Detailed Office Action
1. This communication is being filed in response to the initial submission having a mailing date of 06/04/2024, in which a three (3) month Shortened Statutory Period for Response has been set.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgements
3. Upon new entry, claims (1 -11) appear pending for examination, of which (1, 10, 11) are the three (3) parallel running independent claims on record.
Information Disclosure Statement
4. The submitted IDS dated on 06/04/2024 is/are in compliance with the provisions of 37 CFR 1.97, being reviewed and considered by the Examiner.
Drawings
5. The submitted Drawings on date 06/04/2024 has been accepted and considered under the 37 CFR 1.121 (d).
Claim interpretation section
6. For the purpose of examination, and under the broadest reasonable interpretation (BRI), consistent with the instant specification and the common knowledge of one of ordinary skill in the art, the below list of terms/limitations will be considered to read as:
_ Term/limitation “a coded aperture camera” will read as (e.g. at least one of “distance between an encoded mask and an image sensor”, or “number of pinholes”, or “size of each of the pinholes”, and/or “a position of each of the pinholes”; [0026]).
_ Term/limitation “a lens-less multi-pinhole camera” will read as (e.g. a multi-pinhole camera (200), Fig. 2, having plurality of pinholes (211, 212) “located at random” or “at an equal interval”; [0056]).
Claim rejection section
35 USC 103
6. 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. 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.
6.1. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied 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 non-obviousness.
6.2. Claims (1 -11) is/are rejected under 35 U.S.C. 103 as being unpatentable over “Face detection and recognition using back propagation NN; hereafter “Vijayalakshmi”), in view of Arbabian; et al. (US 11,418,773 B2; hereafter “Arbabian”).
Claim 1. Vijayalakshmi discloses the basics of the invention as claimed - An information processing method comprising: (e.g. a computerized system for object recognition (i.e. facial), for car/robotic industries, employing camera sensing information, imputed to a similar back-propagated neural networks (BPNN), able to provide effective and efficient output predictions; [page 1 -3]).
More specifically Vijayalakshmi discloses - by a computer, (e.g. a computerized system of the same; [page 1])
training a first neural network model so as to receive a first operation parameter for an operation of a first sensor and (e.g. see Fig. 2; [page 3])
second sensing data obtained by an operation of a second sensor and output first sensing data obtained by the operation of the first sensor using the first operation parameter; (e.g. see Fig. 2; [page 3])
generating a third neural network model including the first neural network model (e.g. combination of BPNN nodes, or other applications, disclosed ; [page 6]) and a second neural network model connected to each other in such a manner that the second neural network model receives the first sensing data output from the trained first neural network model and outputs an identification result of the first sensing data; (e.g. similarly using pinout (i.e. inputs/outputs) connectivity from every previous layer (Fig. 2),
training the second neural network model by backpropagation using an error difference between: (e.g. see error difference quantization and analysis, Fig. 2; [page 3])
the identification result which the third neural network model outputs after receiving the second sensing data and the first operation parameter; e.g. similarly using pinout (i.e. inputs/outputs) connectivity from every previous layer (Fig. 2),
and correct identification information corresponding to the second sensing data; (e.g. identification techniques implemented [pages 1 -3])
and acquiring a second operation parameter by updating the first operation parameter via the first neural network model by the backpropagation; e.g. similarly using pinout (i.e. inputs/outputs) connectivity from every previous layer (Fig. 2).
Given the teachings of Vijayalakshmi; et al. as a whole, and under the obvious assumption and purpose of his papers, it is noted that some of the functional steps and/or components as listed (i.e. camera sensor, NN layers, etc), are missed or not fully described in the papers.
For the purpose of additional clarification and in the same field of endeavor, Arbabian teaches (e.g. a BPNN of the same, as shown in Figs. (19 -20), capable of training sensing image data, from plurality of camera sources (Fig. 3 -4), able to accurately make predictions on data fed into the NN layer models (Fig. 19), further comprising a learning phase, to optimize the models to correctly predict the output for a given sensor’s inputs [Arbabian; 25: 55; 27: 15 – 29: 45].)
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention, to modify the teachings of Vijayalakshmi, with the BPNN system implementation of Arbabian, in order to provide – (e.g. a model able to predict the output for a given set of sensing inputs, evaluated over several epochs to reliably provide the output that is specified as corresponding to the given input for the greatest number of inputs for the training dataset. [Arbabian; 26: 10].)
Claim 2. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 1, wherein the first sensor is a coded aperture camera, and the first operation parameter includes at least one of a distance between an encoded mask and an image sensor, the number of pinholes, a size of each of the pinholes, and a position of each of the pinholes; (e.g. see Figs (19 -20) system, that employes distance constrains, different viewpoint positions, for plurality of sensing techniques, such pinhole camera types (Fig. 3); [Arbabian; 7: 05]), and multicamera lens type (Fig. 4, 12, 13); with lens focal adjustment (Figs. 12 -13) [Arbabian; 16: 20]; the same motivation applies herein.)
Examiner notes is taken - regarding the well-known and common use, of pinhole multi-lens camera/sensor type(s), and/or the like it, way before the invention was made/filed.
Claim 3. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 1, wherein the first sensor is a lens-less multi-pinhole camera, and the first operation parameter includes at least one of a focal distance of the lens-less multi-pinhole camera, the number of pinholes, a size of each of the pinholes, and a position of each of the pinholes; (The same rationale and motivation apply as given to Claim 2 above.)
Claim 4. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 1, wherein the second sensing data includes an image having a smaller blur than an image included in the first sensing data; (e.g. see motion blur determined, and taken into account; [Arbabian; Cols. 18-19]; the same motivation applies herein.)
Claim 5. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 4, wherein the second sensor is a camera including a lens, a diaphragm, and an imaging element. (The same rationale and motivation apply as given to Claim 2 above.)
Claim 6. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 4, wherein the second sensor is a pinhole camera. (The same rationale and motivation apply as given to Claim 2 above.)
Claim 7. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 1, wherein the second sensing data includes images captured at different viewpoint positions. (The same rationale and motivation apply as given to Claim 2 above.)
Claim 8. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 7, wherein the second sensing data includes images captured at a plurality of viewpoint positions. (Same rationale and motivation apply as given to Claim 2 above.)
Claim 9. Vijayalakshmi/Arbabian discloses - The information processing method according to claim 8, wherein the first sensing data includes an image formed by superimposing a plurality of images acquired respectively through a plurality of pinholes, and the second sensing data includes an image captured at a viewpoint position corresponding to a position of each of the pinholes. (The same rationale and motivation apply as given to Claim 2 above.)
Claim 10. Vijayalakshmi/Arbabian discloses - An information processing system, comprising: a first training part that trains a first neural network model so as to receive a first operation parameter for an operation of a first sensor and second sensing data obtained by an operation of a second sensor and output first sensing data obtained by the operation of the first sensor using the first operation parameter;
a generation part that generates a third neural network model including the first neural network model and a second neural network model connected to each other in such a manner that the second neural network model receives the first sensing data output from the trained first neural network model and outputs an identification result of the first sensing data;
a second training part that trains the second neural network model by backpropagation using an error difference between: the identification result which the third neural network model outputs after receiving the second sensing data and the first operation parameter; and
correct identification information corresponding to the second sensing data; and
an acquisition part that acquires a second operation parameter by updating the first operation parameter via the first neural network model by the backpropagation. (Current lists all the same elements as recite in Claim 1 above, but in “System form” instead, and is/are therefore on the same premise.)
Claim 11. Vijayalakshmi/Arbabian discloses - A non-transitory computer-readable storage medium that stores an information processing program for causing a computer to execute, by the information processing program, processing comprising:
training a first neural network model so as to receive a first operation parameter for an operation of a first sensor and second sensing data obtained by an operation of a second sensor and output first sensing data obtained by the operation of the first sensor using the first operation parameter;
generating a third neural network model including the first neural network model and a second neural network model connected to each other in such a manner that the second neural network model receives the first sensing data output from the trained first neural network model and outputs an identification result of the first sensing data;
training the second neural network model by backpropagation using an error difference between: the identification result which the third neural network model outputs after receiving the second sensing data and the first operation parameter; and correct identification information corresponding to the second sensing data;
and acquiring a second operation parameter by updating the first operation parameter via the first neural network model by the backpropagation. (Current lists all the same elements as recite in Claim 1 above, but in “CRM form” instead, and is/are therefore on the same premise.)
Prior Art Citations
7. The following List of prior art, made of record and not relied upon, is/are considered
pertinent to applicant's disclosure:
7.1. Patent documentation
US 10,911,732 B2 Arbabian; et al. G06F16/55; H04N13/246; G06T7/80;
US 11,418,773 B2 Arbabian; et al. G06F16/55; H04N13/246; G06T7/80;
US 11,488,325 B2 Deng; et al. G06T7/80; G06T7/11;
US 11,403,739 B2 Chen; et al. G06T5/00; G06T7/80; H04N5/357;
US 12,675,911 B2 Wakai; et al. G06T7/00; G06T7/80;
7.2. Non-Patent documentation:
_ Adaptively Directed Image Restoration Using Resilient backpropagation neural network; Nawaz - April-2023
_ Applying Back-propagation in Convolutional Neuron networks for image classification; Zhang - Jun-2017
_ Face detection and recognition using back propagation NEURAL NETWORKS; Vijayalakshmi - Nov-2017
CONCLUSIONS
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS PEREZ-FUENTES (luis.perez-fuentes@uspto.gov) whose telephone number is (571) 270 -1168. The examiner can normally be reached on Monday-Friday 8am-5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, WILLIAM VAUGHN can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is (571) 272 -3922. 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, 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 system, please call (800) 786 -9199 (USA OR CANADA) or (571) 272 -1000.
/LUIS PEREZ-FUENTES/
Primary Examiner, Art Unit 2481.
Prosecution Insights