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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Status of Claims
Claims 1-17 are now pending.
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 USC 119 (a)-(d) to application JP2024-171486 filed 09/30/2024. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. As such, the effective filing date of the application is 09/30/2024.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 7, 8 and 10-12 and 14-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ardiyanto et al. (Human Motion Prediction Considering Environmental Context), hereinafter Ardiyanto.
Regarding claim 1, Ardiyanto discloses:
A system configured to predict movement of a person, the system comprising:
one or more memories storing instructions; and one or more processors that execute the instructions to:
acquire information indicating a position and a moving direction of a person (see first section 3.2, laser range finder and camera. See further the laser-based person tracker described in sections 3.1 and 3.2, and derivative of position as described in section 2.2.2.1);
acquire a graph that includes nodes and edges and indicates a movement route (see at least section 2.1 which describes how a graph is extrapolated from a 2D map generated by a SLAM algorithm. See further figure 2.)
determine a first edge of the graph corresponding to the position of the person based on the position of the person (see section 2.2 which describes how st={xt,yt} is an input to the prediction scheme as described in section 2.2)
and predicting a position corresponding a point on the first edge or on one or more edges located in the moving direction of the person with respect to the first edge as a movement destination of the person (see first Fig. 2, the edge between the “junction node” and “hallway node”. See further section 2.2.3. “particle filter-based predictor” which describes the incorporation of a probabilistic sequence model of human motion into a particle filter-based algorithm that yields a goal as well as a confidence.)
Regarding claim 2, Ardiyanto discloses:
The system according to claim 1, wherein the one or more processors execute the instructions to:
select a second edge connected to the first edge at a first node located in the moving direction of the person among nodes to which the first edge is connected (see at least fig. 2, the edge between the “hallway node” and the junction node aligned with the hallway on the right side of the map)
and predict a position corresponding to a point on the second edge as the movement destination of the person (see at least Fig. 1, the two “going away” paths in the perspective map. See further Fig. 5, the top left set of perspective map camera views.)
Regarding claim 7, Ardiyanto discloses:
The system according to claim 1, wherein the one or more processors execute the instructions to determine an edge closest to the position of the person as the first edge (see section 2.2 which describes how st={xt,yt} is an input to the prediction scheme as described in section 2.2)
Regarding claim 8, Ardiyanto discloses:
The system according to claim 1, wherein the one or more processors execute the instructions to determine the first edge further based on the moving direction of the person (see first section 3.2, laser range finder and camera. See further the laser-based person tracker described in sections 3.1 and 3.2, and derivative of position as described in section 2.2.2.1)
Regarding claim 10, Ardiyanto discloses:
The system according to claim 1, wherein the graph is created based on an environmental feature (see fig. 2, indoor corridor and junctions)
Regarding claim 11, Ardiyanto discloses:
The system according to claim 1, wherein the edges are set along a movable region of the person or an outer edge of a static obstacle (see at least Fig. 2 and 5, indoor walls and Skeleton)
Regarding claim 12, Ardiyanto discloses:
The system according to claim 1, wherein the nodes are set for at least one of a position where two or more roads intersect, an entrance of a building, a vending machine, and an uppermost step or a lowermost step of a staircase (see at least Fig. 2 junctions)
Regarding claim 14, Ardiyanto discloses:
The system according to claim 1, wherein the system is installed in a mobile body, the system further comprising:
a sensor; a controller configured to control a movement of the mobile body based on a prediction result of the movement destination of the person; and a driving unit configured to move the mobile body in accordance with the control by the controller, wherein the one or more processors execute the instructions to predict the position of the person based on an output from the sensor (see at least section 3.2, Predicting the human motion on a robot, wherein a mobile robot is equipped by a “laser range finder.” See further Fig. 5.)
Regarding claim 15, Ardiyanto discloses:
The system according to claim 14, wherein the controller is configured to control the movement of the mobile body based on the prediction result of the movement destination in such a manner that the mobile body moves ahead of the person or follows the person (see at least section 3.2, Predicting the human motion on a robot, wherein a mobile robot is equipped by a “laser range finder.” See further Fig. 5.)
Regarding claim 16, Ardiyanto discloses:
A method of predicting movement of a person, comprising:
acquiring information indicating a position and a moving direction of a person (see first section 3.2, laser range finder and camera. See further the laser-based person tracker described in sections 3.1 and 3.2, and derivative of position as described in section 2.2.2.1);
acquiring a graph that includes nodes and edges and indicates a movement route (see at least section 2.1 which describes how a graph is extrapolated from a 2D map generated by a SLAM algorithm. See further figure 2.)
determining a first edge of the graph corresponding to the position of the person based on the position of the person (see section 2.2 which describes how st={xt,yt} is an input to the prediction scheme as described in section 2.2)
and predicting a position corresponding a point on the first edge or on one or more edges located in the moving direction of the person with respect to the first edge as a movement destination of the person (see first Fig. 2, the edge between the “junction node” and “hallway node”. See further section 2.2.3. “particle filter-based predictor” which describes the incorporation of a probabilistic sequence model of human motion into a particle filter-based algorithm that yields a goal as well as a confidence.)
Regarding claim 17, Ardiyanto discloses:
A non-transitory computer-readable medium storing a program executable by a computer to perform a method of predicting movement of a person, the method comprising:
acquiring information indicating a position and a moving direction of a person (see first section 3.2, laser range finder and camera. See further the laser-based person tracker described in sections 3.1 and 3.2, and derivative of position as described in section 2.2.2.1);
acquiring a graph that includes nodes and edges and indicates a movement route (see at least section 2.1 which describes how a graph is extrapolated from a 2D map generated by a SLAM algorithm. See further figure 2.)
determining a first edge of the graph corresponding to the position of the person based on the position of the person (see section 2.2 which describes how st={xt,yt} is an input to the prediction scheme as described in section 2.2)
and predicting a position corresponding a point on the first edge or on one or more edges located in the moving direction of the person with respect to the first edge as a movement destination of the person (see first Fig. 2, the edge between the “junction node” and “hallway node”. See further section 2.2.3. “particle filter-based predictor” which describes the incorporation of a probabilistic sequence model of human motion into a particle filter-based algorithm that yields a goal as well as a confidence.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 3-6, 9 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ardiyanto in view of Tenzer et al. (US 12152889 B1), hereinafter Tenzer.
Regarding claim 3, Ardiyanto discloses:
The system according to claim 2.
Ardiyanto does not explicitly disclose, but Tenzer, in an analogous field of endeavor teaches:
wherein the one or more processors execute the instructions to selecting the second edge from among a plurality of edges connected to the first edge at the first node based on angles between (i) an orientation of the first edge or the moving direction of the person and (ii) each of the plurality of edges (see First Figures 4A and 4B. See further col. 16, lines 19-36: “FIGS. 4A and 4B, with reference to FIGS. 1 through 3, provide an illustrated notional example of a digraph G (FIG. 4A) and its associated line digraph L(G) (FIG. 4B). Each edge 410 in G becomes a vertex 412 in L(G); each path of two edges in G becomes an edge 414 in L(G). Line digraphs are a natural generalization of the more well-known line graphs, which are undirected. Conveniently, an edge 410 in L(G) matches the concept of “turns” which is defined above. As a result, the vertices 412 of L(G) are referred to as e∈E (because they are the same as G's edges 410) and the edges 414 of L(G) as t E T. Line digraph. After constructing L(G), turn restrictions can be enforced by pruning its edges. If a particular turn τ=(e.sub.1, e.sub.2) is prohibited (e.g., no U-turn), the corresponding edge t is deleted. Conversely, if restrictions require a turn (e.g., right turn only), all other turns {(e.sub.1, e′)|e′≠e.sub.2} are removed. Equivalently, these turns may be given as an infinite cost.”
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Ardiyanto with the methods as taught by Tenzer. This is because as stated by Tenzer col. 7 lines 58-67: “The processor 25 is further configured for outputting generated trajectories 5 to improve routing choices for a GPS 50 and to provide the route choices for selection when navigating from an origin to a destination in the road network 30. Accordingly, the system 100 combines geocoordinate data 20, machine learning (i.e., INR model 10), and a routing algorithm 1 to predict and generate multiple route choices for travelers in uncertain traffic conditions, offering them a range of options based on the real-world traffic situations.”
Regarding claim 4, the combination of Ardiyanto and Tenzer teaches:
The system according to claim 3.
Ardiyanto does not explicitly teach, but Tenzer, in an analogous field of endeavor teaches:
wherein an angle between the first edge and the second edge is a largest angle of angles between the first edge and each of the plurality of edges (see First Figures 4A and 4B. See further col. 16, lines 19-36: “FIGS. 4A and 4B, with reference to FIGS. 1 through 3, provide an illustrated notional example of a digraph G (FIG. 4A) and its associated line digraph L(G) (FIG. 4B). Each edge 410 in G becomes a vertex 412 in L(G); each path of two edges in G becomes an edge 414 in L(G). Line digraphs are a natural generalization of the more well-known line graphs, which are undirected. Conveniently, an edge 410 in L(G) matches the concept of “turns” which is defined above. As a result, the vertices 412 of L(G) are referred to as e∈E (because they are the same as G's edges 410) and the edges 414 of L(G) as t E T. Line digraph. After constructing L(G), turn restrictions can be enforced by pruning its edges. If a particular turn τ=(e.sub.1, e.sub.2) is prohibited (e.g., no U-turn), the corresponding edge t is deleted. Conversely, if restrictions require a turn (e.g., right turn only), all other turns {(e.sub.1, e′)|e′≠e.sub.2} are removed. Equivalently, these turns may be given as an infinite cost.”
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Ardiyanto with the methods as taught by Tenzer. This is because as stated by Tenzer col. 7 lines 58-67: “The processor 25 is further configured for outputting generated trajectories 5 to improve routing choices for a GPS 50 and to provide the route choices for selection when navigating from an origin to a destination in the road network 30. Accordingly, the system 100 combines geocoordinate data 20, machine learning (i.e., INR model 10), and a routing algorithm 1 to predict and generate multiple route choices for travelers in uncertain traffic conditions, offering them a range of options based on the real-world traffic situations.”
Regarding claim 5, the combination of Ardiyanto and Tenzer teaches:
The system according to claim 3.
Ardiyanto does not explicitly teach, but Tenzer, in an analogous field of endeavor teaches:
wherein the one or more processors execute the instructions to select, as the second edge, an edge extending in a direction closer to the moving direction of the person from among the plurality of edges (see First Figures 4A and 4B. See further col. 16, lines 19-36: “FIGS. 4A and 4B, with reference to FIGS. 1 through 3, provide an illustrated notional example of a digraph G (FIG. 4A) and its associated line digraph L(G) (FIG. 4B). Each edge 410 in G becomes a vertex 412 in L(G); each path of two edges in G becomes an edge 414 in L(G). Line digraphs are a natural generalization of the more well-known line graphs, which are undirected. Conveniently, an edge 410 in L(G) matches the concept of “turns” which is defined above. As a result, the vertices 412 of L(G) are referred to as e∈E (because they are the same as G's edges 410) and the edges 414 of L(G) as t E T. Line digraph. After constructing L(G), turn restrictions can be enforced by pruning its edges. If a particular turn τ=(e.sub.1, e.sub.2) is prohibited (e.g., no U-turn), the corresponding edge t is deleted. Conversely, if restrictions require a turn (e.g., right turn only), all other turns {(e.sub.1, e′)|e′≠e.sub.2} are removed. Equivalently, these turns may be given as an infinite cost.”
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Ardiyanto with the methods as taught by Tenzer. This is because as stated by Tenzer col. 7 lines 58-67: “The processor 25 is further configured for outputting generated trajectories 5 to improve routing choices for a GPS 50 and to provide the route choices for selection when navigating from an origin to a destination in the road network 30. Accordingly, the system 100 combines geocoordinate data 20, machine learning (i.e., INR model 10), and a routing algorithm 1 to predict and generate multiple route choices for travelers in uncertain traffic conditions, offering them a range of options based on the real-world traffic situations.”
Regarding claim 6, the combination of Ardiyanto and Tenzer teaches:
The system according to claim 3.
Ardiyanto does not explicitly teach, but Tenzer, in an analogous field of endeavor teaches:
wherein the one or more processors execute the instructions to select the second edge further based on a distance between the position of the person and each of the plurality of edges (see First Figures 4A and 4B. See further col. 16, lines 19-36: “FIGS. 4A and 4B, with reference to FIGS. 1 through 3, provide an illustrated notional example of a digraph G (FIG. 4A) and its associated line digraph L(G) (FIG. 4B). Each edge 410 in G becomes a vertex 412 in L(G); each path of two edges in G becomes an edge 414 in L(G). Line digraphs are a natural generalization of the more well-known line graphs, which are undirected. Conveniently, an edge 410 in L(G) matches the concept of “turns” which is defined above. As a result, the vertices 412 of L(G) are referred to as e∈E (because they are the same as G's edges 410) and the edges 414 of L(G) as t E T. Line digraph. After constructing L(G), turn restrictions can be enforced by pruning its edges. If a particular turn τ=(e.sub.1, e.sub.2) is prohibited (e.g., no U-turn), the corresponding edge t is deleted. Conversely, if restrictions require a turn (e.g., right turn only), all other turns {(e.sub.1, e′)|e′≠e.sub.2} are removed. Equivalently, these turns may be given as an infinite cost.”
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Ardiyanto with the methods as taught by Tenzer. This is because as stated by Tenzer col. 7 lines 58-67: “The processor 25 is further configured for outputting generated trajectories 5 to improve routing choices for a GPS 50 and to provide the route choices for selection when navigating from an origin to a destination in the road network 30. Accordingly, the system 100 combines geocoordinate data 20, machine learning (i.e., INR model 10), and a routing algorithm 1 to predict and generate multiple route choices for travelers in uncertain traffic conditions, offering them a range of options based on the real-world traffic situations.”
Regarding claim 9, Ardiyanto discloses:
The system according to claim 1.
Ardiyanto does not explicitly teach, but Tenzer, in an analogous field of endeavor teaches:
wherein the one or more processors execute the instructions to determine the first edge from among a plurality of the edges constituting the graph based on both (i) a distance between each of the plurality of edges and the position of the person and (ii) similarity between a direction of each of the plurality of edges and the moving direction of the person (see First Figures 4A and 4B. See further col. 16, lines 19-36: “FIGS. 4A and 4B, with reference to FIGS. 1 through 3, provide an illustrated notional example of a digraph G (FIG. 4A) and its associated line digraph L(G) (FIG. 4B). Each edge 410 in G becomes a vertex 412 in L(G); each path of two edges in G becomes an edge 414 in L(G). Line digraphs are a natural generalization of the more well-known line graphs, which are undirected. Conveniently, an edge 410 in L(G) matches the concept of “turns” which is defined above. As a result, the vertices 412 of L(G) are referred to as e∈E (because they are the same as G's edges 410) and the edges 414 of L(G) as t E T. Line digraph. After constructing L(G), turn restrictions can be enforced by pruning its edges. If a particular turn τ=(e.sub.1, e.sub.2) is prohibited (e.g., no U-turn), the corresponding edge t is deleted. Conversely, if restrictions require a turn (e.g., right turn only), all other turns {(e.sub.1, e′)|e′≠e.sub.2} are removed. Equivalently, these turns may be given as an infinite cost.”
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Ardiyanto with the methods as taught by Tenzer. This is because as stated by Tenzer col. 7 lines 58-67: “The processor 25 is further configured for outputting generated trajectories 5 to improve routing choices for a GPS 50 and to provide the route choices for selection when navigating from an origin to a destination in the road network 30. Accordingly, the system 100 combines geocoordinate data 20, machine learning (i.e., INR model 10), and a routing algorithm 1 to predict and generate multiple route choices for travelers in uncertain traffic conditions, offering them a range of options based on the real-world traffic situations.”
Regarding claim 13, Ardiyanto discloses:
The system according to claim 1.
Ardiyanto does not disclose but Tenzer, in an analogous field of endeavor teaches wherein the graph is created based on pedestrian movement data (see at least Fig. 2A and 2B.)
It would have been prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention, with a reasonable expectation for success, to combine the invention of Ardiyanto with the methods as taught by Tenzer. This is because as stated by Tenzer col. 7 lines 58-67: “The processor 25 is further configured for outputting generated trajectories 5 to improve routing choices for a GPS 50 and to provide the route choices for selection when navigating from an origin to a destination in the road network 30. Accordingly, the system 100 combines geocoordinate data 20, machine learning (i.e., INR model 10), and a routing algorithm 1 to predict and generate multiple route choices for travelers in uncertain traffic conditions, offering them a range of options based on the real-world traffic situations.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH NELESKI whose telephone number is (571)272-6064. The examiner can normally be reached 10 - 6.
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/E.R.N./Examiner, Art Unit 3658
/JASON HOLLOWAY/
Primary Examiner, Art Unit 3658
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