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 .
DETAILED ACTION
1. This Final Office action is in reply to the Applicant amendment filed on 23 March 2026.
2. Claims 1, 2, 5, 7, 9, 10, 14, 17, 18, 20 have been amended.
3. Claims 1-20 are currently pending and have been examined. The Information Disclosure Statement filed 16 January 2026 has been considered by the Examiner. A signed copy is enclosed with this Office Action.
Response to Amendment
In the previous office action, Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter (abstract idea). Applicants have not amended Claims 1-20 to provide statutory support and the rejection is maintained.
Response to Arguments
Applicant’s arguments filed 23 March 2026 have been fully considered but they are not persuasive. In the remarks regarding the 35 USC § 101 rejection for Claims 1-20, Applicant basically argues that: (1) the claims are not directed to an abstract idea, and even if they were, they would amount to significantly more than the abstract idea. Examiner respectfully disagrees. Still commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, and the Examiner details the maintained rejection under 35 U.S.C. 101 in the below rejection with further explanation. However the Examiner respectfully disagrees. The claims still recite Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; Certain methods of organizing human activity –marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). Claims 1-16 are each focused to a statutory category of invention, namely “method/system” sets. However, “computer program product” Claims 17-20 do not recite that the product is non-transitory. In at least paragraphs 45-47 in the un-published specification recites: “In the computer 102, the volatile memory 118 is located in a single package and is internal to computer 102, but alternatively or additionally, the volatile memory 118 may be distributed over multiple packages and/or located externally with respect to computer 102”. Volatile memory is non-statutory and is thus not considered a statutory category of invention. See MPEP 2106.03. Despite this failure to pass Step 1, the Examiner continues below in further detail to the next steps of the analysis. In summary as indicated below through Steps 1-2B, the recitation of generic computer components (computer; K*/planner algorithm; processor set; storage medium”, etc.) to perform the claim limitations amount to no more than mere instruction to apply the exception using generic computer components. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. For at least these reasons, the rejection is maintained.
Applicant submits that: (2) Katz et al. (Katz) (US 2019/0340525) in view of Petit, Travis Rivera. "A Formal Verification of Strong Stubborn Set Based Pruning" (2020) does not teach or suggest in amended Claim 1: “…does not teach or suggest determining, by the computer prior to determining a set of solutions associated with the planning problem, one or more extended stubborn sets associated with the single goal planning problem based on the at least one stubborn set; Petit does not overcome the above-noted deficiency of Katz” [see Remarks pages 17-19]. With regard to argument (2), the Examiner respectfully disagrees. As seen below with further clarification in the maintained prior art rejection, Katz in view of Petit teach these claim limitations. Additionally, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). It is noted that any citations to specific, pages, columns, paragraphs, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123. The Examiner has a duty and responsibility to the public and to Applicant to interpret the claims as broadly as reasonably possible during prosecution. In re Prater, 415 F.2d 1 393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969). For at least these reasons, the rejection is maintained.
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 a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1-16 are each focused to a statutory category of invention, namely “method/system” sets. However, “computer program product” Claims 17-20 still do not recite that the product is non-transitory. In at least paragraphs 45-47 in the un-published specification recites: “In the computer 102, the volatile memory 118 is located in a single package and is internal to computer 102, but alternatively or additionally, the volatile memory 118 may be distributed over multiple packages and/or located externally with respect to computer 102”. Volatile memory is non-statutory and is thus not considered a statutory category of invention. See MPEP 2106.03. Despite this failure to pass Step 1, the Examiner proceeds to the next steps of the analysis.
Step 2A: Prong One: Claims 1-20 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention as directed to an abstract idea without significantly more. The claims recite steps for a computer-implemented method for robot path planning utilizes computer vision or algorithm-driven heuristics to parse tasks as recited in representative Claim 1 as:
“receiving, by a computer, a first input associated with a planning problem, wherein the first input comprises a plurality of actions;
transforming, by the computer, the planning problem into a single goal planning problem based on the reception of the first input;
generating, by the computer, at least one stubborn set associated with the single goal planning problem;
determining, by the computer prior to determining a set of solutions associated with the planning problem, one or more extended stubborn sets associated with the single goal planning problem based on the at least one stubborn set, wherein the one or more extended stubborn sets comprises the at least one stubborn set and at least one task action of a set of task actions associated with the single goal planning problem;
determining, by the computer, a pruned search space associated with the single goal planning problem based on the one or more extended stubborn sets, wherein a number of one or more actions in the pruned search space is less than a number of the plurality of actions in the first input;
determining, by the computer, the set of solutions associated with the planning problem based on the pruned search space;
and transmitting, by the computer, the set of solutions to a robot to control the robot to execute the set of solutions”
These abstract idea limitations identified above under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “determining a set of solutions associated with goal planning problems and a set of solutions to a robot to control the robot to execute the set of solutions”. The claim step limitations as currently recited are based on user input utilizing a generic computer. The claims are directed to the use of an abstract mathematical method (the stubborn set method) to solve a generic problem (a planning problem). Under 35 U.S.C. 101 Step 2A, these claims fall into the judicial exception category of abstract ideas, specifically a Mathematical concept/formula, a method of organizing human activity, and Mental processes.
As the claims are now amended, the steps are analyzed as follows:
Mathematical Concepts: The steps involving generating a "stubborn set" and creating a "pruned search space" rely on mathematical algorithms, Boolean logic operations, and set theory to reduce problem variables. This falls squarely under the judicial exception of a mathematical concept/relationship.
Method of Organizing Human Activity: Steps such as taking a generic input and arranging/grouping it into a restricted set of task actions can also be classified under method of organizing human activity.
Mental Processes: The transformation of a multi-goal planning problem to a single-goal planning problem and the algorithmic derivation of extended sets mimic human analytical and planning processes. Therefore, it falls under the category of mental processes (when performed conceptually or purely logically).
See MPEP § 2106.04 (a) II C. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception.
Prong Two: Claims 1-20: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 9 recite additional elements directed to “computer; K*/planner algorithm; processor set; storage medium” (e.g., see Applicants’ un-published Specification ¶’s 40-50). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. “computer program product” Claims 17-20 do not recite that the product is non-transitory and thus fail this step of the analysis. See MPEP § 2106.05(f) (h).
Step 2B: As explained in MPEP § 2106.05, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea nor recites additional elements that integrate the judicial exception into a practical application. The additional elements of “computer; K*/planner algorithm; processor set; storage medium”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. “computer program product” Claims 17-20 do not recite that the product is non-transitory and thus fail this step of the analysis.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ un-published Specification ¶’s 38-52) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “computing environment 100” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Katz et al. (Katz) (US 2019/0340525) in view of Petit, Travis Rivera. "A Formal Verification of Strong Stubborn Set Based Pruning" (2020).
With regard to Claims 1, 4, 9, 12, 17, 20, Katz teaches a computer-implemented method/system/ computer program product comprising (system; processor; computer readable storage medium): a processor set configured to/solving partially ordered top-quality planning (for improving performance of at least one hardware processor solving a top-k planning), the computer program product comprising a computer-readable storage medium having program instructions embodied therewith (see at least paragraphs 5-8), the program instructions executable by a system to cause the system to (see at least paragraphs 124-130):
receiving, by a computer, a first input associated with a planning problem (method M2.1 that receives a planning problem and a set of plans (solutions to the planning problem) and returns a planning problem that preserves exactly all solutions of the input planning problem, except for the input solutions), wherein the first input comprises a plurality of actions (Consider classical planning tasks as captured by the well-known SAS+ formalism, extended with action costs. In such a planning task Π=[AltContent: rect]O, s.sub.0, s*, cost[AltContent: rect], V is a finite set of finite-domain state variables. Each variable v∈V is associated with a finite domain D(v) of variable values. A partial assignment p maps a subset of variables vars(p).Math.V to values in their domains. For a variable v∈V and partial assignment p, the value of v in p is denoted by p[v] if v∈vars(p) and it is said that p[v] is undefined if v.Math.vars(p). A partial assignment s with vars(s)=V, is called a state. State s is consistent with partial assignment p if they agree on all variables in vars(p), shortly denoted by p.Math.s. The product S=Π.sub.v∈D(v) is called the state space of planning task Π. The state s.sub.0 is called initial state of Π and the partial assignment s* is called the goal of Π. A state s is called a goal state if s*.Math.s and the set of all goal states is denoted by S.sub.s*. The finite set O is a set of actions, each action is a pair [AltContent: rect]pre, eff[AltContent: rect] where pre is a partial assignment called precondition and eff is a partial assignment called effect. Further each action o has an associated natural number cost(o), called cost. An action o=[AltContent: rect]pre, eff[AltContent: rect] is applicable in state s if pre.Math.s. Applying action o in state s results in a state denoted by s[[o]] where s[[o]][v]=eff[v] for all v∈vars(eff) and =s[[o]][v]=s[v] for all other variables) (see at least paragraphs 36-40, 50-53);
transforming, by the computer, the planning problem into a single goal planning problem (State s is consistent with partial assignment p if they agree on all variables in vars(p), shortly denoted by p.Math.s. The product S=Π.sub.v∈D(v) is called the state space of planning task Π. The state s.sub.0 is called initial state of Π and the partial assignment s* is called the goal of Π. A state s is called a goal state if s*.Math.s and the set of all goal states is denoted by S.sub.s*.) based on the reception of the first input (Thus, all the following actions o′ are mapped to either o′.sup.e or o′.sup.1, and the preconditions of these actions are restricted to V and v=0, the sequence of actions r.sup.−1(φ achieves the goal values on V and thus is a plan) (see at least paragraphs 50-57, 67-72);
generating, by the computer, at least one set associated with the single goal planning problem (First, given two plans π.sub.1 and π.sub.2, if these plans intersect, i.e., pass through the same state s, then additional plans may be devised out of these two by following one of the plans until the state s and the other plan from the state s onwards. In general, a set of plans P induces a directed graph G(P) over the states of Π with edges annotated by the actions on the plans. Each path in G(P) from the initial state to some goal state is a plan for Π; Let Π be a planning task and P be a set of optimal plans for Π. Then, any path in G(P) from s.sub.0 to some goal state of Π corresponds to an optimal plan for Π.) (see at least paragraphs 67-72);
determining, by the computer prior to determining a set of solutions associated with the planning problem, one or more extended sets associated with the single goal planning problem based on the at least one set (The exemplary ITERATIVETOPKMULTIPLE(Π,k) algorithm illustrated in FIG. 14 works as follows. Once a plan is found, it is extended to a set of plans P and then to the graph G(P), which is forbidden in the next iteration. Further, the plans encoded by G(P) are extracted and partitioned into two sets, optimal plans T and non-optimal ones B. In the next iterations, the set T is extended with optimal plans T′ from that iteration, as well as all plans of the same cost as those in T′ from the set B. The algorithm is thus iterating until the set T consists of at least k plans or no more plans exist), wherein the one or more extended sets comprises of at least one task action of a set of task actions associated with the single goal planning problem (G(P) can be viewed as a compact representation for a set of plans P of a planning task Π. Hence, often more plans are represented by G(P) as compared to P. Proof is now provided regarding the correspondence of paths in G(P) and plans for Π. Consider Lemma 1. Let Π be a planning task and P be a set of plans for Π. Then, any path in G(P) from s.sub.0 to some goal state of Π corresponds to a plan for Π. By way of proof, let s.sub.0, s.sub.1, . . . , s.sub.n with s.sub.n∈S.sub.s* be some path in G(P). Each edge (s.sub.i−1, s.sub.i) corresponds to some action o.sub.i on a plan in P, and thus o.sub.i is applicable in s.sub.i−1, giving o.sub.1 . . . o.sub.n being a plan for Π) (see at least paragraphs 67-72, 96);
determining, by the computer, a search space (a search is performed on a tree of reformulations, invoking an existing planner in each node. As the number of successors of each node is the number of actions in the found plan, the clear down side of such an approach is the large number of invocations of the underlying planner. On the positive side, the approach exhibits an anytime behavior, with the first plan found rather quickly) associated with the single goal planning problem based on the one or more extended sets, wherein a number of one or more actions in the search space is less than a number of the plurality of actions in the first input ((Consider classical planning tasks as captured by the well-known SAS+ formalism, extended with action costs. In such a planning task Π=[AltContent: rect]O, s.sub.0, s*, cost[AltContent: rect], V is a finite set of finite-domain state variables. Each variable v∈V is associated with a finite domain D(v) of variable values. A partial assignment p maps a subset of variables vars(p).Math.V to values in their domains. For a variable v∈V and partial assignment p, the value of v in p is denoted by p[v] if v∈vars(p) and it is said that p[v] is undefined if v.Math.vars(p). A partial assignment s with vars(s)=V, is called a state. State s is consistent with partial assignment p if they agree on all variables in vars(p), shortly denoted by p.Math.s. The product S=Π.sub.v∈D(v) is called the state space of planning task Π. The state s.sub.0 is called initial state of Π and the partial assignment s* is called the goal of Π. A state s is called a goal state if s*.Math.s and the set of all goal states is denoted by S.sub.s*. The finite set O is a set of actions, each action is a pair [AltContent: rect]pre, eff[AltContent: rect] where pre is a partial assignment called precondition and eff is a partial assignment called effect. Further each action o has an associated natural number cost(o), called cost. An action o=[AltContent: rect]pre, eff[AltContent: rect] is applicable in state s if pre.Math.s. Applying action o in state s results in a state denoted by s[[o]] where s[[o]][v]=eff[v] for all v∈vars(eff) and =s[[o]][v]=s[v] for all other variables)) (see at least paragraphs 50-57, 67-72);
determining, by the computer, the set of solutions associated with the planning problem based on the search space (includes obtaining a specification of a planning problem in a planning language (e.g., PDDL, STRIPS, SAS+, and/or ADL); obtaining, in a first iteration, at least one solution to the planning problem (e.g., step 301); modifying, in the first iteration, the planning problem to forbid the at least one solution (e.g. step 305); and repeating the obtaining of the at least one solution and the modifying to forbid the at least one solution, for a plurality of additional iterations, after the first iteration, until a desired number, k, of solutions to the planning problem is found or until no further solutions exist, whichever comes first (decision block 307, e.g.)) (see at least paragraphs 50-57, 67-72, 109);
transmitting, by the computer, the set of solutions (includes obtaining a specification of a planning problem in a planning language (e.g., PDDL, STRIPS, SAS+, and/or ADL); obtaining, in a first iteration, at least one solution to the planning problem (e.g., step 301); modifying, in the first iteration, the planning problem to forbid the at least one solution (e.g. step 305); and repeating the obtaining of the at least one solution and the modifying to forbid the at least one solution, for a plurality of additional iterations, after the first iteration, until a desired number, k, of solutions to the planning problem is found or until no further solutions exist, whichever comes first (decision block 307, e.g.)) to a robot to control the robot to execute the set of solutions (One example is control of industrial robots or the like. Thus, in one or more embodiments, plans are used for task planning for robots. The skilled artisan will appreciate that for a robot to pick up a cup from a table, several micro-actions (e.g., joint and motor movements) will typically need to be performed. In one or more embodiments, planning is not carried out at the level of joint and motor movements, but rather on the level of macro-actions such as “move from Point A to Point B”; “use the arm to pick up object Z”; and the like. The robot is provided with high level plan generated using aspects of the invention and the robot then translates that plan into micro-actions. Reference is made to Torsten Jandt et al., “b-it-bots RoboCup@Work Team Description Paper,” 20th RoboCup International Symposium, Leipzig, Jun. 30-Jul. 4, 2016, the complete disclosure of which is hereby expressly incorporated herein by reference in its entirety for all purposes. As disclosed therein, the existing finite state machines (FSMs) are refactored to very small and clear state machines covering only basic actions, such as move-to-location, perceive-object, grasp-object or place-object) (see at least paragraphs 50-57, 67-72, 109-117);
Katz does not specifically teach stubborn/pruned. Petit teaches stubborn (Strong Stubborn Sets were conceived with the idea of exploiting facts about independent operators to narrow down tree or graph searches, for instance if op is in a Strong Stubborn Set and is applicable in a state s, then only the operators it is dependent of must also belong to that set, although further conditions must also be met to ensure the desired behaviour when pruning)/pruned (State space pruning is a domain independent technique that narrows down that list while preserving optimality, thus reducing the computation overhead needed and making the use of some solvers feasible and more efficient) in analogous art of stubborn set based pruning for the purposes of: “Classical Planning is a branch of artificial intelligence that studies single agent, static, deterministic, fully observable, discrete search problems. A common challenge in this field is the explosion of states to be considered when searching for the goal. One technique that has been developed to mitigate this is Strong Stubborn Set based pruning, where on each state expansion, the considered successors are restricted to Strong Stubborn Sets, which exploit the properties of independent operators to cut down the tree or graph search. We adopt the definitions of the theory of Strong Stubborn Sets from the SAS+ setting to transition systems and validate a central theorem about the correctness of Strong Stubborn Set based pruning for transition systems” (see at least page 1, last paragraph; page 2, second paragraph in section 1.1; Abstract).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include A Formal Verification of Strong Stubborn Set Based Pruning as taught by Petit in the system of Katz, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With regard to Claims 2, 10, 18, Katz teaches wherein the first input further comprises a set of finite-domain state variables (finite-domain state variables), an initial state (initial state), a goal state (goal state), a cost associated with each action of the plurality of actions (action costs), a cost threshold ( plan π is optimal if its cost is minimal among all plans in P.sub.Π), and the set of task actions (actions) (see at least paragraphs 48, 52).
With regard to Claims 3, 11, 19, Katz teaches wherein the set of solutions corresponds to a set of plans, and wherein a cost associated with each plan of the set of plans is less than the cost threshold (see at least paragraph 52).
With regard to Claims 4, 12, 20, Katz teaches:
executing, by the computer, a planner algorithm based on the search space (see at least paragraph 98);
determining, by the computer, the set of solutions associated with the planning problem based on the execution of the planner algorithm (see at least paragraphs 98, 109).
With regard to Claims 5, 14, Katz teaches wherein the set of solutions corresponds to a set of plans to be executed by the robot (see at least paragraphs 5-8, 50-53, 113-122).
With regard to Claims 6, 13, Katz teaches wherein the planner algorithm corresponds to a K* planner algorithm (k* algorithm) (see at least paragraph 32).
With regard to Claims 7, 15, Katz teaches:
identifying, by the computer, one or more duplicate plans in the set of plans (see at least paragraph 47);
removing, by the computer, the one or more duplicate plans from the set of plans to update the set of plans (see at least paragraphs 47, 109);
rendering, by the computer, the updated set of plans associated with the planning problem (see at least paragraphs 47, 109).
With regard to Claims 8, 16, Katz teaches:
determining, by the computer, a time period associated with the determination of the set of solutions associated with the planning problem (see at least paragraph 105);
rendering, by the computer, the time period, wherein the time period is indicative of a time taken for the determination of the set of solutions associated with the planning problem (see at least paragraph 105).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Riepshoff et a. (US 2011/0173042)
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS L MANSFIELD whose telephone number is (571)270-1904. The examiner can normally be reached M-Thurs, alt. Fri. (9-6).
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624
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