DETAILED ACTION
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 .
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of JP2022-103353, filed in Japan on 6/28/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/24/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Preliminary Amendment
Applicant submitted a preliminary amendment on 9/24/2024. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Specification
The following guidelines illustrate the preferred layout for the specification of a utility application. These guidelines are suggested for the applicant’s use.
Arrangement of the Specification
As provided in 37 CFR 1.77(b), the specification of a utility application should include the following sections in order. Each of the lettered items should appear in upper case, without underlining or bold type, as a section heading. If no text follows the section heading, the phrase “Not Applicable” should follow the section heading:
(a) TITLE OF THE INVENTION.
(b) CROSS-REFERENCE TO RELATED APPLICATIONS.
(c) STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT.
(d) THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT.
(e) INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A READ-ONLY OPTICAL DISC, AS A TEXT FILE OR AN XML FILE VIA THE PATENT ELECTRONIC SYSTEM.
(f) STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINT INVENTOR.
(g) BACKGROUND OF THE INVENTION.
(1) Field of the Invention.
(2) Description of Related Art including information disclosed under 37 CFR 1.97 and 1.98.
(h) BRIEF SUMMARY OF THE INVENTION.
(i) BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S).
(j) DETAILED DESCRIPTION OF THE INVENTION.
(k) CLAIM OR CLAIMS (commencing on a separate sheet).
(l) ABSTRACT OF THE DISCLOSURE (commencing on a separate sheet).
(m) SEQUENCE LISTING. (See MPEP § 2422.03 and 37 CFR 1.821 - 1.825). A “Sequence Listing” is required on paper if the application discloses a nucleotide or amino acid sequence as defined in 37 CFR 1.821(a) and if the required “Sequence Listing” is not submitted as an electronic document either on read-only optical disc or as a text file via the patent electronic system.
The specification still contains previous claims 1-56 on pages 55-63 of the specification. Appropriate action is required.
The abstract of the disclosure is objected to because the references to the images (100, 101, and 110) should not be included. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 6 and 13 are objected to because of the following informalities:
Claim 6 states “wherein estimation the position of the target part includes”. This is incorrect verbiage.
Claim 13 states “the target part in the estimation target is displayed in a more emphasized manner than the associated region.” More emphasized is unclear and needs to be further specified.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
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 nonobviousness.
Claims 1, 18, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Masko (U.S. Patent Pub. No. 2021/0350554) in view of Zheng (CN113597616A).
Regarding Claim 1, Masko teaches an information processing apparatus comprising:
at least one memory configured to store instructions; and at least one processor configured to execute the instructions to (¶66 There may be provided a computer program, which when run on a computer, causes the computer to configure any apparatus, including a circuit, controller, converter, or device disclosed herein or perform any method disclosed herein. The computer program may be a software implementation, and the computer may be considered as any appropriate hardware, including a digital signal processor, a microcontroller, and an implementation in read only memory (ROM), erasable programmable read only memory (EPROM) or electronically erasable programmable read only memory (EEPROM), as non-limiting examples:)
acquire an estimation target image including an image of a target part in an estimation target; and (¶95 an image of an eye of a user, such as the reference-image or the sample-image, may comprise a digital image produced by an image sensor. )
extract a target part feature related to a portion associated with the target part from a feature of the estimation target image (Fig. 2; ¶84 A controller of the system may employ image processing (such as digital image processing) for extracting features in the image. The controller may for example identify the location of the pupil 230 in the one or more images captured by the image sensor. The controller may determine the location of the pupil 230 using a pupil-detection process. The controller may also identify corneal reflections 232 or glints in the image of the eye. The corneal reflections may correspond to reflections of light emitted by one or more illuminators of the eye-tracking system. The controller may estimate a corneal center based on the corneal reflections 232.,) based on a training result acquired by performing training by using a reference image including an image of a target part and a target image including an image of the target part (¶116 After a ML network eye-tracking-algorithm has been trained with differential-training-images, the algorithm may be subsequently used in the systems and methods disclosed herein to determine unknown eye-data of a sample-image. The eye-data can be determined based on a differential-image and reference-eye-data as described above)
Masko does not explicitly disclose estimates a position of the target part, based on the target part feature.
Zheng is in the same field of art of image analysis. Further, Zheng teaches estimates a position of the target part, based on the target part feature (¶118 according to the corresponding relationship between the original image and the position of the extracted face region, and the corresponding relationship between the recognized human eye region in the face region, the 2D coordinates of the pupil point in the original image are deduced)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Masko by estimating pupil position based on the images that is taught by Zheng; thus, one of ordinary skilled in the art would be motivated to combine the references to be able to accurately locate the three-dimensional (3D) spatial position of the pupil point in the camera coordinate system (Zheng ¶10).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 18, Masko in view of Zheng discloses the information processing apparatus according to claim 1, wherein
the target part includes at least one of a pupil center, an outer corner of an eye, and an inner corner of an eye (Zheng, ¶13 The pupil point is the center point of the pupil. Determine the position of the pupil point in the image according to the first area of the heat map)
The reasons for combining Masko and Zheng are similar to that stated in the rejection of claim 1.
Regarding claim 20, claim 20 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Masko further teaching on:
An information processing method comprising, by one or more computers (¶66 There may be provided a computer program, which when run on a computer, causes the computer to configure any apparatus, including a circuit, controller, converter, or device disclosed herein or perform any method disclosed herein.)
Regarding claim 21, claim 21 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Masko further teaching on:
A non-transitory computer readable medium storing a program for causing one or more computers to execute (¶67 The computer program may be provided on a computer readable medium, which may be a physical computer readable medium such as a disc or a memory device, or may be embodied as a transient signal. Such a transient signal may be a network download, including an internet download. There may be provided one or more non-transitory computer-readable storage media storing computer-executable instructions that, when executed by a computing system, causes the computing system to perform any method disclosed herein.)
Claims 2-7, 14-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Masko (U.S. Patent Pub. No. 2021/0350554) in view of Zheng (CN113597616A) in view of Chae (U.S. Patent Pub. No. 2024/0371129).
Regarding Claim 2, Masko in view of Zheng teaches the information processing apparatus according to claim 1, wherein the at least one processor configured further to execute the instructions (see claim 1)
Masko in view of Zheng does not explicitly disclose generate similarity degree information indicating a degree of similarity between the feature of the estimation target image and a reference feature being a feature of the reference image, and
extract the target part feature from the feature of the estimation target image, based on the similarity degree information.
Chae is in the same field of art of image analysis. Further, Chae teaches generate similarity degree information indicating a degree of similarity between the feature of the estimation target image and a reference feature being a feature of the reference image, and (Chae 2024/0371129 ¶37 The cosine similarity loss is a loss corresponding to the cosine similarity between a first feature amount F1 of the first training image TI1 calculated by the fully connected layer of the learning model M and a second feature amount F1 of the second training image TI1 calculated by the fully connected layer of the learning model M.)
extract the target part feature from the feature of the estimation target image, based on the similarity degree information (Chae, Fig. 1; The similarity loss is used for the estimation module) (Masko teaches uses a difference (a comparison of two images just opposite of similarity,) ¶28 The eye-tracking system may be configured to determine the candidate region of the sample-image by: determining candidate-pixel-locations of the corresponding differential-image based on the pupil-area and the differential-intensity-values.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Masko in view of Zheng by generating a similarity and using that to determine the feature that is taught by Chae; thus, one of ordinary skilled in the art would be motivated to combine the references to improve an accuracy of a learning model which estimates a numerical value relating to an object included in an image (Chae ¶8).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 3, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 2, wherein
the similarity degree information is generated, based on the feature of the estimation target image and the reference feature (Masko, ¶28 The reference-image and each sample-image may comprise a pixel-array of pixel-locations, each pixel-location having an intensity-value. The eye-tracking system may be configured to determine the difference between the reference-image and the sample-image by matrix subtraction of the corresponding pixel-arrays to define the differential-image as a pixel-array of differential-intensity-values.)
Regarding Claim 4, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 3, wherein
the training result includes a learned feature extraction model subjected to machine training for extracting the feature of the estimation target image, and the feature of the estimation target image is acquired by using the feature extraction model with the estimation target image as an input (Masko, ¶113 Eye-gaze-tracking systems can utilize machine learning (ML) networks (such as deep learning networks) with images as input to the network. The disclosed systems and methods can pre-process images by subtracting one or more reference-images from subsequently received images to create new differential-images as input for the network. Such systems can then use the pre-processed (differential) images to calculate eye-data in any way that is known in the art. The one or more reference-images may be captured during a personal calibration with known stimuli points. Systems and methods employing differential-images can be used to both: (i) train a ML network eye-tracking-algorithm; and (ii) determine unknown eye-data for sample-images.)
Regarding Claim 5, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 2,
wherein the training result includes a learned position estimation model subjected to machine training for estimating an in-region position indicating a position of the target part in an associated region being associated with the target part feature (Zheng, ¶15 a deep neural network can be used to predict the heat map of the pupil point, and the probability distribution of the pupil point can be represented by the heat map. It is easier for the deep neural network to return the heat map based on the image than directly returning the pupil point coordinates, and it is more effective for occlusion, light, and large eyeballs,)
the in-region position is estimated by using the position estimation model with the target part feature as an input, and a position of the target part is estimated, based on the in-region position (Zheng, ¶13 Determine the position of the pupil point in the image according to the first area of the heat map, where the probability value corresponding to the pixel in the first area of the heat map is greater than the first threshold. The second area in the image is determined, where the probability value corresponding to the pixel in the heat map of the second area is greater than the second threshold, and the second threshold is less than or equal to the first threshold.)
The reasons for combining Masko, Zheng, and Chae are similar to that stated in the rejection of claims 1 and 2. In addition, this same reasoning is pertinent and applicable to the rejections of claims 6, 7, 14, and 17 below.
Regarding Claim 6, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 5, wherein
estimation the position of the target part includes determining a region position indicating a position of the associated region, estimating the in-region position by using the position estimation model with the target part feature as an input, and estimating the position of the target part, based on the region position and the in-region position (Zheng, ¶17 the center position of the first region of the heat map can be used as the position of the pupil point in the image, which has better robustness. For example, the argmax function can be used for the heat map of the human eye area to find the point with the highest probability value (that is, the probability value), the second highest point, the second highest point lower than the second highest point, etc.)
Regarding Claim 7, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 6, wherein
estimation the position of the target part further includes converting the estimated position of the target part into a position in the estimation target image (Zheng, ¶17 the center position of the first region of the heat map can be used as the position of the pupil point in the image, which has better robustness. For example, the argmax function can be used for the heat map of the human eye area to find the point with the highest probability value (that is, the probability value), the second highest point, the second highest point lower than the second highest point, etc.)
Regarding Claim 14, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 2, wherein
the estimation target is a person (Masko, ¶8 receive one or more sample-images of the eye of the user,)
the target part is a pupil center (Masko, ¶23 perform a pupil-detection process on one or more initial-images of the eye of the user to determine reference-pupil-data associated with each initial-image,)
the reference image is a one eye image including only a predetermined eye (Masko, ¶7 receive a reference-image of an eye of a user, the reference-image being associated with reference-eye-data,)
the target image is a both eyes image including both eyes, and (Masko, ¶79 The system 100 comprises an image sensor 120 (e.g. a camera) for capturing images of the eyes of the user.)
the degree of similarity is a spatial cosine degree of similarity (Chae, ¶37 the learning device 20 calculates the cosine similarity loss based on the first training image TI1 and the second training image TI2. The cosine similarity loss is a loss corresponding to the cosine similarity between a first feature amount F1 of the first training image TI1 calculated by the fully connected layer of the learning model M and a second feature amount F1 of the second training image TI1 calculated by the fully connected layer of the learning model M.)
Regarding Claim 15, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 14, wherein
the target part is plural, and (Masko, ¶137 Determining the candidate regions limits a search area for pupils in the sample-image)
the at least one processor configured further to execute the instructions to:
estimate a line-of-sight direction of a person being the estimation target, based on a position of each of the plurality of target parts (Masko, ¶117 determining an eye-gaze may comprise an intermediate step of determining pupil-data, such as pupil-position. In such examples, the reference-gaze-data may comprise reference-pupil-data and the eye-data-analyser may determine pupil-data of the sample-image based on the differential-image and the reference-gaze-data using a ML eye-tracking-algorithm.)
Regarding Claim 17, Masko in view of Zheng in view of Chae discloses the information processing apparatus according to claim 15, wherein
the at least one processor configured further to execute the instructions to:
output an alert related to the estimation target, based on the line-of-sight direction (Zheng, ¶14 From the above, 3D pupil point positioning can be realized, providing technical support for gaze tracking, eye movement, human-computer interaction and other technical directions, providing an accurate and stable starting point for gaze technology, and ensuring the stability of gaze estimation … In driver monitoring, the driver's distraction can also be judged based on the three-dimensional position of the pupil.)
Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Masko (U.S. Patent Pub. No. 2021/0350554) in view of Zheng (CN113597616A) in view of Chae (U.S. Patent Pub. No. 2024/0371129) in view of Liu (U.S. Patent Pub. No. 2021/0026446).
Regarding Claim 1, Masko in view of Zheng in view of Chae teaches the information processing apparatus according to claim 5, wherein the at least one processor configured further to execute the instructions (see claim 5)
Masko in view of Zheng in view of Chae does not explicitly disclose obtain a position loss being a loss related to the in-region position estimated by using the position estimation model with the target part feature of a feature of the target image as an input, based on a position loss function; and correct the position estimation model, based on the position loss.
Liu is in the same field of art of image analysis. Further, Liu teaches obtain a position loss being a loss related to the in-region position estimated by using the position estimation model with the target part feature of a feature of the target image as an input, based on a position loss function; and correct the position estimation model, based on the position loss (¶61 Referring to FIG. 3, in operation 301, a gaze tracking apparatus obtains output position information by inputting an input face image of a user to a neural network model. In operation 303, the gaze tracking apparatus calculates a loss value of the output position information. During the training, parameters of the neural network (e.g., weights of connections between nodes at the different layers) may be updated to minimize the loss function by minimizing the difference between true and predicted gaze point distances.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Masko in view of Zheng in view of Chae by obtaining a position loss and correcting the model that is taught by Liu; thus, one of ordinary skilled in the art would be motivated to combine the references to accurately predict gaze position information in real time (Liu ¶3).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 9, Masko in view of Zheng in view of Chae in view of Liu discloses the information processing apparatus according to claim 8, wherein
the position loss function is a function that does not include, in the position loss, a loss related to a position estimated by using the position estimation model with, as an input, the target part feature input to the position estimation model in a case where the target part feature is different from a portion associated with the target part (Liu, ¶61 Referring to FIG. 3, in operation 301, a gaze tracking apparatus obtains output position information by inputting an input face image of a user to a neural network model. In operation 303, the gaze tracking apparatus calculates a loss value of the output position information. During the training, parameters of the neural network (e.g., weights of connections between nodes at the different layers) may be updated to minimize the loss function by minimizing the difference between true and predicted gaze point distances.)
The reasons for combining Masko, Zheng, Chae and Liu are similar to that stated in the rejection of claim 8. In addition, this same reasoning is pertinent and applicable to the rejection of claim 10 below.
Regarding Claim 10, Masko in view of Zheng in view of Chae in view of Liu discloses the information processing apparatus according to claim 8, wherein
the at least one processor configured further to execute the instructions to:
obtain a similarity degree loss being a loss related to the similarity degree information, based on the similarity degree information and a similarity degree loss function, and the training result includes a learned feature extraction model subjected to machine training for extracting the feature of the estimation target image, the feature extraction model is corrected, based on the similarity degree loss (Chae, ¶37 the learning device 20 calculates the cosine similarity loss based on the first training image TI1 and the second training image TI2. The cosine similarity loss is a loss corresponding to the cosine similarity between a first feature amount F1 of the first training image TI1 calculated by the fully connected layer of the learning model M and a second feature amount F1 of the second training image TI1 calculated by the fully connected layer of the learning model M.)
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Masko (U.S. Patent Pub. No. 2021/0350554) in view of Zheng (CN113597616A) in view of Chae (U.S. Patent Pub. No. 2024/0371129) in view of Victor-Faichney (U.S. Patent No. 10948997).
Regarding Claim 12, Masko in view of Zheng in view of Chae teaches the information processing apparatus according to claim 5, wherein the at least one processor configured further to execute the instructions (see rejection of claim 5)
Masko in view of Zheng in view of Chae does not explicitly disclose display, on a display, a position of the target part in the estimation target and the associated region over the estimation target image in an overlapping manner.
Victor-Faichney is in the same field of art of image analysis. Further, Victor-Faichney teaches display, on a display, a position of the target part in the estimation target and the associated region over the estimation target image in an overlapping manner (Col 14 Lines 28-29: At block 604, process 600 can access a mapping of notification types (the “notification” is looked at as an object being displayed i.e. position of pupil determined in claim 1) to display properties... Examples of the display properties that notification types can be mapped to include… how the notification reacts to other virtual objects (e.g., overlapping them, moving them, causing them to become at least partially transparent, etc.), or any other output (visual, auditory, haptic) that can be applied to a notification.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Masko in view of Zheng in view of Chae by overlapping the displayed objects that is taught by Victor-Faichney; thus, one of ordinary skilled in the art would be motivated to combine the references since in order to display objects according to user specifications (Victor-Faichney Col 14).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 13, Masko in view of Zheng in view of Chae in view of Victor-Faichney discloses the information processing apparatus according to claim 12, wherein
the position of the target part in the estimation target is displayed in a more emphasized manner than the associated region (Victor-Faichney, Col 14 Lines 28-29: At block 604, process 600 can access a mapping of notification types (the “notification” is looked at as an object being displayed i.e. position of pupil determined in claim 1) to display properties... Examples of the display properties that notification types can be mapped to include… how the notification reacts to other virtual objects (e.g., overlapping them, moving them, causing them to become at least partially transparent, etc.), or any other output (visual, auditory, haptic) that can be applied to a notification.)
The reasons for combining Masko, Zheng, Chae and Victor-Faichney are similar to that stated in the rejection of claim 12.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Masko (U.S. Patent Pub. No. 2021/0350554) in view of Zheng (CN113597616A) in view of Chae (U.S. Patent Pub. No. 2024/0371129) in view of Yachida (U.S. Patent Pub. No. 2023/0386038).
Regarding Claim 16, Masko in view of Zheng in view of Chae teaches the f information processing apparatus according to claim 15 (see rejection of claim 15.)
Masko in view of Zheng in view of Chae does not explicitly disclose wherein the plurality of target parts further include an outer corner of an eye and an inner corner of an eye.
Yachida is in the same field of art of image analysis. Further, Yachida teaches wherein the plurality of target parts further include an outer corner of an eye and an inner corner of an eye (¶51 the position of the element of the eye that is estimated by the element position estimation unit 14 includes a gravity center position of the pupil of the subject P and positions of an outer eye corner and an inner eye corner. Note that any one of the positions of the outer eye corner and the inner eye corner may be used, or a position of another freely selected point may be used instead.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Masko in view of Zheng in view of Chae by using the inner and outer corner of an eye as the target parts that is taught by Yachida; thus, one of ordinary skilled in the art would be motivated to combine the references to suitably evaluate a change in state of an eye of a subject (Yachida ¶13).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Allowable Subject Matter
Claim 11 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 11, no prior art teaches the information processing apparatus according to claim 10, wherein correcting the feature extraction model includes
obtaining an integrated loss acquired by integrating the position loss and the similarity degree loss, correcting the feature extraction model, based on the integrated loss, and correcting the position estimation model, based on the integrated loss.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
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/DUSTIN BILODEAU/Examiner, Art Unit 2664
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