DETAILED ACTION
This is the first action is regarding application number 17/967,484 filed 10/17/2022. Claims 1-20 and have been canceled and claims 21-40 have been added. Claims 21-40 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 21-40 are rejected under 35 U.S.C. 101 because they claimed invention is directed to an abstract idea without significantly more.
Regarding claim 21:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining which cases have fields to impute in the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing fields to impute. See 2106.04.(a)(2).III.C.
The claim recites determining conviction scores for each particular case… based on a certainty function which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites removing the particular case from the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user removing a case and performing an interpretation. See 2106.04.(a)(2).III.C.
The claim recites adding the particular case back into the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user adding a case that was removed and performing an interpretation. See 2106.04.(a)(2).III.C.
The claim recites wherein the certainty function is associated with a certainty that a particular set of data fits a model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user making a judgement on whether data is correct. See 2106.04.(a)(2).III.C.
The claim recites determining an order in which to impute missing data for one or more cases with missing fields based on the conviction scores which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order. See 2106.04.(a)(2).III.C.
The claim recites determining an order of the conviction scores for the cases that have features to impute which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order. See 2106.04.(a)(2).III.C.
The claim recites determining the order … based on the order of the conviction scores for the cases that have features to impute which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user selecting/choosing data. See 2106.04.(a)(2).III.C.
The claim recites modifying the case with the imputed data, wherein the modified case becomes part of the … reasoning model in place of the original case to create an updated … reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user changing data of a set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
…that has fields to impute in the computer-based reasoning model …(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
wherein the method is performed by one or more computing devices(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
with a control system(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
causing…control of a system with the updated computer-based reasoning model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
computer-based(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Additional elements (b) (c) (d) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) (b) (c) (d) and (e) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 22:
The rejection of claim 21 is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining…the order of the conviction scores for the cases that have features to impute from highest to lowest conviction score, with the case with a highest conviction score being first in the order which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order from high to low and choosing data at the highest first. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 23:
The rejection of claim 21 is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining the action to take which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user making a choice. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
receiving a request for an action to take in the system, including a context for the system(recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g))
based at least in part on the context for the system and the updated computer-based reasoning model(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
causing the control system to perform the determined action in the system(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Further, additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ).
Additional elements (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Additional elements (c) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 24:
The rejection of claim 21 is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining the order in which to impute data … from lowest to highest conviction score, with the case with a lowest conviction score being first in the order which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order from low to high and choosing data at the lowest first. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
for the missing fields for the features in the cases that have features to impute based on the order of conviction scores for the cases that have features to impute(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 25:
The rejection of claim 21 is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining two or more cases with the highest conviction score which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user selecting/choosing two cases. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 26:
The rejection of claim 21 is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim does not contain elements that would warrant a Step 2A Prong 1 analysis.
Subject Matter Eligibility Analysis Step 2A Prong 2:
wherein the machine learning model for the computer-based reasoning model's data has been trained using the data in the computer-based reasoning model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding claim 27:
The rejection of claim 26 is incorporated and further:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining an update to the machine learning model based on the updated computer- based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user selecting a change to make in a model. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
The claim does not contain elements that would warrant a Step 2A Prong 2 analysis.
Subject Matter Eligibility Analysis Step 2B:
The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 28:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining which cases have fields to impute in the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing fields to impute. See 2106.04.(a)(2).III.C.
The claim recites determining conviction scores for each particular case… based on a certainty function which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites removing the particular case from the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user removing a case and performing an interpretation. See 2106.04.(a)(2).III.C.
The claim recites adding the particular case back into the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user adding a case that was removed and performing an interpretation. See 2106.04.(a)(2).III.C.
The claim recites wherein the certainty function is associated with a certainty that a particular set of data fits a model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user making a judgement on whether data is correct. See 2106.04.(a)(2).III.C.
The claim recites determining an order in which to impute missing data for one or more cases with missing fields based on the conviction scores which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order. See 2106.04.(a)(2).III.C.
The claim recites determining an order of the conviction scores for the cases that have features to impute which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order. See 2106.04.(a)(2).III.C.
The claim recites determining the order … based on the order of the conviction scores for the cases that have features to impute which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user selecting/choosing data. See 2106.04.(a)(2).III.C.
The claim recites modifying the case with the imputed data, wherein the modified case becomes part of the…reasoning model in place of the original case to create an updated…reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user changing data of a set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
…that has fields to impute in the computer-based reasoning model …(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
One or more non-transitory storage media storing instructions which, when executed by one or more computing devices(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
with a control system(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
causing…control of a system with the updated computer-based reasoning model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
computer-based(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Additional elements (b) (c) (d) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) (b) (c) (d) and (e) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 29:
The rejection of claim 28 is incorporated and further:
Claim 29 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 22 found in claim 29.
Regarding Claim 30:
The rejection of claim 28 is incorporated and further:
Claim 30 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 23 found in claim 30.
Regarding Claim 31:
The rejection of claim 28 is incorporated and further:
Claim 31 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 24 found in claim 31.
Regarding claim 32:
The rejection of claim 29 is incorporated and further
Claim 32 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 25 found in claim 32.
Regarding Claim 33:
The rejection of claim 28 is incorporated and further:
Claim 33 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 26 found in claim 33.
Regarding Claim 34:
The rejection of claim 33 is incorporated and further:
Claim 34 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 27 found in claim 34.
Regarding claim 35:
Subject Matter Eligibility Analysis Step 2A Prong 1:
The claim recites determining which cases have fields to impute in the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing fields to impute. See 2106.04.(a)(2).III.C.
The claim recites determining conviction scores for each particular case… based on a certainty function which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites removing the particular case from the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user removing a case and performing an interpretation. See 2106.04.(a)(2).III.C.
The claim recites adding the particular case back into the computer-based reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user adding a case that was removed and performing an interpretation. See 2106.04.(a)(2).III.C.
The claim recites wherein the certainty function is associated with a certainty that a particular set of data fits a model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user making a judgement on whether data is correct. See 2106.04.(a)(2).III.C.
The claim recites determining an order in which to impute missing data for one or more cases with missing fields based on the conviction scores which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order. See 2106.04.(a)(2).III.C.
The claim recites determining an order of the conviction scores for the cases that have features to impute which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user choosing an order. See 2106.04.(a)(2).III.C.
The claim recites determining the order … based on the order of the conviction scores for the cases that have features to impute which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))).
The claim recites determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user selecting/choosing data. See 2106.04.(a)(2).III.C.
The claim recites modifying the case with the imputed data, wherein the modified case becomes part of the…reasoning model in place of the original case to create an updated…reasoning model which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass a user changing data of a set. See 2106.04.(a)(2).III.C.
Subject Matter Eligibility Analysis Step 2A Prong 2:
…that has fields to impute in the computer-based reasoning model …(merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)))
A system comprising one or more computing devices, which one or more computing devices are configured to perform(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
with a control system(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
causing…control of a controllable system with the updated computer-based reasoning model(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
computer-based(merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)))
Subject Matter Eligibility Analysis Step 2B:
Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)).
Additional elements (b) (c) (d) and (e) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f).
The additional element(s) (a) (b) (c) (d) and (e) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible.
Regarding Claim 36:
The rejection of claim 35 is incorporated and further:
Claim 36 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 22 found in claim 36.
Regarding Claim 37:
The rejection of claim 35 is incorporated and further:
Claim 37 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 23 found in claim 37.
Regarding Claim 38:
The rejection of claim 35 is incorporated and further:
Claim 37 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 24 found in claim 37.
Regarding Claim 39:
The rejection of claim 35 is incorporated and further:
Claim 39 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 26 found in claim 39.
Regarding Claim 40:
The rejection of claim 39 is incorporated and further:
Claim 40 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 27 found in claim 40.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 21, 23-28, 30-35 and 37-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al.(“Efficient Missing Data Imputation for Supervised Learning”, henceforth known as Zhang) and further in view of Patino et al.(“Neural Networks for Advanced Control of Robot Manipulators” henceforth known as Patino)
Regarding claim 21:
Zhang discloses performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired(Zhang, Page 5, Col. 1, Figure 1, “Repeat the following two steps until convergence (k iterations). For i=1 to (Number of missing values) Impute CIMV(i) utilizing all the dataset based on the kNN algorithm” where iterating until filled-in values are stabilized (converged) is considered continuing until imputation is no longer needed/no cases remain with missing fields)
Zhang discloses determining which cases have fields to impute in the computer-based reasoning model(Zhang, Page 3, Col. 2, Paragraph 2, “So it is reasonable for us to impute missing values with instances that have observed values including those instances which contain some missing values based on the above analysis” where the analysis is considered determining what cases have fields to impute.)
Zhang discloses determining conviction scores for each particular case that has fields to impute in the computer-based reasoning model based on a certainty function associated with removing the particular case from the computer-based reasoning model and adding the particular case back into the computer-based reasoning mode and wherein the certainty function is associated with a certainty that a particular set of data fits a model (Zhang, Page 4, Col. 1, Equations 1, Equation 2 and Paragraph 2, “We use the F-measure which is commonly used and was firstly introduced in information retrieval and natural language processing communities to express Sign(i,j). The F-measure requires us to specify a desired trade-off between Ri and Wi through a variable Sign(i,j). That is to say, using the F-measure allows users to specify their own desired tradeoff in terms of Ri and Wi. In fact, the F-measure is a harmonic mean of Ri and Wi.” where Sign(i,j) is the harmonic mean of Ri(inverse missing-rate certainty(how complete the case is)) and Wi(mutual information certainty(how predictive the attribute is)) is considered a conviction score as the joint conviction represents how confident the imputation of the specific missing value will be accurate and fit and, for each missing field, a computed imputed value(using KNN) is used to remove the previous imputed/missing value and a new imputed value is added)
Zhang discloses determining an order in which to impute missing data for one or more cases with missing fields based on the conviction scores, wherein the order in which to impute missing data for the one or more cases with missing fields based on the conviction scores comprises determining an order of the conviction scores for the cases that have features to impute, and determining the order in which to impute data for the missing fields for the features in the cases that have features to impute based on the order of the conviction scores for the cases that have features to impute(Zhang, Page 4, Col 2., Paragraph 1, “With Eq. 2, we can rank all missing values by the Sign(i,j) values (in ascending order), and select the missing value with the least Sign(i,j) values to impute. I” where ranking and selecting based off conviction score is considered determining an order…to impute missing data for…cases with missing fiends based on conviction scores wherein the order to impute...comprises determining an order of the conviction scores … and determining the order... based on the order of the conviction scores for the cases that have features to impute)
Zhang discloses for each of the one or more cases with missing fields to impute, and based on the order in which to impute data for the missing fields for the features in the cases that have features to impute determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case and modifying the case with the imputed data, wherein the modified case becomes part of the computer-based reasoning model in place of the original case to create an updated computer-based reasoning model;(Zhang, Page 5, Col. 1, Figure 1, “Repeat the following two steps until convergence (k iterations). For i=1 to (Number of missing values) Impute CIMV(i) utilizing all the dataset based on the kNN algorithm” where the use of k-NN predictor per case to impute based on model information and filled-in instance replacing case information is considered for each…case with missing fields…determine imputed data for a missing field based on the case, an imputation model and missing fields in the case and modifying the case with imputed data that becomes part of the computer-based reasoning model where k-NN is the imputation model)
Zhang discloses wherein the method is performed by one or more computing devices(Zhang, Figure 1, where the pseudocode and proposed algorithm using kNN is executed on a computer)
Zhang does not disclose, however Patino discloses causing, with a control system, control of a system with the updated computer-based reasoning model(Patino, Page 343, Col. 2, Paragraph 3, “This paper presents a feedback adaptive neurocontroller for robots which combines feedforward neural networks with adaptive and robust control techniques… The neurocontroller is based on a set of fixed NNs…with each NN representing the robot inverse model for a specified and adequate payload condition.” where the neurocontroller(control system) uses trained neural networks(computer-based reasoning model) with adaptive controls to apply control based on payload conditions(control a system))
References Zhang and Patino are analogous art because they are from the same field of endeavor of using computer-based reasoning models to solve problems.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Zhang and Patino before him or her, to modify the model of Zhang to include the control system using a neural network of Patino as any missing or dropped packets/occluded vision labels or intermittent sensors would allow the system to repair missing data to make more accurate control law calculations/lower the control error. The suggestion for doing this would be “In all design and analysis of NN-based control systems, it is important to take into account the NN learning error and its influence on the control error of the plant. In addition, the neurocontroller is modified to build a robust adaptive controller to NN learning errors, which leads to reach a global asymptotic stability and zero convergence of control errors.”(Patino, Page 344, Col. 1, Paragraph 1)
Regarding claim 23:
The rejection of claim 21 is incorporated and further:
Patino discloses wherein causing control of the system comprises receiving a request for an action to take in the system, including a context for the system(Patino, Page 345, Col. 2, Paragraph 2, “For a desired motion trajectory, specified in joint-space…it should be found: 1) an adaptive control law… and 2) a parameter update” where a desired motion trajectory for the robot is considered a receiving a request for an action to take in the system and specifying in joint-space is considered a context of the system as the joint-space is the joint information for the robot)
Patino discloses determining the action to take based at least in part on the context for the system and the updated computer-based reasoning model(Patino, Page 343, Col. 2, Paragraph 3, “A stable controller-parameter adjustment mechanism…is constructed to adjust the coefficients of a linear combination of NN outputs to uncertain dynamic parameters, such as link inertia or payloads” and “the control law… is a feedback PD control action… the NN-based feedback control law” where the control law being a control action for a robot and the robot’s action being determined by a NN-based feedback control law is considered determining an action to take based on the context of the system(robot information) and a computer-based reasoning model(neural network)) causing the control system to perform the determined action in the system(Patino, Page 344, Col. 1, Paragraph 4, “…the vector of joint displacements in robot coordinates…is the vector of applied joint torques or forces (vector of control inputs in robot coordinates)” where using the joint actuators with the control law computed from the neurocontroller to apply joint torque/forces is considered performing a determined action caused by the control system)
Regarding claim 24:
The rejection of claim 21 is incorporated and further
Zhang discloses wherein determining the order ... to impute comprises determining the order in which to impute data for the missing fields for the features in the cases that have features to impute based on the order of conviction scores for the cases that have features to impute from lowest to highest conviction score, with the case with a lowest conviction score being first in the order. (Zhang, Page 4, Figure 1 pseudo code, where the pseudo code ranks Sign(i,j) values and selects values in ascending or weighted order depending on α parameter is considered ordering with lowest conviction score being first)
Regarding claim 25:
The rejection of claim 21 is incorporated and further
Zhang discloses wherein determining the order … to impute comprises determining two or more cases with the highest conviction score(Zhang, Page 4, Col 2., Paragraph 1, “With Eq. 2, we can rank all missing values by the Sign(i,j) values (in ascending order), and select the missing value with the least Sign(i,j) values to impute” where ranking by Sign is considered determining two or more cases with the highest conviction score)
Regarding claim 26:
The rejection of claim 21is incorporated and further
Zhang discloses wherein determining imputed data for the missing field comprises determining the imputed data based on a machine learning model for the computer-based reasoning model's data, wherein the machine learning model for the computer- based reasoning model's data has been trained using the data in the computer-based reasoning model(Zhang, Page 4, Figure 1, “Impute CIMV(i) utilizing all the dataset based on the kNN algorithm” where determining imputed values using ML predictor build from the dataset itself (instance-based k-NN) is a learning model who behavior is determined by and updated with data)
Regarding claim 27:
The rejection of claim 26 is incorporated and further
Zhang discloses determining an update to the machine learning model based on the updated computer- based reasoning model(Zhang, Page 4, Figure 1, where the pseudo code updating the data for the predictor/model used in the next iteration is considered updating the machine learning model based on updated computer-based reasoning model)
Regarding claim 28:
Zhang discloses one or more non-transitory storage media storing instructions which, when executed by one or more computing devices(Zhang, Figure 1, where the pseudocode and proposed algorithm using kNN is executed on a computer)
Zhang discloses performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired(Zhang, Page 5, Col. 1, Figure 1, “Repeat the following two steps until convergence (k iterations). For i=1 to (Number of missing values) Impute CIMV(i) utilizing all the dataset based on the kNN algorithm” where iterating until filled-in values are stabilized (converged) is considered continuing until imputation is no longer needed/no cases remain with missing fields)
Zhang discloses determining which cases have fields to impute in the computer-based reasoning model(Zhang, Page 3, Col. 2, Paragraph 2, “So it is reasonable for us to impute missing values with instances that have observed values including those instances which contain some missing values based on the above analysis” where the analysis is considered determining what cases have fields to impute.)
Zhang discloses determining conviction scores for each particular case that has fields to impute in the computer-based reasoning model based on a certainty function associated with removing the particular case from the computer-based reasoning model and adding the particular case back into the computer-based reasoning mode and wherein the certainty function is associated with a certainty that a particular set of data fits a model(Zhang, Page 4, Col. 1, Equations 1, Equation 2 and Paragraph 2, “We use the F-measure which is commonly used and was firstly introduced in information retrieval and natural language processing communities to express Sign(i,j). The F-measure requires us to specify a desired trade-off between Ri and Wi through a variable Sign(i,j). That is to say, using the F-measure allows users to specify their own desired tradeoff in terms of Ri and Wi. In fact, the F-measure is a harmonic mean of Ri and Wi.” where Sign(i,j) is the harmonic mean of Ri(inverse missing-rate certainty(how complete the case is)) and Wi(mutual information certainty(how predictive the attribute is)) is considered a conviction score as the joint conviction represents how confident the imputation of the specific missing value will be accurate and fit and, for each missing field, a computed imputed value(using KNN) is used to remove the previous imputed/missing value and a new imputed value is added)
Zhang discloses determining an order in which to impute missing data for one or more cases with missing fields based on the conviction scores, wherein the order in which to impute missing data for the one or more cases with missing fields based on the conviction scores comprises determining an order of the conviction scores for the cases that have features to impute, and determining the order in which to impute data for the missing fields for the features in the cases that have features to impute based on the order of the conviction scores for the cases that have features to impute(Zhang, Page 4, Col 2., Paragraph 1, “With Eq. 2, we can rank all missing values by the Sign(i,j) values (in ascending order), and select the missing value with the least Sign(i,j) values to impute. I” where ranking and selecting based off conviction score is considered determining an order…to impute missing data for…cases with missing fiends based on conviction scores wherein the order to impute...comprises determining an order of the conviction scores … and determining the order... based on the order of the conviction scores for the cases that have features to impute)
Zhang discloses for each of the one or more cases with missing fields to impute, and based on the order in which to impute data for the missing fields for the features in the cases that have features to impute determining imputed data for a missing field of the missing fields based on the case, and an imputation model, and the missing fields in the case and modifying the case with the imputed data, wherein the modified case becomes part of the computer-based reasoning model in place of the original case to create an updated computer-based reasoning model;(Zhang, Page 5, Col. 1, Figure 1, “Repeat the following two steps until convergence (k iterations). For i=1 to (Number of missing values) Impute CIMV(i) utilizing all the dataset based on the kNN algorithm” where the use of k-NN predictor per case to impute based on model information and filled-in instance replacing case information is considered for each…case with missing fields…determine imputed data for a missing field based on the case, an imputation model and missing fields in the case and modifying the case with imputed data that becomes part of the computer-based reasoning model where k-NN is the imputation model)
Zhang discloses causing, with a control system, control of a system with the updated computer-based reasoning model(Zhang, Page 4, Figure 1 and Page 1, Col. 2, Paragraph 4, “We propose an EMstyle iterative imputation method, in which each missing attribute-value is iteratively filled using a predictor constructed from the known values and predicted values of the missing attribute-values from the previous iterations” where figure 1’s EM-style pseudo code controls a system(a computer) that updates a computer-based reasoning mode when imputation(See Also Page 1, Col. 2, Paragraph 1, “…an interesting feature of the algorithm is that the E and M steps collapse into a single step because the data being filled in are the modal-updating and the filled-in values and update the model at the same time” where modal updating is the process of adjusting a computation model))
Regarding Claim 30:
The rejection of claim 28 is incorporated and further:
Claim 30 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 23 found in claim 30.
Regarding Claim 31:
The rejection of claim 28 is incorporated and further:
Claim 31 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 24 found in claim 31.
Regarding Claim 33:
The rejection of claim 28 is incorporated and further:
Claim 33 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 26 found in claim 33.
Regarding Claim 34:
The rejection of claim 33 is incorporated and further:
Claim 34 is rejected under that same claim analysis due to the substantially similarity of the limitations and additional elements of claim 27 found in claim 34.
Regarding claim 35:
Zhang discloses performing the following until there are no more cases in a computer-based reasoning model with missing fields for which imputation is desired(Zhang, Page 5, Col. 1, Figure 1, “Repeat the following two steps until convergence (k iterations). For i=1 to (Number of missing values) Impute CIMV(i) utilizing all the dataset based on the kNN algorithm” where iterating until filled-in values are stabilized (converged) is considered continuing until imputation is no longer needed/no cases remain with missing fields)
Zhang discloses determining which cases have fields to impute in the computer-based reasoning model(Zhang, Page 3, Col. 2, Paragraph 2, “So it is reasonable for us to impute missing values with instances that have observed values including those instances which contain some missing values based on the above analysis” where the analysis is considered determining what cases have fields to impute.)
Zhang discloses determining conviction scores for each particular case that has fields to impute in the computer-based reasoning model based on a certainty function associated with removing the particular case from the computer-based reasoning model and adding the particular case back into the computer-based reasoning mode and wherein the certainty function is associated with a certainty that a particular set of data fits a model(Zhang, Page 4, Col. 1, Equations 1, Equation 2 and Paragraph 2, “We use the F-measure which is commonly used and was firstly introduced in information retrieval and natural language processing communities to express Sign(i,j). The F-measure requires us to specify a desired trade-off between Ri and Wi through a variable Sign(i,j). That is to say, using the F-measure allows users to specify their own desired tradeoff in terms of Ri and Wi. In fact, the F-measure is a harmonic mean of Ri and Wi.” where Sign(i,j) is the harmonic mean of Ri(inverse missing-rate certainty(how complete the case is)) and Wi(mutual information certainty(how predictive the attribute is)) is considered a conviction score as the joint conviction represents how confident the imputation of the specific missing value will be accurate and fit and, for each missing field, a computed imputed value(using KNN) is used to remove the previous imputed/missing value and a new imputed value is added)
Zhang discloses determining an order in which to impute missing data for one or more cases with missing fields based on the conviction scores, wherein the order in which to impute missing data for the one or more cases with missing fields based on the conviction scores comprises determining an order of the conviction scores for the cases that have features to impute, and determining the order in which to impute data for the missing fields for the features in the cases that have features to impute based on the order of the conviction scores for the cases that have features to impute(Zhang, Page 4, Col 2., Paragraph 1, “With Eq. 2, we can rank all missing values by the Sign(i,j) values (in ascending order), and select the missing value with the least Sign(i,j) values to impute. I” where ranking and selecting based off conviction score is considered determining an order…to impute missing data for…cases with missing fiends based on conviction scores wherein the order to impute...comprises determining an order of the conviction scores … and determining the order... based on the order of the conviction scores for the cases that have features to impute)
Zhang discloses for each of the one or more cases with missing fields to impute,