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 (IDS) submitted on 12/15/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With regard to Claim 1,
Step 2A, Prong 1
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claim 1 recites:
A computer-implemented method, comprising: obtaining, from a user, one or more configuration requirements associated with a machine-learning (ML) algorithm; and determining the ML algorithm based on the one or more configuration requirements.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process. Determining a ML algorithm to use based on one or more configuration requirements is a mental process.
Step 2A, Prong 1 (Yes).
Step 2A, Prong 2
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The additional element is the obtaining step.
The obtaining step is mere data gathering and is insignificant extra-solution activity. See MPEP 2106.05(g).
Step 2A, Prong 2 (No).
Step 2B
This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above:
The obtaining step is mere data gathering and is insignificant extra-solution activity. This element amounts to receiving or transmitting data over a network and has been found by the courts to be well-understood, routine and conventional activity. See MPEP 2106.05(d), subsection II.
Step 2B (No).
Claim 1 is ineligible.
Claims 12 and 20 are similar in scope and rejected likewise.
Dependent Claims:
Claims 2, 3, 8-11: Each of these dependent claims elaborate on the mental step.
Claims 4-7: These dependent claims elaborate on the details of the data that is collected.
Thus, these claims are ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 4, 12, 13, 15, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by McKay (US 20210192394 A1).
Regarding claim 1, McKay discloses “obtaining, from a user, one or more configuration requirements associated with a machine-learning (ML) algorithm” (See [0047]; McKay discloses obtaining a use case configuration from an end user for a machine learning algorithm)
“determining the ML algorithm based on the one or more configuration requirements” (See [0092]; McKay discloses selecting an ML algorithm to use based on the configuration requirements from the ML labeler configuration).
Regarding claim 2, McKay discloses “the one or more configuration requirements comprises a target use case” (See [0047]; McKay discloses obtaining a use case configuration from an end user for a machine learning algorithm)
“determining the ML algorithm comprises selecting, from among a plurality of predefined ML algorithms, the ML algorithm based on the target use case” (See [0049], [0092]; McKay discloses selecting a ML algorithm to use based on the configuration requirements from the ML labeler configuration. McKay also discloses that multiple ML algorithms can be specified before determining a ML algorithm to use).
Regarding claim 4, McKay discloses “the one or more configuration requirements further comprise a storage path associated with training data” (See [0099]; McKay discloses a training data storage device that stores training data)
“obtaining the training data based on the storage path” (See [0099]; McKay discloses retrieving data from the training data storage device)
“training the ML algorithm based on the training data” (See [0099]; McKay discloses training the ML model based on the training data from the training data storage. An algorithm is the procedure/recipe used to output a model, so the ML model is what is being trained).
Regarding claims 12 and 20, these claims are similar in scope to claim 1.
Regarding claim 13, this claim is similar in scope to claim 2.
Regarding claim 15, this claim is similar in scope to claim 4.
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.
Claims 3, 9, 10, 14 are rejected under 35 U.S.C. 103 as being unpatentable over McKay (US 20210192394 A1) in view of Koch (US 20210033688 A1).
Regarding claim 3, McKay discloses “the selecting of the ML algorithm is further based on the architecture of the ML algorithm” (See [0047]; McKay discloses a use case template lists what configuration and architecture is needed, and specifies what ML algorithm to select)
McKay fails to explicitly disclose, “the one or more configuration requirements further comprise a configuration of a computing platform for executing the ML algorithm; the method further comprises determining an architecture of the ML algorithm based on the configuration of the computing platform”.
Koch teaches “the one or more configuration requirements further comprise a configuration of a computing platform for executing the ML algorithm;” (See [0060]; Koch discloses a computer system 600 configuring a machine learning algorithm to execute on the computer system 600 by generating quantitative susceptibility maps).
“the method further comprises determining an architecture of the ML algorithm based on the configuration of the computing platform” (See [0061]; Koch discloses determining parameters (architecture) of a ML algorithm when using a ML algorithm in a computer system 600).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Koch before them to modify McKay to configure a computing platform to run the algorithm and an architecture to configure the algorithm. One would be motivated to do so in order to define what type of computer system could be used to run the algorithm, as well as defining what parameters are needed for the algorithm, see e.g., [0061], where Koch defines the type of computer system and parameters needed for the algorithm to execute.
Regarding claim 9, McKay fails to explicitly disclose, “providing the ML algorithm to a device for deployment”.
Koch teaches “providing the ML algorithm to a device for deployment” (See [0007]; Koch discloses that a machine learning algorithm is provided to a computer system to be implemented).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Koch before them to modify McKay to provide a ML algorithm to a device so it can be implemented on that device. One would be motivated to do so in order to run the algorithm on a device, see e.g., [0007], where Koch implements the algorithm on a computer system so it can be executed.
Regarding claim 10, McKay fails to explicitly disclose, “obtaining, from the user, an instruction indicating the device for deployment; wherein the providing of the ML algorithm is based on the instruction”.
Koch teaches “obtaining, from the user, an instruction indicating the device for deployment” (See [0059], [0060]; Koch discloses that a computer system 600 may obtain instructions from a user's input 602, and these instructions may include implementing (deploying) machine learning algorithms on a computer system 600).
“wherein the providing of the ML algorithm is based on the instruction” (See [0059], [0060]; Koch discloses that ML algorithms can be provided to the computer system 600 from the given instructions from the user's input 602).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Koch before them to modify McKay to obtain instructions for deploying the algorithm on a device and to specify what algorithm should be used for deployment. One would be motivated to do so in order to instruct how an algorithm should be deployed on the selected device and to specify what algorithm should be used, see e.g., [0059] and [0060], where Koch explains that instructions can be received to configure a device to implement algorithms on a computer system and can also provide the algorithms to be used.
Regarding claim 14, this claim is similar in scope to claim 3.
Claim Rejections - 35 USC § 103
Claims 5, 16 are rejected under 35 U.S.C. 103 as being unpatentable over McKay (US 20210192394 A1) in view of Gunnarsson (US 20210268313 A1).
Regarding claim 5, McKay fails to explicitly disclose, “determining, based on the training data, one or more training settings for the training of the ML algorithm; the one or more training settings comprises a value of each of one or more hyper-parameters and/or a loss function; the training of the ML algorithm is further based on the one or more training settings”.
Gunnarsson teaches “determining, based on the training data, one or more training settings for the training of the ML algorithm” (See [0077]; Gunnarsson discloses selecting optimization (training) settings that include selecting loss functions based on training data for the purpose of training the machine learning model).
“the one or more training settings comprises a value of each of one or more hyper-parameters and/or a loss function” (See [0077]; The optimization (training) settings comprises of a loss function and hyperparameters)
“the training of the ML algorithm is further based on the one or more training settings” (See [0077]; Training the machine learning model is based on the selected optimization (training) settings).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Gunnarsson before them to modify McKay to configure training settings with hyper-parameters and/or a loss function for the purpose of training the ML algorithm. One would be motivated to do so in order to train the algorithm using a specific configuration of hyper-parameters or a loss function, see e.g., [0077], where Gunnarsson describes training a machine learning model using a specific configuration of training settings that include using a loss function.
Regarding claim 16, this claim is similar in scope to claim 5.
Claim Rejections - 35 USC § 103
Claims 6, 17 are rejected under 35 U.S.C. 103 as being unpatentable over McKay (US 20210192394 A1) in view of Rubens (US 20170213101 A1) and Sahu (US 20230205674 A1).
Regarding claim 6, McKay fails to explicitly disclose, “selecting, among a plurality of predefined preprocessing algorithms, at least one preprocessing algorithm based on the metadata associated with the training data; and processing the training data using the at least one selected preprocessing algorithm; wherein the training of the ML algorithm is based on the processed training data”.
Rubens teaches “selecting, among a plurality of predefined preprocessing algorithms, at least one preprocessing algorithm based on the metadata associated with the training data;” (See [0067]; Rubens discloses selecting a preprocessing algorithm based on metadata).
“processing the training data using the at least one selected preprocessing algorithm” (See [0072]; Rubens discloses using preprocessing algorithms to process raw data, which may comprise of image data or image metadata, for the purpose of training a model)
“wherein the training of the ML algorithm is based on the processed training data” (See [0072]; Rubens discloses training a model using processed training data that consists of image data that was processed by a preprocessing algorithm).
Rubens fails to explicitly disclose, “determining, based on the training data, metadata associated with the training data”.
Sahu teaches “determining, based on the training data, metadata associated with the training data” (See [0045]; Sahu discloses determining metadata associated with the training data).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Rubens before them to modify McKay to select at least one preprocessing algorithm based on metadata from training data, and training the ML algorithm by processing the training data with the preprocessing algorithm. Additionally, it would have been obvious to a person having ordinary skill in the art having McKay and Sahu before them to modify McKay to determine what should be metadata based on its association with the training data. One would be motivated to prepare training data with a preprocessing algorithm for the purpose of training the ML algorithm, see e.g., [0072], where Rubens teaches training a model by processing raw data and using the processed data to train a model. One would also be motivated to extract metadata from training data to use as a set of parameters to select a preprocessing algorithm, see e.g., [0045], where Sahu teaches using metadata associated with training data as a set of parameters to be used for machine learning.
Regarding claim 17, this claim is similar in scope to claim 6.
Claim Rejections - 35 USC § 103
Claims 7, 18 are rejected under 35 U.S.C. 103 as being unpatentable over McKay (US 20210192394 A1) in view of Brantjes (US 20220026810 A1).
Regarding claim 7, McKay fails to explicitly disclose, “selecting, among a plurality of predefined features associated with the training data, at least one feature for the training of the ML algorithm; determining, based on the training data, the at least one selected feature; wherein the training of the ML algorithm is based on the at least one determined feature”.
Brantjes teaches “selecting, among a plurality of predefined features associated with the training data, at least one feature for the training of the ML algorithm” (See [0054]; Brantjes discloses selecting at least one predefined feature for training a machine learning model).
“determining, based on the training data, the at least one selected feature” (See [0054]; Brantjes discloses determining at least one predefined feature for training a machine learning model)
“wherein the training of the ML algorithm is based on the at least one determined feature” (See [0054]; Brantjes discloses training a machine learning model using at least one predefined feature).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Brantjes before them to modify McKay to select a feature from a selection of predefined features and use it to train the ML algorithm. One would be motivated to train the model using the most relevant features to improve model performance.
Regarding claim 18, this claim is similar in scope to claim 7.
Claim Rejections - 35 USC § 103
Claims 8, 19 are rejected under 35 U.S.C. 103 as being unpatentable over McKay (US 20210192394 A1) in view of Asif (US 20220101184 A1).
Regarding claim 8, McKay fails to explicitly disclose, “the one or more configuration requirements further comprise a minimum target performance of the ML algorithm; determining a performance of the selected ML algorithm; selecting, among the plurality of predefined ML algorithms, a further ML algorithm, if the performance of the selected ML algorithm is worse than the minimum target performance of the ML algorithm”.
Asif teaches “the one or more configuration requirements further comprise a minimum target performance of the ML algorithm; determining a performance of the selected ML algorithm” (See [0035]; Asif discloses gathering configuration requirements that comprise target performance requirements for the model).
“determining a performance of the selected ML algorithm” (See [0044]; Asif discloses evaluating (determining) a performance of a selected student model)
“selecting, among the plurality of predefined ML algorithms, a further ML algorithm, if the performance of the selected ML algorithm is worse than the minimum target performance of the ML algorithm” (See [0036], [0045], [0046]; Asif discloses selecting a new model if the current model fails to meet the performance requirements, and a new model is selected by acquiring a known predefined model from a repository of predefined models known to be accurate).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Asif before them to modify McKay to designate a certain target performance for a ML algorithm and to select a different ML algorithm if the currently selected ML algorithm performs below the target performance. One would be motivated to do so in order to obtain a ML algorithm that meets the target performance, see e.g., [0004], where Asif teaches finding a ML model with the highest performance when training a model to meet performance standards.
Regarding claim 19, this claim is similar in scope to claim 8.
Claim Rejections - 35 USC § 103
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over McKay (US 20210192394 A1) in view of Koch (US 20210033688 A1) and Warren (US 20210279570 A1).
Regarding claim 11, McKay fails to explicitly disclose, “the ML algorithm comprises a plurality of neural networks”.
Koch teaches “the ML algorithm comprises a plurality of neural networks” (See [0019]; Koch discloses training a plurality of neural networks using a ML algorithm by training a different neural network for each of the different resolution sizes).
Koch fails to explicitly disclose, “the method further comprises serializing the plurality of neural networks based on the one or more configuration requirements”.
Warren teaches “the method further comprises serializing the plurality of neural networks based on the one or more configuration requirements” (See [0056]; Warren discloses using serialized representations of neural networks).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having McKay and Koch before them to modify McKay to use a plurality of neural networks for the algorithm. Additionally, it would have been obvious to a person having ordinary skill in the art having McKay and Warren before them to modify McKay to serialize the neural networks. One would be motivated to train multiple neural networks for various resolutions and parcel sizes, see e.g., [0019], where Koch trains a different neural network for each different resolution. One would also be motivated to generate serialized representations of neural networks for the purpose of converting neural networks into a standardized format that can be analyzed mathematically, see e.g., [0056], where Warren serializes neural networks as a mathematical description for use in mathematical equations and operations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID KIM whose telephone number is (571)272-4331. The examiner can normally be reached 7:30 AM - 4:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Ell can be reached at (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.K./Examiner, Art Unit 2141
/BEN M RIFKIN/Primary Examiner, Art Unit 2123
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