DETAILED ACTION
The present application, filed on 7/12/2023 is being examined under the AIA first inventor to file provisions.
The following is a second non-final Office Action in response to Applicant’s amendments filed on 5/12/2026.
a. Claims 1, 3, 9, 11, 13-15 are amended
Overall, claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
35 USC 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 USC 101 because the claimed invention is not directed to patent eligible subject matter. The claimed matter is directed to a judicial exception, i.e. an abstract idea, not integrated into a practical application, and without significantly more.
Per Step 1 of the multi-step eligibility analysis, claims 1-10 are directed to a computer implemented method, claims 11-13 are directed to a computer implemented method, claims 14 are directed to computer executable instructions stored on a non-transitory storage medium, and claims 15-20 are directed to a system.
Thus, on its face, each independent claim and the associated dependent claims are directed to a statutory category of invention.
[INDEPENDENT CLAIMS]
Per Step 2A.1. Independent claim 1, (which is representative of independent claims 14) is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 1, (which is representative of independent claims 14) recite an abstract idea, shown in bold below:
[A] A processor-implemented method
[B] receiving image data obtained by an image sensor, wherein the image data comprises labels corresponding to defect codes;
[C] training a first model to predict confidences of labels for data samples in a training dataset comprising the image data, including
[D] using a corrected data sample obtained by correcting an incorrect label based on a corresponding confidence detected by the first model and an estimated corrected label generated by a second model;
[E] training the second model to estimate correct labels for the data samples, including
[F] estimating a correct other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model with respect to the other incorrect label; and
[G] automatically correcting the other incorrect label with an estimated correct other label to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time.
Independent claim 1, (which is representative of independent claims 14) recites: training a first and using corrected data samples and a corrected label ([C], [D]); training a second model and estimating a correct label ([E], [F]); and correcting the incorrect label ([G]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: utilizing a deep learning model for correcting labels and automating labeling.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing “following rules or instructions”. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)).
In addition, or alternatively, under its broadest reasonable interpretation, covers reasonable performance of limitations expressing observation, evaluation in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. For example, the step “training a first model to predict confidences of labels for data samples in a training dataset comprising the image data”, as drafted in the context of this claim, encompasses the user manually or mentally guiding, exercising model, without physical aid. Further, the step “using a corrected data sample obtained …”, as drafted in the context of this claim, encompasses the user manually or mentally using data, without physical aid. Further, the step “training the second model to estimate correct labels for the data samples,”, as drafted in the context of this claim, encompasses the user manually or mentally guiding, exercising a model, without physical aid. Further, the step “estimating a correct other label corresponding to another incorrect label …”, as drafted in the context of this claim, encompasses the user manually or mentally making an estimation, without physical aid. Further, the step “automatically correcting the other incorrect label with an estimated correct other label to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time.”, as drafted in the context of this claim, encompasses the user manually or mentally making a correction, without physical aid. … These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)). The use of a physical aid would not negate the mental nature of this limitation (see MPEP 2106.04(a)(2) iii B)
Accordingly, it is concluded that independent claim 1, (which is representative of independent claims 14) recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “processor,” “computer-readable storage medium” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea utilizing a deep learning model for correcting labels and automating labeling, and do not serve to integrate the identified abstract idea into a practical application.
The additional elements in the independent claims, shown not bolded above, recite: receiving an image ([B]). When considered individually, they amount to nothing more than receiving data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling) into a practical application (see MPEP 2106.05(f)(2)).
Therefore, the additional claim elements of independent claim 1, (which is representative of independent claims 14) do not integrate the identified abstract idea into a practical application and the claims are directed to the recited judicial exception.
Per Step 2B. Independent claim 1, (which is representative of independent claims 14) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 1, 11 are deemed ineligible.
Per Step 2A.1. Independent claim 11 is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 11 recite an abstract idea, shown in bold below:
[A] A process-implemented automatic labeling method
[B] detecting whether a label for a data sample is an incorrect label by applying the data sample to a first model,
[C] wherein the data sample comprises image data obtained by an image sensor,
[D] wherein the first model comprises a first neural network that is trained to
[E] detect the incorrect label comprised in the data sample based on confidence of the label to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time.
Independent claim 11 recites: detecting an incorrect label ([B], [E]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: utilizing a deep learning model for correcting labels and automating labeling.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing “following rules or instructions”. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)).
In addition, or alternatively, under its broadest reasonable interpretation, covers reasonable performance of limitations expressing observation, evaluation in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. For example, the step “detecting whether a label for a data sample is an incorrect label by applying the data sample to a first model”, as drafted in the context of this claim, encompasses the user manually or mentally assessing the correctness of a label, without physical aid. Further, the step “detect the incorrect label comprised in the data sample based on confidence of the label”, as drafted in the context of this claim, encompasses the user manually or mentally detecting an incorrect label, without physical aid. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)). The use of a physical aid would not negate the mental nature of this limitation (see MPEP 2106.04(a)(2) iii B)
Accordingly, it is concluded that independent claim 11 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the additional elements “wherein the first model comprises a first neural network” as applied to the detection model, are nothing more than (a) descriptive limitations of claim elements, such as describing the nature, structure and/or content of other claim elements, or (b) general links to the computing environment, which amount to instructions to “apply it,” or equivalent (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea utilizing a deep learning model for correcting labels and automating labeling, and do not serve to integrate the identified abstract idea into a practical application.
Therefore, the additional claim elements of independent claim 11 do not integrate the identified abstract idea into a practical application and the claims are directed to the recited judicial exception.
Per Step 2B. Independent 11 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 11 are deemed ineligible.
Per Step 2A.1. Independent claim 15 is rejected under 35 USC 101 because the independent claim is directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The limitations of the independent claim 15 recite an abstract idea, shown in bold below:
[A] An electronic device, comprising: a communication system; and a processor configured to, based on confidences of labels for data samples in a training dataset received by the communication system,
[B] iteratively train a first model to detect incorrect labels in the training dataset and
a second model to
[C] estimate correct labels corresponding to the incorrect labels, and
[D] generate a data sample in which an incorrect label is corrected using at least one of the first model or the second model,
[E] wherein the training dataset comprises image data obtained by an image sensor and labels corresponding to defect codes,
wherein the processor is further configured to:
[F] train the first model to predict the confidences, including using the corrected data sample generated by correcting the incorrect label based on a corresponding confidence detected by the first model and an estimated corrected label generated by the second model,
[G] train the second model to estimate correct labels for the data samples, including estimating a corrected other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model with respect to the other incorrect label, and
[H] automatically correct the other incorrect label with the estimated correct other label to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time.
Independent claim 15 recites: training a model to detect incorrect labels and a model with correct labels ([B], [C]); generate data samples with correct label along with a confidence level ([D], [F]); and training a model to estimate the correct labels and to correct incorrect labels ([G], [H]), which, based on the claim language and in view of the application disclosure, represents a process aimed at: utilizing a deep learning model for correcting labels and automating labeling.
This is a combination that, under its broadest reasonable interpretation, covers performance of limitations expressing “following rules or instructions”. These fall under the Certain Methods of Organizing Human Activity, i.e., Managing Personal Behavior or Relationships, or Interactions Between People grouping of abstract ideas (see MPEP 2106.04(a)(2)).
In addition, or alternatively, under its broadest reasonable interpretation, covers reasonable performance of limitations expressing observation, evaluation in the human mind. Nothing in the claim elements precludes the steps from being practically performed in the human mind. For example, the step “iteratively train a first model to detect incorrect labels in the training dataset”, as drafted in the context of this claim, encompasses the user manually or mentally training a model, without physical aid. Further, the step “estimate correct labels corresponding to the incorrect labels”, as drafted in the context of this claim, encompasses the user manually or mentally estimating the correctness of a label, without physical aid. Further, the step “generate a data sample in which an incorrect label is corrected …”, as drafted in the context of this claim, encompasses the user manually or mentally generate a label correcting sample, without physical aid. Further, the step “train the first model to predict the confidences …”, as drafted in the context of this claim, encompasses the user manually or mentally training a first model, without physical aid. Further, the step “train the second model to estimate correct labels for the data samples”, as drafted in the context of this claim, encompasses the user manually or mentally training a second model, without physical aid. Further, the step “automatically correct the other incorrect label with the estimated correct other label”, as drafted in the context of this claim, encompasses the user manually or mentally proceeding with correcting labels, without physical aid. These limitations fall under the Mental Processes, i.e., Concepts Performed in the Human Mind grouping of abstract ideas (see MPEP 2106.04(a)(2)). The use of a physical aid would not negate the mental nature of this limitation (see MPEP 2106.04(a)(2) iii B)
Accordingly, it is concluded that independent claim 15 recites an abstract idea that corresponds to a judicial exception.
[INDEPENDENT CLAIMS – Additional Elements]
Per Step 2A.2. The identified abstract idea is not integrated into a practical application because the additional elements in the independent claims only amount to instructions to apply the judicial exception to a computer, or are a general link to a technological environment (see MPEP 2106.05(f); MPEP 2106.05(h)).
For example, the added elements “communication system,” “processor” recite computing elements at a high level of generality, generally linking the use of a judicial exception to a particular technological environment (see MPEP 2106.05(h)), or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
These additional elements of the independent claims do not preclude from carrying out the identified abstract idea utilizing a deep learning model for correcting labels and automating labeling, and do not serve to integrate the identified abstract idea into a practical application.
Therefore, the additional claim elements of independent claim 15 do not integrate the identified abstract idea into a practical application and the claims are directed to the recited judicial exception.
Per Step 2B. Independent claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when the independent claim is reevaluated as a whole, as an ordered combination under the considerations of Step 2B, the outcome is the same like under Step 2A.2.
Overall, it is concluded that independent claims 15 are deemed ineligible.
[DEPENDENT CLAIMS]
Dependent claim 2, which is representative of dependent claims 16, recites:
iteratively trains the first model to detect incorrect labels and the second model to estimate the correct labels; the iterative training further comprises:
determining the confidence comprising a first probability of each of the labels being correct and a second probability of each of the labels being incorrect;
training the first model by updating first parameters of the first model, to predict confidence,
using the corrected data samples obtained by correcting the incorrect labels in the first data sample.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 2 (which is representative of dependent claims 16) is deemed ineligible.
Dependent claim 3 recites:
sampling a second data sample comprising the correct labels based on a Bernoulli distribution.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to “receiving or transmitting data over a network, e.g., using the Internet to gather or provide data”, which has been recognized by a controlling court as "well-understood, routine and conventional computing functions" when claimed generically as they are in these dependent claims. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling) into a practical application (see MPEP 2106.05(d) II)).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 3 is deemed ineligible.
Dependent claim 4, which is representative of dependent claims 17, recites:
updating the first parameters of the first model based on a maximum likelihood corresponding to the corrected data samples.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to receiving/transmitting data, processing data, storing results or transmitting data that serves merely to implement the abstract idea using computing components for performing computer functions (corresponding to the words “apply it” or an equivalent), or merely uses a computer as a tool to perform the identified abstract idea. Thus, it is concluded that these claim elements do not integrate the identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling) into a practical application (see MPEP 2106.05(f)(2)).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 4 (which is representative of dependent claims 17) is deemed ineligible.
Dependent claim 5, which is representative of dependent claims 18, recites:
training the first model by applying respective regularization penalties for the confidences to the updated first parameters.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 5 (which is representative of dependent claims 18) is deemed ineligible.
Dependent claim 6, which is representative of dependent claims 19, recites:
determining initial parameter values of the first model based on a calculated cross-entropy loss.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 6 (which is representative of dependent claims 19) is deemed ineligible.
Dependent claim 7, which is representative of dependent claims 20, recites:
estimating a probability of the correct other label corresponding to the other incorrect label of the first data sample;
training the second model by updating second parameters of the second model, to estimate the other correct label, using a first data sample comprising the estimated probability of the other correct label.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 7 (which is representative of dependent claims 20) is deemed ineligible.
Dependent claim 8 recites:
classifying the data samples in the training dataset into a first data sample comprising the incorrect labels and a second data sample comprising the correct labels based on a distribution of the confidences;
wherein the data samples are mixed such that the first data sample comprises
training data and the incorrect label corresponding to the training data and the second data sample comprises the training data and the correct label corresponding to the training data.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 8 is deemed ineligible.
Dependent claim 12 recites:
wherein the data sample comprises input data and the label corresponding to the input data, and the method further comprises:
generating a correct label corresponding to the incorrect label by applying the input data to a second model, as the label is determined as the incorrect label,
wherein the second model comprises a second neural network that is trained to estimate the correct label corresponding to the data sample comprising the incorrect label.
When considered individually, these added claim elements further elaborate on the abstract idea identified in the independent claims, because the dependent claim continues to recite the identified abstract idea: utilizing a deep learning model for correcting labels and automating labeling. The elements in this dependent claim are comparable to performance of limitations expressing mathematical concepts like mathematical relationships, mathematical calculations. These fall under the Mathematical Concepts. i.e., mathematical relationships, mathematical formulas or equations, or mathematical calculations grouping of abstract ideas (see MPEP 2106.04(a)(2) I).
The dependent claim elements have the same relationship to the underlying abstract idea (utilizing a deep learning model for correcting labels and automating labeling) as outlined in the independent claims analysis above. Thus, the dependent claim elements are not directed to any specific improvements of the independent claims and do not practically or significantly alter how the identified abstract idea would be performed. When considered as a whole, as an ordered combination, the dependent claim further elaborates on the previously identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling).
Therefore, dependent claim 12 is deemed ineligible.
Dependent claims 9, 10, 13 recite:
wherein the training data comprises another image data of a semiconductor obtained by an image sensor.
wherein the first model and the second model are each trained based on an expectation-maximization (EM) algorithm.
wherein the input data comprises another image data of a semiconductor obtained by the image sensor.
These further elements in the dependent claims do not perform any claimed method steps. They describe the nature, structure and/or content of other claim elements – the training data; the first model; the second model; the input data – and as such, cannot change the nature of the identified abstract idea (utilizing a deep learning model for correcting labels and automating labeling), from a judicial exception into eligible subject matter, because they do not represent significantly more (see MPEP 2106.07). The nature, form or structure of the other claim elements themselves do not practically or significantly alter how the identified abstract idea would be performed and do not provide more than a general link to a technological environment.
Therefore, dependent claims 9, 10, 13 are deemed ineligible.
When the dependent claims are considered as a whole, as an ordered combination, the claim elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. The most significant elements, which form the abstract concept, are set forth in the independent claims. The fact that the computing devices and the dependent claims are facilitating the abstract concept is not enough to confer statutory subject matter eligibility, since their individual and combined significance do not transform the identified abstract concept at the core of the claimed invention into eligible subject matter. Therefore, it is concluded that the dependent claims of the instant application, considered individually, or as a as a whole, as an ordered combination, do not amount to significantly more (see MPEP 2106.07(a)II).
In sum, Claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION – The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5, 11-13, 18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
Claim 11 is rejected under 35 U.S.C. 112(b) because the scope of the claim cannot be ascertained. The claim recites: “… based on confidence of the label.” However, no confidence of the label has been determined.
Claims 5, 18 are rejected under 35 U.S.C. 112(b) because the scope of the claim cannot be ascertained. The claim recites: “… applying respective regularization penalties …” However, no regularization penalties have been determined.
The remainder of the claims are rejected by virtue of dependency. The reference is provided for the purpose of compact prosecution.
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 difference 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 the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Petit et al (“Computerized Medical Imaging and Graphics”).
Regarding Claims 1, 14: Petit first embodiment discloses: A processor-implemented method comprising:
training a first model to predict confidences of labels for data samples in a training dataset comprising the image data, including using a corrected data sample obtained by correcting an incorrect label based on a corresponding confidence detected by the first model and an estimated corrected label generated by a second model; {see at least sections 1, 3.2, 3.2.1, 3.2.2, Algorithm 2: training the confidence network based on the correct label estimation}
training the second model to estimate correct labels for the data samples, including {see at least sections 1, 3. 2, 3. 2 .1, 3.2.2, Algorithms l, 2: training the network to predict correct labels, relabeling)}
estimating a correct other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model with respect to the other incorrect label; and {see at least page 2; “… in order to estimate the unknown complete ground-truth labels …”}
automatically correcting the other incorrect label with an estimated correct other label to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time. {see at least: relabeling in the next iteration. The claim element “to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time” consists entirely of language disclosing at most a reason to have performed earlier method steps (intended use or field of use), but does not affect the functions in a manipulative sense (see MPEP 2103 I C) and imparts neither structure nor functionality to the claimed method (see MPEP 2111.05, MPEP 2114 and authorities cited therein), so it is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.}
Petit first embodiment does not disclose, however, Petit second embodiment discloses:
receiving image data obtained by an image sensor, wherein the image data comprises
labels corresponding to defect codes; {see at least page 2 “… import of organ labels …”; page 3, “input images”; page 9, paragraph 4.4. external sources of images. The reference does not disclose the term “obtained by an image sensor”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Petit first embodiment to include the elements of Petit second embodiment. One would have been motivated to do so, in order to provide data to the algorithm. In the instant case, Petit first embodiment evidently discloses correcting labels and automating labeling. Petit second embodiment is merely relied upon to illustrate the functionality of receiving data in the same or similar context. Since both correcting labels and automating labeling, as well as receiving data are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Petit first embodiment, as well as Petit second embodiment would function in the same manner in combination as they do in their separate embodiments, it is concluded that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Petit. **Examiner notes that the reference is being used here as a one-reference combination in this 103 rejection because the reference teaches two clearly different embodiments within the same cited reference.**
Regarding Claims 2, 16: Petit discloses the limitations of Claims 1, 15. Petit further discloses: wherein the respective trainings of the first and second models are iterative trainings that, based on the confidences of the labels in the training dataset, {see at least INERRANT (Iterative coNfidencE Relabeling of paRtial ANnoTations)}
iteratively trains the first model to detect incorrect labels and the second model to estimate the correct labels; the iterative training further comprises: {see at least page2, fig1 INERRANT (Iterative coNfidencE Relabeling of paRtial ANnoTations)}
determining the confidence comprising a first probability of each of the labels being correct and a second probability of each of the labels being incorrect; {see at least page 3, paragraph 2.3 probability of the predicted class}
training the first model by updating first parameters of the first model, to predict confidence, {see at least sections 1, 3.2, 3.2.1, 3.2.2, Algorithm 2: training the confidence network based on the correct label estimation}
using the corrected data samples obtained by correcting the incorrect labels in the first data sample. {see at least page 4, “… only leverages correct labels.”}
Regarding Claims 3: Petit discloses the limitations of Claims 1, 15. Petit further discloses: wherein the classifying comprises:
sampling a second data sample comprising the correct labels based on a Bernoulli distribution. {see at least page 4, paragraph 3.2.1 “easy positive samples”. The reference does not disclose the term “based on a Bernoulli distribution”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
Regarding Claims 4, 17: Petit discloses the limitations of Claims 1, 15. Petit further discloses: wherein the updating of the first parameters comprises:
updating the first parameters of the first model based on a maximum likelihood corresponding to the corrected data samples. {see at least page8, paragraph “table 7”, “…training on updated dataset.”}
Regarding Claims 5, 18: Petit discloses the limitations of Claims 1, 15. Petit further discloses: wherein the training of the first model comprises:
training the first model by applying respective regularization penalties for the confidences to the updated first parameters. {see at least sections 1, 3.2, 3.2.1, 3.2.2, Algorithm 2: training the confidence network based on the correct label estimation}
Regarding Claims 6, 19: Petit discloses the limitations of Claims 1, 15. Petit further discloses: wherein the training of the first model further comprises:
determining initial parameter values of the first model based on a calculated cross-entropy loss. {see at least page 4, “… the binary cross entropy to train our model …”. The reference does not disclose the term “based on a calculated cross-entropy loss”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
Regarding Claims 7, 20: Petit discloses the limitations of Claims 1, 15. Petit further discloses: wherein the training of the second model comprises:
estimating a probability of the correct other label corresponding to the other incorrect label of the first data sample; {see at least page 3, paragraph 2.3 probability of the predicted class}
training the second model by updating second parameters of the second model, to estimate the other correct label, using a first data sample comprising the estimated probability of the other correct label. {see at least sections 1, 3. 2, 3. 2 .1, 3.2.2, Algorithms l, 2: training the network to predict correct labels, relabeling)}
Regarding Claims 8: Petit discloses the limitations of Claims 1, 15. Petit further discloses: further comprising:
classifying the data samples in the training dataset into a first data sample comprising the incorrect labels and a second data sample comprising the correct labels based on a distribution of the confidences; {see at least page 2, paragraph 2.3, properly separating correct predictions from errors. The reference does not disclose the term “based on a distribution of the confidences”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
wherein the data samples are mixed such that the first data sample comprises
training data and the incorrect label corresponding to the training data and the second data sample comprises the training data and the correct label corresponding to the training data. {see at least page2, fig1 INERRANT (Iterative coNfidencE Relabeling of partial ANnoTations)}
Regarding Claims 9: Petit discloses the limitations of Claims 1, 15. Petit further discloses:
wherein the training data comprises another image data of a semiconductor obtained by the image sensor. {see at least page 9, paragraph 4.4. external sources of images. The reference does not disclose the term “image data of a semiconductor obtained by an image sensor”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
Regarding Claims 10: Petit discloses the limitations of Claims 1, 15. Petit further discloses:
wherein the first model and the second model are each trained based on an expectation-maximization (EM) algorithm. {see at least sections 1, 3.2, 3.2.1, 3.2.2, Algorithm 2: training the confidence network based on the correct label estimation; sections 1, 3. 2, 3. 2 .1, 3.2.2, Algorithms l, 2: training the network to predict correct labels, relabeling. The reference does not disclose the term “based on an expectation-maximization (EM) algorithm”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
Regarding Claim 11: Petit first embodiment discloses: An automatic labeling method, comprising:
detecting whether a label for a data sample is an incorrect label by applying the data sample to a first model, {see at least sections 1, 3.2, 3.2.l., 3.2.2, confidence estimation network.}
wherein the first model comprises a first neural network that is trained to
detect the incorrect label comprised in the data sample based on confidence of the label to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time. {see at least: optional feature is disregarded. The claim element “to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time” consists entirely of language disclosing at most a reason to have performed earlier method steps (intended use or field of use), but does not affect the functions in a manipulative sense (see MPEP 2103 I C) and imparts neither structure nor functionality to the claimed method (see MPEP 2111.05, MPEP 2114 and authorities cited therein), so it is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.}
Petit first embodiment does not disclose, however, Petit second embodiment discloses:
wherein the data sample comprises image data obtained by an image sensor, {see at least see at least page 9, paragraph 4.4. external sources of images. The reference does not disclose the term “obtained by an image sensor”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Petit first embodiment to include the elements of Petit second embodiment. One would have been motivated to do so, in order to provide data to the algorithm. In the instant case, Petit first embodiment evidently discloses correcting labels and automating labeling. Petit second embodiment is merely relied upon to illustrate the functionality of data type in the same or similar context. Since both correcting labels and automating labeling, as well as data type are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Petit first embodiment, as well as Petit second embodiment would function in the same manner in combination as they do in their separate embodiments, it is concluded that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Petit. **Examiner notes that the reference is being used here as a one-reference combination in this 103 rejection because the reference teaches two clearly different embodiments within the same cited reference.**
Regarding Claims 12: Petit discloses the limitations of Claims 1, 15. Petit further discloses:
wherein the data sample comprises input data and the label corresponding to the input data, and the method further comprises: {see at least page 2, paragraph 2.2, “… input images …”}
generating a correct label corresponding to the incorrect label by applying the input data to a second model, as the label is determined as the incorrect label, {see at least page 2, rc1, “… maximize the number of correct labels …”}
wherein the second model comprises a second neural network that is trained to estimate the correct label corresponding to the data sample comprising the incorrect label. {see at least sections 1, 3. 2, 3. 2 .1, 3.2.2, Algorithms l, 2: training the network to predict correct labels, relabeling)}
Regarding Claims 13: Petit discloses the limitations of Claims 1, 15. Petit further discloses:
wherein the input data comprises another image data of a semiconductor obtained by the image sensor. {see at least page 2 “… import of organ labels …”; page 3, “input images”; page 9, paragraph 4.4. external sources of images. The reference does not disclose the term “image data of a semiconductor obtained by an image sensor”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
Regarding Claim 15: Petit first embodiment discloses: An electronic device, comprising: a communication system; and a processor configured to, based on confidences of labels for data samples in a training dataset received by the communication system, {see at least page 2, fig2}
iteratively train a first model to detect incorrect labels in the training dataset and
a second model to estimate correct labels corresponding to the incorrect labels, and {see at least page2, fig1 INERRANT (Iterative coNfidencE Relabeling of paRtial ANnoTations)}
generate a data sample in which an incorrect label is corrected using at least one of the first model or the second model, {see at least page 4, paragraph 3.2.1 relabeling (reads on correcting labels)}
wherein the processor is further configured to:
train the first model to predict the confidences, including using the corrected data sample generated by correcting the incorrect label based on a corresponding confidence detected by the first model and an estimated corrected label generated by the second model, {see at least sections 1, 3.2, 3.2.1, 3.2.2, Algorithm 2: training the confidence network based on the correct label estimation}
train the second model to estimate correct labels for the data samples, including estimating a corrected other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model with respect to the other incorrect label, and {see at least sections 1, 3. 2, 3. 2 .1, 3.2.2, Algorithms l, 2: training the network to predict correct labels, relabeling)}
automatically correct the other incorrect label with the estimated correct other label to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time. {see at least: relabeling in the next iteration. The claim element “to generate a defect classification system robust against noise thereby improving performance in accuracy and increasing efficiency in terms of cost and time” consists entirely of language disclosing at most a reason to have performed earlier method steps (intended use or field of use), but does not affect the functions in a manipulative sense (see MPEP 2103 I C) and imparts neither structure nor functionality to the claimed method (see MPEP 2111.05, MPEP 2114 and authorities cited therein), so it is considered but given no patentable weight. The reference is provided for the purpose of compact prosecution.}
Petit first embodiment does not disclose, however, Petit second embodiment discloses:
wherein the training dataset comprises image data obtained by an image sensor and labels corresponding to defect codes, and {see at least page1, medical image datasets. The reference does not disclose the term “obtained by an image sensor”. However, this difference is only found in the non-functional descriptive material and does not affect how the claimed invention functions (i.e., the descriptive material does not have any claim function in the claimed method; see MPEP 2111.05). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability}
It would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Petit first embodiment to include the elements of Petit second embodiment. One would have been motivated to do so, in order to provide data to the algorithm. In the instant case, Petit first embodiment evidently discloses correcting labels and automating labeling. Petit second embodiment is merely relied upon to illustrate the functionality of data type in the same or similar context. Since both correcting labels and automating labeling, as well as data type are implemented through well-known computer technologies in the same or similar context, combining their features as outlined above using such well-known computer technologies (i.e., conventional software/hardware configurations), would be reasonable, according to one of ordinary skill in the art. Moreover, since the elements disclosed by Petit first embodiment, as well as Petit second embodiment would function in the same manner in combination as they do in their separate embodiments, it is concluded that their resulting combination would be predictable. Accordingly, the claimed subject matter is obvious over Petit. **Examiner notes that the reference is being used here as a one-reference combination in this 103 rejection because the reference teaches two clearly different embodiments within the same cited reference.**
The prior art made of record and not relied upon which, however, is considered pertinent to applicant's disclosure:
US 20200117991 A1 Suzuki; Kanata et al. LEARNING APPARATUS, DETECTING APPARATUS, LEARNING METHOD, AND DETECTING METHOD A feature model, which calculates a feature value of an input image, is trained on a plurality of first images. First feature values corresponding one-to-one with the first images are calculated using the feature model, and feature distribution information representing a relationship between a plurality of classes and the first feature values is generated. When a detection model which determines, in an input image, each region with an object and a class to which the object belongs is trained on a plurality of second images, second feature values corresponding to regions determined within the second images by the detection model are calculated using the feature model, an evaluation value, which indicates class determination accuracy of the detection model, is modified using the feature distribution information and the second feature values, and the detection model is updated based on the modified evaluation value.
US 20220327808 A1 Bergen; Leon et al. Systems And Methods For Evaluating The Error Rate Of Human-Generated Data Systems, apparatuses, and methods for more efficiently and effectively determining the accuracy with which a human evaluates a set of data, as this may reduce the error in the assessment of a model's performance. This can be helpful in situations where human inputs are used to confirm the output of a machine learning generated classification and in situations where it is desired to evaluate the accuracy of data that may have been labeled or annotated by a human curator. This may assist in reducing the need for new validation/test data when evaluating a new model. The system and methods described can be used to evaluate the accuracy of a trained Machine Learning (ML) model, and as a result, allow a comparison between models based on different ML algorithms.
US 20210406644 A1 SALMAN; Nader et al. ACTIVE LEARNING FRAMEWORK FOR MACHINE-ASSISTED TASKS An active learning framework is provided that employs a plurality of machine learning components that operate over iterations of a training phase followed by an active learning phase. In each iteration of the training phase, the machine learning components are trained from a pool of labeled observations. In the active learning phase, the machine learning components are configured to generate metrics used to control sampling of unlabeled observations for labeling such that newly labeled observations are added to a pool of labeled observations for the next iteration of the training phase. The machine learning components can include an inspection (or primary) learning component that generates a predicted label and uncertainty score for an unlabeled observation, and at least one additional component that generates a quality metric related to the unlabeled observation or the predicted label. The uncertainty score and quality metric(s) can be combined for efficient sampling of observations for labeling.
US 20210245659 A1 Golov; Gil ARTIFICIAL INTELLIGENCE-BASED PERSISTENCE OF VEHICLE BLACK BOX DATA The disclosed embodiments are directed to improving the persistence of pre-accident data in vehicles. In one embodiment a method is disclosed comprising receiving events broadcast over a vehicle bus; classifying the events using a machine learning model, the classifying comprising indicating that a collision is imminent; and copying data from a cyclic buffer of a black box device into a long-term storage device in response to the classifying.
US 20220224659 A1 El Ghazzal; Sammy AUTOMATED MESSAGING REPLY-TO An automated messaging reply-to system can automatically select which message a potential reply message is replying to. The automated messaging reply-to system can obtain a message thread, a potential reply message, and a context. The automated messaging reply-to system can filter the message thread and generate model inputs based on the remaining messages, the potential reply message, and the context. The automated messaging reply-to system can apply the model input to a machine learning model, which can generate reply scores for the remaining messages. After generating reply scores, the automated messaging reply-to system can determine whether the remaining message with the highest reply score qualifies as an originating message being replied to. The automated messaging reply-to system can cause display of the potential reply message as a reply-to for the determined originating message.
US 20200311616 A1 Rajkumar; Nareshkumar et al. EVALUATING ROBOT LEARNING Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating robot learning. In some implementations, one or more computers receive object classification examples from a plurality of robots. Each object classification example includes (i) an embedding that a robot generated using a machine learning model, and (ii) an object classification corresponding to the embedding. The object classification examples are evaluated based on a similarity of the received embeddings with respect to other embeddings. A subset of the object classification examples is selected based on the evaluation of the quality of the embeddings. The subset of the object classification examples is distributed to the robots in the plurality of robots.
US 20230315879 A1 JAVIDI; Bahram et al. SYSTEMS AND METHODS FOR PROTECTING MACHINE LEARNING MODELS AGAINST ADVERSARIAL ATTACKS Embodiments pertain to systems configured to and methods for analyzing a scene comprising one or more objects. The system may be configured to perform the following: obtaining a set of optically encrypted image data describing a scene, including applying an optical manipulation to light incoming to an image acquisition device, whereby the image acquisition device outputs the set of optically encrypted image data, and wherein the optical manipulation is based on an encryption key; providing the set of optically encrypted image data to a machine learning model trained in accordance with the encryption key; and receiving from the machine learning model a prediction related to the scene.
US 20220058347 A1 Singaraju; Gautam et al. TECHNIQUES FOR PROVIDING EXPLANATIONS FOR TEXT CLASSIFICATION A chatbot system is configured to execute code to perform determining, by the chatbot system, a classification result for an utterance and one or more anchors each anchor of the one or more anchors corresponding to one or more anchor words of the utterance. For each anchor of the one or more anchors, one or more synthetic utterances are generated, and one or more classification results for the one or more synthetic utterances are determined. A report is generated by the chatbot system comprising a representation of a particular anchor of the one or more anchors, the particular anchor corresponding to a highest confidence value among the one or more anchors. The one or more synthetic utterances may be used to generate a new training dataset for training a machine-learning model. The training dataset may be refined according to a threshold confidence values to filter out datasets for training.
US 20210174231 A1 SAWADA; Azusa et al. MODEL GENERATION DEVICE, MODEL GENERATION METHOD, AND NON-TRANSITORY RECODING MEDIUM Disclosed is a model generation device capable of mitigating the risk of overlooking a phenomenon of interest in machine learning. The model generation device determines whether or not a label of a first data is similar to a label of a second data. The model generation device assigns the label of the second data to the first data when determining that the label of the first data is similar to the label of the second data based on a degree of similarity between observation information representing a state where the first data is observed and observation information representing a state where the second data is observed. The model generation device calculates model representing a relevance between data information containing the first data and the second data and label information containing the assigned label and the label of the second data.
US 10305766 B1 Zhang; Ce et al. Coexistence-insensitive presence detection A system and method include processing logic receiving, from a wireless transceiver of a first device, first data indicative of channel state information (CSI) of a first communication link between the wireless transceiver and a wireless transmitter of a second device, the first device and the second device being located in a building. The logic pre-preprocesses the first data to generate input vectors composed of statistical parameter values derived from sets of discrete samples of the first data. The logic processes, through a long short-term memory (LSTM) layer of a neural network, the input vectors to generate multiple hidden state values of the neural network. The logic processes, through a set of additional layers of the neural network, respective hidden state values of the multiple hidden state values to determine that a human is present in the building.
US 12148417 B1 Cardella; Aidan Thomas et al. Label confidence scoring Devices and techniques are generally described for confidence score generation for label generation. In some examples, first data may be received from a first computing device. In various further examples, first label data classifying at least one aspect of the first data may be received. First metadata associated with how the first label data was generated may be received. In some cases, the first label data may be generated by a first user. In various examples, a first machine learning model may generate a first confidence score associated with the first label data based at least in part on the first data and second data related to label generation by the first person. In various examples, output data comprising the first confidence score may be sent to the first computing device.
US 11678011 B1 Fu; Sai-Wai et al. Mobile distributed security response The invention concerns a video feed monitoring app configured to be implemented with an interface and a processor. The interface may be configured to receive requests from an operator and present visual content to a display. The processor may be configured to process the requests and update the display. The video feed monitoring app may be configured to receive video streams from smart security devices, receive a priority signal, select a subset of the video streams in response to the priority signal and a user preference and arrange the subset of the video streams on the display. The priority signal may be generated externally based on events detected. The subset of the video streams may be updated based on the priority signal. Updating the subset of the video streams may comprise displaying the video streams that comprise events and removing the video streams that do not comprise events.
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