DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: Amendment filed on 7/20/2026.
Claims 1-17 and 20-22 are pending. Claims 1, 14, and 20 are independent. Claims 18-19 are previously canceled.
The previous rejection of claims 1-17 and 20-22 under 35 USC § 102 and 35 USC § 103 have been withdrawn in view of the amendment.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-11,14-16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al. (US2020/0034742) and Kurniawan et al. (US11,449,797) as made of reference in IDS dated 7/1/2026.
In regards to claim 1, Dirac et al. discloses a computer-implemented method comprising:
receiving, by one or more processors and automatically via a workspace data hook that integrates with a third-party workspace, a data artifact (i) associated with a machine learning model trained utilizing the a third-party workspace (Dirac et al. para[0047], some machine learning models may be created and trained, e.g., by a group of model developers or data scientists using the MLS APIs, and then published for use by another community of users.); and
linking, by the one or more processors, the machine learning model with the representation (Dirac et al. para[0047], alias is associated with a pointer to the machine learning model).
Dirac et al. does not explicitly disclose (ii) comprising data associated with training configuration, or operation of the machine learning model;
generating, by the one or more processors, at least one representation for the machine learning model based on the data artifact.
However Kurniamwan et al. discloses (ii) comprising data associated with training configuration, or operation of the machine learning model (Kurniamwan et al. col7 ln31-52, The programs, dependency descriptors, configuration and hyper-parameter settings may be included in the local machine learning artifact);
generating, by the one or more processors, at least one representation for the machine learning model (Dirac et al. para[0047], generates alias representing a model published by model developers) based on the data artifact (Kurniamwan et al. col8 ln24-39, utilize the artifacts to automatically generate a representation of a dynamically deployable software execution environment which satisfies the software dependencies and includes the training programs).
It would have been obvious to one of ordinary kill in the art before the filing date of the invention to have combined the alias generation of Dirac et al. with the workflow automation system of Kurniamwan et al. in order to validate and deploy models while protecting data security (Kurniamwan et al. col2 ln36-52) .
In regards to claim 2, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 1, further comprising:
receiving, by the one or more processors, a search query (Dirac et al. para[0108], interface may be used to search for domain-specific recipes available from a machine learning service);
identifying, by the one or more processors and based on the search query, a stored machine learning model, wherein the stored machine learning model comprises the machine learning model (Dirac et al. para[0109], for selected problem domain, links to five example recipes are shown); and
retrieving, by the one or more processors, the stored machine learning model in response to the search query (Dirac et al. para[0109], user may be able to download the text version of the recipe for inclusion in a subsequent MLS API invocation).
In regards to claim 3, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 2, further comprising:
causing, by the one or more processors, rendering of a user interface comprising an t indication of the stored machine learning model (Dirac et al. fig. 15, para[0108], Fig. 15 illustrates an example of a programmatic interface that may be used to search for domain-specific recipes available from a machine learning service)
In regards to claim 5, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 2, wherein the 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 (Dirac et al. para[0047], model developers in different communities publish machine learning models to be used by a wider audience).
In regards to claim 6, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 1, wherein the workspace data hook dynamically retrieves the data artifact via the third-party workspace in real-time during training of the machine learning model (Dirac et al. para[0076], published model updated as model developers test and validate new versions).
In regards to claim 7, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 1, wherein the workspace data hook retrieves the data artifact via the third-party workspace upon initiation of publication of the machine learning model to a model centralization system (Dirac et al. para[0076], published model updated as model developers test and validate new versions).
In regards to claim 8, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 7, wherein the model centralization system maintains a first-party workspace providing access to the at least one third-party workspace (Dirac et al. fig. 3 para[0061], Security container 390A may be used exclusively for a customer C1 (e.g., to run customer-provided machine learning modules, or third-party modules specified by the customer), while security container 390B may be used exclusively for a different customer C2 in the depicted example.).
In regards to claim 9, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 1, wherein (i) the third- party workspace comprises a plurality of third-party workspaces, and (ii) a third-party workspace of the plurality of third-party workspaces integrated via the workspace data hook (Dirac et al. fig. 3 para[0061], Security container 390A may be used exclusively for a customer C1 (e.g., to run customer-provided machine learning modules, or third-party modules specified by the customer), while security container 390B may be used exclusively for a different customer C2 in the depicted example).
In regards to claim 10, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 1, wherein the data artifact comprises data representing a model type corresponding to the machine learning model, a training data set utilized to train the machine learning model, data representing characteristic of the training data set utilized to train the machine learning model, metadata associated with a user profile utilized to train the machine learning model, data representing an accuracy of the machine learning model, or any combination thereof (Dirac et al. para[0072], MLS artifacts may include, among others, data sources , statistics, feature processing recipes , model predictions, evaluations, modifiable or in-development models, and published models or aliases).
In regards to claim 11, Dirac et al. as modified by Kurniamwan et al. discloses the computer-implemented method of claim 1, further comprising:
generating, by the one or more processors, evaluation data corresponding to the machine learning model based on the data artifact (Dirac et al. para[0072], MLS artifacts may include statistics and evaluations);
determining, by the one or more processors, that the evaluation data satisfies minimum evaluation threshold (Dirac et al. para[0076], determines minimum throughput rate for alias); and
storing, by the one or more processors, the machine learning model in response to determining that the evaluation data satisfies the minimum evaluation threshold (Dirac et al. para[0076],stores model if it can meet minimum throughput rate).
Claims 14-16 recites substantially similar limitations to claims 1-3. Thus claims 14-16 are rejected along the same rationale as claims 1-3.
Claim 20 recites substantially similar limitations to claim 1. This claim 20 is rejected along the same rationale as claim 1.
Claim(s) 4, 12-13, 17, and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dirac et al. in view of Kurniawan et al., Balasubramanian et al. (US2024/0070210).
In regards to claim 4, Dirac et al. as modified by Kurniamwan et al. substantially discloses the computer-implemented method of claim 2. Dirac et al does not explicitly disclose wherein identifying the stored machine learning model comprises:
mapping, by the one or more processors, at least a portion of the search query to a particular location in a keyword embedding space; and
determining, by the one or more processors, that the representation is relevant to the search query based on a distance between the particular location and second location in the keyword embedding space, wherein the second location is associated with the representation.
However Balasubramanian et al. discloses wherein identifying the at least one stored machine learning model comprises:
mapping, by the one or more processors, at least a portion of the search query to a particular location in a keyword embedding space (Balasubramanian et al. para[0054], The candidate keyword selection module also has access to query embedding model configured to convert a search term or a keyword into a query embedding); and
determining, by the one or more processors, that the at least one representation is relevant to the search query based on a distance between the particular location and at least one second location in the keyword embedding space, wherein the at least one second location is associated with the at least one representation (Balasubramanian et al. para[0055], identify one or more candidate embeddings from the query embedding space based on a proximity of each candidate embedding to the embedding for the seed keyword).
It would have been obvious to one of ordinary skill in the art to have combined the alias generation method of Dirac et al. with the keyword identification method of Balasubramanian et al. in order to identify a terms correlated to related content (Balasubramanian et al. para[0003]).
In regards to claim 12, Dirac et al. as modified by Kurniamwan et al. substantially discloses the computer-implemented method of claim 1. Dirac et al. does not explicitly disclose wherein generating the representation associated with the machine learning model comprises:
applying, by the one or more processors, the data artifact to a keyword generation model trained to output the at least one representation based on the data artifact.
However Balasubramanian et al. discloses disclose wherein generating the at least one representation associated with the machine learning model comprises:
applying, by the one or more processors, the at least one data artifact to a keyword generation model trained to output the at least one representation based on the at least one data artifact (Balasubramanian et al. para[0050], generates set of seed keywords based on item data).
It would have been obvious to one of ordinary skill in the art to have combined the alias generation method of Dirac et al. with the keyword identification method of Balasubramanian et al. in order to identify a terms correlated to related content (Balasubramanian et al. para[0003]).
In regards to claim 13, Dirac et al. as modified by Kurniamwan et al. substantially discloses the computer-implemented method of claim 1. Dirac et al. does not explicitly disclose wherein generating the representation associated with the machine learning model comprises:
applying, by the one or more processors, the data artifact to a keyword generation rule set that defines the representation based on the at least one data artifact.
However Balasubramanian et al. discloses wherein generating the representation associated with the machine learning model comprises:
applying, by the one or more processors, the data artifact to a keyword generation rule set that defines the representation based on the at least one data artifact (Balasubramanian et al. para[0050], generates set of seed keywords based on item data).
It would have been obvious to one of ordinary skill in the art to have combined the alias generation method of Dirac et al. with the keyword identification method of Balasubramanian et al. in order to identify a terms correlated to related content (Balasubramanian et al. para[0003]).
Claims 17 recites substantially similar limitations to claim 3. Thus claim 17 is rejected along the same rationale as claim 4.
In regards to claim 21, Dirac et al. as modified by Kurniamwan et al. substantially discloses the computer-implemented method of claim 1. Dirac et al. does not explicitly disclose wherein the representation comprises at least one model keyword associated with the machine learning model.
However Balasubramanian et al. discloses wherein the representation comprises at least one model keyword associated with the machine learning model (Balasubramanian et al. para[0050], The title and/or description of these similar items may further be parsed to identify indicative words as keywords.)
It would have been obvious to one of ordinary skill in the art to have combined the alias generation method of Dirac et al. with the keyword identification method of Balasubramanian et al. in order to identify a terms correlated to related content (Balasubramanian et al. para[0003]).
In regards to claim 22, Dirac et al. as modified by Kurniamwan et al. substantially discloses the computer-implemented method of claim 1. Dirac et al. does not explicitly disclose wherein the representation includes an embedding of the machine learning model in an embedding space shared with at least one other embedding associated with at least one other machine learning model.
However Balasubramanian et al. discloses wherein the representation includes an embedding of the machine learning model in an embedding space shared with at least one other embedding associated with at least one other machine learning model (Balasubramanian et al. para[0050], The set of seed keywords may be inferred from similar or adjacent items or products in the content item embedding space based on K nearest neighbor or cosine similarity relative to the embedding of the content item).
It would have been obvious to one of ordinary skill in the art to have combined the alias generation method of Dirac et al. with the keyword identification method of Balasubramanian et al. in order to identify a terms correlated to related content (Balasubramanian et al. para[0003]).
Response to Arguments
Applicant’s arguments with respect to claims 1-17, and 20-22 have been considered but are moot because the arguments do not apply the current rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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