DETAILED ACTION
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 .
Claims 11-28 are presented for the examination.
§ 101 2. 35 U.S.C. 101 reads as follows
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 11, 15, 17, 27, 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to Claims 11, 27, 28 have been rejected under 35 USC 101 for abstract idea without significantly more. Under Step 2A, Prong 1, the “ determining an activated vehicle function which is operated during a journey ” recite a mental process since “determining” is a function that can be reasonably performed in the human mind with the aid of pen and paper through observation, evaluation, judgment, opinion.
Under Prong 2, the additional element “ providing a graph, wherein the graph comprises a plurality of nodes which are interconnected via edges, each node of the plurality of nodes is representative of at least one vehicle function, and each edge is representative of a probability of a transition from one of the plurality of nodes to another of the plurality of nodes, allocating the activated vehicle function to a first node of the plurality of nodes; and allocating the resources of the vehicle depending on the determined transition probability.” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception, Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f).
Under Step 2B, the additional elements “ providing a graph, wherein the graph comprises a plurality of nodes which are interconnected via edges, each node of the plurality of nodes is representative of at least one vehicle function, and each edge is representative of a probability of a transition from one of the plurality of nodes to another of the plurality of nodes ” - this generally have been a mental process although the graph could be a generic computer component describes in actual computer hardware.
, “ allocating the activated vehicle function to a first node of the plurality of nodes; and allocating the resources of the vehicle depending on the determined transition probability” - this is mere instructions to apply the mental process under mpep 2106.05(f), amounts to merely generally linking the use of the judicial exception to a particular technological environment or field or use, and is merely applying the judicial exception, therefore, does not amount to significantly more, hence, cannot provide an inventive concept.
As to Claims 15, 17 have been rejected under 35 USC 101 for abstract idea without significantly more. Under Step 2A, Prong 1, the “ determining impossible connections of temporary graph nodes; determining transition probabilities between the possible combinations of display devices and vehicle functions during a plurality of journeys of the vehicle” recite a mental process since “determining” is a function that can be reasonably performed in the human mind with the aid of pen and paper through observation, evaluation, judgment, opinion.
Under Prong 2, the additional element “ providing a temporary graph, wherein the temporary graph comprises temporary graph nodes which comprise a plurality of possible combinations of display devices and vehicle functions of the vehicle, each temporary graph node having two counter-directional edges is connected to each of the other temporary graph nodes, and each of the counter-directional edge comprises a weighting which is representative of a transition probability, herein transition probabilities of the impossible connections of temporary graph nodes are set to 0, generating and storing the graph depending on the determined transition probabilities and the temporary graph; and providing the graph” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component, or merely a generic computer or generic computer components to perform the judicial exception, Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f).
Under Step 2B, the additional elements “ providing a temporary graph, wherein the temporary graph comprises temporary graph nodes which comprise a plurality of possible combinations of display devices and vehicle functions of the vehicle, each temporary graph node having two counter-directional edges is connected to each of the other temporary graph nodes, and each of the counter-directional edge comprises a weighting which is representative of a transition probability, herein transition probabilities of the impossible connections of temporary graph nodes are set to 0 ” - this generally have been a mental process although the graph could be a generic computer component describes in actual computer hardware.
, “ generating and storing the graph depending on the determined transition probabilities and the temporary graph; and providing the graph” - this is mere instructions to apply the mental process under mpep 2106.05(f), amounts to merely generally linking the use of the judicial exception to a particular technological environment or field or use, and is merely applying the judicial exception, therefore, does not amount to significantly more, hence, cannot provide an inventive concept.
5. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application. See MPEP 2106.05(d). Thus, the claim is not patent eligible.
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.
6. Claim 28 is rejected under 35 U.S.C. 101 because they are directed to non-statutory subject matter.
7. Claim 28 is rejected under 35 U.S.C. 101 as non-statutory because it is not tangibly embodied.
8. Claim 28 recites “ computer-readable storage medium” in the preamble. However, the specification does not define this medium to be hardware, memory or non-transitory computer medium; therefore, claim 28 is non-statutory.
The following is a quotation of 35 U.S.C. 112(f):
9. Element in Claim for a Combination. —An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AlA35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination maybe expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection |, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre- AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: A device which configured to in claim 27.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AlA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
10. Claim(s) 11, 14, 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Saunders( US 20220035372 A1) in view of Husain( US 20210072033 A1) and further in view of Murray(US 20200398428 A1).
As to claim 11, Sauders teaches providing a graph, wherein the graph comprises a plurality of nodes which are interconnected via edges, each node of the plurality of nodes is representative of at least one vehicle function ( present disclosure relates generally to robotics and, in particular, to one or more of the design, construction, operation or use of autonomous robots such as autonomous or semi-autonomous vehicles, para[0001]/ which the robot(s) 204 are caused to execute the mission, the task allocation service 404 is configured to construct a task graph in which the mission is modeled. The task graph is expressed as a directed graph and includes selected task nodes representing the selected tasks that are connected by edges representing transitions between the selected tasks. In some of these examples, the task allocation service is further configured to provide mission data including the task graph to the robot(s), and the task graph is accessible onboard the robot(s) to cause the robot(s) to execute the mission, para[0065]/ a robot may be managed to execute tasks to implement these behaviors with specific parameters and capabilities, and these tasks may be modeled in a task graph of nodes).
, and each edge is representative of a probability of a transition from one of the plurality of nodes to another of the plurality of nodes( a task graph 608 in which the mission is modeled, the edges 614, 622, 626 representing transitions between the tasks (represented by the task nodes 612, 620, 624) may be associated with levels of contingency. This association, then, may direct the transition from one task to another when the level of contingency is reported to the task manager service 606., para[0096]/ In the illustrated task graph, the edges are associated with levels of contingency for level 1, level 2 and level 3 contingencies, as well as a level 0 representing a nominal transition between task nodes in a sequence of task nodes. In some examples, one or more of the edges may be associated with multiple levels of contingency so that the same transition occurs when any of the multiple levels of contingency are detected, para[0097], ln 10-20).
determining the probability of transition of the allocated first node to a further node of the plurality of nodes( the method 1100 further includes for the robot 204 of the one or more robot, detecting occurrence of the contingency event during execution of any one of the selected tasks, as shown at block 1120. The method includes transitioning to the alternate task graph, and calling on the task library 616 to execute the one or more alternate tasks and thereby cause the robot to execute one or more other maneuvers, as shown at blocks 1122 and 1124, para[0123], ln 8-18/ The task graph 700 includes selected task nodes 702 representing the selected tasks in a nominal sequence that are connected by edges 704 representing transitions between the selected tasks. The task graph includes or is associated with alternate task nodes 706 representing alternate tasks to be executed when a contingency event (e.g., local contingency event, global contingency event) occurs during execution of a selected task in the nominal sequence of selected tasks. In the illustrated task graph, the edges are associated with levels of contingency for level 1, level 2 and level 3 contingencies, as well as a level 0 representing a nominal transition between task nodes in a sequence of task nodes. In some examples, one or more of the edges may be associated with multiple levels of contingency so that the same transition occurs when any of the multiple levels of contingency are detected, para[0097])
Husain teaches determining an activated vehicle function which is operated during a journey(the rideshare router may notify the user that an unmanned aerial vehicle (UAV) taxi was chosen for some or all of the journey due to terrestrial road traffic conditions between point A and point B, para[0026], ln 13-20/ Once trained, the first trained model may be used by the rideshare router 110 to determine how to schedule a requested journey (or package transport) the first model may output indications of what modes of transport are preferable, which of multiple alternative “paths” (e.g., roadways, waterways, UAV sectors, etc.) are acceptable for various legs of the journey, whether it would be preferable to start the journey later, etc. In such examples, information into the first trained model may include information input by user(s), information determined from public/private sources, information as measured by particular sensors, information posted to a blockchain, etc, para[0088], ln 6-18/ Referring to FIG. 5, a particular illustrative example of a system 500 operable to train models is shown, para[0091], ln 1-3/ Fig, 5 Alternatively, the activation function may be randomly or pseudo-randomly selected from a set of allowed activation functions, and different nodes of a model may have different types of activation functions, para[0114], ln 11-17/ Fig.5)
allocating resources of a vehicle, allocating the activated vehicle function to a first node of the plurality of nodes( the rideshare router may notify the user that an unmanned aerial vehicle (UAV) taxi was chosen for some or all of the journey due to terrestrial road traffic conditions between point A and point B, para[0026], ln 13-20/different nodes of a model may have different types of activation functions. In other implementations, the activation function assigned to each node may be randomly or pseudo-randomly selected (from the set of allowed activation functions) for each node the particular model, para[0114], ln 14-20);
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Sauders with Husain to incorporate the above feature because this predicts vehicle maintenance, calculate a vehicle health score, determine natural language generation of explanations regarding vehicle health, and even calculate a carbon footprint score.
Murray teaches allocating the resources of the vehicle depending on the determined transition probability( The robotic system 100 may employ other forms of robots 102, for example autonomous vehicles, para[0112], ln 12-16/ determines or assesses a likelihood or probability that a motion or transition (represented by an edge) will result in a collision with an obstacle, para[0164], ln 2-6/ or nodes in the planning graph 300 where there is a probability that direct transition between the nodes will cause a collision with an obstacle, the motion planner (e.g., cost setter 254, FIG. 2) assigns a cost value or weight to the edges of the planning graph 300 transitioning between those nodes (e.g., edges 310a, 310b, 310c, 310d, 310e, 310f, 310g, 310h) indicating the probability of a collision with the obstacle, para[0165], ln 1-10/ , the motion planner has assigned a cost value or weight of zero to those edges in the planning graph 300 which represent transitions or motions of the robot 102, 202 that do not have any or have very little probability of a collision with an obstacle. For each of a number of edges of the planning graph 300 with a respective probability of a collision with an obstacle in the environment above the defined threshold probability of a collision, the motion planner assigns a cost value or weight with a value substantially greater than zero. In the present example, the motion planner has assigned a cost value or weight of greater than zero to those edges in the planning graph 300 which have a relatively high probability of collision with an obstacle. The particular threshold used for the probability of collision may vary. For example, the threshold may be 40%, 50%, 60% or lower or higher probability of collision. Also, assigning a cost value or weight with a value greater than zero may include assigning a weight with a magnitude greater than zero that corresponds with the respective probability of a collision. For example, as shown in the planning graph 300, the motion planner has assigned a cost value or weight of 5 to edges 310f and 310i that have a higher probability of collision, but has assigned a cost value or weight with a lower magnitude of 1 to edges 310p and 310g, which the motion planner determined have a much lower probability of collision. In other implementations, the cost values or weights may present a binary choice between collision and no collision, there being only two cost values or weights to select from in assigning cost values or weights to the edges, para[0166]).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Sauders and Husain with Murray to incorporate the above feature because this allows task is used to refer to a robotic task in which a robot transitions from a pose to a pose without colliding with obstacles in its environment.
As to claim 14, Murray teaches the resources are allocated at least temporarily depending on a relationship between the determined transition probability and a threshold value( para[0165], ln 1-10/ para[0166]) for the same reason as to claim 1 above.
As to claims 27-28, they are rejected for the same reason as to claim 1 above. In additional, Sauders teaches computer-readable storage medium (the computer-readable storage medium being non-transitory, para[0019], ln 1-6).
11. Claim(s) 12, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Saunders( US 20220035372 A1) in view of Husain( US 20210072033 A1) in view of Murray(US 20200398428 A1) and further in view of YUN(US 20180102987 A1).
As to claim 12, Yun teaches wherein each node of the plurality of nodes is representative of a combination of a displayed content of a display device and the at least one vehicle function( the communication nodes (i.e., gateways, switches, end nodes, etc.) constituting the vehicle network may be connected in a star topology, a bus topology, a ring topology, a tree topology, a mesh topology, or the like, para[0054], ln 1-6/ Here, the second function corresponding to the first function may mean a function related to the first function among a plurality of functions performed at the vehicle. For example, if the second communication node is a transmission of the vehicle and the first function is a function of shifting a gear of the vehicle into a reverse position (i.e., ‘R’ position), the second function may be a function of outputting images photographed at the rear of the vehicle. That is, the third communication node performing the second function may be a display device capable of outputting images photographed at the rear of the vehicle, para[0069], ln 5-20).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Sauders, Husain and Murray with Yun to incorporate the above feature because this provides a display device capable of outputting images photographed on the left side or the right side of the vehicle.
As to claim 13, Murray teaches the resources are allocated at least temporarily depending on a relationship between the determined transition probability and a threshold value(para[0165], ln 1-10/ para[0166]) for the same reason as to claim 1 above.
12. Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Saunders( US 20220035372 A1) in view of Husain( US 20210072033 A1) in view of Murray(US 20200398428 A1) in view of YUN(US 20180102987 A1) and further in view of Tepper( US 20120167075 A1).
As to claim 15, Yun teaches providing the graph further comprises: providing a temporary graph, wherein the temporary graph comprises temporary graph nodes which comprise a plurality of possible combinations of display devices and vehicle functions of the vehicle,( the communication nodes (i.e., gateways, switches, end nodes, etc.) constituting the vehicle network may be connected in a star topology, a bus topology, a ring topology, a tree topology, a mesh topology, or the like, para[0054], ln 1-6/ Here, the second function corresponding to the first function may mean a function related to the first function among a plurality of functions performed at the vehicle. For example, if the second communication node is a transmission of the vehicle and the first function is a function of shifting a gear of the vehicle into a reverse position (i.e., ‘R’ position), the second function may be a function of outputting images photographed at the rear of the vehicle. That is, the third communication node performing the second function may be a display device capable of outputting images photographed at the rear of the vehicle, para[0069], ln 5-20).
para[0069], ln 5-20 );
Yun teaches graph includes a plurality of possible combinations of display devices and vehicle functions of the vehicle and Sauders teaches determining transition probabilities between vehicle functions during a plurality of journeys of the vehicle( In some examples in which the mission data 604 includes a task graph 608 in which the mission is modeled, the edges 614, 622, 626 representing transitions between the tasks (represented by the task nodes 612, 620, 624) may be associated with levels of contingency. This association, then, may direct the transition from one task to another when the level of contingency is reported to the task manager service 606, para[0096]/ In the illustrated task graph, the edges are associated with levels of contingency for level 1, level 2 and level 3 contingencies, as well as a level 0 representing a nominal transition between task nodes in a sequence of task nodes. In some examples, one or more of the edges may be associated with multiple levels of contingency so that the same transition occurs when any of the multiple levels of contingency are detected, para[0097], ln 11-20/ he method includes, for a robot of the one or more robots, accessing mission data 604 for the mission including or associated with tasks that are executable to cause the robot to execute maneuvers, and executing the tasks according to the mission data to cause the robot to execute the maneuvers, as shown at blocks 1202 and 1204 of FIG. 12A. The method includes, as the tasks are executed, monitoring at least one of the robot or the environment, and detecting an event based on the monitoring, during execution of a task of the tasks, as shown at blocks 1206 and 1208. The method includes mapping the event to a level of contingency of a plurality of levels of contingency, as shown at block 1210. The method includes transitioning from the task to another of the tasks according to the level of contingency, and executing the other of the tasks, as shown at blocks 1212 and 1214., para[0124]);
each temporary graph node having two counter-directional edges is connected to each of the other temporary graph node( determining a task graph in which the mission is modeled, the task graph expressed as a directed graph and including selected task nodes representing the selected tasks that are connected by edges representing transitions between the selected tasks; and causing the one or more robots to execute the mission using the task graph and a task library of tasks including a selected task executable to cause the one or more robots to execute a maneuver, para[0011], ln 6-18/ In response, the task manager service is configured to transition from the task to another of the tasks according to the level of contingency, and execute the other of the tasks., para[0093], ln 13-17), and each of the counter-directional edge comprises a weighting which is representative of a transition probability(According to some example implementations, events that are detectable by the contingency monitor 628 service may be may be classified into levels of contingency. In some examples, events may be classified into levels of contingency corresponding to the alternate task graphs 510, 512, 514 shown in FIG. 5C for some global contingency events. In other examples, the events may be classified into levels of contingency as follows: Land immediately (level 1) Land as soon as possible (level 2) Land as soon as practical (level 3) Hold pattern (level 4), Other robot or mission-specific contingencies The levels of contingency may be ordered such as in order of priority (e.g., land immediately being the highest, followed by land as soon as possible, followed by and as soon as practical, and so forth, para[0087] to para[0092]/ para[0094);
generating and provide the graph depending on the determined transition probabilities and the temporary graph( In some examples in which the mission data 604 includes a task graph 608 in which the mission is modeled, the edges 614, 622, 626 representing transitions between the tasks (represented by the task nodes 612, 620, 624) may be associated with levels of contingency. This association, then, may direct the transition from one task to another when the level of contingency is reported to the task manager service 606, para[0069]/ The method includes determining a task graph 608 in which the mission is modeled, as shown at block 1104. The task graph is expressed as a directed graph 610 and includes selected task nodes 612 representing the selected tasks that are connected by edges 614 representing transitions between the selected tasks, para[0117], ln 7-17 / Each task node 702 may be connected to one or more edges that represent transition from the task node (egress), and these one or more edges may cover all of the levels of contingency. For some tasks, however, the robot may be configured to ignore one or more levels of contingency. The task graph 700 may further include additional edges and nodes to cover these situations. As shown, for example, the task graph includes additional edges 708 associated with one or more levels of contingency, para[0098], ln 1-12/ FIG. 5D illustrates the task graph 500 connected to, for example, the third alternate task graph 514 for the third global contingency event. The selected task nodes 502 are associated with the third alternate task graph, shown for one of the selected task nodes as a dashed edge 516 that represents association of the selected task with the alternate task graph., para[0075]) ;
Tepper teaches determining impossible connections of temporary graph nodes, wherein transition probabilities of the impossible connections of temporary graph nodes are set to 0( a prediction graph of blocks is generated. That is, assuming that the program to be streamed has been divided into blocks, each of those blocks is represented by a node in the graph, para[0031], ln 1-6/ predict what unit of software will be used next, one has to have a notion of what constitutes a "unit" of software. In one example, a unit of software is a "component"--i.e., a functional unit of the software, para[0019], ln 1-6/ the values in the cells correspond to the labels on the labeled edges in FIG. 3. (For non-existent edges, the cell value is zero, thereby indicating that there is a zero probability of transitioning from a first node to a second node if no edge from the first node to the second node exists.), para[0033], ln 8-20).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Yun, Sauders, Husain and Murray with Tepper to incorporate the above feature because this determines the probability that a particular block will follow a "sequence" of blocks, the relevant length of the sequence is a single block.
As to claim 16, Yun teaches the temporary graph comprises temporary graph nodes which comprise all possible combinations of display devices and vehicle functions of the vehicle( para[0054], ln 1-6/ para[0069], ln 5-20) for the same reason as to claim 12 above.
13. Claim(s) 17, 18, 21, 23 are rejected under 35 U.S.C. 103 as being unpatentable over YUN(US 20180102987 A1) in view of Saunders( US 20220035372 A1) in view of Tepper( US 20120167075 A1) and further in view of Mandi( US 11112795 B1).
As to claim 17, Yun teaches providing the graph further comprises: providing a temporary graph, wherein the temporary graph comprises temporary graph nodes which comprise a plurality of possible combinations of display devices and vehicle functions of the vehicle,( the communication nodes (i.e., gateways, switches, end nodes, etc.) constituting the vehicle network may be connected in a star topology, a bus topology, a ring topology, a tree topology, a mesh topology, or the like, para[0054], ln 1-6/ Here, the second function corresponding to the first function may mean a function related to the first function among a plurality of functions performed at the vehicle. For example, if the second communication node is a transmission of the vehicle and the first function is a function of shifting a gear of the vehicle into a reverse position (i.e., ‘R’ position), the second function may be a function of outputting images photographed at the rear of the vehicle. That is, the third communication node performing the second function may be a display device capable of outputting images photographed at the rear of the vehicle, para[0069], ln 5-20).
para[0069], ln 5-20 );
Yun teaches graph includes a plurality of possible combinations of display devices and vehicle functions of the vehicle and Sauders teaches determining transition probabilities between vehicle functions during a plurality of journeys of the vehicle( In some examples in which the mission data 604 includes a task graph 608 in which the mission is modeled, the edges 614, 622, 626 representing transitions between the tasks (represented by the task nodes 612, 620, 624) may be associated with levels of contingency. This association, then, may direct the transition from one task to another when the level of contingency is reported to the task manager service 606, para[0096]/ In the illustrated task graph, the edges are associated with levels of contingency for level 1, level 2 and level 3 contingencies, as well as a level 0 representing a nominal transition between task nodes in a sequence of task nodes. In some examples, one or more of the edges may be associated with multiple levels of contingency so that the same transition occurs when any of the multiple levels of contingency are detected, para[0097], ln 11-20/ the method includes, for a robot of the one or more robots, accessing mission data 604 for the mission including or associated with tasks that are executable to cause the robot to execute maneuvers, and executing the tasks according to the mission data to cause the robot to execute the maneuvers, as shown at blocks 1202 and 1204 of FIG. 12A. The method includes, as the tasks are executed, monitoring at least one of the robot or the environment, and detecting an event based on the monitoring, during execution of a task of the tasks, as shown at blocks 1206 and 1208. The method includes mapping the event to a level of contingency of a plurality of levels of contingency, as shown at block 1210. The method includes transitioning from the task to another of the tasks according to the level of contingency, and executing the other of the tasks, as shown at blocks 1212 and 1214., para[0124])
each temporary graph node having two counter-directional edges is connected to each of the other temporary graph node( determining a task graph in which the mission is modeled, the task graph expressed as a directed graph and including selected task nodes representing the selected tasks that are connected by edges representing transitions between the selected tasks; and causing the one or more robots to execute the mission using the task graph and a task library of tasks including a selected task executable to cause the one or more robots to execute a maneuver, para[0011], ln 6-18/ In response, the task manager service is configured to transition from the task to another of the tasks according to the level of contingency, and execute the other of the tasks., para[0093], ln 13-17), and each of the counter-directional edge comprises a weighting which is representative of a transition probability(According to some example implementations, events that are detectable by the contingency monitor 628 service may be may be classified into levels of contingency. In some examples, events may be classified into levels of contingency corresponding to the alternate task graphs 510, 512, 514 shown in FIG. 5C for some global contingency events. In other examples, the events may be classified into levels of contingency as follows: Land immediately (level 1) Land as soon as possible (level 2) Land as soon as practical (level 3) Hold pattern (level 4), Other robot or mission-specific contingencies The levels of contingency may be ordered such as in order of priority (e.g., land immediately being the highest, followed by land as soon as possible, followed by and as soon as practical, and so forth, para[0087] to para[0092]/ para[0094).
generating and provide the graph depending on the determined transition probabilities and the temporary graph( In some examples in which the mission data 604 includes a task graph 608 in which the mission is modeled, the edges 614, 622, 626 representing transitions between the tasks (represented by the task nodes 612, 620, 624) may be associated with levels of contingency. This association, then, may direct the transition from one task to another when the level of contingency is reported to the task manager service 606, para[0069]/ The method includes determining a task graph 608 in which the mission is modeled, as shown at block 1104. The task graph is expressed as a directed graph 610 and includes selected task nodes 612 representing the selected tasks that are connected by edges 614 representing transitions between the selected tasks, para[0117], ln 7-17 / Each task node 702 may be connected to one or more edges that represent transition from the task node (egress), and these one or more edges may cover all of the levels of contingency. For some tasks, however, the robot may be configured to ignore one or more levels of contingency. The task graph 700 may further include additional edges and nodes to cover these situations. As shown, for example, the task graph includes additional edges 708 associated with one or more levels of contingency, para[0098], ln 1-12/ FIG. 5D illustrates the task graph 500 connected to, for example, the third alternate task graph 514 for the third global contingency event. The selected task nodes 502 are associated with the third alternate task graph, shown for one of the selected task nodes as a dashed edge 516 that represents association of the selected task with the alternate task graph., para[0075]);
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Yun with Sauders to incorporate the above feature because this allows improve existing systems to enhance their efficiency and operation to achieve one or more mission objective of a deployment of a robot vehicle.
Tepper teaches determining impossible connections of temporary graph nodes, wherein transition probabilities of the impossible connections of temporary graph nodes are set to 0( a prediction graph of blocks is generated. That is, assuming that the program to be streamed has been divided into blocks, each of those blocks is represented by a node in the graph, para[0031], ln 1-6/ predict what unit of software will be used next, one has to have a notion of what constitutes a "unit" of software. In one example, a unit of software is a "component"--i.e., a functional unit of the software, para[0019], ln 1-6/ the values in the cells correspond to the labels on the labeled edges in FIG. 3. (For non-existent edges, the cell value is zero, thereby indicating that there is a zero probability of transitioning from a first node to a second node if no edge from the first node to the second node exists.) , para[0033], ln 8-20).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Yun and Sauders with Tepper to incorporate the above feature because this determines the probability that a particular block will follow a "sequence" of blocks, the relevant length of the sequence is a single block.
Mandi teaches generating and storing the graph depending on the temporary graph( then generates a computer-readable awareness graph using the first maneuver data for the first vehicle and the second maneuver data for the second vehicle. The awareness graph comprises nodes and directed edges. Each node in the nodes is assigned to a maneuver that is to be executed by a vehicle in a vicinity of the autonomous vehicle, col 2, ln 62-67 to col3, ln 1-3/ Concurrently with or subsequent to generating the awareness graph, the autonomous vehicle may cause a third node to be added to the awareness graph. The third node is assigned to the third maneuver that is to be executed by the autonomous vehicle. In an example, the third maneuver may be executing a left turn, and positions of the first vehicle and the second vehicle (as indicated by the first maneuver data and the second maneuver data) may fall within the third regions of interest for the third maneuver. Based on the third regions of interest in the third maneuver data, the autonomous vehicle may cause a second directed edge and a third directed edge to be added to the awareness graph, col 3, ln 33-45/ The autonomous vehicle may then generate a subgraph of the awareness graph by selecting a subset of nodes in the nodes and a subset of directed edges in the directed edges. The subset of nodes and the subset of directed edges are those that are relevant for the current maneuver that is about to be executed by the autonomous vehicle (i.e., the third maneuver). For instance, the subset of nodes may include the first node, the second node, the third node. The subset of directed edges may include the first directed edge, the second directed edge, and the third directed edge., col 4, ln 1-10/ In addition to storing executable instructions, the memory 1104 may also store maneuver data, awareness graphs, subgraphs of awareness graphs, computer-implemented machine learning models, etc, col 15, ln 3-10).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Yun, Sauders and Tepper with Mandi to incorporate the above feature because this resolve every intersection that is to occur between the surrounding vehicles at each timestamp, some of which are not relevant to navigating the autonomous vehicle.
As to claim 18, Mandi teaches a plurality of graphs are generated and stored, and each graph of the plurality of graphs corresponds to vehicle(col 15, ln 3-10/ col 2, ln 1-9).
As to claim 21, Mandi teaches a plurality of graphs are generated and stored(col 15, ln 3-10) and Zarokian teaches each graph of the plurality of graphs corresponds to a user of the vehicle(para[0100]).
As to claim 23, Yun the temporary graph comprises temporary graph nodes which comprise all possible combinations of display devices and vehicle functions of the vehicle( para[0069], ln 5-20).
14. Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over YUN(US 20180102987 A1) in view of Saunders( US 20220035372 A1) in view of Tepper( US 20120167075 A1) in view of Mandi( US 11112795 B1) and further in view of ELISHA(US 20210241625 A1).
As to claim 19, ELISHA teaches all graphs are grouped into various sets by means of a cluster algorithm(The VRP consists of finding tours for all N vehicles, which all start at the depot, such that each intermediate node is visited exactly once and the total cost of visiting all nodes is minimized. The cost metric can be defined in terms of distance, time, number of drivers, etc. A common variant is where a time window [a.sub.i, b.sub.i] is imposed on the visit of each customer. The nodes to be visited (rides to complete) are structed as a graph, where the edges (arches) between nodes include an estimated driving time from drop-off ride.sub.i to pick-up ride.sub.j, a distance between rides, a time lag between rides, etc, para[0039], ln 11-26/ In one embodiment, the routing module 201 processes a subset of the enhanced ride data and/or the shared ride data corresponding to a subset of rides in a cluster to provide a ride sub-graph. By way of example, the ride sub-graph is a directed acyclic sub-graph (sub-DAG) that describes all possible (feasible) connections between rides in a VRP sub-problem for a cluster via parallel processes 415a-415k, k is the number of clusters. In other words, all rides in the subset belongs to one cluster, and all rides that belong to the cluster must appear in the same subset. In one embodiment, such ride sub-graph is constructed per cluster, by re-constructing the ride sub-graph from the respective ride data (using process 407). In another embodiment, such ride sub-graph is constructed per cluster, by extracting only the relevant nodes and edges in the ride graph generated in process 407 associated with the ride subset, para[0049]).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Yun, Sauders, Tepper and Mandi with ELISHA to incorporate the above feature because this separately compute, for each cluster of the one or more clusters, a solution to a multiple vehicle routing problem for the set of rides in said each cluster.
15. Claims 20, 24 are rejected under 35 U.S.C. 103 as being unpatentable over YUN(US 20180102987 A1) in view of Saunders( US 20220035372 A1) in view of Tepper( US 20120167075 A1) and further in view of Mandi( US 11112795 B1) and further in view of
Zarokian(US 20240086432 A1).
As to claim 20, Zarokian teaches each graph of the plurality of graphs corresponds to a user of the vehicle( determine a specific focus of data or specific subsets of data for generating custom graphs. For example, a user may want to generate a specific industry graph of the aerospace industry, but only for a specific type of aircraft or for a specific category of aircraft, such as helicopters versus airplanes, para[0100]).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Yun, Sauders, Tepper and Mandi with Zarokian to incorporate the above feature because this allows Graphs of multiple entities can be linked to each other using a unique identifier, such as a company name, and users can easily navigate from one graph to another using these links.
As to claim 24, it is rejected for the same reason as to claim 21 above.
16. Claims 22, 25, 26 are rejected under 35 U.S.C. 103 as being unpatentable over YUN(US 20180102987 A1) in view of Saunders( US 20220035372 A1) in view of Tepper( US 20120167075 A1) in view of Mandi( US 11112795 B1) and further in view of ELISHA(US 20210241625 A1).
As to claim 22, ELISHA teaches all graphs are grouped into various sets by means of a cluster algorithm (The VRP consists of finding tours for all N vehicles, which all start at the depot, such that each intermediate node is visited exactly once and the total cost of visiting all nodes is minimized. The cost metric can be defined in terms of distance, time, number of drivers, etc. A common variant is where a time window [a.sub.i, b.sub.i] is imposed on the visit of each customer. The nodes to be visited (rides to complete) are structed as a graph, where the edges (arches) between nodes include an estimated driving time from drop-off ride.sub.i to pick-up ride.sub.j, a distance between rides, a time lag between rides, etc, para[0039], ln 11-26/ In one embodiment, the routing module 201 processes a subset of the enhanced ride data and/or the shared ride data corresponding to a subset of rides in a cluster to provide a ride sub-graph. By way of example, the ride sub-graph is a directed acyclic sub-graph (sub-DAG) that describes all possible (feasible) connections between rides in a VRP sub-problem for a cluster via parallel processes 415a-415k, k is the number of clusters. In other words, all rides in the subset belongs to one cluster, and all rides that belong to the cluster must appear in the same subset. In one embodiment, such ride sub-graph is constructed per cluster, by re-constructing the ride sub-graph from the respective ride data (using process 407). In another embodiment, such ride sub-graph is constructed per cluster, by extracting only the relevant nodes and edges in the ride graph generated in process 407 associated with the ride subset, para[0049]).
It would have been obvious to one of the ordinary skill in the art before the effective filling date of claimed invention was made to modify the teaching of Yun, Sauders, Tepper and Mandi with ELISHA to incorporate the above feature because this separately compute, for each cluster of the one or more clusters, a solution to a multiple vehicle routing problem for the set of rides in said each cluster.
As to claim 25, it is rejected for the same reason as to claim 22 above.
As to claim 26, Mandi teaches plurality of graphs are generated and stored( col 15, ln 3-10) , and ELISHA teaches each graph of the plurality of graphs corresponds to a user of the vehicle( para[0039], ln 11-26/ para[0049]) for the same reasons as to claims 17 and 22.
Conclusion
US 20220197306 A1 teaches the MPS may then build a motion plan for the robot. In some embodiments, the motion plan is a motion planning, The MPS may perform a least cost analysis on the motion planning graph to determine a set of transitions or path from the “start state” to the “goal state”.
US 20210133057 A1 teaches a failure state includes determining 802, from a plurality of predefined states, the failure state. The plurality of predefined states each correspond to a different combination of functional nodes.
US 20110210973 teaches More specifically, these graphs are assumed to have a generic parameter "p", communication radius may not reach another edge of communication radius.
US 9419854 B1 teaches a software agent such as: a monitoring agent, a target recognition agent, or a shopping service agent, a robot, or a car or other software, device, computing service, or platform agent. The edge 112 is a direct network connection between two nodes from the nodes A102-E110.
US 8423538 B1 teaches graph G naturally lends itself to a Markov process interpretation. The weights on the edges of the graph are computed based on the probability of transitions between the states. Hence, each node can be viewed as a Markov state with the edge weights being the transition probabilities between the corresponding states.
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/LECHI TRUONG/ Primary Examiner, Art Unit 2194