DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is made non-final.
Claims 1-22 are pending in the case. Claims 1, 21, and 22 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for domestic benefit of provisional application 63/127,792 filed on 12/18/2020.
Specification
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:
obtaining unit as recited in claim 1 and invokes 112(f), is interpreted as a processor, integrated circuit, or equivalent thereof performing the claimed functions as supported in paragraph [0078] of Applicant's Specification.
generating unit as recited in claims 1-10, and 14-20, invokes 112(f), is interpreted as a processor, integrated circuit, or equivalent thereof performing the claimed functions as supported in paragraph [0078] of Applicant's Specification.
learning unit as recited in claims 11-13 and invokes 112(f), is interpreted as a processor, integrated circuit, or equivalent thereof performing the claimed functions as supported in paragraph [0078] of Applicant's Specification.
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 § 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.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-22 rejected under 35 U.S.C. 101 because the claimed invention is directed an abstract idea without significantly more.
Step 1: Claims 1-20 are directed to an apparatus (a machine), Claim 21 is directed to a method (a process), and Claim 22 is directed to a non-transitory computer-readable medium (a machine). Therefore, claims 1-22 are directed to a process, machine, manufacture or composition of matter.
Claim 1
Step 2A, Prong 1: The claim recites an abstract idea.
The claim recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“uses the dataset and generates a model in such a way that there is a decrease in variability in weight”
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements add insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)] and therefore fail to integrate the judicial exception into a practical application. The limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
“obtains a dataset of training data to be used for training of a model”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to integrate the judicial exception into a practical application.
“an obtaining unit”
“a generating unit”
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception and therefor fail to amount to significantly more than the judicial exception. The courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) [see MPEP 2106.05(d)(II)].
“obtains a dataset of training data to be used for training of a model”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to amount to significantly more than the judicial exception.
“an obtaining unit”
“a generating unit”
Claim 2
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model in such a way that there is a decrease in standard deviation or dispersion of the weight”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 3
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using post-conversion training data obtained by conversion in such a way that there is a decrease in variability in the weight of the model”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 4
Step 2A, Prong 1: The claim recites the same abstract idea as claim 3.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using the post-conversion training data obtained by normalization of the training data”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 5
Step 2A, Prong 1: The claim recites the same abstract idea as claim 3.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using the post-conversion training data obtained by converting the training data into vectors”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 6
Step 2A, Prong 1: The claim recites the same abstract idea as claim 3.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“converts the training data into the post-conversion training data.”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 7
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“when the training data points to an item related to a numerical value, the generating unit normalizes the training data and generates the post conversion training data.”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 8
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“using a predetermined conversion function for normalizing the training data, the generating unit generates the post-conversion training data by normalizing the training data”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 9
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“when the training data points to an item related to a category, the generating unit converts the training data into vectors and generates the postconversion training data”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 10
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“using a vector conversion model for embedding the training data, …generates the post-conversion training data by converting the training data into vectors”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 11
Step 2A, Prong 1: The claim recites an abstract idea and corresponds to the system of claim 1.
The claim recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the vector conversion model by performing a training operation”
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements add insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)] and therefore fail to integrate the judicial exception into a practical application. The limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
“generates the vector conversion model by performing a training operation”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to integrate the judicial exception into a practical application.
“learning unit”
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception and therefor fail to amount to significantly more than the judicial exception. The courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) [see MPEP 2106.05(d)(II)].
“generates the vector conversion model by performing a training operation”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to amount to significantly more than the judicial exception.
“learning unit”
Claim 12
Step 2A, Prong 1: The claim recites the same abstract idea as claim 11.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the vector conversion model that is trained in features of the training data”
Step 2A, Prong 2: For the same reasons as provided for claim 11, there are no additional elements, including the “learning unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 11, there are no additional elements, including the “learning unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 13
Step 2A, Prong 1: The claim recites the same abstract idea as claim 11.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the vector conversion model in such a way that there is a decrease in variability in distribution of vectors output by the vector conversion model”
Step 2A, Prong 2: For the same reasons as provided for claim 11, there are no additional elements, including the “learning unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 11, there are no additional elements, including the “learning unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 14
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using a partial data group generated from the dataset based on a predetermined range”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 15
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using the partial data group that is generated from the dataset, in which sets of training data are associated to time, based on a time window indicating a predetermined time range”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 16
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using the partial data group in which a plurality of sets of partial data overlappingly contains a single set of training data”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 17
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model, with data corresponding to each of the partial data group serving as data to be input to a model”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 18
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using batch normalization”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 19
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“generates the model using the batch normalization in which input of each layer of the model is normalized”
Step 2A, Prong 2: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that integrate the judicial exception into a practical application.
Step 2B: For the same reasons as provided for claim 1, there are no additional elements, including the “generating unit” [see MPEP 2106.05(f)], in this claim that amount to significantly more than the judicial exception.
Claim 20
Step 2A, Prong 1: The claim recites the same abstract idea as claim 1.
The claim further recites data gathering. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“receiving the model learnt by the model generation server from the model generation server”
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements add insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)] and therefore fail to integrate the judicial exception into a practical application. The limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
“sending data to be used in generation of the model to an external model generation server, requesting the model generation server to learn the model”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to integrate the judicial exception into a practical application.
“model generation server”
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception and therefor fail to amount to significantly more than the judicial exception. The courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) [see MPEP 2106.05(d)(II)].
“sending data to be used in generation of the model to an external model generation server, requesting the model generation server to learn the model, and receiving the model learnt by the model generation server from the model generation server”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to amount to significantly more than the judicial exception.
“model generation server”
Claim 21
Step 2A, Prong 1: The claim recites an abstract idea that corresponds to the system of claim 1.
The claim recites a mathematical concept. The claim involves statistical data processing, which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“using the dataset and generating a model in such a way that there is a decrease in variability in weight”
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements add insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)] and therefore fail to integrate the judicial exception into a practical application. The limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
“obtaining a dataset of training data to be used for training of a model”
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception and therefor fail to amount to significantly more than the judicial exception. The courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) [see MPEP 2106.05(d)(II)].
“obtaining a dataset of training data to be used for training of a model”
Claim 22
Step 2A, Prong 1: The claim recites an abstract idea, that corresponds to claim 1 by simply adding a “non-transitory computer-readable storage medium”.
The claim recites a storage medium containing a program that executes training data processing which is a form of mathematical analysis and is considered an abstract idea [see MPEP 2106.04(a)(2)(I)].
“obtaining a dataset of training data to be used for training of a model”
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements add insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(g)] and therefore fail to integrate the judicial exception into a practical application. The limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
“obtaining a dataset of training data to be used for training of a model”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to integrate the judicial exception into a practical application.
“non-transitory storage-readable medium”
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception and therefor fail to amount to significantly more than the judicial exception. The courts have recognized that receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) [see MPEP 2106.05(d)(II)].
“obtaining a dataset of training data to be used for training of a model”
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fail to amount to significantly more than the judicial exception.
“non-transitory storage-readable medium”
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-8, and 20-22 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ando (US Patent 12,026,935 B2).
Regarding claim 1, Ando teaches an information processing apparatus (Col. 8, lines 32-33 and
FIG. 2: training device 10/information processing apparatus) comprising:
an obtaining unit that obtains a dataset of training data to be used for training of a model; (FIG. 2, and Col 8, lines 41-67: The obtaining unit invokes 112(f) and corresponds to the acquisition section 20 in combination with processing section 30. Thus, the obtaining unit is a communication interface in combination with, for example, a processor, integrated circuit, or equivalent thereof.; FIG. 3 and Col. 8, lines 41-58: FIG. 3 shows that an acquisition section 20, takes an image to be processed to produce a trained model; Fig. 4 S102 and Col. 11 lines 59-64: FIG. 4 is a flowchart of the training of the model, and step S102 acquires the input image from the acquisition unit, which takes an image from a plurality of images which corresponds to a dataset of training data; FIG. 4 S111 and Col. 18, lines 50-67: The acquisition section obtains a new input image from the dataset for the iterative process, FIG. 4 starting at step S102 of training the model, at step S111 a decision is made to retrain the model at S102.) and
a generating unit that uses the dataset and generates a model in such a way that there is a decrease in variability in weight (Col 8, lines 59-67: The generating unit invokes 112(f) and corresponds to the processing section 30. Thus, the generating unit is, for example, a processor, integrated circuit, or equivalent thereof.; FIG. 3 and Col. 8, lines 35-40: “processing section 30 that performs the machine learning based on the input image.” The processing section 30 from FIG. 3, takes the input image form the acquisition section 20 to create a model.; FIG. 3, shows an input image acquired form the acquisition section 20. The data is then sent to the processing section 30, which performs iterative operations to output a trained model corresponding to generating a model.; Col. 11 lines 48-62: initializing weights using a normal distribution with a controlled standard deviation. This in combination with FIG. 5A, FIG. 5B and Col. 11 lines 15-38, leads to a convergence of optimized weights. In addition, the iterative weight updating process described in, FIG. 4 step S108-S111 and Col. 17 lines 3-67 cont. Col. 18 lines 1-23, describes various methods for calculating an output difference between predicted and actual values, including squared error, Huber loss, and probability-based divergence. These loss functions are used to guide iterative training and weight adjustment. Minimization of the output difference converges the model, which corresponds to a decrease in variability in weight.).
Regarding claim 2, Ando further teaches the information processing apparatus according to
claim 1, wherein the generating unit generates the model in such a way that there is a decrease in standard deviation or dispersion of the weight (FIG. 5A and Col. 10, lines 61-67 cont. Col. 11, lines 1-9: The training process includes adjusting weight coefficients between layers of a neural network. Specifically, each node’s output is calculated using weights and biases, and backpropagation is applied to refine those weights throughout the training.; FIG. 4 S109-S111 and Col. 17 lines 56-67 cont. Col. 18 line 1-67: The iterative training process in which weight coefficients update each layer of the neural network through backpropagation (S109-S110) and refined through repeated training cycles (S102-S111) until a condition is met. This process adjusts weights towards convergence to reduce prediction error. As the model converges, the dispersion of weight values is decreased as the weight values are optimized throughout the training process.).
Regarding claim 3, Ando further teaches the information processing apparatus according to
claim 1, wherein the generating unit generates the model using post-conversion training data obtained by conversion in such a way that there is a decrease in variability in the weight of the model (FIG. 3, FIG. 4 S104-S111 and Col. 17 lines 3-67 cont. Col. 18, lines 1-67: Discusses taking in the training data and applying data augmentation (conversion)FIG. 4 steps S104-S105, and outputs the first and second augmented image which corresponds with post-conversion training data. FIG. 4 S106-S111 show that by using the post-conversion training data the model is updated in a way that it decreases variability of the weight. This is done iteratively until a condition is met, S111.).
Regarding claim 4, Ando further teaches the information processing apparatus according to
claim 3, wherein the generating unit generates the model using the post-conversion training data obtained by normalization of the training data (FIG. 4 step S104-S105 and Col. 12, lines 15-23: The first data augmentation includes at least one of the color correction process, as detailed in Col. 12, lines 24-63, the brightness correction process, as detailed in Col. 12, lines 64-67 cont. Col. 13, lines 1-58, the smoothing process, as detailed in Col. 13, lines 58-67 cont. Col. 13, lines 1-14, an image sharpening process, as detailed in Col. 14, lines 15-26, a noise addition process, as detailed in Col. 14, lines 27-51, and an affine transformation process, as detailed in Col. 14, lines 52-67 cont. Col. 15, lines 1-10. The first data augmentation may be any one of these processes, or may be a combination of two or more of these processes. Each process is an example of normalization of the input image. Steps 104-105 produce augmented images from the normalized image, which correspond to post-conversion training data; FIG. 4 step S106, and FIG. 7: FIG. 4 step S106 takes the augmented images and inputs them into the neural network. FIG. 7 then illustrates how the augmented images/post-conversion training data are used to generate the model.).
Regarding claim 6, Ando further teaches the information processing apparatus according to
claim 3, wherein the generating unit converts the training data into the post-conversion training data (FIG. 4, S104-S106 and Col. 9, lines 42-48: “The data augmentation section 31 acquire an input image from the acquisition section 20, and applies data augmentation to the input image. The data augmentation section 31 performs a process of generating the first augmented image by applying the first data augmentation to the input image, and a process of generating the second augmented image by applying the second data augmentation to the input image.”; Col 15. Lines 18-21: “That is, the data augmentation section 31 performs image conversion of a similar type to that performed in step S104 as the second data augmentation.” The augmented images produced from image conversion from steps, S104 and S105 correspond to post-conversion training data.).
Regarding claim 7, Ando further teaches the information processing apparatus according to
claim 3, wherein, when the training data points to an item related to a numerical value, the generating unit normalizes the training data and generates the post-conversion training data (FIG. 4 step S104-S105 and Col. 12, lines 24-63: For example, the color correction process normalizes the training images by converting red(R), green(G) and blue(B) pixel values into hue(H), saturation(S), and brightness(V) values using Expression 1. This produces post-conversion training data by normalizing the data where the RGB vales relates to a numerical value. Note that color correction process is one example of normalization and at least one other normalization process, as supported in Col. 12, lines 15-23, may additionally or alternatively occur).
Regarding claim 8 Ando further teaches the information processing apparatus according to
claim 7, wherein, using a predetermined conversion function for normalizing the training data, the generating unit generates the post-conversion training data by normalizing the training data (Fig. 4, step S104-S105 and Col. 12, lines 24-63: For example, the color correction process normalizes the input image according to the function Expression 1, producing the augmented images corresponding to the post-conversion training data. Note that color correction process is one example of normalization and at least one other normalization process, as supported in Col. 12, lines 15-23, may additionally or alternatively occur; FIG. 4 step S106, FIG. 6A-6F, FIG. 7; FIG. 6A and FIG. 6D represent input images, after normalization, FIG. 6B, 6C, 6E and 6F create the augmented images which corresponds to the post-conversion training data. FIG. 4 step S106 takes the augmented images and inputs them into the neural network. FIG. 7 illustrates how the augmented images are processed to generate the output data, which corresponds to post-conversion training data used for model generation.).
Regarding claim 20 Ando further teaches the information processing apparatus according to
claim 1, wherein the generating unit generates the model by
sending data to be used in generation of the model to an external model generation server, requesting the model generation server to learn the model, and
receiving the model learnt by the model generation server from the model generation server
(FIG. 13, and Col. 27, lines 38-52: Teaches generating the model by sending data from a terminal device to an external server system, performing training of the model and returning the trained model to the terminal device.).
Regarding claim 21 Ando further teaches an information processing method implemented in
an information processing apparatus, comprising (Col. 6, lines 11-26: Describes the image processing apparatus executes the information processing method.) steps corresponding to the information processing apparatus of claim 1 is therefore rejected on the same premise.
Regarding claim 22 Ando further teaches a non-transitory computer-readable storage medium
having stored therein an information processing program that causes a computer to execute (Col. 10, lines 13-36: Describes the computer-readable storage medium which stores the information processing method program within.) operations corresponding to the information processing apparatus of claim 1 is therefore rejected on the same premise.
Claim Rejections - 35 USC § 103
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.
Claim(s) 5 and 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ando (US Patent 12,026,935 B2) in view of Guo et al. (US Patent 11,062,180 B2).
Regarding claim 5, Ando teaches the information processing apparatus according to claim 3.
Although Ando teaches the information processing apparatus, Ando does not explicitly teach wherein the generating unit generates the model using the post-conversion training data obtained by converting the training data into vectors.
Guo teaches wherein the generating unit generates the model using the post-conversion training data obtained by converting the training data into vectors (FIG. 5, FIG. 6, FIG. 7A, and Col. 17, lines 62-67 cont. Col. 18, lines 1-64: Describes how training data is converted to vectors 506-508 and, then through transformations FIG. 5 and FIG. 6, is converted to post-conversion training data to generate a model FIG. 7A).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ando by incorporating the teachings of Guo to include converting training data into vectors. Doing so would improve computational efficiency and reduce model complexity by transforming high-dimensional data into compact vector representations. This allows neural networks to process categorized training inputs more efficiently in a lower-dimensional space.
Regarding claim 9, Ando teaches the information processing apparatus according to claim 6.
Although Ando teaches the information processing apparatus, Ando does not explicitly teach wherein, when the training data points to an item related to a category, the generating unit converts the training data into vectors and generates the post-conversion training data.
Guo teaches wherein, when the training data points to an item related to a category, the generating unit converts the training data into vectors and generates the post-conversion training data (FIG 5. Blocks 502 and 506, FIG. 6 blocks 608-614 and Col. 17, lines 6