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 .
DETAILED ACTION
This action is responsive to the Application filed on 03/02/2026.
Claims 1-5 and 7-11 are pending in the case. Claims 1 and 10-11 are independent claims. Claims 1 and 10-11 have been currently amended. Claim 6 have been canceled.
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.
Claim(s) 1-5 and 7-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
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, i.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 itis 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. Applicant is advised to consult the 2019 PEG for more details of the analysis.
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Claim(s) 1 is drawn to a computer-implemented method, claim 10 is drawn to a device and claim 11 is to a non-transitory computer-readable medium, therefore each of these claim groups falls under one of four categories of statutory subject matter (machine/products/apparatus, process/method, manufactures and compositions of mater; Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significant more (Step 2A, see below). Independent claims 1, 10 and 11 are non-verbatim but similar in claim construction, hence share the same rationale that the claimed inventions are directed to non-statutory subject matter as follows:
Regarding claim 1:
Claim 1 recites: A computer-implemented method for determining a similarity of data sets, comprising the following steps: predefining a first data set that includes a plurality of first embeddings;
predefining a second data set that includes a plurality of second embeddings; training a first model on the first data set; training a second model on the second data set;
determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model;
determining a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model;
determining a map that optimally maps the set of first features onto the set of second features; and
determining a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 1 is directed to an abstract idea, specifically, a mental process-concepts that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as a “mathematical concept”, a mathematical calculation, mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number in light of the specification. See MPEP § 2106.04(a)(2)(I)(C).
Independent claim 1 recites in part:
determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can mentally analyze relationships between data this is received, and determine, based on judgement and opinion, figuring out which important details or traits will be used in the first version of the model.
determining a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can mentally analyze relationships between data this is received, and determine, based on judgement and opinion, figuring out which important details or traits will be used in the second version of the model.
determining a map that optimally maps the set of first features onto the set of second features
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, with pen and paper one can connect the first group of data to the second group of data.
determining a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
The limitation above is broadly and reasonably interpreted as a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C). For example, determining a similarity as a function of a distance from a reference is a core mathematical concept. A distance function, or metric, quantifies the difference or "distance" between any two objects in a set.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 1 recites in part:
“A computer-implemented method for determining a similarity of data sets, comprising the following steps:” as drafted, amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
“predefining a first data set that includes a plurality of first embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“predefining a second data set that includes a plurality of second embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“training a first model on the first data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
“training a second model on the second data set”, as drafted, amount to insignificant extra-solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 1 recites in part:
“A computer-implemented method for determining a similarity of data sets, comprising the following steps:” as drafted, amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
“predefining a first data set that includes a plurality of first embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“predefining a second data set that includes a plurality of second embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“training a first model on the first data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
“training a second model on the second data set”, as drafted, amount to insignificant extra-solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 10:
Claim 10 recites: A device configured to determine a similarity of digital data sets, the device configured to:
predefine a first data set that includes a plurality of first embeddings;
predefine a second data set that includes a plurality of second embeddings;
train a first model on the first data set; train a second model on the second data set;
determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model;
determine a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model;
determine a map that optimally maps the set of first features onto the set of second features; and
determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 10 is directed to an abstract idea, specifically, a mental process-concepts that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as a “mathematical concept”, a mathematical calculation, mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number in light of the specification. See MPEP § 2106.04(a)(2)(I)(C).
Independent claim 10 recites in part:
determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can mentally analyze relationships between data this is received, and determine, based on judgement and opinion, figuring out which important details or traits will be used in the first version of the model.
determine a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can mentally analyze relationships between data this is received, and determine, based on judgement and opinion, figuring out which important details or traits will be used in the second version of the model.
determine a map that optimally maps the set of first features onto the set of second features
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, with pen and paper one can connect the first group of data to the second group of data.
determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
The limitation above is broadly and reasonably interpreted as a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C). For example, determining a similarity as a function of a distance from a reference is a core mathematical concept. A distance function, or metric, quantifies the difference or "distance" between any two objects in a set.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
“A device configured to determine a similarity of digital data sets, the device configured to:”, as drafted, amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
“predefine a first data set that includes a plurality of first embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“predefine a second data set that includes a plurality of second embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“train a first model on the first data set”, as drafted, amount to insignificant extra-solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
“train a second model on the second data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 10 recites in part:
“A device configured to determine a similarity of digital data sets, the device configured to:”, as drafted, amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
“predefine a first data set that includes a plurality of first embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“predefine a second data set that includes a plurality of second embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“train a first model on the first data set”, as drafted, amount to insignificant extra-solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
“train a second model on the second data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Regarding claim 11
Claim 11 recites: A non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for determining a similarity of digital data sets, the instructions, when executed by a computer, causing the computer to perform the following steps:
predefining a first data set that includes a plurality of first embeddings;
predefining a second data set that includes a plurality of second embeddings;
training a first model on the first data set;
training a second model on the second data set;
determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model;
determining a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model;
determining a map that optimally maps the set of first features onto the set of second features; and
determining a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Claim 11 is directed to an abstract idea, specifically, a mental process-concepts that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). As well as a “mathematical concept”, a mathematical calculation, mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number in light of the specification. See MPEP § 2106.04(a)(2)(I)(C).
Independent claim 11 recites in part:
determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can mentally analyze relationships between data this is received, and determine, based on judgement and opinion, figuring out which important details or traits will be used in the first version of the model.
determining a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, one can mentally analyze relationships between data this is received, and determine, based on judgement and opinion, figuring out which important details or traits will be used in the second version of the model.
determining a map that optimally maps the set of first features onto the set of second features
The limitation above is broadly and reasonably interpreted as a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III). For example, with pen and paper one can connect the first group of data to the second group of data.
determining a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set.
The limitation above is broadly and reasonably interpreted as a mathematical calculation, as directed to “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C). For example, determining a similarity as a function of a distance from a reference is a core mathematical concept. A distance function, or metric, quantifies the difference or "distance" between any two objects in a set.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
Independent claim 11 recites in part:
“A non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for determining a similarity of digital data sets, the instructions, when executed by a computer, causing the computer to perform the following steps:” as drafted, amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
“predefining a first data set that includes a plurality of first embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“predefining a second data set that includes a plurality of second embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“training a first model on the first data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
“training a second model on the second data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
First, the additional elements directed to generally linking the use of a judicial exception to a particular technological environment or field of use are deemed insufficient to transform the judicial exception to a patentable invention because the claimed limitations generally link the judicial exception to the technology environment, see MPEP 2106.05(h). However, they are included below for the sake of completeness.
Second, the additional elements mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception. See MPEP 2106.05(f). However, they are included below for the sake of completeness.
Independent claim 11 recites in part:
“A non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for determining a similarity of digital data sets, the instructions, when executed by a computer, causing the computer to perform the following steps:” as drafted, amount to an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception and reciting only the idea of a solution or outcome, i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is unclear how the “computer” is used nor the specification makes it clear how these actions are performed. Thus, these additional elements are recited in a manner that represent no more than mere instructions to apply the judicial exceptions on a computer. See MPEP § 2106.05(f) and § 2106.04(d).
“predefining a first data set that includes a plurality of first embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“predefining a second data set that includes a plurality of second embeddings” as drafted, amount to an additional element that amounts to insignificant extra-solution activity (includes both pre-solution and post-solution activity.) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
“training a first model on the first data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
“training a second model on the second data set”, as drafted, amount to insignificant extra solution activity, as merely appending a type of environment to the beginning of the abstract idea in the form of a machine learning model for performing the mental activities of data observation and judgement.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. The claims are not eligible subject matter.
Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole the independent claim limitations do not recite what have the courts have identified as “significantly more”.
Furthermore, regarding dependent claims 2-9 which are dependent on claim 1, the claims are directed to a judicial exception without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under Step 2A and 2B:
Claim 2 incorporates the rejection of independent claim 1 and all elements are part of the abstract idea as shown above. Additional limitation recited in dependent claim 2 does not integrate the judicial exception into a practical application.
Claim 3 incorporates the rejection of independent claim 1 and all elements are part of the abstract idea as shown above. Additional limitation recited in dependent claim 3 does not integrate the judicial exception into a practical application.
Claim 4 incorporates the rejection of independent claim 1 and all elements are part of the abstract idea as shown above. Additional limitation recited in dependent claim 4 does not integrate the judicial exception into a practical application.
Claim 5 incorporates the rejection of independent claim 1 and all elements are part of the abstract idea as shown above. Additional limitation recited in dependent claim 5 that does not integrate the judicial exception into a practical application and no additional element recognized as well understood, routine, and conventional."
Claim 7 incorporates the rejection of independent claim 1 and all elements are part of the abstract idea as shown above. A mathematical calculation, mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C).
Claim 8 incorporates the rejection of independent claim 1 and all elements are part of the abstract idea as shown above. A mathematical calculation, mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number”. See MPEP § 2106.04(a)(2)(I)(C).
Claim 9 incorporates the rejection of independent claim 1 and all elements are part of the abstract idea as shown above. Additional limitation recited in dependent claim 9 does not integrate the judicial exception into a practical application.
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.
Claims 1-5 and 8-11 are rejected under 35 U.S.C 103 as being unpalatable over WIERZYNSKI et al. (Pub. No.: 20170024641 A1), hereinafter referred to as WIERZYNSKI in view of Schatz et al. Pub No.: 20190019105 A1), hereinafter referred to as Schatz and further in view of Haim et al. (US Patent No. 11,308,077 B2), hereinafter referred to as Haim.
With respect to claim 1, WIERZYNSKI disclose:
A computer-implemented method for determining a similarity of data sets, comprising the following steps: predefining a first data set that includes a plurality of first embeddings (In paragraph [0067], WIERZYNSKI discloses a first training set D; The first training set D includes data x and a corresponding y. The data of the training set may be referred to as an image.)
predefining a second data set that includes a plurality of second embeddings (In paragraph [0072], WIERZYNSKI discloses the second training set D', the second training set D' includes the second data x′.sub.i and the second label y. The data of the training set may be referred to as an image.)
training a first model on the first data set (In Fig.6 and paragraph [0080], WIERZYNSKI discloses training a first neural network using the first training set.)
training a second model on the second data set (In Fig.6 and paragraph [0082], WIERZYNSKI discloses training a second neural network on the second data and second labels.)
With respect to claim 1, WIERZYNSKI does not explicitly discloses:
Determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model
Determining a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model
Determining a map that optimally maps the set of first features onto the set of second features
Determining a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
However, it is known by Schatz to disclose:
Determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model (In paragraph [0047], Schatz discloses determining features/embeddings from another embedding space. Training a machine learning model to translate embeddings from one embedding space (second type) into another embedding space (first type).)
Determining a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model (In paragraph [0045], Schatz discloses that embeddings of the second type can be created from the sparse features. At block 404, one or more pooled embeddings of the second type can be created from the embeddings of the second type. )
WIERZYNSKI and Schatz are analogous pieces of art because both references concern training a machine learning model, improving systems and methods. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify and transfer learning in the neural network as taught by WIERZYNSKI with a training set of embeddings of a first type and a training set of embeddings of a second type as taught by Schatz. The motivation for doing so would have been to improve systems and methods for transferring learning into neural networks (See [0003] of WIERZYNSKI.)
With respect to claim 1, WIERZYNSKI in view of Schatz do not explicitly disclose:
Determining a map that optimally maps the set of first features onto the set of second features
Determining a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
However, it is known by Haim to disclose:
Determining a map that optimally maps the set of first features onto the set of second features (In Col. 11, lines 38–64, Haim discloses an overall similarity score between at least two datasets. The column similarity scores for each source-target dataset pair are combined to find an overall similarity score between each source-target dataset pair. (Furthermore, WIERZYNSKI discloses the first and second images.))
Determining a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set (In Col. 14, lines 39-63, Haim discloses training a classification model for the target domain via a transfer learning process that utilizes the at least one source dataset that is most similar to the target dataset. The system determines which features are most important for telling the at least two datasets apart, finding the maximal similarity score for each source-target dataset pair. Performing feature selection includes identifying the remaining columns within the source-target dataset pair as the selected features.)
WIERZYNSKI in view of Schatz and Haim are analogous pieces of art because both references concern training a machine learning model, improving systems and methods. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, identifying one or more source datasets that are similar to a target dataset as taught by Haim. The motivation for doing so would have been to optimize the process involving performing several steps for each source-target dataset pair (See (Col. 10, lines 1-5) of Haim).
Regarding claim 2, WIERZYNSKI in view of Schatz and Haim discloses elements of claim 1. In addition, WIERZYNSKI disclose:
The method as recited in claim 1, wherein each first embedding of the plurality of first embeddings represents a digital image from a plurality of first digital images, each second embedding of the plurality of second embeddings represents a digital image from a plurality of second digital images (In paragraph [0067], WIERZYNSKI discloses a first training set D; The first training set D includes data x and a corresponding y. The data of the training set may be referred to as an image. In paragraph [0072], WIERZYNSKI discloses the second training set D', the second training set D' includes the second data x′.sub.i and the second label y. The data of the training set may be referred to as an image.)
Regarding claim 3, WIERZYNSKI in view of Schatz and Haim discloses elements of claim 1. In addition, Schatz disclose:
The method as recited in claim 1, wherein each first embedding of the plurality of first embeddings represents a portion of a first corpus, and each second embedding of the plurality of second embeddings represents a portion of a second corpus (In paragraph [0047], Schatz discloses a training set of first-type embeddings and a training set of second-type embeddings. The output first-type embedding corresponds to the input second-type embedding.)
Regarding claim 4, WIERZYNSKI in view of Schatz and Haim discloses elements of claim 1. In addition, WIERZYNSKI disclose:
The method as recited in claim 1, wherein the first model includes an artificial neural network with an input layer and an output layer, for each second embedding situated at the input layer of the first model, a last layer prior to the output layer, between the input layer and the output layer, being determined that characterizes a feature associated with the second embedding, and/or the second model includes an artificial neural network with an input layer and an output layer, for each second embedding situated at the input layer of the second model, a last layer prior to the output layer, between the input layer and the output layer, being determined that characterizes a feature associated with the second embedding (The examiner selects the first portion: In paragraph [0033-0034], WIERZYNSKI discloses the input/output of a first layer of the NN, in which the output of a first layer of neurons becomes an input to a second layer of neurons, the output of a second layer of the NN becomes and input to a third layer of neurons, and so on.)
Regarding claim 5, WIERZYNSKI in view of Schatz and Haim discloses elements of claim 1. In addition, WIERZYNSKI disclose:
The method as recited in claim 4, wherein the artificial neural networks have the same architecture of an architecture of a classifier, or have layers whose output characterizes the features have the same dimensions (The Examiner selects the first portion: In paragraph [0067-0070], WIERZYNSKI discloses a first neural network and a subsequent neural network in a transfer-learning framework. The subsequent model is derived from or based on the first network.)
Regarding claim 8, WIERZYNSKI in view of Schatz and Haim discloses elements of claim 1. In addition, WIERZYNSKI disclose:
The method as recited in claim 1, wherein the similarity is determined as a function of a norm of the distance of the map from the reference (In paragraph [0054], WIERZYNSKI discloses a normalization layer (Norm), and a pooling layer. The normalization layer may be used to normalize the output of the convolution filters. For example, the normalization layer may provide whitening or lateral inhibition. The pooling layer may provide down sampling aggregation over space for local invariance and dimensionality reduction.)
Regarding claim 9, WIERZYNSKI in view of Schatz and Haim discloses elements of claim 1. In addition, WIERZYNSKI disclose:
The method as recited in claim 1, wherein the second model is trained or becomes trained for a classification of embeddings, at least one embedding of a digital image or of a portion of a corpus being detected or received, and the embedding being classified by the second model (In Fig.6 and paragraph [0082], WIERZYNSKI discloses training a second neural network on the second data and second labels.
With respect to claim 10, WIERZYNSKI disclose:
A device configured to determine a similarity of digital data sets, the device configured to: predefine a first data set that includes a plurality of first embeddings (In paragraph [0067], WIERZYNSKI discloses a first training set D; The first training set D includes data x and a corresponding y. The data of the training set may be referred to as an image.)
predefine a second data set that includes a plurality of second embeddings (In paragraph [0072], WIERZYNSKI discloses the second training set D', the second training set D' includes the second data x′.sub.i and the second label y. The data of the training set may be referred to as an image.)
train a first model on the first data set (In Fig.6 and paragraph [0080], WIERZYNSKI discloses training a first neural network using the first training set.)
train a second model on the second data set (In Fig.6 and paragraph [0082], WIERZYNSKI discloses training a second neural network on the second data and second labels.)
With respect to claim 10, WIERZYNSKI does not explicitly discloses:
Determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model
Determine a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model
Determine a map that optimally maps the set of first features onto the set of second features
Determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
However, it is known by Schatz to disclose:
Determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model (In paragraph [0047], Schatz discloses determining features/embeddings from another embedding space. Training a machine learning model to translate embeddings from one embedding space (second type) into another embedding space (first type).)
Determine a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model (In paragraph [0045], Schatz discloses that embeddings of the second type can be created from the sparse features. At block 404, one or more pooled embeddings of the second type can be created from the embeddings of the second type. )
WIERZYNSKI and Schatz are analogous pieces of art because both references concern training a machine learning model, improving systems and methods. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify and transfer learning in the neural network as taught by WIERZYNSKI with a training set of embeddings of a first type and a training set of embeddings of a second type as taught by Schatz. The motivation for doing so would have been to improve systems and methods for transferring learning into neural networks (See [0003] of WIERZYNSKI.)
With respect to claim 10, WIERZYNSKI in view of Schatz do not explicitly disclose:
Determine a map that optimally maps the set of first features onto the set of second features
Determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
However, it is known by Haim to disclose:
Determine a map that optimally maps the set of first features onto the set of second features (In Col. 11, lines 38–64, Haim discloses an overall similarity score between at least two datasets. The column similarity scores for each source-target dataset pair are combined to find an overall similarity score between each source-target dataset pair. (Furthermore, WIERZYNSKI discloses the first and second images.))
Determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set (In Col. 14, lines 39-63, Haim discloses training a classification model for the target domain via a transfer learning process that utilizes the at least one source dataset that is most similar to the target dataset. The system determines which features are most important for telling the at least two datasets apart, finding the maximal similarity score for each source-target dataset pair. Performing feature selection includes identifying the remaining columns within the source-target dataset pair as the selected features.)
WIERZYNSKI in view of Schatz and Haim are analogous pieces of art because both references concern training a machine learning model, improving systems and methods. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, identifying one or more source datasets that are similar to a target dataset as taught by Haim. The motivation for doing so would have been to optimize the process involving performing several steps for each source-target dataset pair (See (Col. 10, lines 1-5) of Haim).
With respect to claim 11, WIERZYNSKI disclose:
A non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for determining a similarity of digital data sets, the instructions, when executed by a computer, causing the computer to perform the following steps: predefining a first data set that includes a plurality of first embeddings (In paragraph [0067], WIERZYNSKI discloses a first training set D; The first training set D includes data x and a corresponding y. The data of the training set may be referred to as an image.)
predefining a second data set that includes a plurality of second embeddings (In paragraph [0072], WIERZYNSKI discloses the second training set D', the second training set D' includes the second data x′.sub.i and the second label y. The data of the training set may be referred to as an image.)
training a first model on the first data set (In Fig.6 and paragraph [0080], WIERZYNSKI discloses training a first neural network using the first training set.)
training a second model on the second data set (In Fig.6 and paragraph [0082], WIERZYNSKI discloses training a second neural network on the second data and second labels.)
With respect to claim 11, WIERZYNSKI does not explicitly discloses:
Determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model
Determine a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model
Determine a map that optimally maps the set of first features onto the set of second features
Determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
However, it is known by Schatz to disclose:
Determining a set of first features of the first model on the second data set, which for each of the second embeddings, includes a feature of the first model (In paragraph [0047], Schatz discloses determining features/embeddings from another embedding space. Training a machine learning model to translate embeddings from one embedding space (second type) into another embedding space (first type).)
Determine a set of second features of the second model on the second data set, which for each of the second embeddings includes a feature of the second model (In paragraph [0045], Schatz discloses that embeddings of the second type can be created from the sparse features. At block 404, one or more pooled embeddings of the second type can be created from the embeddings of the second type. )
WIERZYNSKI and Schatz are analogous pieces of art because both references concern training a machine learning model, improving systems and methods. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify and transfer learning in the neural network as taught by WIERZYNSKI with a training set of embeddings of a first type and a training set of embeddings of a second type as taught by Schatz. The motivation for doing so would have been to improve systems and methods for transferring learning into neural networks (See [0003] of WIERZYNSKI.)
With respect to claim 11, WIERZYNSKI in view of Schatz do not explicitly disclose:
Determine a map that optimally maps the set of first features onto the set of second features
Determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set
However, it is known by Haim to disclose:
Determine a map that optimally maps the set of first features onto the set of second features (In Col. 11, lines 38–64, Haim discloses an overall similarity score between at least two datasets. The column similarity scores for each source-target dataset pair are combined to find an overall similarity score between each source-target dataset pair. (Furthermore, WIERZYNSKI discloses the first and second images.))
Determine a similarity as a function of a distance of the map from a reference, wherein a training data set is determined that includes the first data set or a portion of the first data set, when the similarity of the first data set to the second data set is greater than a similarity of a third data set to the second data set, and otherwise the training data set is determined as a function of the third data set, and wherein, in a training, the second model is pretrained with data of the training data set and then being trained with data of the second data set (In Col. 14, lines 39-63, Haim discloses training a classification model for the target domain via a transfer learning process that utilizes the at least one source dataset that is most similar to the target dataset. The system determines which features are most important for telling the at least two datasets apart, finding the maximal similarity score for each source-target dataset pair. Performing feature selection includes identifying the remaining columns within the source-target dataset pair as the selected features.)
WIERZYNSKI in view of Schatz and Haim are analogous pieces of art because both references concern training a machine learning model, improving systems and methods. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, identifying one or more source datasets that are similar to a target dataset as taught by Haim. The motivation for doing so would have been to optimize the process involving performing several steps for each source-target dataset pair (See (Col. 10, lines 1-5) of Haim).
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered, but are not persuasive.
Pertaining to rejection under 101
The applicant’s remarks/arguments on Pages 6 and 7 emphasize the improvement of the “second model being pretrained with data from the training data and then being trained with data from the second data set.” However, the Examiner respectfully believes nowhere in the claims recites an improvement to the second model. The applicant further recites [0031] explaining that “better performance is achieved”, despite that it does not expressly require that the second model itself be improved or achieve any particular performance gain. Paragraph [0031] does not disclose how or how they are training the second model only with second data 102. Therefore, examiner's 101 rejections is maintained, and further 101 analysis have been written above.
Pertaining to rejection under 103
Thus, the applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights