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 .
Status of Claims
• This action is in reply to the Application Number 18/393,425 filed on 12/21/2023.
• Claims 1-6, 8-12, 14-19 are currently pending and have been examined.
• This action is made FINAL in response to the “Amendment” and “Remarks” filed on 12/17/2025.
• The examiner would like to note that this application is now being handled by examiner Kai Wang.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy has been filed in Application No. 18/393,425 filed on 12/21/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/29/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
The claims are objected to because the lines are crowded too closely together, making reading difficult. Substitute claims with lines one and one-half or double spaced on good quality paper are required. See 37 CFR 1.52(b).
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) 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-AIA 35 U.S.C. 112, sixth paragraph:
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 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-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 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-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is 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 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-AIA 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: perception unit in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 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-AIA 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-AIA 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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 8-10, 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Truong ("Toward Socially Aware Robot Navigation in Dynamic and Crowded Environments: A Proactive Social Motion Model," in IEEE Transactions on Automation Science and Engineering, vol. 14, no. 4, pp. 1743-1760, Oct. 2017) in view of Ishikawa (US20230112368A1), further in view of Huang (US 20190145778 A1)and Diaz (US20180100743A1).
Regarding Claims 1, 8 and 16:
Truong teaches:
A method for generating a navigation path for an autonomous system, wherein the autonomous system comprises one or more sensing devices configured to observe an environment in which the autonomous system is configured to navigate, and a processor configured to execute the method, the environment comprising human entities and non-human entities, the method comprising: (Truong, abstract, “we propose an effective proactive social motion model (PSMM) that enables a mobile service robot to navigate safely and socially in crowded and dynamic environments”, and page 1755, “We used an Eddie mobile robot platform equipped with a Microsoft Kinect sensor and a laser rangefinder”, and page 1756, “Intel core i7 2.2-GHz laptop”, and Fig 3 (e ) depicts an environment of human–object social interaction space.)
receiving, by the processor, first data comprising characteristics of the human and non-human entities; (Truong, page 1755, “We used an Eddie mobile robot platform equipped with a Microsoft Kinect sensor… The standard Kinect sensor composed of an infrared light projector, a depth sensor, an RGB camera, and a multiarray microphone”, and page [1756], “estimate the human position and velocity”, Fig 7 depicts the walls, objects of non-human entities.)
receiving, by the processor, first instructions causing the autonomous system to identify a destination in the environment; (Truong, page 1755, “the robot proactively planned its route to avoid the humans when navigating toward its given destination”)
generating a navigation path comprising waypoints to be followed by the autonomous system to reach the destination from a current position of the autonomous system, (Truong, page 1744, “this method can be used with any local and global path planning method (even those with given waypoints) to navigate mobile robots toward their destinations”)
the waypoints being based on the first data and defining segmental paths between two consecutive waypoints,( Truong, page 1751, “path planning method (even a set of waypoints) to navigate the robot toward a destination while socially and safely avoiding humans, and taking into account human group social interactions, and human–object social interaction in a socially acceptable manner”, Fig.15 depicts defining segmental paths from start waypoint to the end waypoint.)
each waypoint and each segmental path being associated with a location cost indicative of a safeness of the autonomous system and the human and non-human entities in its surroundings if the autonomous system is located at said waypoint, ( Truong, page 1744, “we have developed human comfortable safety indices (HCSIs) including social individual index (SII), social group index (SGI), and relative motion index (RMI) to evaluate the proposed framework”, and Fig. 14 shows the human comfortable safety indices (HCSIs) of the autonomous system and the human and non-human entities in its surroundings if the autonomous system is located at each waypoint and segmental path.) Examiner note: Truong teaches human comfortable safety indices (HCSIs) as a location cost to measure the physical safety and comfort as well as socially acceptable behaviors of the mobile robot and human–object interactions at each waypoint and each segmental path.
the safeness being based on a distance between the autonomous system and the human and non-human entities (Truong, page 1751, “the physical safety of a human is violated if the relative distance between a robot and a human is less than dp”, page 1752, “SGI value corresponding to the distance between the robot and the center of the social interaction space”)
and the location cost being determined based on the first data and the second data when the location of the waypoint and associated segmental paths is determined, (Truong, page [1756], “estimate the human position and velocity”, Fig 7 depicts the walls, objects of non-human entities, page 1748, “this information including relative positions, human actions, social cues, and social constraints should be incorporated into the socially aware navigation framework to ensure human comfort and safety, and to generate socially acceptable behaviors for the mobile robot”, page 1744, “we have developed human comfortable safety indices (HCSIs) including social individual index (SII), social group index (SGI), and relative motion index (RMI) to evaluate the proposed framework”, page 1751, “path planning method (even a set of waypoints) to navigate the robot toward a destination while socially and safely avoiding humans, and taking into account human group social interactions, and human–object social interaction in a socially acceptable manner”)
executing, by the processor, second instructions causing the autonomous system to navigate along the navigation path; (Truong, Fig.14 and page 1757, Truong teaches first instruction causing the robot (a)avoiding a standing person and a group of two standing people, then second instruction causing the robot (b) avoiding a group of two people interacting with an object in page 1757.)
and during navigation of the autonomous system from a first one of the waypoints to a second one of the waypoints along a corresponding segmental path: (Truong, Fig.14 depicts the start point as first waypoint and destination point is the second waypoint.)
accessing, by the processor, third data generated by the one or more sensing devices during navigation of the autonomous system comprising updates of the characteristics of the human and non-human entities located in a vicinity of the corresponding segmental path; (Truong, page 1756, “fuse the human information detected by laser data as presented in [54] and Kinect sensor data as explained in [55] using a particle filter”, and Fig. 13 (b) and page 1753, “the robot was planned to navigate from the start position (Start) through landmarks (A)–(G), and then to return to the start position while dealing with … human–object interactions… a group of two people looking at an interesting object”) Examiner note: Truong teaches updating the human information detected by sensor devices during the navigation.
the location cost being determined based on the third data and the second data, (Truong, , page 1744, “we have developed human comfortable safety indices (HCSIs) including social individual index (SII), social group index (SGI), and relative motion index (RMI) to evaluate the proposed framework”, page 1751, “path planning method (even a set of waypoints) to navigate the robot toward a destination while socially and safely avoiding humans, and taking into account human group social interactions, and human–object social interaction in a socially acceptable manner” page 1756, “estimate the human position and velocity”, page 1748, “this information including … social cues, and social constraints should be incorporated into the socially aware navigation framework to ensure human comfort and safety”) Examiner note: Truong teaches human comfortable safety indices (HCSIs) as a location cost based on the third data by estimate the human position and velocity using sensing devices and the second data using social cues, and social constraints information.
Truong does not explicitly teach, but Ishikawa teaches:
receiving, by the processor, second data comprising social and physical norms associated with the human entities and non-human entities.( Ishikawa, para[65], “receives information”, para [80], “physical norms and social norms ”, and Fig. 3 depicts social and physical norms associated with the human entities (human) and non-human entities (sofa))
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Robot Navigation control method of Truong to include these above aforementioned teachings from Ishikawa in order to include receiving, by the processor, second data comprising social and physical norms associated with the human entities and non-human entities. One of ordinary skill in the art would have been motivated to make this modification in order to provide a harmonious coexistence with the people in its surroundings.
Truong does not teach, but Huang teaches:
each waypoint and each segmental path being associated with a location cost (Huang, para [55-56], “the local trajectory cost function as: Q(t j)=αj(1+κj)(1+νj)e −(o k −l j ) 2, Where κj measures the mean curvature along tj and νj computes the average angle between successive camera view direction pairs along the trajectory.”)
the generating the navigation path comprising computing a global cost, the global cost being a combination of the location costs of the waypoints and of the segmental paths; (Huang, para [47], “the global cost function is made of local trajectory cost function and transit cost function. The global cost function represents the overall cost or total cost of the UAV flight path”)
each sub-waypoint and each sub- segmental path being associated with a location cost if the autonomous system is located at said sub-waypoint. (Huang, para [55-56], “the local trajectory cost function as: Q(t j)=αj(1+κj)(1+νj)e −(o k −l j ) 2, Where κj measures the mean curvature along tj and νj computes the average angle between successive camera view direction pairs along the trajectory.”)
the generating the navigation sub-path comprising computing a global sub-cost, the global sub-cost being a combination of the location costs of the sub-waypoints and of the sub-segmental paths; (Huang, para [47], “the global cost function is made of local trajectory cost function and transit cost function. The global cost function represents the overall cost or total cost of the UAV flight path”) Examiner note: Huang teaches computing a global cost function for the local trajectory including the waypoint and segmental path, and it is obvious to apply the same algorithm to calculate the global sub-cost for the sub-path and sub-waypoint.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Robot Navigation control method of Truong to include these above aforementioned teachings from Huang in order to include each waypoint and each segmental path being associated with a location cost, the generating the navigation path comprising computing a global cost, the global cost being a combination of the location costs of the waypoints and of the segmental paths; the generating the navigation sub-path comprising computing a global sub-cost, the global sub-cost being a combination of the location costs of the sub-waypoints and of the sub-segmental paths. One of ordinary skill in the art would have been motivated to make this modification in order to “making the flight of the UAV safer”(Huang, Description).
Truong does not teach, but Diaz teaches:
generating, by the processor, a sub-path comprising sub-waypoints to be followed by the autonomous system and defining sub-segmental paths between the first one of the waypoints and the second one of the waypoints, (Diaz, claim 1, “computing a subpath”, para [04], “a subpath defines a way of reaching a waypoint from another waypoint.”, and para[02], “autonomous vehicles”)
when the location of the sub-waypoint and associated sub segmental paths is determined, (Diaz, claim 1, “computing a subpath”, para [04], “a subpath defines a way of reaching a waypoint from another waypoint.”, and para[02], “autonomous vehicles”)
and executing, by the processor, third instructions causing the autonomous system to navigate along the sub-path to reach the second sub-waypoint. (Diaz, Fig.3 depicts the third instruction of causing the robot navigate along the sub-path (dash line from 302 to 314) to reach the second sub-waypoint nearby 314, and claim 1 “guiding the vehicle according to the optimal path.”)
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Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Robot Navigation control method of Truong to include these above aforementioned teachings from Diaz in order to include generating, by the processor, a sub-path comprising sub-waypoints to be followed by the autonomous system and defining sub-segmental paths between the first one of the waypoints and the second one of the waypoints, when the location of the sub-waypoint and associated sub segmental paths is determined, and executing, by the processor, third instructions causing the autonomous system to navigate along the sub-path to reach the second sub-waypoint. One of ordinary skill in the art would have been motivated to make this modification in order to “finding an optimal (e.g. safest, shortest, etc.) route between two points”(Diaz, Description).
Regarding Claims 2 and 9:
Truong in view of Ishikawa, Huang and Diaz, as shown in the rejection above, discloses the limitations of claim 1. Truong does not teach, but Huang teaches:
The method of claim 1, wherein each segmental path between a given waypoint and a consecutive waypoint is associated with a location cost indicative of a safeness of the autonomous system and entities in its surroundings when the autonomous system is navigating from the given waypoint to the consecutive waypoint along the segmental path, the location cost of the segmental paths being determined based on the first data, and the global cost further rely upon the location costs of the waypoints and the location costs of segmental paths. (Huang, para [123], “specify a sequence of up to 99 waypoints (physical locations to which the drone will fly)… The drone then travels from one waypoint to the next ”, para [55-56], “the local trajectory cost function as: Q(t j)=αj(1+κj)(1+νj)e −(o k −l j ) 2, Where κj measures the mean curvature along tj and νj computes the average angle between successive camera view direction pairs along the trajectory. In our implementation, we compute κj and νj by sampling the trajectory densely, with one sample every 10 meters. The weight αj counts the number of local extreme a of the elevation and the distance from the geometric center of the land mark along tj. Finally, the last term of (6) is designed to decrease as the trajectory length increases for carefully-observed landmarks, and vice versa for casual landmarks”, and para [62], “By expanding the bounding curve, the UAV flight path can be kept at a distance from the landmark, making the flight of the UAV safer”, para [47], “the global cost function is made of local trajectory cost function and transit cost function. The global cost function represents the overall cost or total cost of the UAV flight path”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Robot Navigation control method of Truong to include these above aforementioned teachings from Huang in order to include wherein each segmental path between a given waypoint and a consecutive waypoint is associated with a location cost indicative of a safeness of the autonomous system and entities in its surroundings when the autonomous system is navigating from the given waypoint to the consecutive waypoint along the segmental path, the location cost of the segmental paths being determined based on the first data, and the global cost further rely upon the location costs of the waypoints and the location costs of segmental paths. One of ordinary skill in the art would have been motivated to make this modification in order to “making the flight of the UAV safer”(Huang, Description).
Regarding Claims 3 and 10:
Truong in view of Ishikawa, Huang and Diaz, as shown in the rejection above, discloses the limitations of claim 1. Truong teaches:
The method of claim 1, wherein a location of the second one of the waypoints is adjusted during navigation of the autonomous system if determination is made, based on the second data, that the autonomous system being located at the second one of the waypoints corresponds to a lack of safety for surrounding entities or for the autonomous system. (Truong, page 1758 and Fig. 15(d), “b) Avoiding a group of two walking people…the robot respected the group interaction space of two people p1 and p2 and proactively moved around the group to not interfere their social interaction,”, and Fig.15 (d) and page 1757, “Experiment 2 (Avoiding Dynamic Humans): In the second experiment, we aimed to examine whether the robot was able to avoid a dynamic person or a group of dynamic people safely and comfortably interaction, e.g., when they were in a social conversation, as seen in Fig. 15(d).”)
Regarding Claims 4 , 14 and 17:
Truong in view of Ishikawa, Huang and Diaz, as shown in the rejection above, discloses the limitations of claim 1. Truong teaches:
The method of claim 1, wherein characteristics of one of the human entities being selected in a group of characteristics comprising: a formation of the human entity, a number of persons comprised in the human entity, an orientation of the persons of the human entity, a location of the human entity, a level of business of the human entity, or a combination thereof. (Truong, page 1745, “2-D Gaussian or linear techniques are used to model the space around the humans-—i.e., social costmaps…to avoid collision with human obstacles in the vicinity of the robot. The costmaps model imports the socio-spatiotemporal characteristics of humans into a local path planner to generate a feasible path for the robot that satisfies socially acceptable behaviors”)
Regarding Claims 5, 15 and 18:
Truong in view of Ishikawa, Huang and Diaz, as shown in the rejection above, discloses the limitations of claim 4. Truong teaches:
The method of claim 4, wherein, upon receiving first instructions causing the autonomous system to identify a destination in the environment, the method further comprises: if determination is made based on the first instructions that the autonomous system has to interact with a human entity: accessing characteristics of the human entity; (Truong, page 1758 and Fig. 15(d), “b) Avoiding a group of two walking people…the robot respected the group interaction space of two people p1 and p2 and proactively moved around the group to not interfere their social interaction”
and determining a destination in a vicinity of the human entity based on the characteristics of the human entity. (Truong, page 1744, “this method can be used with any local and global path planning method (even those with given waypoints) to navigate mobile robots toward their destinations”, and page 1758 and Fig. 15(d), “the robot respected the group interaction space of two people p1 and p2 and proactively moved around the group to not interfere their social interaction”)
Claim(s) 6, 12, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Truong ("Toward Socially Aware Robot Navigation in Dynamic and Crowded Environments: A Proactive Social Motion Model," in IEEE Transactions on Automation Science and Engineering, vol. 14, no. 4, pp. 1743-1760, Oct. 2017) in view of Ishikawa (US20230112368A1), further in view of Huang (US 20190145778 A1) and Diaz (US20180100743A1), Higueras, ("Robot local navigation with learned social cost functions," 2014 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vienna, Austria, 2014).
Regarding Claims 6, 12 and 19:
Truong in view of Ishikawa, Huang and Diaz, as shown in the rejection above, discloses the limitations of claim 1, 8, 16. Truong teaches:
The method of claim 1, wherein locations of the waypoints are determined based on the social and physical norms associated with the entities, (Truong, page 1746, “To ensure human safety and comfort when working in human–robot shared workspaces, mobile robots must distinguish humans from other obstacles, recognize human features from the socio-spatiotemporal characteristics of an individual human and a group of humans, and then incorporate such information into their navigation systems”, and page 1751, “we obtain the proactive social motion controller for the socially aware robot navigation framework. This can be used with any path planning method (even a set of waypoints) to navigate the robot toward a destination while socially and safely avoiding humans”)
Truong does not explicitly teach, but Ishikawa teaches:
…based on the social and physical norms, ( Ishikawa, para [80], “based on physical norms and social norms ”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Robot Navigation control method of Truong to include these above aforementioned teachings from Ishikawa in order to include determining the local cost based on social and physical norms. One of ordinary skill in the art would have been motivated to make this modification in order to provide a harmonious coexistence with the people in its surroundings.
Truong does not teach, but Huang teaches:
the location cost associated with each waypoint and each segmental path being further determined…(Huang, para [123], “specify a sequence of up to 99 waypoints (physical locations to which the drone will fly)… The drone then travels from one waypoint to the next ”, para [55-56], “the local trajectory cost function as: Q(t j)=αj(1+κj)(1+νj)e −(o k −l j ) 2, Where κj measures the mean curvature along tj and νj computes the average angle between successive camera view direction pairs along the trajectory.”)
and generating the navigation path comprises minimizing a global cost, the global cost comprising a combination of the location cost of each of the waypoints and each segmental path. (Huang,para [62], “By expanding the bounding curve, the UAV flight path can be kept at a distance from the landmark, making the flight of the UAV safer”, para [47], “the global cost function is made of local trajectory cost function and transit cost function. The global cost function represents the overall cost or total cost of the UAV flight path”, and para [108], “minimize the overall cost of the resulting trajectory.”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Robot Navigation control method of Truong to include these above aforementioned teachings from Huang in order to include the location cost associated with each waypoint and each segmental path being further determined, and generating the navigation path comprises minimizing a global cost, the global cost comprising a combination of the location cost of each of the waypoints and each segmental path. One of ordinary skill in the art would have been motivated to make this modification in order to “making the flight of the UAV safer”(Huang, Description).
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Truong ("Toward Socially Aware Robot Navigation in Dynamic and Crowded Environments: A Proactive Social Motion Model," in IEEE Transactions on Automation Science and Engineering, vol. 14, no. 4, pp. 1743-1760, Oct. 2017) in view of Ishikawa (US20230112368A1), further in view of Huang (US 20190145778 A1) and Diaz (US20180100743A1), Eddie Robot Platform, (https://forums.parallax.com/uploads/attachments/49183/85661.pdf, 2011).
Regarding Claim 11:
Truong in view of Ishikawa, Huang and Diaz, as shown in the rejection above, discloses the limitations of claim 8. Truong teaches:
and a perception unit configured to provide the characteristics and the updates of the characteristics of the human and non-human entities in the vicinity of the autonomous system (Truong, and page 1755, “We used an Eddie mobile robot platform equipped with a Microsoft Kinect sensor and a laser rangefinder”, and page 1756, “fuse the human information detected by laser data as presented in [54] and Kinect sensor data as explained in [55] using a particle filter”, and Fig 3 (e ) depicts an environment of human–object social interaction space.)
Truong does not teach, but Eddie Robot Platform teaches:
The autonomous system of claim 8, further comprising: a network device communicably connected to a network and configured for receiving the characteristics of the human and non-human entities and the updates of the characteristics of the entities;( Eddie Robot Platform, page 1, “Simple USB connectivity between control board and your laptop ….Communication Interface: Serial commands over USB interface”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the Robot Navigation control method of Truong to include these above aforementioned teachings from Eddie Robot Platform in order to include a network device communicably connected to a network and configured for receiving the characteristics of the entities and the updates of the characteristics of the entities. One of ordinary skill in the art would have been motivated to make this modification in order to create a mobile robot with your own laptop and Kinect camera.
RESPONSE TO ARGUMENTS
Claim Rejections - 35 USC § 103. Applicant’s arguments filed on 12/17/2025 with respect to claims 1, 8, 16 (See applicant’s response, page 9, “Rejections under 35 U.S.C. 103”) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kai Wang whose telephone number is (571) 270-5633. The examiner can normally be reached Mon-Fri 8:30-5:30 Eastern.
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/KAI NMN WANG/Examiner, Art Unit 3664
/REDHWAN K MAWARI/Primary Examiner, Art Unit 3664
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