DETAILED ACTION
This action is in response to the original filling on 05/16/2023. Claims 1-20 are pending for examination.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/08/2023 is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 8, and 15 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4 and 14 Copending Application No. 18/318,052 and in view of Vogeti et al (US 20220129785 A1, published 04/28/2022, hereinafter Vogeti).
This is a provisional nonstatutory patenting rejection.
Although the claims are not identical, they are not patentably distinct from each other because the claims of the present invention are similar in scope to the claims of copending Application No. 18/318,052. For example, the table below shown similarity and different between the instant application and the copending application 18/318,052.
18/318,057 (Instant Application)
18/318,052
1: A system for format-agnostic publishing of a machine learning model, comprising:
a processor; a non-transitory computer-readable medium; and stored instructions translatable by the processor for executing:
receiving a request to publish a machine learning model, the request comprising a machine learning model schema and a machine learning model package comprising one or more files; publishing the machine learning model based on the machine learning model schema and the machine learning model package, comprising: validating the machine learning model schema and the machine learning model package to determine whether the request to publish the machine learning model is valid; wherein, if the request to publish the machine learning model is valid, converting the machine learning model to a common machine learning model format and deploying the converted machine learning model; and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid.
11) A system application programming interface (API) based machine learning model publication, the system comprising: a processor; a non-transitory computer-readable medium; and instructions stored on the non-transitory computer-readable medium and translatable by the processor for: receiving, from a client device through an API of the system, a request to publish a machine learning model trained using a third-party machine learning modeling application, the request containing a machine learning model package;
converting the machine learning model to a standard format supported by the system;
13) The system of claim 12, wherein, prior to the converting, the API wrapper calls a model validation API for validating the machine learning model obtained from the machine learning model package, and wherein the API wrapper calls the model conversion API responsive to the machine learning model being valid.
14. The system of claim 13, wherein validation of the machine learning model comprises at least one of: checking whether the machine learning model is of a valid supported model type; verifying whether the machine learning model is packaged correctly based on the valid supported model type; determining whether the machine learning model is free of malware; determining whether the machine learning model package contains any unwarranted file or system call; or where the machine learning model package is a zip file, validating a file name of the zip file.
8) A method for format-agnostic publishing of a machine learning model, comprising: receiving a request to publish a machine learning model, the request comprising a machine learning model schema and a machine learning model package comprising one or more files; publishing the machine learning model based on the machine learning model schema and the machine learning model package, comprising: validating the machine learning model schema and the machine learning model package to determine whether the request to publish the machine learning model is valid; wherein, if the request to publish the machine learning model is valid, converting the machine learning model to a common machine learning model format and deploying the converted machine learning model; and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid.
1) A method for application programming interface (API) based machine learning model publication, the system comprising: a processor; a non-transitory computer-readable medium; and instructions stored on the non-transitory computer-readable medium and translatable by the processor for: receiving, from a client device through an API of the system, a request to publish a machine learning model trained using a third-party machine learning modeling application, the request containing a machine learning model package;
converting the machine learning model to a standard format supported by the system;
3) The system of claim 2, wherein, prior to the converting, the API wrapper calls a model validation API for validating the machine learning model obtained from the machine learning model package, and wherein the API wrapper calls the model conversion API responsive to the machine learning model being valid.
4) The system of claim 3, wherein validation of the machine learning model comprises at least one of: checking whether the machine learning model is of a valid supported model type; verifying whether the machine learning model is packaged correctly based on the valid supported model type; determining whether the machine learning model is free of malware; determining whether the machine learning model package contains any unwarranted file or system call; or where the machine learning model package is a zip file, validating a file name of the zip file.
18/318,057 (Instant Application)
18/318,052
15) A computer programming product comprising a non-transitory computer-readable medium storing instructions for format-agnostic publishing of a machine learning model, the instructions translatable by a processor for: receiving a request to publish a machine learning model, the request comprising a machine learning model schema and a machine learning model package comprising one or more files; publishing the machine learning model based on the machine learning model schema and the machine learning model package, comprising: validating the machine learning model schema and the machine learning model package to determine whether the request to publish the machine learning model is valid; wherein, if the request to publish the machine learning model is valid, converting the machine learning model to a common machine learning model format and deploying the converted machine learning model; and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid.
1) A method for application programming interface (API) based machine learning model publication, the system comprising: a processor; a non-transitory computer-readable medium; and instructions stored on the non-transitory computer-readable medium and translatable by the processor for: receiving, from a client device through an API of the system, a request to publish a machine learning model trained using a third-party machine learning modeling application, the request containing a machine learning model package;
converting the machine learning model to a standard format supported by the system;
3) The system of claim 2, wherein, prior to the converting, the API wrapper calls a model validation API for validating the machine learning model obtained from the machine learning model package, and wherein the API wrapper calls the model conversion API responsive to the machine learning model being valid.
4) The system of claim 3, wherein validation of the machine learning model comprises at least one of: checking whether the machine learning model is of a valid supported model type; verifying whether the machine learning model is packaged correctly based on the valid supported model type; determining whether the machine learning model is free of malware; determining whether the machine learning model package contains any unwarranted file or system call; or where the machine learning model package is a zip file, validating a file name of the zip file.
The co-pending 18/318,052 did not teach the request comprising a machine learning model schema; publishing the machine learning model based on the machine learning model schema; validating the machine learning model schema; and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid as recited in claims 1, 8 and 15 of the instant application.
However, Vogeti teaches: the request comprising a machine learning model schema (Paragraph [0018]: Thereafter, once an account or other information for an ML predictive service has been registered and/or established, a user or other client entity may request use of the ML predictive service. Initially, the user may wish to upload, register, and/or deploy one or more ML models (requesting use of the service also entails requesting use for publishing/upload. Further, the uploaded files may be in a specific format for the prediction service such that...(format analogous to schema)); publishing the machine learning model based on the machine learning model schema (Paragraph [0015]: In a first phase, the client may upload one or more files, such as a compressed folder (e.g., .zip folder) having the files and corresponding data for the ML model.(uploading/publishing is analogous) Further, the uploaded files may be in a specific format for the prediction service such that...(format analogous to schema)); validating the machine learning model schema (Paragraph [0021]: Thereafter, in a second phase, the service provider may implement verification processes to verify the ML model is valid and proper for deployment the ML prediction service. This may include whether the ML model can be hosted and utilized in a production computing environment (e.g., ML prediction engine) of the service provider..... In this regard, the ML model deployer may utilize the requirements text file (e.g., a .txt file) to determine whether the code packages are supported by the programming code and ML frameworks of the ML prediction service.); and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid. (Paragraph [0023]: Thus, some features may be provided as input to the ML model and the output decisions may be decided by the ML model from those input features. If the corresponding decisions match and/or are expected from the test data, the ML model deployer may validate the ML model for deployment. However, if not, the user may be alerted of the incorrect decisions and model prediction errors.)
It would have been obvious to a person have ordinary skill in the art before the effective filling date to have incorporated the concept of having the request comprising a machine learning model schema; publishing the machine learning model based on the machine learning model schema; validating the machine learning model schema; and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid as suggested by Vogeti into the instant application because both of these systems are addressing the need to deploying machine learning model. Doing so would be improving the instant application by allowing the end users and client devices to upload and host ML models and request predictive services from multiple ML models (Vogeti, paragraph [0001]).
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.
Claim 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 15
Step 1: Claims 1, 8, and 15 recite a system, a method, and a product; therefore, they are directed to the statutory categories of a machine, a method, and a product of manufacture.
Step 2A Prong 1: The claims recite, inter alia:
“validating the machine learning model schema and the machine learning model package to determine whether the request to publish the machine learning model is valid;” and “wherein, if the request to publish the machine learning model is valid, converting the machine learning model to a common machine learning model format” and “and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid.” Under its broadest reasonable interpretation in light of the specification, these limitation encompasses the mental process of evaluation or judgement that is practically capable of being performed in the human mind with the aid of pen and paper.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of “A system for format-agnostic publishing of a machine learning model, comprising: a processor; a non-transitory computer-readable medium; and stored instructions translatable by the processor for executing:” amount to no more than mere instructions to apply an exception (see MPEP § 2106.05(f)). The claim invokes computers or other machinery merely as a tool to perform an existing process. The additional elements of “receiving a request to publish a machine learning model, the request comprising a machine learning model schema and a machine learning model package comprising one or more files;” and “publishing the machine learning model based on the machine learning model schema and the machine learning model package, comprising:” amount to no more than mere data gathering and output (see MPEP § 2106.05(g)). The additional elements of “and deploying the converted machine learning model;” amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. (see MPEP § 2106.05(f)).
Step 2B: The claims do not contain significantly more than the judicial exception. “A system for format-agnostic publishing of a machine learning model, comprising: a processor; a non-transitory computer-readable medium; and stored instructions translatable by the processor for executing:” amount to no more than mere instructions to apply an exception (see MPEP § 2106.05(f)). The claim invokes computers or other machinery merely as a tool to perform an existing process. The additional elements of “receiving a request to publish a machine learning model, the request comprising a machine learning model schema and a machine learning model package comprising one or more files;” and “publishing the machine learning model based on the machine learning model schema and the machine learning model package, comprising:” amount to no more than mere data gathering and output (see MPEP § 2106.05(g)) and is considered well-understood, routine, conventional activity similar to gathering statistics and presenting offers (see MPEP 2106.05(d)(II)). The additional elements of “and deploying the converted machine learning model;” amounts to no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. (see MPEP § 2106.05(f)). Nothing in the claims provides significantly more than that abstract idea. As such, the claims are ineligible.
Claims 7, 14, and 20
Step 1: Claims recite a system, a method, and a product; therefore, they are directed to the statutory categories of a machine, a method, and a product of manufacture.
Step 2A Prong 1: The claims recite, inter alia: “wherein validating the machine learning model package comprises at least one of: validating that the package is free of malware or, validating that the package comprises at least one file and at least one file directory.”
Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluation or judgement that is practically capable of being performed in the human mind with the aid of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claims 2-6, 9-13, and 16-19
Step 1: Claims recite a system, a method, and a product; therefore, they are directed to the statutory categories of a machine, a method, and a product of manufacture.
Step 2A Prong 1: Claims 2-6, 9-13, and 16-19, merely narrow the previously recited abstract limitations. For the reasons described above with respect to claims 1, 8, and 15, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
Claims 2, 9, and 16 recite the additional element of “the request to publish the machine learning model is received from a machine learning model training application, the machine learning model schema comprising an identification of a machine learning model format, wherein: publishing of the machine learning model is agnostic of the identification of the machine learning model format of the machine learning model.”. These elements amount to mere instructions to apply an exception (see MPEP § 2106.05(f)).
Claims 3, 10, and 17 recite the additional elements of “wherein the machine learning model schema comprises an identification of a machine learning model format of the machine learning model.”. These elements amount to no more than linking a particular exception to a field of use or technological environment, limited to a particular data source (see MPEP § 2106.05(h)),
Claims 4, 11, and 18 recite the additional element of “wherein the machine learning model schema comprises metadata in a JavaScript Object Notation (JSON) format.”. These elements amount to no more than linking a particular exception to a field of use or technological environment, limited to a particular data source (see MPEP § 2106.05(h)).
Claims 5-6, 12-13, and 19 recite the additional elements of, inter alia: “wherein the machine learning model package is compressed.” and “wherein the machine learning model package is a zip file.”. These elements amount to mere instructions to apply an exception (see MPEP § 2106.05(f)).
Step 2B: The claims do not contain significantly more than the judicial exception.
Claims 2, 9, and 16 recite the additional element of “the request to publish the machine learning model is received from a machine learning model training application, the machine learning model schema comprising an identification of a machine learning model format, wherein: publishing of the machine learning model is agnostic of the identification of the machine learning model format of the machine learning model.”. These elements amount to mere instructions to apply an exception (see MPEP § 2106.05(f)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d).
Claims 3, 10, and 17 recite the additional elements of “wherein the machine learning model schema comprises an identification of a machine learning model format of the machine learning model.”. These elements amount to no more than linking a particular exception to a field of use or technological environment, limited to a particular data source (see MPEP § 2106.05(h)),
Claims 4, 11, and 18 recite the additional element of “wherein the machine learning model schema comprises metadata in a JavaScript Object Notation (JSON) format.”. These elements amount to no more than linking a particular exception to a field of use or technological environment, limited to a particular data source (see MPEP § 2106.05(h)).
Claims 5-6, 12-13, and 19 recite the additional elements of, inter alia: “wherein the machine learning model package is compressed.” and “wherein the machine learning model package is a zip file.”. These elements amount to mere instructions to apply an exception (see MPEP § 2106.05(f)), and are well understood, routine, conventional activity in the art (see MPEP § 2106.05(d)).
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.
Claims 1, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vogeti et al. (US 20220129785 A1, published 04/28/2022, hereinafter Vogeti), and in view of Cella et al. (US 12585231 B2, published 03/24/2026, hereinafter Cella)
Regarding Claim 1, Vogeti teaches a system for format-agnostic publishing of a machine learning model, comprising:
a processor (Paragraph [0033], one or more processors);
a non-transitory computer-readable medium (Paragraph [0033], one or more computer readable mediums); and
stored instructions translatable by the processor for executing: (Paragraph [0033]: Client device 110 and service provider server 130 may each include one or more processors, memories, and other appropriate components for executing instructions such as program code and/or data stored on one or more computer readable mediums to implement the various applications, data, and steps described herein. For example, such instructions may be stored in one or more computer readable media such as memories or data storage devices internal and/or external to various components of system 100, and/or accessible over network 150. Paragraph [0089]: Components of computer system 800 also include a system memory component 814 (e.g., RAM), a static storage component 816 (e.g., ROM), and/or a disk drive 817. Computer system 800 performs specific operations by processor(s) 812 and other components by executing one or more sequences of instructions contained in system memory component 814. Logic may be encoded in a computer readable medium, which may refer to any medium that participates in providing instructions to processor(s) 812 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. In various embodiments, non-volatile media includes optical or magnetic disks, volatile media includes dynamic memory, such as system memory component 814, and transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus 802. In one embodiment, the logic is encoded in non-transitory computer readable medium. In one example, transmission media may take the form of acoustic or light waves, such as those generated during radio wave, optical, and infrared data communications.)
receiving a request to publish a machine learning model, the request comprising a machine learning model schema and a machine learning model package comprising one or more files; (Paragraph [0019]: The user may select to upload an ML model and provide a folder or file with the data for the ML model. Paragraph [0024]: In order to deploy the ML model, a deployment request may then be generated and issued to the ML prediction service. The deployment request may correspond to an operation to deploy and host the ML model in a live production computing environment. Paragraph [0018]: Thereafter, once an account or other information for an ML predictive service has been registered and/or established, a user or other client entity may request use of the ML predictive service. Initially, the user may wish to upload, register, and/or deploy one or more ML models (requesting use of the service also entails requesting use for publishing/upload. Further, the uploaded files may be in a specific format for the prediction service such that...(format analogous to schema))
publishing the machine learning model based on the machine learning model schema and the machine learning model package, (Paragraph [0015]: In a first phase, the client may upload one or more files, such as a compressed folder (e.g., .zip folder) having the files and corresponding data for the ML model.(uploading/publishing is analogous) Further, the uploaded files may be in a specific format for the prediction service such that...(format analogous to schema))
comprising: validating the machine learning model schema and the machine learning model package to determine whether the request to publish the machine learning model is valid; (Paragraph [0021]: Thereafter, in a second phase, the service provider may implement verification processes to verify the ML model is valid and proper for deployment the ML prediction service. This may include whether the ML model can be hosted and utilized in a production computing environment (e.g., ML prediction engine) of the service provider..... In this regard, the ML model deployer may utilize the requirements text file (e.g., a .txt file) to determine whether the code packages are supported by the programming code and ML frameworks of the ML prediction service) and:
and if the request to publish the machine learning model is invalid, generating a response that the request to publish the machine learning model is invalid. (Paragraph [0023]: Thus, some features may be provided as input to the ML model and the output decisions may be decided by the ML model from those input features. If the corresponding decisions match and/or are expected from the test data, the ML model deployer may validate the ML model for deployment. However, if not, the user may be alerted of the incorrect decisions and model prediction errors.)
However, Vogeti fails to disclose: wherein, if the request to publish the machine learning model is valid, converting the machine learning model to a common machine learning model format and deploying the converted machine learning model;.
In the same field of endeavor, Cella teaches wherein, if the request to publish the machine learning model is valid, converting the machine learning model to a common machine learning model format and deploying the converted machine learning model; (Column 68, Lines 37-59: In embodiments, the user interface 3020 may facilitate access to the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management. Column 342, Lines 34-51: As described above, the model optimization circuit 9434 may perform the validation with reference to validation data/requirements stored in the storage 9450. Column 217, Lines 25-37: In embodiments, the data structuring system 8106 is configured to process and structure data into a format that can be consumed by an enterprise digital twin. In embodiments, processing by the data structuring system 8106 may include compression, computation, filtering, aggregation, multiplexing, selective switching, batching, packetization, streaming, summarization, fusion, fragmentation, encoding, decoding, transcoding, encryption, decryption, duplication, deduplication, normalization, cleansing, identification, copying, storage, decompression, syndication, augmentation (e.g., by metadata), content inspection, classification, extraction, transformation, loading, formatting, error correction, data structuring, and/or many other processing actions.) Cella defines the machine learning model thusly: (Column 110, Lines 53-58: Referring to FIG. 43, the artificial intelligence system 1160 may define a machine learning model. The artificial intelligence system 1160 may also define the digital twin system 1700 to create a digital replica of one or more of the value chain entities).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated the concept of checking if the request to publish the machine learning model is valid, converting the machine learning model to a common machine learning model format and deploying the converted machine learning model as suggested by Cella into the reference of Vogeti because both of these systems are addressing the need to deploy machine learning models. Doing so would improve the Vogeti by allowing users to convert data efficiently so that they would not be overwhelmed (Cella Column 2, Lines 33-41).
Regarding Claim 3, the combination of Vogeti and Cella teaches the invention as claimed in Claim 1 including wherein the machine learning model schema comprises an identification of a machine learning model format of the machine learning model (Vogeti Paragraph [0024] The ML model may be deployed and hosted within a directory that includes the files and data for the ML model, such as the model artifacts, metadata, and the like. The directory may correspond to a name or file path that identifies the ML model and allows for retrieval of the ML model's artifacts for execution of the ML model and determination of predictions.).
Regarding Claim 4, the combination of Vogeti and Cella teaches the invention as claimed in claim 3 including wherein the machine learning model schema comprises metadata in a JavaScript Object Notation (JSON) format. (Vogeti Paragraph [0020] Any metadata for the model may also be provided, such as a model name, type, description, entity, documentation, build, configuration, and the like. Vogeti Paragraph [0057] The metadata for the ML model may include a programming code and/or type used by the ML model, a description of the ML model, the corresponding input data and features processable by the ML model with corresponding output predictions, a model build or version number, and the like.).
Regarding Claim 5, the combination of Vogeti and Cella teaches the invention as claimed in claim 1 including wherein the machine learning model package is compressed (Vogeti Paragraph [0015] In a first phase, the client may upload one or more files, such as a compressed folder (e.g., .zip folder) having the files and corresponding data for the ML model.).
Regarding Claim 6, the combination of Vogeti and Cella teaches the invention as claimed in claim 5 including wherein the machine learning model package is a zip file (Vogeti Paragraph [0015] In a first phase, the client may upload one or more files, such as a compressed folder (e.g., .zip folder) having the files and corresponding data for the ML model.).
Regarding Claim 7, the combination of Vogeti and Cella teaches the invention as claimed in claim 1 including wherein validating the machine learning model package comprises at least one of: validating that the package is free of malware or, validating that the package comprises at least one file and at least one file directory (Vogeti Paragraph [0048] Thereafter, in a second phase of ML model deployment, ML model deployer 142 may verify and validate the ML model package and data. This may correspond to using the requirements file to determine whether the ML frameworks used and provided by ML model platform (e.g., Tensorflow, Scikit-Learn, H2O, or the like) are capable of deploying the corresponding ML model.).
Regarding Claims 8 and 10-14, they are method claims that corresponding to the system claims 1 and 3-7. Therefore, they are rejected for the same reason as system claims 1 and 3-7 above.
Regarding Claims 15 and 17-20, they are product claims that corresponding to the system claims 1, 3-4 and 6-7 above. Therefore, they are rejected for the same reason as system claims 1, 3-4 and 6-7 above.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Vogeti et al. (US 20220129785 A1, published 04/28/2022, hereinafter Vogeti), in view of Cella et al. (US 12585231 B2, published 043/24/2026, hereinafter Cella), as applied in claims 1, 8 and 15 above, and further in view of Khare et al. (US 11249827 B2, published 02/15/2022, hereinafter Khare).
Regarding Claim 2, the combination of Vogeti and Cella teaches all the limitations of Claim 1 including wherein the request to publish the machine learning model is received from a machine learning model training application, the machine learning model schema comprising an identification of a machine learning model format, wherein: (Vogeti Paragraph [0019]: The user may select to upload an ML model and provide a folder or file with the data for the ML model. Vogeti Paragraph [0024]: In order to deploy the ML model, a deployment request may then be generated and issued to the ML prediction service. The deployment request may correspond to an operation to deploy and host the ML model in a live production computing environment. Vogeti Paragraph [0018]: Thereafter, once an account or other information for an ML predictive service has been registered and/or established, a user or other client entity may request use of the ML predictive service. Initially, the user may wish to upload, register, and/or deploy one or more ML models (requesting use of the service also entails requesting use for publishing/upload. Further, the uploaded files may be in a specific format for the prediction service such that...(format analogous to schema) (Instant application paragraph [0012], uses "upload" as a synonym for publishing: The docker images corresponding to the converted ML models can then be posted, uploaded, or published to the docker registry for deployment to a managed cluster (in a cloud and/or on-prem))).
The combination of Vogeti and Cella fails to disclose: publishing of the machine learning model is agnostic of the identification of the machine learning model format of the machine learning model.
In the same field of endeavor, Khare teaches: (Column 2, Lines 54-59: In some embodiments, a producer provides a container to the web services model repository service 121 using a model/algorithm container registry 105. This container is shared as an image. In some embodiments, a model/algorithm container registry 105 is a fully-managed container registry that allows for storing, managing, and deploying of container images. Column 3, Lines 4-10: The publishing/listing agent 125 publishes received code or containers, lists containers, and responds to queries. Each of these actions are detailed more below. Published algorithms, models, and data are stored in algorithm/model/data store 123 (of course, this storage may be spread across many physical devices). The store 123 may also store pipelines and/or notebooks. Column 3, Lines 11-27: In some embodiments, container images include one or more layers, where each layer represents executable instructions. Some or all of the executable instructions together represent an algorithm that defines a machine learning model. The executable instructions (e.g., the algorithm) can be written in any programming language (e.g., Python, Ruby, C++, Java, etc.). In some embodiments, the virtual machine instances are utilized to containers. In some embodiments, each virtual machine instance includes an operating system (OS), a language runtime, and one or more machine learning (ML) training containers).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated format identification agnostic publishing of machine learning models as suggested by Khare into the combination of Vogeti and Cella. Doing so would be desirable because more and more users are beginning to engage with artificial intelligence systems. While the desire to use machine learning models/algorithms is high, not all programmers and/or system administrators have the time or requisite knowledge to produce this content or integrate it into a pipeline of actions (Khare, Column 2, Lines 19-26).
Regarding claims 9 and 16, they are method and product claims that corresponding to the system claim 2 above. Therefore, they are rejected for the same reason as system claim 2 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. (US 11301762 B1) discloses Machine learning models trained in different frameworks translated into a common format.
Hiremath et al. (US 11853272 B2) discloses receiving input data used to train, test, and validate machine learning models.
Tyagi et al. (S. Tyagi, A. Baghela, K. M. Dar, A. Patel, S. Kothari and S. Bhosale, "Malware Detection in PE files using Machine Learning," 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON), Raigarh, Chhattisgarh, India, 2023, pp. 1-6) discloses malware detection in files.
Lopez Garcia et al. (Á. López García et al., "A Cloud-Based Framework for Machine Learning Workloads and Applications," in IEEE Access, vol. 8, pp. 18681-18692, 2020) discloses the entire machine learning development lifecycle.
Jianing et al. (CN 115994584 A) discloses machine learning model construction.
Konecki et al. (M. Konecki, R. Kudelić and A. Lovrenčić, "Efficiency of lossless data compression," 2011 Proceedings of the 34th International Convention MIPRO, Opatija, Croatia, 2011, pp. 810-815).
Srisatish et al. (CN 110520872 A) discloses converting and validating machine learning models.
SRINIVASAN et al. (US 20190306237 A1) discloses JSON metadata.
Chen et al. (US 11301762 B1) discloses machine learning models trained in different frameworks translated into a common format.
Ambati et al. (CN 110520872 A) teaches converting and validation of machine learning models
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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