DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The present application is examined under the claims filed on 10/09/2023.
Claims 1-20 are rejected.
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. The analysis of the claims
will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg.
50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
The claim states: “A computer-implemented method comprising:” therefore it is directed to a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “generating, by the one or more processors, an embedded representation of the machine learning model based on the at least one data artifact;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Receiving, by one or more processors and automatically via at least one workspace data hook, at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace, wherein the at least one workspace data hook integrates with the at least one third- party workspace”. This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
“Storing, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Receiving, by one or more processors and automatically via at least one workspace data hook, at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace, wherein the at least one workspace data hook integrates with the at least one third-party workspace”. This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)).
“Storing, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(iv)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “identifying, by the one or more processors and based on[[by]] processing the search query, at least one stored machine learning model, wherein the at least one stored machine learning model is retrieved based on at least one embedded representation of the at least one stored machine learning model in the embedding space;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Receiving, by the one or more processors, a search query;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
“Retrieving, by the one or more processors, the at least one stored machine learning model in response to the search query.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Receiving, by the one or more processors, a search query;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
“Retrieving, by the one or more processors, the at least one stored machine learning model in response to the search query.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Causing rendering, by the one or more processors, of a user interface comprising at least one indication of the at least one stored machine learning model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Causing rendering, by the one or more processors, of a user interface comprising at least one indication of the at least one stored machine learning model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “determining, by the one or more processors, the at least one embedded representation of the at least one stored machine learning model is proximate to the query embedded location.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Generating, by the one or more processors, a query embedded location by applying the search query to a query embedding model;” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Generating, by the one or more processors, a query embedded location by applying the search query to a query embedding model;” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“The computer-implemented method of claim 2, wherein the at least one stored machine learning model comprises a plurality of machine learning models, the plurality of machine learning models comprising at least a first machine learning model trained via a first third-party workspace and a second machine learning model trained via a second third- party workspace.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “The computer-implemented method of claim 2, wherein the at least one stored machine learning model comprises a plurality of machine learning models, the plurality of machine learning models comprising at least a first machine learning model trained via a first third-party workspace and a second machine learning model trained via a second third- party workspace.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“The computer-implemented method of claim 1, wherein the search query comprises free text search data that is parseable to map to the embedding space.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “The computer-implemented method of claim 1, wherein the search query comprises free text search data that is parseable to map to the embedding space.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“The computer-implemented method of claim 1, wherein the at least one workspace data hook dynamically retrieves the at least one data artifact via the at least one third-party workspace in real-time during training of the machine learning model.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “The computer-implemented method of claim 1, wherein the at least one workspace data hook dynamically retrieves the at least one data artifact via the at least one third-party workspace in real-time during training of the machine learning model.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“The computer-implemented method of claim 1, wherein the at least one workspace data hook retrieves the at least one data artifact via the at least one third-party workspace upon initiation of publication of the machine learning model to a model centralization system.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “The computer-implemented method of claim 1, wherein the at least one workspace data hook retrieves the at least one data artifact via the at least one third-party workspace upon initiation of publication of the machine learning model to a model centralization system.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“The computer-implemented method of claim 1, wherein the model centralization system maintains a first-party workspace providing access to the at least one third-party workspace.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “The computer-implemented method of claim 1, wherein the model centralization system maintains a first-party workspace providing access to the at least one third-party workspace.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “wherein the machine learning model is assigned to a particular cluster based on the at least one data artifact.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Applying, by the one or more processors, at least a portion of the at least one data artifact to a clustering model, wherein the clustering model is specially configured to generate N different clusters of machine learning models defined within the embedding space.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Applying, by the one or more processors, at least a portion of the at least one data artifact to a clustering model, wherein the clustering model is specially configured to generate N different clusters of machine learning models defined within the embedding space.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “wherein the embedding model is specially configured to map an embedded representation of the machine learning model to a particular location in the embedding space based on the portion of the at least one data artifact.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Applying, by the one or more processors, at least a portion of the at least one data artifact to an embedding model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Applying, by the one or more processors, at least a portion of the at least one data artifact to an embedding model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
The claim states: “A system comprising at least one memory and one or more processors communicatively coupled to the at least one memory, the by one or more processors configured to:” therefore it is directed to a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “generate, by the one or more processors, an embedded representation of the machine learning model based on the at least one data artifact;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Receive, by the one or more processors and automatically via at least one workspace data hook, at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace wherein the at least one workspace data hook integrates with the at least one third- party workspace;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
“Store, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Receive, by the one or more processors and automatically via at least one workspace data hook, at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace wherein the at least one workspace data hook integrates with the at least one third- party workspace;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)).
“Store, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(iv)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “identify, by the one or more processors and based on[[by]] processing the search query, at least one stored machine learning model, wherein the at least one stored machine learning model is retrieved based on at least one embedded representation of the at least one stored machine learning model in the embedding space;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Receive, by the one or more processors, a search query;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
“Retrieve the at least one stored machine learning model in response to the search query.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Receive, by the one or more processors, a search query;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)).
“Retrieve the at least one stored machine learning model in response to the search query.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(iv)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Cause, by the one or more processors, rendering of a user interface comprising at least one indication of the at least one stored machine learning model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Cause, by the one or more processors, rendering of a user interface comprising at least one indication of the at least one stored machine learning model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “determine, by the one or more processors, the at least one embedded representation of the at least one stored machine learning model is proximate to the query embedded location.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Generate, by the one or more processors, a query embedded location by applying the search query to a query embedding model;” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Generate, by the one or more processors, a query embedded location by applying the search query to a query embedding model;” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“The system of claim 12, wherein the at least one workspace data hook dynamically retrieves the at least one data artifact via the at least one third-party workspace in real-time during training of the machine learning model.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “The system of claim 12, wherein the at least one workspace data hook dynamically retrieves the at least one data artifact via the at least one third-party workspace in real-time during training of the machine learning model.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“The system of claim 12, wherein the at least one workspace data hook retrieves the at least one data artifact via the at least one third-party workspace upon initiation of publication of the machine learning model to a model centralization system.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “The system of claim 12, wherein the at least one workspace data hook retrieves the at least one data artifact via the at least one third-party workspace upon initiation of publication of the machine learning model to a model centralization system.” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 18
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “wherein the machine learning model is assigned to a particular cluster based on the at least one data artifact.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Apply, by the one or more processors, at least a portion of the at least one data artifact to a clustering model, wherein the clustering model is specially configured to generate N different clusters of machine learning models defined within the embedding space.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Apply, by the one or more processors, at least a portion of the at least one data artifact to a clustering model, wherein the clustering model is specially configured to generate N different clusters of machine learning models defined within the embedding space.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 19
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “, wherein the embedding model is specially configured to map an embedded representation of the machine learning model to a particular location in the embedding space based on the portion of the at least one data artifact.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Apply, by the one or more processors, at least a portion of the at least one data artifact to an embedding model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Apply, by the one or more processors, at least a portion of the at least one data artifact to an embedding model.” This amounts to generic instructions to perform a process. Thus, the additional elements amount to no more than the recitation of the words “apply it” (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
Claim 20
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
The claim states: “At least one non-transitory computer-readable storage medium having instructions that, when executed by one or more processors, cause the one or more processors to:” therefore it is directed to a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or
natural phenomenon?
The claim states: “generate, by the one or more processors, an embedded representation of the machine learning model based on the at least one data artifact;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the
judicial exception into a practical application?
The claim states the additional elements:
“Receive, by the one or more processors and automatically via at least one workspace data hook, at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace wherein the at least one workspace data hook integrates with the at least one third- party workspace;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)).
“Store, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly
more than the judicial exception?
The claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements “Receive, by the one or more processors and automatically via at least one workspace data hook, at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace wherein the at least one workspace data hook integrates with the at least one third- party workspace;” This is receiving or transmitting data over a network, which amounts to an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(d)(II)(i)).
“Store, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model.” This is storing and retrieving information in memory, which is a well understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(iv)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and the claim is subject matter ineligible.
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, 6, 8, 12-15, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Gur et al (Gur et al, “Machine Learning Model Repository Management and Search Engine”, US12340293, 2019, hereinafter Gur) in view of Umurzokov (Umurzokov, “Event-Driven APIs with Webhook and API Gateway”, 2022).
Regarding claim 1 Gur discloses:
“receiving, by one or more processors, [and automatically via at least one workspace data hook], at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace” (“The present invention provides a mechanism for maintaining a repository of ML algorithms and for searching and retrieving ML algorithms for training ML models”(Gur, par.[0017]), “The ML models and the ML algorithms used to train these ML models may comprise many different variants of third party ML models/algorithms, many in- house implementations of third party ML models/algorithms, and many novel ML models/algorithms designed with their own configurations. Moreover, the data sets being analyzed may vary from images to text to natural language questions, etc” (Gur, par. [0023]). Gur teaches receiving (retrieving) data artifacts (data sets) associated with training by third party ML models/algorithms.)
“Generating, by the one or more processors, an embedded representation of the machine learning model based on the at least one data artifact” (“The machine learning framework further operates to index, by the machine learning framework, the plurality of machine learning algorithms to generate and store in a machine learning algorithm index data storage, a machine learning algorithm metadata model for each machine learning algorithm in the plurality of machine learning algorithms.” (Gur, par. [0004]). Gur teaches generating embedded representations (indexing) of machine learning algorithms/models.)
“Storing, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model” (“The machine learning framework operates to register, in a machine learning algorithm repository, a plurality of machine learning algorithms, wherein each machine learning algorithm is an algorithm used to train a machine learning model to perform a related task” (Gur, par. [0004]). Gur teaches storing of a plurality of machine learning models in a repository (embedding space) based on the aforementioned indexing.)
Gur does not disclose:
“[Receiving, by one or more processors and] automatically via at least one workspace data hook, [at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace], wherein the at least one workspace data hook integrates with the at least one third- party workspace”
Umurzokov discloses:
“[Receiving, by one or more processors and] automatically via at least one workspace data hook, [at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace], wherein the at least one workspace data hook integrates with the at least one third- party workspace” (“A webhook is a software architecture approach that allows applications and services to submit a web-based notification to other applications whenever a specific event occurs... Webhooks are generally used to notify clients of events, in real-time, as they occur.” (Umurzokov). Umurzokov teaches integrating webhooks with third party applications to receive data during real time events.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gur and Umurzokov. Gur teaches a framework for organizing, storing, and searching for machine learning models. Umurzokov teaches integrating webhooks into applications to receive live data when certain events are triggered. One of ordinary skill would have motivation to combine the framework of Umurzokov with the webhooks of Umurzokov so that “An event producer, such as an API server, can send event notifications to a webhook when something interesting happens” (Umurzokov). This would encompass events in the framework of Gur associated with organizing, storing and searching for machine learning models.
Regarding claim 2 Gur in view of Umurzokov discloses:
“Receiving, by the one or more processors, a search query” (“in addition, the machine learning framework further operates to receive, via a user interface of the machine learning framework, a user specification of at least one analytics pipeline task for which at least one machine learning model is to be trained, and convert, by a machine learning algorithm search criteria generation engine of the machine learning framework, the user specification to one or more machine learning algorithm search criteria” (Gur, par. [0004]). Gur teaches receiving a user input which is then converted into a search query (search criteria) via a machine learning algorithm search criteria generation engine).
“Identifying, by the one or more processors and based on processing the search query, at least one stored machine learning model, wherein the at least one stored machine learning model is retrieved based on at least one embedded representation of the at least one stored machine learning model in the embedding space” (“Moreover, the machine learning framework operates to search, by a machine learning algorithm search engine of the machine learning framework, the machine learning algorithm index data storage, based on the one or more machine learning algorithm search criteria, to identify at least one matching machine learning algorithm having a corresponding machine learning algorithm metadata model that matches the one or more machine learning algorithm search criteria”(Gur, par. [0004]). Gur teaches a search of the machine learning model index storage (embedding space) based on the search query (search criteria) and embedded representation (index) in order to identify a matching machine learning model).
“Retrieving, by the one or more processors, the at least one stored machine learning model in response to the search query” (“Additionally, the machine learning framework operates to output, via the user interface, information describing the at least one matching machine learning algorithm” (Gur, par. 4). Gur teaches retrieving and outputting stored machine learning models in response to the search and identification mentioned above.).
Regarding claim 3 Gur in view of Umurzokov discloses:
“Causing rendering, by the one or more processors, of a user interface comprising at least one indication of the at least one stored machine learning model” (“Additionally, the machine learning framework operates to output, via the user interface, information describing the at least one matching machine learning algorithm” (Gur, par. [0004]). Gur teaches rendering of information (output, via the user interface) indicating at least one stored machine learning model (at least one matching machine learning algorithm)).
Regarding claim 4 Gur in view of Umurzokov discloses:
“Generating, by the one or more processors, a query embedded location by applying the search query to a query embedding model” (“Moreover, the machine learning framework operates to search, by a machine learning algorithm search engine of the machine learning framework, the machine learning algorithm index data storage, based on the one or more machine learning algorithm search criteria, to identify at least one matching machine learning algorithm having a corresponding machine learning algorithm metadata model that matches the one or more machine learning algorithm search criteria” (Gur, par. [0004]), “Additionally, the machine learning framework operates to output, via the user interface, information describing the at least one matching machine learning algorithm” (Gur, par. [0004]). Gur teaches applying a search query (search criteria) to a query embedding model (machine learning algorithm search engine) to generate a query embedded location (machine learning algorithm metadata model, output via the user interface).).
“Determining, by the one or more processors, the at least one embedded representation of the at least one stored machine learning model is proximate to the query embedded location” (“Moreover, the machine learning framework operates to search, by a machine learning algorithm search engine of the machine learning framework, the machine learning algorithm index data storage, based on the one or more machine learning algorithm search criteria, to identify at least one matching machine learning algorithm having a corresponding machine learning algorithm metadata model that matches the one or more machine learning algorithm search criteria” (Gur, par. [0004]). Gur teaches determining the embedded representation (index) of the stored machine learning model is proximate (matching) to the query embedded location (metadata model).).
Regarding claim 6 Gur in view of Umurzokov discloses:
“The computer-implemented method of claim 1, wherein the search query comprises free text search data that is parseable to map to the embedding space” (“The search, in one illustrative embodiment in which a Lucene index is utilized, involves performing a text based search of terms in the ML algorithm metadata models based on terms in the search criteria to find ML algorithm metadata models matching the search criteria. It should be appreciated that while a Lucene index is used in the described examples, the present invention is not limited to such and any index or search engine mechanisms may be used without departing from the spirit and scope of the present invention, e.g., Solr, Elastic Search, or other types of indices and search engines” (Gur, par. [0042]). Gur teaches a free text search data (text based search terms) that is parseable to map (Lucene index) to the embedding space.)
Regarding claim 8 Gur in view of Umurzokov discloses:
“Wherein the at least one workspace data hook retrieves the at least one data artifact via the at least one third-party workspace upon initiation of publication of the machine learning model to a model centralization system” (“A webhook is used for event-driven integrations and is one of the many ways applications can communicate with each other. They allow you to send real-time data from one system to another when a given event occurs” (Umurzokov), “The machine learning framework operates to register, in a machine learning algorithm repository, a plurality of machine learning algorithms” (Gur, par. [0004]). Gur teaches publication (register) of the machine learning model to a model centralization system (machine learning algorithm repository). Umurzokov teaches at least one workspace data hook retrieves the at least one data artifact via at least one third party workspace (send real time data from one system to another)).
Regarding claim 12 Gur discloses:
“receive, by the one or more processors and [automatically via at least one workspace data hook], at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace” (“The present invention provides a mechanism for maintaining a repository of ML algorithms and for searching and retrieving ML algorithms for training ML models”(Gur, par.[0017]), “The ML models and the ML algorithms used to train these ML models may comprise many different variants of third party ML models/algorithms, many in- house implementations of third party ML models/algorithms, and many novel ML models/algorithms designed with their own configurations. Moreover, the data sets being analyzed may vary from images to text to natural language questions, etc” (Gur, par. [0023]). Gur teaches receiving (retrieving) data artifacts (data sets) associated with training by third party ML models/algorithms.)
“Generate, by the one or more processors, an embedded representation of the machine learning model based on the at least one data artifact” (“The machine learning framework further operates to index, by the machine learning framework, the plurality of machine learning algorithms to generate and store in a machine learning algorithm index data storage, a machine learning algorithm metadata model for each machine learning algorithm in the plurality of machine learning algorithms.” (Gur, par. [0004]). Gur teaches generating embedded representations (indexing) of machine learning algorithms/models.)
“Store, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model” (“The machine learning framework operates to register, in a machine learning algorithm repository, a plurality of machine learning algorithms, wherein each machine learning algorithm is an algorithm used to train a machine learning model to perform a related task” (Gur, par. [0004]). Gur teaches storing of a plurality of machine learning models in a repository (embedding space) based on the aforementioned indexing.)
Gur does not disclose:
“[Receive, by one or more processors and] automatically via at least one workspace data hook [at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace] wherein the at least one workspace data hook integrates with the at least one third- party workspace”
Umurzokov discloses:
“[Receive, by one or more processors and] automatically via at least one workspace data hook [at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace] wherein the at least one workspace data hook integrates with the at least one third- party workspace” (“A webhook is a software architecture approach that allows applications and services to submit a web-based notification to other applications whenever a specific event occurs... Webhooks are generally used to notify clients of events, in real-time, as they occur.” (Umurzokov). Umurzokov teaches integrating webhooks with third party applications to receive data during real time events.)
Regarding claim 13 Gur in view of Umurzokov discloses:
“Receive, by the one or more processors, a search query” (“in addition, the machine learning framework further operates to receive, via a user interface of the machine learning framework, a user specification of at least one analytics pipeline task for which at least one machine learning model is to be trained, and convert, by a machine learning algorithm search criteria generation engine of the machine learning framework, the user specification to one or more machine learning algorithm search criteria” (Gur, par. [0004]). Gur teaches receiving a user input which is then converted into a search query (search criteria) via a machine learning algorithm search criteria generation engine).
“Identify, by the one or more processors and based on[[by]] processing the search query, at least one stored machine learning model, wherein the at least one stored machine learning model is retrieved based on at least one embedded representation of the at least one stored machine learning model in the embedding space” (“Moreover, the machine learning framework operates to search, by a machine learning algorithm search engine of the machine learning framework, the machine learning algorithm index data storage, based on the one or more machine learning algorithm search criteria, to identify at least one matching machine learning algorithm having a corresponding machine learning algorithm metadata model that matches the one or more machine learning algorithm search criteria”(Gur, par. [0004]). Gur teaches a search of the machine learning model index storage (embedding space) based on the search query (search criteria) and embedded representation (index) in order to identify a matching machine learning model).
“Retrieve, by the one or more processors, the at least one stored machine learning model in response to the search query” (“Additionally, the machine learning framework operates to output, via the user interface, information describing the at least one matching machine learning algorithm” (Gur, par. 4). Gur teaches retrieving and outputting stored machine learning models in response to the search and identification mentioned above.).
Regarding claim 14 Gur in view of Umurzokov discloses:
“Cause, by the one or more processors, rendering of a user interface comprising at least one indication of the at least one stored machine learning model” (“Additionally, the machine learning framework operates to output, via the user interface, information describing the at least one matching machine learning algorithm” (Gur, par. [0004]). Gur teaches rendering of information (output, via the user interface) indicating at least one stored machine learning model (at least one matching machine learning algorithm)).
Regarding claim 15 Gur in view of Umurzokov discloses:
“Generate, by the one or more processors, a query embedded location by applying the search query to a query embedding model” (“Moreover, the machine learning framework operates to search, by a machine learning algorithm search engine of the machine learning framework, the machine learning algorithm index data storage, based on the one or more machine learning algorithm search criteria, to identify at least one matching machine learning algorithm having a corresponding machine learning algorithm metadata model that matches the one or more machine learning algorithm search criteria” (Gur, par. [0004]), “Additionally, the machine learning framework operates to output, via the user interface, information describing the at least one matching machine learning algorithm” (Gur, par. [0004]). Gur teaches applying a search query (search criteria) to a query embedding model (machine learning algorithm search engine) to generate a query embedded location (machine learning algorithm metadata model, output via the user interface).).
“Determine, by the one or more processors, the at least one embedded representation of the at least one stored machine learning model is proximate to the query embedded location” (“Moreover, the machine learning framework operates to search, by a machine learning algorithm search engine of the machine learning framework, the machine learning algorithm index data storage, based on the one or more machine learning algorithm search criteria, to identify at least one matching machine learning algorithm having a corresponding machine learning algorithm metadata model that matches the one or more machine learning algorithm search criteria” (Gur, par. [0004]). Gur teaches determining the embedded representation (index) of the stored machine learning model is proximate (matching) to the query embedded location (metadata model).).
Regarding claim 17 Gur in view of Umurzokov discloses:
“Wherein the at least one workspace data hook retrieves the at least one data artifact via the at least one third-party workspace upon initiation of publication of the machine learning model to a model centralization system” (“A webhook is used for event-driven integrations and is one of the many ways applications can communicate with each other. They allow you to send real-time data from one system to another when a given event occurs” (Umurzokov), “The machine learning framework operates to register, in a machine learning algorithm repository, a plurality of machine learning algorithms” (Gur, par. [0004]). Gur teaches publication (register) of the machine learning model to a model centralization system (machine learning algorithm repository). Umurzokov teaches at least one workspace data hook retrieves the at least one data artifact via at least one third party workspace (send real time data from one system to another)).
Regarding claim 20 Gur discloses:
“receive, by the one or more processors and [automatically via at least one workspace data hook], at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace” (“The present invention provides a mechanism for maintaining a repository of ML algorithms and for searching and retrieving ML algorithms for training ML models”(Gur, par.[0017]), “The ML models and the ML algorithms used to train these ML models may comprise many different variants of third party ML models/algorithms, many in- house implementations of third party ML models/algorithms, and many novel ML models/algorithms designed with their own configurations. Moreover, the data sets being analyzed may vary from images to text to natural language questions, etc” (Gur, par. [0023]). Gur teaches receiving (retrieving) data artifacts (data sets) associated with training by third party ML models/algorithms.)
“Generate, by the one or more processors, an embedded representation of the machine learning model based on the at least one data artifact” (“The machine learning framework further operates to index, by the machine learning framework, the plurality of machine learning algorithms to generate and store in a machine learning algorithm index data storage, a machine learning algorithm metadata model for each machine learning algorithm in the plurality of machine learning algorithms.” (Gur, par. [0004]). Gur teaches generating embedded representations (indexing) of machine learning algorithms/models.)
“Store, by the one or more processors, the embedded representation of the machine learning model in an embedding space shared with at least one other embedded representation associated with at least one other machine learning model” (“The machine learning framework operates to register, in a machine learning algorithm repository, a plurality of machine learning algorithms, wherein each machine learning algorithm is an algorithm used to train a machine learning model to perform a related task” (Gur, par. [0004]). Gur teaches storing of a plurality of machine learning models in a repository (embedding space) based on the aforementioned indexing.)
Gur does not disclose:
“[Receive, by one or more processors and] automatically via at least one workspace data hook, [at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace] wherein the at least one workspace data hook integrates with the at least one third- party workspace”
Umurzokov discloses:
“[Receive, by one or more processors and] automatically via at least one workspace data hook [at least one data artifact associated with training of at least a machine learning model trained utilizing at least one third-party workspace] wherein the at least one workspace data hook integrates with the at least one third- party workspace” (“A webhook is a software architecture approach that allows applications and services to submit a web-based notification to other applications whenever a specific event occurs... Webhooks are generally used to notify clients of events, in real-time, as they occur.” (Umurzokov). Umurzokov teaches integrating webhooks with third party applications to receive data during real time events.)
Claims 5, 7, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Gur et al (Gur et al, “Machine Learning Model Repository Management and Search Engine”, US12340293, 2019, hereinafter Gur) and Umurzokov (Umurzokov, “Event-Driven APIs with Webhook and API Gateway”, 2022) in view of Lutkevich (Lutkevich, “Hugging Face”, 9/13/2023).
Regarding claim 5 Lutkevich discloses:
“The computer-implemented method of claim 2, wherein the at least one stored machine learning model comprises a plurality of machine learning models, the plurality of machine learning models comprising at least a first machine learning model trained via a first third-party workspace and a second machine learning model trained via a second third- party workspace” (“Through Spaces and the Hugging Face Transformers library, researchers and developers can share models with the community. Other users can download these models and use them in their own applications”, “Researchers and developers can share data sets for training machine learning models or discover data sets to train their models through the Datasets library” (Lutkevich). Lutkevich teaches a system for sharing and downloading models and data sets for training models via third parties. It would be obvious and inferred that the availability of both third party models and data sets for training models would result in third party models trained by third party data sets. As a result, Lutkevich teaches at least a first machine learning model trained via a first third-party workspace and a second machine learning model trained via a second third- party workspace.)
Regarding claim 7 Lutkevich discloses:
“The at least one data artifact via the at least one third-party workspace” (“Through Spaces and the Hugging Face Transformers library, researchers and developers can share models with the community. Other users can download these models and use them in their own applications” (Lutkevich). Lutkevich teaches at least one data artifact (download these models) and at least one third-party workspace (developers can share models… other users can download these models).)
Lutkevich does not disclose:
“The computer-implemented method of claim 1, wherein the at least one workspace data hook dynamically retrieves [the at least one data artifact via the at least one third-party workspace] in real-time during training of the machine learning model”
Umurzokov discloses:
“The computer-implemented method of claim 1, wherein the at least one workspace data hook dynamically retrieves [the at least one data artifact via the at least one third-party workspace] in real-time during training of the machine learning model” (“A webhook is used for event-driven integrations and is one of the many ways applications can communicate with each other. They allow you to send real-time data from one system to another when a given event occurs” (Umurzokov). Umurzokov teaches a data hook (webhook) that dynamically retrieves data in real time during training of the machine learning model (send real-time data from one system to another when a given event occurs).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gur and Umurzokov with Lutkevich. Gur teaches a framework for organizing, storing, and searching for machine learning models and Umurzokov teaches webhooks for dynamically sending and receiving live data during event triggers. Lutkevich teaches Hugging Face, a system for sharing and storing machine learning models and training data to and from third parties. One of ordinary skill would have motivation to combine the systems of Gur and Umurzokov with the system of Lutkevich in order to “provide access to a vast community, continuously updated models, and documentation and tutorials” (Lutkevich), “integrate multiple ML frameworks” (Lutkevich), and “help users bypass restrictive compute and skill requirements typical of AI development. The fact that Hugging Face provides pre-trained models, fine-tuning scripts and APIs for deployment makes the process of creating LLMs easier” (Lutkevich).
Regarding claim 16 Lutkevich discloses:
“The at least one data artifact via the at least one third-party workspace” (“Through Spaces and the Hugging Face Transformers library, researchers and developers can share models with the community. Other users can download these models and use them in their own applications” (Lutkevich). Lutkevich teaches at least one data artifact (download these models) and at least one third-party workspace (developers can share models… other users can download these models).)
Lutkevich does not disclose:
“The system of claim 12, wherein the at least one workspace data hook dynamically retrieves [the at least one data artifact via the at least one third-party workspace] in real-time during training of the machine learning model”
Umurzokov discloses:
“The system of claim 12, wherein the at least one workspace data hook dynamically retrieves [the at least one data artifact via the at least one third-party workspace] in real-time during training of the machine learning model” (“A webhook is used for event-driven integrations and is one of the many ways applications can communicate with each other. They allow you to send real-time data from one system to another when a given event occurs” (Umurzokov). Umurzokov teaches a data hook (webhook) that dynamically retrieves data in real time during training of the machine learning model (send real-time data from one system to another when a given event occurs).)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Gur et al (Gur et al, “Machine Learning Model Repository Management and Search Engine”, US12340293, 2019, hereinafter Gur) and Umurzokov (Umurzokov, “Event-Driven APIs with Webhook and API Gateway”, 2022) in view of Batra et al (Batra et al, “Enabling a third-party data service to update custom data objects”, US11138176, 2016, hereinafter Batra).
Regarding claim 9 Gur in view of Batra discloses:
“The computer-implemented method of claim 1, wherein the model centralization system maintains a first-party workspace providing access to the at least one third-party workspace” (“Batra FIG. 1, elements 102,104,106,108,110”. Batra teaches a user selecting a data object (102) an option to select from multiple third party services (106), and outputting data object from third party to user (110).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gur and Umurzokov with Batra. Gur teaches a framework for organizing, storing, and searching for machine learning models and Umurzokov teaches webhooks for dynamically sending and receiving live data during event triggers. Batra teaches a system for accessing and allowing third party services to update first party data. One of ordinary skill would have motivation to combine Gur, Umurzokov, and Batra in order to “quickly and easily creates metadata that maps between the fields of a custom object and the data fields used by a third-party data service, without the need to create code that remains hard-coded and inflexible when the database user attempts to apply the laboriously created code to a different data object - data service combination” (Batra par. [0011]). This would allow the system of Gur and Umurzokov to update data based on changes in third party services (model training and updates).
Claims 10, 11, 18, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gur et al (Gur et al, “Machine Learning Model Repository Management and Search Engine”, US12340293, 2019, hereinafter Gur) and Umurzokov (Umurzokov, “Event-Driven APIs with Webhook and API Gateway”, 2022) in view of Jing et al (Jing et al, “Data-Driven Techniques for Model Ensembles”, US20210342707, 2020, hereinafter Jing).
Regarding claim 10 Gur in view of Umurzokov further in view of Jing discloses:
“Applying, by the one or more processors, at least a portion of the at least one data artifact to a clustering model, wherein the clustering model is specially configured to generate N different clusters of machine learning models defined within the embedding space, and wherein the machine learning model is assigned to a particular cluster based on the at least one data artifact” (“According to one embodiment of the present disclosure, a method is provided. The method includes generating a plurality of residues by processing a plurality of input records using a plurality of machine learning (ML) models; identifying a plurality of data clusters by evaluating, using a clustering model, the plurality of input records and the plurality of residues; generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models” (Jing, par. [0003]). Jing teaches generating N different clusters of machine learning models (generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models) defined within the embedding space, wherein the machine learning model is assigned to a particular cluster based on the at least one data artifact (plurality of input records and the plurality of residues).)
Regarding claim 11 Gur in view of Umurzokov further in view of Jing discloses:
“Applying, by the one or more processors, at least a portion of the at least one data artifact to an embedding model, wherein the embedding model is specially configured to map an embedded representation of the machine learning model to a particular location in the embedding space based on the portion of the at least one data artifact” (“According to one embodiment of the present disclosure, a method is provided. The method includes generating a plurality of residues by processing a plurality of input records using a plurality of machine learning (ML) models; identifying a plurality of data clusters by evaluating, using a clustering model, the plurality of input records and the plurality of residues; generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models” (Jing, par. [0003]). Jing teaches at least a portion of the at least one data artifact (the plurality of input records and the plurality of residues) to an embedding model (clustering model), wherein the embedding model is specially configured to map an embedded representation (ensemble) of the machine learning model to a particular location in the embedding space (generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models).
Regarding claim 18 Gur in view of Umurzokov further in view of Jing discloses:
“Apply, by the one or more processors, at least a portion of the at least one data artifact to a clustering model, wherein the clustering model is specially configured to generate N different clusters of machine learning models defined within the embedding space, and wherein the machine learning model is assigned to a particular cluster based on the at least one data artifact” (“According to one embodiment of the present disclosure, a method is provided. The method includes generating a plurality of residues by processing a plurality of input records using a plurality of machine learning (ML) models; identifying a plurality of data clusters by evaluating, using a clustering model, the plurality of input records and the plurality of residues; generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models” (Jing, par. [0003]). Jing teaches generating N different clusters of machine learning models (generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models) defined within the embedding space, wherein the machine learning model is assigned to a particular cluster based on the at least one data artifact (plurality of input records and the plurality of residues).)
Regarding claim 19 Gur in view of Umurzokov further in view of Jing discloses:
“Apply, by the one or more processors, at least a portion of the at least one data artifact to an embedding model, wherein the embedding model is specially configured to map an embedded representation of the machine learning model to a particular location in the embedding space based on the portion of the at least one data artifact” (“According to one embodiment of the present disclosure, a method is provided. The method includes generating a plurality of residues by processing a plurality of input records using a plurality of machine learning (ML) models; identifying a plurality of data clusters by evaluating, using a clustering model, the plurality of input records and the plurality of residues; generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models” (Jing, par. [0003]). Jing teaches at least a portion of the at least one data artifact (the plurality of input records and the plurality of residues) to an embedding model (clustering model), wherein the embedding model is specially configured to map an embedded representation (ensemble) of the machine learning model to a particular location in the embedding space (generating a first ensemble for a first data cluster of the plurality of data clusters, wherein the first ensemble comprises one or more of the plurality of ML models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gur and Umurzokov with Jing. Gur teaches a framework for organizing, storing, and searching for machine learning models and Umurzokov teaches webhooks for dynamically sending and receiving live data during event triggers. Jing teaches using model ensembles for grouping machine learning models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Gur, Umurzokov, and Jing in order to have “an effective technique to improve prediction accuracy, as compared to using individual models” (Jing, par. [0002]) when using the framework of Gur and Umurzokov.
Conclusion
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/HOWIE CHAN/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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