Detailed Action
Claims 1-20 are presently 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 .
Specification
The disclosure is objected to because of the following informalities: All mathematical notations starting from paragraph 31 to paragraph 46 including Charts 1 and 2, and Table 1 are not legible.
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: Claim 2 recites “wherein identifying the CACBO problem includes an agent selecting the CACBO problem and a plurality of features”. Nowhere in the specification provides proper support or antecedent basis for the “identifying … an agent selecting the CACBO problem and a plurality of features.”
Claim 12 is a system claim which recites the same features as claim 2 and is objected to at least the same reasons.
Appropriate correction is required.
Claim Objections
Claims 9 is objected to because of the following informalities: “… using a contextual combinatorial combinatorial bandit approach” should read “… using a contextual combinatorial bandit approach” in line 2.
Claim 18 is objected to because of the following informalities: “… at each iteration step T using a contextual combinatorial combinatorial bandit approach” should read “… using a contextual combinatorial bandit approach” in line 3.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 11-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 11, the claim does not fall within at least one of the four categories of patent eligible subject matter because, under the broadest reasonable interpretation in light of the specification, they are directed to software per se. Specifically, claim 1 recites “A computing system, comprising: a machine learning system for implementing …” However, nowhere in the specification is the “computing system, comprising: a machine learning system” defined as excluding purely software instantiations of the computing system and the machine learning system. As neither claim 12-19 add hardware to the claim, they are also non-statutory. Examiner recommends amending the claims to recite the hardware on which the machine learning system runs explicitly.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Step 1: Claim 1 recites a method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm. Therefore, it is directed to the statutory category of processes.
2A Prong 1: A method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, the method comprising: (directed to mathematical formula – spec [0032]-[0033])
identifying the Context-Attentive Combinatorial Bandit with Observations problem having multiple arms; (mental process of evaluation – selecting problems with multiple arms can be done in one’s mind)
identifying a plurality of parameters including a total number of features N, a number of initially observed features V, an initially observed features set CV, a number of observed additional features U, a distribution parameter, and a function λ(t) which is computed differently for stationary and nonstationary cases; (directed to mathematical calculations - spec [0038])
initializing the initially observed features, the initially observed features set and the observed additional features; (directed to mathematical calculations - spec [0038])
identifying a plurality of subsets CV(t) for each time t from a plurality of predetermined times t; (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
sampling a vector parameter for each context feature for a plurality of context features; (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
identifying a best subset of features; and (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
selecting an arm based on the best subset of features. (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
2A Prong 2: The claim does not include additional elements. This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 2,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein identifying the CACBO problem includes an agent selecting the CACBO problem and a plurality of features, where each of the multiple arms includes an unknown and independent probability-law of reward. (mental process of evaluation – selecting problems with multiple arms can be done in one’s mind)
2A Prong 2: The claim does not include additional elements. This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 3,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein identifying a plurality of parameters includes an agent observing the total number of features N, the number of initially observed features V, and the number of observed additional features U. (mathematical calculations - spec [0038])
2A Prong 2: The claim does not include additional elements. This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 4,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein identifying a plurality of parameters includes determining the function λ(t) for stationary cases differently than determining the function λ(t) for nonstationary cases. (mathematical calculations - spec [0038])
2A Prong 2: The claim does not include additional elements. This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 5,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein initializing includes initializing the initially observed features, the initially observed features set and the observed additional features to a predetermined initial value. (directed to mathematical calculations - spec [0038])
2A Prong 2: The claim does not include additional elements. This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 6,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein identifying a plurality of subsets CV(t) includes performing an iteration of T steps and observing values CV(t) for each iteration step T that are within the initially observed features set CV. (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 7,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein sampling the vector parameter includes, for each iteration step T, obtaining a sample of the vector parameter from a corresponding multivariate Gaussian distribution separately for each feature not yet observed to generate an estimated vector parameter. (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 8,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein identifying the best subset of features includes selecting the best subset of features at each iteration step T. (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 9,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein identifying the best subset of features includes using a contextual combinatorial combinatorial bandit approach. (mathematical concept of math calculation – spec [0038], Fig. 2, Algorithm 2)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 10,
Step 1: Processes, as above.
2A Prong 1: The method of Claim 1, wherein selecting the arm includes using a contextual combinatorial bandit approach based on a context that the best subset of features. (mental process of evaluation – selecting the best arm can be done in one’s mind without a computer component)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 11,
Claim 11 is a system claim which recites the same features as the claim 1, and is rejected for at least the same reasons. Additional limitations of claim 11 not addressed in claim 1 are addressed below.
Step 1: Non statutory, as above. For purpose of the examination, the examiner treats the claim as statutory category of a machine.
2A Prong 1: Rejected for at least the same reasons as claim 1.
2A Prong 2: A computing system, comprising: a machine learning system for implementing a method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, wherein the method includes: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: A computing system, comprising: a machine learning system for implementing a method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, wherein the method includes: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Claim 12 is a system claim which recites the same features as claim 2 and is rejected for at least the same reasons.
Claim 13 is a system claim which recites the same features as claim 3 and is rejected for at least the same reasons.
Claim 14 is a system claim which recites the same features as claim 4 and is rejected for at least the same reasons.
Claim 15 is a system claim which recites the same features as claim 5 and is rejected for at least the same reasons.
Claim 16 is a system claim which recites the same features as claim 6 and is rejected for at least the same reasons.
Claim 17 is a system claim which recites the same features as claim 7 and is rejected for at least the same reasons.
Regarding claim 18,
Step 1: Non statutory, as above. For purpose of the examination, the examiner treats the claim as the statutory category of an apparatus.
2A Prong 1: The computing system of Claim 11, wherein identifying the best subset of features includes selecting the best subset of features at each iteration step T using a contextual combinatorial combinatorial bandit approach. (mental process of evaluation – selecting the best features and arms can be done in one’s mind without a computer component)
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 19 is a system claim which recites the same features as claim 9 and is rejected for at least the same reasons.
Regarding claim 20,
Claim 20 recites the same features as claim 1 and is rejected for at least the same reasons. Additional limitations of claim 20 not addressed in claim 1 are addressed below.
Step 1: Claim 20 recites a computer program product comprising a computer readable storage medium having program instructions. Therefore, it is directed to the statutory category of a product.
2A Prong 1: Rejected as claim 1.
2A Prong 2: A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations for implementing a method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, the method comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
2B: A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations for implementing a method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, the method comprising: (mere instructions to apply an exception using a generic computer component MPEP 2106.05(f))
Claim Rejections - 35 USC § 102
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 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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bouneffouf et al. (“Double-Linear Thompson Sampling for Context-Attentive Bandits”, 2020, hereinafter ‘Bouneffouf’).
Regarding claim 1, Bouneffouf teaches:
A method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, the method comprising: ([page 4, 4 Context Attentive Thompson Sampling (CATS), lines 1-8] and [page 5, Algorithm 2] discloses Context Attentive Thompson Sampling algorithm)
identifying the Context-Attentive Combinatorial Bandit with Observations problem having multiple arms; ([page 4, 4 Context Attentive Thompson Sampling (CATS), lines 1-8] discloses that the algorithm receives Context-Attentive Thompson Sampling CATS problem with k number of arms and features i)
identifying a plurality of parameters including a total number of features N, a number of initially observed features V, an initially observed features set CV, a number of observed additional features U, a distribution parameter, and a function λ(t) which is computed differently for stationary and nonstationary cases; ([page 4, 4 Context Attentive Thompson Sampling (CATS), lines 9-12] The algorithm takes the total number of features N, the number of features initially observed V, the set of observed features CV (i.e., the initially observed features set), the number of additional features to observe U, the distribution parameter α used in linear Thompson Sampling, and a function of time λ(t). [page 6, 5 Experiments, 2nd para, lines 1-11] λ(t) is set as 1 for stationary cases and defined by the GP-UCB algorithm for nonstationary cases)
initializing the initially observed features, the initially observed features set and the observed additional features; ([page 5, Algorithm 2, line 2] The observations are initialized as follows:
∀
k
∈
K
,
A
k
≔
I
N
+
1
,
g
k
≔
0
N
+
1
,
μ
^
k
≔
0
N
+
1
,
a
n
d
∀
i
∈
N
,
B
k
≔
I
N
+
1
,
z
k
≔
0
N
+
1
,
θ
^
k
≔
0
N
+
1
.
i
denotes the initially observed features and it is supported by [page 4, last para, lines 1-2]. [Algorithm 2, line 18]
B
k
denotes the initially observed features set as it is calculated based on CV(t). [Algorithm 2, line 16]
A
k
denotes the observed additional features because it is calculated by combining the initially observed features set V and the additional features U. See the instant spec paragraph [0038] and Figure 2 operational block 204)
identifying a plurality of subsets CV(t) for each time t from a plurality of predetermined times t; ([page 5, Algorithm 2, lines 3-4] and [page 4, last para, lines 1-6] For each t=1,2,…,T CV(t) are observed)
sampling a vector parameter for each context feature for a plurality of context features; ([page 5, Algorithm 2, line 5-7] and [page 4, last para, lines 1-6] Sample the vector parameters
θ
^
i
are sampled for each feature
i
∈
C
V
from the posterior distribution
N
(
θ
^
i
,
α
2
B
i
-
1
)
)
identifying a best subset of features; and ([page 5, Algorithm 2, lines 8-9] and [page 5, paragraph, lines 1-4] Select the subset of best estimated features
C
U
(
t
)
at time t)
selecting an arm based on the best subset of features. ([page 5, Algorithm 2, lines 14-15] and [page 5, paragraph, lines 1-4] discloses selecting arm k(t) based on
C
U
(
t
)
by calculating
C
V
+
U
(
t
)
)
Regarding claim 2, Bouneffouf teaches:
The method of Claim 1, wherein identifying the CACBO problem includes an agent selecting the CACBO problem and a plurality of features, where each of the multiple arms includes an unknown and independent probability-law of reward ([page 6, 5 Experiments, lines 14-18] CATS-fix, CALINUCB, CATS 1 and TSRC was performed on several publicly available datasets, as well as on a proprietary corporate dialog orchestration dataset. Publicly available Covertype and CNAE-9 were featured in the original TSRC paper and Warfarin Sharabiani et al. [2015] is a historically popular dataset for evaluating bandit methods. These datasets (problems) were selected in order to perform the CACBO method. [lines 9-11] and [Algorithm 1, lines 6-8] shows that the rewards are unknown and given independently to each context and the action taken in that context)
Regarding claim 3, Bouneffouf teaches:
The method of Claim 1, wherein identifying a plurality of parameters includes an agent observing the total number of features N, the number of initially observed features V, and the number of observed additional features U. ([page 4, 4 Context Attentive Thompson Sampling (CATS), lines 9-12] The algorithm takes the total number of features N, the number of features initially observed V, the number of additional features to observe U, the set of observed features CV, the distribution parameter α used in linear Thompson Sampling, and a function of time λ(t))
Regarding claim 4, Bouneffouf teaches:
The method of Claim 1, wherein identifying a plurality of parameters includes determining the function λ(t) for stationary cases differently than determining the function λ(t) for nonstationary cases. ([page 6, 5 Experiments, 2nd para, lines 1-11] λ(t) is set as 1 for stationary cases and defined by the GP-UCB algorithm for nonstationary cases)
Regarding claim 5, Bouneffouf teaches:
The method of Claim 1, wherein initializing includes initializing the initially observed features, the initially observed features set and the observed additional features to a predetermined initial value. ([page 5, Algorithm 2, line 2] The observations are initialized as follows:
∀
k
∈
K
,
A
k
≔
I
N
+
1
,
g
k
≔
0
N
+
1
,
μ
^
k
≔
0
N
+
1
,
a
n
d
∀
i
∈
N
,
B
k
≔
I
N
+
1
,
z
k
≔
0
N
+
1
,
θ
^
k
≔
0
N
+
1
.
i
denotes the initially observed features and it is supported by [page 4, last para, lines 1-2]. [Algorithm 2, line 18]
B
k
denotes the initially observed features set as it is calculated based on CV(t). [Algorithm 2, line 16]
A
k
denotes the observed additional features because it is calculated by combining the initially observed features set V and the additional features U. See the instant spec paragraph [0038] and Figure 2 operational block 204)
Regarding claim 6, Bouneffouf teaches:
The method of Claim 1, wherein identifying a plurality of subsets CV(t) includes performing an iteration of T steps and observing values CV(t) for each iteration step T that are within the initially observed features set CV. ([page 5, Algorithm 2, lines 3-4] and [page 4, last para, lines 1-6] For each t=1,2,…,T CV(t) are observed. [page 4, 4 Context Attentive Thompson Sampling (CATS), lines 9-12] shows that CV(t) denotes the set of observed features CV)
Regarding claim 7, Bouneffouf teaches:
The method of Claim 1, wherein sampling the vector parameter includes, for each iteration step T, obtaining a sample of the vector parameter from a corresponding multivariate Gaussian distribution separately for each feature not yet observed to generate an estimated vector parameter. ([page 5, Algorithm 2, line 5-7] and [page 4, last para, lines 1-6] Sample the vector parameters
θ
^
i
are sampled for each feature
i
∈
C
V
from the posterior distribution
N
(
θ
^
i
,
α
2
B
i
-
1
)
)
Regarding claim 8, Bouneffouf teaches:
The method of Claim 1, wherein identifying the best subset of features includes selecting the best subset of features at each iteration step T. ([Algorithm 2, lines 8-9] and [page 5, paragraph, lines 1-4] Select the subset of best estimated features
C
U
(
t
)
at time t)
Regarding claim 9, Bouneffouf teaches:
The method of Claim 1, wherein identifying the best subset of features includes using a contextual combinatorial combinatorial bandit approach. ([page 3, 3rd para under Algorithm 1, Contextual Combinatorial Bandit, lines 1-5] The contextual combinatorial bandit, the agent sequentially observes a context, selects a subset, and observes the reward corresponding to the selected subset. [page 5, Algorithm 2, lines 8-9] and [page 5, paragraph, lines 1-4] shows that the observation of the context [Algorithm 2, lines 5-7], selection of the best subset at time t [Algorithm 2, lines 8-9], and observation of the reward at time t [Algorithm 2, lines 14-15] is performed sequentially, which indicates that the approach is performed using a contextual combinatorial bandit approach)
Regarding claim 10, Bouneffouf teaches:
The method of Claim 1, wherein selecting the arm includes using a contextual combinatorial bandit approach based on a context that the best subset of features. ([page 3, 3rd para under Algorithm 1, Contextual Combinatorial Bandit, lines 1-5] The contextual combinatorial bandit, the agent sequentially observes a context, selects a subset, and observes the reward corresponding to the selected subset. [page 5, Algorithm 2, lines 8-9] and [page 5, paragraph, lines 1-4] shows that the observation of the context [Algorithm 2, lines 5-7], selection of the best subset at time t [Algorithm 2, lines 8-9], and observation of the reward at time t [Algorithm 2, lines 14-15] is performed sequentially, which indicates that the approach is performed using a contextual combinatorial bandit approach)
Regarding claim 11, Bouneffouf teaches:
A computing system, comprising: a machine learning system for implementing a method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, wherein the method includes: ([page 4, 4 Context Attentive Thompson Sampling (CATS), lines 1-8] and [page 5, Algorithm 2] discloses Context Attentive Thompson Sampling algorithm. [2 Related Work, 2nd para, lines 1-3] The Context Attentive Bandit problem is related to the budgeted learning problem)
Claim 11 is a system claim which recites the same features as claim 1, and is rejected for at least the same reasons.
Claim 12 is a system claim which recites the same features as claim 2 and is rejected for at least the same reasons.
Claim 13 is a system claim which recites the same features as claim 3 and is rejected for at least the same reasons.
Claim 14 is a system claim which recites the same features as claim 4 and is rejected for at least the same reasons.
Claim 15 is a system claim which recites the same features as claim 5, and is rejected for at least the same reasons.
Claim 16 is a system claim which recites the same features as claim 6, and is rejected for at least the same reasons.
Claim 17 is a system claim which recites the same features as claim 7, and is rejected for at least the same reasons.
Regarding claim 18, Bouneffouf teaches:
The computing system of Claim 11, wherein identifying the best subset of features includes selecting the best subset of features at each iteration step T using a contextual combinatorial combinatorial bandit approach. ([page 3, 3rd para under Algorithm 1, Contextual Combinatorial Bandit, lines 1-5] The contextual combinatorial bandit, the agent sequentially observes a context, selects a subset, and observes the reward corresponding to the selected subset. [page 5, Algorithm 2, lines 8-9] and [page 5, paragraph, lines 1-4] shows that the observation of the context [Algorithm 2, lines 5-7], selection of the best subset at time t [Algorithm 2, lines 8-9], and observation of the reward at time t [Algorithm 2, lines 14-15] is performed sequentially, which indicates that the approach is performed using a contextual combinatorial bandit approach)
Claim 19 is a system claim which recites the same features as claim 9 and is rejected for at least the same reasons.
Regarding claim 20, Claim 20 is a computer program product claim which recites the same features as the claim 1, and is rejected for at least the same reasons.
Bouneffouf teaches:
A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations for implementing a method for solving a Context-Attentive Combinatorial Bandit with Observations (CACBO) problem using a Context-Attentive Combinatorial Thompson Sampling with Observations (CACTSO) algorithm, the method comprising: ([page 6, 5 Experiments, lines 14-18] CATS-fix, CALINUCB, CATS 1 and TSRC was performed on several publicly available datasets, as well as on a proprietary corporate dialog orchestration dataset. Publicly available Covertype and CNAE-9 were featured in the original TSRC paper and Warfarin Sharabiani et al. [2015] is a historically popular dataset for evaluating bandit methods. This paragraph shows that the experiment is performed using conventional computer components. [page 4, 4 Context Attentive Thompson Sampling (CATS), lines 1-8] and [page 5, Algorithm 2] discloses Context Attentive Thompson Sampling algorithm)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220343080 A1 (This prior art is pertinent because it teaches selecting and processing multi-arms problems using a machine learning algorithm)
Daulton et al., “Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints”, 2019 (This prior art is pertinent because it discloses performing Thompson sampling to solve contextual bandit problems)
Lin et al., “Contextual Bandit with Adaptive Feature Extraction”, 2018 (This prior art is pertinent because it discloses solving Contextual Bandit problem using feature extractions)
Bouneffouf et al., “Context Attentive Bandits: Contextual Bandit with Restricted Context”, 2017 (This prior art is pertinent because it discloses contextual bandit problem solutions based on a sampling method [page 4, Algorithm 2])
US-11151467-B1 (This prior art is pertinent because it discloses selecting problems and features to be input to a machine learning model)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 8:00AM – 5:00PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at (571)270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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