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 . This action is responsive to the application filed on 09/14/2023. Claims 1-19 are presented in the case. Claims 1, 9, 13, 15 and 17 are independent claims.
Priority
Applicant's claim for the benefit of a 35 U.S.C. § 371 National Stage of International Patent Application No. PCT/IB2021/052289, filed on March 18, 2021 is acknowledged.
Information Disclosure Statement
The information disclosure statement submitted on 09/14/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-12 are directed to a method, claim 13 is directed to a medium and claims 15-18 are directed to a controller. Therefore, the claims are eligible under Step 1 for being directed to a process, a manufacture and a machine respectively.
Independent claims 1, 13 and 15:
Step 2A Prong 1:
Claims recite:
forming an input vector, the input vector comprising the first state value, the second state value, and temporal feature - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
after inputting the input vector into the trained ML model, obtaining a probability vector from the ML model, the probability vector comprising, for each device included in the set of devices, a state change prediction value indicating a likelihood that the device will change state at the particular future point in time - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses a mathematical concept of a mathematical calculation of calculating using mathematical methods to determine a probability vector comprising, for each device included in the set of devices, a state change prediction value indicating a likelihood that the device will change state at the particular future point in time;
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
A method for predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time; A non-transitory computer readable storage medium storing a computer program comprising instructions which when executed by processing circuitry of a controller, causes the controller to perform; A controller, the controller comprising:
processing circuitry; and a memory, the memory containing instructions executable by the processing circuitry - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
for a first device within the set of devices, obtaining a first state value indicating the current state of the first device - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
for a second device within the set of devices, obtaining a second state value indicating the current state of the second device - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
inputting the input vector into a trained machine learning (ML) model - the step recited at a high level of generality, and amounts to mere data inputting, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
A method for predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time; A non-transitory computer readable storage medium storing a computer program comprising instructions which when executed by processing circuitry of a controller, causes the controller to perform; A controller, the controller comprising:
processing circuitry; and a memory, the memory containing instructions executable by the processing circuitry - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
for a first device within the set of devices, obtaining a first state value indicating the current state of the first device - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
for a second device within the set of devices, obtaining a second state value indicating the current state of the second device - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g));
inputting the input vector into a trained machine learning (ML) model - the step recited at a high level of generality, and amounts to mere data inputting, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 2 and 16:
Step 2A Prong 1:
Claims recite:
generating a first device vector, the first device vector comprising the first state value indicating the current state of the first device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper;
generating a second device vector, the second device vector comprising the second state value indicating the current state of the second device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper, wherein
forming the input vector comprises concatenating the first device vector with the second device vector - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Dependent claim 3:
Step 2A Prong 1:
Claim recites:
the first device vector further comprises a first spatial feature value indicating the current location of the first device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
the second device vector further comprises a second spatial feature value indicating the current location of the second device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Dependent claim 4:
Step 2A Prong 1:
Claim recites:
the first device vector further comprises a first type value indicating a type of the first device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
the second device vector further comprises a second type value indicating a type of the second device - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Dependent claim 5:
Step 2A Prong 1: The claim recites the abstract ideas of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the temporal feature comprises a set of one or more time values indicating the current time - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the temporal feature comprises a set of one or more time values indicating the current time - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claim 6:
Step 2A Prong 1: The claim recites the abstract ideas of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the set of one or more time values comprises: an hour value specifying an hour of the day; a day value specifying a day of the week; and/or a month value specifying a month of the year - the step recited at a high level of generality, and amounts to selecting a particular data source or type of data to be manipulated, which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the set of one or more time values comprises: an hour value specifying an hour of the day; a day value specifying a day of the week; and/or a month value specifying a month of the year - viewed individually or in combination, describes selecting a particular data source or type of data to be manipulated similar to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display described in MPEP § 2106.05(g).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claim 7:
Step 2A Prong 1: The claim recites the abstract ideas of claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
wherein the ML model was generated using a temporal convolutional network (TCN) - the step recited at a high level of generality, and amounts to merely indicating a field of use or technological environment in which the judicial exception is performed (see MPEP § 2106.05(h)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
wherein the ML model was generated using a temporal convolutional network (TCN) - generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claim 8:
Step 2A Prong 1:
Claim recites:
deciding whether or not to activate the first device based on the state change prediction value indicating the likelihood that the first device will change state at the particular future point in time - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and selecting data based on judgement, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly more. As such, the claims are ineligible.
Independent claims 9 and 17:
Step 2A Prong 1:
Claims recite:
obtaining a training dataset, the training dataset comprising a set of feature-label pairs including at least a first feature-label pair, each feature-label pair comprising at least a first feature vector and at least a first label vector - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
after obtaining the first and second state values, generating the first feature vector of the first feature-label pair, wherein the first feature vector of the first feature-label pair comprises the first state value, the second state value, and a first temporal feature indicating the first point in time - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
after obtaining the third and fourth state values, generating the first label vector of the first feature-label pair, wherein the first label vector of the first feature-label pair comprises the third state value, the fourth state value, and a second temporal feature indicating the second point in time - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
A method for producing a machine learning (ML) model for use in predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time; A controller for producing a machine learning (ML) model for use in predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time, the controller comprising: processing circuitry; and a memory, the memory containing instructions executable by the processing circuitry - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
generating the ML model using the training dataset as an input to a temporal convolutional network (TCN) - the step recited at a high level of generality, and amounts to merely indicating a field of use or technological environment in which the judicial exception is performed (see MPEP § 2106.05(h)).
wherein obtaining the training dataset comprises:
for a first device within the set of devices, obtaining a first state value indicating the state of the first device at a first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
for a second device within the set of devices, obtaining a second state value indicating the state of the second device at the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
obtaining a third state value indicating the state of the first device at a subsequent second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
obtaining a fourth state value indicating the state of the second device at the subsequent second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
A method for producing a machine learning (ML) model for use in predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time; A controller for producing a machine learning (ML) model for use in predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time, the controller comprising: processing circuitry; and a memory, the memory containing instructions executable by the processing circuitry - These limitations amount to components of a general purpose computer that applies a judicial exception, by use of conventional computer functions (see MPEP § 2106.05(b)).
generating the ML model using the training dataset as an input to a temporal convolutional network (TCN) - generally linking the use of the judicial exception to indicate a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
wherein obtaining the training dataset comprises:
for a first device within the set of devices, obtaining a first state value indicating the state of the first device at a first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
for a second device within the set of devices, obtaining a second state value indicating the state of the second device at the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
obtaining a third state value indicating the state of the first device at a subsequent second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
obtaining a fourth state value indicating the state of the second device at the subsequent second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claims 10 and 18:
Step 2A Prong 1:
Claims recite:
the first feature vector of the first feature-label pair further comprises the first location value and the second location value - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
obtaining a first location value indicating the location of the first device at the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)); and
obtaining a second location value indicating the location of the second device at the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
obtaining a first location value indicating the location of the first device at the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)); and
obtaining a second location value indicating the location of the second device at the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claim 11:
Step 2A Prong 1:
Claims recite:
the first feature-label pair further comprises a second feature vector - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
after obtaining the fifth and sixth state values, generating the second feature vector of the first feature-label pair, wherein the second feature vector of the first feature-label pair comprises the fifth state value, the sixth state value, and a second temporal feature indicating the second point in time - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
for the first device within the set of devices, obtaining a fifth state value indicating the state of the first device at a second point in time that precedes the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)); and
for the second device within the set of devices, obtaining a sixth state value indicating the state of the second device at the second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
for the first device within the set of devices, obtaining a fifth state value indicating the state of the first device at a second point in time that precedes the first point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)); and
for the second device within the set of devices, obtaining a sixth state value indicating the state of the second device at the second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Dependent claim 12:
Step 2A Prong 1:
Claims recite:
the second feature vector of the first feature-label pair further comprises the third location value and the fourth location value - Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating data and generating data, which is observing, evaluating and judging that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: This judicial exception is not integrated into a practical application because they recite the additional elements:
obtaining a third location value indicating the location of the first device at the second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)); and
obtaining a fourth location value indicating the location of the second device at the second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
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 claims are thus directed to the abstract idea.
Step 2B: The claims do not include additional elements that amount to significantly more than the judicial exception.
The additional elements:
obtaining a third location value indicating the location of the first device at the second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)); and
obtaining a fourth location value indicating the location of the second device at the second point in time - the steps recited at a high level of generality, and amounts to mere data gathering which is a form of insignificant extra-solution activity (see MPEP § 2106.05(g)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8, 13 and 15-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Demetriou et al. (hereinafter Demetriou), US 20190081857 A1.
Regarding independent claim 1, Demetriou teaches a method for predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time (Abstract, Examples herein relate to determining a next state in which to transition multiple IoT devices within an environment. Examples disclose determining, via operation of a state machine, a current state of the multiple IoT devices within the environment. The state machine receives contextual information. Based on the current state and the contextual information, the state machine determine a next state of the multiple IoT devices in which to transition of the multiple IoT devices within the environment), the method comprising:
for a first device within the set of devices, obtaining a first state value indicating the current state of the first device (Fig. 4; [0035] At operation 402 by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state; Fig. 5; [0039] At operation 502, the state machine determines the current state of the multiple IoT devices within the environment);
for a second device within the set of devices, obtaining a second state value indicating the current state of the second device (Fig. 4; [0035] At operation 402 by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state; Fig. 5; [0039] At operation 502, the state machine determines the current state of the multiple IoT devices within the environment);
forming an input vector, the input vector comprising the first state value, the second state value, and temporal feature ([0017] FIG. 1 is an example environment 102 including state machine 108 to determine current state 104 of multiple IoT device 106a-106d and transition to next state 114; [0018] Current state 104 represents a particular condition of multiple IoT devices 106a-106d within environment 102 at a specific time. As such, current state 104 further includes a status of each of multiple IoT devices 106a10-6d. In this manner, current state 104 comprises various statuses of multiple IoT devices 106a-106d at the present moment. The status of each of multiple IoT devices 106a-106d is considered the position of the each device at a particular time. The status may include a standby, non-operational, low powered, off, normal operation, operational, on, etc.. Each of these statuses represents the condition of each device; [0036] At operation 404, the state machine receives contextual information that includes at least one of either a spatial attribute and/or temporal attribute … Using the contextual information, the state machine can anticipate the transition to the next state of the multiple IoT devices within the environment. In a further implementation, crowdsourcing information is received in addition to the contextual information to predict the next state; [0040] At operation 504, the state machine models the state of each IoT device and in turn the current state. Modeling the current state, the state machine models a state diagram as a way to describe the behavior or statuses of the multiple IoT devices. The state diagrams include a finite number of states, and based on receipt of contextual information received at operation 508, the state machine determines the current state of the given environment);
inputting the input vector into a trained machine learning (ML) model ([0032] At module 308, the current state extracts the statuses of the multiple IoT devices and feeds them as testing attributes to the machine learning model 316); and
after inputting the input vector into the trained ML model, obtaining a probability vector from the ML model, the probability vector comprising, for each device included in the set of devices, a state change prediction value indicating a likelihood that the device will change state at the particular future point in time ([0045] At operations 518-520, the likelihood of each state is determined to predict which state in which to transition the multiple IoT devices. At these operations, a probabilistic model may be used to calculate the probability or likelihood of each state. In turn, the state machine can identify which state to transition the multiple IoT devices within the environment).
Regarding dependent claim 2, Demetriou teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Demetriou further teaches
further comprising:
generating a first device vector, the first device vector comprising the first state value indicating the current state of the first device (Fig. 2A, Si 1; [0025]-[0026]); and
generating a second device vector, the second device vector comprising the second state value indicating the current state of the second device (Fig. 2A, Sj 2; [0025]-[0026]), wherein
forming the input vector comprises concatenating the first device vector with the second device vector (Fig. 2A, HS; [0025]-[0026]; [0035] At operation 402 by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state … the state machine models the current state of the multiple IoT devices by modeling the state of each multiple IoT devices).
Regarding dependent claim 3, Demetriou teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Demetriou further teaches wherein
the first device vector further comprises a first spatial feature value indicating the current location of the first device ([0027] The transition between states, or a change in the house state occurs based on modifications or changes in the contextual information. A state transition function δ is a function that given a set of inputs applied on a set of states, outputs a new set of state. In this example, the set of states is the house state HS and thus δ becomes, where Σ is the input (e.g., contextual information):
δ: S×Σ−>S
[0028] Let T denote the set of temporal attributes, L the set of spatial attributes and N the notifications that may trigger an adaption to another state, then:
Σ={T, L, N}; Fig. 5, 510; [0042] At operation 508, the state machine receives the contextual information. In one implementation, the contextual information includes at least one of a spatial attribute, temporal attribute, or combination thereof as at operations 510-512. The spatial attribute as at operation 510 infers a user location relative to the given environment while the temporal attribute as at operation 512 is used to infer time), and
the second device vector further comprises a second spatial feature value indicating the current location of the second device ([0027] The transition between states, or a change in the house state occurs based on modifications or changes in the contextual information. A state transition function δ is a function that given a set of inputs applied on a set of states, outputs a new set of state. In this example, the set of states is the house state HS and thus δ becomes, where Σ is the input (e.g., contextual information):
δ: S×Σ−>S
[0028] Let T denote the set of temporal attributes, L the set of spatial attributes and N the notifications that may trigger an adaption to another state, then:
Σ={T, L, N}; Fig. 5, 510; [0042] At operation 508, the state machine receives the contextual information. In one implementation, the contextual information includes at least one of a spatial attribute, temporal attribute, or combination thereof as at operations 510-512. The spatial attribute as at operation 510 infers a user location relative to the given environment while the temporal attribute as at operation 512 is used to infer time).
Regarding dependent claim 4, Demetriou teaches all the limitations as set forth in the rejection of claim 2 that is incorporated. Demetriou further teaches wherein
the first device vector further comprises a first type value indicating a type of the first device ([0031] FIG. 3 illustrates an overall example system architecture that includes different environments 302 and 304. IoT device types, statuses, spatial attribute(s), temporal attribute(s), and other information is communicated from participating environments 302 and 304 through an overlay protocol to a hosted and managed cloud 300), and
the second device vector further comprises a second type value indicating a type of the second device ([0031] FIG. 3 illustrates an overall example system architecture that includes different environments 302 and 304. IoT device types, statuses, spatial attribute(s), temporal attribute(s), and other information is communicated from participating environments 302 and 304 through an overlay protocol to a hosted and managed cloud 300).
Regarding dependent claim 5, Demetriou teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Demetriou further teaches wherein the temporal feature comprises a set of one or more time values indicating the current time ([0012] Additionally, using a state machine to model a state of multiple IoT devices within the environment. The state machine incorporates both contextual information such as time attributes and a location of the user relative to the environment and crowdsourcing information; [0021] Contextual information 110 is information used to infer time and/or user's location relative to environment 102. In one implementation, contextual information 110 includes at least one temporal attribute or spatial attribute or combination thereof. The temporal attribute is a property that may be used in infer time, such as a time of day, season of year, etc.).
Regarding dependent claim 6, Demetriou teaches all the limitations as set forth in the rejection of claim 5 that is incorporated. Demetriou further teaches wherein the set of one or more time values comprises: an hour value specifying an hour of the day; a day value specifying a day of the week; and/or a month value specifying a month of the year ([0036] The temporal attribute is a feature used to infer the time. The time can be as fine-grained as a specific timestamp on a specific day of a specific month or as coarse grained as a season and/or year. In this implementation, the temporal attribute may be defined as time information including a collection of a minute, hour, day, week, season, and/or year, etc.).
Regarding dependent claim 8, Demetriou teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Demetriou further teaches further comprising:
deciding whether or not to activate the first device based on the state change prediction value indicating the likelihood that the first device will change state at the particular future point in time ([0045] At operations 518-520, the likelihood of each state is determined to predict which state in which to transition the multiple IoT devices. At these operations, a probabilistic model may be used to calculate the probability or likelihood of each state. In turn, the state machine can identify which state to transition the multiple IoT devices within the environment; [0046] At operation 522, based on determining the next state in which to transition the multiple IoT devices, the individual status of each of the IoT devices is transitioned to attain the next state. In one implementation, a handler may transmit a communication to each of the impacted IoT devices indicating a status to achieve to transition to the next state).
Regarding independent claim 13, it is a medium claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Demetriou further teaches a non-transitory computer readable storage medium storing a computer program comprising instructions which when executed by processing circuitry of a controller, causes the controller to perform the method of claim 1 (Fig. 6; [0048]; [0050]).
Regarding independent claim 15, it is a controller claim that corresponding to the method of claim 1. Therefore, it is rejected for the same reason as claim 1 above. Demetriou further teaches a controller, the controller comprising: processing circuitry; and a memory, the memory containing instructions executable by the processing circuitry, wherein the controller is configured to perform the method of claim 1 (Fig. 6; [0048]; [0049]).
Regarding dependent claim 16, it is a controller claim that corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claim 2 above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 7, 9-12 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Demetriou, in view of Zhang et al. (hereinafter Zhang), US 20210035437 A1, this reference included in IDS filed 09/14/2023.
Regarding dependent claim 7, Demetriou teaches all the limitations as set forth in the rejection of claim 1 that is incorporated. Demetriou does not explicitly teach wherein the ML model was generated using a temporal convolutional network (TCN).
However, in the same field of endeavor, Zhang teaches wherein the ML model was generated using a temporal convolutional network (TCN) ([0047] The sensed information from 301 and the electronic records information from 303 are provided as inputs at 323 to the recurrent neural network (RNN). More specifically, the present example implementation may include a long short-term memory (LSTM) RNN or deep convolutional neural network (CNN), including a plurality of frames 325; [0054] this approach may learn the importance of features during temporal relationship mining. Alternatively, other example implementations or alternatives may also be used, using alternative sequence models, such as gated recurrent units (GRU) and temporal convolution nets (TCN)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of using temporal convolution nets (TCN) as alternative sequence model to generate ML model using the training dataset as an input as suggested in Zhang into Demetriou’s system because both of these systems are addressing generating a prediction associated with a likelihood of the state transition based on the input. It would be obvious as a matter of design choice to use alternative sequence model to generate ML model.
Regarding independent claim 9, Demetriou teaches a method for producing a machine learning (ML) model for use in predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time ([0011] the present disclosure provides a state machine engine to model behavior or states of multiple IoT devices within an environment such as a home and/or building. Based on the states of the multiple IoT devices, the state machine receives contextual information to indicate transitions to the next states for the multiple IoT devices; [0012] Additionally, using a state machine to model a state of multiple IoT devices within the environment. The state machine incorporates both contextual information such as time attributes and a location of the user relative to the environment and crowdsourcing information. Using these different sources of information, the state transitions are calculated to predict which state in which to transition the multiple IoT devices. This provides an adaptive feature that allows the state transitions to change based on the information), the method comprising:
obtaining a training dataset, the training dataset comprising a set of feature-label pairs including at least a first feature-label pair, each feature-label pair comprising at least a first feature vector and at least a first label vector ([0025] For example, in FIG. 2A, the time of day and user's location can be used as contextual information to switch a status of a light bulb from off to on and a coffee maker from off to on to transition from a current state to next state; [0026] In FIG. 2A, an example smart home 202 is used as the state machine where a vertex represents various home states 204 and 214 to include the set of multiple IoT devices' statuses. The various equations below reference how the state machine model can be leveraged to automate an environment (e.g., house). Although the house environment is used, other environments could include an office, stadium, etc. As such, in a more formalized fashion, D below may represent the set of IoT devices that can be found in home 202. Additionally, let be the status i of device j and the house state (HS) 202.
D={d1, d2, . . . , dd}, where |D|=d
HS={Si 1, Sj 2, . . . , Sk n}
[0027] The transition between states, or a change in the house state occurs based on modifications or changes in the contextual information. A state transition function δ is a function that given a set of inputs applied on a set of states, outputs a new set of state. In this example, the set of states is the house state HS and thus δ becomes, where Σ is the input (e.g., contextual information):
δ: S×Σ−>S
[0028] Let T denote the set of temporal attributes, L the set of spatial attributes and N the notifications that may trigger an adaption to another state, then:
Σ={T, L, N}); and
generating the ML model using the training dataset as an input 302 and 304. IoT device types, statuses, spatial attribute(s), temporal attribute(s), and other information is communicated from participating environments 302 and 304 through an overlay protocol to a hosted and managed cloud 300. This cloud 300, includes a crowdsourcing handler 306 that collects the crowdsourcing information to use as input to a home state to global home state 308 to identify the patterns that are common among environments 302 and 304. Feature extraction 310 extracts features from the collected information and uses the information to train the machine learning models seen in FIGS. 2A-2C in training module 312 and testing module 314) wherein obtaining the training dataset comprises:
for a first device within the set of devices, obtaining a first state value indicating the state of the first device at a first point in time (Fig. 1, 104; [0017] FIG. 1 is an example environment 102 including state machine 108 to determine current state 104 of multiple IoT device 106a-106d; Fig. 4; [0035] At operation 402 by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state; Fig. 5; [0039] At operation 502, the state machine determines the current state of the multiple IoT devices within the environment);
for a second device within the set of devices, obtaining a second state value indicating the state of the second device at the first point in time (Fig. 1, 104; [0017] FIG. 1 is an example environment 102 including state machine 108 to determine current state 104 of multiple IoT device 106a-106d; Fig. 4; [0035] At operation 402 by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state; Fig. 5; [0039] At operation 502, the state machine determines the current state of the multiple IoT devices within the environment);
after obtaining the first and second state values, generating the first feature vector of the first feature-label pair, wherein the first feature vector of the first feature-label pair comprises the first state value, the second state value, and a first temporal feature indicating the first point in time ([0018] Current state 104 represents a particular condition of multiple IoT devices 106a-106d within environment 102 at a specific time. As such, current state 104 further includes a status of each of multiple IoT devices 106a10-6d. In this manner, current state 104 comprises various statuses of multiple IoT devices 106a-106d at the present moment. The status of each of multiple IoT devices 106a-106d is considered the position of the each device at a particular time. The status may include a standby, non-operational, low powered, off, normal operation, operational, on, etc.. Each of these statuses represents the condition of each device; [0036] At operation 404, the state machine receives contextual information that includes at least one of either a spatial attribute and/or temporal attribute … Using the contextual information, the state machine can anticipate the transition to the next state of the multiple IoT devices within the environment. In a further implementation, crowdsourcing information is received in addition to the contextual information to predict the next state; [0040] At operation 504, the state machine models the state of each IoT device and in turn the current state. Modeling the current state, the state machine models a state diagram as a way to describe the behavior or statuses of the multiple IoT devices. The state diagrams include a finite number of states, and based on receipt of contextual information received at operation 508, the state machine determines the current state of the given environment; Fig. 2B, HSi; [0029] Turning now to FIG. 2B, a state transition Σ between states 204 and 214 is illustrated. Each model 216 and 218 represent the various statuses of the IoT devices within home state 202. Given a specific user, the state machine can learn over time the state transition that may apply to that specific user. If there are multiple users in given environment, then spatial attributes to infer user location and other information would be more specific to avoid conflicting behaviors);
obtaining a third state value indicating the state of the first device at a subsequent second point in time (Fig. 1, 114; [0017] State machine 108, coupled to controller 112, receives contextual information 110 to transitions multiple IoT devices 106a-106d from current state 104 to next state 114);
obtaining a fourth state value indicating the state of the second device at the subsequent second point in time (Fig. 1, 114; [0017] State machine 108, coupled to controller 112, receives contextual information 110 to transitions multiple IoT devices 106a-106d from current state 104 to next state 114); and
after obtaining the third and fourth state values, generating the first label vector of the first feature-label pair, wherein the first label vector of the first feature-label pair comprises the third state value, the fourth state value, and a second temporal feature indicating the second point in time ([0018] Over time, the statuses of multiple IoT devices 106a-106d may change to create next state 114; [0023] Next state 114 represents a modification of status among multiple IoT devices 106a-106d to adapt to different scenarios based on contextual information 110. As illustrated by next state 114, based on a user awakening and/or daybreak, light bulb 106a modifies status from off to on, coffee maker 106b may remain in a power off mode, computer 106c turns from off to on, and mobile device 106d may remain in standby mode; Fig. 2B, HSi+1; [0029] Turning now to FIG. 2B, a state transition Σ between states 204 and 214 is illustrated. Each model 216 and 218 represent the various statuses of the IoT devices within home state 202. Given a specific user, the state machine can learn over time the state transition that may apply to that specific user. If there are multiple users in given environment, then spatial attributes to infer user location and other information would be more specific to avoid conflicting behaviors).
Demetriou does not explicitly teach generating the ML model using the training dataset as an input to a temporal convolutional network (TCN).
However, in the same field of endeavor, Zhang teaches generating the ML model using the training dataset as an input to a temporal convolutional network (TCN) ([0047] The sensed information from 301 and the electronic records information from 303 are provided as inputs at 323 to the recurrent neural network (RNN). More specifically, the present example implementation may include a long short-term memory (LSTM) RNN or deep convolutional neural network (CNN), including a plurality of frames 325; [0054] this approach may learn the importance of features during temporal relationship mining. Alternatively, other example implementations or alternatives may also be used, using alternative sequence models, such as gated recurrent units (GRU) and temporal convolution nets (TCN)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of using temporal convolution nets (TCN) as alternative sequence model to generate ML model using the training dataset as an input as suggested in Zhang into Demetriou’s system because both of these systems are addressing generating a prediction associated with a likelihood of the state transition based on the input. It would be obvious as a matter of design choice to use alternative sequence model to generate ML model.
Regarding dependent claim 10, the combination of Demetriou and Zhang teaches all the limitations as set forth in the rejection of claim 9 that is incorporated. Demetriou further teaches further comprising
obtaining a first location value indicating the location of the first device at the first point in time ([0017] State machine 108, coupled to controller 112, receives contextual information 110 to transitions multiple IoT devices 106 a-106 d from current state 104 to next state 114; [0021] Contextual information 110 is information used to infer time and/or user's location relative to environment 102. In one implementation, contextual information 110 includes at least one temporal attribute or spatial attribute or combination thereof. The temporal attribute is a property that may be used in infer time, such as a time of day, season of year, etc. The spatial attribute is a property used to infer the user's location relative to environment 102, such as user is within environment 102 or outside of environment 102); and
obtaining a second location value indicating the location of the second device at the first point in time ([0017] State machine 108, coupled to controller 112, receives contextual information 110 to transitions multiple IoT devices 106 a-106 d from current state 104 to next state 114; [0021] Contextual information 110 is information used to infer time and/or user's location relative to environment 102. In one implementation, contextual information 110 includes at least one temporal attribute or spatial attribute or combination thereof. The temporal attribute is a property that may be used in infer time, such as a time of day, season of year, etc. The spatial attribute is a property used to infer the user's location relative to environment 102, such as user is within environment 102 or outside of environment 102), wherein
the first feature vector of the first feature-label pair further comprises the first location value and the second location value ([0021] As seen in FIG. 1, contextual information 110 may include a time (e.g., alarm clock and sun) and user's location relative to environment 102 (e.g., user in bed). In this example, in the morning, the user may have awoken and as such, multiple IoT devices 106a-106d which may have been in a powered down or off status, may power on as indicated by next state 114; [0025] For example, in FIG. 2A, the time of day and user's location can be used as contextual information to switch a status of a light bulb from off to on and a coffee maker from off to on to transition from a current state to next state).
Regarding dependent claim 11, the combination of Demetriou and Zhang teaches all the limitations as set forth in the rejection of claim 9 that is incorporated. Demetriou further teaches further comprising wherein
the first feature-label pair further comprises a second feature vector, and
obtaining the training dataset further comprises:
for the first device within the set of devices, obtaining a fifth state value indicating the state of the first device at a second point in time that precedes the first point in time ([0024] FIGS. 2A-2C illustrate various example states of multiple IoT devices within an environment based on contextual information to adapt to different scenarios; [0025] For example, in FIG. 2A, the time of day and user's location can be used as contextual information to switch a status of a light bulb from off to on and a coffee maker from off to on to transition from a current state to next state; [0029] Turning now to FIG. 2B, a state transition Σ between states 204 and 214 is illustrated. Each model 216 and 218 represent the various statuses of the IoT devices within home state 202. Given a specific user, the state machine can learn over time the state transition that may apply to that specific user. If there are multiple users in given environment, then spatial attributes to infer user location and other information would be more specific to avoid conflicting behaviors; [0035] At operation 402 by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state. As such, having different IoT devices within an environment, such as a light switch, coffee maker, soda machine, etc. each may have a different status. The various statuses for the different IoT devices forms the current state. Depending on a user's location and/or time of day, these IoT devices may modify their particular statuses. In this case, the contextual information, e.g., user's location and/or time, may modify the statuses of the IoT devices to comprise the next state. For example, the presence of the user within the environment may indicate to turn on a light while the absence of the user and/or nighttime may indicate to turn off the light. In another implementation, the state machine models the current state of the multiple IoT devices by modeling the state of each multiple IoT devices; [0036] The temporal attribute is a feature used to infer the time. The time can be as fine-grained as a specific timestamp on a specific day of a specific month or as coarse grained as a season and/or year. In this implementation, the temporal attribute may be defined as time information including a collection of a minute, hour, day, week, season, and/or year, etc.);
for the second device within the set of devices, obtaining a sixth state value indicating the state of the second device at the second point in time ([0024] FIGS. 2A-2C illustrate various example states of multiple IoT devices within an environment based on contextual information to adapt to different scenarios; [0025] For example, in FIG. 2A, the time of day and user's location can be used as contextual information to switch a status of a light bulb from off to on and a coffee maker from off to on to transition from a current state to next state; [0029] Turning now to FIG. 2B, a state transition Σ between states 204 and 214 is illustrated. Each model 216 and 218 represent the various statuses of the IoT devices within home state 202. Given a specific user, the state machine can learn over time the state transition that may apply to that specific user. If there are multiple users in given environment, then spatial attributes to infer user location and other information would be more specific to avoid conflicting behaviors; [0035] At operation 402 by operation of the state machine, the current state of the multiple IoT devices within the environment is determined. The current state includes a status of each of the multiple IoT devices to comprise the current state. As such, having different IoT devices within an environment, such as a light switch, coffee maker, soda machine, etc. each may have a different status. The various statuses for the different IoT devices forms the current state. Depending on a user's location and/or time of day, these IoT devices may modify their particular statuses. In this case, the contextual information, e.g., user's location and/or time, may modify the statuses of the IoT devices to comprise the next state. For example, the presence of the user within the environment may indicate to turn on a light while the absence of the user and/or nighttime may indicate to turn off the light. In another implementation, the state machine models the current state of the multiple IoT devices by modeling the state of each multiple IoT devices; [0036] The temporal attribute is a feature used to infer the time. The time can be as fine-grained as a specific timestamp on a specific day of a specific month or as coarse grained as a season and/or year. In this implementation, the temporal attribute may be defined as time information including a collection of a minute, hour, day, week, season, and/or year, etc.);
after obtaining the fifth and sixth state values, generating the second feature vector of the first feature-label pair, wherein the second feature vector of the first feature-label pair comprises the fifth state value, the sixth state value, and a second temporal feature indicating the second point in time ([0018] Over time, the statuses of multiple IoT devices 106a-106d may change to create next state 114; [0023] Next state 114 represents a modification of status among multiple IoT devices 106a-106d to adapt to different scenarios based on contextual information 110. As illustrated by next state 114, based on a user awakening and/or daybreak, light bulb 106a modifies status from off to on, coffee maker 106b may remain in a power off mode, computer 106c turns from off to on, and mobile device 106d may remain in standby mode; Fig. 2B, HSi+1; [0029] Turning now to FIG. 2B, a state transition Σ between states 204 and 214 is illustrated. Each model 216 and 218 represent the various statuses of the IoT devices within home state 202. Given a specific user, the state machine can learn over time the state transition that may apply to that specific user. If there are multiple users in given environment, then spatial attributes to infer user location and other information would be more specific to avoid conflicting behaviors).
Regarding dependent claim 12, the combination of Demetriou and Zhang teaches all the limitations as set forth in the rejection of claim 11 that is incorporated. Demetriou further teaches further comprising
obtaining a third location value indicating the location of the first device at the second point in time ([0017] State machine 108, coupled to controller 112, receives contextual information 110 to transitions multiple IoT devices 106 a-106 d from current state 104 to next state 114; [0021] Contextual information 110 is information used to infer time and/or user's location relative to environment 102. In one implementation, contextual information 110 includes at least one temporal attribute or spatial attribute or combination thereof. The temporal attribute is a property that may be used in infer time, such as a time of day, season of year, etc. The spatial attribute is a property used to infer the user's location relative to environment 102, such as user is within environment 102 or outside of environment 102); and
obtaining a fourth location value indicating the location of the second device at the second point in time ([0017] State machine 108, coupled to controller 112, receives contextual information 110 to transitions multiple IoT devices 106 a-106 d from current state 104 to next state 114; [0021] Contextual information 110 is information used to infer time and/or user's location relative to environment 102. In one implementation, contextual information 110 includes at least one temporal attribute or spatial attribute or combination thereof. The temporal attribute is a property that may be used in infer time, such as a time of day, season of year, etc. The spatial attribute is a property used to infer the user's location relative to environment 102, such as user is within environment 102 or outside of environment 102), wherein
the second feature vector of the first feature-label pair further comprises the third location value and the fourth location value ([0021] As seen in FIG. 1, contextual information 110 may include a time (e.g., alarm clock and sun) and user's location relative to environment 102 (e.g., user in bed). In this example, in the morning, the user may have awoken and as such, multiple IoT devices 106a-106d which may have been in a powered down or off status, may power on as indicated by next state 114; [0025] For example, in FIG. 2A, the time of day and user's location can be used as contextual information to switch a status of a light bulb from off to on and a coffee maker from off to on to transition from a current state to next state).
Regarding independent claim 17, it is a controller claim that corresponding to the method of claim 9. Therefore, it is rejected for the same reason as claim 9 above. Demetriou further teaches a controller for producing a machine learning (ML) model for use in predicting, for each device included in a set of devices, whether the device will change state at a particular future point in time, the controller comprising: processing circuitry; and a memory, the memory containing instructions executable by the processing circuitry, wherein the controller is configured to perform the method of claim 9 (Fig. 6; [0048]; [0049]).
Regarding dependent claim 18, it is a controller claim that corresponding to the method of claim 10. Therefore, it is rejected for the same reason as claim 10 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
KUMAR et al. (US 20210326723 A1) discloses a predictive program, in a run-time phase, receives a current value for a remotely sourced forecast as run-time input into an artificial intelligence model. The artificial intelligence model has been trained on training data including a time series of locally sourced measurements for a parameter and a time series of remotely sourced forecast data for the parameter. The predictive program outputs a predicted forecast offset between the current value of a remotely sourced forecast and a future locally sourced measurement for the parameter. The predictive program outputs from the artificial intelligence model a predicted forecast offset based on the run-time input.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY P HOANG whose telephone number is (469)295-9134. The examiner can normally be reached M-TH 8:30-5:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JENNIFER WELCH can be reached at 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AMY P HOANG/ Examiner, Art Unit 2143
/JENNIFER N WELCH/ Supervisory Patent Examiner, Art Unit 2143
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