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 .
Examiner’s Note
Providing supporting paragraph(s) for each limitation of amended/new claim(s) in Remarks is strongly requested for clear and definite claim interpretations by Examiner (e.g., to avoid rejections under 35 U.S.C § 112(a) “Lack of written description”)
Applicant can schedule interviews (via Automated Interview Request (AIR)) at any stage of the prosecution (e.g., Non-Final, Final, and After-Final) to discuss any issues related to, for example, rejections under 35 U.S.C § 101 and § 102/103, for moving toward allowance.
Priority
Acknowledgment is made of applicant's claim for the present application filed on 08/04/2023.
Response to Arguments
Applicant's arguments filed on 06/08/2026 have been fully considered but they are not persuasive.
In Remarks, regarding 35 USC § 101, Applicant contends:
This integrated architecture imposes meaningful limits on any alleged abstract idea and transforms abstract data analysis into a tangible improvement to the operational reliability of a distributed machine learning system.
…
The amended claims are directed to specific and concrete improvements to computer technology, namely, a system for dynamically selecting and deploying reliable machine learning model ensembles to edge computing nodes based on health score vectors including feature health scores for sensors used by machine learning models.
Examiner’s response:
The examiner understands the applicant’s assertion.
However, it appears that each processing step is just applying the abstract idea to a general field of endeavor with additional elements. In addition, improvements to technology or technical field are not necessarily reflected in the claims. Thus, the claim does not integrate the judicial exception into a practical application, and the claim does not amount to significantly more than the judicial exception.
The examiner understands the applicant’s assertion “The operations are embedded in an end-to-end architecture for sensor health-aware model selection and distributed deployment. This integrated architecture imposes meaningful limits on any alleged abstract idea and transforms abstract data analysis into a tangible improvement to the operational reliability of a distributed machine learning system” and “The amended claims are directed to specific and concrete improvements to computer technology, namely, a system for dynamically selecting and deploying reliable machine learning model ensembles to edge computing nodes based on health score vectors including feature health scores for sensors used by machine learning models”.
However, as rejected under Claim Rejections - 35 USC § 101, overall, the claims are interpreted as combinations of abstract ideas and additional elements. Clustering heath score vectors can be practically performed in the human mind, selecting ML models can be practically performed in the human mind as well. In addition, deploying ML models can be interpreted as an insignificant extra-solution activity. Thus, the claims do not integrate the judicial exception into a practical application, and the claims do not amount to significantly more than the judicial exception.
The limitations do not clearly show e.g., improvements in computer technology and improvements to other technical fields. Rather, the improvements in Remarks are about just improving the abstract ideas of the independent claims. It doesn’t seem that the specification and/or the independent claims clearly show how the inventive concept of the claims enables improvements and how they are tied together. The applicant may need to amend the claims to show how the claim languages and improvements are tied together.
To find a valid improvement to a technology, MPEP 2106.04(d)(1) says the specification must explain the improvement and that the claim must reflect the disclosed improvement. Furthermore, the improvement should not be merely a consequence of the abstract idea. See MPEP 2106.05(a). An improvement in the abstract idea itself is not an improvement to technology.
For at least these reasons, Applicant's arguments are not convincing.
Applicant’s arguments regarding 35 USC § 102/103 with respect to the independent claims have been considered but are moot because the arguments are directed to amended limitation(s) that has/have not been previously examined.
Claim Objections
Claim(s) 3-5, 15-17 is/are objected to because of the following informalities.
Claim(s) 3 is/are objected to because of the following informalities: it appears that “the model score vectors” (line 4) needs to read “the set of model score vectors” or something else. Appropriate correction is required. In addition, claim(s) 15 is/are objected to for the same reason.
Claim(s) 3, 15 each recite(s) limitations that raise issues of indefiniteness as set forth above, and their dependent claims are objected to at least based on their direct and/or indirect dependency from the claims listed above. Appropriate explanation and/or amendment is required.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 5, 7, 17 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim(s) 5 recite(s) the limitation “the models” (last line). There is insufficient antecedent basis for this limitation in the claim. It is not clear what it is referring to since it may indicate “machine learning models” (claim 1, line 5), or “the models” (claim 1, line 7), or “a set of top K performing models” (claim 1, line 8) or something else. It appears it may need to read “models”, or something else. For the purposes of examination, “models” is used. In addition, claim(s) 7, 17 is/are rejected for the same reason.
Claim(s) 5, 7, 17 each recite(s) limitations that raise issues of indefiniteness as set forth above, and their dependent claims are rejected at least based on their direct and/or indirect dependency from the claims listed above. Appropriate explanation and/or amendment is required.
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.
Regarding claim 1
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“… comprising:
…:
clustering health score vectors received from nodes operating in an environment to obtain clusters of the health score vectors, …;
comparing a model score distribution for an ensemble of the models with model score distributions per cluster, to obtain a set of top K performing models for each cluster;
…, identifying an associated health score vector for the new data and … select the set of top K performing models; and
…”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). In particular, the claim recites an additional element(s) (“at least one processing device including a processor coupled to a memory; the at least one processing device being configured to implement the following steps”, “using the set of top K performing models corresponding to a cluster for the associated health score vector to”) – using a device and/or a model to process data. The device and the model in each step are recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In particular, the claim recites an additional element (“the health score vectors including feature health scores for sensors used by machine learning models, the feature health scores characterizing a reliability of the sensors based on deviations of sensor outputs from expected behavior”). This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)
In particular, the claim recites an additional element(s) (“upon receiving new data for prediction”) – the act of receiving data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of receiving data is recited at a high-level of generality (i.e., as a generic act of receiving performing a generic act function of receiving data) such that it amounts no more than a mere act to apply the exception using a generic act of receiving. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In particular, the claim recites an additional element(s) (“deploying the clusters and model ensembles to the nodes”) – the act of transmitting data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of transmitting data is recited at a high-level of generality (i.e., as a generic act of performing a generic act function of transmitting data) such that it amounts no more than a mere act to apply the exception using a generic act of transmitting. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer component to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. MPEP 2106.05(f).
This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h).
As discussed above, the claim recites the additional element(s) of receiving data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible.
As discussed above, the claim recites the additional element(s) of transmitting data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible.
Regarding claim 2
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“… select a model among the set of top K performing models for generating the inferences”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). In particular, the claim recites an additional element(s) (“causing the nodes to generate inferences using the deployed clusters and model ensembles to”) – using a device and/or a model to process data. The device and the model in each step are recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer component to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. MPEP 2106.05(f).
Regarding claim 3
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“for each cluster,
determining a set of model score vectors per cluster, …, and
using the set of model score vectors to construct the model score distributions per cluster”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites an additional element (“the model score vectors containing model scores”). This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h).
Regarding claim 4
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“wherein the model scores are determined by generating a vector for each model, …”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites an additional element (“the vector including feature importance scores for each feature of a corresponding model and the feature health scores for each feature of each sensor used by the corresponding model”). This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h).
Regarding claim 5
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“wherein the feature importance scores are arranged in a first matrix and the feature health scores are arranged in a second matrix among the machine learning models”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
The limitations of
“wherein each vector is a dot product of a corresponding first matrix and a corresponding second matrix, and”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation based on mathematical relationships and/or mathematical formulas or equations and/or mathematical calculations. That is, nothing in the claim element precludes the step from practically being performed based on mathematical relationships and/or mathematical formulas or equations and/or mathematical calculations.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation based on mathematical relationships and/or mathematical formulas or equations and/or mathematical calculations, but for the recitation of generic computer components, then it falls within the “Mathematical concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites an additional element (“wherein the vector includes a model score for each of the models”). This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h).
Regarding claim 6
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“wherein the model score distributions are constructed using distribution fitting”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim does not recite additional elements. Thus, the claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claim is not patent eligible.
Regarding claim 7
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“wherein the model score distribution for the ensemble of the models is compared with the model score distributions per cluster using a probability distance measure”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim does not recite additional elements. Thus, the claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claim is not patent eligible.
Regarding claim 8
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1: The claim recites the abstract idea identified above regarding claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites an additional element (“wherein the feature health scores are collected according to a pre-determined period that is specified for each node”). This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h).
Regarding claim 9
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1: The claim recites the abstract idea identified above regarding claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
In particular, the claim recites an additional element(s) (“wherein the health score vectors are received at a near-edge node configured to accumulate the health score vectors prior to transmission to a central node”) – the act of receiving/transmitting data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of receiving/transmitting data is recited at a high-level of generality (i.e., as a generic act of performing a generic act function of receiving/transmitting data) such that it amounts no more than a mere act to apply the exception using a generic act of transmitting. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In particular, the claim recites an additional element (“wherein the near-edge node is an intermediate node positioned in a computing hierarchy between the nodes operating in the environment and the central node”). This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not integrate the abstract idea into a practical application. See MPEP 2106.05(h)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, the claim recites the additional element(s) of receiving/transmitting data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible.
This is a recitation of a particular type or source of model/data to be used in performing the abstract idea. Limiting the abstract idea to a particular type or source of model/data is an attempt to limit the abstract idea to a particular field of use or technological environment, which does not amount to significantly more than the abstract idea. See MPEP 2106.05(h).
Regarding claim 10
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“wherein the clusters and model ensembles are reset periodically for re-clustering”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim does not recite additional elements. Thus, the claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus, the claim is not patent eligible.
Regarding claim 11
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“wherein the health score vectors are clustered …”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). In particular, the claim recites an additional element(s) (“using unsupervised multi-dimensional clustering”) – using a device and/or a model to process data. The device and the model in each step are recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer component to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. MPEP 2106.05(f).
Regarding claim 12
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claim recites a system; therefore, it falls into the statutory category of a machine.
Step 2A Prong 1:
The limitations of
“wherein the health score vectors are clustered … based on labels …”, as drafted, are a machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, the limitations in the context of this claim encompass the user mentally thinking with a physical aid (e.g., pencil and paper).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites additional elements that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). In particular, the claim recites an additional element(s) (“using supervised multi-dimensional clustering”) – using a device and/or a model to process data. The device and the model in each step are recited at a high-level of generality (i.e., as a generic computer performing a generic computer function of processing data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
In particular, the claim recites an additional element(s) (“labels received from the nodes”) – the act of receiving data. The claim is adding an insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g). The act of receiving data is recited at a high-level of generality (i.e., as a generic act of receiving performing a generic act function of receiving data) such that it amounts no more than a mere act to apply the exception using a generic act of receiving. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a generic computer component to perform each step amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible. MPEP 2106.05(f).
As discussed above, the claim recites the additional element(s) of receiving data at a high-level of generality and is adding an insignificant extra-solution activity – see MPEP 2106.05(g). However, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood, routine, and conventional. See MPEP 2106.05(d)(II) – “Receiving or transmitting data over a network” or “Storing and retrieving information in memory”. Accordingly, this additional element does not provide an inventive concept and significantly more than the abstract idea. Thus, the claim is not patent eligible.
Regarding claim 13
The claim is rejected for the reasons set forth in the rejection of Claim 1 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 14
The claim is rejected for the reasons set forth in the rejection of Claim 2 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 15
The claim is rejected for the reasons set forth in the rejection of Claim 3 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 16
The claim is rejected for the reasons set forth in the rejection of Claim 4 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 17
The claim is rejected for the reasons set forth in the rejection of Claim 5 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 18
The claim is rejected for the reasons set forth in the rejection of Claim 9 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 19
The claim is rejected for the reasons set forth in the rejection of a combination of Claims 11 and 12 under 35 U.S.C. 101, mutatis mutandis, as reciting an abstract idea without integrating the judicial exception into a practical application nor providing significantly more than the judicial exception.
Regarding claim 20
The claim recites “A non-transitory processor-readable storage medium having stored thereon program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:” to perform precisely the system of Claim 1. As performance of an abstract idea on generic computer components (see MPEP 2106.05(f)) and “Storing and retrieving information in memory” (see MPEP 2106.05(g) on Insignificant Extra-Solution Activity, and MPEP 2106.05(d) on Well-Understood, Routine, Conventional Activity) cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, the claim is rejected for reasons set forth in the rejection of Claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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) 1-2, 6-8, 10, 13-14, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lakshmanan et al. (US 2022/0004935 A1) in view of Teh et al. (Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection) in view of Manmatha et al. (Modeling Score Distributions for Combining the Outputs of Search Engines) in view of Seo et al. (MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing)
Regarding claim 1
Lakshmanan teaches
A system comprising:
at least one processing device including a processor coupled to a memory;
(Lakshmanan [fig(s) 1] “Computing System”;)
the at least one processing device being configured to implement the following steps:
(Lakshmanan [fig(s) 1] “Computing System”;)
(Note: Hereinafter, if a limitation has bold brackets (i.e. [·]) around claim languages, the bracketed claim languages indicate that they have not been taught yet by the current prior art reference but they will be taught by another prior art reference afterwards.)
clustering health score vectors received from nodes operating in an environment to obtain clusters of the health score vectors, the health score vectors including feature health scores for sensors used by machine learning models, the feature health scores characterizing [a reliability of the sensors based on deviations of sensor outputs from expected behavior];
(Lakshmanan [fig(s) 6] [fig(s) 8] “Clusterizer” [fig(s) 9] “Receive a deep feature vector from a feature extractor of an ensemble learning system, the deep feature vector extracted from input data”, “Cluster the deep feature vector into a plurality of clusters based on a distance into tile plurality of clusters” [par(s) 11-14] “FIG. 6 is a block diagram depicting an example neural network topology for ensemble learning for deep feature defect detection of implementations of the disclosure.” [par(s) 93-100] “As shown in FIG. 6, neural network topology 600 (referred to herein as topology 600) depicts a two-staged machine learning model that can learn from distributed data sources. The distributed data sources 610 provide input data, such as sensor data. The distributed data sources 610 may be distributed throughout an organizational settings, such as a production environment or a manufacturing environment, for example. The distributed data source may include, but are not limited to, a camera 611, time-series data 612, light data 613, audio data 614, LIDAR data 615, or 3D camera data 616. In one implementations, the input data can be of high dimensionality. … In order to make the data simple for the two-staged ML model system described herein to process, the input data used for anomaly detection is obtained as a "feature vector" (also referred to as a deep feature herein) by using DL model 730. DL model 730 is a pre-trained deep learning network model used as a primary feature extractor.” [par(s) 101-113] “Model ensemble 830 includes an ensemble of trained secondary probabilistic model(s), such as Ml 831, M2 832, M3 833, M4 834, through Mn 835. … Method 900 begins at block 910 where the processing device may receive a deep feature vector from a feature extractor of an ensemble learning system, the deep feature vector extracted from input data. In one implementation, the input data includes sensor data. Then, at block 920, the processing device may cluster the deep feature vector into a plurality of clusters based on a distance into the plurality of clusters.”;)
[comparing] a model score distribution for an ensemble of the models with model score distributions per cluster, to obtain a set of top K performing model[s] for each cluster;
(Lakshmanan [fig(s) 6] [fig(s) 8] “Clusterizer” [fig(s) 9] [par(s) 148] “the probabilistic machine learning model is part of an ensemble of probabilistic machine learning models trained to predict a likelihood of a defect among deep feature vectors grouped into clusters corresponding to each the probabilistic machine learning models of the ensemble.” [par(s) 101-109] “Model ensemble 830 includes an ensemble of trained secondary probabilistic model(s), such as M1 831, M2 832, M3 833, M4 834, through Mn 835. Each secondary probabilistic model 831-835 in the model ensemble 830 is tuned for a corresponding data cluster 821-825 created by clusterizer 820. For example, as shown in FIG. 8, M1 831 is tuned for Cl 821, M2 832 is tuned for C2 822, M3 833 is tuned for C3 823, M4 834 is tuned for C4 824, and so on through Mn 835 being tuned for Cn 825. In some implementations, probabilistic models 831-835 for the underlying data are created using algorithms, such as GMM or other Bayesian models. In one implementations, every time a new data point is added to a particular cluster 821-825, the corresponding probabilistic model gets tuned to make the model more accurate. In implementations herein, the ensemble 830 of probabilistic machine learning models 831-835 are trained to predict a likelihood (e.g., output 840) of a defect among deep feature vectors grouped into clusters corresponding to each of the probabilistic machine learning models 831-835 of the ensemble 830” [par(s) 110-113] “Then, at block 920, the processing device may cluster the deep feature vector into a plurality of clusters based on a distance into the plurality of clusters.”;)
upon receiving new data for prediction, identifying an associated health score vector for the new data and using the set of top K performing model[s] corresponding to a cluster for the associated health score vector to select the set of top K performing model[s]; and
(Lakshmanan [fig(s) 6] [fig(s) 8] “Clusterizer” [fig(s) 9] [par(s) 148] “the probabilistic machine learning model is part of an ensemble of probabilistic machine learning models trained to predict a likelihood of a defect among deep feature vectors grouped into clusters corresponding to each the probabilistic machine learning models of the ensemble.” [par(s) 101-109] “In some implementations, each time a new deep feature 810 is received at the second stage 800, the clusterizer 820 is trained to add the deep feature to an existing cluster 821-825. … Model ensemble 830 includes an ensemble of trained secondary probabilistic model(s), such as M1 831, M2 832, M3 833, M4 834, through Mn 835. Each secondary probabilistic model 831-835 in the model ensemble 830 is tuned for a corresponding data cluster 821-825 created by clusterizer 820. For example, as shown in FIG. 8, M1 831 is tuned for Cl 821, M2 832 is tuned for C2 822, M3 833 is tuned for C3 823, M4 834 is tuned for C4 824, and so on through Mn 835 being tuned for Cn 825. In some implementations, probabilistic models 831-835 for the underlying data are created using algorithms, such as GMM or other Bayesian models. In one implementations, every time a new data point is added to a particular cluster 821-825, the corresponding probabilistic model gets tuned to make the model more accurate. In implementations herein, the ensemble 830 of probabilistic machine learning models 831-835 are trained to predict a likelihood (e.g., output 840) of a defect among deep feature vectors grouped into clusters corresponding to each of the probabilistic machine learning models 831-835 of the ensemble 830” [par(s) 110-113] “Then, at block 920, the processing device may cluster the deep feature vector into a plurality of clusters based on a distance into the plurality of clusters.”;)
However, Lakshmanan does not appear to explicitly teach:
the feature health scores characterizing [a reliability of the sensors based on deviations of sensor outputs from expected behavior];
[comparing] a model score distribution for an ensemble of the models with model score distributions per cluster, to obtain a set of top K performing model[s] for each cluster;
upon receiving new data for prediction, identifying an associated health score vector for the new data and using the set of top K performing model[s] corresponding to a cluster for the associated health score vector to select the set of top K performing model[s]; and
deploying the clusters and model ensembles to the nodes.
(Note: Hereinafter, if a limitation has one or more bold underlines, the one or more underlined claim languages indicate that they are taught by the current prior art reference, while the one or more non-underlined claim languages indicate that they have been taught already by one or more previous art references.)
Teh teaches
the feature health scores characterizing a reliability of the sensors based on deviations of sensor outputs from expected behavior;
(Teh [sec(s) I] “Our proposed approach is a fully automated anomaly detection framework that uses unsupervised feature selection on systematically engineered time series features along with PCA to detect sensor data anomalies. The novel unsupervised feature selection approach can be applied to newly deployed sensors for which no anomalies have been observed, yet. The algorithm automatically selects time series features based on hypothesis testing with respect to their abilities to predict statistics of near-future values. Therefore, the feature selection is unsupervised with respect to the non-existing labels of sensor anomalies. Combining the selected features with PCA allows anomaly detection to be more reliable and fully automated without the need for labelled ground truth data. Once the model is trained, anomaly detection can be done in real-time on the edge devices themselves. The handful of selected features reduces the complexity of the error detection problem which makes it less computationally intensive to train an anomaly detection model and saves bandwidth by detecting anomalies early and not unnecessarily transmitting error-ridden data.”;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lakshmanan with the top K models of Teh.
One of ordinary skill in the art would have been motived to combine in order to detect anomalies very reliably despite any anomalies in training have not seen.
(Teh [sec(s) I] “Our evaluation of two real-world datasets that were both one to three months long, demonstrates that the three-day calibration phase works reliably for anomaly detection problems, despite not seeing any anomalies in training. This is vital for real-world applications where oftentimes it is difficult to obtain labelled datasets and the calibration phase is very short with respect to the deployment phase.”)
However, the combination of Lakshmanan, Teh does not appear to explicitly teach:
[comparing] a model score distribution for an ensemble of the models with model score distributions per cluster, to obtain a set of top K performing model[s] for each cluster;
upon receiving new data for prediction, identifying an associated health score vector for the new data and using the set of top K performing model[s] corresponding to a cluster for the associated health score vector to select the set of top K performing model[s]; and
deploying the clusters and model ensembles to the nodes.
Manmatha teaches
comparing a model score distribution for an ensemble of the models with model score distributions per cluster, to obtain a set of top K performing models for each cluster;
(Manmatha [sec(s) 1] “The approach proposed here allows us to combine the outputs of search engines using the probabilities derived from the model of score distributions. In this paper we examine two approaches to combination. The first involves averaging the probabilities which is optimal in the sense of minimizing the Bayes’ error if the search engines are treated as independent classifiers [18]. The second approach involves using the probabilities to discard “bad” engines while keeping the “good” ones. We show that the combination approaches proposed using these techniques do as well as the best combination techniques proposed in the literature. In addition, our technique is less ad-hoc and easier to justify. The technique can also be extended to multi-lingual and multi-modal combination.” [sec(s) 6] “We have demonstrated how to model the score distributions of a number of text search engines. Specifically, it was shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents.” [sec(s) Abs] “It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities.”;)
upon receiving new data for prediction, identifying an associated health score vector for the new data and using the set of top K performing models corresponding to a cluster for the associated health score vector to select the set of top K performing models; and
(Manmatha [sec(s) 1] “The approach proposed here allows us to combine the outputs of search engines using the probabilities derived from the model of score distributions. In this paper we examine two approaches to combination. The first involves averaging the probabilities which is optimal in the sense of minimizing the Bayes’ error if the search engines are treated as independent classifiers [18]. The second approach involves using the probabilities to discard “bad” engines while keeping the “good” ones. We show that the combination approaches proposed using these techniques do as well as the best combination techniques proposed in the literature. In addition, our technique is less ad-hoc and easier to justify. The technique can also be extended to multi-lingual and multi-modal combination.” [sec(s) 6] “We have demonstrated how to model the score distributions of a number of text search engines. Specifically, it was shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents.” [sec(s) Abs] “It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities.”;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lakshmanan, Teh with the top K models of Manmatha.
One of ordinary skill in the art would have been motived to combine in order to perform well as the best combination techniques proposed in the literature. In addition, the system is less ad-hoc and easier to justify, and can be extended to multi-lingual and multi-modal combination.
(Manmatha [sec(s) 1] “We show that the combination approaches proposed using these techniques do as well as the best combination techniques proposed in the literature. In addition, our technique is less ad-hoc and easier to justify. The technique can also be extended to multi-lingual and multi-modal combination.”)
However, the combination of Lakshmanan, Teh, Manmatha does not appear to explicitly teach:
deploying the clusters and model ensembles to the nodes.
Seo teaches
deploying the clusters and model ensembles to the nodes.
(Seo [sec(s) 1] “First, it supports efficient multi-video stream processing by analyzing the resources in an edge cluster environment. It supports flexible scale in and scale out by periodically detecting resource changes in the cluster. It also minimizes delay through efficient distribution of tasks. • Second, we constructed high-level feature network (HFN) and low-level feature network (LFN) networks that lighten the scaled YOLOv4 [16] model. The model lightweighting based on the training features of the deep learning model provides improved detection accuracy even on more complex data than the trained set. • Third, we implemented the deep learning model ensemble in a distributed environment to maximize the benefits of distributed processing. In addition to improving processing speed, object detection can continue even when some services fail. … If a new device added to the cluster has enough GPU and memory, it can deploy a large deep learning model and make inferences. However, for a smartphone or micro device, deploying a large deep learning model may cause inferences to experience delays or failures.” [sec(s) 3.1] “As shown in Figure 1, the MDED framework consists of microservices that perform video object detection, a MongoDB service, and persistent volumes. To make the framework suitable for distributed processing, a cluster consists of a set of multiple individual nodes. The nodes’ environments vary widely, and microservices are automatically deployed that are appropriate for each node’s resources. Microservices are built on top of Docker [35] containers and provide services in the form of Kubernetes [36] pods, the smallest unit that can be deployed on a single node. Microservices are organized into four types: front microservices, preprocessing microservices, inferencing microservices, and postprocessing microservices.”;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lakshmanan, Teh Manmatha with the deployment of Seo.
One of ordinary skill in the art would have been motived to combine in order to show good performance in terms of accuracy and execution time.
(Seo [sec(s) 5] “Compared with the existing deep learning model (Scaled YOLOv4), the deep learning model improved by the proposed framework showed good performance in terms of accuracy and execution time. For an image with a resolution of 640, the performance was 2 FPS faster than the existing model; meanwhile, for an image with a resolution of 1280, the accuracy was up to 0.18 AP faster than the existing model. This shows that the proposed method can be used to obtain improved detection results in quasi‐real time, even for unfamiliar data that have not been trained. model improved by the proposed framework showed good performance in terms of accuracy and execution time. For an image with a resolution of 640, the performance was 2 FPS faster than the existing model; meanwhile, for an image with a resolution of 1280, the accuracy was up to 0.18 AP faster than the existing model. This shows that the proposed method can be used to obtain improved detection results in quasi-real time, even for unfamiliar data that have not been trained”)
Regarding claim 2
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
Seo further teaches
causing the nodes to generate inferences using the deployed clusters and model ensembles to select a model among the set of top K performing models for generating the inferences.
(Seo [sec(s) 1] “First, it supports efficient multi-video stream processing by analyzing the resources in an edge cluster environment. It supports flexible scale in and scale out by periodically detecting resource changes in the cluster. It also minimizes delay through efficient distribution of tasks. • Second, we constructed high-level feature network (HFN) and low-level feature network (LFN) networks that lighten the scaled YOLOv4 [16] model. The model lightweighting based on the training features of the deep learning model provides improved detection accuracy even on more complex data than the trained set. • Third, we implemented the deep learning model ensemble in a distributed environment to maximize the benefits of distributed processing. In addition to improving processing speed, object detection can continue even when some services fail. … If a new device added to the cluster has enough GPU and memory, it can deploy a large deep learning model and make inferences. However, for a smartphone or micro device, deploying a large deep learning model may cause inferences to experience delays or failures.” [sec(s) 3.1] “As shown in Figure 1, the MDED framework consists of microservices that perform video object detection, a MongoDB service, and persistent volumes. To make the framework suitable for distributed processing, a cluster consists of a set of multiple individual nodes. The nodes’ environments vary widely, and microservices are automatically deployed that are appropriate for each node’s resources. Microservices are built on top of Docker [35] containers and provide services in the form of Kubernetes [36] pods, the smallest unit that can be deployed on a single node. Microservices are organized into four types: front microservices, preprocessing microservices, inferencing microservices, and postprocessing microservices.”;)
The combination of Lakshmanan, Teh, Manmatha, Seo is combinable with Seo for the same rationale as set forth above with respect to claim 1.
Regarding claim 6
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
Manmatha further teaches
wherein the model score distributions are constructed using distribution fitting.
(Manmatha [sec(s) 1] “The approach proposed here allows us to combine the outputs of search engines using the probabilities derived from the model of score distributions. In this paper we examine two approaches to combination. The first involves averaging the probabilities which is optimal in the sense of minimizing the Bayes' error if the search engines are treated as independent classifiers [18]. The second approach involves using the probabilities to discard “bad” engines while keeping the “good” ones.” [sec(s) 6] “We have demonstrated how to model the score distributions of a number of text search engines. Specifically, it was shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. It was then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution may be fitted to the score distribution. These distributions were used to map the scores of a search engine to probabilities.” [sec(s) Abs] “It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities.”;)
The combination of Lakshmanan, Teh, Manmatha, Seo is combinable with Manmatha for the same rationale as set forth above with respect to claim 1.
Regarding claim 7
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
Manmatha further teaches
wherein the model score distribution for the ensemble of the models is compared with the model score distributions per cluster using a probability distance measure.
(Manmatha [sec(s) 1] “scores from different search engines can be very different since they are often the result of computing some metric (or non-metric) distance over sets of features. … The approach proposed here allows us to combine the outputs of search engines using the probabilities derived from the model of score distributions. In this paper we examine two approaches to combination. The first involves averaging the probabilities which is optimal in the sense of minimizing the Bayes’ error if the search engines are treated as independent classifiers [18]. The second approach involves using the probabilities to discard “bad” engines while keeping the “good” ones. We show that the combination approaches proposed using these techniques do as well as the best combination techniques proposed in the literature. In addition, our technique is less ad-hoc and easier to justify. The technique can also be extended to multi-lingual and multi-modal combination.” [sec(s) 2] “We observe here that the empirical data for a large number of search engines clearly shows that the two distributions are not similar.” [sec(s) 3] “The Kolmogorov-Smirnov (KS) test for significances shows that we cannot eliminate the null hypothesis that the distribution is a Gaussian. In other words, a Gaussian is not inconsistent with the data.” [sec(s) 6] “We have demonstrated how to model the score distributions of a number of text search engines. Specifically, it was shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents.” [sec(s) Abs] “It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities.”;)
The combination of Lakshmanan, Teh, Manmatha, Seo is combinable with Manmatha for the same rationale as set forth above with respect to claim 1.
Regarding claim 8
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
Lakshmanan further teaches
wherein the feature health scores are collected according to a pre-determined period that is specified for each node.
(Lakshmanan [fig(s) 6] [fig(s) 8] “Clusterizer” [fig(s) 9] [par(s) 11-14] “FIG. 6 is a block diagram depicting an example neural network topology for ensemble learning for deep feature defect detection of implementations of the disclosure.” [sec(s) 47] “The secondary ML models can be located centrally and, as they are data-specific, the secondary ML models are more granularly trained to distinguish outliers.” [par(s) 93-100] “The distributed data source may include, but are not limited to, a camera 611, time-series data 612, light data 613, audio data 614, LIDAR data 615, or 3D camera data 616. In one implementations, the input data can be of high dimensionality. … In order to make the data simple for the two-staged ML model system described herein to process, the input data used for anomaly detection is obtained as a "feature vector" (also referred to as a deep feature herein) by using DL model 730. DL model 730 is a pre-trained deep learning network model used as a primary feature extractor.” [par(s) 101-113] “Model ensemble 830 includes an ensemble of trained secondary probabilistic model(s), such as Ml 831, M2 832, M3 833, M4 834, through Mn 835. … Method 900 begins at block 910 where the processing device may receive a deep feature vector from a feature extractor of an ensemble learning system, the deep feature vector extracted from input data. In one implementation, the input data includes sensor data. Then, at block 920, the processing device may cluster the deep feature vector into a plurality of clusters based on a distance into the plurality of clusters.”;)
Regarding claim 10
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
Seo further teaches
wherein the clusters and model ensembles are reset periodically for re-clustering.
(Seo [algorithm 1] “While(True): Sleep(5)” [fig(s) 1] [sec(s) 1] “First, it supports efficient multi-video stream processing by analyzing the resources in an edge cluster environment. It supports flexible scale in and scale out by periodically detecting resource changes in the cluster. It also minimizes delay through efficient distribution of tasks. • Second, we constructed high-level feature network (HFN) and low-level feature network (LFN) networks that lighten the scaled YOLOv4 [16] model. The model lightweighting based on the training features of the deep learning model provides improved detection accuracy even on more complex data than the trained set. • Third, we implemented the deep learning model ensemble in a distributed environment to maximize the benefits of distributed processing. In addition to improving processing speed, object detection can continue even when some services fail. … If a new device added to the cluster has enough GPU and memory, it can deploy a large deep learning model and make inferences. However, for a smartphone or micro device, deploying a large deep learning model may cause inferences to experience delays or failures.” [sec(s) 3.1] “As shown in Figure 1, the MDED framework consists of microservices that perform video object detection, a MongoDB service, and persistent volumes. To make the framework suitable for distributed processing, a cluster consists of a set of multiple individual nodes. The nodes’ environments vary widely, and microservices are automatically deployed that are appropriate for each node’s resources. Microservices are built on top of Docker [35] containers and provide services in the form of Kubernetes [36] pods, the smallest unit that can be deployed on a single node. Microservices are organized into four types: front microservices, preprocessing microservices, inferencing microservices, and postprocessing microservices. … The front microservice periodically updates the information about the overall resource and usable resources of the edge node, and automatically determines whether to generate new microservices as it receives new input data.”;)
The combination of Lakshmanan, Teh, Manmatha, Seo is combinable with Seo for the same rationale as set forth above with respect to claim 1.
Regarding claim 13
The claim is a method claim corresponding to the system claim 1, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the system claim.
Regarding claim 14
The claim is a method claim corresponding to the system claim 2, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the system claim.
Regarding claim 20
The claim is a processor-readable storage medium claim corresponding to the system claim 1, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the system claim.
Claim(s) 3-5, 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lakshmanan et al. (US 2022/0004935 A1) in view of Teh et al. (Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection) in view of Manmatha et al. (Modeling Score Distributions for Combining the Outputs of Search Engines) in view of Seo et al. (MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing) in view of TANAKA et al. (US 2021/0081438 A1)
Regarding claim 3
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
wherein the model score distributions per cluster are obtained using steps comprising: (See claim 1)
However, the combination of Lakshmanan, Teh, Manmatha, Seo does not appear to explicitly teach:
for each cluster,
determining a set of model score vectors per cluster, the model score vectors containing model scores, and
using the set of model score vectors to construct the model score distributions per cluster.
TANAKA teaches
for each cluster,
determining a set of model score vectors per cluster, the model score vectors containing model scores, and
(TANAKA [par(s) 27-28] “The feature extracting unit 103 extracts a predetermined feature from each item of digital data DD included in the data set DG obtained from the storage unit 102 and generates a feature vector set BG that is a set of feature vectors indicating the extracted features. The feature extracting unit 103 then sends the feature vector set BG to the clustering determining unit 104. Examples of methods of extracting features from digital data DD that is vibration data include filter bank analysis, wavelet analysis, linear predictive coding (LPC) analysis, and cepstrum analysis. The clustering determining unit 104 performs a trial of clustering on the basis of the feature vector set BG obtained from the feature extracting unit 103 and the label set RG obtained from the storage unit 102, determines the possibility of clustering, and determines the homogeneity of the data sets. The clustering determining unit 104 then sends a determination result RE to the output unit 105. Here, for the possibility of clustering, the clustering determining unit 104 determines whether or not clustering can be performed, but, alternatively, for example, the degree to which clustering is successful may be determined.” [par(s) 10-12] “processing circuitry to generate a feature vector set by extracting a predetermined feature from each of the multiple items of digital data and generating feature vectors indicating the extracted features, the feature vector set including the feature vectors, and to determine homogeneity of the data set by performing a trial of supervised clustering on the feature vector set by using the label set and determining possibility of the clustering.” [par(s) 19] “In the following embodiments, a case is assumed where the homogeneity of a data set indicating the vibration of a motor is determined. When multivariate analysis or machine learning is used to determine the soundness of a motor on the basis of the vibration of the motor, the data sets used for the learning must be homogeneous.”;)
using the set of model score vectors to construct the model score distributions per cluster.
(TANAKA [par(s) 27-28] “The clustering determining unit 104 performs a trial of clustering on the basis of the feature vector set BG obtained from the feature extracting unit 103 and the label set RG obtained from the storage unit 102, determines the possibility of clustering, and determines the homogeneity of the data sets. The clustering determining unit 104 then sends a determination result RE to the output unit 105. Here, for the possibility of clustering, the clustering determining unit 104 determines whether or not clustering can be performed, but, alternatively, for example, the degree to which clustering is successful may be determined.” [par(s) 19] “In the following embodiments, a case is assumed where the homogeneity of a data set indicating the vibration of a motor is determined. When multivariate analysis or machine learning is used to determine the soundness of a motor on the basis of the vibration of the motor, the data sets used for the learning must be homogeneous.” [par(s) 33] “Specifically, when linear discriminant analysis is used, the clustering determining unit 104 performs a trial of clustering by calculating a matrix that transforms feature vectors so that the feature vectors of the same cluster can approach each other (variance is small) and the feature vectors of different clusters can be apart from each other (variance is large).” [par(s) 34] “Specifically, the clustering determining unit 104 performs supervised clustering to classify each of the feature vectors included in the feature vector set BG into any one of multiple clusters, applies a parametric distribution to the feature vectors classified into the clusters, and determines the possibility of clustering by using the degree of divergence of the clusters.” [par(s) 35] “For example, the clustering determining unit 104 may apply a normal distribution to multiple feature vectors and measure the degree of divergence in the Mahalanobis distance or the Bhattacharyya distance.”;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lakshmanan, Teh Manmatha, Seo with the model score vectors of TANAKA.
One of ordinary skill in the art would have been motived to combine in order to obtain a highly accurate degree of divergence in consideration of the shape of the distribution and improve the accuracy of the determination of homogeneity.
(TANAKA [par(s) 75] “] By using a normal distribution as the above-mentioned parametric distribution and measuring the degree of divergence of the clusters by the Mahalanobis distance, a highly accurate degree of divergence in consideration of the shape of the distribution can be obtained, and the accuracy of the determination of homogeneity can be improved. Since the method of calculating the Mahalanobis distance is implemented in many numerical calculation libraries as in the numerical calculation related to the normal distribution, the cost for the implementation of the data processing device can be reduced”)
Regarding claim 4
The combination of Lakshmanan, Teh, Manmatha, Seo, TANAKA teaches claim 3.
TANAKA further teaches
wherein the model scores are determined by generating a vector for each model, the vector including feature importance scores for each feature of a corresponding model and the feature health scores for each feature of each sensor used by the corresponding model.
(TANAKA [par(s) 27-28] “The feature extracting unit 103 extracts a predetermined feature from each item of digital data DD included in the data set DG obtained from the storage unit 102 and generates a feature vector set BG that is a set of feature vectors indicating the extracted features. … Examples of methods of extracting features from digital data DD that is vibration data include filter bank analysis, wavelet analysis, linear predictive coding (LPC) analysis, and cepstrum analysis.” [par(s) 19] “In the following embodiments, a case is assumed where the homogeneity of a data set indicating the vibration of a motor is determined. When multivariate analysis or machine learning is used to determine the soundness of a motor on the basis of the vibration of the motor, the data sets used for the learning must be homogeneous.” [par(s) 33] “Specifically, when linear discriminant analysis is used, the clustering determining unit 104 performs a trial of clustering by calculating a matrix that transforms feature vectors so that the feature vectors of the same cluster can approach each other (variance is small) and the feature vectors of different clusters can be apart from each other (variance is large).” [par(s) 34-35] “Specifically, the clustering determining unit 104 performs supervised clustering to classify each of the feature vectors included in the feature vector set BG into any one of multiple clusters, applies a parametric distribution to the feature vectors classified into the clusters, and determines the possibility of clustering by using the degree of divergence of the clusters. For example, the clustering determining unit 104 may apply a normal distribution to multiple feature vectors and measure the degree of divergence in the Mahalanobis distance or the Bhattacharyya distance” [par(s) 37] “Here, the clustering determining unit 104 may perform the projective transformation by discriminant analysis or on the basis of a margin maximization criterion”;)
The combination of Lakshmanan, Teh, Manmatha, Seo, TANAKA is combinable with TANAKA for the same rationale as set forth above with respect to claim 3.
Regarding claim 5
The combination of Lakshmanan, Teh, Manmatha, Seo, TANAKA teaches claim 4.
TANAKA further teaches
wherein the feature importance scores are arranged in a first matrix and the feature health scores are arranged in a second matrix, wherein each vector is a dot product of a corresponding first matrix and a corresponding second matrix, and wherein the vector includes a model score for each of the models among the machine learning models.
(TANAKA [par(s) 33] “Specifically, when linear discriminant analysis is used, the clustering determining unit 104 performs a trial of clustering by calculating a matrix that transforms feature vectors so that the feature vectors of the same cluster can approach each other (variance is small) and the feature vectors of different clusters can be apart from each other (variance is large).” [par(s) 27-28] “The feature extracting unit 103 extracts a predetermined feature from each item of digital data DD included in the data set DG obtained from the storage unit 102 and generates a feature vector set BG that is a set of feature vectors indicating the extracted features. … Examples of methods of extracting features from digital data DD that is vibration data include filter bank analysis, wavelet analysis, linear predictive coding (LPC) analysis, and cepstrum analysis.” [par(s) 10] “processing circuitry to generate a feature vector set by extracting a predetermined feature from each of the multiple items of digital data and generating feature vectors indicating the extracted features, the feature vector set including the feature vectors, and to determine homogeneity of the data set by performing a trial of supervised clustering on the feature vector set by using the label set and determining possibility of the clustering.” [par(s) 37] “Here, the clustering determining unit 104 may perform the projective transformation by discriminant analysis or on the basis of a margin maximization criterion”;)
The combination of Lakshmanan, Teh, Manmatha, Seo, TANAKA is combinable with TANAKA for the same rationale as set forth above with respect to claim 3.
Regarding claim 15
The claim is a method claim corresponding to the system claim 3, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the system claim.
Regarding claim 16
The claim is a method claim corresponding to the system claim 4, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the system claim.
Regarding claim 17
The claim is a method claim corresponding to the system claim 5, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the system claim.
Claim(s) 9, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lakshmanan et al. (US 2022/0004935 A1) in view of Teh et al. (Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection) in view of Manmatha et al. (Modeling Score Distributions for Combining the Outputs of Search Engines) in view of Seo et al. (MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing) in view of Shi et al. (Edge Computing: Vision and Challenges)
Regarding claim 9
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
Lakshmanan further teaches
wherein the health score vectors are received at a near[-edge] node configured to [accumulate] the health score vectors prior to [transmission] to a central node, [wherein the near-edge node is an intermediate node positioned in a computing hierarchy between the nodes operating in the environment and the central node].
(Lakshmanan [fig(s) 6] [fig(s) 8] “Clusterizer” [fig(s) 9] [par(s) 11-14] “FIG. 6 is a block diagram depicting an example neural network topology for ensemble learning for deep feature defect detection of implementations of the disclosure.” [sec(s) 47] “The secondary ML models can be located centrally and, as they are data-specific, the secondary ML models are more granularly trained to distinguish outliers.” [par(s) 93-100] “The distributed data source may include, but are not limited to, a camera 611, time-series data 612, light data 613, audio data 614, LIDAR data 615, or 3D camera data 616. In one implementations, the input data can be of high dimensionality. … In order to make the data simple for the two-staged ML model system described herein to process, the input data used for anomaly detection is obtained as a "feature vector" (also referred to as a deep feature herein) by using DL model 730. DL model 730 is a pre-trained deep learning network model used as a primary feature extractor.” [par(s) 101-113] “Model ensemble 830 includes an ensemble of trained secondary probabilistic model(s), such as Ml 831, M2 832, M3 833, M4 834, through Mn 835. … Method 900 begins at block 910 where the processing device may receive a deep feature vector from a feature extractor of an ensemble learning system, the deep feature vector extracted from input data. In one implementation, the input data includes sensor data. Then, at block 920, the processing device may cluster the deep feature vector into a plurality of clusters based on a distance into the plurality of clusters.”;)
However, the combination of Lakshmanan, Teh, Manmatha, Seo does not appear to explicitly teach:
wherein the health score vectors are received at a near[-edge] node configured to [accumulate] the health score vectors prior to [transmission] to a central node, [wherein the near-edge node is an intermediate node positioned in a computing hierarchy between the nodes operating in the environment and the central node].
Shi teaches
wherein the health score vectors are received at a near-edge node configured to accumulate the health score vectors prior to transmission to a central node, wherein the near-edge node is an intermediate node positioned in a computing hierarchy between the nodes operating in the environment and the central node.
(Shi [sec(s) II.B] “Edge computing refers to the enabling technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. Here we define “edge” as any computing and network resources along the path between data sources and cloud data centers. For example, a smart phone is the edge between body things and cloud, a gateway in a smart home is the edge between home things and cloud, a micro data center and a cloudlet [14] is the edge between a mobile device and cloud.” [sec(s) III.A] “As we mentioned, the users’ shopping cart data and related operations (e.g., add an item, update an item, delete an item) both can be cached at the edge node. The new shopping cart view can be generated immediately upon the user request reaching the edge node. Of course, the data at the edge node should be synchronized with the cloud, however, this can be done in the background. Another issue involves the collaboration of multiple edges when a user moves from one edge node to another. One simple solution is to cache the data to all edges the user may reach. … 2) Content filtering/aggregating could be done at the edge nodes to reduce the data volume to be transferred. 3) Real-time applications such as vision-aid entertainment games, augmented reality, and connected health, could make fast responses by using edge nodes. Thus, by leveraging edge computing, the latency and consequently the user experience for time-sensitive application could be improved significantly.” [fig(s) 6] “ID Time Data” [sec(s) IV.C] “We can easily define the table with id, time, name, data (e.g.,{0000, 12:34:56PM 01/01/2016, kitchen.oven2.temperature3, 78}) such that any edge thing’s data can be fitted in.”;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lakshmanan, Teh Manmatha, Seo with the score vector transmission of Shi.
One of ordinary skill in the art would have been motived to combine in order to improve significantly the latency and consequently the user experience for time-sensitive application by leveraging edge computing.
(Shi [sec(s) III] “At the bottom line, we can improve the interactive services quality by reducing the latency. Similar applications also include the following. 1) Navigation applications can move the navigating or searching services to the edge for a local area, in which case only a few map blocks are involved. 2) Content filtering/aggregating could be done at the edge nodes to reduce the data volume to be transferred. 3) Real-time applications such as vision-aid entertainment games, augmented reality, and connected health, could make fast responses by using edge nodes. Thus, by leveraging edge computing, the latency and consequently the user experience for time-sensitive application could be improved significantly.”)
Regarding claim 18
The claim is a method claim corresponding to the system claim 9, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the system claim.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lakshmanan et al. (US 2022/0004935 A1) in view of Manmatha et al. (Modeling Score Distributions for Combining the Outputs of Search Engines) in view of Teh et al. (Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection) in view of Seo et al. (MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing) in view of Doggett et al. (US 20220309345 A1)
Regarding claim 11
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
However, the combination of Lakshmanan, Teh, Manmatha, Seo does not appear to explicitly teach:
wherein the health score vectors are clustered using unsupervised multi-dimensional clustering.
Doggett teaches
wherein the health score vectors are clustered using unsupervised multi-dimensional clustering.
(Doggett [fig(s) 3] [par(s) 44] “In addition to using ML model based embedder 226 to map embeddings 352a-352j onto continuous multi-dimensional vector space 350, software code 110, when executed by processing hardware 104, may further perform an unsupervised clustering process to identify clusters each corresponding respectively to a different content category with respect to the similarity metric being used to compare content. FIG. 3B shows subspace 300 of continuous multi-dimensional vector space 350 including embeddings 352a-352j. FIG. 3B also shows distinct clusters 354a, 354b, 354c, and 354d (hereinafter “clusters 354a-354d”), each of which identifies a different category of content with respect to a particular similarity metric”; Note that Lakshmanan teaches “health score vectors.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lakshmanan, Teh, Manmatha, Seo with the unsupervised multi-dimensional clustering of Doggett.
One of ordinary skill in the art would have been motived to combine in order to advantageously result in refining and improving the classification or regression performance.
(Doggett [par(s) 56] “In some implementations, the method outlined by flowchart 470 may conclude with action 474 described above. However, in other implementations, the method outlined by flowchart 470 may also include further training ML model based embedder 226 using contrastive learning and the at least one new label discovered in action 474 (action 475). That is to say, action 475 is optional. When included in the method outlined by flowchart 470, action 475 may be performed by software code 110, executed by processing hardware 104 of computing platform 102, and may advantageously result in refining and improving the future. classification or regression performance of system 100. With respect to the actions included in flowchart 470, it is noted that actions 471, 472, 473, and 474 (hereinafter “actions 471-474”), or actions 471-474 and 475, may be performed as an automated process from which human involvement may be omitted”)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lakshmanan et al. (US 2022/0004935 A1) in view of Manmatha et al. (Modeling Score Distributions for Combining the Outputs of Search Engines) in view of Teh et al. (Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection) in view of Seo et al. (MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing) in view of Xu et al. (US 20240119170 A1) in view of TANAKA et al. (US 2021/0081438 A1)
Regarding claim 12
The combination of Lakshmanan, Teh, Manmatha, Seo teaches claim 1.
However, the combination of Lakshmanan, Teh, Manmatha, Seo does not appear to explicitly teach:
wherein the health score vectors are clustered using supervised multi-dimensional clustering based on labels received from the nodes.
Xu teaches
wherein the [health] score vectors are clustered using [supervised] multi-dimensional clustering [based on] labels received from the nodes.
(Xu [fig(s) 1-3] [par(s) 15] “In some embodiments, for the ML model on-line training workflow: the user console (100), responsive to the embedding scan workflow, thereafter receives data labels submitted by the user for the embedding vector, and submits a model training request with the data labels on the ML pipeline; the ML agent (200) polls and retrieves the on-line training request on the ML pipeline with the data labels, and downloads downloads, instantiates, and runs the ML training model; the ML data engine (300) samples multi-dimensional vector data points of the embedding vector into a number of groups by way of K-Means clustering; annotates the embedding vector in the groups with the user-provided data labels for classification for named entity recognition; applies a semi-supervised machine learning by way of K-Nearest Neighbors to propagate document labels to surrounding unlabeled data points; and applies supervised machine learning by way of a Neural Network to train the ML prediction model using embedding vector with labels as inputs”;)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lakshmanan, Teh Manmatha, Seo with the labels from nodes of Xu.
One of ordinary skill in the art would have been motived to combine in order to improve protection of sensitive and private data during on-line machine learning.
(Xu [par(s) 1-4] “The present invention relates generally inference methods or machines, and more specifically, to on-line machine learning and classification of big data in the cloud and traditional enterprise computer environments, and more particular, to protection of sensitive and private data during on-line machine learning. … Machine learning models have been used to dis cover and classify sensitive data for such purposes. How ever, many of the machine learning based data discovery and classification products available in the market require the transfer of data in clear text from one location to a centralized server at another location for model training. This poses a risk to data security and the possibility of data leaks, since data in the clear is unprotected”)
However, the combination of Lakshmanan, Teh, Manmatha, Seo, Xu does not appear to explicitly teach:
wherein the [health] score vectors are clustered using [supervised] multi-dimensional clustering [based on] labels received from the nodes.
TANAKA teaches
wherein the health score vectors are clustered using supervised multi-dimensional clustering based on labels received from the nodes.
(TANAKA [par(s) 10-12] “An information processing device according to an aspect of the present disclosure includes: a memory to store a data set including multiple items of digital data and a label set including multiple labels, each of the multiple labels being added to each of the multiple items of digital data; and processing circuitry to generate a feature vector set by extracting a predetermined feature from each of the multiple items of digital data and generating feature vectors indicating the extracted features, the feature vector set including the feature vectors, and to determine homogeneity of the data set by performing a trial of supervised clustering on the feature vector set by using the label set and determining possibility of the clustering.” [par(s) 19] “In the following embodiments, a case is assumed where the homogeneity of a data set indicating the vibration of a motor is determined. When multivariate analysis or machine learning is used to determine the soundness of a motor on the basis of the vibration of the motor, the data sets used for the learning must be homogeneous.”;)
The combination of Lakshmanan, Teh, Manmatha, Seo, Xu is combinable with TANAKA for the same rationale as set forth above with respect to claim 3.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lakshmanan et al. (US 2022/0004935 A1) in view of Manmatha et al. (Modeling Score Distributions for Combining the Outputs of Search Engines) in view of Teh et al. (Expect the Unexpected: Unsupervised Feature Selection for Automated Sensor Anomaly Detection) in view of Seo et al. (MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing) in view of Doggett et al. (US 20220309345 A1) in view of Xu et al. (US 20240119170 A1) in view of TANAKA et al. (US 2021/0081438 A1)
Regarding claim 19
The claim is a method claim corresponding to a combination of the system claims 11 and 12, and is directed to largely the same subject matter. Thus, it is rejected for the same reasons as given in the rejections of the combination of the system claims.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dong et al. (ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization) teaches ranking zoo models.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SEHWAN KIM/Examiner, Art Unit 2129
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