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 .
Claim Interpretation
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.
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.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: “reliability output means outputting, on a basis of an output of the machine learning model when input data is received as an input, reliability of the output for the input data”, “generation means generating new training data on a basis of the input data in a case where the reliability satisfies a predetermined condition”, and “learning control means training the machine learning model with the new training data” in claim 1.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends 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 remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
Limitation: reliability output means outputting, on a basis of an output of the machine learning model when input data is received as an input, reliability of the output for the input data
Prong 1: The use of the word “means” in the claim language recites a generic placeholder for performing the claimed function
Prong 2: “means outputting, on a basis of an output of the machine learning model when input data is received as an input, reliability of the output for the input data” is modifying the generic place holder with functional language
Prong 3: The claim links this function to the machine learning model providing structural language to the claim
112(f) Applies: No.
Limitation: generation means generating new training data on a basis of the input data in a case where the reliability satisfies a predetermined condition
Prong 1: the use of “means” in the claim language recites a generic placeholder for performing the claimed function
Prong 2: “generating new training data on a basis of the input data in a case where the reliability satisfies a predetermined condition” is modifying the generic place holder with functional language.
Prong 3: The specification links the generating training data step to a machine learning model in [0070] “The processing in S109 is the processing of generating, after the training (inference) of a machine learning model, additional training data on the basis of data input to that machine learning model. The details of the processing in S108 and S109 are described later.”
112(f) Applies: Yes, due to lack of recited structural language in the limitation the limitation will be interpreted as generating training data after the training of a machine learning model, new training data on a basis of the input data in a case where the reliability satisfies a predetermined condition
Limitation: and learning control means training the machine learning model with the new training data.
Prong 1: the use of “means” in the claim language recites a generic placeholder for performing the claimed function
Prong 2: “training the machine learning model with the new training data.” is modifying the generic place holder with functional language
Prong 3: The claim links this function to a machine learning model providing structural to the claim
112(f) Applies: No
Claim Rejections - 35 USC § 112
Claim’s 1-12 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “reliability” in claim 1 is a relative term which renders the claim indefinite. The term “reliability” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 101
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-12 are rejected under U.S.C 101 as being directed towards an abstract idea without adding significantly more
Regarding Claim 1
Step 1: “An information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: is a mental process that can be done with the aid of pen and paper, a person can observe an output and determine if it is reliable or not
generation means generating new training data on a basis of the input data in a case where the reliability satisfies a predetermined condition is a mental process that can be done with the aid of pen and paper, a person can generate data on the basis of an input satisfying a condition
Step 2A Prong 2: The additional limitations a machine learning model trained with training data is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
reliability output means outputting, on a basis of an output of the machine learning model when input data is received as an input reliability of the output for the input data is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) – examiners note: outputting on the basis of input data amounts to nothing more than mere data gathering
and learning control means training the machine learning model with the new training data are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of training a machine learning model with data
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation a machine learning model trained with training data is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
reliability output means outputting, on a basis of an output of the machine learning model when input data is received as an input reliability of the output for the input data is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g). Furthermore, the additional element is directed to receiving or transmitting data over a network – examiners note: outputting on the basis of input data amounts to nothing more than mere data gathering.
and learning control means training the machine learning model with the new training data are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of training a machine learning model with data
Regarding Claim 2
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitations a trained estimation model configured to output an estimation result on a basis of the output of the machine learning model, wherein the reliability output means outputs the reliability of the output of the machine learning model for the input data on a basis of an output of the estimation model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) - examiners note: high level recitation outputting an estimation from a model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation a trained estimation model configured to output an estimation result on a basis of the output of the machine learning model, wherein the reliability output means outputs the reliability of the output of the machine learning model for the input data on a basis of an output of the estimation model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) - examiners note: high level recitation outputting an estimation from a model
Regarding Claim 3
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 2
Step 2A Prong 2: The additional limitation wherein the input data includes an image in which a target object is captured, the estimation model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the output of the machine learning model, and the reliability output means outputs the reliability on a basis of the image is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the input data includes an image in which a target object is captured, the estimation model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the output of the machine learning model, and the reliability output means outputs the reliability on a basis of the image is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Regarding Claim 4
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 3
Step 2A Prong 2: The additional limitation wherein the estimation model outputs an image in which each point indicates a positional relation with the keypoint, and the reliability output means outputs the reliability on a basis of a variation of candidates for positions of a plurality of the keypoints each generated from points different from each other included in the image output by the estimation model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting an image with keypoints with a metric to ensure its reliable
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the estimation model outputs an image in which each point indicates a positional relation with the keypoint, and the reliability output means outputs the reliability on a basis of a variation of candidates for positions of a plurality of the keypoints each generated from points different from each other included in the image output by the estimation model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting an image with keypoints with a metric to ensure its reliable
Regarding Claim 5
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: wherein the reliability output means outputs the reliability on a basis of information indicating a difference between the output of the estimation model when an input image is input to the estimation model and the output of the estimation model when a processed image obtained by processing the input image by predetermined processing is input to the estimation model is a mental process that can be done with the aid of pen and paper, a person can observe information and determine if its reliable or not
Step 2A Prong 2 and Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Regarding Claim 6
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 3
Step 2A Prong 2: The additional limitation wherein the machine learning model outputs information indicating whether the input data includes the target object or not are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting information with a machine learning model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the machine learning model outputs information indicating whether the input data includes the target object or not are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of outputting information with a machine learning model
Regarding Claim 7
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 2
Step 2A Prong 2: The additional limitation wherein the estimation model is trained with estimation training data, the generation means generates new estimation training data on a basis of the input data in a case where the reliability satisfies the predetermined condition, and the learning control means trains the machine learning model with the new training data are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating new training data for a model
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the estimation model is trained with estimation training data, the generation means generates new estimation training data on a basis of the input data in a case where the reliability satisfies the predetermined condition, and the learning control means trains the machine learning model with the new training data are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating new training data for a model
Regarding Claim 8
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 1
Step 2A Prong 2: The additional limitation wherein the input data includes an image in which a target object is captured, the machine learning model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the input data, and the reliability output means outputs the reliability on a basis of the image are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of model taking in an image as data and outputting a keypoint with a reliability output.
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the input data includes an image in which a target object is captured, the machine learning model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the input data, and the reliability output means outputs the reliability on a basis of the image are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of model taking in an image as data and outputting a keypoint with a reliability output.
Regarding Claim 9
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 8
Step 2A Prong 2: The additional limitation wherein the training data includes a plurality of learning images rendered from a three-dimensional shape model and ground truth images each serving as ground truth data for a corresponding one of the learning images is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the training data includes a plurality of learning images rendered from a three-dimensional shape model and ground truth images each serving as ground truth data for a corresponding one of the learning images is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Regarding Claim 10
Step 1: “The information processing system” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: Recites the abstract ideas of Claim 2
Step 2A Prong 2: The additional limitation wherein the generation means generates new training data including a first additional image obtained by processing the input data by first processing and a second additional image obtained by processing the input data by second processing different from the first processing, and the learning control means trains the machine learning model on a basis of a difference between an output when the first additional image is input to the machine learning model and an output when the second additional image is added to the machine learning model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating training data and training a model on it
Step 2B: The additional elements, taken either alone or in combination with other limitations of the claim, do not amount to significantly more than the abstract idea itself. The additional limitation wherein the generation means generates new training data including a first additional image obtained by processing the input data by first processing and a second additional image obtained by processing the input data by second processing different from the first processing, and the learning control means trains the machine learning model on a basis of a difference between an output when the first additional image is input to the machine learning model and an output when the second additional image is added to the machine learning model are an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “AI system” or “machine learning” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d) – examiners note: high level recitation of generating training data and training a model on it
Regarding Claim 11
Step 1: “An information processing method” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Regarding Claim 12
Step 1: “A non-transitory, computer readable storage medium” falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater).
Step 2A Prong 1: See the analysis of Claim 1
Step 2A Prong 2: See the analysis of Claim 1
Step 2B: See the analysis of Claim 1
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1-2, 7, and 11-12 are rejected under U.S.C. 102 (a)(1) as being anticipated by Yuki et al (JP2021137747A) (“Yuki”)
Regarding Claim 1, Yuki teaches an information processing system comprising: a machine learning model trained with training data ([0054] teaches model relearning with learning data)
reliability output means outputting, on a basis of an output of the machine learning model when input data is received as an input, reliability of the output for the input data ([0050] teaches outputting an estimation result that represents the estimation results and the estimation accuracy representing the reliability of the estimation results. [0049] teaches inputting the estimation target which functions as input data teaching this limitation.)
generation means generating new training data on a basis of the input data in a case where the reliability satisfies a predetermined condition ([0054] teaches adding learning data once it exceeds a set threshold, that way increasing the reliability of the estimation model generated. The set threshold is interpreted as a predetermined condition here teaching the limitation)
and learning control means training the machine learning model with the new training data ([0054] teaches the model relearning with the new added data)
Regarding Claim 2, Yuki teaches all the limitations of Claim 1
Yuki also teaches a trained estimation model configured to output an estimation result on a basis of the output of the machine learning model, wherein the reliability output means outputs the reliability of the output of the machine learning model for the input data on a basis of an output of the estimation model ([0050] teaches outputting an estimation result and estimation accuracy that represents the reliability of the estimation result)
Regarding Claim 7, Yuki teaches all the limitations of Claim 2
Yuki also teaches wherein the estimation model is trained with estimation training data, the generation means generates new estimation training data on a basis of the input data in a case where the reliability satisfies the predetermined condition, and the learning control means trains the machine learning model with the new training data. ([0054] teaches adding learning data based off the estimation accuracy being greater than the threshold. The data here is being interpreted as estimation data. The estimation results are generated based off of the estimation target [0049] teaching the generating estimation data part of the limitation on the basis on input data. The estimation accuracy is a reflection on the reliability of the data, and it has to exceed a set threshold value to be added to the learning data teaching the limitation)
Regarding Claim 11, see the analysis of Claim 1
Regarding Claim 12, see the analysis of Claim 1
Claim Rejections - 35 USC § 103
Claims 3 is rejected under U.S.C. 103 over Yuki et al (JP2021137747A) (“Yuki”), in view of Ayako et al (JP2020160804A) (“Ayako”)
Regarding Claim 3, Yuki teaches all the limitations of Claim 2
Yuki does not teach wherein the input data includes an image in which a target object is captured, the estimation model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the output of the machine learning model, and the reliability output means outputs the reliability on a basis of the image.
However, Ayako does teach wherein the input data includes an image in which a target object is captured, the estimation model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the output of the machine learning model, and the reliability output means outputs the reliability on a basis of the image ([0031] teaches taking input data, with the input data being an image. That image is then run through image recognition with [Fig 4] detailing the process of the image recognition and additional learning being done by machine learning [0042]. This process also includes an inference result that will indicate coordinate information which is being interpreted as a keypoint for pose estimation. The inference result also includes a likelihood information on the certainty of inference in the case of object detection and that is being interpreted as a reliability output teaching this limitation)
Yuki and Ayako are analogous art because they both deal with processing data with a machine learning model
It would have been obvious to combine Yuki with the image processing technology of Ayako. Doing so would increase the accuracy of the data recognition ([Ayako-Problem to be solved])
Claims 4 is rejected under U.S.C. 103 over Yuki et al (JP2021137747A) (“Yuki”), in view of Ayako et al (JP2020160804A) (“Ayako”), and Engedal (US8452080B2) (Engedal)
Regarding Claim 4, The combination of Yuki and Ayako teaches all the limitations of Claim 3
The combination of Yuki and Ayako does not teach wherein the estimation model outputs an image in which each point indicates a positional relation with the keypoint, and the reliability output means outputs the reliability on a basis of a variation of candidates for positions of a plurality of the keypoints each generated from points different from each other included in the image output by the estimation model.
However, Engedal does teach wherein the estimation model outputs an image in which each point indicates a positional relation with the keypoint, and the reliability output means outputs the reliability on a basis of a variation of candidates for positions of a plurality of the keypoints each generated from points different from each other included in the image output by the estimation model. ([Abstract] teaches outputting image 3D image based off of the pose estimation of 2D image data based off of key points and use those keypoints to determine the 3D camera pose estimate. The pose estimate selector is being interpreted as an estimation model. [Column 10: Lines 28-33] teaches only accepting key points with similar mean intensity to enhance the reliability of the keypoint matching, with this being used for posing estimation for the 3D camera pose it is being interpreted that it is an output estimation. The keypoint matching here would only use reliable key points so that is interpreted as a reliability output teaching this limitation)
Yuki, Ayako and Engedal are analogous processing data with a model
It would have been obvious to combine Yuki with the image processing technology of Ayako and the positional relation of Engedal. Doing so would lead to a more effective model when it comes to real time processing (Engedal-Column 1: Lines 36-24])
Claims 5 is rejected under U.S.C. 103 over Yuki et al (JP2021137747A) (“Yuki”), in view of Nabeto et al (US20230141150A1) (“Nabeto”)
Regarding Claim 5, Yuki teaches all the limitations of Claim 2
Yuki does not teach wherein the reliability output means outputs the reliability on a basis of information indicating a difference between the output of the estimation model when an input image is input to the estimation model and the output of the estimation model when a processed image obtained by processing the input image by predetermined processing is input to the estimation model.
However, Nabeto does teach wherein the reliability output means outputs the reliability on a basis of information indicating a difference between the output of the estimation model when an input image is input to the estimation model and the output of the estimation model when a processed image obtained by processing the input image by predetermined processing is input to the estimation model. ([0051-0054] teaches a recognition unit inputting an image into an estimation model or inputting the processed image after processing the image by acquisition unit into the estimation model. It can be reasonably interpreted since two different images are being inputted into an estimation model that the estimation model is able to recognize the different image before calculating the reliability. The recognition unit is also being used to calculate the reliability of the image teaching this limitation)
Yuki and Nabeto are analogous art because processing data with a model
It would have been obvious to combine Yuki with the reliability output of Nabeto. Doing so would lead to a more accurate model with data recognition ([Nabeto-0008])
Claims 6 is rejected under U.S.C. 103 over Yuki et al (JP2021137747A) (“Yuki”), in view of Ayako et al (JP2020160804A) (“Ayako”) and Chong et al (WO2021232849A1) (“Chong”)
Regarding Claim 6, the combination of Yuki and Ayako teaches all the limitations of Claim 3
The combination of Yuki and Ayako does not teach wherein the machine learning model outputs information indicating whether the input data includes the target object or not.
However, Chong does teach wherein the machine learning model outputs information indicating whether the input data includes the target object or not ([Abstract] teaches indicating if the target object is in an abnormal state or not and then that leading to not including the target object or not in the input data.)
Yuki, Ayako, and Chong are analogous art because they deal with processing data with a model
It would have been obvious to combine Yuki with the image processing technology of Ayako and the target data indication technology of Chong. Doing so would lead to more accurate analysis results ([Chong-Background technique])
Claim 8 is rejected under U.S.C. 103 over Yuki et al (JP2021137747A) (“Yuki”), in view of Cao et al (NPL: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields) (“Cao”)
Regarding Claim 8, Yuki teaches all the limitations of Claim 1
Yuki does not teach wherein the input data includes an image in which a target object is captured, the machine learning model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the input data, and the reliability output means outputs the reliability on a basis of the image.
However, Cao does teach wherein the input data includes an image in which a target object is captured, the machine learning model outputs an image indicating a keypoint for pose estimation of the target object on a basis of the input data, and the reliability output means outputs the reliability on a basis of the image ([Page 2-Column 1: Lines 14-17] teaches taking in an image input and outputting the 2d locations of key points for each person in the image. These keypoints are used to output 2D keypoints for all people in the image [Page 2-Column 1: Lines 26-28]. It can be reasonably interpreted that the output 2D keypoints is a form of pose estimation. With the target object being the original poses in the input image. They are comparing their accuracy results against already established methods, and this is being interpreted as a reliability output on the basis of the image teaching this limitation [Page 5-Column 2: Lines 25-28])
Yuki and Cao are analogous art because they both deal with processing data with a model
It would have been obvious to combine Yuki with the image output technology of Cao. Doing so would lead to a more accurate model with real time performance ([Cao-Abstract])
Claim 9 is rejected under U.S.C. 103 over Yuki et al (JP2021137747A) (“Yuki”), in view of Cao et al (NPL: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields) (“Cao”), and Sun (US 12682651 B2) (“Sun”)
Regarding Claim 9, The combination of Yuki and Cao teaches all the limitations of Claim 8
The combination of Yuki and Cao does not teach wherein the training data includes a plurality of learning images rendered from a three-dimensional shape model and ground truth images each serving as ground truth data for a corresponding one of the learning images.
However, Sun does teach wherein the training data includes a plurality of learning images rendered from a three-dimensional shape model and ground truth images each serving as ground truth data for a corresponding one of the learning images ([Abstract] teaches obtaining image data and ground truth data, with the ground truth data used to test accuracy of the machine learning model. The image data generated by the simulator is an image of a virtual world surrounding a vehicle mode, it can be reasonably interpreted that this image is of a three-dimensional shape model teaching this limitation [Column 1: Lines 54-67])
Yuki, Cao, and Sun are analogous art because they both deal with processing data with a model.
It would have been obvious to combine Yuki with the image output technology of Cao, and the ground truth images of Sun. Doing so would lead to more accurate model detection ([Sun-Column 2: Lines 10-16])
Claim 10 is rejected under U.S.C. 103 over Yuki et al (JP2021137747A) (“Yuki”), in view of Miyazaki (US12020466B2) (“Miyazaki”)
Regarding Claim 10, Yuki teaches all the limitations of Claim 2
Yuki does not teach wherein the generation means generates new training data including a first additional image obtained by processing the input data by first processing and a second additional image obtained by processing the input data by second processing different from the first processing, and the learning control means trains the machine learning model on a basis of a difference between an output when the first additional image is input to the machine learning model and an output when the second additional image is added to the machine learning model.
However, Miyazaki does teach wherein the generation means generates new training data including a first additional image obtained by processing the input data by first processing and a second additional image obtained by processing the input data by second processing different from the first processing, and the learning control means trains the machine learning model on a basis of a difference between an output when the first additional image is input to the machine learning model and an output when the second additional image is added to the machine learning model ([Column 9: Lines 52-67] teaches using a filter on an image and then using a different filter on that image to cancel out the first filter to generate two different images used to generate training data with two different images. The two different filters are being interpreted as a processing technique and with the two different images it can be reasonably interpreted that these are additional images that are being used in model training, and the model now recognizes them as different images teaching this limitation.)
Yuki and Miyazaki are analogous art because they both deal with processing data with a model.
It would have been obvious to combine Yuki with the two different image generating technique of Miyazaki. Doing so would allow for more efficient training data generation ([Miyazaki-Column 2: Lines 45-54)
Conclusion
The prior arts are made of record and relied upon is considered to applicant’s disclosure
Banaszczyk et al US 20200401793 A1 (2020-12-24) ([Abstract] “NOVELTY - The apparatus comprises a detector (310) to identify a first subject in first image captured by a first image capture device based on a first set of two-dimensional kinematic keypoints in the first image. The first set of two-dimensional kinematic keypoints are provided corresponding to multiple joints of first subject. The first image capture device (325) is associated with a first view of the first subject. A multi-view associator (330) is provided to verify first subject using first image and second image captured by a second image capture device. The second image capture device associated with a second view of the first subject. The second view is different than the first view. A keypoint generator (340) is provided to generate three-dimensional keypoints for the first subject using the first set of two-dimensional kinematic keypoints and a second set of keypoints in the second image. A biomechanics analyzer (345) is provided to determine a performance metric for the first subject.”)
Lepetit V et al US 20180137644 A1 (2018-05-17) ([Abstract] “NOVELTY - The method involves obtaining an image (800) including the object (820). The two-dimensional projections of a three-dimensional bounding box of the object are determined. The two-dimensional projections of the three-dimensional bounding box are being determined by applying a trained regressor to the image. The trained regressor is trained to predict two-dimensional projections of the three-dimensional bounding box of the object in poses. The three-dimensional pose of the object is estimated using two-dimensional projections of the three-dimensional bounding box.”)
Ahmed M T et al US 20200380723 A1 (2020-12-03) ([Abstract] “NOVELTY - The computer readable medium involves acquiring (501) an image data sequence containing images of an object in a scene along a time. A pose of the object through an object pose tracking algorithm is tracked (502). A first pose of the object in an image of the image data sequence is acquired (502A). The 2D features of the object is extracted (502B) from the image. A training dataset containing the extracted 2D features and the corresponding first pose in the one or more memories are stored (503). The tracking of the pose is performed in an online learning environment.”)
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/URIAH VENDELL MOORE/Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
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