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 the Claims
Claims 1-9, 12-14 and 16-19 are pending for examination.
Claims 1 and 16 are independent Claims.
Claims 1-9, 12-14 and 16-19 are rejected under 35 U.S.C. §103.
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, 4, 6-9, 12-13, 16-17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galloway et al. (U.S. 2018/0233227 hereinafter Galloway) in view of Biswas et al. (U.S. 12,039,177 hereinafter Biswas) in further view of Mueller et al. (U.S. 2021/0326717 hereinafter Mueller).
As Claim 1, Galloway teaches a test and measurement machine learning model development system, the system comprising:
a user interface (Galloway (¶0062 line 1-2), user interface 300);
one or more ports to allow the system to connect to one or more data sources (Galloway (¶0052 line 1-3), electrocardiogram sensor 115 provides electrocardiogram data 120);
one or more memories (Galloway (¶0076 line 2), memory); and
one or more processors configured to execute code to cause the one or more processors (Galloway (¶0075 line 2, ¶0076 line 1-2), computer system 600) to:
receive data from the one or more data sources (Galloway (¶0070, fig. 5 item 510), measurement system receive electrocardiogram data);
apply one or more modules from a library of signal processing (Galloway (¶0071 line 1-4), measurements system may filter electrocardiogram data) and feature extraction modules to the data to produce training data (Galloway (¶0047 line 4-7), system determines which features to extract and/or analyze from a sensed bio-signal)
apply one or more machine learning models to the training data (Galloway (¶0047 line 1-3), model training service may train a model based on the electrocardiogram training data);
provide monitoring of the one or more machine learning models (Galloway (¶0048 last 4 lines, ¶0073), machine model training service may update machine learning model); and
save the one or more machine learning models to at least one of the one or more memories (Galloway (¶0074 last 5 lines, ¶0078 line 1-3), readable storage medium stores instructions), machine learning model is used to test electrocardiograms).
Galloway may not explicitly disclose:
display, on the user interface, one or more application user interfaces, the application user interfaces to allow a user to provide user inputs to configure components of the machine learning model development system ;
use an application programming interface to configure machine learning model development the system based on the user inputs;
based upon the user inputs;
Biswas teaches:
display, on the user interface (Biswas (col. 20 line 64-67), “the graphical user interface may include options for selecting a machine learning system, changing configurations of a machine learning system, storing training datasets for training machine learning systems, selecting training sets for training particular machine learning systems, and efficiently viewing results of running a particular machine learning system”), one or more application user interfaces, the application user interfaces to allow a user to provide user inputs to configure components of the machine learning model development system (Biswas (¶0021 line 6-17, figs. 6-8), system displays user interface for configuring machine learning);
use an application programming interface to configure machine learning model development the system based on the user inputs (Biswas (col. 26 line 38-43, fig. 9 item 912), machine learning system is configured based on the configuration file);
based upon the user inputs (Biswas (col. 26 line 61-64, fig. 9 item 914), machine learning system trains model based on user inputs);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify user interface of Galloway instead be a machine learning configuration user interface taught by Biswas, with a reasonable expectation of success. The motivation would be to “By providing a graphical user interface for selecting machine learning configurations, such as parameter values and training data, the machine learning server computer
creates a more efficient method of generating a machine learning system” (Biswas (col. 28 line 4-8)).
Balloway in view of Biswas may not explicitly disclose:
the application programming interface including a machine learning facade layer to map to one or more machine learning toolkits and to allow the user to develop new machine learning models;
Mueller teaches:
the application programming interface including a machine learning facade layer (Mueller (¶0078 line 5-8, fig. 8), “an interactive code exploration may be presented to a user (e.g., via a web browser as a web application) that allows the user to explore code, run code, modify and run code, etc.”) to map to one or more machine learning toolkits (Mueller (¶0078 last 4 lines, ¶0080 line 1-13, fig. 8), “a first pipeline "FP BASELINE" is defined with a first "FPO" step, which is defined (as a training job to learn the transformations) with values for a source directory, instance types and counts, and other non-illustrated values such as an ML framework version to be used, a set of tags to be applied, an identifier of a feature processing strategy (e.g., a baseline strategy that performs a 1-hot encoding of all categorical variables and does a median-impute null values with indicators; a quadratic strategy that does a hash-encoding of categorical variables, bucketization of numerics, addition of cartesian-product features for predictive feature combinations; or other strategies) to use”) and to allow the user to develop new machine learning models (Mueller (¶0078 line 12-16), “Moreover, users can "run" this code (e.g., via selecting a user interface element such as a button, causing the application to send a request to the provider network for the code to actually be executed) in its original form or in modified form (e.g., by the user editing the code).”);
Galloway in view of Biswas discloses a system/method to create a machine learning model. Mueller discloses a façade layer (user interface) for creating/editing new machine learning model code. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify user interface of Galloway in view of Biswas instead be a interactive code exploration taught by Mueller, with a reasonable expectation of success. The motivation would be to allow user to conveniently to “view[ing] and/or modify[ing] an automated machine learning pipeline exploration” (Mueller (¶0078 line 2-4)).
As Claim 4, besides Claim 1, Galloway in view of Biswas in further view of Mueller teaches further comprising a connection to one or more real-time data sources (Galloway (¶0066 line 3-5), real-time data of an individual heart-beats).
As Claim 6, besides Claim 1, Galloway in view of Biswas in further view of Mueller teaches wherein the code to cause the one or more processors to receive data from the one or more data sources comprises code to cause the one or more processors to receive data from one or more of a database, cloud storage, a data and waveform simulation tool, stored waveform files, and an acquisition from one or more test and measurement instruments (Galloway (¶0070, fig. 5 item 510), measurement system receive electrocardiogram data).
As Claim 7, besides Claim 6, Galloway in view of Biswas in further view of Mueller teaches wherein the application programming interface includes a query interface to allow the user to search the data (Galloway (¶0063 line 1-4), user interface 300 provides option for recording electrocardiogram data).
As Claim 8, besides Claim 1, Galloway in view of Biswas in further view of Mueller teaches wherein the one or more memories comprise a feature store, and wherein the one or more processors are further configured to execute code to cause the one or more processors to store the training data in the feature store (Galloway (¶0052 line 1-3), analyte analysis system stores electrocardiogram data).
As Claim 9, besides Claim 1, Galloway in view of Biswas in further view of Mueller teaches wherein the one or more memories comprise a connection to at least one of a cloud storage, a cloud data lake storage, and an embedded database (Galloway (¶0075 line 5-11), the machine may operate in a distributed or network environment).
As Claim 12, besides Claim 1, Galloway in view of Biswas in further view of Mueller teaches wherein the code to cause the one or more processors to apply one or more machine learning models to the training data comprises code to cause the one or more processors to apply a machine learning model from a library of one or more saved machine learning models (Galloway (¶0055 last 5 lines), multiple machine learning models are saved in the library).
As Claim 13, besides Claim 11, Galloway in view of Biswas in further view of Mueller teaches wherein the library of one or more saved machine learning models includes one or more of a trained machine learning model for performing glitch detection, a trained machine learning model for performing high speed signal classification, a trained machine learning model for performing tuning of optical transceivers, and a trained machine learning model for performing Transmitter Distortion and Eye Closure Quaternary measurements (Galloway (¶0074 last 5 lines, ¶0078 line 1-3), machine learning model is used to test electrocardiograms).
As Claim 16-17, the Claims are rejected for the same reasons as Claim 1.
As Claim 19, the Claim is rejected for the same reasons as Claim 6.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galloway and Biswas in view of Mueller in further view of Kelly et al. (U.S. 2017/0207987 hereinafter Kelly).
As Claim 2, besides Claim 1, Galloway in view of Biswas in further view of Mueller does not explicitly disclose:
wherein the application user interfaces comprise application user interfaces for data processing and feature extraction, training, predictor definitions, visualizations, and data labeling.
Kelly teaches:
wherein the application user interfaces comprise application user interfaces for data processing and feature extraction (Kelly (¶0029, fig. 5), standard as 100G Ethernet KR4), training (Kelly (¶0029, fig. 5), you can select mark for training data), predictor definitions (Kelly (¶0029, fig. 5), trigger on as All frames), visualizations (Kelly (¶0029, fig. 5), acquire and analyze includes review result table), and data labeling (Kelly (¶0029, fig. 5), data labeling as lane 1, lane 2, lane 3 or lane 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify user interface of Galloway in view of Biswas in further view of Mueller instead be a user interface taught by Kelly, with a reasonable expectation of success. The motivation would be to allow user to select “one or more lanes to be monitored. The user can also select whether to mark a frame marker, control channel, or training data” (Kelly (¶0029 last 2 lines)) (Teaching, Suggestion, or Motivation).
Claim(s) 3 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galloway, Biswas and Mueller in view of Tan et al. (U.S. 2016/0323094 hereinafter Tan) in further view of Smith et al. (U.S. 2021/0249032 hereinafter Smith).
As Claim 3, besides Claim 1, Galloway in view of Biswas in further view of Mueller teaches:
wherein the library of signal processing and feature extraction modules comprise modules for filtering (Galloway (¶0071 line 1-4), measurements system may filter electrocardiogram data), measurements (Galloway (¶0063 line 1-4), user interface 300 provides option for recording electrocardiogram data), tensor building (Galloway (¶0020 line 2-6), machine learning for training tensors or layer) and feature extraction (Galloway (¶0047 line 4-7), system determines which features to extract and/or analyze from a sensed bio-signal).
Galloway in view of Biswas in further view of Mueller does not explicitly disclose:
clock recovery, continuous time linear equalization, de-embedding.
Tan teaches:
clock recovery (Tan (¶0035 line 2), clock recovery), continuous time linear equalization (Tan (¶0035 line 8-9), linear equalization), de-embedding (Tan (¶0035 line 10), de-emphasis).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify module of Galloway in view of Biswas in further view of Mueller instead be a module taught by Tan, with a reasonable expectation of success. The motivation would be to allow user to select “improved techniques to recover a stable clock from data signals that have SSC and signal integrity issues such as ISI, cross coupling and other noise, insertion loss, or reflections” (Tan (¶0016)) (Teaching, Suggestion, or Motivation).
Galloway and Biswas in view of Mueller in further view of Tan may not explicitly disclose:
spectrograms
Smith teaches:
spectrograms (Smith (¶0011), spectrogram)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify module of Galloway and Biswas in view of Mueller in further view of Tan instead be a module taught by Smith, with a reasonable expectation of success. The motivation would be so that “Visual representations can be used as input for machine learning applications in which the visual representations, mathematically manipulated, provide enhanced performance for pattern recognition of characteristics of the audio signal i.e. a 2-or 3-dimensional version of the audio data enhances the machine learning system's detection accuracy” (Smith (¶0011 last 9 lines)).
As Claim 18, the Claim is rejected for the same reasons as Claim 3.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galloway and Biswas in view of Mueller in further view of Krishnakumar et al. (U.S. 2020/0225287 hereinafter Krishnakumar).
As Claim 5, besides Claim 4, Galloway in view of Biswas in further view of Mueller does not explicitly disclose:
wherein the connection to one or more real-time date sources comprises a REST API Krishnakumar teaches:
wherein the connection to one or more real-time date sources comprises a REST API (Krishnakumar (¶0029 line 3), REST API).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify a module of Galloway in view of Biswas in further view of Mueller instead be a API taught by Krishnakumar, with a reasonable expectation of success. The motivation would be to allow “Since REST can be used over nearly any protocol, developers do not need to install libraries or additional software” (Krishnakumar (¶0029 line 5-6)) (Teaching, Suggestion, or Motivation).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Galloway and Biswas in view of Mueller in further view of Lang et al. (U.S. 2019/0258953 hereinafter Lang).
As Claim 14, besides Claim 12, Galloway in view of Biswas in further view of Mueller does not explicitly disclose:
wherein the one or more saved machine learning models are files formatted in accordance with Open Neural Network Exchange (ONNX) standard
Lang teaches:
wherein the one or more saved machine learning models are files formatted in accordance with Open Neural Network Exchange (ONNX) standard (Lang (¶0329 line 2-4), ONNX standard).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify user interface of Galloway in view of Biswas in further view of Mueller instead be a user interface taught by Krishnakumar, with a reasonable expectation of success. The motivation would be to “minimizes the data modification extensions need to ML toolkits” (Lang (¶0329)) (Teaching, Suggestion, or Motivation).
Response to Arguments
Rejections under 35 U.S.C. §102:
As Claims 1, 4, 6-9, 10-13, 16-17 and 19-20, Applicant argues that cited references do not disclose “application programming interface …” because Galloway and Biswas are related to already formed machine learning models (last 3 paragraphs of page 7 in the remarks).
Applicant’s arguments are moot because new reference Mueller teaches the limitation(s). Mueller teaches a user interface for editing codes of machine learning model.
Other independent/dependent Claims are rejected for the same reason(s) above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bansal et al. (U.S. 2021/0097444) discloses a system/method for editing machine learning codes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147
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