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 .
DETAILED ACTION
This office action is in response to the application filed on 10/14/2024. Claims 1-8 are pending in this application. Claims 1 and 7 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. KR 10-2023-0154794, filed on November 9, 2023. Foreign priority information is presented in the application data sheet under 37 CFR 1.76.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to (an) abstract idea(s) without significantly more.
Claim 1 recites:
A computing device for operating an artificial intelligence (AI) service, comprising:
a communication interface configured to transmit and receive signals in a wired or wireless communication manner; a memory storing computer-readable instructions; and at least one processor configured to execute the instructions to
scan a local intelligence storage, in which a deep learning model for an AI application based on a container in a computing device is stored,
identify information on the deep learning model, and
provide the information on the deep learning model to an outside through the communication interface
wherein the local intelligence storage stores the deep learning model independently from the container.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes. Claim 1 is a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: (an) abstract idea(s).
The “identify” limitation in #3 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. These limitations in the context of this claim encompasses evaluating information associated with a deep learning model to identify information concerning the model. Such evaluation or identification of information can practically be performed through human observation and evaluation.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The limitations associated with #1 above are recited at a high level of generality (i.e., as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s).
The “scan” limitation of #2 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “scan” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
The “provide” limitation of #4 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception. The limitation “provide” in the context of this claim encompasses merely sending the information to another container. See MPEP 2106.05(f).
The “stores” limitation of #5 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s). See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than generic computer components, insignificant extra-solution activity, and instructions to apply an exception. Mere insignificant extra-solution activity and instructions to apply an exception using generic computer components cannot provide an inventive concept.
Therefore, claim 1 is directed to an abstract idea without significantly more.
Claim 2 merely describes the physical or logical relationship of the model storage to the computing device of claim 1. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception.
Therefore, claim 2 is directed to an abstract idea without significantly more.
Claim 3 merely describes where the deep learning model information is stored in claim 1. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception.
Therefore, claim 3 is directed to an abstract idea without significantly more.
Claim 4 merely further limits the information identified in claim 1 to information concerning a model name, model version, model creator, or model operating environment. These limitations merely define the particular informational content subjected to the identified mental process. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception.
Therefore, claim 4 is directed to an abstract idea without significantly more.
Claim 5 recites:
The computing device of claim 1, wherein the at least one processor,
in response to receiving an installation request of a deep learning model through the communication device,
is configured to install the deep learning model corresponding to the installation request to be located on the local intelligence storage.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes. Claim 5 is a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: an abstract idea.
Claim 5 incorporates the mental process identified with respect to claim 1.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The “receiving” limitation in #6 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
The “install” limitation in #7 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s). See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than gathering data, and mere instructions to apply the exception. Mere insignificant extra-solution activity and instructions to apply the exception cannot provide an inventive concept.
Therefore, claim 5 is directed to an abstract idea without significantly more.
Claim 6 recites:
The computing device of claim 5, wherein the at least one processor is configured to
broadcast information on the installed deep learning model to the container within computing resources.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes. Claim 6 is a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: an abstract idea.
Claim 6 incorporates the mental process identified with respect to claim 1.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The “broadcast” limitation in #8 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “broadcast” in the context of this claim encompasses merely transmitting and displaying data/information. See MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than insignificant extra-solution activity. Mere insignificant extra-solution activity cannot provide an inventive concept.
Therefore, claim 6 is directed to an abstract idea without significantly more.
Claim 7 recites:
A method of managing a deep learning model in a computing device, which is performed by the computing device, the method comprising:
storing a deep learning model for an artificial intelligence (AI) application based on a container in a local intelligence storage that is independent from the container;
scanning the local intelligence storage to identify information on the deep learning model installed on the computing device;
and providing the identified information on the deep learning model through a network interface.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes. Claim 7 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: an abstract idea.
The “identify” limitation in #10 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. These limitations in the context of this claim encompasses evaluating information associated with a deep learning model to identify information concerning the model. Such evaluation or identification of information can practically be performed through human observation and evaluation.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The “scanning” limitation of #10 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “scanning” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
The “providing” limitation of #11 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception. The limitation “providing” in the context of this claim encompasses merely sending the information to another container. See MPEP 2106.05(f).
The “storing” limitation of #9 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s). See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than gathering data, and instructions to apply the exception. Mere insignificant extra-solution activity and instructions to apply the exception cannot provide an inventive concept.
Therefore, claim 7 is directed to an abstract idea without significantly more.
Claim 8 recites:
The method of claim 7, further comprising:
receiving an installation request of a deep learning model through the network interface;
installing the deep learning model corresponding to the installation request to be located on the local intelligence storage;
and broadcasting information on the installed deep learning model to the container within computing resources.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Yes. Claim 8 is a process.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes: an abstract idea.
Claim 8 incorporates the mental process identified with respect to claim 7.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The “receiving” limitation of #12 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g).
The “broadcast” limitation in #14 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity. The limitation “broadcast” in the context of this claim encompasses merely transmitting and displaying data/information. See MPEP 2106.05(g).
The “installing” limitation in #13 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s). See MPEP 2106.05(f).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than gathering data, and instructions to apply the exception. Mere insignificant extra-solution activity and instructions to apply the exception cannot provide an inventive concept.
Therefore, claim 8 is directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites the limitation "communication device" on line 3 of the claim. There is insufficient antecedent basis for this limitation in the claim. It is unclear if the “communication ” refers to the “communication interface” as found in claim 1 or a separate component. Claim 6 inherits the limitations of claim 5, and as such, claim 6 is also rejected. For the purposes of the prior art rejections “communication device” is interpreted as meaning the “communication interface”.
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 1-4 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Rogers et al. (US 20210117859 A1) in view of NVIDIA (NVIDIA TensorRT Inference Server).
Regarding claim 1, Rogers teaches: A computing device for operating an artificial intelligence (AI) service, comprising: a communication interface configured to transmit and receive signals in a wired or wireless communication manner; a memory storing computer-readable instructions; and at least one processor configured to execute the instructions to: ([FIG. 11] Memory Device 1120 containing Instruction, Network Controller 1134, Processor(s) 1102).
Identify information on the deep learning model, ([0037] “Information for this model can be detected 406 in the manifest, and the corresponding model (and version, etc.) can be obtained from a model store, or other such source, and stored in a local directory, such as a model-specific directory or sub-directory of a local storage volume.”) Detecting model information in a manifest teaches identifying information on a deep learning model.
Wherein the local intelligence storage stores the deep learning model independently from the container. ([0025] “As mentioned, there can be one or more deep learning (DL) models 128, or neural network models, available to the inferencing application 128, which can be stored at the edge server 120, at least initially in storage 132 external to an application container 126,”) Storage external to an application container corresponds to storing deep learning models independently from the container.
Rogers fails to disclose: (1) Scan a local intelligence storage, in which a deep learning model for an AI application based on a container in a computing device is stored, (2) Provide the information on the deep learning model to an outside through the communication interface.
However, NVIDIA teaches: (1) Scan a local intelligence storage, in which a deep learning model for an AI application based on a container in a computing device is stored, and (2) Provide the information on the deep learning model to an outside through the communication interface. ([Model Management] “Changes to the model repository will be detected and the server will attempt to load and unload models as necessary based on those changes. Changes to the model repository may not be detected immediately because the server polls the repository periodically.” [HTTP and GRPC API] “Performing an HTTP GET to /api/status returns status information about the server and all the models being served. Performing an HTTP GET to /api/status/<model name> returns information about the server and the single model specified by <model name>.”) Periodically polling the model repository for changes corresponds to scanning the local repository for available models. Providing HTTP API endpoints for obtaining model information corresponds to providing the information on the deep learning model through a communication interface.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to combine the teachings of Rogers with the repository scanning and information providing API functionality of NVIDIA. In order to enable the system to automatically detect changes in the model repository and to provide that information to remote clients. One of ordinary skill in the art would have reasonably expected success as Rogers teaches that the TensorRT Inference Server may be used as the model management platform [0028].
Claim 7 is a method claim with equivalent limitations to those of claim 1 and therefore is rejected using the same reasoning as claim 1 above.
Regarding claim 2, Rogers fails to disclose: Wherein the local intelligence storage is configured to independently operate from the computing device. However, NVIDIA further teaches: Wherein the local intelligence storage is configured to independently operate from the computing device. ([NVIDIA TensorRT Inference Server] “Model repositories may reside on a locally accessible file system (e.g. NFS), in Google Cloud Storage or in Amazon S3.”) A model repository stored in a cloud storage solution corresponds to intelligence storage operating independently from the computing device.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to modify the local model repository of Rogers with the cloud-based system of NVIDIA as it would allow storage system to be maintained separately from the computing device while the models remain accessible to the inference server.
Regarding claim 3, Rogers further teaches: Wherein the local intelligence storage is configured as a specific volume within a storage device included in the computing device. ([0037] “the corresponding model (and version, etc.) can be obtained from a model store, or other such source, and stored in a local directory, such as a model-specific directory or sub-directory of a local storage volume.”) Storing models in a specific directory in local storage volume corresponds to the local intelligence storage being a specific volume in a storage device.
Regarding claim 4, Rogers fails to disclose: Wherein the information on the deep learning model includes information related to at least one of a model name, a model version, a model creator, or a model operating environment of the deep learning However, NVIDIA further teaches: Wherein the information on the deep learning model includes information related to at least one of a model name, a model version, a model creator, or a model operating environment of the deep learning model. ([Running the Server] “use the Status API to query the server’s status. From the host system use curl to access the HTTP endpoint to request server status … This status shows configuration information”) NVIDIA teaches that each model has information associated with it including the model’s name which corresponds to the deep learning models including information related to model name.
[AltContent: textbox (Figure 1 Shows a response to a Status API call including model information)]
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Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to modify the local model information of Rogers to include the model name and version as taught by NVIDIA. This enables the system to identify and distinguish the available models and to publish the information to the client services.
Claims 5-6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Rogers, in view of NVIDIA, in view of Lewis (US 20220083363 A1).
Regarding claim 5, Rogers, in view of NVIDIA, teaches the system of claim 1 above.
Rogers further teaches: (2) Install the deep learning model corresponding to the installation request to be located on the local intelligence storage. ([0035] “A model fetcher can fetch models from a model store 206 according to a manifest and at least one policy, which may indicate a frequency (e.g., daily or hourly) with which to check for updates. This model fetcher … can write each model to an appropriate versioned directory, or sub-directory, for that model.”) Fetching a new model after an update then writing the file to the model repository corresponds to installing the deep learning model on the local intelligence storage based on the occurrence of an event.
However, Rogers, in view of NVIDIA, fails to disclose: (1) In response to receiving an installation request of a deep learning model through the communication device
Lewis further teaches: (1) In response to receiving an installation request of a deep learning model through the communication device ([claim 14] “identifying, over a network, a request and a deployment identifier associated with the request; retrieving, over the network and based on the deployment identifier and from a file server, one or more machine learning models;”) Installing a new model due to a user request corresponds to installing a deep learning model into a store due to receiving an installation request.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of claimed invention to modify the model installation mechanism of Rogers such that retrieval and installation of a selected deep learning model is initiated in response to an installation request event (in place of an update event), as taught by Lewis, in order to permit a client or administrator to select and deploy a desired machine learning model on demand rather than relying solely on periodic model-update detection.
Regarding claim 6, Rogers, in view of NVIDIA, in view of Lewis, teaches the system of claim 5 above.
Rogers further teaches: wherein the at least one processor is configured to broadcast information on the installed deep learning model to the container within computing resources. ([0038] “A new version of the model can be mounted 510 to the application container along with the new configuration information ... The inferencing application, while executing on the edge server, can be enabled 514 to detect the new configuration information and automatically switch to the new version of the model,”) Under the broadest reasonable interpretation “broadcast” consistent with the specification encompasses providing information corresponding to a deep learning model to a container. Thus, providing the configuration information of the new model version to the inferencing application corresponds to broadcasting the information to a container within computing resources.
Regarding claim 8, Rogers, in view of NVIDIA, teaches the method of claim 7 above.
Rogers further teaches: (2) Installing the deep learning model corresponding to the installation request to be located on the local intelligence storage for the same reasons set forth above with respect to claim 5; and (3) Broadcasting information on the installed deep learning model to the container within computing resources for the same reasons set forth above with respect to claim 6.
However, Rogers, in view of NVIDIA, fails to disclose: (1) Receiving an installation request of a deep learning model through the network interface
Lewis further teaches: (1) Receiving an installation request of a deep learning model through the network interface for the same reasons set forth above with respect to claim 5.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Rogers to receive a model deployment request over the network and install the model identified by the request, as taught by Lewis, in order to provide on-demand deployment of selected machine learning models using Rogers’s existing local model-storage architecture.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(US 20210224684 A1) Parallel Model Deployment for Artificial Intelligence Using a Primary Storage System: Teaches an AI model pipeline in which trained AI models are stored in a primary storage system and provided to a model server for deployment and inference.
(US 20240152779 A1) Apparatus and method for sharing augmented intelligence model of containerized artificial intelligence module: Teaches separating an AI model storage from its container, downloading the model into a running container.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Luke Thomson whose telephone number is 571-272-0511. The examiner can normally be reached on Monday-Friday from 9:00 AM to 6:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do, can be reached at telephone number 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUKE THOMSON/Examiner, Art Unit 2193
/Chat C Do/Supervisory Patent Examiner, Art Unit 2193
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