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 19/102,984 filed 2/11/2025 with preliminary amendments has been examined.
Claim 20 has been amended.
In this Office Action Claims 1-20 are currently pending.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in a 371 filing of PCT/CN2022/111830 with the filing date of 08/11/2022. It is noted, however, that applicant has not filed a certified copy of the PCT/CN2022/111830 application as required by 37 CFR 1.55.
Examiner’s Note:
Claims 7 and 18 recite: “parsing and pas-tag patterns;”;
However, the specification discloses:
“parsing and pos-tag patterns;” (see specification para. [0038]).
As a result, it appears the intended spelling of “pas-tag” limitations is actually “pos-tag” as spelled in the specification.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 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)
obtaining a topic for each entity cluster based on a co-occurring keyword list.
The limitation of obtaining a topic for each entity cluster based on a co-occurring keyword list, 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, nothing in the claim element precludes the step from
practically being performed in the mind. For example, but for the processor language,
“obtaining” in the context of this claim encompasses the user manually determining
generic “topics” using generic “keyword lists” and “co-occurrences” steps. Similarly, the limitation(s) of obtaining; conducting; extracting; generating; clustering and retrieving, 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 language, obtaining; conducting; extracting; generating; clustering and retrieving in the context of this claim encompasses the user manually receiving generic “text data” and “entity”/”keyword” lists and performing generic extracting/pre-processing and “clustering” 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 obtaining “topics” steps using generic “text data” using generic “entity”/”keyword” lists and performing generic extracting/pre-processing and “clustering” steps 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 with a memory to perform both the obtaining; conducting; extracting; generating; clustering and retrieving and obtaining steps. The processor with a memory in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “obtaining” topics) 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 with a memory to perform
both the obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “generate the entity embedding list with an embedding model based on the
entity list according to semantic meaning, wherein each member of the entity embedding list is an embedding vector in a k-dimension embedding space, and k is a positive integer”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generate the entity embedding list with an embedding model based on the entity list according to semantic meaning, wherein each member of the entity embedding list is an embedding vector in a k-dimension embedding space, and k is a positive integer” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “generate the entity embedding list with an embedding model based on the entity list according to semantic meaning, wherein each member of the entity embedding list is an embedding vector in a k-dimension embedding space, and k is a positive integer” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “wherein: the embedding model is fine-tuned by using a subset of the entity list as a training sample set”.
(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 embedding model is fine-tuned by using a subset of the entity list as a training sample set” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 embedding model is fine-tuned by using a subset of the entity list as a training sample set” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “generate a plurality of first entity clusters based on the entity embedding list
with a first layer clustering model and a first similarity; generate a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity; and combine a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generate a plurality of first entity clusters based on the entity embedding list with a first layer clustering model and a first similarity; generate a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity; and combine a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “generate a plurality of first entity clusters based on the entity embedding list with a first layer clustering model and a first similarity; generate a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity; and combine a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “wherein: the first layer clustering model and the second layer clustering model are based on a clustering algorithm of density-based spatial clustering of applications with noise (DB SCAN); and the first similarity is smaller than the second similarity”.
(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 first layer clustering model and the second layer clustering model are based on a clustering algorithm of density-based spatial clustering of applications with noise (DB SCAN); and the first similarity is smaller than the second similarity” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 first layer clustering model and the second layer clustering model are based on a clustering algorithm of density-based spatial clustering of applications with noise (DB SCAN); and the first similarity is smaller than the second similarity” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “wherein: the first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being
larger than a first threshold except the largest cluster; and the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold”.
(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 first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being larger than a first threshold except the largest cluster; and the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being larger than a first threshold except the largest cluster; and the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “construct a parsing-tree based on the pre-processed text data according to
pre-defined rules of parsing and pas-tag patterns; extract the entity list based on the parsing-tree, each entity in the entity list corresponding to a noun chunk; extract a verb list based on the parsing-tree; and merge the entity list and the verb list to obtain the keyword list”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “construct a parsing-tree based on the pre-processed text data according to pre-defined rules of parsing and pas-tag patterns; extract the entity list based on the parsing-tree, each entity in the entity list corresponding to a noun chunk; extract a verb list based on the parsing-tree; and merge the entity list and the verb list to obtain the keyword list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “construct a parsing-tree based on the pre-processed text data according to pre-defined rules of parsing and pas-tag patterns; extract the entity list based on the parsing-tree, each entity in the entity list corresponding to a noun chunk; extract a verb list based on the parsing-tree; and merge the entity list and the verb list to obtain the keyword list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 8, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “wherein each entity in the entity list is not in a stop-word list or a spam list”.
(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 each entity in the entity list is not in a stop-word list or a spam list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 each entity in the entity list is not in a stop-word list or a spam list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “wherein: the text data comprises at least one of the following: a survey, a ticket, a comment, or an online review”.
(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 text data comprises at least one of the following: a survey, a ticket, a comment, or an online review” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 text data comprises at least one of the following: a survey, a ticket, a comment, or an online review” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “remove anomaly text or formatted templates from the text data; process the text data according to a set of natural language processes (NLPs ); and in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translate the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “remove anomaly text or formatted templates from the text data; process the text data according to a set of natural language processes (NLPs ); and in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translate the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “remove anomaly text or formatted templates from the text data; process the text data according to a set of natural language processes (NLPs ); and in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translate the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “obtain a key sentence for each entity cluster of the plurality of entity clusters based on the co-occurring keyword list”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “obtain a key sentence for each entity cluster of the plurality of entity clusters based on the co-occurring keyword list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “obtain a key sentence for each entity cluster of the plurality of entity clusters based on the co-occurring keyword list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “provide, based on the obtained topic, at least one of the following: a feedback
to the text data, a sentiment analysis to the text data, a content tagging to the text data”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “provide, based on the obtained topic, at least one of the following: a feedback to the text data, a sentiment analysis to the text data, a content tagging to the text data” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “provide, based on the obtained topic, at least one of the following: a feedback to the text data, a sentiment analysis to the text data, a content tagging to the text data” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 13 recites: (Step 2a, Prong One)
obtaining a topic for each entity cluster based on a co-occurring keyword list.
The limitation of obtaining a topic for each entity cluster based on a co-occurring keyword list, 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, nothing in the claim element precludes the step from
practically being performed in the mind. For example, but for the processor language,
“obtaining” in the context of this claim encompasses the user manually determining
generic “topics” using generic “keyword lists” and “co-occurrences” steps. Similarly, the limitation(s) of obtaining; conducting; extracting; generating; clustering and retrieving, 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 language, obtaining; conducting; extracting; generating; clustering and retrieving in the context of this claim encompasses the user manually receiving generic “text data” and “entity”/”keyword” lists and performing generic extracting/pre-processing and “clustering” 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 obtaining “topics” steps using generic “text data” using generic “entity”/”keyword” lists and performing generic extracting/pre-processing and “clustering” steps 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 with a memory to perform both the obtaining; conducting; extracting; generating; clustering and retrieving and obtaining steps. The processor with a memory in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “obtaining” topics) 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 with a memory to perform
both the obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “generating the entity embedding list with an embedding model based on the
entity list according to semantic meaning, wherein each member of the entity embedding list is an embedding vector in a k-dimension embedding space, and k is a positive integer”.
(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 the entity embedding list with an embedding model based on the entity list according to semantic meaning, wherein each member of the entity embedding list is an embedding vector in a k-dimension embedding space, and k is a positive integer” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 the entity embedding list with an embedding model based on the entity list according to semantic meaning, wherein each member of the entity embedding list is an embedding vector in a k-dimension embedding space, and k is a positive integer” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 15, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “generating a plurality of first entity clusters based on the entity embedding list
with a first layer clustering model and a first similarity; generating a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity; and combining a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters”.
(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 a plurality of first entity clusters based on the entity embedding list with a first layer clustering model and a first similarity; generating a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity; and combining a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 a plurality of first entity clusters based on the entity embedding list with a first layer clustering model and a first similarity; generating a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity; and combining a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “wherein: the first layer clustering model and the second layer clustering model are based on a clustering algorithm of density-based spatial clustering of applications with noise (DB SCAN); and the first similarity is smaller than the second similarity”.
(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 first layer clustering model and the second layer clustering model are based on a clustering algorithm of density-based spatial clustering of applications with noise (DB SCAN); and the first similarity is smaller than the second similarity” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 first layer clustering model and the second layer clustering model are based on a clustering algorithm of density-based spatial clustering of applications with noise (DB SCAN); and the first similarity is smaller than the second similarity” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “wherein: the first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being larger than a first threshold except the largest cluster; and the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold”.
(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 first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being larger than a first threshold except the largest cluster; and the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being larger than a first threshold except the largest cluster; and the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “constructing a parsing-tree based on the pre-processed text data according to
pre-defined rules of parsing and pas-tag patterns; extracting the entity list based on the parsing-tree, each entity in the entity list corresponding to a noun chunk; extracting a verb list based on the parsing-tree; and merging the entity list and the verb list to obtain the keyword list”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “constructing a parsing-tree based on the pre-processed text data according to pre-defined rules of parsing and pas-tag patterns; extracting the entity list based on the parsing-tree, each entity in the entity list corresponding to a noun chunk; extracting a verb list based on the parsing-tree; and merging the entity list and the verb list to obtain the keyword list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “constructing a parsing-tree based on the pre-processed text data according to pre-defined rules of parsing and pas-tag patterns; extracting the entity list based on the parsing-tree, each entity in the entity list corresponding to a noun chunk; extracting a verb list based on the parsing-tree; and merging the entity list and the verb list to obtain the keyword list” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “wherein the conducting pre-processing on the text data to obtain the pre-processed text data comprises: removing anomaly text or formatted templates from the text data; processing the text data according to a set of natural language processes (NLPs); and
in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translating the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data”.
(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 conducting pre-processing on the text data to obtain the pre-processed text data comprises: removing anomaly text or formatted templates from the text data; processing the text data according to a set of natural language processes (NLPs); and in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translating the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 conducting pre-processing on the text data to obtain the pre-processed text data comprises: removing anomaly text or formatted templates from the text data; processing the text data according to a set of natural language processes (NLPs); and in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translating the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data” steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “the product comprising: machine-readable media other than a transitory signal; and instructions stored on the machine-readable media, wherein when a processor executes the instructions, the processor is configured to perform the method in claim 13” (i.e. using a media style claim).
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “the product comprising: machine-readable media other than a transitory signal; and instructions stored on the machine-readable media, wherein when a processor executes the instructions, the processor is configured to perform the method in claim 13” (i.e. using a media style claim) steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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 “the product comprising: machine-readable media other than a transitory signal; and instructions stored on the machine-readable media, wherein when a processor executes the instructions, the processor is configured to perform the method in claim 13” (i.e. using a media style claim) steps to perform both the aforementioned obtaining; conducting; extracting; generating; clustering and retrieving and obtaining 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, 9, 11-13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1.
As to claim 1, (and substantially similar claim 13), Gao discloses:
a system for topic discovery,
(Gao [0010] FIG. 3 discloses an example block diagram example block diagram illustrating how topics that exist within a document are used to boost the relevance score of individual keywords.; see also [0005] A system to extract relevant keywords or business
tags that describe a company's business is provided.)
the system comprising: a memory storing instructions; and
a processor in communication with the memory, wherein, when the processor
executes the instructions, the instructions are configured to cause the processor to:
(Gao [0059, 0081-0085])
obtain text data,
(Gao [0019-0020] [0019] the system disclosed herein uses a smart crawler to identify and crawl web pages…[0020] These pages of the companies' website serve to provide textual descriptions of product offerings, solutions, or services that make up the companies' business)
conduct pre-processing on the text data to obtain pre-processed text data,
(Gao teaches crawling websites, i.e. “pre-processing on the text data to obtain pre-processed text data” See Fig. 1 “Identify and crawl companies' web pages globally 102”; see also [0019] At
an operation 102, the system disclosed herein uses a smart crawler to identify and crawl web pages from a number of companies' websites.)
extract an entity list and a keyword list based on the pre-processed text data,
(Gao teaches generating a list of companies and a list of keywords based on crawled text data, i.e. “extract an entity list and a keyword list” see [0040] Similarly, the operation 116 may also produce for each keyword, a ranked and scored list of companies; see also [0021] In one implementation, the operation 102 outputs a list of keywords extracted from the web pages for a company and the frequency of each of such keywords.;
See also [0018] Disclosed herein is an automated system and method to extract relevant keywords (i.e. business tags) that describe a company's business. )
generate an entity embedding list based on the entity list,
(Gao teaches generating company term vectors, i.e. “entity embedding list based on the entity list” See [0023] Subsequently, an operation 106 extracts keyword phrases from the text descriptions and counts keyword phrases that appear for each company, forming a vector of
term frequencies to represent each company, where a term is an n-gram, a chain of n words. Specifically, the operation 106 generates a list of candidate descriptive phrases that
may provide description of a company; see also [0042] In one machine learning application, Representation Learning techniques are applied by an operation 118 on the TF-IDF or relevance vectors to generalize or project companies in the high dimensional n-gram space into a lower dimensional topic space. Such techniques include using Singular Value Decomposition,
Latent Dirichlet Allocation, Hierarchical Dirichlet Processes, Non-negative Matrix Factorization, Neural Network Autoencoders, and others)
clusterize the entity list based on the entity embedding list to obtain a plurality of entity clusters, each entity cluster comprising at least one entity,
(Gao teaches clustering a broad set of companies into subsets or groups of companies that are
similar to each other, i.e. “clusterize the entity list based on the entity embedding list to obtain a plurality of entity clusters, each entity cluster comprising at least one entity” see [0043-0044] [0043] Given that similar companies are close together in the topic vector space, a clustering algorithm is also applied at an operation 120 to automatically segment a broad set of companies into subsets or groups of companies that are similar to each other. Such clustering techniques include, but are not limited to K-Means, Spectral Clustering, DBSCAN, OPTICS, Hierarchical Clustering, and Affinity Propagation. [0044] A technique disclosed herein also allows to automatically extract relevant n-gram keywords to describe each cluster of companies. For a cluster or any set of companies, the constituent companies' n-gram vector representations
are merged into one n-gram vector via an aggregating function, a simple example of which is just the vector sum.)
obtain a topic for each entity cluster of the plurality of entity clusters based on the co-occurring keyword list
(Gao teaches automatically extracting relevant n-gram keywords to describe each
cluster of companies, i.e. “obtain a topic for each entity cluster of the plurality of entity clusters based on the co-occurring keyword list” see [0041-0044] [0041] Clustering and Cluster Tagging
[0042] The TF-IDF and Relevance based semantic representations of companies can be used to directly drive product applications as well as implicitly support downstream machine learning applications. In one machine learning application, Representation Learning techniques are applied by an operation 118 on the TF-IDF or relevance vectors to generalize or project companies in the high dimensional n-gram space into a lower dimensional topic space. Such
techniques include using Singular Value Decomposition, Latent Dirichlet Allocation, Hierarchical Dirichlet Processes, Non-negative Matrix Factorization, Neural Network Autoencoders, and others. Companies that are close together in the topic space, e.g. according to Euclidean or Cosine distance, are effectively similar to each other in terms of their business, product offerings, solutions or services. [0043] Given that similar companies are close together in
the topic vector space, a clustering algorithm is also applied at an operation 120 to automatically segment a broad set of companies into subsets or groups of companies that are similar to each other. Such clustering techniques include, but are not limited to K-Means, Spectral Clustering, DBSCAN, OPTICS, Hierarchical Clustering, and Affinity Propagation. [0044] A technique disclosed herein also allows to automatically extract relevant n-gram keywords to describe each
cluster of companies.).
Gao does not explicitly disclose:
retrieve a co-occurring keyword list based on the plurality of entity clusters, the entity list, and the keyword list, and
However, Lowe discloses:
retrieve a co-occurring keyword list based on the plurality of entity clusters, the entity list, and the keyword list, and
(Lowe teaches determining keyword identifiers based on co-occurrence of terms in documents indicated by clusters and lists, i.e. “retrieve a co-occurring keyword list based on the plurality of entity clusters, the entity list, and the keyword list” see [0090] A threshold number (e.g., the second and third) of the resulting vectors may correspond to dimensions in a concept space, where the concepts that emerge correspond to co-occurrence of terms in documents indicated by clusters in the space. Documents may be clustered according to their corresponding vectors in the concept space, or similarity of documents may be determined by some embodiments by comparing their respective vectors in this space, e.g., based on cosine similarity or other measures.;
See also [0037] For example, a Boolean operator in the conjunctive form, "and," between two keywords may cause the system to identify index values (i.e., document identifiers)
associated in the index with the keywords on either side of the operator. Embodiments may then determine which document identifiers appear in both lists to identify results. Similarly, a Boolean operator in the disjunctive form, "or," may cause the system to append index values associated
with each of the keywords on either side of the operator. In some embodiments, duplicate index values may be condensed into a single representative entry. In some embodiments, the responsive entries may be ranked, for example, based on the number of times that the keywords appear in the documents, or based on both the number of times the keywords appear in the documents and the context in which those keywords appear, for example, in association with other terms related to those keywords ( e.g., having a greater than a threshold co-occurrence rate).)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply co-occurrences based on clusters as taught by Lowe, to the system of Gao,since it was known in the art that query systems provide determining which document identifiers appearing in lists to identify results where the system to append index values associated with each of the keywords and where duplicate index values may be condensed into a single representative entry and responsive entries may be ranked, for example, based on the number of times that the keywords appear in the documents, or based on both the number of times the keywords appear in the documents and the context in which those keywords appear, for example, in association with other terms related to those keywords ( e.g., having a greater than a threshold co-occurrence rate) where these techniques may both help the user find the needle in the haystack, as well as develop an understanding of the haystack itself. (Lowe [0037-0038]).
As to claim 9, Gao as modified discloses the system according to claim 1, wherein:
the text data comprises at least one of the following:
a survey, a ticket, a comment, or an online review
(Gao [0022] Thus, for example, the operation 104 may extract keywords from other source, such as a news article, a Linkedin™ page, Wikipedia™ page about the company, a consumer product review website, AdWords purchased by the company, etc.).
As to claim 11, Gao as modified discloses the system of claim 1, wherein, when the processor executes the instructions, the instructions are configured to further cause the processor to:
obtain a key sentence for each entity cluster of the plurality of entity clusters
based on the co-occurring keyword list
(Gao teaches extract relevant n-gram keywords to describe each cluster of companies based on a count of how often the n-gram occurs i.e. “obtain a key sentence for each entity cluster of the plurality of entity clusters based on the co-occurring keyword list”
See [0023-0024][0023] Subsequently, an operation 106 extracts keyword phrases from the text descriptions and counts keyword phrases that appear for each company, forming a vector of
term frequencies to represent each company, where a term is an n-gram, a chain of n words. Specifically, the operation 106 generates a list of candidate descriptive phrases that
may provide description of a company. For example, for the company selling footwear, one such phrase may be "running shoe." Another such phrase may be "low-impact shoe," etc. The operation 106 extracts such keyword phrases for the company and documents the frequency of each of these keyword phrases. [0024] The descriptive phrases are also referred to as
n-grams. For example, for a company selling footwear a monogram may be "shoes," a bi-gram may be "running shoe," a tri-gram may be "altitude running shoe," etc. The operation 106 generates the count for each such n-grams related to the company. In one implementation, the operation 106 generates then-grams across the websites of the companies globally to determine then-grams that are used more often to describe a company or a product. Each of the
n-grams in this collection of n-gram is related to a count of how often the n-gram occurs. In one implementation, an n-gram having a higher count is ranked higher.;
see also [0044] A technique disclosed herein also allows to automatically extract relevant n-gram keywords to describe each cluster of companies. For a cluster or any set of companies,
the constituent companies' n-gram vector representations are merged into one n-gram vector via an aggregating function, a simple example of which is just the vector sum. From this merged n-gram vector, the relevance scoring algorithm described earlier is applied to boost the strengths of relevant n-grams).
As to claim 12, Gao as modified discloses the system of claim 1, wherein, when the processor executes the instructions, the instructions are configured to further cause the processor to:
provide, based on the obtained topic, at least one of the following:
a feedback to the text data, a sentiment analysis to the text data, a content tagging to the text
data (Gao teaches content tagging see [0041-0044] [0041] Clustering and Cluster Tagging …The top n-grams by relevance can be used to tag each cluster so that they are readily human
understandable. FIG. 7 below illustrates a detailed view of the clusters with various business tags, such as "Application Development," "Mobile Products," etc. see also [0056] FIGS. 6 and 7 illustrate example visualizations exhibiting the non-overlap layout, clustering, and cluster tagging.; and [0077] Another application of the similarity measure between key-phrases is the enhancement of the algorithm described above for automatically tagging companies with keywords that describe the company's business).
Claim 20, Gao discloses a product for topic discovery, the product comprising:
machine-readable media other than a transitory signal; (Gao [0060-0062])
and
instructions stored on the machine-readable media, wherein when a
processor executes the instructions, the processor is configured to perform the
method claim 13 (Gao [0059, 0081-0085]).
Claim(s) 2, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1, in view of Wang et al., US Pub. No. 2010/0169317 A1.
As to claim 2, Gao/Lowe do not disclose:
wherein, when the instructions are configured to cause the processor to generate the entity embedding list based on the entity list, the instructions are configured to cause the processor to:
generate the entity embedding list with an embedding model based on the
entity list according to semantic meaning, wherein each member of the entity
embedding list is an embedding vector in a k-dimension embedding space, and k is
a positive integer;
however, Wang discloses:
the system according to claim 1, wherein, when the instructions are configured to cause the processor to generate the entity embedding list based on the entity list, the instructions are configured to cause the processor to:
generate the entity embedding list with an embedding model based on the
entity list according to semantic meaning, wherein each member of the entity
embedding list is an embedding vector in a k-dimension embedding space, and k is
a positive integer
(Wang teaches using a weighted vector space model, and measuring similarity of a snippet to an attribute can be measured with the cosine of the angle formed by the two TF-IDF feature vectors in the k-dimensional space, i.e. “generate the entity embedding list with an embedding model based on the entity list according to semantic meaning, wherein each member of the entity embedding list is an embedding vector in a k-dimension embedding space” see [0065-0068] [0065] An alternative approach uses the known TF-IDF (term frequency-inverse document frequency) weighted vector space model, which in general represents an attribute (cluster) with a TF-IDF weighted vector of the terms including attribute names, the co-occurring adjectives and (optionally) the co-occurring verbs. More particularly, each attribute is represented by a vector of TF-IDF weights of terms. A snippet is also represented by a TF-IDF weighted vector, and
the cosine between the two vectors is used to measure the similarity between the snippet and the attribute. The attribute most similar to the snippet is then assigned to the snippet.
[0066] Thus a vector is constructed for each attribute: [0067] A=(xi, x2 , ... ,xk), where x, stands for the TF-IDF feature for the i th term in the vocabulary. Similary, a TFIDF feature vector is formed for each snippet as [0068] The similarity of a snippet to an attribute can be
measured with the cosine of the angle formed by the two TF-IDF feature vectors in the k-dimensional space).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply a weighted vector space model, as taught by Wang, to the system of Gao/Lowe, since it was known in the art that query systems provide a TF-IDF (term frequency-inverse document frequency) weighted vector space model, which in general represents an attribute
(cluster) with a TF-IDF weighted vector of the terms including attribute names, the co-occurring adjectives and (optionally) the co-occurring verbs where each attribute is represented by a vector of TF-IDF weights of terms where a snippet is also represented by a TF-IDF weighted vector, and the cosine between the two vectors is used to measure the similarity between the snippet and the attribute. (Wang [0065]).
Referring to claim 14, this dependent claim recites similar limitations as claim 2;
therefore, the arguments above regarding claim 2 are also applicable to claim 14.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1, in view of Wang et al., US Pub. No. 2010/0169317 A1, in view of Kannan et al. US
Pub. No. 2020/0380215 A1.
As to claim 3, Gao/Lowe/Wang do not disclose:
wherein: the embedding model is fine-tuned by using a subset of the entity list as a training sample set;
However, Kannan discloses:
the system according to claim 2, wherein: the embedding model is fine-tuned by using a subset of the entity list as a training sample set
(Kannan teaches language specific training sets for fine-tuning, i.e. “the embedding model is fine-tuned by using a subset of the entity list as a training sample set”
See [0035] Moreover, applying the language-specific adaptor modules 300 to the encoder network 200, which include domain-specific adjustments to the activations output from
each LSTM layer 216, offer a second extension to the architecture of the multilingual E2E speech recognition model 150 by fine-tuning the model 150 on each individual language during training, yet still maintaining the parameter efficiency of having a single global, multilingual E2E model 150.;
see also [0044] This multi-stage training results in a single model having a small number of parameters that are specific to each language, which are typically less than 10% of the original
model size. Such enhancement allows for efficient parameter sharing across languages, as well as per-language specialization. For instance, the encoder network 200 is mostly
shaped by dominant languages, whereas less dominant Ianguages may have distinct acoustic features that can benefit from small per-layer adjustments. Adding the language specific
adapter modules 300 allows the model to specialize in each language in the same way that fine-tuning the whole model would, but in a much more parameter-efficient way. Specifically, in one tested implementation, the capacity added for each language is only about two percent of the
original model size.)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply language specific training sets, as taught by Kannan, to the system of Gao/Lowe/Wang, since it was known in the art that query systems provide multi-stage training results in a single model having a small number of parameters that are specific to each language, which are typically less than 10% of the original model size where such enhancement allows for efficient parameter sharing across languages, as well as per-language specialization where for instance, the encoder network is mostly shaped by dominant languages, whereas less dominant languages may have distinct acoustic features that can benefit from small per-layer adjustments where adding the language specific adapter modules allows the model to specialize in each language in the same way that fine-tuning the whole model would, but in a much more parameter-efficient way where specifically the capacity added for each language is only about two percent of the original model size. (Kannan [0044]).
Claim(s) 4-5, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1, in view of Wang et al., US Pub. No. 2010/0169317 A1, in view of Chen et al., US Patent No., 10,496,691 B1.
As to claim 4, Gao/Lowe do not disclose:
wherein, when the instructions are configured to cause the processor to clusterize the entity list based on the entity embedding list to obtain the plurality of entity clusters, the instructions are configured to cause the processor to:
generate a plurality of first entity clusters based on the entity embedding list
with a first layer clustering model and a first similarity;
generate a plurality of second entity clusters based on a largest cluster in the
plurality of the first entity clusters with a second layer clustering model and a second
similarity; and
combine a first subset of the plurality of the first entity clusters and a second
subset of the plurality of the second entity clusters to obtain the plurality of entity clusters;
However, Chen discloses:
the system according to claim 1, wherein, when the instructions are configured to cause the processor to clusterize the entity list based on the entity embedding list to obtain the plurality of entity clusters, the instructions are configured to cause the processor to:
generate a plurality of first entity clusters based on the entity embedding list
with a first layer clustering model and a first similarity;
(Chen teaches multiple levels of clustering using similarities/cluster scores, i.e., “generate a plurality of first entity clusters based on the entity embedding list with a first layer clustering model and a first similarity” see col. 6 ln. 41-46: The clustering engine 122 may perform rounds of clustering (e.g., first level, second level, etc.) and evaluate the clusters after each round. The clustering engine 122 may evaluate potential clusters based on several metrics. Accordingly, the clustering engine 122 may calculate a cluster score for each first-level cluster.;
see also col. 6 ln. 23-28: One clustering method may include merging clusters based on entity ontology, merging the most similar clusters first, and then applying distance-based clustering to generate the final clusters. Cluster similarity may be based on an embedding similarity, although other conventional similarity measures can be used.; see also col. 6 ln. 32-40: The embedding similarity between two search items can be represented as the cosine similarity within the embedding space. In this particular example, when two entities are ontologically related (i.e.,
synonyms, hypernyms, or co-hypernyms) the two entities are a candidate pair for further clustering. Candidate pairs may be evaluated for similarity ( e.g., using embedding similarity or another similarity measure), and the pairs with the highest similarity are evaluated for merging first)
generate a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity; and
(Chen teaches multiple levels of clustering using similarities/cluster scores, i.e., “generate a plurality of second entity clusters based on a largest cluster in the plurality of the first entity clusters with a second layer clustering model and a second similarity” see col. 6 ln. 41-46: The clustering engine 122 may perform rounds of clustering (e.g., first level, second level, etc.) and evaluate the clusters after each round. The clustering engine 122 may evaluate potential clusters based on several metrics. Accordingly, the clustering engine 122 may calculate a cluster score for each first-level cluster.;
See also col. 8 ln. 1-11: The rounds of clustering based on entity ontology merging most similar clusters first may be considered the first stage of a two-stage clustering process. After the first stage, e.g., one or more rounds of clustering based on entity 5 ontology merging the most similar clusters first, the clustering engine 122 may perform a second stage, applying a
distance-based clustering, such as hierarchical agglomerative clustering, to generate final clusters. The distance-based clustering may merge the most similar clusters, e.g., based on the embedding space distance.)
combine a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters
(Chen teaches merging groups of clusters i.e. “combine a first subset of the plurality of the first entity clusters and a second subset of the plurality of the second entity clusters to obtain the plurality of entity clusters” see col. 8 ln. 10-22: The clustering result of each round of distance-based clustering may be evaluated using the evaluation criteria described above. In some implementations, the system may adjust the similarity score, 15 e.g., the embedding similarity, for cluster pairs that include entities are related in the entity ontology, e.g., are synonyms, hypemyms, or co-hypemyms to favor similarity ( e.g., reducing the distance value). This favors merging clusters with related entities and can result in more coherent clusters. The final clusters from this method may represent first final clusters that can be used to present the search items to the requestor.;
See also col. 6 ln. 56-61: “If the cluster score for the candidate cluster is higher, the
combination is considered beneficial and the clustering engine 122 may proceed with combining the first cluster and the second cluster. Thus, the clustering engine uses the cluster metrics to generate the highest quality, or best cluster candidates.”).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply a multi level clustering, as taught by Chen, to the system of Gao/Lowe, since it was known in the art that query systems provide multi-level clustering for the clustering engine may perform rounds of clustering (e.g., first level, second level, etc.) and evaluate the
clusters after each round where the clustering engine may evaluate potential clusters based on several metrics and accordingly, the clustering engine may calculate a cluster score
for each first-level cluster, which provides improves the clustering by better simulating nuanced search item similarity not captured in conventional hierarchical clustering methods. (Chen col. 6 ln. 1-60).
As to claim 5, Gao as modified discloses the system according to claim 4, wherein:
the first layer clustering model and the second layer clustering model are
based on a clustering algorithm of density-based spatial clustering of applications
with noise (DB SCAN); and
(Gao teaches using “DBSCAN” see [0043-0044] [0043] Given that similar companies are close together in the topic vector space, a clustering algorithm is also applied at an operation 120 to automatically segment a broad set of companies into subsets or groups of companies that are similar to each other. Such clustering techniques include, but are not limited to K-Means, Spectral Clustering, DBSCAN, OPTICS, Hierarchical Clustering, and Affinity Propagation)
And Chen as modified discloses:
the first similarity is smaller than the second similarity
(Chen col. 15 ln. 48-49: “Thus, the system may pair entities with a small distance ( close distance) in the embedding space.”;).
Referring to claim 15, this dependent claim recites similar limitations as claim 4;
therefore, the arguments above regarding claim 4 are also applicable to claim 15.
Referring to claim 16, this dependent claim recites similar limitations as claim 5;
therefore, the arguments above regarding claim 5 are also applicable to claim 16.
Claim(s) 6, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1, in view of Wang et al., US Pub. No. 2010/0169317 A1, in view of Chen et al., US Patent No., 10,496,691 B1, in view of Manning et al., US Pub. No. 2017/0052958.
As to claim 6, Gao/Lowe/Chen do not disclose:
wherein: the first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being larger than a first threshold except the largest cluster; and
the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold;
However, Manning discloses:
as modified discloses the system according to claim 4, wherein:
the first subset of the plurality of the first entity clusters comprises every cluster of the plurality of the first entity clusters that has a number of members being larger than a first threshold except the largest cluster; and
(Manning [0029] According to yet another aspect, the computer executable instructions further cause the processor to: output the first cluster of record pairs to a client computing device; receive, from the client computing device, a second threshold; identify a second cluster of record pairs, wherein each pair of the second cluster has a record in common with at least one other pair in the second cluster, and wherein each pair in the second cluster has a respective match score above the second threshold; and output the second cluster to the client computing device.)
the second subset of the plurality of the second entity clusters comprises every cluster of the plurality of the second entity clusters that has a number of members being larger than a second threshold
(Manning [0029] According to yet another aspect, the computer executable instructions further cause the processor to: output the first cluster of record pairs to a client computing device; receive, from the client computing device, a second threshold; identify a second cluster of record pairs, wherein each pair of the second cluster has a record in common with at least one other pair in the second cluster, and wherein each pair in the second cluster has a respective match score above the second threshold; and output the second cluster to the client computing device.;
See also [0110] In some implementations, when a blocking model generates a group that exceeds or does not satisfy a threshold for a graph metric, this may indicate that the blocking model is not effective. For example, when a blocking model generates a group of records having a large diameter ( e.g., a diameter that exceeds a particular threshold), this may be an indication that at least some records within the group are not closely related to other records within the group. Thus, the system may validate the effectiveness of blocking models,
determine that the blocking model needs to be improved or discarded, and the like.)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply a multiple/second thresholds, as taught by Manning, to the system of Gao/Lowe/Chen, since it was known in the art that query systems provide that a new cluster may be generated via the new cluster may be generated by identifying a first pair of records that is not in a cluster and generating a cluster that contains the first pair of records where no pairs of records are in clusters and any pair may be arbitrarily chosen as the first pair where the first pair may have a match score above a threshold, may have the highest match score of any pair not already in a cluster, or may be chosen according to other criteria where thereafter, a second pair of records may be identified as a candidate for inclusion in the cluster. (Manning [0165-0166]).
Referring to claim 17, this dependent claim recites similar limitations as claim 6;
therefore, the arguments above regarding claim 6 are also applicable to claim 17.
Claim(s) 7, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1, in view of Patil et al., US Pub. No. 2023/0061773 A1.
As to claim 7, Gao/Lowe do not disclose:
wherein, when the instructions are configured to cause the processor to extract the entity list and the keyword list based on the pre-processed text data, the instructions are configured to cause the processor to:
construct a parsing-tree based on the pre-processed text data according to pre-defined rules of parsing and pas-tag patterns;
extract the entity list based on the parsing-tree, each entity in the entity list
corresponding to a noun chunk;
extract a verb list based on the parsing-tree; and
merge the entity list and the verb list to obtain the keyword list;
However, Patil discloses:
the system according to claim 1, wherein, when the instructions are configured to cause the processor to extract the entity list and the keyword list based on the pre-processed text data, the instructions are configured to cause the processor to:
construct a parsing-tree based on the pre-processed text data according to pre-defined rules of parsing and pas-tag patterns;
(Patil teaches generating dependency parse trees, the constituency parse trees and using part-of-speech (POS) tags, i.e. construct a parsing-tree based on the pre-processed text data according to pre-defined rules of parsing and pas-tag patterns
see [0052] Thereafter, the ULDG component 304 marks one or more co-references present in the filtered set of technical documents using the one or more natural language processing algorithms. The part-of-speech (POS) tags, the lemmas, the dependency parse trees, the constituency parse trees, and the one or more co-references are further identified as linguistic information associated with the text data provided in the one or more technical documents. Once the linguistic information associated with the text data is available, the ULDG component 304 creates the ULDG based on the identified linguistic information.)
extract the entity list based on the parsing-tree, each entity in the entity list corresponding to a noun chunk;
(Patil [0054] Thereafter, the domain specific term identification component 306 extracts one or more noun phrases present in the filtered set of technical documents. Further, the extracted one or more technical terms, the one or more keywords and the one or more noun phrases are
identified as one or more candidate domain specific technical terms by the domain specific concept identification component 306.; see also [0055] Thereafter, the domain
specific term clustering algorithm segregates the domain specific technical terms denoted by noun phrases from the non-domain specific technical terms.)
extract a verb list based on the parsing-tree;
(Patil [0047] (iv) information from the dependency parse trees such as number of edges, number of hops between subject and verb, in-degree and out-degree of the
nodes, (v) named entities and the time expressions tags; see also Fig. 10a and 0b)
and
merge the entity list and the verb list to obtain the keyword list
(Patil [0080] At step 412 of the present disclosure, the one or more hardware processors 206 of the TQGS 200 create a concept graph (CG) by populating a concept graph data
structure using the identified one or more domain specific technical terms and the additional information obtained corresponding to the technical domain. The information obtained at step 408 and step 410 is merged by the hardware processors 206 to populate the concept graph data structure, thereby creating the CG. The CG further helps in identifying additional details about the technical domain.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply construct a parsing-tree, as taught by Patil, to the system of Gao/Lowe, since it was known in the art that query systems provide natural language processing algorithms with part-of-speech (POS) tags, the lemmas, the dependency parse trees, the constituency parse trees, and the one or more co-references identified as linguistic information associated with the text data provided in the one or more technical documents where once linguistic information associated with the text data is available, a domain specific concept identification component can include suitable logic and/or interfaces for identifying one or more concepts, i.e., one or more technical terms and one or more keywords denoting different notions in the text data and for selecting one or more domain specific technical terms that are related to a technical domain of the text data from the identified technical terms and keywords. (Patil [0052-0053]).
Referring to claim 18, this dependent claim recites similar limitations as claim 7;
therefore, the arguments above regarding claim 7 are also applicable to claim 18.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1, in view of Patil et al., US Pub. No. 2023/0061773 A1, in view of Medalion et al., US Pub. No. 2021/0287261.
As to claim 8, Gao/Lowe/Patil do not disclose:
Wherein each entity in the entity list is not in a stop-word list or a spam list.
However, Medalion discloses:
the system according to claim 7, wherein
each entity in the entity list is not in a stop-word list or a spam list
(Medalion [0025] For example, the text preparation module 108 may be configured to clean up business descriptions or invoice text. Text preparation module 108 may be configured
to remove stop words, perform lemmatization processes, and calculate term frequency-inverse document frequency values. For example, stop words may be identified using a pre-defined list of stop words.; see also claim 2 “removing the detected stop words from the merchant data
associated with the merchant;”).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply stop word removal, as taught by Medalion, to the system of Gao/Lowe/Patil, since it was known in the art that query systems provide for removing stop words in order to allow for a text preparation module which may be configured to clean up business descriptions or invoice text where the text preparation module may be configured to remove stop words, perform lemmatization processes, and calculate term frequency-inverse document frequency values where the stop words may be identified using a pre-defined list of stop words and thus allow for an embedding module may be configured to embed lemmatized text to vector form within a continuous vector space. (Medalion [0025-0026]).
Claim(s) 10, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Gao et al., US Pub. No. 2018/0060437 A1, in view of Lowe et al., US Pub. No. 2017/0293676 A1, in view of Dubbels et al., US Pub. No. 2014/0164407 A1, in view of Horvitz et al., US Pub. No. 2006/0293893.
As to claim 10, Gao/Lowe do not disclose:
wherein, when the instructions are configured to cause the processor to conduct pre-processing on the text data to obtain the pre-processed text data, the instructions are configured to cause the
processor to:
remove anomaly text or formatted templates from the text data;
process the text data according to a set of natural language processes (NLPs );
However, Dubbels discloses:
as modified discloses the system according to claim 1, wherein, when the instructions are configured to cause the processor to conduct pre-processing on the text data to obtain the pre-processed text data, the instructions are configured to cause the
processor to:
remove anomaly text or formatted templates from the text data;
(Dubbels teaches preprocessing text by normalizing text into a common format/removing formatting, parsing items of interest and removing extra spaces, i.e. “remove anomaly text or formatted templates from the text data”
See [0045] FIG. 5 is a model of the commonly formatted objects, according to one embodiment described herein. As shown, each object 115 is assigned an item of interest. The item of interest may be a specific topic discussed in the text documents ingested by the preprocessing system. In one embodiment, the preprocessor may parse the text document to identify the main topic of the text document which is referred to herein as the "item of interest".;
See also [0032] Portion 320 of the properties file 210 illustrate a list of formatting elements that the preprocessing system should search for when parsing the text document. Here, the formatting elements are headers that include both HTML tags and corresponding labels. For header.002, the properties file 210 instructs the parser to look for the "<strong>" and
"</strong>" HTML tags that encapsulate the regular expression strings "discussion/general" or "discussion". Moreover, the entries may contain other operators ( e.g., [],*,and+) that
define how the preprocessor searches for the strings. The operators may be used to, for example, remove extra spaces, serve as wildcards, perform logical comparisons, and the like;
see also [0048] Because of the text is arranged in the manner shown in FIG. 5, the annotator can parse the text without regards to various original formats used to arrange the text.)
process the text data according to a set of natural language processes (NLPs );
(Dubbles teaches NPL processing see [0049] In one embodiment, each time the preprocessing
system 110 receives new text documents, the preprocessor 215 may update the appropriate object or objects 115 to include the new text. The preprocessor 215 may then transmit the updated objects 115 to the natural language processing pipeline 225 where the annotators update the metadata associated with the stored text. The annotated objects 115 may
then be stored in the data store 120 where the objects 115 can be accessed by a natural-language processing computer system to answer questions, diagnose diseases, generate financial reports, and the like.)
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply preprocessing/normalizing/ natural language processing, as taught by Dubbels, to the system of Gao/Lowe, since it was known in the art that natural language systems provide for a preprocessor that populates the data store could execute on a computing system in the cloud and receive the particular text documents where in such a case, the use could transmit the text documents to the preprocessor which then generates the data store at storage location in the cloud where doing so allows a user to access this information from any computing system attached to a network connected to the cloud (e.g., the Internet). (Dubbels [0024-0025]).
Gao/Lowe/Dubbels do not disclose:
and
in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translate the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data;
However, Horovitz discloses:
and
in response to a first portion of the text data being in a different language in comparison to a second portion of the text data, translate the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data;
(Horvitz teaches translation and inferencing of utterances/words from a larger segment of text for resolving/translating between different languages i.e. “translate the first portion of the text data into a language being same as the second portion of the text data, the first portion of the text data being a smaller portion than the second portion of the text data” see [0071-0072] [0071] FIG. 16 illustrates a device-to-device translation system 1600 between a user and a FLS recipient according to an aspect. A user 1602 utilizes a portable wireless device (PWD) 1604 (which includes the translation architecture of the subject innovation) to communicate wirelessly with the FLS recipient 1606 via a recipient device 1608 (which also includes the translation architecture of the subject innovation). The user 1602 inputs speech signals to the user PWD 1604, which are then processed into translated output and
communicated wirelessly to the recipient device 1608. The recipient device 1608 translates the user speech into user text, which can be displayed on the recipient device 1608, and/or output as translated user speech to the recipient 1606. Similarly, the user device 1604 translates the recipient speech into recipient text, which can be displayed on the user device 1604, and/or output as translated recipient speech to the user 1602. [0072] If both the user 1602 and the recipient 1606 are located in the nearly the same context, either or both devices 1604 or/and 1608 can perform the context and/or concept processing described supra, to enhance translation. Thus, the modalities 1610 and 1612 of either or both devices 1604 or/and 1608, respectively,; See also [0033] With this approach, reasoning is applied about communication goals based on the concept or situation at the current focus of attention, the user with provided appropriately triaged choices and, text and/or speech translations are presented for perception.;
see also [0033-0034][0034] The inferences can also take as input an utterance from the user as part of the evidence in reasoning about concept, situation, goals, and or disambiguating the latter. The system understanding or reformulation of the question, need, or intention can then be echoed back to the user for confirmation. [0035] The inferencing by the system can provide a deep focusing based on the listening, and can also employ words recognized from the user's utterance to further focus the inference.; see also [0008] if necessary, and then, allow a user to refine or select an utterance, text strings, and/or images to relay to a speaker of a foreign language. The mobile device can optionally provide a means for the other person to enter
information or select utterances for relay responses back to the device owner.).
It would have been obvious to one having ordinary skill in the art at the time of the effective filing date to apply translation and inferencing, as taught by Horvitz, to the system of Gao/Lowe/Dubbels , since it was known in the art that natural language systems provide for provide a system includes an adaptive automatic speech recognition (ASR) component which provides that reasoning is applied about communication goals based on the concept or situation at the current focus of attention, the user with provided appropriately triaged choices and, text and/or speech translations are presented for perception and that processes sensed data of a current context and/or concept, and facilitates a speech recognition process based on the sensed data where a historical activities component of the system stores historical data associated with the speech recognition process where in other words, as the user interacts with the system, this interactive data is stored in a datastore as a basis for future analysis and inferencing where the system can further include a language opportunity component that improves the speech recognition process by pushing a training session of one or more terms to a user, which training session increases the likelihood of success when using the one or more terms during a future speech recognition process. (Horvitz [0033-0036]).
Referring to claim 19, this dependent claim recites similar limitations as claim 10;
therefore, the arguments above regarding claim 10 are also applicable to claim 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Epasto et al., US Pub. No.: US 2022/0075897 A1, teaches a computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition;
Lightner et al., US Pub. No.: 2017/0286837 A1, teaches a method for performing automated discovery of new topics from unlimited documents related to any subject domain, employing a multicomponent extension of Latent Dirichlet Allocation (MCLDA) topic models, to discover related topics in a corpus. The resulting data may contain millions of term vectors from any subject domain identifying the most distinguished cooccurring topics that users may be interested in, for periodically building new topic ID models using new content, which may be employed to compare one by one with existing model to measure the significance of changes, using term vectors differences with no correlation with a Periodic New Model, for periodic updates of automated discovery of new topics, which may be used to build a new topic ID model in-memory database to allow query-time linking on massive data-set for automated discovery of new topics.
CONTACT INFORMATION
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/Evan Aspinwall/Primary Examiner, Art Unit 2152