Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, line 2, “techical system” should be changed to -- technical system--.
Claim 1, line 11, “tje” should be changed to -- the --.
Claim 11, line 8, “represective” should be changed to -- respective --.
Claim 13, line 6, “techical system” should be changed to -- technical system--.
Claim 13, line 17, “tje” should be changed to -- the --.
Claim 14, line 3, “techical system” should be changed to -- technical system--.
Claim 14, line 13, “tje” should be changed to -- the --.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a computer-implemented method (claims 1 - 12), a device with structural components (claim 13), and a non-transitory computer-readable medium with structural components (claim 14). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claim 1,
Step 2A, Prong One
The claim recites in part:
determining a state of a technical system, the techical system including an infrastructure element or a road user, wherein a first node represents a first object which is the technical system, a second node represents a second object which is a further infrastructure element or a further road user, wherein an edge between the first node and the second node represents a relationship between the first and second objects,
For example, a human can determine the relationship between a vehicle and its surrounding environment.
determining a prediction which characterizes a behavior of one of the first and second objects, the determination being in accordance with information about the first and second objects and in accordance with a representation of a knowledge graph which includes the first node, the second node, and the edge;
For example, a human can predict the behavior of a vehicle based on past interactions with its surrounding environment.
determining the state of the technical system in accordance with the prediction.
For example, a human can determine what state the vehicle should operate in or where the vehicle should go next.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The recitation of technical system amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
The recitation of technical system amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 2,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The recitation of “controlling the technical system in accordance with
the determined state“ amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
The recitation of “controlling the technical system in accordance with
the determined state“ amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites in part:
(i) the first object is associated with a first class and the first class is represented by the first node, or (ii) the second object is associated with a second class and the second class is represented by the second node.
For example, a human can separate the objects and determine which class each object belongs. Humans have been categorizing data before computers were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites in part:
wherein the first class represents a passenger car, or a truck, or a three-wheeled vehicle, or a two-wheeled vehicle, or a horse rider, or a pedestrian, or a road segment, or an intersection, or a lane, or a guardrail, or a warning beacon, or a traffic light, or a footpath, or a road marking, or a roadway boundary.
For example, a human can separate the objects and determine which class each object belongs. Humans have been categorizing data before computers were ever invented.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites in part:
the second class represents a passenger car, or a truck, or a three-wheeled vehicle, or a two-wheeled vehicle, or a horse rider, or a pedestrian, or a road segment, or an intersection, or a lane, or a guardrail, or a warning beacon, or a traffic light, or a footpath, or a road marking, or a roadway boundary.
For example, a human can separate the objects and determine which class each object belongs. Humans have been categorizing data before computers were ever invented.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
the first and second objects are infrastructure elements associated with one another, wherein the edge represents a relationship of the infrastructure elements associated with one another, including interconnected lanes of a multi-lane road or a traffic light relevant to a lane, or a traffic sign ,or a traffic regulation.
For example, a human can separate the objects and determine the relationships between the objects.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites in part:
wherein the first and second objects are road users associated with one another, wherein the edge represents a relationship of the road users associated with one another, including road users located in the same lane or in different lanes of a multi-lane road
For example, a human can separate the objects and determine the relationships between the objects.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim recites in part:
wherein the first object is an infrastructure element and the second object is a road user, and the infrastructure element and the road user are associated with one another, wherein the edge represents the relationship between the infrastructure element and the road user.
For example, a human can separate the objects and determine the relationships between the objects.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim recites in part:
wherein a further node represents environment information including: a time of day, or a day of the week, or visibility, or a temperature, or a roadway condition, or a roadway type, and wherein a further edge between the further node and the first node represents a relationship between the environment information and the first object
For example, a human can separate the objects and determine the relationships between the objects.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 10,
Step 2A, Prong One
The claim recites in part:
wherein a further node represents a traffic regulation or a behavior pattern, a further edge between the further node and the first node represents a relationship between the first object and the traffic regulation represented by the further node or the behavior pattern represented by the further node.
For example, a human can separate the objects and determine the relationships between the objects.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 11,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the representation of the knowledge graph, which includes the first and second nodes and the edge, is trained in accordance with training data including information about the first and second objects, wherein each of the first and second objects are classified in a respective class, wherein the edge which represents the relationship between the first and second nodes of the knowledge graph, which each represent one of the represective classes, is determined using an ontology which specifies relationships between the respective classes which the first and second nodes represent.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the representation of the knowledge graph, which includes the first and second nodes and the edge, is trained in accordance with training data including information about the first and second objects, wherein each of the first and second objects are classified in a respective class, wherein the edge which represents the relationship between the first and second nodes of the knowledge graph, which each represent one of the represective classes, is determined using an ontology which specifies relationships between the respective classes which the first and second nodes represent.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
As to claims 12,
Step 2A, Prong One
The claim is directed to the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the prediction is determined in accordance with test data which include information, unknown during training, about the first and second objects or an environment, which are mapped onto the prediction by the representation of the knowledge graph trained with training data.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the prediction is determined in accordance with test data which include information, unknown during training, about the first and second objects or an environment, which are mapped onto the prediction by the representation of the knowledge graph trained with training data.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons above.
The claim further recites a device, at least one processor, and at least one non-transitory memory, which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 14 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons above.
The claim further recites a non-transitory computer-readable medium and at least one processor which are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
`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.
Claim(s) 1 - 10, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over JOHNSON et al (US 2019/0304190) in view of Michener et al (US 2021/0055112).
As to claim 1, JOHNSON et al figure 5 shows and teaches a computer-implemented method for determining a state of a technical system (paragraph [0007]… a controller of the system controlling a display screen to render a graphical representation that includes indications of person-objects-entities of interest that are in a field-of-view of the display screen and out of the field-of-view of the display screen, as well as relationships therebetween, the person-objects-entities of interest being determined using the knowledge graph of FIG. 4, in accordance with some embodiments)(Examiner’s Note: “controlling a display screen to render a graphical representation… being determined using the knowledge graph” reads on “determining a state of a technical system”), the techical system including an infrastructure element or a road user, wherein a first node represents a first object which is the technical system, a second node represents a second object which is a further infrastructure element or a further road user, wherein an edge between the first node and the second node represents a relationship between the first and second objects (paragraph [0141]…The knowledge graph 170 includes a plurality of nodes, each corresponding to a POE of interest. While the knowledge graph 170, and the subset 470, are depicted herein with icons and/or graphics representing nodes, as described hereafter, such icons and/or graphics are provided for clarity and may not be represented in the knowledge graph 170, and the subset 470 until the knowledge graph 170, and the subset 470 are explicitly rendered at a display screen, For example, as depicted, a node 471 corresponds to a building of interest, for example at which a crime has occurred and which may be associated with a work assignment. The node 471 graphically represents building (e.g. via an icon of a building). Furthermore, data that identifies the building may be stored in association with the node 471, including, but not limited to, one or more images of the building, a location of the building, entities and/r business entities located in the building, and the like ; paragraph [0143]… The node 475 corresponds to a vehicle of interest, for example a type of vehicle that the witness to the crime reported as leaving the scene of the crime, for example a white van. The node 475 graphically represents a vehicle (e.g. via an icon of a vehicle). Furthermore, data that identifies the vehicle on may be stored in association with the node 475, including, but not limited to, an image of the vehicle, a description of the vehicle, a license plate number, a description of a reported driver of the vehicle, registration information associated with the vehicle and the like ; paragraph [0146]…The knowledge graph 170 hence includes relationships between nodes represented in FIG. 4 as lines between the nodes. For example, solid lines 480, 482, 484 between the nodes 471, 473, 477 in FIG. 4 represent relationships between nodes that correspond to field-of-view POEs of interest, while dashed lines 486, 488 between the nodes 473, 475 and the node 477 represent relationships between nodes that correspond to field-of-view POEs of interest and a node that corresponds to an out-of-field-of-view POE of interest) (Examiner’s Note: “The node 475 corresponds to a vehicle of interest” reads on “wherein a first node represents a first object which is the technical system” ; “a node 471 corresponds to a building of interest” reads on “a second node represents a second object which is a further infrastructure element or a further road user” ; “solid line 482 between the nodes 471 and 475 represent relationships between nodes that correspond to field-of-view POEs of interest” reads on “wherein an edge between the first node and the second node represents a relationship between the first and second objects”), the method comprising:
determining a prediction which characterizes a behavior of one of the first and second objects, the determination being in accordance with information about the first and second objects and in accordance with a representation of a knowledge graph which includes the first node, the second node, and the edge (paragraph [0086]…one or more devices of the system 100 may be generally configured to update and/or maintain the knowledge graph 170 based on explicit information (e.g. time of day, observed locations of POEs of interest, and the like) and/or derived information (e.g. predicted locations of POEs, predicted relationships between POEs derived from descriptive information of POEs and/or work reports, and the like)).
JOHNSON et al fails to explicitly show/teach determining the state of the technical system in accordance with the prediction.
However, Michener et al teaches determining the state of the technical system in accordance with the prediction (paragraph [0077]…Stated differently, the randomness measure 322, 326, 332 may quantify how predictable or unpredictable occurrences of a given particular feature type will be within the new map dataset. Accordingly, in the turn restriction example, a high randomness measure 322, 326, 332 may indicate that occurrences of turn restrictions within the new map dataset 304 cannot be easily predicted, and therefore, that the presence of a turn restriction in the new map dataset 304 is a random occurrence. In such a scenario, it may be determined that the new map dataset 304 was not generated by a computer-process (which would likely be based on certain nearby map features 306, 308 and therefore be less random and predictable), but rather was generated based on random, real-world verification of the turn restrictions).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was mad, for JOHNSON et al to determine the state of the technical system in accordance with the prediction, as in Michener et al, for the purpose of incorporating new mapping data which may enable location-based applications to calculate process more accurate ETAs or ETDs, more accurate pickup point determinations, speed limit advisories, etc.
As to claim 2, JOHNSON et al figure 5 shows and teaches the method, further comprising: controlling the technical system in accordance with the determined state (paragraph [0137]…any such changes and/or input may cause the knowledge graph 170 to be updated which may cause the graphical representation to be updated. Put another way, the controller 220 may be further configured to: dynamically update the graphical representation as the knowledge graph 170 changes).
As to claim 3, JOHNSON et al figure 5 shows and teaches the method, (i) the first object is associated with a first class and the first class is represented by the first node, or (ii) the second object is associated with a second class and the second class is represented by the second node (paragraph [0141]…The knowledge graph 170 includes a plurality of nodes, each corresponding to a POE of interest. While the knowledge graph 170, and the subset 470, are depicted herein with icons and/or graphics representing nodes, as described hereafter, such icons and/or graphics are provided for clarity and may not be represented in the knowledge graph 170, and the subset 470 until the knowledge graph 170, and the subset 470 are explicitly rendered at a display screen, For example, as depicted, a node 471 corresponds to a building of interest, for example at which a crime has occurred and which may be associated with a work assignment. The node 471 graphically represents building (e.g. via an icon of a building). Furthermore, data that identifies the building may be stored in association with the node 471, including, but not limited to, one or more images of the building, a location of the building, entities and/r business entities located in the building, and the like ; paragraph [0143]… The node 475 corresponds to a vehicle of interest, for example a type of vehicle that the witness to the crime reported as leaving the scene of the crime, for example a white van. The node 475 graphically represents a vehicle (e.g. via an icon of a vehicle). Furthermore, data that identifies the vehicle on may be stored in association with the node 475, including, but not limited to, an image of the vehicle, a description of the vehicle, a license plate number, a description of a reported driver of the vehicle, registration information associated with the vehicle and the like.) (Examiner’s Note: “The node 475 corresponds to a vehicle of interest” reads on “the first object is associated with a first class and the first class is represented by the first node” ; “a node 471 corresponds to a building of interest” reads on “second object is associated with a second class and the second class is represented by the second node”).
As to claim 4, JOHNSON et al figure 5 shows and teaches the method wherein the first class represents a passenger car, or a truck, or a three-wheeled vehicle, or a two-wheeled vehicle, or a horse rider, or a pedestrian, or a road segment, or an intersection, or a lane, or a guardrail, or a warning beacon, or a traffic light, or a footpath, or a road marking, or a roadway boundary (paragraph [0143]… The node 475 corresponds to a vehicle of interest, for example a type of vehicle that the witness to the crime reported as leaving the scene of the crime, for example a white van. The node 475 graphically represents a vehicle (e.g. via an icon of a vehicle). Furthermore, data that identifies the vehicle on may be stored in association with the node 475, including, but not limited to, an image of the vehicle, a description of the vehicle, a license plate number, a description of a reported driver of the vehicle, registration information associated with the vehicle and the like.) (Examiner’s Note: “vehicle of interest” reads on “first class represents a passenger car, or a truck, or a three-wheeled vehicle, or a two-wheeled vehicle”).
As to claim 5, JOHNSON et al figure 5 shows and teaches the method wherein the second class represents a passenger car, or a truck, or a three-wheeled vehicle, or a two-wheeled vehicle, or a horse rider, or a pedestrian, or a road segment, or an intersection, or a lane, or a guardrail, or a warning beacon, or a traffic light, or a footpath, or a road marking, or a roadway boundary (paragraph [0141]…The knowledge graph 170 includes a plurality of nodes, each corresponding to a POE of interest. While the knowledge graph 170, and the subset 470, are depicted herein with icons and/or graphics representing nodes, as described hereafter, such icons and/or graphics are provided for clarity and may not be represented in the knowledge graph 170, and the subset 470 until the knowledge graph 170, and the subset 470 are explicitly rendered at a display screen, For example, as depicted, a node 471 corresponds to a building of interest, for example at which a crime has occurred and which may be associated with a work assignment. The node 471 graphically represents building (e.g. via an icon of a building). Furthermore, data that identifies the building may be stored in association with the node 471, including, but not limited to, one or more images of the building, a location of the building, entities and/r business entities located in the building, and the like) (Examiner’s Note: “building of interest” reads on “second class represents a roadway boundary”).
As to claim 6, Michener et al shows and teaches the method wherein the first and second objects are infrastructure elements associated with one another, wherein the edge represents a relationship of the infrastructure elements associated with one another, including interconnected lanes of a multi-lane road or a traffic light relevant to a lane, or a traffic sign ,or a traffic regulation (paragraph [0062]…Each of the map features 306, 308 of the mapping data 200 is represented as a node on the knowledge graph 400, represented by an ID number (e.g., ID 68 of the node 468 corresponds to the turn restriction 68). In particular, the nodes 462, 466, 468, 476, 478, 480, 482, 483 represent, respectively, the traffic direction 62, the turn restriction 66, the turn restriction 68, the road segment 76, the road segment 78, the road segment 80, the intersection 82, and the intersection 83. The knowledge graph 400 also includes other nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424 that represent values corresponding to the nodes 462, 466, 468, 476, 478, 480, 482, 483. Such nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424 may be considered as terminal nodes. In certain implementations, terminal nodes of the same value may be implemented by a single node. For example, nodes 406, 414, 422 may be represented by a single node including the value “Road Segment.” The nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 462, 466, 468, 476, 478, 480, 482, 483 are connected by edges. The edges connect two nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 462, 466, 468, 476, 478, 480, 482, 483 and may designate a relationship type between the nodes, as illustrated. The depicted relationship types and corresponding definitions are provided below in Table 1, although additional relationship types are possible depending on a desired implementation and the map features 306, 308 included in the new map dataset 304)(Examiner’s Note: “nodes may be represented by a single node including the value “Road Segment.” The nodes are connected by edges. The edges connect two nodes and may designate a relationship type between the nodes” reads on “the first and second objects are infrastructure elements associated with one another, wherein the edge represents a relationship of the infrastructure elements associated with one another, including interconnected lanes of a multi-lane road”).
It would have been obvious for the first and second objects to be infrastructure elements associated with one another, wherein the edge represents a relationship of the infrastructure elements associated with one another, including interconnected lanes of a multi-lane road or a traffic light relevant to a lane, or a traffic sign ,or a traffic regulation, for the same reasons as above.
As to claim 7, Michener et al shows and teaches the method wherein the first and second objects are road users associated with one another, wherein the edge represents a relationship of the road users associated with one another, including road users located in the same lane or in different lanes of a multi-lane road (paragraph [0062]…Each of the map features 306, 308 of the mapping data 200 is represented as a node on the knowledge graph 400, represented by an ID number (e.g., ID 68 of the node 468 corresponds to the turn restriction 68). In particular, the nodes 462, 466, 468, 476, 478, 480, 482, 483 represent, respectively, the traffic direction 62, the turn restriction 66, the turn restriction 68, the road segment 76, the road segment 78, the road segment 80, the intersection 82, and the intersection 83. The knowledge graph 400 also includes other nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424 that represent values corresponding to the nodes 462, 466, 468, 476, 478, 480, 482, 483. Such nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424 may be considered as terminal nodes. In certain implementations, terminal nodes of the same value may be implemented by a single node. For example, nodes 406, 414, 422 may be represented by a single node including the value “Road Segment.” The nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 462, 466, 468, 476, 478, 480, 482, 483 are connected by edges. The edges connect two nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 462, 466, 468, 476, 478, 480, 482, 483 and may designate a relationship type between the nodes, as illustrated. The depicted relationship types and corresponding definitions are provided below in Table 1, although additional relationship types are possible depending on a desired implementation and the map features 306, 308 included in the new map dataset 304)(Examiner’s Note: “the traffic direction 62, the turn restriction 66, the turn restriction 68, the road segment 76, the road segment 78, the road segment 80, the intersection 82, and the intersection 83” reads on “the first and second objects are road users associated with one another, wherein the edge represents a relationship of the road users associated with one another, including road users located in the same lane or in different lanes of a multi-lane road”).
It would have been obvious for the first and second objects are road users associated with one another, wherein the edge represents a relationship of the road users associated with one another, including road users located in the same lane or in different lanes of a multi-lane road, for the same reasons as above.
As to claim 8, JOHNSON et al figure 5 shows and teaches the method wherein the first object is an infrastructure element and the second object is a road user, and the infrastructure element and the road user are associated with one another, wherein the edge represents the relationship between the infrastructure element and the road user.
(paragraph [0141]…The knowledge graph 170 includes a plurality of nodes, each corresponding to a POE of interest. While the knowledge graph 170, and the subset 470, are depicted herein with icons and/or graphics representing nodes, as described hereafter, such icons and/or graphics are provided for clarity and may not be represented in the knowledge graph 170, and the subset 470 until the knowledge graph 170, and the subset 470 are explicitly rendered at a display screen, For example, as depicted, a node 471 corresponds to a building of interest, for example at which a crime has occurred and which may be associated with a work assignment. The node 471 graphically represents building (e.g. via an icon of a building). Furthermore, data that identifies the building may be stored in association with the node 471, including, but not limited to, one or more images of the building, a location of the building, entities and/r business entities located in the building, and the like ; paragraph [0143]… The node 475 corresponds to a vehicle of interest, for example a type of vehicle that the witness to the crime reported as leaving the scene of the crime, for example a white van. The node 475 graphically represents a vehicle (e.g. via an icon of a vehicle). Furthermore, data that identifies the vehicle on may be stored in association with the node 475, including, but not limited to, an image of the vehicle, a description of the vehicle, a license plate number, a description of a reported driver of the vehicle, registration information associated with the vehicle and the like ; paragraph [0146]…The knowledge graph 170 hence includes relationships between nodes represented in FIG. 4 as lines between the nodes. For example, solid lines 480, 482, 484 between the nodes 471, 473, 477 in FIG. 4 represent relationships between nodes that correspond to field-of-view POEs of interest, while dashed lines 486, 488 between the nodes 473, 475 and the node 477 represent relationships between nodes that correspond to field-of-view POEs of interest and a node that corresponds to an out-of-field-of-view POE of interest) (Examiner’s Note: “The node 475 corresponds to a vehicle of interest” reads on “the second object is a road user” ; “a node 471 corresponds to a building of interest” reads on “the first object is an infrastructure element”).
As to claim 9, JOHNSON et al figure 5 shows and teaches the method wherein a further node represents environment information including: a time of day, or a day of the week, or visibility, or a temperature, or a roadway condition, or a roadway type, and wherein a further edge between the further node and the first node represents a relationship between the environment information and the first object (paragraph [0086]…Alternatively, one or more devices of the system 100 may be generally configured to update and/or maintain the knowledge graph 170 based on explicit information (e.g. time of day, observed locations of POEs of interest, and the like) and/or derived information (e.g. predicted locations of POEs, predicted relationships between POEs derived from descriptive information of POEs and/or work reports, and the like)).
As to claim 10, Michener et al shows and teaches the method, wherein a further node represents a traffic regulation or a behavior pattern, a further edge between the further node and the first node represents a relationship between the first object and the traffic regulation represented by the further node or the behavior pattern represented by the further node (paragraph 0062]… Each of the map features 306, 308 of the mapping data 200 is represented as a node on the knowledge graph 400, represented by an ID number (e.g., ID 68 of the node 468 corresponds to the turn restriction 68). In particular, the nodes 462, 466, 468, 476, 478, 480, 482, 483 represent, respectively, the traffic direction 62, the turn restriction 66, the turn restriction 68, the road segment 76, the road segment 78, the road segment 80, the intersection 82, and the intersection 83. The knowledge graph 400 also includes other nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424 that represent values corresponding to the nodes 462, 466, 468, 476, 478, 480, 482, 483. Such nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424 may be considered as terminal nodes. In certain implementations, terminal nodes of the same value may be implemented by a single node. For example, nodes 406, 414, 422 may be represented by a single node including the value “Road Segment.” The nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 462, 466, 468, 476, 478, 480, 482, 483 are connected by edges. The edges connect two nodes 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 462, 466, 468, 476, 478, 480, 482, 483 and may designate a relationship type between the nodes, as illustrated. The depicted relationship types and corresponding definitions are provided below in Table 1, although additional relationship types are possible depending on a desired implementation and the map features 306, 308 included in the new map dataset 304).
It would have been obvious for the first and second objects are road users associated with one another, a further node represents a traffic regulation or a behavior pattern, a further edge between the further node and the first node represents a relationship between the first object and the traffic regulation represented by the further node or the behavior pattern represented by the further node, for the same reasons as above.
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons above.
Claim 14 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons above.
Claim(s) 11 and 12 /are rejected under 35 U.S.C. 103 as being unpatentable over JOHNSON et al (US 2019/0304190) in view of Michener et al (US 2021/0055112) and in further view of Stetson et al (US 2020/0081445).
As to claim 11, JOHNSON et al figure 5 shows and teaches a knowledge graph.
JOHNSON et al and Michener et al both fail to show/teach the representation of the knowledge graph, which includes the first and second nodes and the edge, is trained in accordance with training data including information about the first and second objects, wherein each of the first and second objects are classified in a respective class, wherein the edge which represents the relationship between the first and second nodes of the knowledge graph, which each represent one of the represective classes, is determined using an ontology which specifies relationships between the respective classes which the first and second nodes represent
However, Stetson et al teaches the representation of the knowledge graph, which includes the first and second nodes and the edge, is trained in accordance with training data including information about the first and second objects, wherein each of the first and second objects are classified in a respective class, wherein the edge which represents the relationship between the first and second nodes of the knowledge graph, which each represent one of the represective classes, is determined using an ontology which specifies relationships between the respective classes which the first and second nodes represent (paragraph [0083]… One of the many applications of the graph-based systems and methods described herein is the ability to understand risk at a more fundamental level. In any complex system, of the most useful things to understand is risk. For example, in commercial domains such as insurance, it may no longer be sufficient to understand risk via the statistics of historical events. Rather, it is now desirable that many sources of risk be understood before they ever occur. Risk can be assigned to nodes in a knowledge graph, where each node represents a scenario and/or class of scenarios. In some embodiments, risk assumptions can be automatically propagated through the graph. Knowledge graphs that encode risk can be converted into manifolds for easier comprehension, and can be used to identify and/or create training data sets that more accurately capture risk profiles for use in AI training. In a variety of embodiments, manifolds can also be projected onto 2-D representations for easier user comprehension. Systems for interfacing with graphs and performing processes similar to those described above are discussed below).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was mad, for JOHNSON et al to determine the state of the technical system in accordance with the prediction, as in Stetson et al, for the purpose of processing of big data sets using graphs, and more specifically, generating more effective training and testing paradigms for AIs by encoding data in a structure easily navigated by both human and AI.
As to claim 12, Stetson et al teaches the prediction is determined in accordance with test data which include information, unknown during training, about the first and second objects or an environment, which are mapped onto the prediction by the representation of the knowledge graph trained with training data (paragraph [0083]… One of the many applications of the graph-based systems and methods described herein is the ability to understand risk at a more fundamental level. In any complex system, of the most useful things to understand is risk. For example, in commercial domains such as insurance, it may no longer be sufficient to understand risk via the statistics of historical events. Rather, it is now desirable that many sources of risk be understood before they ever occur. Risk can be assigned to nodes in a knowledge graph, where each node represents a scenario and/or class of scenarios. In some embodiments, risk assumptions can be automatically propagated through the graph. Knowledge graphs that encode risk can be converted into manifolds for easier comprehension, and can be used to identify and/or create training data sets that more accurately capture risk profiles for use in AI training. In a variety of embodiments, manifolds can also be projected onto 2-D representations for easier user comprehension. Systems for interfacing with graphs and performing processes similar to those described above are discussed below).
It would have been obvious for the prediction is determined in accordance with test data which include information, unknown during training, about the first and second objects or an environment, which are mapped onto the prediction by the representation of the knowledge graph trained with training data, for the same reason as before.
Conclusion
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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