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 office action is in response to submission of application on 12/04/2023.
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 7 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 7 contains the limitation “generating an uncertainty model by calculating an Area Under the Curve (AUC) of the PDF on both sides of the decision boundary in an output score space” at line 9. There is nothing in the specification that describes “generating an uncertainty model” by calculating an Area Under the Curve. The specification recites “In this case, the probability that true Y lies on either side of the decision boundary may be measured, for example, by calculating Area Under the Curve (AUC) of this PDF on both sides of the decision boundary.” (Specification, paragraph [0037], line 7.) The “generating” could mean creating a new model by calculating an AUC, for which there is no support in the specification or description for how it would be accomplished. It could mean training the model. Or it could simply mean calculating the AUC. Therefore, the claim is indefinite and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-8 are directed to a method (i.e., a process), claims 9-14 are directed to a computer program product (i.e., a product/article of manufacture), and claims 15-20 are directed to a system (i.e., a machine/apparatus); therefore, all pending claims are directed to one of the four categories of invention.
Step 2A, Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 recites limitations of:
selecting between the first output and the second output as a final output, wherein the selecting is based at least in part on a confidence level on the second output and a predetermined confidence threshold – mental process (observation, evaluation, judgement, opinion) as a human mind can select from outputs based on a confidence level and a threshold.
which is an abstract idea, something that can be accomplished by the human mind, or with the aid of pen and paper
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements of:
A computer-implemented method – computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
receiving input feature data from a plurality of entities – inputting data is insignificant, extra-solution activity. See MPEP 2106.05(g).
the plurality of entities comprises deterministic entities and uncertain entities – description of the types of sources of input merely identifies a technology or field of use. See MPEP 2106.05(h).
the uncertain entities are subject to an indeterministic state – description of the types of sources of data merely identifies a technology or field of use. See MPEP 2016.05(h).
processing the input feature data derived from the deterministic entities using a first model to generate a first output – processing data with a machine learning model without a description of the model is mere instructions to apply. See MPEP 2106.05(f)(3). Machine learning models recited at a high level are construed as generic models used to implement the abstract idea. See MPEP 2106.05(f)(1).
processing the input feature data derived from the deterministic entities and the uncertain entities using a second model to generate a second output - processing data with a machine learning model without a description of the model is mere instructions to apply. See MPEP 2106.05(f)(3). Machine learning models recited at a high level are construed as generic models used to implement the abstract idea. See MPEP 2106.05(f)(1).
The additional elements do not integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The additional elements of:
A computer-implemented method - computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
receiving input feature data from a plurality of entities - inputting data is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well -understood, routine and conventional. See MPEP 2106.05(d)(II)(i).
the plurality of entities comprises deterministic entities and uncertain entities - description of the types of sources of input merely identifies a technology or field of use. See MPEP 2106.05(h).
the uncertain entities are subject to an indeterministic state - description of the types of sources of data merely identifies a technology or field of use. See MPEP 2016.05(h).
processing the input feature data derived from the deterministic entities using a first model to generate a first output - processing data with a machine learning model without a description of the model is mere instructions to apply. See MPEP 2106.05(f)(3). Machine learning models recited at a high level are construed as generic. See MPEP 2106.05(f)(1).
processing the input feature data derived from the deterministic entities and the uncertain entities using a second model to generate a second output - processing data with a machine learning model without a description of the model is mere instructions to apply. See MPEP 2106.05(f)(3). Machine learning models recited at a high level are construed as generic. See MPEP 2106.05(f)(1).
The limitations do not amount to significantly more than the abstract idea. Therefore, claim 1 is not patent eligible.
Independent claims 9 and 15 recite similar limitations and a similar analysis applies. Claim 9 recites the additional elements of “A computer program product comprising a non-transient machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations” – computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2). Claim 15 recites the additional elements of “A system comprising: at least one programmable processor; and a non-transient machine-readable medium storing instructions that, when executed by the processor, cause the at least one programmable processor to perform operations” - computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
The additional elements do not integrate the abstract idea into a practical application. Nor do they amount to significantly more. Therefore, the independent claims are not patent eligible.
The above analysis similarly applies to the dependent claims.
Claims 2, 10, and 16 recite the additional elements of “the input feature data is derived from the plurality of entities in a distributed and cloud data streaming platform” - description of the source of the data merely identifies a technology or field of use. See MPEP 2106.05(h), and “the indeterministic state of the uncertain entities is caused by out-of-order or out-of-sync processing of data transmissions in the platform” - description of the data merely identifies a technology or field of use. See MPEP 2106.05(h).
Claims 3, 11, and 17 recite the additional elements of “constructing a joint Probability Distribution Function (PDF) by running the first model and the second model on a same dataset derived from the deterministic entities and the uncertain entities” – mathematical concepts (relationships, formulas or equations, calculations), and “the joint PDF provides intrinsic dependency between joint scoring behavior of the first model and the second model when the uncertain entities are in the indeterministic state”- description of the result of a mental process. See MPEP 2106.05(f)(3), and “the confidence level on the second output is based at least in part on the intrinsic dependency” - description of the result of a mental process. See MPEP 2106.05(f)(3).
Claims 4, 12, and 18 “selecting the second output if the first output and the second output lies on a same side of a decision boundary” – mental process (observation, evaluation, judgement, opinion) as a human mind can make a selection based on an evaluation of outputs.
Claims 5, 13, and 19 recite the additional elements of “selecting the second output if the first output and the second output lies on a same side of a decision boundary and the second output is within an uncertainty threshold” – mental process (observation, evaluation, judgement, opinion) as a human mind can make a selection based on an evaluation of outputs compared to a threshold.
Claim 6 recites the additional elements of “calculating a quantitative uncertainty measure of the second output and determine whether first output and the second output lies on contrary sides of the decision boundary” – mathematical concepts (relationships, formulas or equations, calculations) , and “selecting the second output if the quantitative uncertainty measure is within the predetermined confidence threshold” – mental process (observation, evaluation, judgement, opinion), and “selecting the first output if the quantitative uncertainty measure is outside of the predetermined confidence threshold” – mental process (observation, evaluation, judgement, opinion), and “the quantitative uncertainty measure is a numerical estimation of an impact of indeterministic states of uncertain input entities on the confidence level of the second output” – details of the mathematical concept (relationships, formulas or equations, calculations).
Claim 7 recites the additional elements of “generating three random variables representing a score of the first model, a score of the second model when the uncertain entities are in a deterministic state, and a score of the second model when the uncertain entities are in the indeterministic state” – mental process (observation, evaluation, judgement, opinion) as a human mind can generate three random variables based on scores, and “forming a joint Probability Distribution Function (PDF) of the three random variables to capture a joint scoring behavior of the models under a possibility of the uncertain entities being in an indeterministic state” – mathematical concepts (relationships, formula or equations, calculations), and “(AUC) of the PDF on both sides of the decision boundary in an output score space” Examiner notes, there is no mention of generating an uncertainty model in the specification of the claimed invention. Instead, the specification recites “In this case, the probability that true Y lies on either side of the decision boundary may be measured, for example, by calculating Area Under the Curve (AUC) of this PDF on both sides of the decision boundary.” (Specification, paragraph [0037], line 7.) The “generating” could mean training the model, or simply could mean calculating the AUC. Based on this, examiner is interpreting the limitation to mean “calculating an Area Under the Curve (AUC) of the PDF on both sides of the decision boundary in an output score space” – mathematical concepts (relationships, formulas or equations, calculations) , and “the uncertainty model captures uncertainty of the second model's score when the uncertain entities are in the indeterministic state” - description of the result of the abstract idea as performed by a generic machine learning model. See MPEP 2106.05(f)(3), and “introducing an additional measure representing a probability that the uncertain entities are in fact in the indeterministic state” – mathematical concepts (relationships, formulas or equations, calculations) , and “the probability is estimated by observing an occurrence rate of the indeterministic state based on prior knowledge” – mental process (observation, evaluation, judgement, opinion) as a human mind can estimate a probability based on an observation, and “calculating the quantitative uncertainty measure based on the probability that the uncertain entity is in the indeterministic state and the AUC of the PDF on the side of the decision boundary that contradicts an observed score of the second model” – mathematical concepts (relationships, formulas or equations, calculations).
Claims 8, 14, and 20 recite the additional elements of “the predetermined confidence threshold is a specific value representing a tolerance for uncertainty in the second output due to the indeterministic state of the uncertain entities” – mathematical concepts (relationships, formulas or equations, calculations) as a predetermined value used for evaluation is a calculation or relationship.
The additional elements do not integrate the abstract idea into a practical application. Nor do they amount to significantly more. Therefore, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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 1, 3-5, 8-9, 11-15, and 17-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhang, et al (Few-shot activity learning by dual Markov logic networks, herein Zhang), and Maupin, et al (Validation Metrics for Deterministic and Probabilistic Data, herein Maupin).
Regarding claim 1,
Zhang teaches A computer-implemented method ( Zhang, abstract, line 6 “Specifically, we pre-train two MLN models using two unmatched training datasets. Then, these models are used to infer the possible labels of unlabeled data simultaneously.” In other words, pre-train two MLN models and then use these models to infer is a computer implemented method.) , comprising:
receiving input feature data from a plurality of entities (Zhang, Table 1, and page 6, column 2, paragraph 6, line 1 “The experiment was carried out on the dataset collected under the WSU CASAS smart home project [19], widely used in HAR. In this dataset, 24 common household activities are collected, each of which consists of several actions [41]. Activities are continuous, and actions are instantaneous. To ensure consistency and meet the requirements of using data in FSL, the activities and their contained actions were simplified so that the model’s judgment was not based on the number of actions. The activities and component actions used in the experiment are shown in Table 1.” And, page 5, column 2, paragraph 3, line 1 “When the two models continue to maintain the same inference results, and the most likely activity label is clearly separable from other activities, this label is deemed the most likely calibration result of the data. Suppose there is no way to meet the convergence conditions.
In that case, it is considered that the data features do not have good distinguishability, and the calibration of the data cannot be achieved. Algorithm 2 is the pseudo-code of this part.
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In other words, household activities are collected is receiving input, data features is feature data, and smart home project is a plurality of entities.) , wherein
[the plurality of entities comprises deterministic entities and uncertain entities, wherein the uncertain entities are subject to an indeterministic state];
processing the input feature data derived from the deterministic entities using a first model to generate a first output (Zhang, Algorithm 1,
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In other words, Algorithm 1 shows processing of input data, M1 is using first model, and from Algorithm 3, y1 is first output. );
processing the input feature data derived from the deterministic entities and the uncertain entities using a second model to generate a second output (Zhang, Algorithm 1.
In other words, Algorithm 1 shows processing of input data, M2 is using second model, and from Algorithm 3, y2 is second output.) ; and
selecting between the first output and the second output as a final output, wherein the selecting is based at least in part on a confidence level on the second output and a predetermined confidence threshold (Zhang, Algorithm 3, In other words, steps 26-29 of algorithm 3 is selecting between the first output and the second output as the final output, ώ2 is confidence level on the second output, and ώ1 is predetermined confidence level.) .
Thus far Zhang does not explicitly teach the plurality of entities comprises deterministic entities and uncertain entities, wherein the uncertain entities are subject to an indeterministic state.
Maupin teaches the plurality of entities comprises deterministic entities and uncertain entities, wherein the uncertain entities are subject to an indeterministic state (Maupin, page 1, column 2, paragraph 3, line 6 “The validation metrics we describe have been chosen to cover situations with purely deterministic data, with uncertainty in one source of data (model results or experimental observations), or uncertainty in both. We demonstrate the importance of using available uncertainty estimates.” And, page 2, column 1, paragraph 3, line 1 “With these conditions in mind, we can categorize available data into one of the following classes:
Type 1: Experimental and predicted (model) data are treated
as point values without uncertainties.
Type 2: Uncertainty estimates for either experimental or
model data are available, while the other set of data is treated
as point values.
Type 3: Uncertainties in both the experimental and predicted
data are acknowledged, and estimates thereof exist.”
Examiner notes that “sources” of the data, i.e., the entities, are not relevant as they are not part of the claimed invention but are instead merely sources of input data. Instead, it is the deterministic data and uncertain data that the entities produce that is relevant. In other words, models is a plurality of entities, purely deterministic data is deterministic data, and uncertainties in the data is uncertain data.)
Both Zhang and Maupin are directed to machine learning models and processing data, among other things. Zhang teaches a computer-implemented method, comprising:
receiving input feature data from a plurality of entities, processing the input feature data derived from the
processing the input feature data derived from the
In view of the teaching of Zhang, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Maupin into Zhang. This would result in a computer-implemented method, comprising: receiving input feature data from a plurality of entities, processing the input feature data derived from the deterministic entities using a first model to generate a first output; processing the input feature data derived from the deterministic entities and the uncertain entities using a second model to generate a second output; and selecting between the first output and the second output as a final output, wherein the selecting is based at least in part on a confidence level on the second output and a predetermined confidence threshold, wherein the plurality of entities comprises deterministic entities and uncertain entities, and wherein the uncertain entities are subject to an indeterministic state.
One of ordinary skill in the art would be motivated to do this because computational modeling and simulation are important to the applicability of computers to modern science and representing data from deterministic and uncertain data sources are essential for representing data from the physical world. (Maupin, Abstract, line 1 “Computational modeling and simulation are paramount to modern science. Computational models often replace physical experiments that are prohibitively expensive, dangerous, or occur at extreme scales. Thus, it is critical that these models accurately represent and can be used as replacements for reality.”)
Regarding claim 3,
The combination of Zhang and Maupin teaches the method of claim 1, further comprising
constructing a joint Probability Distribution Function (PDF) by running the first model and the second model on a same dataset derived from the deterministic entities and the uncertain entities (Zhang, page 5, column 1, paragraph 1, line 1 “When the two models have completed the pre-calibration of the unlabeled data, the two models may predict the same label. Although the model structure is the same, since the training data of the two models are not exactly the same, the weights cannot be exactly the same after the same iteration times. When the two models predict the same, it means that they are consistent with the characteristics of the data.” And, page 5, column 1, paragraph 2, line 1 “Combining the predicted label with the unlabeled data can get possible complete data, and the newly generated data can be used to expand Cdata to get C’data.” And, page 5, column 2, paragraph 2, line 1 “The MN in the MLN is a joint distribution model composed of a set of variables.” In other words, two models is a first model and a second model, C’data is the same data set, and joint distribution model is joint probability distribution.) , wherein
the joint PDF provides intrinsic dependency between joint scoring behavior of the first model and the second model when the uncertain entities are in the indeterministic state (Zhang, See above mapping. In other words, the joint distribution provides intrinsic dependency between the classification of the first and second models.) , and wherein
the confidence level on the second output is based at least in part on the intrinsic dependency (Zhang, See above mapping, and, page 6, column 2, paragraph 1, line 2 “The performance of the two models in the test dataset cannot be exactly the same, so it is important to adjust their confidence dynamically.” In other words, joint distribution is the output is subject to a dependency of the two models, and adjust the confidence dynamically is the confidence level on the second output is based in part on the dependency.) .
Regarding claim 4,
The combination of Zhang and Maupin teaches the method of claim 1, further comprising
selecting the second output if the first output and the second output lies on a same side of a decision boundary (Zhang, Algorithms 1, 2, and 3. In other words, from algorithm 1, step 9, if L1 equal L2 is the second output lies on the same side of the decision boundary, and since they are equal, the second one is selected since it is the same as the first.).
Regarding claim 5,
The combination of Zhang and Maupin teaches the method of claim 4, further comprising
selecting the second output if the first output and the second output lies on a same side of a decision boundary and the second output is within an uncertainty threshold (Zhang, Algorithm 3. In other words, if y1_p < y2_p then return y2 is if the second output is within a threshold, return y2.) .
Regarding claim 8,
The combination of Zhang and Maupin teaches the method of claim 1, wherein
the predetermined confidence threshold is a specific value representing a tolerance for uncertainty in the second output due to the indeterministic state of the uncertain entities (Zhang, page 6, column 2, paragraph 2,line 1 “Combined with the relevant knowledge of gray models [39, 40], this experiment finally determines the model confidence based on the model’s performance on the test dataset and combined with the least square method. The least square method needs to construct the generalized Lagrangian function and get the best weight value through the extreme point. In addition, the use of a matrix can also simplify the calculation, so the confidence can be determined within the effective time without causing a time burden to the overall processing process. When the confidence ω1 and ω2 representing model 1 and model 2 are determined, the final probability of unlabeled data in L1 can be obtained according to the possible probability of L1 in model 1 and model 2, respectively. L2 predicted by model 2 will be treated in the same way.” Examiner notes the specification of the instant application recites “In some embodiments, the predetermined confidence threshold is a specific value representing a tolerance for uncertainty in the second output due to the indeterministic state of the uncertain entities.” (Specification, paragraph [0047], line 17.) Therefore, examiner is interpreting that tolerance is simply the confidence threshold. In other words,ω2 is a predetermined confidence threshold representing a tolerance for uncertainty in the second output.)
Claims 9, and 11-14 are computer program product comprising a non-transient machine-readable medium claims corresponding to method claims 1, 3-5, and 8, respectively. Otherwise, they are not patentably distinct. The combination of Zhang and Maupin teaches a non-transient machine-readable medium (Zhang, page 11, column 1, paragraph 1, line 6 “…completed in the Ubuntu 16.04 virtual machine with 2G memory, two processors, and a 100G hard disk.” In other words, 2G of memory and 100G hard disk is non-transient machine-readable medium.) Therefore, claims 9 and 11-14 are rejected for the same reasons as claims 1, 3-5, and 8, respectively.
Claims 15, and 17-20 are system claims comprising at least one programmable processor; and a non-transient machine-readable medium claims corresponding to method claims 1, 3-5, and 8, respectively. Otherwise, they are not patentably distinct. The combination of Zhang and Maupin teaches a system comprising at least one programmable processor and a non-transient machine-readable medium (Zhang, page 11, column 1, paragraph 1, line 6 “…completed in the Ubuntu 16.04 virtual machine with 2G memory, two processors, and a 100G hard disk.” In other words, 2G of memory, two processors and 100G hard disk is at least one programmable processor and a non-transient machine-readable medium.) Therefore, claims 15 and 17-20 are rejected for the same reasons as claims 1, 3-5, and 8, respectively.
Claims 2, 10, and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over Zhang, Maupin, and Jerad, et al (Deterministic Timing for the Industrial Internet of Things, herein Jerad).
Regarding claim 2,
The combination of Zhang and Maupin teaches the method of claim 1, wherein
Thus far, the combination of Zhang and Maupin does not explicitly teach the input feature data is derived from the plurality of entities in a distributed and cloud data streaming platform, and wherein the indeterministic state of the uncertain entities is caused by out-of-order or out-of-sync processing of data transmissions in the platform.
Jerad teaches the input (Jerad, abstract, line 1 “This paper is about reconciling the highly asynchronous untimed interactions that prevail on the Internet with time-sensitive operations of Things in the Internet of Things (IoT). Specifically, this paper addresses a design pattern that is widely used on the Internet called asynchronous atomic callbacks (AAC).” In other words, highly asynchronous untimed interactions are uncertain data, asynchronous is out-of-sync, and operations on the Internet of Things is distributed and cloud data. Examiner notes that input feature data is previously mapped in claim 1.)
Both Jerad and the combination of Zhang and Maupin are directed to deterministic and uncertain data processing, among other things. The combination of Zhang and Maupin teaches the method of claim 1, but does not explicitly teach the uncertain data is caused by out-of-sync transmissions of data from a distributed and cloud data streaming platform. Jerad teaches the uncertain data is caused by out-of-sync transmissions of data from a distributed and cloud data streaming platform.
In view of the teaching of the combination of Zhang and Maupin, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Jerad into the combination of Zhang and Maupin. This would result in the method of claim 1, where the uncertain data is caused by out-of-sync transmissions of data from distributed and cloud data streaming platform.
One of ordinary skill in the art would be motivated to do this because the Internet of Things is highly asynchronous and indeterminant and it would be advantageous for machine learning models to be able to successfully process this data more safely. (Jerad, abstract, line 1 “This paper is about reconciling the highly asynchronous untimed interactions that prevail on the Internet with time-sensitive operations of Things in the Internet of Things (IoT). Specifically, this paper addresses a design pattern that is widely used on the Internet called asynchronous atomic callbacks (AAC). We show that it is possible and practical to endow AACs with temporal semantics that can make system behaviors more repeatable and testable and can make the interactions between cyber services and physical Things safer.”)
Claim 10 is a computer program product claim corresponding to method claim 2. Otherwise, they are not patentably distinct. Therefore, claim 10 is rejected for the same reasons as claim 2.
Claim 16 is a system claim corresponding to method claim 2. Otherwise, they are not patentably distinct. Therefore, claim 16 is rejected for the same reasons as claim 2.
The prior art made of record and not used is considered pertinent to applicant’s disclosure.
Chang, et al, “Data Uncertainty Learning in Face Recognition” discloses a method that applies data uncertainty learning to face recognition, such that the feature (mean) and uncertainty (variance) are learnt simultaneously where two separate learning methods are used.
Cheng, et al “Dual-model hybrid pattern recognition method based on a fiber optic line-based sensor with a large amount of data” discloses a dual-model hybrid pattern recognition method based on a fiber optic line-based sensor with a large amount of data is proposed. The ResNet18 model for classification is used, and to reduce the false positive rate, the over-zero rate and short-time energy are extracted from the intrusion signal, and a support vector machine (SVM) is used.
Krzysztofowicz, R. “Bayesian theory of probabilistic forecasting via deterministic hydrologic model” discloses a Bayesian forecasting system( BFS) for producing a probabilistic forecast of a hydrologic prediction via any deterministic catchment model.
Zhang, et al “Hybrid Algorithm Based on MDF-CKF and RF for GPS/INS System During GPS Outages” discloses a dual-model solution for global positioning system (GPS)/inertial navigation system (INS) during GPS outages, which integrates with multiple-decrease factor cubature Kalman Filter (MDF-CKF) and random forest (RF) that can be used for modeling and compensating the velocity and positioning errors/
Allowable Subject Matter
The claims have been searched but no prior art which anticipates or renders the following claims obvious have been found.
Claim 6 would be allowable if found eligible and rewritten in independent form including all of the limitations of the base claim and any intervening claims. Dependent claim 7 inherits the particular limitations from claim 6 and similarly would be allowable.
Conclusion
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/Bart I Rylander/Examiner, Art Unit 2124
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