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 .
2. This action is responsive to the following communication: Original claims filed 9/20/23. This action is made non-final.
3. Claims 1-20 are pending in the case. Claims 1, 11 and 20 are independent claims.
Claim Objections
4. Claims 2-10 and 12-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
6. Claim 1, 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mattivi (US 20240211370) in view of McPeak (US 20240039916) and further in view of Lou (US 20240386243).
Regarding claim 1, Mattivi discloses a computer-implemented method for executing a machine learning model, the method comprising:
performing a first set of training iterations (the interactive user interface provides continuous feedback on the performance of a machine learning model to enable a user to refine, add, and/or remove natural language rules through iterations of the machine learning model development, refinement, and/or conversion process, see paragraph 0020) to convert a prediction learning network into a first trained prediction learning network based on a first support set of training data (in this way, the performance metrics may allow a user to track a progress of the predictive machine learning model as rules are added/modified/converted at each iteration of an iterative model training process, see paragraph 0048), wherein the first support set of training data is associated with a first set of classes (a predictive machine learning model may include a classification model trained, using one or more semi-supervised and/or supervisory training techniques, to output a classification output predicted to correspond to an input data object, see paragraph 0100);
Mattivi does not disclose executing a first trained representation learning network to convert a first data sample into a first latent representation, wherein the first trained representation learning network is generated by training a representation learning network using a first query set of training data, a first set of self-supervised losses associated with the first query set of training data, and a first set of supervised losses associated with the first query set of training data, and wherein the first query set of training data is associated with a second set of classes that is different from the first set of classes.
However, McPeak discloses wherein the machine learning model may be trained by generating embeddings for different segments of code and metadata within a resource. For example, lines of code and metadata that include a user identifier may be converted into a semantic representation in latent space using a supervised machine learning model. Owner determination module 208 may then use an unsupervised machine learning model to determine the distance in latent space between one or more example owner embedding representations and each semantic representation. Where a distance is below a threshold, owner determination module 208 may determine that the user identifier within the corresponding text to the latent representation is the owner (see paragraph 0030).
The combination of Mattivi and McPeak would have resulted in the iterative training teachings of Mattivi to further incorporate McPeak’s teachings of utilizing latent spaces to provide the data samples. One would have been motivated to have combined the teachings because a user in Mattivi is already interested in iterative teachings and predictions systems. As such, the combination of teachings would have resulted in a predictive invention to one of ordinary skill in the art.
Mattivi does not disclose executing the first trained prediction learning network to convert the first latent representation into a first prediction of a first class that is not included in the second set of classes.
However, Lou discloses wherein for example, the account interaction prediction system 102 utilizes the initial state neural network 114 to convert the initial state features 312 into a latent vector space that represents probability values of the user account taking on one or more hidden states. For instance, the account interaction prediction system 102 utilizes the initial state neural network 114 to determine patterns in the initial state features 312 that resemble patterns from training data, and to generate the initial state embeddings 412 as outputs according to the training of the initial state neural network 114 (see paragraph 0070).
The combination of Mattivi and Lou would have resulted in the iterative training teachings of Mattivi to further incorporate Lou’s teachings of utilizing latent spaces to provide the data samples. One would have been motivated to have combined the teachings because a user in Mattivi is already interested in iterative teachings and predictions systems. As such, the combination of teachings would have resulted in a predictive invention to one of ordinary skill in the art.
Regarding claim 11, the subject matter of the claim is substantially similar to claim 1 and as such the same rationale of rejection applies.
Regarding claim 20, the subject matter of the claim is substantially similar to claim 1 and as such the same rationale of rejection applies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID E CHOI whose telephone number is (571)270-3780. The examiner can normally be reached on M-F: 7-2, 7-10 (PST). If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bechtold, Michelle T. can be reached on (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID E CHOI/Primary Examiner, Art Unit 2148
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