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
Application 19/281,922 filed on 7/28/2025 has been examined.
In this Office Action, Claims 1-20 are pending.
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.
Initially, Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because independent claim(s) 1 does not recite statutory computer hardware/processors (only generic methods without even generically described computer processing elements, like a “computer-implemented method”, for example) without limitation and thus the claim(s) is/are directed to a signal per se and/or mere information in the form of data, and dependent claims 2-7 do not correct this deficiency. See generally guidance on the New Form Paragraphs for Subject Matter Eligibility Rejections under the 2019 Revised Patent Subject Matter Eligibility Guidance (1 7.05.01 Rejection, 35 U.S.C. 101, Nonstatutory (Not One of the Four Statutory Categories); Available via: https://www.uspto.gov/sites/default/files/documents/form_para_for_2019peg_20190108.pdf
Additionally, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites: (Step 2a, Prong One)
utilizing and entity deduplication model to detect and remove duplicate entities from a
database.
The limitation of utilizing and entity deduplication model to detect and remove duplicate entities from a database, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic method/model, and a generic “machine learning process” nothing in the claim element precludes the step from practically being
performed in the mind. For example, but for the model/machine learning process language, “utilizing” in the context of this claim encompasses the user manually determining generic “duplicate entities” using generic “detect and remove” steps. Additionally, note the recent
and relevant decision Recentive Analytics, Inc. v. Fox Corp. (CAFC Case: 23-2437) explaining
that “we hold only that patents that do no more than claim the application of generic
machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”). Similarly, the limitation(s) of evaluating; inputting; measuring; and modifying, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the method/model/machine learning process language, evaluating; inputting; measuring; and modifying in the context of this claim encompasses the user manually receiving generic “entities” and performing generic “evaluating”/”measuring”/”modifying” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic generating of utilizing of a generic model to detect and remove duplicates
is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a method and a generic “machine learning process” to perform both the evaluating; inputting; measuring; and modifying; and utilizing steps. The method and a generic “machine learning process” in both steps is recited at a high level of generality (i.e., as a generic method performing a generic computer function of “utilizing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a method and a machine learning process to perform both the evaluating; inputting; measuring; and modifying; and utilizing steps
amounts to no more than mere instructions to apply the exception using a generic computer
component. Mere instructions to apply an exception using a generic computer component
cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 2, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and
inputting the error value into the machine learning process for matching with corresponding
entity feature vectors for the pair of entities”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and inputting the error value into the machine learning process for matching with corresponding entity feature vectors for the pair of entities” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract
idea into a practical application because it does not impose any meaningful limits on practicing
the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and inputting the error value into the machine learning process for matching with corresponding entity feature vectors for the pair of entities” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 3, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract
idea into a practical application because it does not impose any meaningful limits on practicing
the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 4, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract
idea into a practical application because it does not impose any meaningful limits on practicing
the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 5, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through
deduplication of entities represented by objects within the CRM database”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through deduplication of entities represented by objects within the CRM database” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract
idea into a practical application because it does not impose any meaningful limits on practicing
the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through deduplication of entities represented by objects within the CRM database” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 6, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “executing, by a machine learning system hosting the machine learning process, a task using a model”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “executing, by a machine learning system hosting the machine learning process, a task using a model” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “executing, by a machine learning system hosting the machine learning process, a task using a model” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 7, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the task includes at least one of classifying events, classifying
entities, classifying relationships, scoring potential recipients of messages, or generating text”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the task includes at least one of classifying events, classifying entities, classifying relationships, scoring potential recipients of messages, or generating text” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the task includes at least one of classifying events, classifying entities, classifying relationships, scoring potential recipients of messages, or generating text” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim 8 recites: (Step 2a, Prong One)
utilizing and entity deduplication model to detect and remove duplicate entities from a
database.
The limitation of utilizing and entity deduplication model to detect and remove duplicate entities from a database, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic processor/memory, and a generic “machine learning process” nothing in the claim element precludes the step from practically being
performed in the mind. For example, but for the processor/memory/machine learning process language, “utilizing” in the context of this claim encompasses the user manually determining generic “duplicate entities” using generic “detect and remove” steps. Additionally, note the recent and relevant decision Recentive Analytics, Inc. v. Fox Corp. (CAFC Case: 23-2437) explaining that “we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”). Similarly, the limitation(s) of evaluating; inputting; measuring; and modifying, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the processor/memory/machine learning process language, evaluating; inputting; measuring; and modifying in the context of this claim encompasses the user manually receiving generic “entities” and performing generic “evaluating”/”measuring”/”modifying” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic generating of utilizing of a generic model to detect and remove duplicates
is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor/memory and a generic “machine learning process” to perform both the evaluating; inputting; measuring; and modifying; and utilizing steps. The method and a generic “machine learning process” in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “utilizing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a processor/memory and a machine learning process to perform both the evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 9, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and inputting the error value into the machine learning process for matching with corresponding
entity feature vectors for the pair of entities”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and inputting the error value into the machine learning process for matching with corresponding entity feature vectors for the pair of entities” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and inputting the error value into the machine learning process for matching with corresponding entity feature vectors for the pair of entities” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 10, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 11, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 12, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through
deduplication of entities represented by objects within the CRM database”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through deduplication of entities represented by objects within the CRM database” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through deduplication of entities represented by objects within the CRM database” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 13, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “executing, by a machine learning system hosting the machine learning process, a task using a model”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “executing, by a machine learning system hosting the machine learning process, a task using a model” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “executing, by a machine learning system hosting the machine learning process, a task using a model” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 14, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein the task includes at least one of classifying events, classifying
entities, classifying relationships, scoring potential recipients of messages, or generating text”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “wherein the task includes at least one of classifying events, classifying entities, classifying relationships, scoring potential recipients of messages, or generating text” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “wherein the task includes at least one of classifying events, classifying entities, classifying relationships, scoring potential recipients of messages, or generating text” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim 15 recites: (Step 2a, Prong One)
utilizing and entity deduplication model to detect and remove duplicate entities from a
database.
The limitation of utilizing and entity deduplication model to detect and remove duplicate entities from a database, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a generic processor/medium, and a generic “machine learning process” nothing in the claim element precludes the step from practically being
performed in the mind. For example, but for the processor/medium/machine learning process language, “utilizing” in the context of this claim encompasses the user manually determining generic “duplicate entities” using generic “detect and remove” steps. Additionally, note the recent and relevant decision Recentive Analytics, Inc. v. Fox Corp. (CAFC Case: 23-2437) explaining that “we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”). Similarly, the limitation(s) of evaluating; inputting; measuring; and modifying, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the processor/medium/machine learning process language, evaluating; inputting; measuring; and modifying in the context of this claim encompasses the user manually receiving generic “entities” and performing generic “evaluating”/”measuring”/”modifying” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic generating of utilizing of a generic model to detect and remove duplicates
is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor/medium and a generic “machine learning process” to perform both the evaluating; inputting; measuring; and modifying; and utilizing steps. The method and a generic “machine learning process” in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “utilizing”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a processor/medium and a machine learning process to perform both the evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 16, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and
inputting the error value into the machine learning process for matching with corresponding
entity feature vectors for the pair of entities”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and inputting the error value into the machine learning process for matching with corresponding entity feature vectors for the pair of entities” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “generating, by a training error determination module, an error value for the pair of entities by processing a duplicate likelihood value with the duplicate entity indication; and inputting the error value into the machine learning process for matching with corresponding entity feature vectors for the pair of entities” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 17, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “training, by the machine learning process, an artificial intelligence system using the pair of entities and the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 18, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “adjusting, by the machine learning process, weights of a neural network of the artificial intelligence system to minimize the error value” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 19, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through
deduplication of entities represented by objects within the CRM database”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through deduplication of entities represented by objects within the CRM database” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “adjusting, by the machine learning process, weights of the entity deduplication model implemented by the artificial intelligence system for facilitating entity resolution through deduplication of entities represented by objects within the CRM database” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 20, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “executing, by a machine learning system hosting the machine learning process, a task using a model”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “executing, by a machine learning system hosting the machine learning process, a task using a model” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “executing, by a machine learning system hosting the machine learning process, a task using a model” steps to perform both the aforementioned evaluating; inputting; measuring; and modifying; and utilizing steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6, 8-10, 13, 15-17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al., US Pub. No. 2021/0173825 A1, in view of Zeng et al., US Pub. No.: US 2020/0210771 A1.
As to claim 1 (and substantially similar claim 8 and claim 15),
Lu discloses a method,
(Lu [0010,0071,0078])
comprising:
evaluating, by a merge evaluator, a pair of entities including a first entity represented by a
first object and a second entity represented by a second object within a customer relationship
management (CRM) database to generate a duplicate entity indication reflecting a duplicate entity status for the pair of entities;
(Lu teaches identify additional pairs of entities in the online system as
Duplicates using scores/predictions, i.e. “evaluating” “a pair of entities” in order to ”generate a duplicate entity indication reflecting a duplicate entity status for the pair of entities” [0015] The trained machine learning model is then used to identify additional pairs of entities in the online system as duplicates and update content outputted by the online system accordingly. For example, the trained machine learning model is applied to remaining entity pairs not included in the training data, and a subset of entity pairs with scores from the machine learning model that exceed a threshold are identified as duplicates;
see also [0048] Identification apparatus 208 and/or another component train machine
learning model 210 to predict positive user-generated labels 232 indicating that a pair of entities contains duplicates 236 and negative user-generated labels 232 indicating that a pair
of entities does not contain duplicates 236, given the corresponding similarity features 220 and/or relationship features 222. In tum, scores 226 outputted by machine learning model 210 from features for entity pairs 242 include values between 0 and 1, with each value representing the probability that a corresponding pair of entities contains duplicates 236.)
inputting the duplicate entity indication into a machine learning process to use the duplicate
entity status as a label for the pair of entities;
(Lu teaches labeling apparatus that generates labels in training data based on annotations/assessments and applying a trained machine learning model to similarity features, i.e. “inputting the duplicate entity indication into a machine learning process to use the duplicate
entity status as a label for the pair of entities”
See [0049-0050] [0049] Next, identification apparatus 208 generates training data 214 for machine learning model 212 based on scores 226 outputted by machine learning model 210. In one or more embodiments, identification apparatus 208 uses stratified sampling of scores 226 to select a subset of entity pairs 242 for inclusion in training data 214.; [0050] After a subset of entity pairs 242 are sampled for inclusion in training data 214, labeling apparatus 206 generates
labels 232 in training data 214 based on user annotations 230 and assessments 234 of the accuracy of annotations 230.
See also Lu teaches feature processing for deduplicating entities with vector similarities/embeddings, i.e. duplicate entity indication into a machine learning process see [0053] [0053] Identification apparatus 208 then applies the trained machine learning model 212 to similarity features 220 and/or relationship features 222 for remaining entity pairs 242 identified by selection apparatus 224 to generate scores 228 that classify each candidate pair 242 as containing or not containing duplicates 236.)
and
utilizing the entity deduplication model to detect and remove duplicate entities from the
CRM database
(Lu [0010] For example, duplicates can be found in records or pages for companies in an online network. The duplicates can then be resolved to improve the integrity and quality of data presented to users in the online network.; see also [0015] In addition, duplicate entities that are not selected or used as canonical entities are suppressed, de-ranked, and/or removed from the online system.)
Lu does not disclose:
measuring, by machine learning process, an accuracy of an entity deduplication model using
the duplicate entity indication as a control against which the accuracy of the entity deduplication
model is measured;
modifying the entity deduplication model based upon the accuracy;
However, Zeng discloses:
measuring, by machine learning process, an accuracy of an entity deduplication model using
the duplicate entity indication as a control against which the accuracy of the entity deduplication
model is measured;
(Zeng teaches the prediction model is evaluated based on accuracy and effectiveness and may be automatically retrained using labels/set aside training data, i.e. “using
the duplicate entity indication as a control against which the accuracy of the entity deduplication
model is measured”
see [0010] Labels for the training data are extracted from a database system that tracks the statuses of already identified entities. The prediction model is evaluated based on accuracy and effectiveness and may be automatically retrained.;
see also [0052] In an embodiment, a portion of the training data is set aside to validate prediction model 120 that was trained based on another portion of the training data. Validation
involves determining the accuracy the trained prediction model 120.;
see also [0062] In a related embodiment, a model condition dashboard is created to
monitor accuracy of prediction model 120 in terms of recall and/or precision. Recall and precision may be calculated for training instances ( representing actual accounts) that are
automatically generated after prediction model 120 is deployed)
modifying the entity deduplication model based upon the accuracy; and
(Zeng teaches automatic retraining of the model if an accuracy threshold is not met, i.e. “modifying the entity deduplication model based upon the accuracy”
See [0062-0069] An alarm or notification may cause prediction model 120 to be manually analyzed or automatically retrained. In a related embodiment, a model condition dashboard is created to monitor accuracy of prediction model 120 in terms of recall and/or precision.
[0069] If a generated value that is based on performance of prediction model 120
is less than a corresponding threshold value, then an alarm or notification is generated, notifying an administrator that performance of prediction model 120 needs to be closely monitored manually. Additionally or alternatively, if one or more of these threshold values is not met, then an automatic retraining of prediction model 120 is triggered.
see also [0082] ML system 100 analyzes the records over time to determine the accuracy and/or effectiveness of the prediction model. Additionally, ML system 100 may "update" the
prediction model by generating training instances based on a subset of the records and training a new prediction based on the training instances;
see also [0019] An example of transaction management system (TMS) 150 is a customer relationship management (CRM) database. TMS 150 and the associated software may be
developed in-house; that is, by the organization that owns or accesses TMS 150. Alternatively, TMS database 150 and its associated software may be provided by a third party, such
as Microsoft Dynamics, Salesforce, SugarCRM, NetSuite, Insightly, and Zoho.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply accuracy determination for deduplication, as taught by Zeng, to the system of Lu, since it was known in the art that deduplication systems provide a machine learning framework to identify high quality entities and track downstream entity conversion are provided where a machine-learned prediction model is trained based on entity data and sub-entity data for the features where labels for the training data are extracted from a database system that tracks the statuses of already identified entities where the prediction model is evaluated based on accuracy and effectiveness and may be automatically retrained and where this provides numerous benefits, such as the machine learning framework being easily applied to multiple units of an enterprise, the dynamic nature of the prediction model, the flexibility to adapt to real-world changes, the ability to expand to a standalone system to assist multiple third-party providers identify high quality entities for their respective enterprise units, and the ability to evaluate the effectiveness of the prediction model. (Zeng [0010-0011]).
As to claim 2, Lu as modified discloses the method of claim 1, comprising:
generating, by a training error determination module, an error value for the pair of entities by
processing a duplicate likelihood value with the duplicate entity indication;
(Lu teaches confidence scores/duplicate likelihoods, i.e. “processing a duplicate likelihood value with the duplicate entity indication” see abstract: “During operation, the system selects training
data for a first machine learning model based on confidence scores representing likelihoods that pairs of entities in an online system are duplicates.”; see also [0012] features associated with each pair of entities are inputted into a first machine learning model to generate a confidence score representing the likelihood that the pair of entities includes duplicates of a single entity.)
and
inputting the error value into the machine learning process for matching with corresponding
entity feature vectors for the pair of entities
(Lu teaches selecting training data for a first machine learning model based on confidence scores, as well as vector/cosine similarity matching, i.e. “inputting the error value into the machine learning process for matching with corresponding entity feature vectors for the pair of entities” see [0073] The selection apparatus and/or identification apparatus select training data for a first machine learning model based on confidence scores from a second machine learning model, which represent likelihoods that pairs of entities in an online system are duplicates
See also [0044] Next, a feature-processing apparatus 204 uses data 202 from event streams 200 and/or data repository 134 to calculate sets of features for entity pairs 242 identified by
selection apparatus 224. First, feature-processing apparatus 204 generates similarity features 220… These similarity features 220 include, but are not limited to,… and/or cosine similarities, Euclidean distances, and/or other measures of vector similarity between semantic embeddings associated with industries or other attributes that describe the entities).
As to claim 3, Lu as modified discloses the method of claim 2, comprising:
training, by the machine learning process, an artificial intelligence system using the pair of
entities and the error value
(Lu teaches selecting training data for a first machine learning model based on confidence scores between a pair of entities, as well as vector/cosine similarity matching, i.e. “training, by the machine learning process, an artificial intelligence system using the pair of
entities and the error value” see [0073] The selection apparatus and/or identification apparatus select training data for a first machine learning model based on confidence scores from a second machine learning model, which represent likelihoods that pairs of entities in an online system are duplicates).
As to claim 6, Lu as modified discloses the method of claim 1, comprising:
executing, by a machine learning system hosting the machine learning process, a task using a model
(Lu teaches an identification apparatus applies one or more machine learning models to predict labels and suppresses, de-ranks, and/or removes duplicate entities, i.e. “executing, by a machine learning system hosting the machine learning process, a task using a model”,
See [0014] The machine learning model includes a decision tree, random forest, deep learning model, wide-and-deep model, and/or another type of model that is trained to predict
the labels, given features for the corresponding pair of entities.;
see also [0048] Identification apparatus 208 and/or another component train machine
learning model 210 to predict positive user-generated labels 232 indicating that a pair of entities contains duplicates 236 and negative user-generated labels 232 indicating that a pair
of entities does not contain duplicates 236, given the corresponding similarity features 220 and/or relationship features 222.;
see also [0054] Identification apparatus 208 also, or instead, suppresses, de-ranks, and/or removes duplicate entities that are not selected or used as canonical entities from data repository 134 and/or the online system.;
See also [0047] An identification apparatus 208 applies one or more machine learning models 210-212 to similarity features 220 and/or relationship features 222 for some or all
entity pairs 242 to identify a subset of entity pairs 242 as duplicates 236 of one another. As shown in FIG. 2, identification apparatus 208 applies machine learning model 210 to some or all similarity features 220 and/or relationship features 222 for entity pairs 242 to generate one set of scores 226 representing likelihoods that entity pairs 242 are duplicates 236.).
Referring to claim 9, this dependent claim recites similar limitations as claim 2;
therefore, the arguments above regarding claim 2 are also applicable to claim 9.
Referring to claim 10, this dependent claim recites similar limitations as claim 3;
therefore, the arguments above regarding claim 3 are also applicable to claim 10.
Referring to claim 13, this dependent claim recites similar limitations as claim 6;
therefore, the arguments above regarding claim 6 are also applicable to claim 13.
Referring to claim 16, this dependent claim recites similar limitations as claim 2;
therefore, the arguments above regarding claim 2 are also applicable to claim 16.
Referring to claim 17, this dependent claim recites similar limitations as claim 3;
therefore, the arguments above regarding claim 3 are also applicable to claim 17.
Referring to claim 20, this dependent claim recites similar limitations as claim 6;
therefore, the arguments above regarding claim 6 are also applicable to claim 20.
Claim(s) 4-5, 7, 11-12, 14, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al., US Pub. No. 2021/0173825 A1, in view of Zeng et al., US Pub. No.: US 2020/0210771 A1, in view of Polaczuk et al., US Pub. No. 2024/0211496 A1.
As to claim 4, Lu/Zeng do not disclose:
adjusting, by the machine learning process, weights of a neural network of the artificial
intelligence system to minimize the error value;
However, Polaczuk discloses:
the method of claim 3, comprising:
adjusting, by the machine learning process, weights of a neural network of the artificial
intelligence system to minimize the error value
(Polaczuk teaches update/optimizing weights of the links defined in the RNN for improving the performance and accuracy of the learning process and the performance of the trained RNNs see [0141-0142] [0141] In some embodiments, the RNNs of the entity attribute prediction module 116 may be trained using a stochastic gradient descent optimization algorithm. Stochastic gradient descent optimization comprises first the estimation
of the loss on one or more training examples, then the calculation of the derivative of the loss (gradient), which is propagated backward through the RNN to update weights of the links defined in the RNN. Weights are updated using a fraction of the back propagated error controlled by a defined learning rate. Meaningful values of the gradients through several layers of the RNN allows training of an effective or optimum RNN.; [0142] In order to improve the performance and accuracy of the learning process and the performance of the trained RNNs, in some embodiments, a gradient clipping technique is performed. Gradient clipping comprises limiting the gradient values to a specific minimum or maximum value if the gradient exceeds an expected range. The maximum gradient value may be defined as a maximum L2 norm of a vector of the weights comprised in the RNN. An L2 norm of a vector is calculated as the square root of the sum of the squared vector values. In some embodiments, the maximum L2 norm
of a vector of the weights comprised in the RNN may be set value in the range of 4 to 5, for example. Incorporating gradient clipping during the training process allows the RNNs comprised in the entity attribute prediction module 116 to learn from longer sequences of entity information 130 that serves as an input to the entity attribute prediction module 116. Gradient clipping thereby enables the entity attribute prediction module 116 to learn from larger amounts
of information while improving the accuracy of the output of the entity attribute prediction module 116.;
see also [0139-0140] [0139] During each iteration, the value of a loss function for the respective task is calculated and based on the value of the loss function, the weights of the neural networks of the multi-task machine learning model are adjusted, using a gradient-descent algorithm, for example. [0140] In some embodiments, the one or more ANNs of the entity attribute prediction module 116 may be Recurrent Neural Networks (RNNs). RNNs are neural networks that are structured to process sequential information or data.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply accuracy determination for deduplication, as taught by Polaczuk, to the system of Lu/Zeng, since it was known in the art that deduplication systems provide for a training process for the multi-task machine learning model of the entity attribute prediction module may be specifically managed to improve the accuracy of the predictions by the entity attribute prediction module where for training of the multi-task machine learning models, the input remains the same (i.e. entity information) but the output depends on the task at hand, where the training process may be equally spaced across the more than one task, where the equal spacing may comprise separating the training data set into separate batches for each task, and performing a training iteration for a first task, followed by a second task, followed by a third task, until an iteration is run for the final task where during each iteration, the value of a loss function for the respective task is calculated and based on the value of the loss function, the weights of the neural networks of the multi-task machine learning model are adjusted, using a gradient-descent algorithm, for example. (Polaczuk [0139]).
As to claim 5, Polaczuk as modified discloses the method of claim 3, comprising:
adjusting, by the machine learning process, weights of the entity deduplication model
implemented by the artificial intelligence system for facilitating entity resolution through
deduplication of entities represented by objects within the CRM database
(Polaczuk teaches update/optimizing weights of the links defined in the RNN for improving the performance and accuracy of the learning process and the performance of the trained RNNs see [0141-0142] [0141] In some embodiments, the RNNs of the entity attribute prediction module 116 may be trained using a stochastic gradient descent optimization algorithm. Stochastic gradient descent optimization comprises first the estimation
of the loss on one or more training examples, then the calculation of the derivative of the loss (gradient), which is propagated backward through the RNN to update weights of the links defined in the RNN. Weights are updated using a fraction of the back propagated error controlled by a defined learning rate. Meaningful values of the gradients through several layers of the RNN allows training of an effective or optimum RNN.; [0142] In order to improve the performance and accuracy of the learning process and the performance of the trained RNNs, in some embodiments, a gradient clipping technique is performed. Gradient clipping comprises limiting the gradient values to a specific minimum or maximum value if the gradient exceeds an expected range. The maximum gradient value may be defined as a maximum L2 norm of a vector of the weights comprised in the RNN. An L2 norm of a vector is calculated as the square root of the sum of the squared vector values. In some embodiments, the maximum L2 norm
of a vector of the weights comprised in the RNN may be set value in the range of 4 to 5, for example. Incorporating gradient clipping during the training process allows the RNNs comprised in the entity attribute prediction module 116 to learn from longer sequences of entity information 130 that serves as an input to the entity attribute prediction module 116. Gradient clipping thereby enables the entity attribute prediction module 116 to learn from larger amounts
of information while improving the accuracy of the output of the entity attribute prediction module 116.;
see also [0139-0140] [0139] During each iteration, the value of a loss function for the respective task is calculated and based on the value of the loss function, the weights of the neural networks of the multi-task machine learning model are adjusted, using a gradient-descent algorithm, for example. [0140] In some embodiments, the one or more ANNs of the entity attribute prediction module 116 may be Recurrent Neural Networks (RNNs). RNNs are neural networks that are structured to process sequential information or data.;
see also [0164] Validation may also comprise deduplication of entity information 130.).
As to claim 7, Polaczuk as modified discloses the method of claim 6, wherein the task includes at least one of classifying events, classifying entities, classifying relationships, scoring potential recipients of messages, or generating text
(Polaczuk teaches an entity attribute prediction module for predicting an industry classification or a label for the entity, category of goods or services offered by the Entity, i.e. “classifying events, classifying entities, classifying relationships” see [0167] At 216, based on the numerical representation or model generated at 214, the entity attribute prediction module 116 may make predictions regarding an entity attribute. Examples of entity attributes may include: number of employees of the entity, an industry classification or a label for the entity, category of goods or services offered by the entity, one or more physical locations or addresses associated with the entity. The determined entity attributes may be useful in providing more directed or tailored services to the entity. For example, if the entity is deemed to have a large number of employees, then automated payroll services may be offered to the entity based on the predicted entity attribute. In some embodiments, 216 may involve predicting or
extracting names of employees and job titles of each employee of an entity based on the numerical representation or model of the entity.).
Referring to claim 11, this dependent claim recites similar limitations as claim 4;
therefore, the arguments above regarding claim 4 are also applicable to claim 11.
Referring to claim 12, this dependent claim recites similar limitations as claim 5;
therefore, the arguments above regarding claim 5 are also applicable to claim 12.
Referring to claim 14, this dependent claim recites similar limitations as claim 7;
therefore, the arguments above regarding claim 7 are also applicable to claim 14.
Referring to claim 18, this dependent claim recites similar limitations as claim 4;
therefore, the arguments above regarding claim 4 are also applicable to claim 18.
Referring to claim 19, this dependent claim recites similar limitations as claim 5;
therefore, the arguments above regarding claim 5 are also applicable to claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cassidy et al., US Pub. No. 2017/0308557A1, teaches a system for cleansing and de-duplicating data in database are provided. The method includes filtering garbage records from a plurality of records based on data fields, and applying cleansing rules to create a cleansed database. A similarity vector is generated, where each vector corresponds to pairwise comparison of distinct data entries in cleansed database. Matching rules are applied to label each vector as one of matched, unmatched and unclassified. The method analyzes the vectors labeled as matched and unmatched to train a machine learning model to identify duplicates in the cleansed database. Unclassified vectors in the cleansed database are labeled as matched or unmatched by applying machine learning model on unclassified vectors. Thereafter, the method processes all the vectors labeled as matched to create clusters of records that are duplicates of each other. Further, records in each cluster are merged to obtain de-duplicated cleansed database using predefined consolidated rules;
Chen et al., US Patent No. 11,514,321 B1, teaches improvements for entity record pairs are extracted from a selected cluster of entity records. Attribute value pairs are obtained from the entity record pairs. Labels are assigned to the attribute value pairs based at least in part on entity-level similarity scores of the entity records from which the attribute value pairs were obtained. A machine learning model is trained, using a data set which includes at least some attribute value pairs to which the labels are assigned, to generate attribute similarity scores for pairs of attribute values;
Dirac et al., US Pub. No. 2015/0379430, teaches a machine learning service, a determination is made that an analysis to detect whether at least a portion of contents of one or more observation records of a first data set are duplicated in a second set of observation records is to be performed. A duplication metric is obtained, indicative of a non-zero probability that one or more observation records of the second set are duplicates of respective observation records of the first set. In response to determining that the duplication metric meets a threshold criterion, one or more responsive actions are initiated, such as the transmission of a notification to a client of the service;
Erenrich et al. US Pub. No. 20180330280 A1, teaches methods are provided for selecting training examples to increase the efficiency of supervised active machine learning processes. Training examples for presentation to a user may be selected according to measure of the model's uncertainty in labeling the examples. A number of training examples may be selected to increase efficiency between the user and the processing system by selecting the number of training examples to minimize user downtime in the machine learning process.
Zhiyanov et al., US Patent. No. 11,625,555 B1, teaches improvements for respective labels are generated automatically for a plurality of record pairs, with a label for a given pair indicating a relationship detected between the records of the pair. One or more machine learning models are trained using the labeled record pairs. The trained versions of the models are stored.
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/Evan Aspinwall/Primary Examiner, Art Unit 2156
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