CTNF 19/068,077 CTNF 83391 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 action is in response to communications filed on 3/3/2025. Accordingly, claims 1- 10 are pending. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA 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. 07-30-06 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: “interface configured to…. computing device configured to…” in claims 1 ; and the same stated features along with an additional limitation of “a device configured to”, as recited in claim 9 . 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. As per claims 2-8 they all depend from claim 1 and have at least the same deficiencies as presented above with respect to claim 1 . Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 1- 10—in particular independent claims 1, 9 & 10— are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The following language in the claims below are not clearly understood and hence render the claims indefinite: As per claims 1, 9 & 10 : the claims recite the limitation of “output output data for evaluating the sensor data…” it is unclear what is meant by this limitation? Whether the claims intend “output data for evaluating the sensor data” or if there is some other process involved with the output data (i.e., an output of the output data); for purposes of examining the examiner will interpret the claims to intend the former. Furthermore, regarding claims 1, 9 & 10: the claims recite the language of a “memory state” it is unclear what is meant by a memory state and/or how a memory state is different from any memory and/or storage associated with a computing device and/or an artificial neural network. For purposes of examining the examiner will interpret this limitation to be any memory associated with the computing device and/or an artificial neural network. As per claims 2-8 they all depend from claim 1 and have at least the same deficiencies as presented above with respect to claim 1 . Furthermore, regarding claim 4: the claim recites the limitation of “at least partially compensate a movement…”; it is unclear how partially compensate is different from compensating (i.e., what distinguishes a partial compensation from any compensation)? For purposes of examining the examiner will interpret this to be any amount of compensation/adjustment to the movement. As per claims 5-6 they all depend from claim 4 and have at least the same deficiencies as presented above with respect to claim 4 . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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- 10— in particular Independent claims 1, 9 & 10 —are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite “receiv[-ing], provid[-ing], adjust[-ing] …” data. These limitations, as drafted, are processes that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. But for the sensor device, computing device and memory language, the claims encompass a user simply comparing the collected data to a predetermined/configurable threshold in his/her mind. The mere nominal recitation of a sensor, processor and memory does not take the claim limitation out of the mental processes grouping. Thus, the claims recite a mental process which is an abstract idea . This judicial exception is not integrated into a practical application. The claims recite the elements of receiving, providing, adjusting, and that a generic computer preform these steps. The receiving and providing steps are recited at a high level of generality (i.e., as a general means of receiving/transmitting and storing data for use in the adjusting step), and as such they amount to mere data gathering, which is a form of insignificant extra-solution activity . The processor that performs the receiving and providing steps are recited at a high level of generality, and merely automates the providing step. Each of the additional limitations are no more than mere instructions to apply the exception using a generic computer component (the processor). The combination of these additional elements are no more than mere instructions to apply the exception using a generic computer component (the processor). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea . The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept. For the receiving, providing and adjusting steps were considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The background does not provide any indication that the processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible. As per claims 2-8 they all depend from claim 1 and have at least the same deficiencies as presented above with respect to claim 1 . Claim Rejections - 35 USC § 102 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. 07-15 AIA Claim s 1-10 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Ulrich et al. (“Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar”; hereinafter Ulrich ) . Ulrich discloses: 1: A device for evaluating sensor data of a sensor device (see Ulrich at least Abstract and Introduction; improving detection and performance of radar-based object detection networks ) , comprising: an interface configured to receive the sensor data from the sensor device (see Ulrich at least Abstract and Introduction; lidar and computer vision community, radar sensors, sensor fusion with cameras and lidar ) ; and a computing device configured to provide input data to an artificial neural network based on the received sensor data, and the artificial neural network uses the input data to output output data for evaluating the sensor data (see Ulrich at least Abstract and Introduction; convolutional neural network (CNN), Object detection using oriented bounding boxes (OBB) and object type classification ) ; wherein the computing device is configured to adjust parameters of the artificial neural network using a memory state, wherein the memory state depends on latent features of the artificial neural network relating to a previous calculation step (see Ulrich at least Abstract and Introduction and fig. 1-3 and Table 1-3; information loss of precise position and geometric structure, deteriorated orientation estimates of OBBs, hybrid architecture ) . 2: wherein the computing device is configured to concatenate latent features of the artificial neural network with the latent features of the memory state (see Ulrich at least Abstract, Introduction and Problem Summary; object state vectors, tracking vehicles, orientation of OBB ) . 3: wherein the latent features include location coordinates with feature vectors (see Ulrich at least Abstract, Introduction and Problem Summary; object state vectors, tracking vehicles, orientation of OBB ) . 4: wherein the computing device is further configured to at least partially compensate a movement of an ego vehicle (see Ulrich at least Abstract, Introduction and Problem Summary; object state vectors, tracking vehicles, orientation of OBB ) . 5: wherein the computing device is configured to transform the memory state as a function of a movement of the ego vehicle (see Ulrich at least Abstract, Introduction and Problem Summary; object state vectors, tracking vehicles, orientation of OBB ) . 6: wherein the artificial neural network includes at least one layer for compensating movements of the ego vehicle (see Ulrich at least Abstract, Introduction and Problem Summary; object state vectors, tracking vehicles, orientation of OBB ) . 7: wherein the sensor device includes at least one of: a radar sensor, LiDAR sensor, and camera sensor (see Ulrich at least Abstract and Introduction; lidar and computer vision community, radar sensors, sensor fusion with cameras and lidar ) . 8: wherein the computing device is configured to generate the input data of the artificial neural network based on sensor data only of a current acquisition step of the sensor device (see Ulrich at least Abstract and Introduction; CNN, GNN, KPConvs, lidar and computer vision community, radar sensors, sensor fusion with cameras and lidar ) . 9: A driver assistance system, comprising: a device configured to evaluate sensor data of a sensor device of a motor vehicle (see Ulrich at least Abstract and Introduction; improving detection and performance of radar-based object detection networks ) , the device including: an interface configured to receive the sensor data from the sensor device (see Ulrich at least Abstract and Introduction; lidar and computer vision community, radar sensors, sensor fusion with cameras and lidar ) , and a computing device configured to provide input data to an artificial neural network based on the received sensor data, and the artificial neural network uses the input data to output output data for evaluating the sensor data (see Ulrich at least Abstract and Introduction; convolutional neural network (CNN), Object detection using oriented bounding boxes (OBB) and object type classification ) , wherein the computing device is configured to adjust parameters of the artificial neural network using a memory state, wherein the memory state depends on latent features of the artificial neural network relating to a previous calculation step (see Ulrich at least Abstract and Introduction and fig. 1-3 and Table 1-3; information loss of precise position and geometric structure, deteriorated orientation estimates of OBBs, hybrid architecture ) . 10: A method for evaluating sensor data of a sensor device, comprising the following steps: generating sensor data using the sensor device (see Ulrich at least Abstract and Introduction; lidar and computer vision community, radar sensors, sensor fusion with cameras and lidar ) ; providing input data for an artificial neural network based on the sensor data (see Ulrich at least Abstract and Introduction; convolutional neural network (CNN), Object detection using oriented bounding boxes (OBB) and object type classification ) ; and outputting output data by the artificial neural network based on the input data for evaluating the sensor data (see Ulrich at least Abstract and Introduction; convolutional neural network (CNN), Object detection using oriented bounding boxes (OBB) and object type classification ) ; wherein parameters of the artificial neural network are adjusted using a memory state, wherein the memory state depends on latent features of the artificial neural network relating to a previous calculation step (see Ulrich at least Abstract and Introduction and fig. 1-3 and Table 1-3; information loss of precise position and geometric structure, deteriorated orientation estimates of OBBs, hybrid architecture ) . 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 Application/Control Number: 19/068,077 Page 2 Art Unit: 3663 Application/Control Number: 19/068,077 Page 3 Art Unit: 3663 Application/Control Number: 19/068,077 Page 4 Art Unit: 3663 Application/Control Number: 19/068,077 Page 5 Art Unit: 3663 Application/Control Number: 19/068,077 Page 6 Art Unit: 3663 Application/Control Number: 19/068,077 Page 7 Art Unit: 3663 Application/Control Number: 19/068,077 Page 8 Art Unit: 3663 Application/Control Number: 19/068,077 Page 9 Art Unit: 3663 Application/Control Number: 19/068,077 Page 10 Art Unit: 3663
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