Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to submission of application on 3/14/2024.
Claims 1-20 are presented for examination.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show the
structural details as described in the specification. The components are not labeled, there are
no descriptions of the components, nor do the drawings provide a description of the steps. As
such, they are merely a black box. Any structural detail that is essential for a proper
understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office
action to avoid abandonment of the application. Any amended replacement drawing sheet
should include all of the figures appearing on the immediate prior version of the sheet, even if
only one figure is being amended. The figure or figure number of an amended drawing should
not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must
be removed from the replacement sheet, and where necessary, the remaining figures must be
renumbered and appropriate changes made to the brief description of the several views of the
drawings for consistency. Additional replacement sheets may be necessary to show the
renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an
application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet”
pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The
objection to the drawings will not be held in abeyance.
Claim Objections
Claims 2, 9, and 16 are objected to because of the following informalities: the claims recite the phrase “ordering the at least one feature based an amount the at least one feature contributes to the clustering”. Does the limitation mean “based on an amount of”? Or does it mean “based on the amount”? Appropriate correction is required. For the purpose of prior art examination, Examiner is interpreting the limitation as “ordering the at least one feature based on the amount the at least one feature contributes to the clustering”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2-3, 9-10, and 16-17 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
The term “contributes” in claims 2-3, 9-10, and 16-17 is a relative term which renders the claim indefinite. The term “contributes” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term could mean a numerical value such as “contributes an amount” but there is no description of what is being contributed or criteria for determining how it is measured. Neither is there a description of a method for calculation in either the claims or specification.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
Claims 1-7 are directed to a method (i.e., a process), claims 8-14 are directed to a system (i.e., a machine/apparatus), and claims 15-20 are directed to a vehicle (i.e., a machine/apparatus); therefore, all pending claims are directed to one of the four categories of invention.
Step 2A: Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 recites limitations of:
obtaining a first decision tree usable in operation of the device, the first decision tree having a first level of having a parent node and a second level having at least a first child node and a second child node, wherein the first child node and the second child node are accessible from the parent node of the first level – obtaining a first decision tree is interpreted as generating a decision tree which is a mental process (observation, evaluation, judgement, opinion) as a human mind can generate a decision tree. Examiner notes, that it’s possible that applicant intends “obtaining a decision tree” to mean inputting a decision tree. In that case, the limitation would be considered an additional element. Inputting data is insignificant, extra-solution activity. See MPEP 2106.05(g). Transmitting data is well-understood, routine and conventional. See MPEP 2106.05(d)(II)(i).
selecting the parent node and identifying at least the first child node and the second child node – mental process (observation, evaluation, judgement, opinion) as a human mind can select a parent node of a decision tree and identify child nodes.
clustering the first child node and the second child node to form a second decision tree having a clustered child node, the clustered child node based on at least one feature in common between the first child node and the second child node – mental process (observation, evaluation, judgement, opinion) as a human mind can group child nodes of a decision tree based on a feature found in common.
determining a semantic meaning for the clustered child node, wherein the semantic meaning for the clustered child node is not present in either the first child node or the second child node – mental process (observation, evaluation, judgement, opinion) as a human mind can determine the semantic meaning for a clustered node of a decision tree.
which are abstract ideas, something that can be accomplished by the human mind, or with the aid of pen and paper.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements of:
A method for increasing an efficiency of operating a device – components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
The additional elements do not integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
The additional elements of:
A method for increasing an efficiency of operating a device – components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2).
The additional elements do not amount to significantly more than the abstract idea. Therefore, claim 1 is not patent eligible.
Independent claims 8 and 15 recite similar limitations and a similar analysis applies. Claim 8 recites the additional elements of “A system for increasing an efficiency of operation of a device, comprising: a processor” - computer components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2). Claim 15 recites the additional elements of “A vehicle, comprising: a processor” - components recited at a high level are construed as generic components used to implement the abstract idea. See MPEP 2106.05(f)(2). The additional elements do not integrate the abstract idea into a practical application. Nor do they amount to significantly more. Therefore, the independent claims are not patent eligible.
The above analysis similarly applies to the dependent claims.
Claims 2, 9, and 16 recite the additional elements of “determine the semantic meaning by ordering the at least one feature based on an amount the at least one feature contributes to the clustering” – mental process (observation, evaluation, judgement, opinion) as a human mind can determine a semantic meaning based on a feature.
Claim 3, 10, and 17 recite the additional elements of “select a distinguishing feature from the at least one feature that contributes most to the clustering and assigning the semantic meaning to the clustered child node based on the distinguishing feature” – mental process (observation, evaluation, judgement, opinion) as a human mind can select a distinguishing feature and assign the semantic meaning to a clustered node.
Claims 4 and 11 recite the additional elements of “the first decision tree includes a third level having nodes, each node of the third level being accessible from a node of the second level” – further details of the result of the mental process. As such, a mental process (observation, evaluation, judgement, opinion), and “assign the clustered child node as a clustered parent node, identify the nodes of the third level associated with the clustered parent node and clustering the identified nodes of the third level” – mental process (observation, evaluation, judgement, opinion) as a human mind can assign a clustered node to be a parent node and further cluster identified nodes.
Claims 5, 12, and 19 recite “operate the device using the first decision tree to take an action based on one of the first child node and the second child node and present a reason for the action based on the semantic meaning for the clustered child node from the second decision tree” – using a decision tree to operate a device is mere instructions to apply. See MPEP 2106.05(f)(3). Claim 19 recites the additional element of “vehicle” in place of “device”. This is also mere instructions to apply.
Claims 6, 13, and 20 recite the additional elements of “the at least one feature includes at least one of: (i) a state of the vehicle; (ii) a value of the action; and (iii) a spatial parameter of the action” – further details of the result of the mental process. As such, a mental process (observation, evaluation, judgement, opinion) as a human mind can identify a feature.
Claims 7 and 14 recite the additional elements of “the parent node is a top node of the first decision tree” – further details of the generation of the decision tree, which is a mental process (observation, evaluation, judgement, opinion) as a human mind can generate a decision tree where a parent node is a top node of the decision tree.
Claim 18 recites the additional elements of “the first decision tree includes
a third level having nodes, each node of the third level being accessible from a node of the second level” – further details of the decision tree. As such, a mental process (observation, evaluation, judgement, opinion), and “assign the clustered child node as a clustered parent node, identify the nodes of the third level associated with the clustered parent node and clustering the identified nodes of the third level” – mental process (observation, evaluation, judgement, opinion).
The additional elements do not integrate the abstract idea into a practical application. Nor do they amount to significantly more. Therefore, claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-11, and 14-18 are rejected under 35 U.S.C. § 103 as being unpatentable over Chang, et al, US 12,649,462 B1 (Vehicle Trajectory Tree Search, herein Chang), and He, et al, US 2018/0357262 A1 (Determining A Hierarchical Concept Tree Using a Large Corpus of Table Values, herein He).
Regarding claim 1,
Chang teaches a method for increasing an efficiency of operating a device (Chang, column 2, line 48 “ The various examples and techniques described herein may be implemented in a number of ways to improve the operation of autonomous vehicles and the functioning of computing systems.” In other words, vehicle is device, technique is method, and improve operation of autonomous vehicles is increasing an efficiency of operating.), comprising:
obtaining a first decision tree usable in operation of the device, the first decision tree having a first level of having a parent node and a second level having at least a first child node and a second child node (Chang, column 2, line 38 “The trajectory planning component may generate and traverse a tree structure a tree structure by determining and evaluating candidate actions for each node in the tree structure (in which each node represents a vehicle state and/or driving environment state), selecting one or more candidate actions at each node based on costs, and iteratively creating and exploring new nodes based on the selected candidate actions, until an end node in the tree structure is reached, to determine one or more potential control trajectories from the current vehicle state to an intended end state.” In other words, generate...a tree structure is obtain a first decision tree, nodes are nodes, tree structure is parent nodes and child nodes, and selecting one or more candidate actions to control trajectories from the current vehicle state is usable in operation of a device.), wherein
the first child node and the second child node are accessible from the parent node of the first level; selecting the parent node and identifying at least the first child node and the second child node (Chang, FIG. 4B, and column 2, line 38 “The trajectory planning component may generate and traverse a tree structure by determining and evaluating candidate actions for each node in the tree structure (in which each node represents a vehicle state and/or driving environment state), selecting one or more candidate actions at each node based on costs, and iteratively creating and exploring new nodes based on the selected candidate actions, until an end node in the tree structure is reached, to determine one or more potential control trajectories from the current vehicle state to an intended end state.”
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In other words, first node 408 is the parent node, traverse the tree is the first child node 414, and the second child node 418 are accessible from the parent node, and selecting one or more candidate actions is selecting the parent node and identifying at least the first child node and the second child node.)
[clustering the first child node and the second child node to form a second decision tree having a clustered child node, the clustered child node based on at least one feature in common between the first child node and the second child node]; and
[determining a semantic meaning for the clustered child node, wherein the semantic meaning for the clustered child node is not present in either the first child node or the second child node].
Thus far, Chang does not explicitly teach clustering the first child node and the second child node to form a second decision tree having a clustered child node, the clustered child node based on at least one feature in common between the first child node and the second child node.
He teaches clustering the first child node and the second child node to form a second decision tree having a clustered child node, the clustered child node based on at least one feature in common between the first child node and the second child node (He, FIG. 5, and paragraph [0018], line 3 “In performing the knowledge discovery, the systems and methods disclosed herein cluster one or more values according to concepts, and attempt to represent the organization of such values according to the clustering.” and, paragraph [0019], line 3 “In the first step, the disclosed systems and methods compute one or more statistical co-occurrence values between all values in the table corpus. These cooccurrence values are then iteratively merged into clusters in a bottom-up, hierarchical manner. This is because natural concepts follow a tree hierarchy, starting from the narrow concept in the leaf nodes, up to root levels with super-categories and broad concepts.”
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FIG. 5
In other words, clustering is clustering, according to concepts is based on at least one common feature, and iteratively merge into clusters in a bottom-up, hierarchical manner is to form a second tree. )
He teaches determining a semantic meaning for the clustered child node, wherein the semantic meaning for the clustered child node is not present in either the first child node or the second child node (He, paragraph [0038], line 4 “In one embodiment, the similarity determination module 216 employs statistical value cooccurrence to compute a similarity between any two values of the table values 222 and/or spreadsheet values 224 to capture the semantic relatedness of these two values. For example, if a first value (e.g., “France.02. -VCSRLCT") co-occurs frequently with a second value ( e.g., "Germany.06.MNFEU") in the same columns of the one or more database tables 112,118 and/or spreadsheet files 114-116, this pair of values is likely to have a high similarity score.” The specification of the instant application recites “In addition to one or more of the features described herein, wherein determining the semantic meaning further includes ordering the at least one feature based an amount the at least one feature contributes to the clustering.” (Specification, paragraph [0004], line 1.) Based on this, Examiner is interpreting that “semantic meaning” refers to the reason, i.e. similarity, for clustering two or more nodes. In other words, values of the table is nodes, capture the semantic relatedness is determining a semantic meaning, and similarity between the two is the semantic meaning is not present in either the first child node or the second child node.)
Both Chang and He are directed to decision trees, among other things. Chang teaches a method for increasing an efficiency of an operating a device, comprising: obtaining a first decision tree usable in operation of the device, the first decision tree having a first level of having a parent node and a second level having at least a first child node and a second child node, wherein the first child node and the second child node are accessible from the parent node of the first level; selecting the parent node and identifying at least the first child node and the second child node; but does not explicitly teach clustering the first child node and the second child node to form a second decision tree having a clustered child node, the clustered child node based on at least one feature in common between the first child node and the second child node; or determining a semantic meaning for the clustered child node, wherein the semantic meaning for the clustered child node is not present in either the first child node or the second child node. He teaches clustering the first child node and the second child node to form a second decision tree having a clustered child node, the clustered child node based on at least one feature in common between the first child node and the second child node; and determining a semantic meaning for the clustered child node, wherein the semantic meaning for the clustered child node is not present in either the first child node or the second child node.
In view of the teaching of Chang, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of He into Chang. This would result in a method for increasing an efficiency of operating a device, comprising: obtaining a first decision tree usable in operation of the device, the first decision tree having a first level of having a parent node and a second level having at least a first child node and a second child node, wherein the first child node and the second child node are accessible from the parent node of the first level; selecting the parent node and identifying at least the first child node and the second child node; clustering the first child node and the second child node to form a second decision tree having a clustered child node, the clustered child node based on at least one feature in common between the first child node and the second child node; and determining a semantic meaning for the clustered child node, wherein the semantic meaning for the clustered child node is not present in either the first child node or the second child node.
One of ordinary skill in the art would be motivated to do this to improve the efficiency of decision trees by clustering in order to reduce the amount of processing required (He, paragraph [0004], line 5 “Entity-concept relationships so discovered may involve substantial post-processing and cleaning before they can be used. Given that each enterprise has its own proprietary data, this is thus difficult to apply and expensive to scale. Accordingly, conceptualizing data at the enterprise level and within private domains is challenging and under-utilized.” ).
Regarding claim 2,
The combination of Chang and He teaches the method of claim 1, further comprising wherein determining the semantic meaning further comprises
ordering the at least one feature based (See interpretation, office action paragraph 5. ) the at least one feature contributes to the clustering (He, paragraph [0038], line 4 “In one embodiment, the similarity determination module 216 employs statistical value cooccurrence to compute a similarity between any two values of the table values 222 and/or spreadsheet values 224 to capture the semantic relatedness of these two values.” And, paragraph [0039], line 1 “Accordingly, a corpus-driven approach is used to define similarity
s: V x V->(0,1) for each pair of values. The similarity determination module 216 may use one or more set-based and/or vector-based equations to determine the similarity values 226, such as Pointwise Mutual Information (PMI), the Sorensen-Dice coefficient, the Jaccard index, or any other statistic used for comparing one or more sample sets and/or values.” And, paragraph [0043], line 6 “In particular, the candidate cluster module 218 collapses all node pairs whose
edge scores are higher than a selected similarity threshold.” Examiner notes, it is not clear what “ordering” means in the limitation. The specification recites “…determining the semantic meaning further includes ordering the at least one feature based an amount the at least one feature contributes to the clustering.” (Specification, paragraph [0004], line 2.) There is no specific description for what the “ordering” refers to. Based on this, Examiner is interpreting that “ordering the at least one feature contributes to clustering” means, edge score for a pair of nodes. In other words, edge score is amount the feature contributes to the clustering. ).
Regarding claim 3,
The combination of Chang and He teaches the method of claim 2, further comprising
selecting a distinguishing feature from the at least one feature that contributes most to the clustering and assigning the semantic meaning to the clustered child node based on the distinguishing feature (He, Eq 1, and, paragraph [0039], line 1 “Accordingly, a corpus-driven approach is used to define similarity s: V x V->(0,1) for each pair of values. The similarity determination module 216 may use one or more set-based and/or vector-based equations to determine the similarity values 226, such as Pointwise Mutual Information (PMI), the Sorensen-Dice coefficient, the Jaccard index, or any other statistic used for comparing one or more sample sets and/or values. Where a first table is assigned as v1 and a second table is assigned as v2, the similarity value between v1 and v2 using the Jaccard index is stated as:” And, paragraph [0043], line 6 “In particular, the candidate cluster module 218 collapses all node pairs whose edge scores are higher than a selected similarity threshold.”
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In other words, similarity is feature, and Jaccard score is assigning the semantic meaning to the clustered node based on the feature.).
Regarding claim 4,
The combination of Chang and He teaches the method of claim 1, wherein
the first decision tree includes a third level having nodes, each node of the third level being accessible from a node of the second level (He, FIG. 5. In other words, FIG. 5 shows the first decision tree which includes a third level having nodes where each node of the third level is accessible from a node of the second level.) , the method further comprising
assigning the clustered child node as a clustered parent node, identifying the nodes of the third level associated with the clustered parent node and clustering the identified nodes of the third level (He, Figures 10A, and 10B
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In other words, construct hierarchical concept tree using…identified candidate clusters is assigning clustered child node as a clustered parent node, and continuously cluster co-occurrence pairs is identify the nodes of the third level associated with the clustered parent node and clustering the identified nodes.) .
17. Regarding claim 7,
The combination of Chang and He teaches the method of claim 1, wherein
the parent node is a top node of the first decision tree (Chang, column 5, line 63 “To evaluate the cost of a candidate action node, the planning component may sum ( or otherwise aggregate) the costs associated with the nodes composing a branch of the tree structure including that candidate action ( e.g., the cost of the candidate action node and the cost of all parent nodes from which the candidate action node depends, tracing back to the root node).” In other words, parent node is parent node, and tracing back to the root node is the parent node is root node which is a top node of the first decision tree.).
Claim 8 is a system claim…comprising a processor that corresponds to method claim 1. Otherwise, they are not patentably distinct. The combination of Chang and He teaches a system comprising a processor (Chang, column 1, paragraph 1, line 1 “Autonomous driving may benefit from computing systems capable of determining driving paths and navigating
along routes from an initial location toward a destination.” And, claim 1 “A vehicle comprising: one or more processors…” In other words, computer system is system, and comprising one or more processors is a system comprising a processor.). Therefore, claim 8 is rejected for the same reasons as claim 1.
Claims 9-11, and 14 are system claims that correspond to method claims 2-4, and 7, respectively. Otherwise, they are not patentably distinct. Therefore, claims 9-11, and 14 are rejected for the same reasons as claims 2-4, and 7, respectively.
Claim 15 is a vehicle comprising a processor claim that corresponds to method claim 1. Otherwise, they are not patentably distinct. The combination of Chang and He teaches a vehicle comprising a processor (Chang, claim 1 “A vehicle comprising: one or more processors…” In other words, a vehicle comprising one or more processors is a vehicle comprising a processor.) Therefore, claim 15 is rejected for the same reasons as claim 1.
Claims 16-18, are vehicle comprising a processor claims that correspond to method claims 2-4, respectively. Otherwise, they are not patentably distinct. Therefore, claims 16-18 are rejected for the same reasons as claims 2-4, respectively.
Claims 5-6, 12-13, and 19-20 are rejected under 35 U.S.C § 103 as being unpatentable over Chang, He, and Brewitt, et al (GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving, herein Brewitt).
Regarding claim 5,
The combination of Chang and He teaches the method of claim 1, further comprising
operating the device using the first decision tree to take an action based on one of the first child node and the second child node (Chang, FIG. 1,
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In other words, autonomous vehicle is device, and FIG. 1 shows operating the device to take an action.) and
Thus far, the combination of Chang and He does not explicitly teach presenting a reason for the action based on the semantic meaning for the clustered child node from the second decision tree.
Brewitt teaches presenting a reason for the action based on the semantic meaning for the clustered child node from the second decision tree (Brewitt, page 1025, column 2, paragraph 1, line 1 “One limitation of current prediction methods is the inability to guarantee safety through formal verification. GRIT can easily be verified due to the computational simplicity of decision tree inference, and the tree representation, which can be mapped into propositional logic.” And, page 1025, column 2, paragraph 1, line 7 “ In order to perform verification, we first represented the model using propositional logic, and then verify a proposition
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by proving that :
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is unsatisfiable. We used the Z3 SMT solver [25] to perform
the verification. In the event that verification of
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fails, the solver provides a counterexample which can be useful to understand why the model makes certain predictions.” In other words, provide a counterexample for why the model makes a prediction is presenting a reason for the action based on the semantic meaning.) .
Both Brewitt and the combination of Chang and He are directed to using decision trees for controlling autonomous vehicles, among other things. The combination and of Chang and He teaches the method of claim 1, but does not explicitly teach presenting a reason for the action based on the semantic meaning for the clustered child node from the second decision tree. Brewitt teaches presenting a reason for the action based on the semantic meaning for the clustered child node from the second decision tree.
In view of the teaching of the combination of Chang and He, it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Brewitt into the combination of Chang and He. This would result in the method of claim 1, and presenting a reason for the action based on the semantic meaning for the clustered child node from the second decision tree.
One of ordinary skill in the art would be motivated to do this in order to make autonomous vehicles safer by making inferences verifiable. (Brewitt, abstract, line 1 “It is important for autonomous vehicles to have the ability to infer the goals of other vehicles (goal recognition), in order to safely interact with other vehicles and predict their future trajectories. This is a difficult problem, especially in urban environments with interactions between many vehicles. Goal recognition methods must be fast to run in real time and make accurate inferences. As autonomous driving is safety critical, it is important to have methods which are human interpretable and for which safety can be formally verified.”)
Regarding claim 6,
The combination of Chang, He, and Brewitt teaches the method of claim 5, wherein the at least one feature includes at least one of:
(i) a state of the device; (ii) a value of the action; and (iii) a spatial parameter of the action (Chang, FIG. 3
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In other words, Vehicle State is state of the device, which is one of: a state of the device, a value of the action, and a spatial parameter of the action.) .
Claims 12-13 are system claims that correspond to method claims 5-6, respectively. Otherwise, they are not patentably distinct. Therefore, claims 12-13 are rejected for the same reasons as claims 5-6, respectively.
Claims 19-20 are vehicle comprising a processor claims that correspond to method claims 5-6, respectively. Otherwise, they are not patentably distinct. Therefore, claims 19-20 are rejected for the same reasons as claims 5-6, respectively.
The prior art made of record and not used is considered pertinent to applicant’s disclosure:
Basak, et al “Interpretable Hierarchical Clustering by Constructing an Unsupervised Decision Tree” discloses a method for hierarchical clustering based on the decision tree approach and presents four different measures for selecting the most appropriate attribute to be used for splitting the data at every branching node (or decision node), and two different algorithms for splitting the data at each decision node.
Narayanan, et al, US 20240174256 A1 “Vehicle Trajectory Tree Search for Off-Route Driving Maneuvers” discloses techniques for using a tree search to determine a trajectory for a vehicle to join a driving route from an initial vehicle state off of the driving route structure.
Van Beek, et al, WO 2020205 55 A1 “Autonomous Vehicle System” discloses an apparatus including an interface to receive sensor data from a plurality of sensors of an autonomous vehicle and including processing circuitry to apply a sensor abstraction process to the sensor data to produce abstracted scene data for the autonomous vehicle.
Widyantoro, et al “An Incremental Approach to Building a Cluster Hierarchy” discloses a novel Incremental Hierarchical Clustering (IHC) algorithm that aims to construct a hierarchy that satisfies homogeneity and monotonicity properties. Working in a bottom-up fashion, a new instance is placed in the hierarchy, and a sequence of hierarchy restructuring process is performed only in regions that have been affected by the presence of the new instance.
Conclusion
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/Bart I Rylander/Examiner, Art Unit 2124
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