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
The present application claims foreign priority based on Japanese Patent Application No. JP2022-196152, filed on 12/08/2022.
A certified copy of Japanese Patent Application No. JP2022-196152 in Japanese has been received (on 09/12/2023), as required by 37 CFR. 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/09/2023 and 02/13/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claim 4 is objected to because of the following informality: “the selection rule includes a rule in which an IoU of a prediction value for the correct answer is smaller than a predetermined threshold value” For the purpose of examination, the Examiner will assume the Applicant meant “the selection rule includes a rule in which an intersection over union (IoU) of a prediction value for the correct answer is smaller than a predetermined threshold value”. Appropriate correction is required.
Claims 10 and 11 are objected to because of the following informalities: a claim can only be a single sentence, for proper claim structure the ending punctuation will need to be changed to either a ';' or ':' . Appropriate correction is required.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: [0057 ]: A model output in the drawing means a prediction value output by the machine learning model 23. For example, when an IoU of the prediction value corresponding to the correct answer is greater than a predetermined threshold value, for example, 0.3, the target selection unit 14 classifies the data as normal data.
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
uncertainty calculation unit configured to calculate (claim 1)… sets (claim 12)…
target selection unit configured to extract (claim 1)…extracts (claim 2)…
tendency analysis planning unit planning unit configured to specify (claim 1) …classifies (claim 6) …proposes (claim 7) …proposes (claim 8)…
a display unit configured to display (claim 9)…
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 6 – 9, 12 and 13 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.
Claims 2 – 12 are rejected as being dependent on a rejected independent claim.
Regarding claims 1, 2, 6 – 9, 12 and 13, various claim limitation reciting uncertainty calculation unit configured to calculate (claim 1)… sets (claim 12)…; target selection unit configured to extract (claim 1)…extracts (claim 2)…; tendency analysis planning unit planning unit configured to specify (claim 1) …classifies (claim 6) …proposes (claim 7) …proposes (claim 8)…; a display unit configured to display (claim 9)… invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is no clear disclosure of the particular structure, either explicitly or inherently, to perform the functions of the claims. As would be recognized by those of ordinary skill in the art, the functions can be performed in any number of ways including in hardware, in software, or a combination of the two. The specification does not provide sufficient details such that one of ordinary skill in the art would understand which structure or structures perform(s) the claim functions.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 2, 6 – 9, 12 and 13 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claims 2 – 12 are rejected as being dependent on a rejected independent claim.
Claims 1, 2, 6 – 9, 12 and 13 contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As discussed above, the disclosure does not provide adequate structure to perform the claimed functions of:
uncertainty calculation unit configured to calculate (claim 1)… sets (claim 12)…
target selection unit configured to extract (claim 1)…extracts (claim 2)…
tendency analysis planning unit planning unit configured to specify (claim 1) …classifies (claim 6) …proposes (claim 7) …proposes (claim 8)…
a display unit configured to display (claim 9)…
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed 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.
Claims 1 – 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed
to an abstract idea without significantly more. The following sections following the 2019 PEG
guidelines for analyzing subject matter eligibility.
The analysis below of the claims’ subject matter eligibility follows the 2019 Revised
Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”)
and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial
Intelligence, 89 Fed. Reg. 58128-58138 (July 17, 2024) (“2024 AI SME Update”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined
whether the claim is directed to one of the four statutory categories of invention, 1.e., process,
machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the
statutory categories, the second step in the analysis is to determine whether the claim is directed
to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first
prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception
(e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis
proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the
claims integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical application, the
analysis proceeds to determining whether the claim is a patent-eligible application of the
exception (Step 2B). If an abstract idea is present in the claim, any element or combination of
elements in the claim must be sufficient to ensure that the claim integrates the judicial exception
into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
calculate, based on a prediction value obtained from a machine learning model using evaluation data for evaluating a shortage of training data as an input and a correct answer for the evaluation data, a data shortage degree representing a training data shortage degree for each piece of the evaluation data; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
extract target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
to specify a tendency of the target data based on a predetermined analysis rule and specify a property of the training data to be added based on the tendency of the target data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
an uncertainty calculation unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a target selection unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a tendency analysis planning unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
an uncertainty calculation unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a target selection unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
a tendency analysis planning unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
As an ordered whole, the claim is directed to a method of updating training data, this is nothing more than using machine learning models to recessively update the model based on training deficiencies. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2 incorporates the rejections of claim 1.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
the selection rule is predetermined including a rule for classifying the target data based on the fluctuation value (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
extracts the target data based on the selection rule and the fluctuation value. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the data shortage degree includes a fluctuation value representing a degree of fluctuation in the prediction value when weight in the machine learning model is modified (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the target selection unit extracts the target data (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the data shortage degree includes a fluctuation value representing a degree of fluctuation in the prediction value when weight in the machine learning model is modified (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the target selection unit extracts the target data (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 3 incorporates the rejections of claim 2.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 2 are incorporated.
wherein the selection rule is predetermined including a rule for setting data, as the target data, in which the fluctuation value is in a predetermined ratio from an upper level. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
Claim 4 incorporates the rejections of claim 3.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 3 are incorporated. Please see the analysis of claim 3 above. Regarding the training data evaluation system in claim 3, these steps cover mental processes based on selecting portions of data.
Therefore, claim 4 is directed to an abstract idea – mental processes (i.e., observation an evaluation/judgement/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the machine learning model is a model for detecting a predetermined object from an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the selection rule includes a rule in which an IoU of a prediction value for the correct answer is smaller than a predetermined threshold value and the data in which the fluctuation value is in the ratio from the upper level is set as the target data. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the machine learning model is a model for detecting a predetermined object from an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the selection rule includes a rule in which an IoU of a prediction value for the correct answer is smaller than a predetermined threshold value and the data in which the fluctuation value is in the ratio from the upper level is set as the target data. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 5 incorporates the rejections of claim 2.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 2 are incorporated. Please see the analysis of claim 2 above. Regarding the training data evaluation system in claim 2, these steps cover mental processes based on selecting portions of data.
Therefore, claim 5 is directed to an abstract idea – mental processes (i.e., observation an evaluation/judgement/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the machine learning model is a model identifying what appears in an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the selection rule includes a rule for setting evaluation data, as the target data, in which the fluctuation value is greater than a predetermined threshold value. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the machine learning model is a model identifying what appears in an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the selection rule includes a rule for setting evaluation data, as the target data, in which the fluctuation value is greater than a predetermined threshold value. (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 6 incorporates the rejections of claim 1.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
wherein the analysis rule is predetermined including a rule for classifying the target data into a plurality of classification groups according to a common property (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
specifies a property of the training data to be added for each classification group. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
the tendency analysis planning unit (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
the tendency analysis planning unit (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 7 incorporates the rejections of claim 6.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 6 are incorporated.
the analysis rule is predetermined including a rule for classifying the target data in which a color variance of an object in the correct answer is greater than a predetermined threshold value into one classification group (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
proposes that an image having the color variance greater than the threshold value is to be generated based on the target data classified into the classification group and is to be added to the training data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the machine learning model is a model for detecting a predetermined object from an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the tendency analysis planning unit (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the machine learning model is a model for detecting a predetermined object from an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the tendency analysis planning unit (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 8 incorporates the rejections of claim 6.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 6 are incorporated.
the analysis rule includes a rule for classifying the target data into a target classification group based on a distribution of pixels in an image in which a contribution to a pixel fluctuation value is greater than a predetermined threshold value (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
proposes that an image having the distribution of the target classification group is to be generated from the target data classified into the target classification group based on the analysis rule and is to be added to the training data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the machine learning model is a model for identifying what appears in an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the tendency analysis planning unit (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the machine learning model is a model for identifying what appears in an image (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the tendency analysis planning unit (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 9 incorporates the rejections of claim 8.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 8 are incorporated. Please see the analysis of claim 8 above. Regarding the training data evaluation system in claim 8, these steps cover mental processes based on selecting portions of data.
Therefore, claim 9 is directed to an abstract idea – mental processes (i.e., observation an evaluation/judgement/opinion).
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
a display unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
display an improvement proposal in which a property common to the target classification group an improvement plan to be added with the training data having the image based on the distribution of the target classification group are associated. (Mere data gathering, Insignificant extra solution activity in MPEP § 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
a display unit configured to (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
display an improvement proposal in which a property common to the target classification group an improvement plan to be added with the training data having the image based on the distribution of the target classification group are associated. (receiving or transmitting data, using components and functions claimed at a high level of generality have been determined by the courts as being well-understood, routine, and conventional activities in the field of computer functions (See MPEP § 2106.05(d)(II)(i))
Claim 10 incorporates the rejections of claim 8.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 8 are incorporated.
wherein the contribution is a value calculated by the following equation.
PNG
media_image1.png
457
610
media_image1.png
Greyscale
(Mathematical Concepts: are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations.)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
Claim 11 incorporates the rejections of claim 8.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 8 are incorporated.
wherein the contribution is a value calculated by the following equation.
PNG
media_image2.png
309
609
media_image2.png
Greyscale
(Mathematical Concepts: are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations.)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
Claim 12 incorporates the rejections of claim 1.
Step 1: The claim recites a training data evaluation system, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated.
sets, as a prediction value for the evaluation data, a prediction value candidate of a rectangle having a highest similarity to a rectangle representing the correct answer to the evaluation data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
wherein the machine learning model is a model in which one or more rectangles that indicate an area that is estimated to be an area in which a predetermined object exists in an image are output as prediction value candidates, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the uncertainty calculation unit (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
wherein the machine learning model is a model in which one or more rectangles that indicate an area that is estimated to be an area in which a predetermined object exists in an image are output as prediction value candidates, (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
the uncertainty calculation (Mere instructions to apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP § 2106.05(f))
The courts have found that adding the words "apply it" (or an equivalent) with the
judicial exception, or mere instructions to implement an abstract idea on a computer does not
qualify as “significantly more”. (See MPEP § 2106.05(I)(A))
Claim 13
Step 1: The claim recites a training data evaluation method using a device having a processing device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
an uncertainty calculation step of calculating, based on a prediction value obtained from a machine learning model using evaluation data for evaluating a shortage of training data as an input and a correct answer for the evaluation data, a data shortage degree representing a training data shortage degree for each piece of the evaluation data; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
a target selection step of extracting target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; and (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
a tendency analysis planning step of specifying a tendency of the target data specifying a tendency of the target data based on a predetermined analysis rule and specifying a property of the training data to be added based on the tendency of the target data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
As an ordered whole, the claim is directed to a method of updating training data, this is nothing more than using machine learning models to recessively update the model based on training deficiencies. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 14
Step 1: The claim recites a training data evaluation program that causes a device having a processing device, which is one of the four statutory categories of eligible matter.
Step 2A Prong 1:
an uncertainty calculation function configured to calculate , based on a prediction value obtained from a machine learning model using evaluation data for evaluating a shortage of training data as an input and a correct answer for the evaluation data, a data shortage degree representing a training data shortage degree for each piece of the evaluation data; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
a target selection function of extracting target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
a tendency analysis planning function of specifying a tendency of the target data based on a predetermined analysis rule and specifying a property of the training data to be added based on the tendency of the target data. (Mental Processes: Can be performed in the human mind, or by a human using a pen and paper, making observations, evaluations and judgments as claimed)
Step 2A Prong 2: The judicial exceptions are not integrated into a practical application. In
particular, the claim recites these additional elements:
The claim does not recite any additional limitations. Therefore, there are no additional elements to integrate the abstract ideas into a practical applications.
Step 2B: The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
The claim does not include additional elements that are sufficient to amount to
significantly more than the judicial exception.
As an ordered whole, the claim is directed to a method of updating training data, this is nothing more than using machine learning models to recessively update the model based on training deficiencies. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
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 (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 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 3, 5, 6, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Malisiewicz (US 20180137642 A1) in view of Takimoto (US 20160210535 A1)
Regarding claim 1, Malisiewicz teaches an uncertainty calculation unit configured to calculate, based on a prediction value obtained from a machine learning model using evaluation data for evaluating a shortage of training data as an input and a correct answer for the evaluation data, a data shortage degree representing a training data shortage degree for each piece of the evaluation data; (See e.g. [0026], ”The machine learning model can repeatedly process the input data, and the parameters (e.g., the weight values) of the machine learning model can be modified in what amounts to a trial-and-error process until the model produces (or “converges” on) the correct or preferred output.” [i.e., trial-and-error process corresponding to evaluating a shortage of training data] “For example, the modification of weight values may be performed through a process referred to as “back propagation.” Back propagation includes determining the difference between the expected model output and the obtained model output, and then determining how to modify the values of some or all parameters of the model to reduce the difference between the expected model output and the obtained model output.” See e.g. [0046], “PCK measures the fraction of annotated instances that are correct when all the ground truth boxes are given as input to the system.” [i.e., ground truth boxes corresponding to correct answer for the evaluation data])
Malisiewicz does not teach a target selection unit configured to extract target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; a tendency analysis planning unit configured to specify a tendency of the target data based on a predetermined analysis rule and specify a property of the training data to be added based on the tendency of the target data.
Takimoto teaches a target selection unit configured to extract target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; (See e.g. [0065], “a possibility that the data having such a tendency that it is insufficient in the training data” [i.e., insufficient in the training data corresponding to the data shortage degree] “is contained in the unselected (T−S) data in the training data set is high. Therefore, a message for urging the user to add training data similar to the unselected (T−S) data as illustrated in FIG. 7A is displayed.” [i.e., adding training data similar to the unselected data corresponding to data to be added to the training data, based on a predetermined selection rule])
a tendency analysis planning unit configured to specify a tendency of the target data based on a predetermined analysis rule and specify a property of the training data to be added based on the tendency of the target data. (See e.g. [0065], “It is also possible to construct in such a manner that a plurality of data is sequentially extracted from the data of the largest score Score(i) among all “i”, it is determined that the data which was not selected in common to those data sets is the data having such a tendency that it is insufficient in the training data,” [i.e., the data having such a tendency that it is insufficient in the training data corresponding to a tendency analysis planning unit configured to specify a tendency of the target data] “and a message as illustrated in FIG. 7B is displayed. By using such a method, it is possible to urge the user to add proper training data.” [i.e., urge the user to add proper training data corresponding to specify a property of the training data to be added based on the tendency of the target data])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz and Takimoto before them, to include Takimoto’s shortage in training data of in Malisiewicz’s training data evaluation model. One would have been motivated to make such a combination in order to reduce excessive adapt training data and increasing performance of the learning model, as suggested by Takimoto (0008)
Regarding claim 2, Malisiewicz and Takimoto teach the system of claim 1. Malisiewicz further teaches wherein the data shortage degree includes a fluctuation value representing a degree of fluctuation in the prediction value when weight in the machine learning model is modified (See e.g. [0026], ”The machine learning model can repeatedly process the input data, and the parameters (e.g., the weight values) of the machine learning model can be modified in what amounts to a trial-and-error process until the model produces (or “converges” on) the correct or preferred output. For example, the modification of weight values may be performed through a process referred to as “back propagation.” Back propagation includes determining the difference between the expected model output and the obtained model output, and then determining how to modify the values of some or all parameters of the model to reduce the difference between the expected model output and the obtained model output.” [i.e., modifying parameters to reduce the differences between expected and obtained output corresponding to a degree of fluctuation in prediction value when weight is modified])
the selection rule is predetermined including a rule for classifying the target data based on the fluctuation value, and (See e.g. [0052], “In R-CNN, the final output is a classification score” [i.e., final output classification score corresponding to classifying the target data based on fluctuation value] “and the bounding box regression values for every region proposal. The bounding box regression” [i.e., bounding box regression corresponding to selection rule] “allows moving the region proposal around and scaling it such that the final bounding box localizes just the object.”)
Malisiewicz does not teach the target selection unit extracts the target data based on the selection rule and the fluctuation value.
Takimoto teaches the target selection unit extracts the target data based on the selection rule and the fluctuation value. (See e.g. [0065], “a possibility that the data having such a tendency that it is insufficient in the training data is contained in the unselected (T−S) data in the training data set is high. Therefore, a message for urging the user to add training data similar to the unselected (T−S) data as illustrated in FIG. 7A is displayed.” [i.e., the unselected data corresponding to the target selection unit])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz and Takimoto before them, to include Takimoto’s shortage in training data of in Malisiewicz’s training data evaluation model. One would have been motivated to make such a combination in order to reduce excessive adapt training data and increasing performance of the learning model, as suggested by Takimoto (0008)
Regarding claim 3, Malisiewicz and Takimoto teach the system of claim 2. Malisiewicz further teaches wherein the selection rule is predetermined including a rule for setting data, as the target data, in which the fluctuation value is in a predetermined ratio from an upper level. (See e.g. [0046], “PCK measures the fraction of annotated instances that are correct when all the ground truth boxes are given as input to the system” [i.e., PCK measures the fraction corresponding to an upper level] “A predicted keypoint was considered correct if its normalized distance from the annotation was less than a threshold (a).” [i.e., annotation was less than a threshold (a) corresponding to the fluctuation value is in a predetermined ratio])
Regarding claim 5, Malisiewicz and Takimoto teach the system of claim 2. Malisiewicz further teaches wherein the machine learning model is a model for identifying what appears in an image, and (See e.g. [0157], “a system for detecting a cuboid in an image is disclosed.”)
the selection rule includes a rule for setting evaluation data, as the target data, in which the fluctuation value is greater than a predetermined threshold value. (See e.g. [0046], “The cuboid detector 200 was evaluated on two tasks: cuboid bounding box detection and cuboid keypoint localization. For detection, a bounding box was correct if the intersection over union (IoU) overlap was greater than 0.5.2.” [i.e., IoU overlap greater than 0.5.2 corresponding to a fluctuation value is greater than a predetermined threshold value])
Regarding claim 6, Malisiewicz and Takimoto teach the system of claim 1.
Malisiewicz does not teach wherein the analysis rule is predetermined including a rule for classifying the target data into a plurality of classification groups according to a common property, and the tendency analysis planning unit classifies the target data into the classification groups based on the analysis rule, and specifies a property of the training data to be added for each classification group.
Takimoto teaches wherein the analysis rule is predetermined including a rule for classifying the target data into a plurality of classification groups according to a common property, and (See e.g. [0067], “an image is input, feature amounts are extracted from the input image, and whether the input image is normal data or abnormal data is classified on the basis of the extracted feature amounts.” [i.e., input image is normal data or abnormal data is classified on the basis of the extracted feature amounts corresponding to classification groups according to a common property])
the tendency analysis planning unit classifies the target data into the classification groups based on the analysis rule, and specifies a property of the training data to be added for each classification group. (See e.g. [0067], “If the inconsistency of the abnormal area has occurred, in step S904, a message for urging an addition of training data or a teaching of a correct abnormal area is displayed.” [i.e., urging an addition of training data or a teaching of a correct abnormal area corresponding to specifies a property of the training data to be added for each classification group])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz and Takimoto before them, to include Takimoto’s shortage in training data of in Malisiewicz’s training data evaluation model. One would have been motivated to make such a combination in order to reduce excessive adapt training data and increasing performance of the learning model, as suggested by Takimoto (0008)
Regarding claim 13, Malisiewicz teaches an uncertainty calculation step of calculating, based on a prediction value obtained from a machine learning model using evaluation data for evaluating a shortage of training data as an input and a correct answer for the evaluation data, a data shortage degree representing a training data shortage degree for each piece of the evaluation data; (See e.g. [0026], ”The machine learning model can repeatedly process the input data, and the parameters (e.g., the weight values) of the machine learning model can be modified in what amounts to a trial-and-error process until the model produces (or “converges” on) the correct or preferred output.” [i.e., trial-and-error process corresponding to evaluating a shortage of training data] “For example, the modification of weight values may be performed through a process referred to as “back propagation.” Back propagation includes determining the difference between the expected model output and the obtained model output, and then determining how to modify the values of some or all parameters of the model to reduce the difference between the expected model output and the obtained model output.” See e.g. [0046], “PCK measures the fraction of annotated instances that are correct when all the ground truth boxes are given as input to the system.” [i.e., ground truth boxes corresponding to correct answer for the evaluation data])
Malisiewicz does not teach a target selection step of extracting target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; and a tendency analysis planning step of specifying a tendency of the target data based on a predetermined analysis rule and specifying a property of the training data to be added based on the tendency of the target data.
Takimoto teaches a target selection step of extracting target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; and (See e.g. [0065], “a possibility that the data having such a tendency that it is insufficient in the training data” [i.e., insufficient in the training data corresponding to the data shortage degree] “is contained in the unselected (T−S) data in the training data set is high. Therefore, a message for urging the user to add training data similar to the unselected (T−S) data as illustrated in FIG. 7A is displayed.” [i.e., adding training data similar to the unselected data corresponding to data to be added to the training data, based on a predetermined selection rule])
a tendency analysis planning step of specifying a tendency of the target data based on a predetermined analysis rule and specifying a property of the training data to be added based on the tendency of the target data. (See e.g. [0065], “It is also possible to construct in such a manner that a plurality of data is sequentially extracted from the data of the largest score Score(i) among all “i”, it is determined that the data which was not selected in common to those data sets is the data having such a tendency that it is insufficient in the training data,” [i.e., the data having such a tendency that it is insufficient in the training data corresponding to a tendency analysis planning unit configured to specify a tendency of the target data] “and a message as illustrated in FIG. 7B is displayed. By using such a method, it is possible to urge the user to add proper training data.” [i.e., urge the user to add proper training data corresponding to specify a property of the training data to be added based on the tendency of the target data])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz and Takimoto before them, to include Takimoto’s shortage in training data of in Malisiewicz’s training data evaluation model. One would have been motivated to make such a combination in order to reduce excessive adapt training data and increasing performance of the learning model, as suggested by Takimoto (0008)
Regarding claim 14, Malisiewicz teaches an uncertainty calculation function configured to calculate, based on a prediction value obtained from a machine learning model using evaluation data for evaluating a shortage of training data as an input and a correct answer for the evaluation data, a data shortage degree representing a training data shortage degree for each piece of the evaluation data; (See e.g. [0026], ”The machine learning model can repeatedly process the input data, and the parameters (e.g., the weight values) of the machine learning model can be modified in what amounts to a trial-and-error process until the model produces (or “converges” on) the correct or preferred output.” [i.e., trial-and-error process corresponding to evaluating a shortage of training data] “For example, the modification of weight values may be performed through a process referred to as “back propagation.” Back propagation includes determining the difference between the expected model output and the obtained model output, and then determining how to modify the values of some or all parameters of the model to reduce the difference between the expected model output and the obtained model output.” See e.g. [0046], “PCK measures the fraction of annotated instances that are correct when all the ground truth boxes are given as input to the system.” [i.e., ground truth boxes corresponding to correct answer for the evaluation data])
Malisiewicz does not teach a target selection function of extracting target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; and a tendency analysis planning function of specifying a tendency of the target data based on a predetermined analysis rule and specifying a property of the training data to be added based on the tendency of the target data.
Takimoto teaches a target selection function of extracting target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; and (See e.g. [0065], “a possibility that the data having such a tendency that it is insufficient in the training data” [i.e., insufficient in the training data corresponding to the data shortage degree] “is contained in the unselected (T−S) data in the training data set is high. Therefore, a message for urging the user to add training data similar to the unselected (T−S) data as illustrated in FIG. 7A is displayed.” [i.e., adding training data similar to the unselected data corresponding to data to be added to the training data, based on a predetermined selection rule])
a tendency analysis planning function of specifying a tendency of the target data based on a predetermined analysis rule and specifying a property of the training data to be added based on the tendency of the target data. (See e.g. [0065], “It is also possible to construct in such a manner that a plurality of data is sequentially extracted from the data of the largest score Score(i) among all “i”, it is determined that the data which was not selected in common to those data sets is the data having such a tendency that it is insufficient in the training data,” [i.e., the data having such a tendency that it is insufficient in the training data corresponding to a tendency analysis planning unit configured to specify a tendency of the target data] “and a message as illustrated in FIG. 7B is displayed. By using such a method, it is possible to urge the user to add proper training data.” [i.e., urge the user to add proper training data corresponding to specify a property of the training data to be added based on the tendency of the target data])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz and Takimoto before them, to include Takimoto’s shortage in training data of in Malisiewicz’s training data evaluation model. One would have been motivated to make such a combination in order to reduce excessive adapt training data and increasing performance of the learning model, as suggested by Takimoto (0008)
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Malisiewicz (US 20180137642 A1) in view of Takimoto (US 20160210535 A1) further in view of Fujimoto (US 20210390704 A1)
Regarding claim 4, Malisiewicz and Takimoto teach the system of claim 3. Malisiewicz teaches wherein the machine learning model is a model for detecting a predetermined object from an image (See e.g. [0157], “a system for detecting a cuboid in an image is disclosed.”)
Malisiewicz and Takimoto do not teach the selection rule includes a rule in which an IoU of a prediction value for the correct answer is smaller than a predetermined threshold value and the data in which the fluctuation value is in the ratio from the upper level is set as the target data.
Fujimoto teaches the selection rule includes a rule in which an IoU of a prediction value for the correct answer is smaller than a predetermined threshold value and the data in which the fluctuation value is in the ratio from the upper level is set as the target data. (See e.g. [0098], “the number of ROIs considered is increased to accommodate the number of ROIs that potentially may occur in historical documents. Additionally or alternatively, the intersection over union (“IOU”) threshold for pairing predictions with ground truth during training is increased.” [i.e., intersection over union (“IOU”) threshold for pairing predictions with ground truth corresponding to an IoU of a prediction value for the correct answer] See e.g. [0100], “the step 801 of training the system may be repeated until segmented images and/or extracted particles are sufficiently similar to ground truth. In embodiments, “sufficiently similar” is a precision of approximately 80%, for example 81%, and/or a recall of approximately 75%, for example 76%.” [i.e., the step 801 of training the system may be repeated until segmented images and/or extracted particles are sufficiently similar to ground truth corresponding to the data in which the fluctuation value is in the ratio from the upper level is set as the target data] “Higher or lower thresholds for precision and/or recall may be utilized as suitable.” [i.e., lower thresholds for precision corresponding to correct answer is smaller than a predetermined threshold value])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz, Takimoto and Fujimoto before them, to include Fujimoto’s intersection over union in Malisiewicz and Takimoto’s training data evaluation model. One would have been motivated to make such a combination in order to improve learned imaged scaling and region proposing, as suggested by Fujimoto (0018)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Malisiewicz (US 20180137642 A1) in view of Takimoto (US 20160210535 A1) further in view of DUBBA (US 20230054688 A1) further in view of KIM (US 20210166345 A1)
Regarding claim 7, Malisiewicz and Takimoto teach the system of claim 6. Malisiewicz further teaches wherein the machine learning model is a model for detecting a predetermined object from an image, (See e.g. [0157], “a system for detecting a cuboid in an image is disclosed.”)
Malisiewicz and Takimoto do not teach the analysis rule is predetermined including a rule for classifying the target data in which a color variance of an object in the correct answer is greater than a predetermined threshold value into one classification group, and the tendency analysis planning unit proposes that an image having the color variance greater than the threshold value is to be generated based on the target data classified into the classification group and is to be added to the training data.
DUBBA teaches the analysis rule is predetermined including a rule for classifying the target data in which a color variance of an object in the correct answer is greater than a predetermined threshold value into one classification group, and (See e.g. [0059], “Yet another example of a predetermined rule involves leveraging the fact that GUI-elements tend to be noisier (in terms of pixel-value variance) than arbitrary larger bounding boxes (since they tend to have more blank space). Knowing this, a predetermined rule may involve providing a threshold on the variance of the pixel-values within a given GUI-element bounding box 128 [i.e., pixel-values within a given GUI-element bounding box corresponding to classifying the target data in which a color variance] “Over a certain threshold, it can be determined that the bounding box 128 is accurate. The above examples are not intended to be limiting on the predetermined rules and it will be apparent to the skilled person that the above rules merely serve as possible examples of predetermined rules and that it is possible to define more predetermined rules. The heuristic model based on predetermined rules, upon which the classification of the estimated GUI interaction information 430 is based, may include a combination of one or more predetermined rules, where the rules “vote” on whether subsets of the estimated GUI interaction information 430 (e.g. a GUI-element bounding box 128) is correct” [i.e., upon which the classification of the estimated GUI interaction information 430 is based, may include a combination of one or more predetermined rules corresponding to an object in the correct answer is greater than a predetermined threshold value into one classification group] “and the model makes a final decision on whether the estimated GUI interaction information 430 is correct based on a majority vote.”
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz, Takimoto and DUBBA before them, to include DUBBA’s pixel variance of an object in Malisiewicz and Takimoto’s training data evaluation model. One would have been motivated to make such a combination in order to allow more efficient and accurate of pixel variation, as suggested by DUBBA (0070)
Malisiewicz, Takimoto and DUBBA do not teach the tendency analysis planning unit proposes that an image having the color variance greater than the threshold value is to be generated based on the target data classified into the classification group and is to be added to the training data.
Kim teaches the tendency analysis planning unit proposes that an image having the color variance greater than the threshold value is to be generated based on the target data classified into the classification group and is to be added to the training data. (See e.g. [0098], “the second AI model may be trained using a plurality of training images and upscaled images corresponding to each of the training images as input/output training data pairs.” [i.e., upscaled images corresponding to each of the training images as input/output training data pairs corresponding to be added to the training data] See e.g. [0101], “In one embodiment, the processor 110 may obtain the feature information for the pixels included in the edge region. Here, the edge is a region where the spatially adjacent pixel values are changing rapidly, and the difference between adjacent pixel values may be greater than or equal to a threshold value.” [i.e., difference between adjacent pixel values may be greater than or equal to a threshold value corresponding to color variance greater than the threshold value] See e.g. [0182], “Subsequently, the processor 110 may input the extracted feature value into a classification network 1213 to obtain upscaling information,” [i.e., classification network 1213 to obtain upscaling information corresponding to the target data classified into the classification group] “for example, an upscaling ratio”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz, Takimoto, DUBBA and Kim before them, to include Kim’s updated training data based on pixel variance of an object in Malisiewicz, Takimoto and DUBBA’s training data evaluation model. One would have been motivated to make such a combination in order to improve the input image quality, as suggested by Kim (0055)
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Malisiewicz (US 20180137642 A1) in view of Takimoto (US 20160210535 A1) further in view of Tasli (US 20190354772 A1)
Regarding claim 8, Malisiewicz and Takimoto teach the system of claim 6. Malisiewicz further teaches wherein the machine learning model is a model for identifying what appears in an image, (See e.g. [0032], “The cuboid detector can learn to detect cuboids in images using a data-driven approach.”)
Malisiewicz and Takimoto do not teach the analysis rule includes a rule for classifying the target data into a target classification group based on a distribution of pixels in an image in which a contribution to a pixel fluctuation value is greater than a predetermined threshold value, and the tendency analysis planning unit proposes that an image having the distribution of the target classification group is to be generated from the target data classified into the target classification group based on the analysis rule and is to be added to the training data.
Tasli teaches the analysis rule includes a rule for classifying the target data into a target classification group based on a distribution of pixels in an image in which a contribution to a pixel fluctuation value is greater than a predetermined threshold value, and (See e.g. [0041], “The quality of the alignment is evaluated in the Registration Evaluation module (504) using a pixelwise similarity metric using sum of squared differences (SSD) between the background and aligned (registered) current image” [i.e., pixelwise similarity metric corresponding classifying the target data into a target classification group]. “This evaluation ensures the quality of matching by comparing the results of the similarity metric with a threshold and the decision to either return to image capture state (201) or to go through with the Background Analysis module (205) is made depending on the result whether it is below or above the threshold” [i.e., Background Analysis module results above the threshold corresponding pixel fluctuation value is greater than a predetermined threshold value]. “The threshold for successful registration is learned from set of training images where ground truth keypoint matches are used for homography estimation. Estimated homography matrix is used to align the images in order to obtain a similarity metric distribution for correctly aligned images.”)
the tendency analysis planning unit proposes that an image having the distribution of the target classification group is to be generated from the target data classified into the target classification group based on the analysis rule and is to be added to the training data. (See e.g., [0042], “FIG. 7 shows the details of the Background Analysis module (205) where a Background Stability Check (601) is initially performed…If the stability is not satisfied (<90% both on precision and recall), system goes back to a new Image Capture state (201). If a successful background registration is observed (≥90% on precision and recall), the active background image is updated (602).” [i.e., background analysis module passing a threshold identifying the target data as a background image corresponding to the target classification group based on the analysis rule and is to be added to the training])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz, Takimoto and Tasli before them, to include Tasli’s identification of pixel fluctuation in Malisiewicz and Takimoto’s training data evaluation model. One would have been motivated to make such a combination in order to increase accuracy and understanding of an image, as suggested by Tasli (0012)
Regarding claim 9, Malisiewicz and Takimoto teaches the system of claim 8.
Malisiewicz and Takimoto do not teach a display unit configured to display an improvement proposal in which a property common to the target classification group and an improvement plan to be added with the training data having the image based on the distribution of the target classification group are associated.
Tasli teaches a display unit configured to display an improvement proposal in which a property common to the target classification group and an improvement plan to be added with the training data having the image based on the distribution of the target classification group are associated. (See e.g., [0042], “FIG. 7 shows the details of the Background Analysis module (205) where a Background Stability Check (601) is initially performed…If the stability is not satisfied (<90% both on precision and recall), system goes back to a new Image Capture state (201). If a successful background registration is observed (≥90% on precision and recall), the active background image is updated (602).” [i.e., background image is update corresponding to improvement plan to be added with the training data] See e.g. [0069], “the FOD analysis module (206) sending the output to the user terminal [i.e., output to the user terminal corresponding to display an improvement proposal] and the user interface showing the results of the analysis.” [i.e., showing the results of the analysis corresponding to improvement plan])
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz, Takimoto and Tasli before them, to include Tasli’s identification of pixel fluctuation in Malisiewicz and Takimoto’s training data evaluation model. One would have been motivated to make such a combination in order to increase accuracy and understanding of an image, as suggested by Tasli (0012)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Malisiewicz (US 20180137642 A1) in view of Takimoto (US 20160210535 A1) further in view of Dimitrova (US 20090196464 A1)
Regarding claim 12, Malisiewicz and Takimoto teach the system of claim 1.
Malisiewicz and Takimoto do not teach wherein the machine learning model is a model in which one or more rectangles that indicate an area that is estimated to be an area in which a predetermined object exists in an image are output as prediction value candidates, the uncertainty calculation unit sets, as a prediction value for the evaluation data, a prediction value candidate of a rectangle having a highest similarity to a rectangle representing the correct answer to the evaluation data.
Dimitrova teaches wherein the machine learning model is a model in which one or more rectangles that indicate an area that is estimated to be an area in which a predetermined object exists in an image are output as prediction value candidates, (See e.g. [0038], “Input video images 20 are scanned from left to right, top to bottom, and rectangles of different sizes in the image are analyzed to determine whether or not it contains a face. Thus, stages of the classifier are applied in succession to a rectangle. Each stage yields a score for the rectangle, which is the sum of the responses of the weak classifiers comprising the stage…If the rectangle's scores pass the thresholds for all stages, it is determined to include a face portion” [i.e., determining if a face portion if present in the input image corresponding to a predetermined object exists in an image], “and the face image is passed to feature extraction 35.” See e.g. [0040], “When implemented, an input rectangular portion of an image is also typically analyzed by h based on the weighted sum of pixels in two or more sub-rectangles of the input rectangle, and the output of h” [i.e., output of h corresponding to output as prediction value]) “is set to 1 if the threshold (as determined from the training) is exceeded for the input rectangle and h=-1 if it does not.”)
the uncertainty calculation unit sets, as a prediction value for the evaluation data, a prediction value candidate of a rectangle having a highest similarity to a rectangle representing the correct answer to the evaluation data. (See e.g. [0038], “If the rectangle's scores pass the thresholds for all stages, it is determined to include a face portion,” [i.e. If the rectangle's scores pass the thresholds for all stages, it is determined to include a face portion corresponding to rectangle having a highest similarity to a rectangle representing the correct answer to the evaluation data] “and the face image is passed to feature extraction 35. If the rectangle is below the threshold for any stage, the rectangle is discarded and the algorithm proceeds to another rectangle in the image.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Malisiewicz, Takimoto and Dimitrova before them, to include Dimitrova’s rectangular estimation area in Malisiewicz and Takimoto’s training data evaluation model. One would have been motivated to make such a combination in order to improve image recognition, as suggested by Dimitrova (0003)
Conclusion
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/K.A.T./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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