DETAILED ACTION
This Action is responsive to Claims filed 08/13/2024.
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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 08/13/2024 and 01/13/2025 were filed before the mailing date of the first Action on the merits. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
Receipt of Drawings filed 08/13/2024 is acknowledged. These Drawings are acceptable.
Status of the Claims
Claims 1, 3, 12-13, and 15 were preliminarily amended. Claims 21-28 were preliminarily cancelled. Claims 1-20 are currently pending.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 7, 9-16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Khamy et al. (US 2021/0374608 A1), hereinafter El-Khamy and Malik et al. (US 2021/0117780 A1), hereinafter Malik.
In regards to Claim 1: The present invention claims: “A method comprising: receiving, by a user equipment, a configuration for a machine learning model, the configuration comprising a plurality of weights determined by a server during a first phase training of the machine learning model;” El-Khamy teaches “Before training begins, global weight factors {Wa, Wb} and factor strengths r may be initialized by the server 101. Once initialized, each training round begins with {W a' Wb, r} being sent to a selected subset of the total number of clients 102." ([0038])
“initiating, by the user equipment, a second phase of training of the machine learning model using local training data at the user equipment to personalize the machine learning model to a user of the user equipment without updating the plurality of weights of the machine learning model, wherein the local training data is applied to the machine learning model to generate at least a first reference embedding mapped to a label, wherein the first reference embedding and the label are stored in a dictionary at the user equipment;” El-Khamy teaches "At 305, the client device receives from the global server 101 a globally updated weight factor dictionary and a globally updated factor strengths vector. At 306, the client device retrains on the dataset of the client, the group of parameters selected by the client device, the globally updated weight factor dictionary and the globally updated factor strengths vector. The method may continue until a desired number of training epoch have occurred. The method ends at 307," ([0043]).
“in response to a condition at the user equipment being satisfied, triggering, by the user equipment, a third phase of training of the machine learning model using at least the local training data at the user equipment to update the plurality of weights of the machine learning model and to further personalize the machine learning model to the user of the user equipment;” El-Khamy teaches "The processor may select a group of parameters for the client device from a global group of parameters, train a model using a dataset of the client device and the group of parameters selected by the client device in which the dataset may be formed from an output of the at least one computing device, update a weight factor dictionary and a factor strengths vector after training the model, send through the communication interface to a global server a client-updated weight factor dictionary and a client-updated factor strengths vector, receive through the communication interface from the global server a globally updated weight factor dictionary and a globally updated factor strengths vector, and retrain the model using the dataset of the client device, the group of parameters selected by the client device, and the globally updated weight factor dictionary and the globally updated factor strengths vector," ([0004]).
“and in response to receiving a second unknown sample at the machine learning model, using, by the user equipment, the machine learning model with the updated weights to perform a second inference task by generating a second embedding to query the dictionary to find a second reference embedding and a corresponding label that identifies the second unknown sample.” El-Khamy teaches "The client device may receive the globally updated weight factor dictionary and a globally updated factor strengths vector by receiving the globally updated weight factor dictionary and a globally updated factor strengths vector that were sent by the global server to a second subset of the N client devices in which the client device may be part of the second subset of client devices. In still another embodiment, the processor may send a request through the communication interface to the global server for a current version of the global group of parameters, may update the model using the current version of the global group of parameters, and may evaluate the model updated using the current version of the global group of parameters to form an inference based on the dataset of the client device," ([0004]).
El-Khamy fails to explicitly teach:
“in response to receiving a first unknown sample at the machine learning model, using, by the user equipment, the machine learning model to perform a first inference task by generating a first embedding that is used to query the dictionary to find at least the first reference embedding and the label that identifies the first unknown sample;” However, Malik, in a similar field of endeavor, teaches "Personalized federated learning (i.e., federated user representation learning) can personalize models in federated learning by learning task-specific user representations (i.e., embeddings) or by personalizing model weights. Personalized federated learning is a simple, scalable, privacy-preserving, and resource-efficient. Personalized federated learning may divide model parameters into federated and private parameters," ([0060]) and "As another example and not by way of limitation, a dictionary trained to map text to a vector representation may be utilized, or such a dictionary may be itself generated via training," ([0133]).
Both El-Khamy and Malik are directed towards distributed/federated learning systems. It would have been obvious to one of ordinary skill in the art to combine the neural network training system of El-Khamy with the support for inferences based on unfamiliar data captured at a local device of Malik, because such systems and methods allow for applying a trained model to unknown data to perform an inference (Malik [0060] and [0133]).
In regards to claim 2: The present invention claims: “wherein in response to the update of the plurality of weights of the machine learning model, the reference embeddings are updated, and/or wherein the receiving further comprises receiving an initial set of one or more reference embedding mapped to corresponding labels.” Malik teaches "All the parameters in the character embedding, BLSTM and MLP layers were federated parameters that were shared across all client systems. These parameters were locally trained, sent back to the server, and averaged as in standard federated averaging. User embeddings were considered private parameters and were jointly trained with the federated parameters, but kept privately on the client systems. Even though user embeddings were trained independently on each client system, they evolved collaboratively through the globally shared model (i.e., embeddings were multiplied by the same shared model weights)" ([0119]).
In regards to claim 3: The present invention claims: “wherein the machine learning model receives inputs from different domains, wherein the different domains include at least one of audio samples, video samples, image samples, biometric samples, bioelectrical samples, electrocardiogram samples, electroencephalogram samples, and/or electromyogram samples.” El-Khamy teaches "The data distribution Di may include information relating to biometric data, medical data, image data, location data, application use data, thermal data, atmospheric data and/or audio data," ([0038]).
In regards to claim 4: The present invention claims: “wherein the dictionary comprises an associative memory contained in the user equipment, wherein the associative memory stores a plurality of reference embeddings, each of which is mapped to a label.” Malik teaches “In particular embodiments, the client system may then store, in the local data store, the trained local personalization model comprising the plurality of updated local model parameters," ([0011]) and "The method may begin at step 710, where client system 130 may receive, from one or more remote servers 510, a current version of a neural network model comprising a plurality of model parameters. At step 720, client system 130 may train the neural network model on a plurality of examples 530 retrieved from a local data store to generate a plurality of updated model parameters, wherein each of the plurality of examples 530 comprises one or more features and one or more labels," ([0107]).
In regards to claim 7: The present invention claims: “wherein the first unknown sample and the second unknown sample comprise speech samples from at least one speaker, wherein the first unknown sample and the second unknown sample comprise image samples, and/or wherein the first unknown sample and the second unknown sample comprise video samples.” Malik teaches "The personalized acoustical model may be trained or refined using the voice of a particular user to recognize that user's speech. In particular embodiments, the personalized language model may then determine the most likely phrase that corresponds to the identified phonetic units for a particular audio input," ([0050]).
In regards to claim 9: The present invention claims: “wherein first reference embedding, the first embedding, and the second embedding each comprise a feature vector generated as an output of the machine learning model.” Malik teaches "In particular embodiments, an object may be represented in the vector space 1400 as a vector referred to as a feature vector or an object embedding," ([0134]).
In regards to claim 10: The present invention claims: “wherein the machine learning model comprises a neural network and/or a convolutional neural network.” El-Khamy teaches "The federated machine-learning system disclosed herein provides a federated-learning system that efficiently uses data in a global model to train neural networks in N local models in a factorized way," ([0026]).
In regards to claim 11: The present invention claims: “wherein the machine learning model is trained using a triplet loss function and/or gradient descent.” Malik teaches “In particular embodiments, the training may be based on one or more iterations of gradient descent, or a stochastic variation of gradient descent (SGD).” ([0096]).
In regards to claim 12: The present invention claims: “wherein at least one layer of the machine learning model uses same weights when processing inputs from different domains.” El-Khamy teaches “Before training begins, global weight factors {Wa, Wb} and factor strengths r may be initialized by the server 101. Once initialized, each training round begins with {Wa, Wb, r} being sent to a selected subset of the total number of clients 102. Each selected (sampled) client then trains the model using their own private data distribution Di for E epochs, updating not only the weight factor dictionary {Wa, Wb} and the factor strengths r, but also variational parameters {i, ci, di} of the client, which controls which factors the client uses. The data distribution Di may include information relating to biometric data, medical data, image data, location data, application use data, thermal data, atmospheric data and/or audio data," ([0038]).
In regards to claims 13-16 and 19: Claims 13-16 and 19 recite similar limitations to claims 1-4 and 7, with the exception of “A system comprising: at least one processor; and at least one memory including code which when executed by the at least one processor causes operations comprising…” of Claim 13; therefore, both sets of claims are similarly rejected.
Claim(s) 5-6 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Khamy and Malik as applied to claims 1 and 13 above, and further in view of Cummings et al. (US 2022/0027792 A1), hereinafter Cummings.
In regards to claim 5: While the combination of El-Khamy and Malik reads on Claim 4 above, the combination fails to explicitly teach “wherein the associative memory comprises a lookup table, content-addressable memory, and/or a hashing function implemented memory, and/or wherein the associative memory comprises a random access memory coupled to digital circuitry that searches the random access memory for a reference embedding.” However, Cummings teaches "The term "map" or "mapping" at least in some embodiments refers to a data item or data structure that includes one or more attribute-value pairs (AVPs), keyvalue pairs (KVPs), tuples, and/or other like data representation. A "map" may be in the form of an associative array, symbol table, dictionary, mapping function, hash table, look-up table, linked list, search tree, database objects (e.g., database records, database fields, attributes, associations between data and/or database entities (also referred to as "relations"), etc.), blocks and/or links between blocks in block chain implementations, and/or some other suitable data structure," ([0169]).
El-Khamy, Malik, and Cummings all relate to neural networks and/or neural network memory structures. It would have been obvious to one of ordinary skill in the art to combine the neural network systems of El-Khamy and Malik with the support for hash tables and look-up tables of Cummings, because such systems and methods allow for querying a data structure for reference embeddings (Cummings [0169]).
In regards to claim 6: The present invention claims: “wherein the dictionary is comprised in magnetoresistive memory using spin orbit torque and/or spin transfer torque.” Cummings teaches "Storage circuitry 458 provides persistent storage of information such as data, applications, operating systems and so forth. In an example, the storage 458 may be implemented via a solid-state disk drive (SSDD) and/or high-speed electrically erasable memory (commonly referred to as "flash memory"). Other devices that may be used for the storage 458 include flash memory cards, such as SD cards, microSD cards, XD picture cards, and the like, and USB flash drives. In an example, the memory device may be or may include memory devices that use chalcogenide glass, multi-threshold level NAND flash memory, NOR flash memory, single or multi-level Phase Change Memory (PCM), a resistive memory, nanowire memory, ferroelectric transistor random access memory (Fe TRAM), anti-ferroelectric memory, magnetoresistive random access memory (MRAM) memory that incorporates memristor technology," ([0096]).
In regards to claims 17-18: Claims 17-18 recite similar limitations to claims 5-6, with the exception of “A system comprising: at least one processor; and at least one memory including code which when executed by the at least one processor causes operations comprising…” of Claim 13; therefore, both sets of claims are similarly rejected.
Claim(s) 8 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Khamy and Malik as applied to claims 1 and 13 above, and further in view of Zhihong, Xu (US 2020/0275873 A1), hereinafter Zhihong.
In regards to claim 8: The combination of El-Khamy and Malik fials to explicitly teach “wherein the first unknown sample and the second unknown sample comprise biometric samples, wherein the biometric samples comprise an electrocardiogram sample, an electroencephalogram sample, and/or an electromyogram signals.” However, Zhihong teaches "performing electrocardiogram analysis based on the electrocardiogram information, performing electroencephalogram analysis based on the electroencephalogram information, performing electrooculogram analysis based on the electrooculogram information, and determining a first emotional stress of the target object based on analysis results of the electrocardiogram analysis, the electroencephalogram analysis and the electrooculogram analysis," ([0024]).
El-Khamy, Malik, and Zhihong are all directed to the use and/or implementation of neural networks in relevant imaging scenarios. It would have been obvious to one of ordinary skill in the art to combine the neural network systems of El-Khamy and Malik with the support for electrocardiogram and electroencephalogram signals of Zhihong, because such systems and methods allow for the use of unique biological signals to perform biometric analysis (Zhihong ([0024]).
In regards to claim 20: Claim 20 recites similar limitations to claim 8, with the exception of “A system comprising: at least one processor; and at least one memory including code which when executed by the at least one processor causes operations comprising…” of Claim 13; therefore, both claims are similarly rejected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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/GRIFFIN TANNER BEAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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