DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-21 filed on 01/10/2024 are pending and being examined. Claims 1, 10, 12, 14, and 18-20 are independent form.
Priority
3. Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Claim Rejections—35 USC § 101
4. 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.
5. Claims 1-21 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to non-statutory subject matter (an abstract idea without significantly more).
5-1. Regarding independent claim 1, the claim recites a focal position estimation method for estimating a focal position when in focus corresponding to an estimation target image, the method comprising:
[1] acquiring an estimation target image; and [2] estimating a focal position when in focus corresponding to the estimation target image and according to a position in the estimation target image, from the acquired estimation target image, by [3] using a focal position estimation model that is generated through machine learning training and that receives information based on an image as its input and outputs information indicating a focal position when in focus according to a position in the image.
Step 1:
With regard to step (1), claim 1, is directed to a focal position estimation method for estimating a focal position when in focus corresponding to an estimation target image. The claim 1 therefore is one of statutory categories of invention, i.e., a process.
Step 2A-1:
With regard to 2A-1, The elements recited in claim 1, as drafted, under their broadest reasonable interpretation, encompass a process(es) which is/are directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts. For example, “estimating a focal position when in focus corresponding to the estimation target image and according to a position in the estimation target image, from the acquired estimation target image” in step [2] in the context of this claim, encompasses mental observation, evaluations, judgments, and/or opinions that can be performed in human mind, or by a human using a pen and paper, therefore the limitation falls within the “mental processes” grouping of abstract ideas. Similarity, “a focal position estimation model that is generated through machine learning training” in step [3] in the context of this claim, is a mathematical model including mathematical calculations and fall within the “mathematical concepts” grouping of abstract ideas. Claim 1 therefore recites an abstract idea. If a claim limitation is directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts, then the claim recites an abstract idea. See MPEP 2106.04(a)(2).
Step 2A-2:
The 2019 PEG defines the phrase "integration into a practical application" to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional elements of “acquiring an estimation target image” in step [1] under their broadest reasonable interpretation, is mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. Therefore, the claim as a whole does not integrate the judicial exception into a practical application.
Step 2B:
As explained above, the “acquiring an estimation target image” in step [1] was considered insignificant extra-solution activity. These conclusions should be reevaluated in Step 2B. The limitation is mere data gathering and/or output recited at high level of generality and amount to receiving (i.e., acquiring), accessing, or transmitting data over a network, which is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The limitations remain insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional elements present mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim therefore is ineligible.
5-2. Regarding dependent claims 2-9, they are dependent from claim 1 and viewed individually, these additional elements are under its broadest reasonable interpretation, either covers performance of the limitation in the mind, performing a mathematical algorithm or extra solution activity for data gathering and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. And, when the claims are viewed as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (i.e., computer-based analysis of generic data). Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
5-3. Regarding independent claims 10 and 12, the claims recite a non-transitory storage medium (claim 10) and a system comprising circuitry (claim 12) and each of which is analogous to apparatus claim 1. Therefore, grounds of rejection analogous to those applied to claim 1 are applicable to claims 10 and 12. Regarding step 2A-2, the claim(s) does/do not integrate the abstract idea into a practical application because the claim(s) does/do not recite any additional elements that impose any meaningful limits on practicing the abstract idea. The claim(s) therefore recites/recite an abstract idea.
Because the claim(s) fails/fail under (2A), the claim(s) needs/need to be further evaluated under (2B). The claim(s) herein does/do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
5-4. Regarding dependent claims 11, 13, they are dependent from claims 10 and 12, respectively, and viewed individually, these additional elements are under its broadest reasonable interpretation, either covers performance of the limitation in the mind, performing a mathematical algorithm or extra solution activity for data gathering and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. And, when the claims are viewed as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (i.e., computer-based analysis of generic data). Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
5-5. Regarding independent claim 14, the claim recites a model generation method for generating a focal position estimation model that receives information based on an image as its input and outputs information indicating a focal position when in focus according to a position in the image, the method comprising:
[1] acquiring a plurality of learning images of the same imaging target at different focal positions, each of which is associated with a focal position, and in-focus position information indicating focal positions when in focus for the plurality of learning images;
[2] generating focus information for learning generation, wherein information based on each of the acquired plurality of learning images is input to the focal position estimation model during training, a calculation is performed according to the focal position estimation model to acquire information indicating a focal position when in focus according to a position in each of the plurality of learning images, and focus information for learning indicating a focal position when in focus according to a position in an image used for machine learning training is generated from the acquired information and the in-focus position information for each of the plurality of learning images; and
[3] learning, wherein machine learning training for generating the focal position estimation model is performed by using the information based on each of the acquired plurality of learning images and the generated focus information for learning corresponding to each of the plurality of learning images generated.
Step 1:
With regard to step (1), claim 14, is directed to a focal position estimation method for generating a focal position estimation model that receives information based on an image as its input and outputs information indicating a focal position when in focus according to a position in the image. The claim 14 therefore is one of statutory categories of invention, i.e., a process.
Step 2A-1:
With regard to 2A-1, The elements recited in claim 14, as drafted, under their broadest reasonable interpretation, encompass a process(es) which is/are directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts. For example, “generating focus information [...] in step [2] and “learning [...]” in step [3] in the context of this claim, are mathematical calculations for training the machine learning model” and fall within the “mathematical concepts” grouping of abstract ideas. Claim 14 therefore recites an abstract idea. If a claim limitation is directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts, then the claim recites an abstract idea. See MPEP 2106.04(a)(2).
Step 2A-2:
The 2019 PEG defines the phrase "integration into a practical application" to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional elements of “acquiring a plurality of learning images of the same imaging target at different focal positions [...]” in step [1] under their broadest reasonable interpretation, is mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. Therefore, the claim as a whole does not integrate the judicial exception into a practical application.
Step 2B:
As explained above, the “acquiring a plurality of learning images of the same imaging target at different focal positions” in step [1] was considered insignificant extra-solution activity. These conclusions should be reevaluated in Step 2B. The limitation is mere data gathering and/or output recited at high level of generality and amount to receiving (i.e., acquiring), accessing, or transmitting data over a network, which is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The limitations remain insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional elements present mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim therefore is ineligible.
5-6. Regarding dependent claims 15-17, they are dependent from claim 14 and viewed individually, these additional elements are under its broadest reasonable interpretation, either covers performance of the limitation in the mind, performing a mathematical algorithm or extra solution activity for data gathering and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. And, when the claims are viewed as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (i.e., computer-based analysis of generic data). Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
5-7. Regarding independent claims 18 and 19, the claims recite a non-transitory storage medium (claim 18) and a system comprising circuitry (claim 19) and each of which is analogous to apparatus claim 14. Therefore, grounds of rejection analogous to those applied to claim 14 are applicable to claims 18 and 19. Regarding step 2A-2, the claim(s) does/do not integrate the abstract idea into a practical application because the claim(s) does/do not recite any additional elements that impose any meaningful limits on practicing the abstract idea. The claim(s) therefore recites/recite an abstract idea.
Because the claim(s) fails/fail under (2A), the claim(s) needs/need to be further evaluated under (2B). The claim(s) herein does/do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. The claim(s) is/are not patent eligible.
5-8. Regarding independent claim 20, claim 20 is directed to a focal position estimation model that is generated through machine learning training and that causes a computer to function to receive information based on an image as its input and output information indicating a focal position when in focus according to a position in the image and therefore is a pure computer program (or an algorithm). A program per se does not fall within the definitions of any of the statutory categories. Its dependent claim 21 does not recite any additional elements that make the claim eligible, and therefore is rejected as well.
Claim Rejections - 35 USC § 112
6. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
7. Claims 1-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre- AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
7-1. Regarding claim 1, the claim recites “acquiring an estimation target image; and estimating a focal position when in focus corresponding to the estimation target image and according to a position in the estimation target image, from the acquired estimation target image, by using a focal position estimation model that is generated through machine learning training and that receives information based on an image as its input and outputs information indicating a focal position when in focus according to a position in the image”. However, it is not clear what the “when in focus” means. Referring to the specification paragraph [0003], it appears that what the applicant means is an object in an image is in focus. The applicant should clarify said condition, for example in accordance with the paragraph, in order to render the definition of the subject matter of the claim clear.
7-2. Regarding independent claims 10, 12, 14, and 18-20, each of them faces the same issue (see “when in focus” in each of the claims) set forth in the rejection of independent claim 1, and thus, is rejected as being indefinite under 35 U.S.C. 112(b).
7-3. The remaining claims are dependent from claims 1, 10, 12, 14, 18, 19, or 20, respectively, therefore, are rejected as being indefinite under 35 U.S.C. 112(b).
Claim Rejections - 35 USC § 102
8. 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.
9. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(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.
10. Claims 1-21 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Sabato et al (US 2022/0383525, hereinafter “Sabato”).
Regarding claim 1, Sabato discloses a focal position estimation method for estimating a focal position when in focus corresponding to an estimation target image (“the computer-implementable method for extracting depth information from a plurality of images taken by a camera at different focus positions” through the CNN 720 shown by fig.1; see para.9 and fig.5), the method comprising:
acquiring an estimation target image (see 803 of fig.5 and para.229: “processing, by the machine learning algorithm [i.e., the convolutional neural network (CNN) disclosed by fig.2a], a captured image whose image features have not yet been extracted, said captured image representing a currently processed image, e.g. input image 101.”); and
estimating a focal position when in focus corresponding to the estimation target image and according to a position in the estimation target image, from the acquired estimation target image, by using a focal position estimation model that is generated through machine learning training and that receives information based on an image as its input and outputs information indicating a focal position when in focus according to a position in the image (see 807 of fig.3 and para.233: “Generating a two-dimensional depth map using the focus positions specified in the predetermined focus schedule and the at least one generated multi-dimensional tensor”. See para.92: wherein “[t]he step of generating a two-dimensional depth map [...] may further comprise, generating, by the machine learning algorithm, at least one multi-dimensional focus probability map [...]” In other words, the machine learning algorithm may estimate one multi-dimensional focus position probability map for the input image using the focus positions specified in the predetermined focus schedule and the at least one generated multi-dimensional tensor.).
Regarding claim 2, 11, 13, Sabato discloses, wherein the focal position estimation model is generated by: acquiring a plurality of learning images of the same imaging target at different focal positions, each of which is associated with a focal position, and in-focus position information indicating focal positions when in focus for the plurality of learning images; generating focus information for learning generation, wherein information based on each of the acquired plurality of learning images is input to the focal position estimation model during training, a calculation is performed according to the focal position estimation model to acquire information indicating a focal position when in focus according to a position in each of the plurality of learning images, and focus information for learning indicating a focal position when in focus according to a position in an image used for machine learning training is generated from the acquired information and the in-focus position information for each of the plurality of learning images; and learning, wherein machine learning training for generating the focal position estimation model is performed by using the information based on each of the acquired plurality of learning images and the generated focus information for learning corresponding to each of the plurality of learning images generated (see the training phase disclosed by fig.6 and para.234—para.244, which comprises capturing a sequence of images focused at different focus positions according to a focus schedule for a plurality of different scenes from the real physical world, obtaining a sequence of focus probability maps, one for each image after a predetermined number of captured images have been processed, remapping the obtained focus probability maps to real distances using the focus positions from the known focus schedule, calculating the loss function between the estimated/predicted depth maps are with respect to the expected known ground truth focus depth maps, and minimizing the loss function and to determine the best model parameters of the machine learning algorithm/convolutional neural network from the training images.).
Regarding claim 3, 15, Sabato discloses, wherein the focal position estimation model is generated by calculating one focal position when in focus, which is common to the plurality of learning images, according to a position in each learning image from the focal position when in focus according to a position in each of the plurality of learning images indicated by the information acquired by using the focal position estimation model during training and generating the focus information for learning from the one focal position when in focus for each of the plurality of learning images (see 801-807 of fig.5: the convolutional neural network (CNN) “[g]enerat[es] a two-dimensional depth map [which is “common” to each of the sequence of images captured at step 801 of fig.5] using the focus positions specified in the predetermined focus schedule and the at least one generated multi-dimensional tensor”; see para.226-para.233).
Regarding claim 4, 16, Sabato discloses, wherein, a feature quantity of the estimation target image is acquired from the acquired estimation target image by using a feature quantity output model that receives information based on an image as its input and outputs a feature quantity of the image input to the focal position estimation model, and the focal position when in focus corresponding to the estimation target image and according to a position in the estimation target image is estimated from the feature quantity by using the focal position estimation model (see, e.g.,para.235: “a training sample comprising a plurality/a sequence 600 of captured images focused at different focus positions according to a focus schedule 620 for a plurality of different scenes from the real physical world can be processed according to the steps described previously to obtain a sequence 640 of focus probability maps, one for each image after a predetermined number of captured images have been processed.”), and
the feature quantity output model is generated by generating two different feature quantity learning images, which are associated with focal positions and correspond to the plurality of learning images, based on information indicating the focal position when in focus according to a position in each of the plurality of learning images, which is acquired by using the focal position estimation model during the training, comparing feature quantities of the two feature quantity learning images with each other according to focal positions associated with the two feature quantity learning images with a combination of the two feature quantity learning images as one unit, and performing machine learning training based on a result of the comparison (see para.24-para.242: The loss function 660 is a measure of how different the estimated/predicted depth maps are with respect to the expected known ground truth depth maps.” “The training of the machine learning algorithm 630 comprising a convolutional neural network is run until the loss function has reached a desired/specified minimum and the optimal model parameters of the convolutional neural network have been determined.” “The minimization of the loss function may be achieved by optimization techniques such as using a gradient descent algorithm.”).
Regarding claim 5, 17, Sabato discloses, wherein the feature quantity output model is generated by performing the machine learning training so that a difference between the feature quantities of the two feature quantity learning images becomes smaller when the two feature quantity learning images are related to the same focal position and the difference between the feature quantities of the two feature quantity learning images becomes larger when the two feature quantity learning images are related to different focal positions (ibid.).
Regarding claim 6, Sabato discloses the focal position estimation method according to claim 1, wherein, an inclination of an imaging target captured in the estimation target image is estimated from the estimated focal position when in focus according to a position in the estimation target image (see para.237: “The scenes captured in the sequence 600 of images of the training sample can be static or dynamic, i.e. there can be movement between images, e.g. due to movement of objects or subjects in the scene and/or due to movement of the camera, e.g. vibrations due to the camera being held in the hand of a user or due to the camera changing its position.”).
Regarding claim 7, Sabato discloses the focal position estimation method according to claim 1, wherein, a focal position when imaging an imaging target captured in the estimation target image is controlled based on the estimated focal position when in focus according to a position in the estimation target image (see para.235: “a training sample comprising a plurality/a sequence 600 of captured images focused at different focus positions according to a focus schedule 620 for a plurality of different scenes from the real physical world can be processed according to the steps described previously to obtain a sequence 640 of focus probability maps, one for each image after a predetermined number of captured images have been processed.”).
Regarding claim 8, Sabato discloses the focal position estimation according to claim 1, wherein, information indicating an in-focus state according to a position in the estimation target image is output based on the estimated focal position when in focus according to a position in the estimation target image (see para.233: “Generating a two-dimensional depth map [i.e., the output of the CNN] using the focus positions specified in the predetermined focus schedule and the at least one generated multi-dimensional tensor”).
Regarding claim 9, Sabato discloses the focal position estimation method according to claim 1, wherein, a plurality of estimation target images of the same imaging target at different focal positions are acquired (see 801 of fig.5 and para.227: “Capturing, 801, a sequence of images of a scene [i.e., the same target] with a camera at different focus positions according to a predetermined focus schedule that specifies a chronological sequence of focus positions of the camera”), and a focal position when in focus according to a position in the estimation target image is estimated from at least one estimation target image among the acquired plurality of estimation target images, and one image is generated from the plurality of estimation target images based on the estimated focal position (see 807 of fig.3 and para.233: “Generating a two-dimensional depth map using the focus positions specified in the predetermined focus schedule and the at least one generated multi-dimensional tensor”. See para.92: wherein “[t]he step of generating a two-dimensional depth map [...] may further comprise, generating, by the machine learning algorithm, at least one multi-dimensional focus probability map [...]”.).
Regarding claims 10, 12, each of them is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
Regarding claim 14, 18, 19, 20, 21, the essential features of each of them are recited by claim 1 or by the combination of claim 1 and claim 2, thus they are interpreted and rejected for the reasons set forth in the rejections of claim 1 and claim 2.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pinkard et al, "Deep learning for single-shot autofocus microscopy", Optical, 2019.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676