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 .
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 1-10, 16-17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Andrzejewski et al. (US 2024/0220629 A1), hereinafter “Andrzejewski”, and in view of Kim et al. (US 2024/0135194 A1), hereinafter “Kim”.
As per claim 1, Andrzejewski teaches a method for generating a personalized output on a user device comprising:
“receiving a user input from the user device” at [0041]-[0045];
(Andrzejewski teaches receiving training data and insights as input into local and global model)
“processing the user input using a shared embedding model to generate an embedded user input, wherein the embedded user input comprises global and local features” at [0041]-[0061];
(Andrzejewski teaches processing training data and insights to generate feature vectors (i.e., “embedded user input”). The generated feature vectors are inputted into local and global model)
“processing a second subset of global feature using a share global model to generate a global intermediate output” at [0041]-[0061], [0074];
(Andrzejewski teaches each entry in the validation data is passed through the global model 216 to obtain global-model insight score G(i) (i.e., “global intermediate output”))
“processing local data comprising the local features using a local model to generate a local intermediate output” at [0041]-[0061], [0072]-[0074];
(Andrzejewski teaches processing the processing local data to generate local-model insight score L(i) (i.e., “local intermediate output”))
“wherein the local model comprises a set of local model parameters that have been sampled from a distribution characterized by the determined one or more parameters of the approximated global posterior distribution of local features” at [0090]-[0104];
(Andrzejewski teaches the local model is a machine learning model comprising a set of parameters that have been sampled from a distribution of local feature)
“combining the global intermediate output and local intermediate output to generate a personalized output on the user device” at [0043]-[0044], [0073]-[0074], [0076]-[0077].
(Andrzejewski teaches combining global-model insight score G(i) and local-model insight score L(i) to generate a personalized score CS(i) on the user device)
Andrzejewski does not teach “determining one or more parameter of an approximated global posterior distribution of local features by processing a first subset of global features using a shared constructor model” as claimed. However, Kim teaches a method for training a machine learning model to update global and local versions of the model, including the steps of “determining one or more parameter of an approximated global posterior distribution of local features by processing a first subset of global features using a shared constructor model” wherein “the local model comprises a set of local model parameters that have been sampled from a distribution characterized by the determined one or more parameters of the approximated global posterior distribution of local features” at [0005]-[0007], [0009]-[0033]. Thus, it would have been obvious to one of ordinary skill in the art to combine Kim with Andrzejewski’s teaching because the model proposed in the present technique achieve significant improvement in prediction accuracy”, as suggested by Kim at [0230].
As per claim 2, Andrzejewski and Kim teach the method of claim 1 discussed above. Andrzejewski also teaches: wherein “the user input comprises a support set and a query set, wherein the first subset of the global features are embedding of the support set, wherein the second subset of the global features are embeddings of the query set, and wherein the local features are embeddings of the query set” at [0068]-[0071] and Fig. 6.
As per claim 3, Andrzejewski and Kim teach the method of claim 2 discussed above. Andrzejewski also teaches: wherein “the personalized output is a prediction of the query set” at [0067]-[0081].
As per claim 4, Andrzejewski and teach the method of claim 1 discussed above. Andrzejewski also teaches: wherein “the shared embedding model comprises a set of shared embedding parameters and is accessible by a plurality of user devices on a central server, and wherein the shared global model comprises a set of shared global parameters and is accessible by the plurality of user devices on the central server” at [0041]-[0066] and Fig. 5
As per claim 5, Andrzejewski and Kim teach the method of claim 1 discussed above. Andrzejewski also teaches: wherein “the local intermediate output comprises a local correction output, and wherein combining the respective pair of global and local intermediate outputs to generate a personalized output on the user device comprises: adding the global intermediate output and the local intermediate output to generate a corrected intermediate output; and processing the corrected intermediate output to generate the personalized output” at [0041]-[0044], [0072]-[0074].
As per claim 6, Andrzejewski and Kim teach the method of claim 5 discussed above. Kim also teaches: wherein “processing the corrected intermediate output to generate the personalized output comprises applying an activation function to the corrected intermediate output” at [0099]-[0107].
As per claim 7, Andrzejewski and Kim teach the method of claim 6 discussed above. Kim also teaches: wherein “the personalized output is indicative of a class in a predicted classification, and wherein the activation function comprises a softmax function” at [0099]-[0107].
As per claim 8, Andrzejewski and Kim teach the method of claim 6 discussed above. Kim also teaches: wherein “the personalized output is a value of a predicted regression, and wherein the activation function comprises a linear function” at [0099]-[0107].
As per claim 9, Andrzejewski and Kim teach the method of claim 5 discussed above. Andrzejewski also teaches: wherein “adding the global intermediate output and the local correction output to generate a corrected intermediate output comprise adding a global intermediate sequence of embeddings and a local intermediate sequence of embeddings to generate a corrected intermediate output sequence of embeddings, and wherein processing the corrected intermediate output sequence of embedding to generate the personalized output comprises: decoding the corrected intermediate output sequence of embedding to generate the personalized output” at [0041]-[0044], [0072]-[0074].
As per claim 10, Andrzejewski and Kim teach the method of claim 1 discussed above. Kim also teaches: wherein “determining the one or more parameters of the approximated global posterior distribution of local features by processing the first subset of global features using a shared constructor model further comprise: determining one or more of mean, variance, or bias parameters of the approximated global posterior distribution of local features” at [0005]-[0007], [0009]-[0033].
Claims 16-17, 20 recite similar limitations as in claims 1-10 and are therefore rejected by the same reasons.
Allowable Subject Matter
Claims 11-15, 18-19 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.
The following is a statement of reasons for the indication of allowable subject matter: Prior art of record does not teach the combination of claimed elements including: “at each of a number of training iterations: receiving a ground truth output corresponding with the user input; determining a loss function based on a discrepancy between the corresponding personalized output and the ground truth output and a divergence between an underlying global posterior distribution of local features and a surrogate posterior distribution, wherein the surrogate posterior distribution has been approximated using variational inference; updating respective sets of shared parameters comprising respective sets of parameters of the shared embedding model, the shared constructor model, and the shared global model on each user device in accordance with minimizing the loss function; and transmitting the updated respective sets of shared parameters to the central server with a corresponding number of samples in the user input for the training iteration” as recited in claims 11,18.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KHANH B PHAM/Primary Examiner, Art Unit 2166
July 9, 2026
Prosecution Insights