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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/28/2023, 06/02/2023, 07/20/2023, 04/04/2025, 09/26/2025 and 12/22/2025 are in compliance with provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed
to an abstract idea without significantly more. The following sections follow the 2019 PEG
guidelines for analyzing subject matter eligibility.
The analysis below of the claims’ subject matter eligibility follows the 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”)
and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial
Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined
whether the claim is directed to one of the four statutory categories of invention, i.e., process,
machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the
statutory categories, the second step in the analysis is to determine whether the claim is directed
to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first
prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception
(e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If
it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis
proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the
claims integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical application, the
analysis proceeds to determining whether the claim is a patent-eligible application of the
exception (Step 2B). If an abstract idea is present in the claim, any element or combination of
elements in the claim must be sufficient to ensure that the claim integrates the judicial exception
into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
creating an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
performing, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space, wherein the reduced version of the label set is a seed set; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
representing the AI planning problem as a lifted successor generation problem comprising a set of tables and at least one join query on the set of tables; (Mathematical Concepts: are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations)
process the at least one join query and generate one or more applicable actions as a solution to the AI planning problem; and (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
receiving an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
automatically and by machine logic (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
executing a lifted successor generation module on the lifted successor generation problem using the seed set to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
outputting the one or more applicable actions for the AI planning problem for further AI operations. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
receiving an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
automatically and by machine logic (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
executing a lifted successor generation module on the lifted successor generation problem using the seed set to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
outputting the one or more applicable actions for the AI planning problem for further AI operations. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to method of lifting successor generator, this is
nothing more than using machine learning models to plan future actions for the model. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2 incorporates the rejections of claim 1.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. Please see the analysis of claim 1 above. Regarding the method steps recited in claim 1, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 2 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein executing the lifted successor generation module comprises executing a preprocessor of the lifted successor generation module to preprocess the set of tables to reduce a size of the set of tables prior to processing the at least one join query. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein executing the lifted successor generation module comprises executing a preprocessor of the lifted successor generation module to preprocess the set of tables to reduce a size of the set of tables prior to processing the at least one join query. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 3 incorporates the rejections of claim 2.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 2 are incorporated. Please see the analysis of claim 2 above. Regarding the method steps recited in claim 2, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 3 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein the preprocessor preprocesses the set of tables to remove one or more rows of one or more of the tables in the set of tables that have non-seed set labels in elements of the one or more rows. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the preprocessor preprocesses the set of tables to remove one or more rows of one or more of the tables in the set of tables that have non-seed set labels in elements of the one or more rows. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 4 incorporates the rejections of claim 2.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 2 are incorporated. Please see the analysis of claim 2 above. Regarding the method steps recited in claim 2, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 4 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein the preprocessor preprocesses the set of tables to remove one or more tables in the set of tables that have only non-seed set labels in the elements of the one or more tables. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the preprocessor preprocesses the set of tables to remove one or more tables in the set of tables that have only non-seed set labels in the elements of the one or more tables. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 5 incorporates the rejections of claim 1.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. Please see the analysis of claim 1 above. Regarding the method steps recited in claim 1, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 5 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
generating, by machine logic, a mutex group of operators from the plurality of operators; and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
executing, by the machine logic, the label reduction operation using the mutex group of operators to reduce a number of labels, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
present in the reduced version of the action space, relative to a number of labels in the original version of the action space. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
generating, by machine logic, a mutex group of operators from the plurality of operators; and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
executing, by the machine logic, the label reduction operation using the mutex group of operators to reduce a number of labels, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
present in the reduced version of the action space, relative to a number of labels in the original version of the action space. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 6 incorporates the rejections of claim 5.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 5 are incorporated. Please see the analysis of claim 5 above. Regarding the method steps recited in claim 5, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 6 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein executing the label reduction operation comprises performing a partial ground of operators in the plurality of operators. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein executing the label reduction operation comprises performing a partial ground of operators in the plurality of operators. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 7 incorporates the rejections of claim 5.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 5 are incorporated. Please see the analysis of claim 5 above. Regarding the method steps recited in claim 5, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 7 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 8 incorporates the rejections of claim 1.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
wherein performing the label reduction on the initial version of the label set comprises identifying schematic operators (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
lifted mutex groups (LMGs) based on the initial version of the action space (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein representing the AI planning problem as a lifted successor generation problem comprising defining the AI planning problem, for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
lifted mutex groups (LMGs) based on the initial version of the action space (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein representing the AI planning problem as a lifted successor generation problem comprising defining the AI planning problem, for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 9 incorporates the rejections of claim 1.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
generating one or more plans for solving the AI planning problem based on the one or more applicable actions in the output. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “making plans based on current state” of base claim 1) cannot meaningfully integrate the judicial exceptions into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e.,
“making plans based on current state” of base claim 1) cannot provide an inventive concept. The claim is not patent eligible.
Claim 10 incorporates the rejections of claim 1.
Step 1: The claim recites a method, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. Please see the analysis of claim 1 above. Regarding the method steps recited in claim 1, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 10 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein the AI planning problem is defined in a Planning Domain Definition Language (PDDL) data structure. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the AI planning problem is defined in a Planning Domain Definition Language (PDDL) data structure. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 11
Step 1: The claim recites a computer program product comprising a computer readable storage medium, with support for the specification, paragraph 27 that recites a computer readable storage medium as non-transitory signals per se, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
create an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
perform, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space, wherein the reduced version of the label set is a seed set; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
represent the AI planning problem as a lifted successor generation problem comprising a set of tables and at least one join query on the set of tables; (Mathematical Concepts: are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations)
process the at least one join query and generate one or more applicable actions as a solution to the AI planning problem; and (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
automatically and by machine logic (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
execute a lifted successor generation module on the lifted successor generation problem using the seed set to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the one or more applicable actions for the AI planning problem for further AI operations. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
automatically and by machine logic (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
execute a lifted successor generation module on the lifted successor generation problem using the seed set to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the one or more applicable actions for the AI planning problem for further AI operations. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to computer program product of lifting successor generator, this is nothing more than using machine learning models to plan future actions for the model. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 12 incorporates the rejections of claim 11.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 11 are incorporated. Please see the analysis of claim 11 above. Regarding the computer program product steps recited in claim 11, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 12 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein executing the lifted successor generation module comprises executing a preprocessor of the lifted successor generation module to preprocess the set of tables to reduce a size of the set of tables prior to processing the at least one join query. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein executing the lifted successor generation module comprises executing a preprocessor of the lifted successor generation module to preprocess the set of tables to reduce a size of the set of tables prior to processing the at least one join query. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 13 incorporates the rejections of claim 12.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 12 are incorporated. Please see the analysis of claim 12 above. Regarding the computer program product steps recited in claim 12, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 13 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein the preprocessor preprocesses the set of tables to remove one or more rows of one or more of the tables in the set of tables that have non-seed set labels in elements of the one or more rows. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the preprocessor preprocesses the set of tables to remove one or more rows of one or more of the tables in the set of tables that have non-seed set labels in elements of the one or more rows. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 14 incorporates the rejections of claim 12.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 12 are incorporated. Please see the analysis of claim 12 above. Regarding the computer program product steps recited in claim 12, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 14 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein the preprocessor preprocesses the set of tables to remove one or more tables in the set of tables that have only non-seed set labels in the elements of the one or more tables. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the preprocessor preprocesses the set of tables to remove one or more tables in the set of tables that have only non-seed set labels in the elements of the one or more tables. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 15 incorporates the rejections of claim 11.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 11 are incorporated. Please see the analysis of claim 11 above. Regarding the computer program product steps recited in claim 11, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 15 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
generating, by machine logic, a mutex group of operators from the plurality of operators; and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
executing, by the machine logic, the label reduction operation using the mutex group of operators to reduce a number of labels, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
present in the reduced version of the action space, relative to a number of labels in the original version of the action space. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
generating, by machine logic, a mutex group of operators from the plurality of operators; and (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
executing, by the machine logic, the label reduction operation using the mutex group of operators to reduce a number of labels, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
present in the reduced version of the action space, relative to a number of labels in the original version of the action space. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 16 incorporates the rejections of claim 15.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of claim 15 above. Regarding the computer program product steps recited in claim 15, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 16 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein executing the label reduction operation comprises performing a partial ground of operators in the plurality of operators. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein executing the label reduction operation comprises performing a partial ground of operators in the plurality of operators. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 17 incorporates the rejections of claim 15.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 15 are incorporated. Please see the analysis of claim 15 above. Regarding the computer program product steps recited in claim 15, these steps cover mental processes based on grouping and identifying data.
Therefore, claim 17 is directed to an abstract idea – Mental Processes (i.e., can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 18 incorporates the rejections of claim 11.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 11 are incorporated.
wherein performing the label reduction on the initial version of the label set comprises identifying schematic operators (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
lifted mutex groups (LMGs) based on the initial version of the action space (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein representing the AI planning problem as a lifted successor generation problem comprising defining the AI planning problem, for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
lifted mutex groups (LMGs) based on the initial version of the action space (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
wherein representing the AI planning problem as a lifted successor generation problem comprising defining the AI planning problem, for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 19 incorporates the rejections of claim 11.
Step 1: The claim recites a computer program product, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 11 are incorporated.
generating one or more plans for solving the AI planning problem based on the one or more applicable actions in the output. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications. (Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., “making plans based on current state” of base claim 1) cannot meaningfully integrate the judicial exceptions into a practical application. See MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception (i.e.,
“making plans based on current state” of base claim 1) cannot provide an inventive concept. The claim is not patent eligible.
Claim 20
Step 1: The claim recites an apparatus, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
create an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
perform, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space, wherein the reduced version of the label set is a seed set; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
represent the AI planning problem as a lifted successor generation problem comprising a set of tables and at least one join query on the set of tables; (Mathematical Concepts: are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations)
process the at least one join query and generate one or more applicable actions as a solution to the AI planning problem; and (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In particular, the claim recites these additional elements:
receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
automatically and by machine logic (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
execute a lifted successor generation module on the lifted successor generation problem using the seed set to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the one or more applicable actions for the AI planning problem for further AI operations. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
automatically and by machine logic (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
execute a lifted successor generation module on the lifted successor generation problem using the seed set to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
output the one or more applicable actions for the AI planning problem for further AI operations. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to an apparatus of lifting successor generator, this is nothing more than using machine learning models to plan future actions for the model. Nothing in the claim provides significantly more than this. As such, the claim is 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2 and 5 – 10 are rejected under 35 U.S.C. 103 as being unpatentable over Corrêa (NPL, Lifted Successor Generation Using Query Optimization Techniques) in view of Horčík (NPL, Endomorphisms of Lifted Planning Problems)
Regarding claim 1, Corrêa teaches performing, automatically and by machine logic, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space, wherein the reduced version of the label set is a seed set; (See e.g. [P80:C1], The ground representation can be exponentially larger in the number of parameters of the action schemas, but there are efficient grounding techniques (e.g., Helmert 2009) that overall seem to make this preprocessing an effective strategy.)(See e.g. [P87:C2], Areces et al. (2014) develop an automatic action schema [automatically and by machine logic] splitting technique that reduces the number of parameters of action schemas [reduced version of the label], at the cost of modifying the state space [that defines a reduced action space] topology.) (See e.g. [P83:C2], Our planner supports the PDDL fragment representing STRIPS [label set is a seed set] (Examiners note: This mapping is in line with the applicant’s provided specification (03/28/2023); [0095]: The parameter seed set problem is NP-Complete. To solve the parameter seed set problem, it is a case considered a (delete-free) STRIPS planning task with action costs.) with equalities, inequalities, and types.)
representing the AI planning problem as a lifted successor generation problem comprising a set of tables and at least one join query on the set of tables; (See e.g. [P81:C2], Given two tables R and S, the basic operations are (i) to rename a column; (ii) to project R into a set of attributes Y , obtaining a relation πY (R) where some columns of R have been removed or rearranged; (iii) to select some tuples from R that either coincide on two different attributes xi and xj (σxi=xj (R)), or for which an attribute xi has a particular constant value c (σxi=c(R)); and (iv) to join R and S. [at least one join query on the set of tables]) (See e.g. [P81:C1], The solution of a planning problem [the AI planning problem] is a sequence of ground actions applicable to s0 that leads to a goal state. This sequence of actions is called a plan.) (See e.g. [P83:C2], We implemented a lifted planner using the successor generator methods previously described)
executing a lifted successor generation module on the lifted successor generation problem using the seed set to process the at least one join query and generate one or more applicable actions as a solution to the AI planning problem; (See e.g. [P83:C2], We implemented a lifted planner using the successor generator methods previously described) (See e.g. [P81:C1], The solution of a planning problem is a sequence of ground actions [generate one or more applicable actions] applicable to s0 that leads to a goal state. This sequence of actions is called a plan.) (See e.g. [P83:C2], Our planner supports the PDDL fragment representing STRIPS [seed set] with equalities, inequalities, and types.)
and outputting the one or more applicable actions for the AI planning problem for further AI operations. (See e.g. [P83:C1], As we discussed earlier, this algorithm is quadratic in the size of the input I and the output U.)(See e.g. [P82:C1], The evaluation of this query program can take time exponential in both I and U [outputting the one or more applicable actions] due to a combinatorial explosion caused by the joins….This mapping can be evaluated in time linear in the size of Ri by a query program consisting of selection and projection operations. [further AI operations])
Corrêa does not teach receiving an artificial intelligence (AI) planning problem including definitions for a plurality of operators; creating an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators;
Horčík teaches receiving an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (See e.g. [P180:C2], PDDL [an artificial intelligence (AI) planning problem] endomorphisms naturally induce endomorphisms of facts and operators [a plurality of operators] on the ground level through the grounding process)
creating an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (See e.g. [P174:C2 & Figure 1]: Now, we informally explain and illustrate on an example which redundant objects our method aims to find. Consider a logistic task where we have a map of cities connected by roads, a fleet of trucks and we are supposed to transport packages to their destinations. [an initial version of an action space]… Assume we are supposed to move packages from the city c0 to c3 and all our trucks are located in c0 [creating an initial version of a label set]. (Examiners notes: the map is being mapped as the action space and the city labels define the maps initial version) To solve the task, it clearly suffices to use the path from c0 to c3 via c2. [the label set including a plurality of labels] Thus the city c1 is in this sense redundant… As states are sets of grounded atoms, they can be viewed as relational first-order structures having the same universe B. Our method searches for a suitable map B (we call it a PDDL endomorphism) allowing us to replace states and actions in a plan involving the city c1 by another states and actions not involving c1. [each label of the plurality of labels respectively corresponding to the operators of the plurality of operators])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa and
Horčík before them, to include Horčík’s plurality of operators which would allow Corrêa’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 2, Corrêa and Horčík teach the method of claim 1. Corrêa teaches wherein executing the lifted successor generation module comprises executing a preprocessor of the lifted successor generation module to preprocess the set of tables to reduce a size of the set of tables prior to processing the at least one join query. (See e.g. [P83:C2], We first obtain a semi-join program computed from the GYO reduction of the precondition query hypergraph. [reduce a size of the set of table]) (See e.g. [P84:C2], To analyze the effect of evaluating acyclic precondition queries efficiently, we ran our breadth-first search with the configuration FRSJ,<, which uses the GYO algorithm in a preprocessing step [preprocessor] to test which queries are cyclic.)
Regarding claim 5, Corrêa and Horčík teach the method of claim 1. Corrêa teaches executing, by the machine logic, the label reduction operation [using the mutex group of operators] to reduce a number of labels, present in the reduced version of the action space, relative to a number of labels in the original version of the action space. (See e.g. [P87:C2], Areces et al. (2014) develop an automatic action schema [executing, by machine logic] splitting technique that reduces the number of parameters of action schemas [reduce a number of labels], at the cost of modifying the state space [reduced version of the action space] topology.) (See e.g. [P85:C1], The main advantage of using the full reducer is that it avoids large intermediate relations. Trying to estimate how often this occurs, we compared the largest intermediate relation size during the expansion of the initial state [relative to a number of labels in the original version of the action space] for J< and FRSJ,<.)
Corrêa does not teach generating, by machine logic, a mutex group of operators from the plurality of operators;
Horčík teaches generating, by machine logic, a mutex group of operators from the plurality of operators; (See e.g. [P176:C2], There are methods for the inference of lifted mutex groups [a mutex group] from the PDDL formulation.) (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [plurality of operators] on the ground level through the grounding process.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa and
Horčík before them, to include Horčík’s plurality of operators which would allow Corrêa’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 6, Corrêa and Horčík teach the method of claim 5. Corrêa teaches wherein executing the label reduction operation (See e.g. [P87:C2], Areces et al. (2014) develop an automatic action schema [executing] splitting technique that reduces the number of parameters of action schemas [label reduction operation], at the cost of modifying the state space topology.)
Corrêa does not teach comprises performing a partial ground of operators in the plurality of operators.
Horčík teaches comprises performing a partial ground of operators in the plurality of operators. (See e.g. [174:C2], Furthermore, Gnad et al. (2019) applied machine learning techniques to learn which actions are more likely to be a part of a plan, resulting in only partially grounded planning task [performing a partial ground] (without a guarantee that the grounded part contains a plan from the original planning task).) (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [plurality of operators] on the ground level through the grounding process.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa and
Horčík before them, to include Horčík’s plurality of operators which would allow Corrêa’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 7, Corrêa and Horčík teach the method of claim 5.
Corrêa does not teach wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded.
Horčík teaches wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded. (See e.g. [P182:C2], lifted mutex groups [mutex group when grounded] are usually inferred to be grounded and used for the translation to finite domain representation) (See e.g. [P180:C2], Let cost I be the set of ground atoms of valued predicates [a set of lifted predicates] in the initial state.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa and
Horčík before them, to include Horčík’s plurality of operators which would allow Corrêa’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 8, Corrêa and Horčík teach the method of claim 1. Corrêa teaches wherein representing the AI planning problem as a lifted successor generation problem comprising defining the AI planning problem, (See e.g. [P88:C1], We have shown how to efficiently perform lifted successor generation [lifted successor generation] in classical planning [Al planning problem] using well-known database techniques.)
Corrêa does not teach wherein performing the label reduction on the initial version of the label set comprises identifying schematic operators and lifted mutex groups (LMGs) based on the initial version of the action space, for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator.
Horčík teaches wherein performing the label reduction on the initial version of the label set comprises identifying schematic operators and lifted mutex groups (LMGs) based on the initial version of the action space (See e.g. [P182:C1], We did not modify the planner, but instead the tasks were pruned first and new reduced domain and problem PDDL files were generated as inputs [initial version of the label set] for the planner.) (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [identifying schematic operators] on the ground level through the grounding process.) (See e.g. [P182:C2], lifted mutex groups [lifted mutex groups (LMGs)] are usually inferred to be grounded and used for the translation to finite domain representation)
for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator. (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [identifying schematic operators] on the ground level through the grounding process.) (See e.g. [P182:C2], lifted mutex groups [lifted mutex groups (LMGs)] are usually inferred to be grounded and used for the translation to finite domain representation)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa and
Horčík before them, to include Horčík’s plurality of operators which would allow Corrêa’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 9, Corrêa and Horčík teach the method of claim 1. Corrêa teaches generating one or more plans for solving the AI planning problem based on the one or more applicable actions in the output. (See e.g. [P80:C2], Our work is a step towards planning [the AI planning problem] directly on the first order representations. We use well-known techniques from database theory to tackle the task of successor generation, one of the key operations when planning using heuristic search. The enumeration of all applicable ground actions [one or more applicable actions] derived from an action schema in a given state s can be seen as a database query [the output] where s is a database and the action precondition is a query.)
Regarding claim 10, Corrêa and Horčík teach the method of claim 1. Corrêa teaches wherein the AI planning problem is defined in a Planning Domain Definition Language (PDDL) data structure. (See e.g. [P83:C2], Our planner supports the PDDL fragment representing STRIPS with equalities, inequalities, and types.)
Claims 3, 4, and 11 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Corrêa (NPL, Lifted Successor Generation Using Query Optimization Techniques) in view of Horčík (NPL, Endomorphisms of Lifted Planning Problems) further in view of Cosic (US 9367806 B1)
Regarding claim 3, Corrêa and Horčík teach the method of claim 2. Corrêa teaches wherein the preprocessor preprocesses the set of tables [to remove one or more rows of one or more of the tables in the set of tables] that have non-seed set labels in elements of the one or more rows. (See e.g. [P84:C2], To analyze the effect of evaluating acyclic precondition queries efficiently, we ran our breadth-first search with the configuration FRSJ,<, which uses the GYO algorithm in a preprocessing step [preprocessor] to test which queries are cyclic.) (See e.g. [P81:C2], Given two tables R and S, the basic operations are (i) to rename a column; (ii) to project R into a set of attributes Y [set labels in elements], obtaining a relation πY (R) where some columns of R have been removed or rearranged; (iii) to select some tuples [one or more rows.] from R that either coincide on two different attributes xi and xj (σxi=xj (R)), or for which an attribute xi has a particular constant value c (σxi=c(R));)
Corrêa and Horčík do not teach remove one or more rows of one or more of the tables in the set of tables
Cosic teaches remove one or more rows of one or more of the tables in the set of tables (See e.g. [C7:L20 – 23], In some embodiments, the operation on data stored in a database comprises one of, or a combination of: accessing, modifying, creating or deleting of a row [remove one or more rows], column, or cell within a table [one or more of the tables] of a database.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa,
Horčík and Cosic before them, to include Cosic’s database manipulation which would allow Corrêa and Horčík’s model to reduce the amount of redundant information in the planning process. One would have been motivated to make such a combination in order to more accurately anticipate future database actions, as suggested by Cosic (US 9367806 B1) (C38:L29 – 33)
Regarding claim 4, Corrêa and Horčík teach the method of claim 2. wherein the preprocessor preprocesses… (See e.g. [P84:C2], To analyze the effect of evaluating acyclic precondition queries efficiently, we ran our breadth-first search with the configuration FRSJ,<, which uses the GYO algorithm in a preprocessing step [preprocessor] to test which queries are cyclic.)
[in the set of tables] that have only non-seed set labels in the elements [of the one or more tables.] (See e.g. [P83:C2], Schemas with parameters of an empty type [non-seed set labels] are removed )
Corrêa and Horčík do not teach the set of tables to remove one or more tables in the set of tables that have only non-seed set labels in the elements of the one or more tables.
Cosic teaches the set of tables to remove one or more tables [in the set of tables that have only non-seed set labels in the elements of the one or more tables.] (See e.g. [C7:L18 – 20], he operation on data stored in a database comprises one of, or a combination of: accessing, modifying, creating or deleting a table [remove one or more tables] of a database.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa,
Horčík and Cosic before them, to include Cosic’s database manipulation which would allow Corrêa and Horčík’s model to reduce the amount of redundant information in the planning process. One would have been motivated to make such a combination in order to more accurately anticipate future database actions, as suggested by Cosic (US 9367806 B1) (C38:L29 – 33)
Regarding claim 11, Corrêa teaches perform, automatically and by machine logic, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space, wherein the reduced version of the label set is a seed set; (See e.g. [P80:C1], The ground representation can be exponentially larger in the number of parameters of the action schemas, but there are efficient grounding techniques (e.g., Helmert 2009) that overall seem to make this preprocessing an effective strategy.)(See e.g. [P87:C2], Areces et al. (2014) develop an automatic action schema [automatically and by machine logic] splitting technique that reduces the number of parameters of action schemas [reduced version of the label], at the cost of modifying the state space [that defines a reduced action space] topology.) (See e.g. [P83:C2], Our planner supports the PDDL fragment representing STRIPS [label set is a seed set] (Examiners note: This mapping is in line with the applicant’s provided specification (03/28/2023); [0095]: The parameter seed set problem is NP-Complete. To solve the parameter seed set problem, it is a case considered a (delete-free) STRIPS planning task with action costs.) with equalities, inequalities, and types.)
represent the AI planning problem as a lifted successor generation problem comprising a set of tables and at least one join query on the set of tables; (See e.g. [P81:C2], Given two tables R and S, the basic operations are (i) to rename a column; (ii) to project R into a set of attributes Y , obtaining a relation πY (R) where some columns of R have been removed or rearranged; (iii) to select some tuples from R that either coincide on two different attributes xi and xj (σxi=xj (R)), or for which an attribute xi has a particular constant value c (σxi=c(R)); and (iv) to join R and S. [at least one join query on the set of tables]) (See e.g. [P81:C1], The solution of a planning problem [the AI planning problem] is a sequence of ground actions applicable to s0 that leads to a goal state. This sequence of actions is called a plan.) (See e.g. [P83:C2], We implemented a lifted planner using the successor generator methods previously described)
execute a lifted successor generation module on the lifted successor generation problem using the seed set to process the at least one join query and generate one or more applicable actions as a solution to the AI planning problem; (See e.g. [P83:C2], We implemented a lifted planner using the successor generator methods previously described) (See e.g. [P81:C1], The solution of a planning problem is a sequence of ground actions [generate one or more applicable actions] applicable to s0 that leads to a goal state. This sequence of actions is called a plan.) (See e.g. [P83:C2], Our planner supports the PDDL fragment representing STRIPS [seed set] with equalities, inequalities, and types.)
and output the one or more applicable actions for the AI planning problem for further AI operations. (See e.g. [P83:C1], As we discussed earlier, this algorithm is quadratic in the size of the input I and the output U.)(See e.g. [P82:C1], The evaluation of this query program can take time exponential in both I and U [outputting the one or more applicable actions] due to a combinatorial explosion caused by the joins….This mapping can be evaluated in time linear in the size of Ri by a query program consisting of selection and projection operations. [further AI operations])
Corrêa does not teach receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; create an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators;
Horčík teaches receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (See e.g. [P180:C2], PDDL [an artificial intelligence (AI) planning problem] endomorphisms naturally induce endomorphisms of facts and operators [a plurality of operators] on the ground level through the grounding process)
create an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (See e.g. [P174:C2 & Figure 1]: Now, we informally explain and illustrate on an example which redundant objects our method aims to find. Consider a logistic task where we have a map of cities connected by roads, a fleet of trucks and we are supposed to transport packages to their destinations. [an initial version of an action space]… Assume we are supposed to move packages from the city c0 to c3 and all our trucks are located in c0 [create an initial version of a label set]. (Examiners notes: the map is being mapped as the action space and the city labels define the maps initial version) To solve the task, it clearly suffices to use the path from c0 to c3 via c2. [the label set including a plurality of labels] Thus the city c1 is in this sense redundant… As states are sets of grounded atoms, they can be viewed as relational first-order structures having the same universe B. Our method searches for a suitable map B (we call it a PDDL endomorphism) allowing us to replace states and actions in a plan involving the city c1 by another states and actions not involving c1. [each label of the plurality of labels respectively corresponding to the operators of the plurality of operators])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa and
Horčík before them, to include Horčík’s plurality of operators which would allow Corrêa’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Corrêa and Horčík do not teach a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to Cosic teaches a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to (See e.g. [C7:L27 – 31], the present disclosure relates to a non-transitory computer readable medium storing a program causing a computer to execute an interface for a database management system)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa,
Horčík and Cosic before them, to include Cosic’s database manipulation which would allow Corrêa and Horčík’s model to reduce the amount of redundant information in the planning process. One would have been motivated to make such a combination in order to more accurately anticipate future database actions, as suggested by Cosic (US 9367806 B1) (C38:L29 – 33)
Regarding claim 12, Corrêa, Horčík and Cosic teach the computer program product of claim 11. Corrêa teaches wherein executing the lifted successor generation module comprises executing a preprocessor of the lifted successor generation module to preprocess the set of tables to reduce a size of the set of tables prior to processing the at least one join query. (See e.g. [P83:C2], We first obtain a semi-join program computed from the GYO reduction of the precondition query hypergraph. [reduce a size of the set of table]) (See e.g. [P84:C2], To analyze the effect of evaluating acyclic precondition queries efficiently, we ran our breadth-first search with the configuration FRSJ,<, which uses the GYO algorithm in a preprocessing step [preprocessor] to test which queries are cyclic.)
Regarding claim 13, Corrêa and Horčík teach the computer program product of claim 12. Corrêa teaches wherein the preprocessor preprocesses the set of tables [to remove one or more rows of one or more of the tables in the set of tables] that have non-seed set labels in elements of the one or more rows. (See e.g. [P84:C2], To analyze the effect of evaluating acyclic precondition queries efficiently, we ran our breadth-first search with the configuration FRSJ,<, which uses the GYO algorithm in a preprocessing step [preprocessor] to test which queries are cyclic.) (See e.g. [P81:C2], Given two tables R and S, the basic operations are (i) to rename a column; (ii) to project R into a set of attributes Y [set labels in elements], obtaining a relation πY (R) where some columns of R have been removed or rearranged; (iii) to select some tuples [one or more rows.] from R that either coincide on two different attributes xi and xj (σxi=xj (R)), or for which an attribute xi has a particular constant value c (σxi=c(R));)
Corrêa and Horčík do not teach remove one or more rows of one or more of the tables in the set of tables
Cosic teaches remove one or more rows of one or more of the tables in the set of tables (See e.g. [C7:L20 – 23], In some embodiments, the operation on data stored in a database comprises one of, or a combination of: accessing, modifying, creating or deleting of a row [remove one or more rows], column, or cell within a table [one or more of the tables] of a database.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa,
Horčík and Cosic before them, to include Cosic’s database manipulation which would allow Corrêa and Horčík’s model to reduce the amount of redundant information in the planning process. One would have been motivated to make such a combination in order to more accurately anticipate future database actions, as suggested by Cosic (US 9367806 B1) (C38:L29 – 33)
Regarding claim 14, Corrêa and Horčík teach the computer program product of claim 12. wherein the preprocessor preprocesses… (See e.g. [P84:C2], To analyze the effect of evaluating acyclic precondition queries efficiently, we ran our breadth-first search with the configuration FRSJ,<, which uses the GYO algorithm in a preprocessing step [preprocessor] to test which queries are cyclic.)
[in the set of tables] that have only non-seed set labels in the elements [of the one or more tables.] (See e.g. [P83:C2], Schemas with parameters of an empty type [non-seed set labels] are removed )
Corrêa and Horčík do not teach the set of tables to remove one or more tables in the set of tables that have only non-seed set labels in the elements of the one or more tables.
Cosic teaches the set of tables to remove one or more tables [in the set of tables that have only non-seed set labels in the elements of the one or more tables.] (See e.g. [C7:L18 – 20], he operation on data stored in a database comprises one of, or a combination of: accessing, modifying, creating or deleting a table [remove one or more tables] of a database.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa,
Horčík and Cosic before them, to include Cosic’s database manipulation which would allow Corrêa and Horčík’s model to reduce the amount of redundant information in the planning process. One would have been motivated to make such a combination in order to more accurately anticipate future database actions, as suggested by Cosic (US 9367806 B1) (C38:L29 – 33)
Regarding claim 15, Corrêa, Horčík and Cosic teach the computer program product of claim 11. Corrêa teaches executing, by the machine logic, the label reduction operation [using the mutex group of operators] to reduce a number of labels, present in the reduced version of the action space, relative to a number of labels in the original version of the action space. (See e.g. [P87:C2], Areces et al. (2014) develop an automatic action schema [executing, by machine logic] splitting technique that reduces the number of parameters of action schemas [reduce a number of labels], at the cost of modifying the state space [reduced version of the action space] topology.) (See e.g. [P85:C1], The main advantage of using the full reducer is that it avoids large intermediate relations. Trying to estimate how often this occurs, we compared the largest intermediate relation size during the expansion of the initial state [relative to a number of labels in the original version of the action space] for J< and FRSJ,<.)
Corrêa and Cosic do not teach generating, by machine logic, a mutex group of operators from the plurality of operators;
Horčík teaches generating, by machine logic, a mutex group of operators from the plurality of operators; (See e.g. [P176:C2], There are methods for the inference of lifted mutex groups [a mutex group] from the PDDL formulation.) (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [plurality of operators] on the ground level through the grounding process.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa, Horčík and Cosic before them, to include Horčík’s plurality of operators which would allow Corrêa and Cosic’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 16, Corrêa, Horčík and Cosic teach the computer program product of claim 15. Corrêa teaches wherein executing the label reduction operation (See e.g. [P87:C2], Areces et al. (2014) develop an automatic action schema [executing] splitting technique that reduces the number of parameters of action schemas [label reduction operation], at the cost of modifying the state space topology.)
Corrêa and Cosic do not teach comprises performing a partial ground of operators in the plurality of operators.
Horčík teaches comprises performing a partial ground of operators in the plurality of operators. (See e.g. [174:C2], Furthermore, Gnad et al. (2019) applied machine learning techniques to learn which actions are more likely to be a part of a plan, resulting in only partially grounded planning task [performing a partial ground] (without a guarantee that the grounded part contains a plan from the original planning task).) (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [plurality of operators] on the ground level through the grounding process.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa, Horčík and Cosic before them, to include Horčík’s plurality of operators which would allow Corrêa and Cosic’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 17, Corrêa, Horčík and Cosic teach the computer program product of claim 15.
Corrêa and Cosic do not teach wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded.
Horčík teaches wherein the mutex group is a lifted mutex group, wherein a lifted mutex group is a set of lifted predicates that produces a mutex group when grounded. (See e.g. [P182:C2], lifted mutex groups [mutex group when grounded] are usually inferred to be grounded and used for the translation to finite domain representation) (See e.g. [P180:C2], Let cost I be the set of ground atoms of valued predicates [a set of lifted predicates] in the initial state.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa, Horčík and Cosic before them, to include Horčík’s plurality of operators which would allow Corrêa and Cosic’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 18, Corrêa, Horčík and Cosic teach the computer program product of claim 11. Corrêa teaches wherein representing the AI planning problem as a lifted successor generation problem comprising defining the AI planning problem, (See e.g. [P88:C1], We have shown how to efficiently perform lifted successor generation [lifted successor generation] in classical planning [Al planning problem] using well-known database techniques.)
Corrêa and Cosic do not teach wherein performing the label reduction on the initial version of the label set comprises identifying schematic operators and lifted mutex groups (LMGs) based on the initial version of the action space, for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator.
Horčík teaches wherein performing the label reduction on the initial version of the label set comprises identifying schematic operators and lifted mutex groups (LMGs) based on the initial version of the action space (See e.g. [P182:C1], We did not modify the planner, but instead the tasks were pruned first and new reduced domain and problem PDDL files were generated as inputs [initial version of the label set] for the planner.) (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [identifying schematic operators] on the ground level through the grounding process.) (See e.g. [P182:C2], lifted mutex groups [lifted mutex groups (LMGs)] are usually inferred to be grounded and used for the translation to finite domain representation)
for each schematic operator, in terms of a subset of LMGs corresponding to the schematic operator. (See e.g. [P180:C2], PDDL endomorphisms naturally induce endomorphisms of facts and operators [identifying schematic operators] on the ground level through the grounding process.) (See e.g. [P182:C2], lifted mutex groups [lifted mutex groups (LMGs)] are usually inferred to be grounded and used for the translation to finite domain representation)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa, Horčík and Cosic before them, to include Horčík’s plurality of operators which would allow Corrêa and Cosic’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Regarding claim 19, Corrêa, Horčík and Cosic teach the computer program product of claim 11. Corrêa teaches generating one or more plans for solving the AI planning problem based on the one or more applicable actions in the output. (See e.g. [P80:C2], Our work is a step towards planning [the AI planning problem] directly on the first order representations. We use well-known techniques from database theory to tackle the task of successor generation, one of the key operations when planning using heuristic search. The enumeration of all applicable ground actions [one or more applicable actions] derived from an action schema in a given state s can be seen as a database query [the output] where s is a database and the action precondition is a query.)
Regarding claim 20, Corrêa teaches perform, automatically and by machine logic, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space, wherein the reduced version of the label set is a seed set; (See e.g. [P80:C1], The ground representation can be exponentially larger in the number of parameters of the action schemas, but there are efficient grounding techniques (e.g., Helmert 2009) that overall seem to make this preprocessing an effective strategy.)(See e.g. [P87:C2], Areces et al. (2014) develop an automatic action schema [automatically and by machine logic] splitting technique that reduces the number of parameters of action schemas [reduced version of the label], at the cost of modifying the state space [that defines a reduced action space] topology.) (See e.g. [P83:C2], Our planner supports the PDDL fragment representing STRIPS [label set is a seed set] (Examiners note: This mapping is in line with the applicant’s provided specification (03/28/2023); [0095]: The parameter seed set problem is NP-Complete. To solve the parameter seed set problem, it is a case considered a (delete-free) STRIPS planning task with action costs.) with equalities, inequalities, and types.)
represent the AI planning problem as a lifted successor generation problem comprising a set of tables and at least one join query on the set of tables; (See e.g. [P81:C2], Given two tables R and S, the basic operations are (i) to rename a column; (ii) to project R into a set of attributes Y , obtaining a relation πY (R) where some columns of R have been removed or rearranged; (iii) to select some tuples from R that either coincide on two different attributes xi and xj (σxi=xj (R)), or for which an attribute xi has a particular constant value c (σxi=c(R)); and (iv) to join R and S. [at least one join query on the set of tables]) (See e.g. [P81:C1], The solution of a planning problem [the AI planning problem] is a sequence of ground actions applicable to s0 that leads to a goal state. This sequence of actions is called a plan.) (See e.g. [P83:C2], We implemented a lifted planner using the successor generator methods previously described)
execute a lifted successor generation module on the lifted successor generation problem using the seed set to process the at least one join query and generate one or more applicable actions as a solution to the AI planning problem; (See e.g. [P83:C2], We implemented a lifted planner using the successor generator methods previously described) (See e.g. [P81:C1], The solution of a planning problem is a sequence of ground actions [generate one or more applicable actions] applicable to s0 that leads to a goal state. This sequence of actions is called a plan.) (See e.g. [P83:C2], Our planner supports the PDDL fragment representing STRIPS [seed set] with equalities, inequalities, and types.)
and output the one or more applicable actions for the AI planning problem for further AI operations. (See e.g. [P83:C1], As we discussed earlier, this algorithm is quadratic in the size of the input I and the output U.)(See e.g. [P82:C1], The evaluation of this query program can take time exponential in both I and U [outputting the one or more applicable actions] due to a combinatorial explosion caused by the joins….This mapping can be evaluated in time linear in the size of Ri by a query program consisting of selection and projection operations. [further AI operations])
Corrêa does not teach receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; create an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators;
Horčík teaches receive an artificial intelligence (AI) planning problem including definitions for a plurality of operators; (See e.g. [P180:C2], PDDL [an artificial intelligence (AI) planning problem] endomorphisms naturally induce endomorphisms of facts and operators [a plurality of operators] on the ground level through the grounding process)
create an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (See e.g. [P174:C2 & Figure 1]: Now, we informally explain and illustrate on an example which redundant objects our method aims to find. Consider a logistic task where we have a map of cities connected by roads, a fleet of trucks and we are supposed to transport packages to their destinations. [an initial version of an action space]… Assume we are supposed to move packages from the city c0 to c3 and all our trucks are located in c0 [create an initial version of a label set]. (Examiners notes: the map is being mapped as the action space and the city labels define the maps initial version) To solve the task, it clearly suffices to use the path from c0 to c3 via c2. [the label set including a plurality of labels] Thus the city c1 is in this sense redundant… As states are sets of grounded atoms, they can be viewed as relational first-order structures having the same universe B. Our method searches for a suitable map B (we call it a PDDL endomorphism) allowing us to replace states and actions in a plan involving the city c1 by another states and actions not involving c1. [each label of the plurality of labels respectively corresponding to the operators of the plurality of operators])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa and
Horčík before them, to include Horčík’s plurality of operators which would allow Corrêa’s model to reduce the amount of action label sets needed for planning. One would have been motivated to make such a combination in order to improve results in AI planning, as suggested by Horčík (NPL, Endomorphisms of Lifted Planning Problems) (P182:C2)
Corrêa and Horčík do not teach at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to Cosic teaches at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to (See e.g. [C16:L51 – 57], The various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Corrêa,
Horčík and Cosic before them, to include Cosic’s database manipulation which would allow Corrêa and Horčík’s model to reduce the amount of redundant information in the planning process. One would have been motivated to make such a combination in order to more accurately anticipate future database actions, as suggested by Cosic (US 9367806 B1) (C38:L29 – 33)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ALLMAN THOMPSON whose telephone number is (571)272-3671. The examiner can normally be reached Monday - Thursday, 6 a.m. - 3 p.m. ET..
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/K.A.T./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125