DETAILED ACTION
Claims 1-9 are pending.
Priority: 1/31/2025(FP)
Assignee: Vessl AI Korea
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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, 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Srinivasan et al.(2018/0300653).
As per claim 1, Srinivasan discloses:
A logical storage management method for heterogeneous storage integrated management in a machine learning environment(Srinivasan, [0018 -- This disclosure relates to a distributed machine learning system. A distributed machine learning system is a set of one or more computing systems that is configured to receive input from one or more external computing systems, which may each be operated by a respective human administrator, whereby the DML systems performs machine learning tasks]), which is performed by a machine learning operations system including an agent and a machine learning operations platform(Srinivasan, [0024 -- The DML system 200 supports a plurality of model architectures and the administrator selects the model architecture from the administrator computing device 104. The administrator computing device 104 includes the administrator selection of the model architecture in a set of hyper parameters 114. The hyper parameters 114 can define any parameters related to the training of a model]), the method comprising:
creating, by the agent, logical storage for a user, which is capable of being managed by integrating a plurality of heterogeneous storage, in response to a storage registration command from the user(Srinivasan, [0038 -- A volume 234 of training data may refer to a set of training data 236 that pertains to a particular training task. A volume 234 may include structured training data 236 and a volume identifier 238. The volume identifier is a unique value assigned to the volume 234 that identifies the volume 234 from other volumes 234 stored in the volume data store 232. When the DML system 200 receives a new set of training data], [0067 -- In some implementations, the DML system 200 receives container requests 120 from a client computing device 106 (e.g., FIG. 1B). In response to such requests, the container manager 218 may be configured to retrieve the model 254 and container image 242 in the manner described above. The container manager 218 may mount the model 254 to the container image 242, thereby obtaining a serving container]);
setting up, by the agent, a logical volume by generating the logical volume and volume metadata for the logical storage based on the logical storage in response to a volume generation command from the user(Srinivasan, [0027 -- The DML system 200 mounts a set of structured training data (also referred to as the “volume” of training data) to the filesystem of the container. The DML system 200 may create a specific directory for input data or may use a default “input” directory in the filesystem], [0038 -- The volume data store 232 stores volumes 234 of training data ]);
and performing, by the machine learning operations platform, a machine learning task by mounting or importing volume data of target storage corresponding to a volume of the logical storage in response to a machine learning task request from the user(Srinivasan, [0027 -- The scheduler of the DML system 200 may then identify one or more computing resources to which it assigns a training or prediction task. Once the scheduler has assigned a task to the computing resources, the DML system 200 may begin running the container configured to perform the task. The DML system 200 mounts a set of structured training data (also referred to as the “volume” of training data) to the filesystem of the container]).
As per claim 8, Srinivasan discloses:
A machine learning operations system(Srinivasan, [0018 -- This disclosure relates to a distributed machine learning system. A distributed machine learning system is a set of one or more computing systems that is configured to receive input from one or more external computing systems, which may each be operated by a respective human administrator, whereby the DML systems performs machine learning tasks]) comprising:
a machine learning operations platform configured to perform machine learning depending on a job specification associated with a machine learning task request, when the machine learning task request is received from a user(Srinivasan, [0024 -- The DML system 200 supports a plurality of model architectures and the administrator selects the model architecture from the administrator computing device 104. The administrator computing device 104 includes the administrator selection of the model architecture in a set of hyper parameters 114. The hyper parameters 114 can define any parameters related to the training of a model]);
and a logical storage agent configured to integrate and manage a plurality of heterogeneous storage(Srinivasan, [0037 -- Storage devices may be located at the same physical location (e.g., in the same device and/or the same data center) or may be distributed across multiple physical locations (e.g., across multiple data centers). The storage system 230 can store a volume data store 232, a container image data store 240, and a model bundle data store ]), wherein the logical storage agent is configured to:
create logical storage for the user, which is capable of being managed by integrating the plurality of heterogeneous storage, in response to a storage registration command from the user(Srinivasan, [0038 -- A volume 234 of training data may refer to a set of training data 236 that pertains to a particular training task. A volume 234 may include structured training data 236 and a volume identifier 238. The volume identifier is a unique value assigned to the volume 234 that identifies the volume 234 from other volumes 234 stored in the volume data store 232. When the DML system 200 receives a new set of training data], [0067 -- In some implementations, the DML system 200 receives container requests 120 from a client computing device 106 (e.g., FIG. 1B). In response to such requests, the container manager 218 may be configured to retrieve the model 254 and container image 242 in the manner described above. The container manager 218 may mount the model 254 to the container image 242, thereby obtaining a serving container]);
and set up a logical volume by generating the logical volume and volume metadata for the logical storage based on the logical storage in response to a volume generation command from the user,(Srinivasan, [0027 -- The DML system 200 mounts a set of structured training data (also referred to as the “volume” of training data) to the filesystem of the container. The DML system 200 may create a specific directory for input data or may use a default “input” directory in the filesystem], [0038 -- The volume data store 232 stores volumes 234 of training data ]);
wherein the machine learning operations platform is configured to: perform a machine learning task by mounting or importing volume data of target storage corresponding to a volume of the logical storage in response to the machine learning task request from the user(Srinivasan, [0027 -- The scheduler of the DML system 200 may then identify one or more computing resources to which it assigns a training or prediction task. Once the scheduler has assigned a task to the computing resources, the DML system 200 may begin running the container configured to perform the task. The DML system 200 mounts a set of structured training data (also referred to as the “volume” of training data) to the filesystem of the container]).
As per claim 9, the method of Srinivasan as recited in claim 1 is incorporated, in addition, Srinivasan discloses:
A non-transitory computer-readable recording medium including instructions causing a computer to execute the method of claim 1(Srinivasan, [0027 -- In response to a training request, the DML system 200 creates a new container from a container image. The DML system 200 retrieves from its memory a container image corresponding to the model architecture type. The container image contains the model architecture (e.g., computer readable instructions that support the matrix operations and other processes tied that define the model architecture) and a filesystem.]);
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Srinivasan et al.(2018/0300653), and further in view of Faulhaber et al.(2019/0155633).
As per claim 2, the method of Srinivasan recited in claim 1 is incorporated, in addition, Srinivasan does not explicitly disclose the following, however Faulhaber discloses:
when the machine learning task on the imported volume data is completed, exporting the corresponding volume data to the target storage(Faulhaber, [0042 -- The virtual machine instance 122 (or the model training system 120 itself) pulls the generated model data from the ML training container 130 and stores the generated model data in the training model data store 175 in an entry associated with the virtual machine instance 122 and/or the machine learning model being trained]).
Therefore it would have been obvious to a POSITA at the time of filing to incorporate the features of Faulhaber into the method/system of Srinivasan for the benefit of providing tremendous flexibility to users to perform complex and powerful tasks simply, through use of a minimal lightweight schema or specification of containers. The users can easily update a model by deploying a new version of the model within the model hosting system without needing to update an application that relies on the model thus this decoupling can greatly reduce the complexity of maintaining the client and the model by separating the two. The helper containers can provide useful support functions for the container so that the container can beneficially remain ignorant to all of these functionalities to enable users to easily create containers that are focused specifically on the machine learning task at hand(Faulhaber, [0020], [0034]-[0043], [00130]-[00156]).
As per claim 4, the method of Srinivasan, Faulhaber as recited in claim 2 is incorporated, in addition, Srinivasan discloses:
wherein the setting up of the logical volume includes: generating, by the agent, a volume generation request in response to the volume generation command from the user,(Srinivasan, [0025 -- The DML system 200 receives the hyper parameters 114 and generates one or more training containers based on the received hyper parameters 114 and the raw data]);
wherein the volume generation request includes logical storage information and volume information(Srinivasan, [0025 -- The raw data 112 may include structured and/or unstructured data. In the case of unstructured data, the DML system 200 may structure the data according to an ontology], [0032 -- The container request 128 may indicate a model bundle. In response to the container request 128, the DML system 200 generates a serving container 132 based on the model bundle and a serving container image.]);
and setting up, by the machine learning operations platform, the logical volume by recording metadata about the volume generation request in a database(Srinivasan, [0045 -- The model bundle data store 250 stores model bundles 252. FIG. 2D illustrates an example of the data contained in or associated with a model bundle 252 according to some implementations of this disclosure. A model bundle 252 can include a model bundle identifier 253, a model 254 (e.g., a matrix of weights) and, in some scenarios, a set of classification labels]).
Therefore it would have been obvious to a POSITA at the time of filing to incorporate the features of Faulhaber into the method/system of Srinivasan for the benefit of providing tremendous flexibility to users to perform complex and powerful tasks simply, through use of a minimal lightweight schema or specification of containers. The users can easily update a model by deploying a new version of the model within the model hosting system without needing to update an application that relies on the model thus this decoupling can greatly reduce the complexity of maintaining the client and the model by separating the two. The helper containers can provide useful support functions for the container so that the container can beneficially remain ignorant to all of these functionalities to enable users to easily create containers that are focused specifically on the machine learning task at hand(Faulhaber, [0020], [0034]-[0043], [00130]-[00156]).
Allowable Subject Matter
Claim(s) 3, 5-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Examiner Notes
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Masson et al.(2026/0023618) in which involves receiving a selection of a task to be executed in association with a machine learning model (MLM) from a client device through a cloud service application programming interface (API). Multiple processors are allocated to execute the task from a shared pool of cloud computing resources, where the shared pool of cloud computing resources is concurrently used for execution of a set of additional tasks received from multiple additional client devices. An execution container is instantiated, where the execution container comprises multiple compute backends. User data is received into the execution container using authorization data. The task is executed in the execution container using the processors, where the authorization data comprises a storage address of the user data, and a password to access the user data and a representation of the password to access the user data(Masson, abstract).
Feng et al.(20170220949) involving dividing a set of data into sub-sets of data, where each sub-set corresponds to one of a set of operation nodes. Each sub-set of data is allocated to a corresponding node for estimating values of parameters based on the sub-set of data. Estimated values of the parameters obtained based on a corresponding sub-set of data allocated to the node are received from each node. The parameters of a machine learning model are estimated based on the estimated values of the parameters generated by some of the set of nodes(Feng, abstract)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARVIND TALUKDAR whose telephone number is (303)297-4475. The examiner can normally be reached M-F, 10 am-6pm EST.
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Arvind Talukdar
Primary Examiner
Art Unit 2132
/ARVIND TALUKDAR/Primary Examiner, Art Unit 2132
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