, Yao et al. discloses the method of claim 1, wherein determining the one or more attributes by one or more processing units comprises executing one or more software libraries that include one or more instructions to obtain the metadata during the training process (Yao et al. para[0048], testing the ML model with testing data to evaluate the performance of the trained ML entity for selection for inference ).
In regards to claim 3, Yao et al. discloses the method of claim 2, further comprising wrapping one or more second software libraries of the training process using the one or more software libraries, the one or more second software libraries including one or more second instructions to train the one or more models (Yao et al. para[0049], testing to check whether the ML model works when deployed in or at the target node).
In regards to claim 4, Yao et al. discloses the method of claim 1, wherein the one or more attributes indicate one or more hardware thresholds for one or more devices to execute the one or more models, the one or more hardware thresholds including at least one of:
one or more central processing unit (CPU) thresholds,
one or more memory thresholds,
one or more graphics processing unit (GPU) thresholds,
one or more data processing unit (DPU) thresholds, or
one or more network hardware unit thresholds (Yao et al. para[0188], hardware platform/configuration data).
In regards to claim 5, Yao et al. discloses the method of claim 1, wherein the metadata is stored in association with the one or more models during the training process (Yao et al. para[0220], report the performance of the ML entity when performing on the validation data in the ML entity training).
In regards to claim 6, Yao et al. discloses the method of claim 1, further comprising storing, in one or more model archives including data for executing the one or more models, the metadata using one or more model cards corresponding to the one or more models (Yao et al. para[0188], model repository stores model data including model metadata).
In regards to claim 8, Yao et al. discloses the method of claim 1, further comprising:
receiving, from an endpoint, a query for the metadata associated with a model (Yao et al. para[0144], receives NF discovery requests from NF instances);
obtaining, based at least on the query, the metadata associated with the one or more models from one or more model archives; and providing the metadata to the endpoint (Yao et al. para[0144], provides information on discovered NF instance to requesting NF instance).
In regards to claim 9, Yao et al. discloses the method of claim 1, further comprising:
receiving, from an endpoint, a request to provide data for executing the one or more models at the endpoint (Yao et al. fig. 1 para[0022], receives request from consumer (endpoint) for executing model);
evaluating at least one of a policy associated with the endpoint or one or more capabilities associated with the endpoint (Yao et al. para[0028], evaluates ML entity works correctly under certain runtime contexts or certain constraints ); and
determining, based at least on the evaluation of at least one of the policy or the one or more capabilities with respect to the metadata, whether to provide the data to the endpoint (Yao et al. para[0029], provides ML entity to consumer (endpoint) after passing validation).
In regards to claim 10, Yao et al. discloses the method of claim 1, wherein at least one attribute of the one or more attributes comprises at least one of:
an identifier corresponding to a model of the one or more models;
information associated with one or more datasets used to train the model;
license information associated with the model;
a risk score associated with the model;
a bias score associated with the model; or
a hardware specification associated with the model (Yao et al. para[0188], hardware platform/configuration data).
In regards to claim 11, Yao et al. discloses a system comprising:
one or more processors to:
receive, from an endpoint, a request to execute a model using one or more devices associated with the endpoint (Yao et al. para[0061], ML training management involves allowing the MnS consumer to request and/or manage the model training/retraining);
obtain metadata corresponding to the model, the metadata indicating at least one or more attributes associated with the model (Yao et al. para[0097], attribute represents the status of the ML model training and includes information the consumer can use to monitor progress and results);
evaluate the one or more attributes with respect to at least one of a policy associated with the endpoint or one or more capabilities associated with the one or more devices (Yao et al. para[0028], evaluates ML entity works correctly under certain runtime contexts or certain constraints ); and
provide, to the endpoint, at least one of: at least a portion of data for executing the model using the one or more devices(Yao et al. para[0029], provides ML entity to consumer (endpoint) after passing validation); or
an indication that the model is unavailable for use with the one or more devices.
In regards to claim 12, Yao et al. discloses the system of claim 11, wherein the one or more attributes indicate one or more hardware thresholds for the one or more devices to execute the model, the one or more hardware thresholds including at least one of:
one or more central processing unit (CPU) thresholds, one or more memory thresholds, one or more graphics processing unit (GPU) thresholds, one or more data processing unit (DPU) thresholds, or one or more network hardware unit thresholds (Yao et al. para[0188], hardware platform/configuration data).
In regards to claim 14, Yao et al. discloses the system of claim 11, wherein the evaluation comprises:
determining, using the metadata, one or more hardware thresholds corresponding to one or more hardware capabilities for executing the model (Yao et al. para[0188], hardware platform/configuration data);
evaluating the one or more hardware thresholds with respect to the one or more capabilities associated with the one or more devices (Yao et al. para[0028], evaluates ML entity works correctly under certain runtime contexts or certain constraints ); and
determining, based at least on the one or more hardware thresholds, whether to provide the data to the endpoint for executing the model (Yao et al. para[0029], provides ML entity to consumer (endpoint) after passing validation).
In regards to claim 15, Yao et al. discloses the system of claim 11, the one or more processors further to:
cause the model to be trained during a training process (Yao et al. para[0061], ML training management involves allowing the MnS consumer to request and/or manage the model training/retraining);
determine, during the training process, the one or more attributes associated with the model (Yao et al. para[0097], attribute represents the status of the ML model training and includes information the consumer can use to monitor progress and results); and
store the one or more attributes as the metadata in association with the model (Yao et al. para[0188], model repository stores models, model parameters, and model metadata).
In regards to claim 16, Yao et al. discloses the system of claim 15, the one or more processors further to:
access one or more software libraries including one or more instructions for obtaining the metadata during the training process (Yao et al. para[0048], testing the ML model with testing data to evaluate the performance of the trained ML entity for selection for inference ); and
execute the one or more software libraries during the training process used to train the model, wherein at least one of the determination of the one or more attributes or the storing of the one or more attributes is based at least on the modification of the training process (Yao et al. para[0049], testing to check whether the ML model works when deployed in or at the target node).
In regards to claim 17, Yao et al. discloses the system of claim 11, the one or more processors further to:
determine that at least one of the policy or the one or more capabilities prevents the one or more devices from executing the model (Yao et al. para[0188], accesses hardware platform/configuration data);
identify, using at least one of the policy or the one or more capabilities, a second model that the one or more devices are capable of executing (Yao et al. para[0189], selects another model); and
sending, to the endpoint, second metadata indicating at least one or more second attributes associated with the second model (Yao et al. para[0196], Additional or alternative model-based metrics may also be predicted).
In regards to claim 18, Yao et al. discloses the system of claim 11, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing one or more generative AI operations; a system for performing operations using a large language model; a system for performing one or more conversational AI operations; a system for generating synthetic data; a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Yao et al. para[0152], the servers can include cloud compute nodes or cloud compute resources).
In regards to claim 19, Yao et al. discloses at least one processor comprising: one or more circuits to generate and store metadata during a training process for one or more models, the metadata indicating at least one or more attributes associated with the one or more models and being stored in association with the one or more models (Yao et al. para[0188], model repository stores models, model parameters, and model metadata).
In regards to claim 20, Yao et al. discloses the processor of claim 19, wherein the processor is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing one or more simulation operations;
a system for performing one or more digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing one or more generative AI operations;
a system implemented using one or more large language models (LLMs);
a system implemented using one or more vision language models (VLMs);
a system for performing operations using a large language model;
a system for performing one or more conversational AI operations;
a system for generating synthetic data;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
a system incorporating one or more virtual machines (VMs); a
system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources (Yao et al. para[0152], the servers can include cloud compute nodes or cloud compute resources).
Claim Rejections - 35 USC § 103
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.
Claim(s) 7 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yao et al. in view of Edelsten et al. (US2020/0050936) as made of reference in IDS dated 07/01/2024.
In regards to claim 7, Yao et al. discloses the method of claim 1. Yao et al. does not explicitly disclose further comprising:
computing, during the training process, one or more uncertainty values associated with the one or more models, the one or more uncertainty values including at least one of a first value indicating a risk score associated with the one or more models or a second value indicating a bias associated with the one or more models; and
storing the one or more uncertainty values as at least a portion of the metadata in association with the one or more models.
However Edelsten et al. discloses further comprising:
computing, during the training process, one or more uncertainty values associated with the one or more models, the one or more uncertainty values including at least one of a first value indicating a risk score associated with the one or more models or a second value indicating a bias associated with the one or more models (Edelsten et al. para[0071], generates bias value associated with model); and
storing the one or more uncertainty values as at least a portion of the metadata in association with the one or more models (Edelsten et al. fig. 6B para[0073], stores bias value with model).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the of Yao et al. with the dataset creation method of Edelsten et al. in order to make application improvements accessible to end users (Edelsten et al. para[0005]).
In regards to claim 13, Yao et al. discloses the system of claim 11, wherein the evaluation comprises:
determining, based at least on the metadata, a risk score associated with the model (Edelsten et al. para[0071], generates bias value associated with model);
evaluating the risk score with respect to a threshold risk score indicated in the policy endpoint (Yao et al. para[0028], evaluates ML entity works correctly under certain runtime contexts or certain constraints ); and
determining, based at least on the evaluation of the risk score, whether to provide the data to the endpoint for executing the model (Yao et al. para[0029], provides ML entity to consumer (endpoint) after passing validation).
However Edelsten et al. discloses
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the of Yao et al. with the dataset creation method of Edelsten et al. in order to make application improvements accessible to end users (Edelsten et al. para[0005]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ezrielev et al. (US12,536,338) teaches managing AI models and identifying poisoned data sets.
Jain et al. (EP 4575880) teaches generating commentary for machine learning model card documents.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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