CTNF 18/719,264 CTNF 96180 DETAILED ACTIONS 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. Priority Acknowledgment is made of applicant’s claim this application being a National Stage of the International Application No. PCT/JP2022/047005, filed on December 21, 2022, and benefit of foreign priority from Japanese Patent Application No. JP2021-214922 filed on December 28, 2021. Information Disclosure Statement The information disclosure statement (“IDS”) filed on 04/30/2026 was reviewed and the listed references were noted. Drawings The 83-page drawings have been considered and placed on record in the file. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-20 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. 07-30-05 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. 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 limitations are: "a learning device" and "a recognition device" in claim 20. 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. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto- processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-35 Claim s 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim s of copending Application No. 18/721,898 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because claims of the instant application are obvious variants of the corresponding ones in the co-pending application . This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented. Instant Application (18/719,264) U.S. Application No. 18/721,898 Claim 1: An information processing apparatus comprising a conversion part configured to convert a first recognizer or a first dataset for performing a recognition process based on a first signal read from a first sensor having a first pixel characteristic or a first signal characteristic into a second recognizer or a second dataset for performing a recognition process based on a second pixel characteristic different from the first pixel characteristic or a second signal characteristic different from the first signal characteristic. Claim 1: An information processing apparatus comprising a conversion part configured to convert, based on an output of a first recognizer that performs a recognition process based on a first signal read from a first sensor, a processing parameter related to a recognition process of a second recognizer that performs the recognition process based on a second signal read from a second sensor having a characteristic different from a characteristic of the first sensor, wherein the conversion part converts the processing parameter to approximate an output of the second recognizer to the output of the first recognizer. 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. Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims does/do not fall within at least one of the four categories of patent eligible subject matter because the claim recites the limitation "an information processing program". Computer programs are not considered as one of the statutory categories of invention because a computer program is merely as set of instructions capable of being executed by a computer. 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. 07-12-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 1-6, 11, and 16-20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Dolan et al., (US 10939042 B1, published 03/02/2021), hereinafter referred to as Dolan . Claim 1 Dolan discloses an information processing apparatus (Dolan, Fig. 5) comprising a conversion part (Dolan, Fig. 1) configured to convert a first recognizer or a first dataset (Dolan, Fig. 1, Global Shutter Image 102) for performing a recognition process based on a first signal read from a first sensor (Dolan, [Col. 2, lines 16-27], “machine learning models may be used to assist control of an autonomous vehicle in part by interpreting data representative of the surrounding environment received from sensors, such as imagers and other sensors. Such machine learning models may be trained using training data representative of sensed objects, including simulated and/or actual images. Due to differences between global shutter image data and rolling shutter image data , a machine learning model trained using global shutter image data may not be able to accurately interpret rolling shutter image data , for example, if imagers coupled to the autonomous vehicle are rolling shutter imagers”) having a first pixel characteristic or a first signal characteristic (Dolan, [Col. 1, lines 6-7], “global shutter images, which record an entire scene in a given field of view at exactly the same instant in time”) into a second recognizer or a second dataset (Dolan, Fig. 1, Global Shutter Image 102 is converted into Simulated Rolling Shutter Image Data 126) for performing a recognition process based on a second pixel characteristic (Dolan, Fig. 4, Machine Learning Model 402, [Col. 11, lines 1 – 5], “FIG. 4 is a block diagram of an example system 400 for converting global shutter image data 116 to simulated rolling shutter image data 126 for input into an example machine learning model 402 as training data for training the machine learning model 402”) different from the first pixel characteristic or a second signal characteristic different from the first signal characteristic (Dolan, [Col. 1, lines 56-67, Col. 2, lines 1-2], “Global shutter images of a scene are recorded such that the entire scene is recorded at exactly the same instant, while in contrast, rolling shutter images are generated by rapidly scanning across, either horizontally or vertically, a field of view, such that not all parts of a still or video rolling shutter image are recorded exactly simultaneously . As a result, rolling shutter images may include distortions, or artifacts, in the images, for example, when the image records objects moving rapidly with respect to the rolling shutter imager (either by motion of the object, the imager, or both) or when rapidly flashing light is recorded. This results in differences between images of a scene captured by a global shutter imager and images of the same scene captured by a rolling shutter imager.” ). Claim 2 Dolan discloses the information processing apparatus according to claim 1 (Dolan, Fig. 5) , a reading unit of the first sensor is one frame (Dolan, Fig. 1, Global Shutter Image 102) , and the second pixel characteristic or the second signal characteristic is based on a second signal read from a second sensor having a reading unit smaller than the one frame (Dolan, Fig. 1, Global Shutter Image 102 is converted into Simulated Rolling Shutter Image Data 126, Rolling Shutter Images consists of sub-frames scanned across either horizontally or vertically) . Claim 3 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) , wherein the conversion part (Dolan, Fig. 1) converts the first dataset into the second dataset (Dolan, Fig. 1, Global Shutter Image 102 is converted into Simulated Rolling Shutter Image Data 126) by approximating the first pixel characteristic or the first signal characteristic to the second pixel characteristic or the second signal characteristic (Dola, Dolan, [Col. 20, lines 29-34], “ In such examples where the new pixel location is not an integer, various interpolation techniques (e.g., such as bilinear, bicubic, and/or polynomial interpolation) may be used. A pixel of the rolling shutter image at the new location may thereafter be set with a value of the corresponding pixel of the first global shutter image.”) when there is a lack of information on the first pixel characteristic or the first signal characteristic with respect to the second pixel characteristic or the second signal characteristic (Dolan, [Col. 7, lines 53-65], “the global shutter image data from the global shutter images 206 may be communicated to the example global-to-rolling shutter image converter 118 to convert the global shutter image data associated with the global shutter images 206 into simulated rolling shutter image data 126 and/or simulated rolling shutter images 128. In the example shown, the global-to-rolling shutter image converter 118 includes a rolling shutter artifact generator 122, including an optical flow field calculator 210 and a timestamp generator 212 configured to generate rolling shutter artifacts 204 based at least in part on the global shutter image data associated with the global shutter images 206.”, The global shutter image lack the information of optical flow field and timestamp which is estimated using an optical flow field calculator and a timestamp generator) . Claim 4 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) , wherein the conversion part (Dolan, Fig. 1) converts the first dataset into the second dataset (Dolan, Fig. 1, Global Shutter Image 102 is converted into Simulated Rolling Shutter Image Data 126) by estimating missing information due to the lack of information when there is the lack of information on the first pixel characteristic or the first signal characteristic with respect to the second pixel characteristic or the second signal characteristic (Dolan, [Col. 7, lines 53-65], “the global shutter image data from the global shutter images 206 may be communicated to the example global-to-rolling shutter image converter 118 to convert the global shutter image data associated with the global shutter images 206 into simulated rolling shutter image data 126 and/or simulated rolling shutter images 128. In the example shown, the global-to-rolling shutter image converter 118 includes a rolling shutter artifact generator 122, including an optical flow field calculator 210 and a timestamp generator 212 configured to generate rolling shutter artifacts 204 based at least in part on the global shutter image data associated with the global shutter images 206.”, The global shutter image lack the information of optical flow field and timestamp which is estimated using the an optical flow field calculator and a timestamp generator) . Claim 5 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) , wherein the conversion part (Dolan, Fig. 1) converts the first pixel characteristic or the first signal characteristic into the second pixel characteristic or the second signal characteristic (Dolan, Fig. 1, Global Shutter Image 102 is converted into Simulated Rolling Shutter Image Data 126) based on preset information when a correspondence relationship between the first pixel characteristic or the first signal characteristic and the second pixel characteristic or the second signal characteristic is unknown (Dolan, [Col. 4, lines 17-41, “The method may also include receiving one or more rolling shutter imager parameters. For example, a rolling shutter imager may have technical characteristics related to performance, such as, for example, scanning orientation associated with scan lines (e.g., related to whether the scan lines are horizontally or vertically oriented), the number of scan lines per frame or field of view, the scan rate of the scan lines, scan line density, total exposure time, high-dynamic range (HDR) capability , etc., any combination of which may be inherent to a particular rolling shutter imager and may affect artifacts associated with rolling shutter image data captured by the particular rolling shutter imager and/or simulated rolling shutter image data generated upon conversion of the global shutter image data into simulated rolling shutter image data. The method, in some examples, may also include generating a rolling shutter image based at least in part on the optical flow field and the rolling shutter imager parameters”, the parameters above cannot be derived from the global shutter image so they are preset as shown in Fig. 1). Claim 6 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) , wherein the conversion part (Dolan, Fig. 1) estimates the second pixel characteristic or the second signal characteristic into which the first pixel characteristic or the first signal characteristic is converted when a correspondence relationship between the first pixel characteristic or the first signal characteristic and the second pixel characteristic or the second signal characteristic is unknown (Dolan, [Col. 4, lines 17-41, “The method may also include receiving one or more rolling shutter imager parameters. For example, a rolling shutter imager may have technical characteristics related to performance, such as, for example, scanning orientation associated with scan lines (e.g., related to whether the scan lines are horizontally or vertically oriented), the number of scan lines per frame or field of view, the scan rate of the scan lines, scan line density, total exposure time, high-dynamic range (HDR) capability, etc., any combination of which may be inherent to a particular rolling shutter imager and may affect artifacts associated with rolling shutter image data captured by the particular rolling shutter imager and/or simulated rolling shutter image data generated upon conversion of the global shutter image data into simulated rolling shutter image data. The method, in some examples, may also include generating a rolling shutter image based at least in part on the optical flow field and the rolling shutter imager parameters.”, [Col. 11, lines 41-25], “The example system 400 may include a global-to-rolling shutter image converter 118 configured to receive (and/or access) the global shutter image data 116 and, in some examples, receive (and/or access) the rolling shutter imager parameters 120.”, the optical flow field is an information that was gathered from the global shutter image, but the rolling shutter image parameters such as such as, for example, scanning orientation associated with scan lines (e.g., related to whether the scan lines are horizontally or vertically oriented), the number of scan lines per frame or field of view, the scan rate of the scan lines, scan line density, total exposure time, high-dynamic range (HDR) capability are unknown , so they are predetermined and added to the simulated rolling shutter image ) . Claim 11 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) , wherein the conversion part (Dolan, Fig. 1) performs preprocessing on the second dataset input to the first recognizer (Dolan, Fig. 4, simulated rolling shutter image data is inputted to machine learning model 402) , the preprocessing being performed to approximate an output of the first recognizer to an output of the second recognizer (Dolan, [Col. 11, lines 2-13], ”converting global shutter image data 116 to simulated rolling shutter image data 126 for input into an example machine learning model 402 as training data for training the machine learning model 402 . As noted above with respect to FIG. 1, the global shutter image data 116 may be data captured in real-time via a global shutter imager, and/or the global shutter image data 116 may be previously stored global shutter image data that has been previously recorded by one or more global shutter imagers and/or that has been generated by a computer, for example, such that the global shutter image data 116 is simulated global shutter image data”) . Claim 16 Dolan discloses the information processing apparatus according to claim 11 (Dolan, Fig. 5) , wherein the conversion part (Dolan, Fig. 1) changes a processing parameter in a processing unit included in the first recognizer based on the first pixel characteristic or the first signal characteristic and the second pixel characteristic or the second signal characteristic (Dolan, [Col. 4, lines 17-32], “The method may also include receiving one or more rolling shutter imager parameters. For example, a rolling shutter imager may have technical characteristics related to performance, such as, for example, scanning orientation associated with scan lines (e.g., related to whether the scan lines are horizontally or vertically oriented), the number of scan lines per frame or field of view, the scan rate of the scan lines, scan line density, total exposure time, high-dynamic range (HDR) capability, etc., any combination of which may be inherent to a particular rolling shutter imager and may affect artifacts associated with rolling shutter image data captured by the particular rolling shutter imager and/or simulated rolling shutter image data generated upon conversion of the global shutter image data into simulated rolling shutter image data.”) , the processing parameter being changed to approximate an output of a predetermined processing unit included in the first recognizer to an output of a processing unit included in the second recognizer, the processing unit corresponding to the predetermined processing unit (Dolan, Fig. 6, step 604, receiving a rolling shutter image parameter, step 608, generating a simulated rolling shutter image based at least in part on the optical flow field and the simulated imager parameter.) . Claim 17 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) , wherein the first pixel characteristic (Dolan, [Col. 1, lines 6-7], “global shutter images, which record an entire scene in a given field of view at exactly the same instant in time”) and the second pixel characteristic (Dolan, Fig. 4, Machine Learning Model 402, [Col. 11, lines 1 – 5], “FIG. 4 is a block diagram of an example system 400 for converting global shutter image data 116 to simulated rolling shutter image data 126 for input into an example machine learning model 402 as training data for training the machine learning model 402”) are at least one of optical linearity, a noise characteristic, a bit length, presence/absence of high dynamic range composition in the first signal and the second signal, a static gradation characteristic, and a shading characteristic of the first signal and the second signal (Dolan, [Col. 4, lines 15-32, “the optical flow field may be calculated using, for example, Lucas-Kanade methods, machine-learned models, etc. The method may also include receiving one or more rolling shutter imager parameters. For example, a rolling shutter imager may have technical characteristics related to performance, such as, for example, scanning orientation associated with scan lines (e.g., related to whether the scan lines are horizontally or vertically oriented), the number of scan lines per frame or field of view, the scan rate of the scan lines, scan line density, total exposure time, high-dynamic range (HDR) capability, etc., any combination of which may be inherent to a particular rolling shutter imager and may affect artifacts associated with rolling shutter image data captured by the particular rolling shutter imager and/or simulated rolling shutter image data generated upon conversion of the global shutter image data into simulated rolling shutter image data.”) . Claim 18 is rejected for similar reasons as those described in claim 1. The additional elements in Claim 18 ( Dolan ) discloses includes: an information processing method (Dolan, Fig. 6) implemented by a processor (Dolan, [Col. 13, lines 10-12], “The vehicle computing device 504 may include one or more processors 516 and memory 518 communicatively coupled with the one or more processors 516.”) . Claim 19 is rejected for similar reasons as those described in claim 1. The additional elements in Claim 19 ( Dolan ) discloses includes: an information processing program (Dolan, [Col. 17, lines 62-66],” The memory 518 and 534 may store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems.”) causing a processor to implement (Dolan, [Col. 13, lines 10-12], “The vehicle computing device 504 may include one or more processors 516 and memory 518 communicatively coupled with the one or more processors 516.”) . Claim 20 is rejected for similar reasons as those described in claim 1. The additional elements in Claim 20 ( Dolan ) discloses includes: an information processing system (Dolan, Fig. 5) comprising: a learning device (Dolan, Fig. 5, vehicle computing device 504) and a recognition device (Dolan, Fig. 5, vehicle computing device 504) including the second recognizer (Dolan, Fig. 4, machine learning model 402) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 7-10 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Dolan in view of Fan et al., "Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video" (October 2021), hereinafter referred to as Fan . Claim 7 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) . Dolan does not explicitly disclose wherein the conversion part converts the second dataset into the first dataset by approximating the second pixel characteristic or the second signal characteristic to the first pixel characteristic or the first signal characteristic when there is a lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. However, Fan teaches wherein the conversion part converts the second dataset into the first dataset (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) by approximating the second pixel characteristic or the second signal characteristic to the first pixel characteristic or the first signal characteristic when there is a lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic (Fan, Section 4, “RS imaging formulation, i.e., estimating the displacement vectors u r→s of pixels from the RS image to the virtual GS image”, “To deliver each RS pixel x on κ-th scanline in frame 1 to its GS canvas defined by the pose corresponding to s-th scanline of frame 1, the RS-aware forward warping displacement vector of pixel x can be formulated”, “Connection between RS undistortion flow and optical flow. Note that the optical flow in Eq. (6) exhibits the pixel displacement over two consecutive RS frames, while the RS undistortion flow in Eq. (10) models the pixel displacement between the RS frame 1 (or frame 2) and the GS frame at scanline s.”) . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan wherein the conversion part converts the second dataset into the first dataset by approximating the second pixel characteristic or the second signal characteristic to the first pixel characteristic or the first signal characteristic when there is a lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Claim 8 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) . Dolan does not explicitly disclose wherein the conversion part converts the second dataset into the first dataset by estimating missing information due to the lack of information when there is the lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. However, Fan teaches wherein the conversion part converts the second dataset into the first dataset (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) by estimating missing information due to the lack of information when there is the lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic (Fan, Section 5, “The proposed RSSR pipeline can be distilled down to two main submodules: the optical flow estimation network F and the middle-scanline RS undistortion flow estimation network U. We first utilize F to obtain bidirectional optical flows, and then encode the relation between optical flows and middle-scanline RS undistortion flows by the middle-scanline correlation maps over U. Finally, we compute the middle-scanline RS undistortion flows to produce two target middle-scanline GS frames by the softmax splatting”) . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan wherein the conversion part converts the second dataset into the first dataset by estimating missing information due to the lack of information when there is the lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Claim 9 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) . Dolan does not explicitly disclose wherein the conversion part converts the second pixel characteristic or the second signal characteristic into the first pixel characteristic or the first signal characteristic based on preset information when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown. However, Fan teaches wherein the conversion part converts the second pixel characteristic or the second signal characteristic into the first pixel characteristic or the first signal characteristic (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) based on preset information when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown (Fa, Section 4, “determined by camera parameters, camera motions and 3D depths”, Section 5, “The proposed RSSR pipeline can be distilled down to two main submodules: the optical flow estimation network F and the middle-scanline RS undistortion flow estimation network U. We first utilize F to obtain bidirectional optical flows, and then encode the relation between optical flows and middle-scanline RS undistortion flows by the middle-scanline correlation maps over U. Finally, we compute the middle-scanline RS undistortion flows to produce two target middle-scanline GS frames by the softmax splatting”) . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan wherein the conversion part converts the second pixel characteristic or the second signal characteristic into the first pixel characteristic or the first signal characteristic based on preset information when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Claim 10 Dolan discloses the information processing apparatus according to claim 2 (Dolan, Fig. 5) . Dolan does not explicitly disclose wherein the conversion part estimates the first pixel characteristic or the first signal characteristic into which the second pixel characteristic or the second signal characteristic is converted when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown. However, Fan teaches wherein the conversion part estimates the first pixel characteristic or the first signal characteristic into which the second pixel characteristic or the second signal characteristic is converted (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown (Fa, Section 1, “we formulate the bidirectional RS undistortion flows to characterize the pixel wise RS-aware pixel displacement, and further advance a calculation method for the mutual conversion between varying RS undistortion flows corresponding to different scan lines. In particular, we prove that the scaling factor is in the interval of (−1,1) when correcting an RS image to its middle-scanline GS image. As a result of utilizing these parameterizations, we propose a data-driven solution for RSSR with good interpretability, which intrinsically encapsulates the complete underlying RS geometry that more sophisticated methods” . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan wherein the conversion part estimates the first pixel characteristic or the first signal characteristic into which the second pixel characteristic or the second signal characteristic is converted when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown.. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Claim 12 Dolan discloses the information processing apparatus according to claim 11 (Dolan, Fig. 5) , wherein the preprocessing (Dolan, Fig. 4, simulated rolling shutter image data is inputted to machine learning model 402). Dolan teaches converting the first image dataset which is the global shutter image into a second image dataset which is the simulated rolling image data. Dolan does not explicitly disclose process of converting the second dataset into the first dataset by approximating the second pixel characteristic or the second signal characteristic to the first pixel characteristic or the first signal characteristic when there is a lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. However, Fan teaches process of converting the second dataset into the first dataset (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) by approximating the second pixel characteristic or the second signal characteristic to the first pixel characteristic or the first signal characteristic when there is a lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic (Fan, Section 4, “RS imaging formulation, i.e., estimating the displacement vectors u r→s of pixels from the RS image to the virtual GS image”, “To deliver each RS pixel x on κ-th scanline in frame 1 to its GS canvas defined by the pose corresponding to s-th scanline of frame 1, the RS-aware forward warping displacement vector of pixel x can be formulated”, “Connection between RS undistortion flow and optical flow. Note that the optical flow in Eq. (6) exhibits the pixel displacement over two consecutive RS frames, while the RS undistortion flow in Eq. (10) models the pixel displacement between the RS frame 1 (or frame 2) and the GS frame at scanline s.”) . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan of process of converting the second dataset into the first dataset by approximating the second pixel characteristic or the second signal characteristic to the first pixel characteristic or the first signal characteristic when there is a lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Claim 13 Dolan discloses the information processing apparatus according to claim 11 (Dolan, Fig. 5) , wherein the preprocessing (Dolan, Fig. 4, simulated rolling shutter image data is inputted to machine learning model 402). Dolan teaches converting the first image dataset which is the global shutter image into a second image dataset which is the simulated rolling image data. Dolan does not explicitly disclose a process of converting the second dataset into the first dataset by estimating missing information due to a lack of information when there is the lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. However, Fan teaches a process of converting the second dataset into the first dataset (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) by estimating missing information due to the lack of information when there is the lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic (Fan, Section 5, “The proposed RSSR pipeline can be distilled down to two main submodules: the optical flow estimation network F and the middle-scanline RS undistortion flow estimation network U. We first utilize F to obtain bidirectional optical flows, and then encode the relation between optical flows and middle-scanline RS undistortion flows by the middle-scanline correlation maps over U. Finally, we compute the middle-scanline RS undistortion flows to produce two target middle-scanline GS frames by the softmax splatting”) . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan of a process of converting the second dataset into the first dataset by estimating missing information due to the lack of information when there is the lack of information on the second pixel characteristic or the second signal characteristic with respect to the first pixel characteristic or the first signal characteristic. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Claim 14 Dolan discloses the information processing apparatus according to claim 11 (Dolan, Fig. 5) , wherein the preprocessing (Dolan, Fig. 4, simulated rolling shutter image data is inputted to machine learning model 402). Dolan teaches converting the first image dataset which is the global shutter image into a second image dataset which is the simulated rolling image data. Dolan does not explicitly disclose a process of converting the second pixel characteristic or the second signal characteristic into the first pixel characteristic or the first signal characteristic based on preset information when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown. However, Fan teaches a process of converting the second pixel characteristic or the second signal characteristic into the first pixel characteristic or the first signal characteristic (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) based on preset information when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown (Fa, Section 4, “determined by camera parameters, camera motions and 3D depths”, Section 5, “The proposed RSSR pipeline can be distilled down to two main submodules: the optical flow estimation network F and the middle-scanline RS undistortion flow estimation network U. We first utilize F to obtain bidirectional optical flows, and then encode the relation between optical flows and middle-scanline RS undistortion flows by the middle-scanline correlation maps over U. Finally, we compute the middle-scanline RS undistortion flows to produce two target middle-scanline GS frames by the softmax splatting”) . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan of a process of converting the second pixel characteristic or the second signal characteristic into the first pixel characteristic or the first signal characteristic based on preset information when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Claim 15 Dolan discloses the information processing apparatus according to claim 11 (Dolan, Fig. 5) , wherein the preprocessing (Dolan, Fig. 4, simulated rolling shutter image data is inputted to machine learning model 402). Dolan teaches converting the first image dataset which is the global shutter image into a second image dataset which is the simulated rolling image data. Dolan does not explicitly disclose a process of estimating the first pixel characteristic or the first signal characteristic into which the second pixel characteristic or the second signal characteristic is converted when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown. However, Fan teaches a process of estimating the first pixel characteristic or the first signal characteristic into which the second pixel characteristic or the second signal characteristic is converted (Fan, Fig. 1, “The RS image is generated by continuously synthesizing the GS image row by row, while our rolling shutter temporal super-resolution (RSSR) pipeline reverses this process, i.e ., extracting the latent GS image sequence from two consecutive RS images ”, Abstract, “our method can be very efficient for explicit propagation to generate GS images under any scanline.”) when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown (Fa, Section 1, “we formulate the bidirectional RS undistortion flows to characterize the pixel wise RS-aware pixel displacement, and further advance a calculation method for the mutual conversion between varying RS undistortion flows corresponding to different scan lines. In particular, we prove that the scaling factor is in the interval of (−1,1) when correcting an RS image to its middle-scanline GS image. As a result of utilizing these parameterizations, we propose a data-driven solution for RSSR with good interpretability, which intrinsically encapsulates the complete underlying RS geometry that more sophisticated methods” . Dolan and Fan are both considered to be analogous to the claimed invention because they are in the same field of image conversion. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus as taught by Dolan to incorporate the teachings of Fan of a process of estimating the first pixel characteristic or the first signal characteristic into which the second pixel characteristic or the second signal characteristic is converted when a correspondence relationship between the second pixel characteristic or the second signal characteristic and the first pixel characteristic or the first signal characteristic is unknown.. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. The motivation for the proposed modification would have been to produce high-quality GS image sequences with rich details (Fan, Abstract) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. JP2017102838A – Ichikawa – teaches adjusting or converting a machine learning model that uses a camera image input to a machine learning model that uses a LiDAR or radar image as an input to perform object recognition. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENISE G ALFONSO whose telephone number is (571)272-1360. The examiner can normally be reached Monday - Friday 7:30 - 5:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached at (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DENISE G ALFONSO/Examiner, Art Unit 2662 /AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662 Application/Control Number: 18/719,264 Page 2 Art Unit: 2662 Application/Control Number: 18/719,264 Page 3 Art Unit: 2662 Application/Control Number: 18/719,264 Page 4 Art Unit: 2662 Application/Control Number: 18/719,264 Page 5 Art Unit: 2662 Application/Control Number: 18/719,264 Page 6 Art Unit: 2662 Application/Control Number: 18/719,264 Page 7 Art Unit: 2662 Application/Control Number: 18/719,264 Page 8 Art Unit: 2662 Application/Control Number: 18/719,264 Page 9 Art Unit: 2662 Application/Control Number: 18/719,264 Page 10 Art Unit: 2662 Application/Control Number: 18/719,264 Page 11 Art Unit: 2662 Application/Control Number: 18/719,264 Page 12 Art Unit: 2662 Application/Control Number: 18/719,264 Page 13 Art Unit: 2662 Application/Control Number: 18/719,264 Page 14 Art Unit: 2662 Application/Control Number: 18/719,264 Page 15 Art Unit: 2662 Application/Control Number: 18/719,264 Page 16 Art Unit: 2662 Application/Control Number: 18/719,264 Page 17 Art Unit: 2662 Application/Control Number: 18/719,264 Page 18 Art Unit: 2662 Application/Control Number: 18/719,264 Page 19 Art Unit: 2662 Application/Control Number: 18/719,264 Page 20 Art Unit: 2662 Application/Control Number: 18/719,264 Page 21 Art Unit: 2662 Application/Control Number: 18/719,264 Page 22 Art Unit: 2662 Application/Control Number: 18/719,264 Page 23 Art Unit: 2662 Application/Control Number: 18/719,264 Page 24 Art Unit: 2662 Application/Control Number: 18/719,264 Page 25 Art Unit: 2662 Application/Control Number: 18/719,264 Page 26 Art Unit: 2662 Application/Control Number: 18/719,264 Page 27 Art Unit: 2662 Application/Control Number: 18/719,264 Page 28 Art Unit: 2662 Application/Control Number: 18/719,264 Page 29 Art Unit: 2662 Application/Control Number: 18/719,264 Page 30 Art Unit: 2662 Application/Control Number: 18/719,264 Page 31 Art Unit: 2662 Application/Control Number: 18/719,264 Page 32 Art Unit: 2662
Prosecution Insights