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 .
DETAILED ACTION
This action is in response to communications filed on 4/22/2025. Claims 1-13, 20-21, 24-44 & 51 have been preliminarily cancelled, no other claims were newly added. Accordingly, claims 14-19, 22-23, 45-50 are pending.
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 14-19, 22-23, 45-50 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Suzuki et al. (WO2021/090897 A1—however for examining/mapping purposes the examiner relied upon the US pub. 20230045416A1).
Suzuki discloses:
14: An information processing device comprising:
a processor, wherein the processor acquires detection information obtained by detecting a surrounding situation of a moving object, point information obtained by regarding an imaged object as a point, and identification information for identifying the object (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13; sensor unit 14, device 100 detects objects—OB11 and OB12 in its environment),
calculates a control variable from a correlation between a variable related to the surrounding situation of the moving object calculated from the detection information and the control variable inferred by using the variable (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13; action planning unit 134, action planning model), and
controls autonomous driving of the moving object on the basis of the control variable, the point information, and the identification information (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13 and par. 72, 78, 137-142; image recognition model, prediction model, action planning model, execution unit, and collision avoidance).
15: wherein a coefficient of the variable for the control variable is determined in advance, and the processor calculates the control variable by using the coefficient (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
16: wherein the processor executes a process of inferring the control variable in a second cycle longer than a first cycle of acquiring the detection information (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
17: wherein the processor executes a process of calculating the control variable in a third cycle shorter than the second cycle (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
18: wherein the processor infers the control variable through multivariate analysis according to an integration method using deep learning (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
19: wherein the processor acquires the point information and the identification information from different processors (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
22: An information processing device comprising:
a processor, wherein the processor acquires detection information from a detection unit that detects a surrounding situation of a moving object in a second cycle shorter than a first cycle of imaging surroundings of the moving object (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13; sensor unit 14, device 100 detects objects—OB11 and OB12 in its environment),
divides a period in which a collision of an obstacle with the moving object based on the detection information is predictable into a plurality of periods (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13; action planning unit 134, action planning model), and
calculates a control variable for controlling autonomous driving of the moving object by using a plurality of input parameters based on the detection information in each of the plurality of periods according to a time series of the plurality of periods (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13 and par. 72, 78, 137-142; image recognition model, prediction model, action planning model, execution unit, and collision avoidance).
23: wherein in a case in which the control variable is calculated in a second or subsequent period of the plurality of periods, the processor calculates the control variable by reducing the number of input parameters compared with the number of input parameters in a previous period (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
45: An information processing device comprising:
an acquisition unit that acquires a plurality of information related to a vehicle from a detection unit including a sensor that detects a surrounding situation of the vehicle (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13; sensor unit 14, device 100 detects objects—OB11 and OB12 in its environment);
a calculation unit that calculates an index value related to the surrounding situation of the vehicle from the plurality of acquired information, and calculates a control variable for controlling a behavior of the vehicle from the calculated index value (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13; action planning unit 134, action planning model); and
a control unit that controls the behavior of the vehicle on the basis of the calculated control variable (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13; execution unit 135),
wherein the calculation unit predicts a collision of a target object with the vehicle on the basis of the plurality of acquired information, and in a case in which the predicted result indicates an unavoidable collision, the calculation unit calculates a control variable such that damage generated in the vehicle is damage that is a predetermined threshold or less in the unavoidable collision and a re-collision in which the vehicle collides with another target object after the collision (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13 and par. 72, 78, 137-142; image recognition model, prediction model, action planning model, and collision avoidance).
46: wherein the acquisition unit acquires information regarding the surrounding situation of the vehicle from another vehicle that is a collision target object (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
47: wherein the acquisition unit acquires information regarding the surrounding situation of the vehicle from an external device installed on a traveling path on which the vehicle travels (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
48: wherein the calculation unit calculates the control variable from the index value through multivariate analysis according to an integration method using deep learning (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
49: wherein the damage generated in the vehicle is at least one of a deformation position and a deformation amount of the vehicle (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
50: wherein a control variable corresponding to the damage generated in the vehicle is at least one of a collision angle and a vehicle speed of the vehicle (see Suzuki at least fig. 1- 14 in particular fig. 1-3, 7-9 & 11-13).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MACEEH ANWARI whose telephone number is 571-272-7591. The examiner can normally be reached on 9-9:30.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached on 571-272-1206. 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.
MACEEH . ANWARI
Primary Examiner
Art Unit 3663
/MACEEH ANWARI/ Primary Examiner, Art Unit 3663
