DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 (i.e., changing from AIA to pre-AIA 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.
Claims 1-20 are subject to review.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/15/2024 is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without reciting significantly more.
Step 1 – is the claim directed to a process, machine, manufacture, or composition of matter?
Claims 1-10 are directed to a “method” which describes one of the four statutory categories of patentable subject, i.e., a process.
Claims 11-19 are directed to a “system” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Claim 20 is directed to a “machine-readable medium” which describes one of the four statutory categories of patentable subject matter, i.e., a manufacture.
Regarding Claim 1
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 1 recites an abstract idea, substantially as follows:
“predicting, using a first machine-learned model, a predicted hazard for the future activity,– is directed to the abstract idea of a mental process i.e., making a prediction based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement or prediction which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“predicting, using the predicted hazard and a second machine-learned model, an impact area for with the predicted hazard, the second machine-learned model trained on a second subset of the historical safety data and a set of impact areas classes;” – is directed to the abstract idea of a mental process i.e., making a prediction based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement or prediction which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“determining, using the predicted hazard, the historical safety data and a natural language processing algorithm, a mitigation action for the predicted hazard and a risk assessment score for the predicted hazard; and” – is directed to the abstract idea of a mental process i.e., determining an action to be taken based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“planning the project using the predicted hazard, the impact area, the mitigation action and the risk assessment score.” – is directed to the abstract idea of a mental process i.e., creating a plan based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 1 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“obtaining a future activity, the future activity associated with a project planned for a future time;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“wherein the first machine-learned model has been trained using a first subset of historical safety data to predict at least one hazard for an input activity, the historical safety data associated with a plurality of activities;” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 1 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“obtaining a future activity, the future activity associated with a project planned for a future time;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“wherein the first machine-learned model has been trained using a first subset of historical safety data to predict at least one hazard for an input activity, the historical safety data associated with a plurality of activities;” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 2
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 2 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 2 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the project comprises constructing one of a well system, a pipeline network, and a processing plant.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 2 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the project comprises constructing one of a well system, a pipeline network, and a processing plant.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 3
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 3 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 3 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the historical safety data comprises historical safety analysis documents, historical incident reports, and historical risk registers.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 3 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the historical safety data comprises historical safety analysis documents, historical incident reports, and historical risk registers.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 4
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 4 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 4 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the first machine-learned model is a random forest classifier.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 4 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the first machine-learned model is a random forest classifier.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 5
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 5 recites an abstract idea, substantially as follows:
“wherein predicting the impact area comprises: applying a semantic search on the dataset to determine the impact area.” – is directed to the abstract idea of a mental process i.e., making a prediction based contextual or semantic analysis which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 5 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the second machine-learned model is a zero-shot classification model configured to generate a dataset of impact areas against activities from the historical safety data,” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 5 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the second machine-learned model is a zero-shot classification model configured to generate a dataset of impact areas against activities from the historical safety data,” – the broadest reasonable interpretation of this imitation is found to be merely outputting data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 6
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 6 recites an abstract idea, substantially as follows:
“wherein the risk assessment score indicates a probability of the predicted hazard occurring or a severity of the predicted hazard should it occur.” – is directed to the abstract idea of a mental process i.e., making a determination is equivalent to observation, evaluation, and making a judgment which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 6 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 6 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 7
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 7 recites an abstract idea, substantially as follows:
“wherein determining the mitigation action comprises: applying a semantic search on the mitigation action dataset to determine the mitigation action using the predicted hazard.” – is directed to the abstract idea of a mental process i.e., making a determination based contextual or semantic analysis which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 7 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the historical safety data comprises a mitigation action dataset that maps hazards to mitigation actions, wherein the mitigation dataset has been generated from a plurality of historical risk registers,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 7 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the historical safety data comprises a mitigation action dataset that maps hazards to mitigation actions, wherein the mitigation dataset has been generated from a plurality of historical risk registers,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 8
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 8 recites an abstract idea, substantially as follows:
“wherein determining the risk assessment score comprises: applying a semantic search on the incident hazards dataset to determine a frequency of the predicted hazard in the incident hazards dataset; and determining the risk assessment score using the frequency. “ – is directed to the abstract idea of a mental process i.e., making a determination is equivalent to observation, evaluation, and making a judgment which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 8 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the historical safety data comprises an incident hazards dataset that maps each of a plurality of historical activities with a corresponding historical hazard that was caused by the respective historical activity, wherein the incident hazards dataset has been generated from a plurality of historical incident reports,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 8 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the historical safety data comprises an incident hazards dataset that maps each of a plurality of historical activities with a corresponding historical hazard that was caused by the respective historical activity, wherein the incident hazards dataset has been generated from a plurality of historical incident reports,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 9
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 9 recites an abstract idea, substantially as follows:
“where in the incident hazards dataset has been generated using an extractive question and answering pipeline applied to the incident hazards dataset.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing an algorithm, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 9 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 9 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 10
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 10 recites an abstract idea, substantially as follows:
“generating a residual risk assessment score using the details of the control measure and the risk assessment score; – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing calculating a score, which is considered to be a mathematical calculation.
“and planning the project using the residual risk assessment score.” – is directed to the abstract idea of a mental process i.e., creating a plan based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 10 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“further comprising: receiving details of a control measure; – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 10 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“further comprising: receiving details of a control measure; – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 11
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 11 recites an abstract idea, substantially as follows:
“predict, using the first machine-learned model, a predicted hazard for the future activity,– is directed to the abstract idea of a mental process i.e., making a prediction based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement or prediction which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“predict, using the predicted hazard and the second machine-learned model, an impact area for the predicted hazard, the second machine-learned model trained on a second subset of the historical safety data and a set of impact areas classes;” – is directed to the abstract idea of a mental process i.e., making a prediction based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement or prediction which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“determine, using the predicted hazard, the historical safety data and a natural language processing algorithm, a mitigation action and a risk assessment score for the predicted hazard; and” – is directed to the abstract idea of a mental process i.e., determining an action to be taken based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“plan the project using the predicted hazard, the impact area, the mitigation action and the risk assessment score.” – is directed to the abstract idea of a mental process i.e., creating a plan based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 11 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“a first machine-learned model; a second machine-learned model; and” – is merely reciting mere instructions to apply an exception, i.e. using generic machine learning using a computer as a tool (see MPEP 2105.05(f)).
“obtain a future activity, the future activity associated with a project planned in for a future time;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“wherein the first machine-learned model has been trained using a first subset of historical safety data to predict at least one hazard for an input activity, the historical safety data associated with a plurality of activities;” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 11 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“a first machine-learned model; a second machine-learned model; and” – is merely reciting mere instructions to apply an exception, i.e. using generic machine learning using a computer as a tool (see MPEP 2105.05(f)).
“obtaining a future activity, the future activity associated with a project planned for a future time;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“wherein the first machine-learned model has been trained using a first subset of historical safety data to predict at least one hazard for an input activity, the historical safety data associated with a plurality of activities;” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 12
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 12 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 12 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the project comprises constructing one of a well system, a pipeline network, and a processing plant.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 12 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the project comprises constructing one of a well system, a pipeline network, and a processing plant.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 13
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 13 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 13 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the historical safety data comprises historical safety analysis documents, historical incident reports, and historical risk registers.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 13 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the historical safety data comprises historical safety analysis documents, historical incident reports, and historical risk registers.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to amount to more than the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 14
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
No, Claim 14 does not recite an abstract idea.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 14 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the first machine-learned model is a random forest classifier.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 14 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the first machine-learned model is a random forest classifier.” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 15
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 15 recites an abstract idea, substantially as follows:
“the computer further configured to: apply a semantic search on the dataset to determine the impact area.” – is directed to the abstract idea of a mental process i.e., making a prediction based contextual or semantic search which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 15 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the second machine-learned model is a zero-shot classification model configured to generate a dataset of impact areas against activities from the historical safety data,” – is merely a recitation of an insignificant extra-solution data outputting (see MPEP 2106.05(g)).
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 15 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the second machine-learned model is a zero-shot classification model configured to generate a dataset of impact areas against activities from the historical safety data,” – the broadest reasonable interpretation of this imitation is found to be merely outputting data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 16
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 16 recites an abstract idea, substantially as follows:
“wherein the risk assessment score indicates a probability of the predicted hazard occurring or a severity of the predicted hazard should it occur.” – is directed to the abstract idea of a mental process i.e., making a determination is equivalent to observation, evaluation, and making a judgment which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 16 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 16 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 17
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 17 recites an abstract idea, substantially as follows:
“the computer further configured to: apply a semantic search on the mitigation action dataset to determine the mitigation action using the predicted hazard.” – is directed to the abstract idea of a mental process i.e., making a determination based contextual or semantic search which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 17 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the historical safety data comprises a mitigation action dataset that maps hazards to mitigation actions, wherein the mitigation dataset has been generated from a plurality of historical risk registers,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 17 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the historical safety data comprises a mitigation action dataset that maps hazards to mitigation actions, wherein the mitigation dataset has been generated from a plurality of historical risk registers,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 18
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 18 recites an abstract idea, substantially as follows:
“the computer further configured to: apply a semantic search on the incident hazards dataset to determine a frequency of the predicted hazard in the incident hazards dataset; and determine the risk assessment score using the frequency. “ – is directed to the abstract idea of a mental process i.e., making a determination is equivalent to observation, evaluation, and making a judgment which are concepts performed in the human mind at a high level (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 18 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“wherein the historical safety data comprises an incident hazards dataset that maps each of a plurality of historical activities with a corresponding historical hazard that was caused by the respective historical activity, wherein the incident hazards dataset has been generated from a plurality of historical incident reports,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 18 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“wherein the historical safety data comprises an incident hazards dataset that maps each of a plurality of historical activities with a corresponding historical hazard that was caused by the respective historical activity, wherein the incident hazards dataset has been generated from a plurality of historical incident reports,” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Regarding Claim 19
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 19 recites an abstract idea, substantially as follows:
“where in the incident hazards dataset has been generated using an extractive question and answering pipeline applied to the incident hazards dataset.” – is directed to the abstract idea of mathematical concepts (See MPEP 2106.04(a)(2)) as it is describing an algorithm, which is considered to be a mathematical calculation.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 19 does not include additional limitations that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 19 does not include additional limitations that amount to significantly more than the judicial exception.
Regarding Claim 20
Steps 2A Prong 1 – is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?
Yes, Claim 20 recites an abstract idea, substantially as follows:
“predicting, using a first machine-learned model, a predicted hazard for the future activity,– is directed to the abstract idea of a mental process i.e., making a prediction based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement or prediction which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“predicting, using the predicted hazard and a second machine-learned model, an impact area for with the predicted hazard, the second machine-learned model trained on a second subset of the historical safety data and a set of impact areas classes;” – is directed to the abstract idea of a mental process i.e., making a prediction based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement or prediction which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“determining, using the predicted hazard, the historical safety data and a natural language processing algorithm, a mitigation action for the predicted hazard and a risk assessment score for the predicted hazard; and” – is directed to the abstract idea of a mental process i.e., determining an action to be taken based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
“planning the project using the predicted hazard, the impact area, the mitigation action and the risk assessment score.” – is directed to the abstract idea of a mental process i.e., creating a plan based on data mirrors the cognitive activity of a person observing the data and making a conclusive judgement which are concepts performed in the human mind (see MPEP 2106.04(a)(2)(III)(C)), and may be performed with the aid of pen and paper, or using a computer as a tool.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, Claim 20 does not include additional limitations that integrate the judicial exception into a practical application. The additional limitation(s):
“obtaining a future activity, the future activity associated with a project planned for a future time;” – is merely a recitation of an insignificant extra-solution data gathering (see MPEP 2106.05(g)).
“wherein the first machine-learned model has been trained using a first subset of historical safety data to predict at least one hazard for an input activity, the historical safety data associated with a plurality of activities;” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application (See MPEP 2106.04).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, Claim 20 does not include additional limitations that amount to significantly more than the judicial exception. The additional limitation(s):
“obtaining a future activity, the future activity associated with a project planned for a future time;” – the broadest reasonable interpretation of this imitation is found to be merely receiving data, which is analogous to receiving or transmitting data over a network, considered WURC under MPEP2106.05(d)(II)(i).
“wherein the first machine-learned model has been trained using a first subset of historical safety data to predict at least one hazard for an input activity, the historical safety data associated with a plurality of activities;” – is merely indicating a field of use or technological environment (see MPEP 2106.06(h)) and fails to integrate the judicial exception.
Therefore, the additional elements, alone or in combination, do not amount to significantly more than the judicial exception (See MPEP 2106.05).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 6-8, 10-11, 16-18, and 20 are rejected under 35 U.S.C 103 as being unpatentable over Man et. al (US 20230394605 A1), hereinafter referred to as Man, in view of NPL reference Kurian et al, “Seeing the forest and the trees: Using machine learning to categorize and analyze incident reports for Alberta oil sands operators”, hereinafter referred to as Kurian.
Regarding Claim 1
Man discloses:
“obtaining a future activity, the future activity associated with a project planned for a future time;” (Man at [0032]: Accordingly, in one aspect, disclosed herein is a method that involves a computing platform (i) receiving one or more data objects related to a construction project,)
“predicting, using a first machine-learned model, a predicted hazard for the future activity, wherein the first machine-learned model has been trained using a first subset of historical safety data to predict at least one hazard for an input activity, the historical safety data associated with a plurality of activities;” (Man at [0084-0086]: At block 306, the computing platform 400 may determine, via one or more machine-learning models trained using historic construction project data […] As a result of this training, the one or more machine-learning models of the risk assessment engine 430 may identify certain data objects, or combinations of data objects, that are predictive of negative outcomes [Examiner Note: mapped to predicting a hazard]; Man at [0087]: The risk assessment engine 430 may be trained in this way with numerous historic construction data sets within a given cohort [Examiner Note: mapped to subset] of similar construction projects [Examiner Note: mapped to plurality of activities])
“determining, using the predicted hazard, the historical safety data and a natural language processing algorithm, a mitigation action for the predicted hazard and a risk assessment score for the predicted hazard; and” (Man at [0073]: As one example, pre-processing may take the form of unsupervised or supervised Natural Language Processing (“NLP”) techniques that analyze user-inputted data in a way that enables the different software engines of the computing platform 400 (discussed further below) to better “understand” the overall context of the data; ) [Examiner Note: The NLP technique is used to preprocess the data objects for the different engines of the computing platform including the risk assessment engine and risk mitigation engine] [0086]: As a result of this training the one or more machine-learning models of the risk assessment engine 430 may identify certain data objects, or combinations of data objects, that are predictive of negative outcomes. This, in turn, may provide a basis to assign these data objects a risk score [Examiner Note: mapped to determining a risk assessment score for predicted hazard]; [0095]: As another example, the risk mitigation engine 440 may automatically generate a suggested action 441 if the risk score of a given data object is above a predetermined threshold value [Examiner Note: mapped to determining a mitigation action for the predicted hazard])
“planning the project using the predicted hazard, the impact area, the mitigation action and the risk assessment score.” (Man at [0094]: The risk mitigation engine 440 may automatically generate suggested actions 441 based on various criteria. As one example, the risk mitigation engine 440 may automatically generate and maintain a list of suggested actions 441 for a given user to take that are within the user's responsibility, and the list may be ordered with the data objects having the highest risk event first (e.g., responding to RFIs, paying invoices, requesting materials). This may provide a useful reference to help the user stay aware of the tasks that are associated with the highest risk and plan their activities on the construction project accordingly.)
However, Man does not disclose:
“predicting, using the predicted hazard and a second machine-learned model, an impact area for with the predicted hazard, the second machine-learned model trained on a second subset of the historical safety data and a set of impact areas classes;”
On the other hand, Kurian discloses:
“predicting, using the predicted hazard and a second machine-learned model, an impact area for with the predicted hazard, the second machine-learned model trained on a second subset of the historical safety data and a set of impact areas classes;” (Kurian at 2.2. Prepare data for machine learning classification: In order to train a machine learning algorithm to rank and categorize risks, many incident reports must be classified to set a guideline for the program [Examiner Note: mapped to training the model on a set of impact areas classes] (Raschka and Mirjalili, 2017). An accepted method for accomplishing this is to separate the data into a training set and a test set, where the entirety of this data must be classified manually. […] As its name implies, the training data are used to train the program in classifying incidents and the test data can then be used to judge the accuracy of different classifiers; Kurian at 3. Results and Discussion: This scale is used to manually classify both the actual and potential risk associated with incidents. And the supervised machine learning algorithm aims to predict consequence [Examiner Note: predicting an impact area] based on the same scale).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Man with the above teachings of Kurian by using a method of planning a project using a predicted hazard and a determined mitigation plan, as taught by Man, and predicting an impact area of a predicted hazard as taught by Kurian. The modification would have been obvious because one of ordinary skill in the art would be motivated to develop mitigation strategies and improve data organization as suggested by Kurian at 1. Introduction “By applying this logic to incident reports, combining several large incident databases allows companies to identify and develop strategies against hazards and latent causes found by other companies that have not yet been identified on their own sites. Using machine learning algorithms, it is also possible to create a system to rank and categorize incidents.”
Regarding Claims 11 and 20, these claims are rejected on the same basis as Claim 1, mutatis mutandis, since both are analogous claims.
Regarding Claim 6
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
The combination of Man and Kurian further discloses:
“wherein the risk assessment score indicates a probability of the predicted hazard occurring or a severity of the predicted hazard should it occur” (Man at [0086]: As above, risk scores may be quantified on a relative 0-100 scale depending on the strength of the correlation between the data object(s) and the negative outcome (perhaps also contemplating the severity of the negative outcome) [Examiner Note: mapped to severity of the predicted hazard])
Regarding Claim 16, this claim is rejected on the same basis as Claim 6, mutatis mutandis, since both are analogous claims.
Regarding Claim 7
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
The combination of Man and Kurian further discloses:
“wherein the historical safety data comprises a mitigation action dataset that maps hazards to mitigation actions, wherein the mitigation dataset has been generated from a plurality of historical risk registers, wherein determining the mitigation action comprises: applying a semantic search on the mitigation action dataset to determine the mitigation action using the predicted hazard.” (Man at [0085]: As one example, negative outcomes within historic construction data sets (e.g., schedule delays, budget overruns, safety issues, repeated work, payment delays, etc.) may be manually identified and quantified based on their severity; Man at [0116]: As discussed above, a computing platform, such as the computing platform 400 shown in FIG. 4 , may generate a suggested action to be taken with respect to a data object, in order to reduce the risk associated with the data object and the construction project as a whole [Examiner Note: mapped to determines the mitigation action using the predicted hazard]; Man at [0073]: As one example, pre-processing may take the form of unsupervised or supervised Natural Language Processing (“NLP”) techniques that analyze user-inputted data in a way that enables the different software engines of the computing platform 400 (discussed further below) to better “understand” the overall context of the data [Examiner Note: mapped to applying a semantic search])
Regarding Claim 17, this claim is rejected on the same basis as Claim 7, mutatis mutandis, since both are analogous claims.
Regarding Claim 8
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
The combination of Man and Kurian further discloses:
“wherein the historical safety data comprises an incident hazards dataset that maps each of a plurality of historical activities with a corresponding historical hazard that was caused by the respective historical activity, wherein the incident hazards dataset has been generated from a plurality of historical incident reports,” (Kurian at 2.5: The incident databases supplied by companies contain many incident reports and the incident date and time, which allows us to calculate frequency of each incident type within a certain time period, and eliminates the need for a human to predict the likelihood)
“wherein determining the risk assessment score comprises: applying […] on the incident hazards dataset to determine a frequency of the predicted hazard in the incident hazards dataset; and determining the risk assessment score using the frequency.” (Kurian at 2.1: “This form of categorization can also be used to determine the frequency of an incident occurring, which is one of the outputs necessary to calculate risk).
“a semantic search” (Man at [0073]: As one example, pre-processing may take the form of unsupervised or supervised Natural Language Processing (“NLP”) techniques that analyze user-inputted data in a way that enables the different software engines of the computing platform 400 (discussed further below) to better “understand” the overall context of the data [Examiner Note: mapped to applying a semantic search])
The same motivation that was utilized for combing Man and Kurian, as set forth in Claim 3, is equally application to Claim 8.
Regarding Claim 18, this claim is rejected on the same basis as Claim 8, mutatis mutandis, since both are analogous claims.
Regarding Claim 10
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
The combination of Man and Kurian further discloses:
“further comprising: receiving details of a control measure;” (Man at [0032]: (v) based on the second risk score for the second data object, automatically generating a suggested action [Examiner Note: mapped to control measure] to be taken with respect to the first data object)
“generating a residual risk assessment score using the details of the control measure and the risk assessment score;” (Man at [0035]: wherein completion of the suggested action [Examiner Note: mapped to using details of the control measure] will lower the second risk score [Examiner Note: the lowered score is mapped to residual risk assessment score] for the second data object)
“and planning the project using the residual risk assessment score” (Man at [0035]: and (vi) causing an indication of the suggested action to be displayed at a client station of a user associated with the construction project [planning the project using the residual risk assessment score])
Claims 2 and 12 are rejected under 35 U.S.C 103 as being unpatentable over Man in view of Kurian, and further in view of Abhulimen, Kingsley E. (US 20120317058 A1), hereinafter referred to as Abhulimen.
Regarding Claim 2
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
However, the combination of Man and Kurian does not disclose:
“wherein the project comprises constructing one of a well system, a pipeline network, and a processing plant.”
On the other hand, Abhulimen discloses:
“wherein the project comprises constructing one of a well system, a pipeline network, and a processing plant.” (Abhulimen at [0002]: The present invention generally relates to a method and expert system for risk assessment and safety management, […] corresponding thereto to complex multifunctional process systems, such as Offshore Platforms/flow lines and Risers, Deepwater Assets, Subsurface drillings, Well Completions and Placements, complex pipeline network, complex refinery, chemical, complex systems, Industry Processes, Power Plants, Electrical Production and Transmission Systems, Construction Projects, Rig Managements etc.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Man and Kurian with the above teachings of Abhulimen by using a method of planning a project according to a predicted hazard, impact area, and its mitigation action as taught by Man and Kurian, and using project data related to a well system, pipeline network, or processing plant, as taught by Abhulimen. The modification would have been obvious because one of ordinary skill in the art would be motivated to predict and manage risk events in complex multifunctional process systems as suggested by Abhulimen at [0002]: “The present invention generally relates to a method and expert system for risk assessment and safety management, more particularly, to a real-time method and system for detecting, predicting, assessing and managing risk events and providing Safety reliability of FPSO process and systems and managing information corresponding thereto to complex multifunctional process systems”.
Regarding Claim 12, this claim is rejected on the same basis as Claim 2, mutatis mutandis, since both are analogous claims.
Claims 3-4 and 13-14 are rejected under 35 U.S.C 103 as being unpatentable over Man in view of Kurian, and further in view of NPL reference Poh et al. “Safety leading indicators for construction sites: A machine learning approach”, hereinafter referred to as Poh.
Regarding Claim 3
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
However, the combination of Man and Kurian does not disclose:
“wherein the historical safety data comprises historical safety analysis documents, historical incident reports, and historical risk registers.”
On the other hand, Poh discloses:
“wherein the historical safety data comprises historical safety analysis documents, historical incident reports, and historical risk registers.” (Poh at 3.1. Data Understanding: Company X provided data from 27 construction projects (consisting of 19 building projects and eight infrastructure projects) over a period of seven years from 2010 to 2016, and 785 safety monthly inspection records [Examiner Note: mapped to safety analysis documents], 418 accident cases [Examiner Note: mapped to historical incident reports] as well as their corresponding monthly project-related attributes; Poh at 4. Results: Table 6 reports the set of 13 input variables selected based on Boruta feature selection technique and the DT model.[...] Interestingly, out of the 13 selected input variables, six of them are project-related: namely Project Type, Project Ownership, Contract Sum, Percent completed, Magnitude of Delay and Project Manpower. The rest of them are safety-related attributes namely Crane/lifting Operations, Scaffold, Mechanical-Elevated Working Platform, Falling Hazards/Openings, Environmental Management, Good Practices [Examiner Note: mapped to historical risk register] and Weighted Safety Inspection Score. This result suggests the significance of project-related attributes in predicting the occurrence and severity of accidents) [Examiner Note: the data from past construction projects contains attributes related to the project, one of which is Good Practices, which is equivalent to a historical risk register]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Man and Kurian with the above teachings of Poh by using a method of planning a project according to a predicted hazard, impact area, and its mitigation action as taught by Man and Kurian, and using historical data that comprises of project related components, as taught by Poh. The modification would have been obvious because one of ordinary skill in the art would be motivated to use machine learning in order to forecast project safety risk and prevent accidents as suggested by Poh at 1. Introduction: “Therefore, this paper aims to use a ML approach to develop a predictive model of accident occurrence and severity, i.e. a ML model capable of providing a validated safety leading indicator, to help construction organizations forecast project safety risk. It is believed that validated leading indicators will enable effective safety leadership and hence prevent accidents”.
Regarding Claim 13, this claim is rejected on the same basis as Claim 3, mutatis mutandis, since both are analogous claims.
Regarding Claim 4
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
However, the combination of Man and Kurian does not disclose:
“wherein the first machine-learned model is a random forest classifier.”
On the other hand, Poh discloses:
“wherein the first machine-learned model is a random forest classifier.” (Poh at Abstract: Five popular ML algorithms were then used to train models for prediction of accident occurrence and severity. During validation, random forest (RF) provided the best prediction performance with an accuracy of 0.78 and has achieved a substantial strength of agreement with Weighted-Kappa Statistics of 0.70)
The same motivation that was utilized for combing Man, Kurian, and Poh, as set forth in Claim 3, is equally application to Claim 4.
Regarding Claim 14, this claim is rejected on the same basis as Claim 4, mutatis mutandis, since both are analogous claims.
Claims 5 and 15 are rejected under 35 U.S.C 103 as being unpatentable over Man in view of Kurian, and further in view of Shukla et al. (US 20250321986 A1), hereinafter referred to as Shukla.
Regarding Claim 5
The combination of Man and Kurian discloses: “The method of claim 1,” and the limitations are shown in the rejection above.
The combination of Man and Kurian further discloses:
“wherein the […] model is […] configured to generate a dataset of impact areas against activities from the historical safety data,” (Kurian at 2.1: It is also necessary to apply labels to the incident reports [Examiner Note: mapped to activities from the historical safety data] in a manner such that an incident report can be evaluated by the risk matrix. When calculating consequence [Examiner Note: mapped to impact area], we consider financial loss, environmental impact, damage to reputation, and worker health) [Examiner Note: consequence is a label that is applied to each of the incident reports, which is considered equivalent generated a data of impact areas against activities]
“wherein predicting the impact area comprises: applying […] on the dataset to determine the impact area.” (Kurian at 3. Results and Discussion: To rate the severity of an incident, the values of the risk matrices from several collaborators were averaged to develop a 5-point scale (see Fig. 4). This scale is used to manually classify both the actual and potential risk associated with incidents. And the supervised machine learning algorithm aims to predict consequence [Examiner Note: mapped to predicting the impact area] based on the same scale)
“a semantic search” (Man at [0073]: As one example, pre-processing may take the form of unsupervised or supervised Natural Language Processing (“NLP”) techniques that analyze user-inputted data in a way that enables the different software engines of the computing platform 400 (discussed further below) to better “understand” the overall context of the data [Examiner Note: mapped to applying a semantic search])
However, the combination of Man and Kurian does not disclose:
On the other hand, Shukla discloses:
“the second machine-learned is a zero-shot classification model” (Shukla at [0020]: That is, to this end, embodiments described herein: utilize text topic and zero shot classification models to translate multimodal technical documentation (e.g., including text and images) into topic relevant metadata; and process queries, pertaining to technical issues, using a multimodal LLM provided with query-related text and image context derived from said topic relevant metadata)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Man and Kurian with the above teachings of Shukla by using semantic search to predict the impact areas of predicted hazards as taught by Man and Kurian, and using a zero-shot classifier, as taught by Shukla. The modification would have been obvious because one of ordinary skill in the art would be motivated to use the zero-shot classifier in order to reduce risk and optimize performance as suggested by Shukla at [0020]: “Embodiments described herein, accordingly, offer near-instant, accurate, and homogeneous solutions for queried technical issues, thereby minimizing service downtime, reducing misinformation risk, and optimizing issue resolution performance.”
Regarding Claim 15, this claim is rejected on the same basis as Claim 5, mutatis mutandis, since both are analogous claims.
Claims 9 and 19 are rejected under 35 U.S.C 103 as being unpatentable over Man in view of Kurian, and further in view of NPL reference Dimitriadis et al. “Enhancing yes/no question answering with weak supervision via extractive question answering”, hereinafter referred to as Dimitriadis.
Regarding Claim 9
The combination of Man and Kurian discloses: “The method of claim 8,” and the limitations are shown in the rejection above.
However, the combination of Man and Kurian does not disclose:
“where in the incident hazards dataset has been generated using an extractive question and answering pipeline applied to the incident hazards dataset.”
On the other hand, Dimitriadis discloses:
“where in the incident hazards dataset has been generated using an extractive question and answering pipeline applied to the incident hazards dataset.” (Dimitriadis at 3.1 Obtaining weak supervision for evidence spans: Fig. 1, Constructing the enriched dataset leveraging an Extractive QA model based on BERT
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[Examiner Note: applying an Extractive QA model to the original dataset to generate the enriched dataset is equivalent to applying using an extractive question and answering pipeline to generate a dataset]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Man and Kurian with the above teachings of Dimitriadis by using a method of planning a project according to a predicted hazard, impact area, and its mitigation action as taught by Man and Kurian, and using an extractive question and answering pipeline to generate a dataset, as taught by Dimitriadis. The modification would have been obvious because one of ordinary skill in the art would be motivated to improve model accuracy and extract relevant parts within texts as suggested by Dimitriadis at 5 Conclusions & future work: “In contrast to previous approaches, this method takes advantage of a pre-trained extractive QA model to guide the learning of a model to answer yes/no questions. The results are better compared to those of conventional yes/no QA models. It is also important to note that not only the accuracy has been improved by the proposed method, but also the model extracts useful parts of texts as presented in Section 4.3. ”
Regarding Claim 19, this claim is rejected on the same basis as Claim 9, mutatis mutandis, since both are analogous claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“A BERT-Based Model for Question Answering on Construction Incident Reports” – recites a method of identifying risky activities and potential hazards associated with those activities.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMIYAH KABIR whose telephone number is (571)270-0722. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMIYAH KABIR/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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