CTNF 19/090,536 CTNF 87688 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION This is a non-final Office Action on the merits in response to communications filed by Applicant on March 26, 2025. Claims 1-8 are currently pending and examined below. Priority 02-26 AIA Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file. Information Disclosure Statement The information disclosure statement(s) (IDS) submitted on is/are being considered by the examiner. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-4, 6-8 are rejected under 35 U.S.C. 102( a)(1) and/or 102(a)(2 ) as being anticipated by Trautman US2020/0246973 (“Trautman”) . Regarding claim(s) 1, 6, 7, 8. Trautman discloses a method for controlling a robot device, the method comprising the following steps: determining, using sensor data which represent a surrounding of the robot device, whether there are one or more humans in a surrounding of the robot device (fig. 3a-3c, [0008] FIG. 3A is another exemplary agent environment for a system for crowd navigation, according to one aspect.) ; based on determining that there are one or more humans in the surrounding of the robot device, determining for each human of the one or more humans: a respective body pose and a respective velocity of the human using the sensor data ([0046] Furthermore, the statistical module 120 may determine attributes or characteristics of the agents 304-312 relative to one another, as shown in FIG. 3C. The characteristics and attributes determined by the statistical module 120 of agents 304-312 relative to one another may be classified as second order interactions. The first order interactions may include, for example, position or a location of the agents 304-312 relative to one another, speeds of the agents 304-312 relative to one another, distances of the agents 304-312 relative to one another, a bearing or direction of travel of the agents 304-312) , a respective predicted motion of the human for a future time period using the sensor data, using the respective body pose, the respective velocity, and the respective predicted motion, a respective occupation area occupied by the human in the future time period ([0043] At block 202 the method 200 includes the statistical module 120 identifying a number of agents. Turning to FIG. 3A, the agents 304-312 are entities moving in a physical environment of the host 302. Depending on the movement, path planning, and/or capability identify the position of the agents 304-312. The agents 304-312 may be biological entities (humans, animals, insects), vehicles, robots, etc. The agents 304-312 may be identified based on sensor data 110 including, visual data, motion data, and physiological data, among others. In this manner, the statistical module 120 may detect or identify one or more of the entities, objects, obstacles, hazards, and/or corresponding attributes or characteristics including agent identification, a position or a location associated with the agents 304-312, such as a lane location, coordinates, position, size of the agents 304-312, trajectory, velocity, acceleration, etc.) ; generating control parameters for controlling the robot device such that a predefined distance of the robot device to the respective occupation area of each of the one or more humans is ensured in the future time period; and controlling the robot device in accordance with the control parameters ([0037] The statistical module 120 may be an artificial neural network that acts as a framework for machine learning, including deep learning. The model module 122 may be a decoder that converts the data generated by the statistical module 120 to a model of the physical environments shown in FIGS. 3A, 3B, and 3C. The predicted labels of the model module 122 may be labels that correspond to future actions based on the sensor data 110. Continuing the vehicular example given above, the label may correspond to a predicted maneuver of the host 302. In some embodiments, the predicted maneuver may include a series of maneuvers (e.g., going-straight, right-turn, left-turn, decelerate, etc.).) . Regarding claim(s) 2. Trautman discloses for each human of the one or more humans, determining a respective activity of the human using the sensor data; wherein the generating of the control parameters includes determining a velocity of the robot device using the respective activity of each of the one or more humans ([0044] The statistical module 120 may also model the characteristics and attributes of the agents 304-312 relative to the host 302, as shown in FIG. 3B. The characteristics and attributes determined by the statistical module 120 of agents 304-312 relative to the host 302 may be classified as first order interactions. The first order interactions may include, for example, an agent identification, how the trajectories of the agents 304-312 coincide with the path planning of the host 302, speeds of the agents 304-312 relative to the host 302, distances of the agents 304-312 from the host 302, a bearing or direction of travel of the agents 304-312 relative to the host 302) . Regarding claim(s) 3. Trautman discloses wherein the determining of the velocity of the robot device includes: determining an average level of human activity in the surrounding of the robot device using the respective activity of each of the one or more humans; and determining, using the average level of human activity, the velocity of the robot device by decreasing a predefined robot velocity with an increasing average level of human activity ([0046] Furthermore, the statistical module 120 may determine attributes or characteristics of the agents 304-312 relative to one another, as shown in FIG. 3C. The characteristics and attributes determined by the statistical module 120 of agents 304-312 relative to one another may be classified as second order interactions. The first order interactions may include, for example, position or a location of the agents 304-312 relative to one another, speeds of the agents 304-312 relative to one another, distances of the agents 304-312 relative to one another, a bearing or direction of travel of the agents 304-312 relative to one another, acceleration of the agents 304-312 relative to one another, distances of the agents 304-312 relative to one another, a, such as a lane location, coordinates, etc. The statistical module 120 may determine if the agents 304-312 are communicating with one another. Interplay between one or more of the agents 304-312 may be determined based on eyelid movement, head movement and/or mouth movement of the agents 304-312. The statistical module 120 may measure the degree of head movement such as the tilt of the head of the agents 304-312 relative to other agents or the angle of the head of the agents 304-312 relative to other agents. For example, the statistical module 120 may identify the interplay of the agents 310 and 312 based on the gap between the agents 310 and 312, corresponding mouth movements between the agents 310 and 312.) . Regarding claim(s) 4. Trautman discloses wherein the determining of the respective occupation area occupied by the human at a point in time in the future time period includes: determining, using the respective body pose of the human, a respective body pose area occupied by the human; and determining, using the respective velocity of the human, the respective occupation area by increasing the respective body pose area with increasing velocity of the human ([0044]The first order interactions may include, for example, an agent identification, how the trajectories of the agents 304-312 coincide with the path planning of the host 302, speeds of the agents 304-312 relative to the host 302, distances of the agents 304-312 from the host 302, a bearing or direction of travel of the agents 304-312 relative to the host 302, acceleration of the agents 304-312 relative to the host 302,) . Regarding claim(s) 5. Trautman discloses wherein the determining of the respective body pose area occupied by the human includes: projecting a respective two-dimensional ellipse onto a ground on which the robot device is positioned, the two-dimensional ellipse bounding the respective body pose of the human in a plan view; and wherein the determining of the respective occupation area includes: increasing a size of the respective two-dimensional ellipse as a function of the respective velocity of the human, wherein a size of the respective two-dimensional ellipse increases with increasing velocity of the human . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Inquiry Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRUC M DO whose telephone number is (571)270-5962. The examiner can normally be reached on 9AM-6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramón Mercado, Ph.D. can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TRUC M DO/Primary Examiner, Art Unit 3658 Application/Control Number: 19/090,536 Page 2 Art Unit: 3658 Application/Control Number: 19/090,536 Page 3 Art Unit: 3658 Application/Control Number: 19/090,536 Page 4 Art Unit: 3658 Application/Control Number: 19/090,536 Page 5 Art Unit: 3658
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