DETAILED ACTION
This office action is in response to the communication filed on February 21, 2025. Claims 1-23 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/26/25 has been considered by the examiner.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Independent claims 1, 8, and 17 respectively recite a system, a method, and a
computer, which are directed to a statutory category such as a process, machine, or an
article of manufacture.
At step 2A, prong one:
Independent claim 1 and similarly independent claims 8 and 17 recites the limitations:
“perform…gross filtering on each piece of content to detect noise and domain relevance of each piece of content to generate a gross filtering result for each piece of content and discard a particular piece of content that does not pass the gross filtering to produce a reduced number of pieces of content”;
A person can mentally or using a pen and paper perform gross filtering on each piece of content to detect noise and domain relevance of each piece of content to mentally or using a pen and paper generate a gross filtering result for each piece of content by mentally or using a pen and paper discarding a particular piece of content that does not pass the gross filtering to produce a reduced number of pieces of content.
“perform…fine filtering to detect noise and relevance to a topic or a persona for each piece of content of the reduced number of pieces of content to generate a fine filtering result for each piece of content of the reduced number of pieces of content and discard a particular piece of content that fails to pass the fine filtering to produce a second reduced number of pieces of content”;
A person can mentally or using a pen and paper perform fine filtering to detect noise and relevance to a topic or a persona for each piece of content of a reduced number of pieces of content to mentally or using a pen and paper generate a fine filtering result for each piece of content of the reduced number of pieces of content by mentally or using a pen and paper discarding a particular piece of content that fails to pass the fine filtering to produce a second reduced number of pieces of content.
“analyze…each piece of content of the second reduced number of pieces of content including the gross filtering result and the fine filtering result to generate one or more noise related flags for each piece of content of the second reduced number of pieces of content”.
A person can mentally or using a pen and paper analyze each piece of content of a second reduced number of pieces of content including the gross filtering result and the fine filtering result to mentally or using a pen and paper generate one or more noise related flags for each piece of content of the second reduced number of pieces of content.
The limitations, as recited above, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
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. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Independent claim 1 recites the limitations:
“a computing device that interacts with the computer system to submit a query and receive search results from the computer system in response to the query, the search results being curated content having noise filtered out of the search results”, which is a step of submitting or transmitting data and receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“receive a plurality of pieces of content based on the query”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a system, comprising: a computer system having a processor and a plurality of lines of instructions executed by the processor”, “a computing device that interacts with the computer system to”, “from the computer system”, “the computer system being configured to:” and “using machine learning” in the steps in claim 1 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole,
because it does not impose any meaningful limits on practicing the abstract idea.
Independent claim 8 recites the limitation:
“receiving, by a computer, a plurality of pieces of content”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “by a computer”, “using machine learning executed by the computer”, and “by the computer” in the steps in claim 8 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole,
because it does not impose any meaningful limits on practicing the abstract idea.
Independent claim 17 recites the limitation:
“receiving a plurality of pieces of content”, which is a step of receiving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a computer, comprising: a processor and a plurality of lines of instructions executed by the processor; the computer being configured to:” and “using machine learning” in the steps in claim 17 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not
integrate the abstract idea into a practical application, even viewing the claims a whole,
because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Independent claims 1, 8, and 17 recites the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Independent claim 1 recites the limitations:
“a computing device that interacts with the computer system to submit a query and receive search results from the computer system in response to the query, the search results being curated content having noise filtered out of the search results”, which is a step of submitting or transmitting data and receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“receive a plurality of pieces of content based on the query”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Independent claim 8 recites the limitation:
“receiving, by a computer, a plurality of pieces of content”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Independent claim 17 recites the limitation:
“receiving a plurality of pieces of content”, which is a step of receiving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 2 recites additional limitations, such as:
“…filter out a piece of content that contains one of misleading information and harmful content, remove a piece of content that is one of grammatically incorrect and poorly structured and discard a piece of content that is not relevant to a domain”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper filter out a piece of content that contains one of misleading information and harmful content, remove a piece of content that is one of grammatically incorrect and poorly structured and discard a piece of content that is not relevant to a domain, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the computer system configured to perform gross filtering is further configured to” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 3 recites additional limitations, such as:
“…generate a noise score for each piece of content in the reduced number of pieces of content, to generate a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generate an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate a noise score for each piece of content in the reduced number of pieces of content, generate a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generate an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the computer system configured to perform fine filtering is further configured to” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 4 recites additional limitations, such as:
“wherein the one or more noise related flags further comprises a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate one or more noise related flags comprising a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 5 recites additional limitations, such as:
“wherein the relevant to domain flag is a relevant to business to business (B2B) domain flag”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate one or more noise related flags wherein a relevant to domain flag is a relevant to business to business (B2B) domain flag, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 6 recites additional limitations, such as:
“…segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the gross filtering and fine filtering are performed based on the plurality of tokens for each piece of content”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the gross filtering and fine filtering are performed based on the plurality of tokens for each piece of content, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the computer system is further configured to” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 7 recites additional limitations, such as:
“wherein the machine learning further comprises one or more of a natural language processing process, a statistical analysis process, a sematic analysis process and a machine learning model process”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 recites additional limitations, such as:
“wherein performing the gross filtering further comprises filtering out a piece of content that contains one of misleading information and harmful content, removing a piece of content that is one of grammatically incorrect and poorly structured and discarding a piece of content that is not relevant to a domain”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 8, because a person can mentally or using a pen and paper perform gross filtering by filtering out a piece of content that contains one of misleading information and harmful content, removing a piece of content that is one of grammatically incorrect and poorly structured and discarding a piece of content that is not relevant to a domain, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 10 recites additional limitations, such as:
“wherein performing the fine filtering further comprises generating a noise score for each piece of content in the reduced number of pieces of content, generating a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generating an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper perform fine filtering by generating a noise score for each piece of content in the reduced number of pieces of content, generating a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generating an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 11 recites additional limitations, such as:
“wherein the one or more noise related flags further comprises a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate one or more noise related flags comprising a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 12 recites additional limitations, such as:
“wherein the relevant to domain flag is a relevant to business to business (B2B) domain flag”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate one or more noise related flags wherein a relevant to domain flag is a relevant to business to business (B2B) domain flag, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 13 recites additional limitations, such as:
“segmenting each piece of content into at least one sentence, segmenting each sentence into a plurality of tokens, wherein the gross filtering and fine filtering are performed based on the plurality of tokens for each piece of content”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the gross filtering and fine filtering are performed based on the plurality of tokens for each piece of content, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the computer system is further configured to” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 14 recites additional limitations, such as:
“wherein the machine learning further comprises one or more of a natural language processing process, a statistical analysis process, a sematic analysis process and a machine learning model process”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 15 recites additional limitations, such as:
“submitting, by a computing device, a query so that the received plurality of pieces of content are in response to the query”, which is a step of submitting or transmitting data and receiving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 16 recites additional limitations, such as:
“presenting, to the computing device, the one or more noise related flags”, which is a step of presenting or outputting data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data outputting, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
At step 2B, the step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of presenting offers and gathering statistics (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 18 recites additional limitations, such as:
“…filter out a piece of content that contains one of misleading information and harmful content, remove a piece of content that is one of grammatically incorrect and poorly structured and discard a piece of content that is not relevant to a domain”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper filter out a piece of content that contains one of misleading information and harmful content, remove a piece of content that is one of grammatically incorrect and poorly structured and discard a piece of content that is not relevant to a domain, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the computer configured to perform gross filtering is further configured to” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 19 recites additional limitations, such as:
“…generate a noise score for each piece of content in the reduced number of pieces of content, to generate a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generate an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate a noise score for each piece of content in the reduced number of pieces of content, generate a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generate an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the computer configured to perform fine filtering is further configured to” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 20 recites additional limitations, such as:
“wherein the one or more noise related flags further comprises a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate one or more noise related flags comprising a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 21 recites additional limitations, such as:
“wherein the relevant to domain flag is a relevant to business to business (B2B) domain flag”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper generate one or more noise related flags wherein a relevant to domain flag is a relevant to business to business (B2B) domain flag, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 22 recites additional limitations, such as:
“…segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the gross filtering and fine filtering are performed based on the plurality of tokens for each piece of content”.
These limitations are directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the gross filtering and fine filtering are performed based on the plurality of tokens for each piece of content, and because the limitations do not recite any additional elements that are sufficient to amount to significantly more.
The additional elements “wherein the computer is further configured to” are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 23 recites additional limitations, such as:
“wherein the machine learning further comprises one or more of a natural language processing process, a statistical analysis process, a sematic analysis process and a machine learning model process”, which are additional elements recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 2-7, 9-16, and 18-23 are also directed to abstract idea without significantly more and 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) 1-4, 7-11, 14-20, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goldenstein (US Pub 2017/0308614) in view of Joy (US Pub 2021/0295183).
With respect to claim 1, Goldenstein discloses a system comprising:
a computer system having a processor and a plurality of lines of instructions executed by the processor (Goldenstein in [0077] and [0084] and in Figure 13 discloses a computer system comprising a processor and memory coupled to the processor, the memory comprising executable instructions executed by the processor to perform operations);
a computing device that interacts with the computer system to submit a query and receive search results from the computer system in response to the query, the search results being curated content having noise filtered out of the search results (Goldenstein in [0005] discloses queries conducted on sources utilizing parameters, sources associated with topic determined based on the parameters, results of queries aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantive content, profanity etc., filtered results assembled for delivery; Goldenstein in [0077] and [0093] and in Figure 14 discloses a computing device interacting with the computer system); and
the computer system being configured (Goldenstein in [0077] and [0093] and in Figure 14 discloses a computing device interacting with the computer system) to:
receive a plurality of pieces of content based on the query (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display);
perform…gross filtering on each piece of content to detect noise and domain relevance of each piece of content to generate a gross filtering result for each piece of content and discard a particular piece of content that does not pass the gross filtering to produce a reduced number of pieces of content (Goldenstein in [0041]-[0044] and in Figure 4 discloses global filtering comprising any appropriate combination of source filtering, duplicate filtering, noise filtering, or profanity filtering, source filtering utilized to remove irrelevant sources and/or spam, source filtering may compare a dictionary of continuously updated domain names and social media usernames with the article source URL, there is a match, the item may be tagged and identified for further review, if information does not pass source filtering, the source and/or information may be removed from the data stream, if information does pass source filtering, upon completion of global filtering, which is interpreted as gross filtering, the process may proceed to channel based filtering, which is interpreted as fine filtering);
perform…fine filtering to detect noise and relevance to a topic or a persona for each piece of content of the reduced number of pieces of content to generate a fine filtering result for each piece of content of the reduced number of pieces of content and discard a particular piece of content that fails to pass the fine filtering to produce a second reduced number of pieces of content (Goldenstein in [0045]-[0049] and in Figure 4 discloses channel based filtering may be performed on the data stream, channel based filtering may comprise any appropriate combination of language filtering, source filtering, or noise filtering, channel filtering operates strictly within a particular intelligence channel, while global filtering operates universally, assume an intelligence channel in which the topic is high performance automobiles, the intelligence channel may include all manufacturers but an intelligence channel could be filtered for use by General Motors to remove all other manufacturers, which is interpreted as fine filtering); and
analyze each piece of content of the second reduced number of pieces of content including the gross filtering result and the fine filtering result to generate…each piece of content of the second reduced number of pieces of content (Goldenstein in [0050] and in Figure 4 discloses analyzing content with global filter and channel based filter and the final result after the two stage of filtering is cleaned up and stored, which is analyzing content after the gross and fine filtering, the final resulting content is stored and available for delivery to the user).
Goldenstein discloses analyzing content to detect noise and filtering content, detecting noise, , however, Goldenstein does not explicitly disclose:
…using machine learning…;
analyze each piece of content…to generate one or more noise related flags for each piece of content….
The Joy reference discloses analyze each piece of content to generate one or more noise related flags for each piece of content (Joy in [0004] discloses classifying an alert as relevant using a relevance classification mode, which is a machine-learning trained model, identifying a proposed solution for the alert using a root cause analysis classification model, which is a machine-learning trained model; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs, relevant alerts used to train an artificial intelligence or machine learning system, monitoring logs to generate alerts as is necessary, alert including identification of the log data; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein and Joy, to have combined Goldenstein and Joy. The motivation to combine Goldenstein and Joy would be to classify content as noise or non-noise based on relevancy using a severity flag (Joy: [0023] and [0051]).
With respect to claim 2, Goldenstein in view of Joy discloses the system of claim 1, wherein the computer system configured to perform gross filtering is further configured to filter out a piece of content that contains one of misleading information and harmful content, remove a piece of content that is one of grammatically incorrect and poorly structured and discard a piece of content that is not relevant to a domain (Goldenstein in [0043] discloses noise filtering performed to remote items that do not add value, are irrelevant to a topic, or the like, noise filtering identifies and removes items of limited relevance and/or items that lack substantive content; Goldenstein in [0044] discloses performing profanity filtering by comparing keywords, phrases, etc. with articles obtained from sources to identify profanity, filter to remove any information determined to be inappropriate; Goldenstein in [0045] discloses performing any appropriate combination of language filtering, source filtering, or noise filtering.
With respect to claim 3, Goldenstein in view of Joy discloses the system of claim 2, wherein the computer system configured to perform fine filtering is further configured to generate a noise score for each piece of content in the reduced number of pieces of content, to generate a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generate an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content (Goldenstein in [0043] discloses noise filtering performed to remote items that do not add value, are irrelevant to a topic, or the like, noise filtering identifies and removes items of limited relevance and/or items that lack substantive content; Goldenstein in [0044] discloses performing profanity filtering by comparing keywords, phrases, etc. with articles obtained from sources to identify profanity, filter to remove any information determined to be inappropriate; Goldenstein in [0045] discloses performing any appropriate combination of language filtering, source filtering, or noise filtering; Goldenstein in [0063] and [0064] discloses influencer metrics weighted based on various characteristics and returned as score, weighting subjected to adjustment based on crowd-sourced information, such as actions of users of a channel such as views and curation of items, and refinement, scores represent online popularity, channel context or relevance indicative of highly ranked items related closely to a topic, determining relevance score by mentions of topic-related keywords in an item, a ratio of related keywords to other words, page rank, and other factors, relevance considers crowd-sourced data such as views and curation by users or items sourced from particular sources or authors).
With respect to claim 4, Goldenstein in view of Joy discloses the system of claim 3, wherein the one or more noise related flags further comprises a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
With respect to claim 7, Goldenstein in view of Joy discloses the system of claim 1, wherein the machine learning further comprises one or more of a natural language processing process, a statistical analysis process, a sematic analysis process and a machine learning model process (Goldenstein in [0025] discloses filtering based on user statistics and preferences; Goldenstein in [0050] and [0082] discloses semantic analysis of content performed to select and enhance content, natural language processing employed to highlight elements of interest and language detection in content; Goldenstein in [0070] discloses an intelligence channel updated in iterative fashion, such as self-learning closed loop, wherein information is fed back to improve development and refine the channel; Joy in [0004] discloses classifying an alert as relevant using a relevance classification mode, which is a machine-learning trained model, identifying a proposed solution for the alert using a root cause analysis classification model, which is a machine-learning trained mode; Joy in [0023] and [0038] discloses relevant alert used to train an artificial intelligence and/or machine learning system, tokenizing data to train one or more machine learning models).
With respect to claim 8, Goldenstein discloses a method, comprising:
receive a plurality of pieces of content based on the query (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display);
perform…gross filtering on each piece of content to detect noise and domain relevance of each piece of content to generate a gross filtering result for each piece of content and discard a particular piece of content that does not pass the gross filtering to produce a reduced number of pieces of content (Goldenstein in [0041]-[0044] and in Figure 4 discloses global filtering comprising any appropriate combination of source filtering, duplicate filtering, noise filtering, or profanity filtering, source filtering utilized to remove irrelevant sources and/or spam, source filtering may compare a dictionary of continuously updated domain names and social media usernames with the article source URL, there is a match, the item may be tagged and identified for further review, if information does not pass source filtering, the source and/or information may be removed from the data stream, if information does pass source filtering, upon completion of global filtering, which is interpreted as gross filtering, the process may proceed to channel based filtering, which is interpreted as fine filtering);
perform…fine filtering to detect noise and relevance to a topic or a persona for each piece of content of the reduced number of pieces of content to generate a fine filtering result for each piece of content of the reduced number of pieces of content and discard a particular piece of content that fails to pass the fine filtering to produce a second reduced number of pieces of content (Goldenstein in [0045]-[0049] and in Figure 4 discloses channel based filtering may be performed on the data stream, channel based filtering may comprise any appropriate combination of language filtering, source filtering, or noise filtering, channel filtering operates strictly within a particular intelligence channel, while global filtering operates universally, assume an intelligence channel in which the topic is high performance automobiles, the intelligence channel may include all manufacturers but an intelligence channel could be filtered for use by General Motors to remove all other manufacturers, which is interpreted as fine filtering); and
analyze each piece of content of the second reduced number of pieces of content including the gross filtering result and the fine filtering result to generate…each piece of content of the second reduced number of pieces of content (Goldenstein in [0050] and in Figure 4 discloses analyzing content with global filter and channel based filter and the final result after the two stage of filtering is cleaned up and stored, which is analyzing content after the gross and fine filtering, the final resulting content is stored and available for delivery to the user).
Goldenstein discloses analyzing content to detect noise and filtering content, detecting noise, , however, Goldenstein does not explicitly disclose:
…using machine learning…;
analyze each piece of content…to generate one or more noise related flags for each piece of content….
The Joy reference discloses analyze each piece of content to generate one or more noise related flags for each piece of content (Joy in [0004] discloses classifying an alert as relevant using a relevance classification mode, which is a machine-learning trained model, identifying a proposed solution for the alert using a root cause analysis classification model, which is a machine-learning trained model; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs, relevant alerts used to train an artificial intelligence or machine learning system, monitoring logs to generate alerts as is necessary, alert including identification of the log data; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein and Joy, to have combined Goldenstein and Joy. The motivation to combine Goldenstein and Joy would be to classify content as noise or non-noise based on relevancy using a severity flag (Joy: [0023] and [0051]).
With respect to claim 9, Goldenstein in view of Joy discloses the method of claim 8, wherein performing the gross filtering further comprises filtering out a piece of content that contains one of misleading information and harmful content, removing a piece of content that is one of grammatically incorrect and poorly structured and discarding a piece of content that is not relevant to a domain (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
With respect to claim 10, Goldenstein in view of Joy discloses the method of claim 9, wherein performing the fine filtering further comprises generating a noise score for each piece of content in the reduced number of pieces of content, generating a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generating an audience relevance score that identifies a target audience in the domain for each piece of content in the reduced number of pieces of content (Goldenstein in [0043] discloses noise filtering performed to remote items that do not add value, are irrelevant to a topic, or the like, noise filtering identifies and removes items of limited relevance and/or items that lack substantive content; Goldenstein in [0044] discloses performing profanity filtering by comparing keywords, phrases, etc. with articles obtained from sources to identify profanity, filter to remove any information determined to be inappropriate; Goldenstein in [0045] discloses performing any appropriate combination of language filtering, source filtering, or noise filtering; Goldenstein in [0063] and [0064] discloses influencer metrics weighted based on various characteristics and returned as score, weighting subjected to adjustment based on crowd-sourced information, such as actions of users of a channel such as views and curation of items, and refinement, scores represent online popularity, channel context or relevance indicative of highly ranked items related closely to a topic, determining relevance score by mentions of topic-related keywords in an item, a ratio of related keywords to other words, page rank, and other factors, relevance considers crowd-sourced data such as views and curation by users or items sourced from particular sources or authors).
With respect to claim 11, Goldenstein in view of Joy discloses the method of claim 10, wherein the one or more noise related flags further comprises a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
With respect to claim 14, Goldenstein in view of Joy discloses the method of claim 8, wherein the machine learning further comprises one or more of a natural language processing process, a statistical analysis process, a sematic analysis process and a machine learning model process (Goldenstein in [0025] discloses filtering based on user statistics and preferences; Goldenstein in [0050] and [0082] discloses semantic analysis of content performed to select and enhance content, natural language processing employed to highlight elements of interest and language detection in content; Goldenstein in [0070] discloses an intelligence channel updated in iterative fashion, such as self-learning closed loop, wherein information is fed back to improve development and refine the channel; Joy in [0004] discloses classifying an alert as relevant using a relevance classification mode, which is a machine-learning trained model, identifying a proposed solution for the alert using a root cause analysis classification model, which is a machine-learning trained mode; Joy in [0023] and [0038] discloses relevant alert used to train an artificial intelligence and/or machine learning system, tokenizing data to train one or more machine learning models).
With respect to claim 15, Goldenstein in view of Joy discloses the method of claim 8 further comprising submitting, by a computing device, a query so that the received plurality of pieces of content are in response to the query (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display).
With respect to claim 16, Goldenstein in view of Joy discloses the method of claim 8 further comprising presenting, to the computing device, the one or more noise related flags (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
With respect to claim 17, Goldenstein discloses a computer (Goldenstein in [0077] and [0084] and in Figure 13 discloses a computer system comprising a processor and memory coupled to the processor, the memory comprising executable instructions executed by the processor to perform operations), comprising:
a processor and a plurality of lines of instructions executed by the processor (Goldenstein in [0077] and [0084] and in Figure 13 discloses a computer system comprising a processor and memory coupled to the processor, the memory comprising executable instructions executed by the processor to perform operations);
the computer being configured (Goldenstein in [0077] and [0084] and in Figure 13 discloses a computer system comprising a processor and memory coupled to the processor, the memory comprising executable instructions executed by the processor to perform operations)to:
receive a plurality of pieces of content based on the query (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display);
perform…gross filtering on each piece of content to detect noise and domain relevance of each piece of content to generate a gross filtering result for each piece of content and discard a particular piece of content that does not pass the gross filtering to produce a reduced number of pieces of content (Goldenstein in [0041]-[0044] and in Figure 4 discloses global filtering comprising any appropriate combination of source filtering, duplicate filtering, noise filtering, or profanity filtering, source filtering utilized to remove irrelevant sources and/or spam, source filtering may compare a dictionary of continuously updated domain names and social media usernames with the article source URL, there is a match, the item may be tagged and identified for further review, if information does not pass source filtering, the source and/or information may be removed from the data stream, if information does pass source filtering, upon completion of global filtering, which is interpreted as gross filtering, the process may proceed to channel based filtering, which is interpreted as fine filtering);
perform…fine filtering to detect noise and relevance to a topic or a persona for each piece of content of the reduced number of pieces of content to generate a fine filtering result for each piece of content of the reduced number of pieces of content and discard a particular piece of content that fails to pass the fine filtering to produce a second reduced number of pieces of content (Goldenstein in [0045]-[0049] and in Figure 4 discloses channel based filtering may be performed on the data stream, channel based filtering may comprise any appropriate combination of language filtering, source filtering, or noise filtering, channel filtering operates strictly within a particular intelligence channel, while global filtering operates universally, assume an intelligence channel in which the topic is high performance automobiles, the intelligence channel may include all manufacturers but an intelligence channel could be filtered for use by General Motors to remove all other manufacturers, which is interpreted as fine filtering); and
analyze each piece of content of the second reduced number of pieces of content including the gross filtering result and the fine filtering result to generate…each piece of content of the second reduced number of pieces of content (Goldenstein in [0050] and in Figure 4 discloses analyzing content with global filter and channel based filter and the final result after the two stage of filtering is cleaned up and stored, which is analyzing content after the gross and fine filtering, the final resulting content is stored and available for delivery to the user).
Goldenstein discloses analyzing content to detect noise and filtering content, detecting noise, , however, Goldenstein does not explicitly disclose:
…using machine learning…;
analyze each piece of content…to generate one or more noise related flags for each piece of content….
The Joy reference discloses analyze each piece of content to generate one or more noise related flags for each piece of content (Joy in [0004] discloses classifying an alert as relevant using a relevance classification mode, which is a machine-learning trained model, identifying a proposed solution for the alert using a root cause analysis classification model, which is a machine-learning trained model; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs, relevant alerts used to train an artificial intelligence or machine learning system, monitoring logs to generate alerts as is necessary, alert including identification of the log data; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein and Joy, to have combined Goldenstein and Joy. The motivation to combine Goldenstein and Joy would be to classify content as noise or non-noise based on relevancy using a severity flag (Joy: [0023] and [0051]).
With respect to claim 18, Goldenstein in view of Joy discloses the computer of claim 17, wherein the computer configured to perform gross filtering is further configured to filter out a piece of content that contains one of misleading information and harmful content, remove a piece of content that is one of grammatically incorrect and poorly structured and discard a piece of content that is not relevant to a domain (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
With respect to claim 19, Goldenstein in view of Joy discloses the computer of claim 18, wherein the computer configured to perform fine filtering is further configured to generate a noise score for each piece of content in the reduced number of pieces of content, to generate a content relevance score in which the noise in each piece of content in the reduced number of pieces of content is determined based on a context of the piece of content in the reduced number of pieces of content and generate an audience relevance score that identified a target audience in the domain for each piece of content in the reduced number of pieces of content (Goldenstein in [0043] discloses noise filtering performed to remote items that do not add value, are irrelevant to a topic, or the like, noise filtering identifies and removes items of limited relevance and/or items that lack substantive content; Goldenstein in [0044] discloses performing profanity filtering by comparing keywords, phrases, etc. with articles obtained from sources to identify profanity, filter to remove any information determined to be inappropriate; Goldenstein in [0045] discloses performing any appropriate combination of language filtering, source filtering, or noise filtering; Goldenstein in [0063] and [0064] discloses influencer metrics weighted based on various characteristics and returned as score, weighting subjected to adjustment based on crowd-sourced information, such as actions of users of a channel such as views and curation of items, and refinement, scores represent online popularity, channel context or relevance indicative of highly ranked items related closely to a topic, determining relevance score by mentions of topic-related keywords in an item, a ratio of related keywords to other words, page rank, and other factors, relevance considers crowd-sourced data such as views and curation by users or items sourced from particular sources or authors).
With respect to claim 20, Goldenstein in view of Joy discloses the computer of claim 19, wherein the one or more noise related flags further comprises a content safe for consumption flag, a well formed content flag, a relevant to domain flag, a noise level flag, a content relevant to query flag and a content relevant to target persona flag (Goldenstein in [0005] and [0038] discloses queries conducted on source utilizing parameters, results of the queries are aggregated and/or filtered to remove irrelevant information, duplicative information, information lacking substantiative content, profanity or the like, filtered results assembled for delivery in an interactive display; Joy in [0023] and [0027] discloses classifying relevant irrelevant or noise and relevant or non-noise alerts received from logs; Joy in [0028] and [0049] discloses alert includes a severity indicator, such as severity flag with several levels, such as critical, high, medium, and low, receiving an indication of the severity of the alert with the alert as a severity flag, alert classified according to its severity flag; Joy in [0050] and [0051] discloses based on the severity flag classifying alerts, severity flag used with relevance classification model to classify alert as relevant or not relevant).
With respect to claim 23, Goldenstein in view of Joy discloses the computer of claim 17, wherein the machine learning further comprises the computer configured to perform one or more of a natural language processing process, a statistical analysis process, a sematic analysis process and a machine learning model process (Goldenstein in [0025] discloses filtering based on user statistics and preferences; Goldenstein in [0050] and [0082] discloses semantic analysis of content performed to select and enhance content, natural language processing employed to highlight elements of interest and language detection in content; Goldenstein in [0070] discloses an intelligence channel updated in iterative fashion, such as self-learning closed loop, wherein information is fed back to improve development and refine the channel; Joy in [0004] discloses classifying an alert as relevant using a relevance classification mode, which is a machine-learning trained model, identifying a proposed solution for the alert using a root cause analysis classification model, which is a machine-learning trained mode; Joy in [0023] and [0038] discloses relevant alert used to train an artificial intelligence and/or machine learning system, tokenizing data to train one or more machine learning models).
Claim(s) 5, 12, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goldenstein (US Pub 2017/0308614) in view of Joy (US Pub 2021/0295183) and in further view of Griffith (US Pub 2009/0320045).
With respect to claim 5, Goldenstein in view of Joy discloses the system of claim 4, Goldenstein discloses determining domain relevance of content and Joy discloses flagging content based on relevancy, however, Goldenstein and Joy do not explicitly disclose:
wherein the relevant to domain flag is a relevant to business to business (B2B) domain flag.
The Griffith reference discloses a relevant to domain flag is a relevant to business to business (B2B) domain flag (Griffith in [0012], [0078], and [0292] discloses a flag must exist for each entry within a repository, flag specifying a target deployment platform, any object within a specific domain entering a system and causing conflicts due to business to business interactions will be flagged, the flag will be stored to notify of the conflict for possible solution).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein, Joy, and Griffith, to have combined Goldenstein, Joy, and Griffith. The motivation to combine Goldenstein, Joy, and Griffith would be to understand information associated with stored entry by specifying a flag for the entry (Griffith: [0078]).
With respect to claim 12, Goldenstein in view of Joy discloses the method of claim 11, Goldenstein discloses determining domain relevance of content and Joy discloses flagging content based on relevancy, however, Goldenstein and Joy do not explicitly disclose:
wherein the relevant to domain flag is a relevant to business to business (B2B) domain flag.
The Griffith reference discloses a relevant to domain flag is a relevant to business to business (B2B) domain flag (Griffith in [0012], [0078], and [0292] discloses a flag must exist for each entry within a repository, flag specifying a target deployment platform, any object within a specific domain entering a system and causing conflicts due to business to business interactions will be flagged, the flag will be stored to notify of the conflict for possible solution).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein, Joy, and Griffith, to have combined Goldenstein, Joy, and Griffith. The motivation to combine Goldenstein, Joy, and Griffith would be to understand information associated with stored entry by specifying a flag for the entry (Griffith: [0078]).
With respect to claim 21, Goldenstein in view of Joy discloses the computer of claim 20, Goldenstein discloses determining domain relevance of content and Joy discloses flagging content based on relevancy, however, Goldenstein and Joy do not explicitly disclose:
wherein the relevant to domain flag is a relevant to business to business (B2B) domain flag.
The Griffith reference discloses a relevant to domain flag is a relevant to business to business (B2B) domain flag (Griffith in [0012], [0078], and [0292] discloses a flag must exist for each entry within a repository, flag specifying a target deployment platform, any object within a specific domain entering a system and causing conflicts due to business to business interactions will be flagged, the flag will be stored to notify of the conflict for possible solution).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein, Joy, and Griffith, to have combined Goldenstein, Joy, and Griffith. The motivation to combine Goldenstein, Joy, and Griffith would be to understand information associated with stored entry by specifying a flag for the entry (Griffith: [0078]).
Claim(s) 6, 13, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Goldenstein (US Pub 2017/0308614) in view of Joy (US Pub 2021/0295183) and in further view of Chatterjee (US Pub 2020/0210817).
With respect to claim 6, Goldenstein in view of Joy discloses the system of claim 1, wherein the computer system is further configured to segment each piece of content…into a plurality of tokens, wherein the gross filtering and fine filtering are performed…for each piece of content (Joy in [0009] and [0016] discloses pre-processing data to remove stop words and special characters, tokenizing data using term frequency-inverse document frequency, training classification models with the tokenized pre-processed data; Joy in [0037] and [0038] discloses tokenize data by converting the data into numerical format, using the tokenized data to train one or more machine learning models).
Goldenstein discloses performing gross filtering and fine filtering are performed for each piece of content and Joy discloses pre-processing data by tokenizing the data into tokens and using the tokenized data to train one or more machine learning models, however, Goldenstein and Joy do not explicitly disclose:
segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the filtering…performed based on the plurality of tokens for each piece of content;
The Chatterjee reference discloses segmenting each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the filtering is performed based on the plurality of tokens for each piece of content (Chatterjee in [0022] and [0025] discloses a preprocessing module receives input data, input data includes text data such as a natural language sentence, process the input data to prepare the input data, segment the natural language sentence into multiple tokens, filter the tokens to remove irrelevant tokens for a particular natural language processing task, tokens filtered to remove punctuations and stop words which may add noise, receive preprocessed input data, such as multiple tokens or prepared input data without noise, further process the preprocessed data to obtain relevant portions, such as further segmented, evaluated, and filtered to obtain relevant tokens, from the relevant portions or tokens determine a set of influential portions, determine a degree of influence score for a given relevant portion; Chatterjee in [0032] and [0050] discloses input data can be a single sentence or a set of sentences, which is segmented into a number of portions and processed to filter-out irrelevant portions while retaining relevant portions).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein, Joy, and Chatterjee, to have combined Goldenstein, Joy, and Chatterjee. The motivation to combine Goldenstein, Joy, and Chatterjee would be to build an improved and more efficient artificial intelligence model by adjusting and modifying the training data for the model to include relevant portions of given input data (Chatterjee: [0003] and [0004]).
With respect to claim 13, Goldenstein in view of Joy discloses the method of claim 8 further comprising segmenting each piece of content…into a plurality of tokens, wherein the gross filtering and fine filtering are performed…for each piece of content (Joy in [0009] and [0016] discloses pre-processing data to remove stop words and special characters, tokenizing data using term frequency-inverse document frequency, training classification models with the tokenized pre-processed data; Joy in [0037] and [0038] discloses tokenize data by converting the data into numerical format, using the tokenized data to train one or more machine learning models)..
Goldenstein discloses performing gross filtering and fine filtering are performed for each piece of content and Joy discloses pre-processing data by tokenizing the data into tokens and using the tokenized data to train one or more machine learning models, however, Goldenstein and Joy do not explicitly disclose:
segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the filtering…performed based on the plurality of tokens for each piece of content;
The Chatterjee reference discloses segmenting each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the filtering is performed based on the plurality of tokens for each piece of content (Chatterjee in [0022] and [0025] discloses a preprocessing module receives input data, input data includes text data such as a natural language sentence, process the input data to prepare the input data, segment the natural language sentence into multiple tokens, filter the tokens to remove irrelevant tokens for a particular natural language processing task, tokens filtered to remove punctuations and stop words which may add noise, receive preprocessed input data, such as multiple tokens or prepared input data without noise, further process the preprocessed data to obtain relevant portions, such as further segmented, evaluated, and filtered to obtain relevant tokens, from the relevant portions or tokens determine a set of influential portions, determine a degree of influence score for a given relevant portion; Chatterjee in [0032] and [0050] discloses input data can be a single sentence or a set of sentences, which is segmented into a number of portions and processed to filter-out irrelevant portions while retaining relevant portions).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein, Joy, and Chatterjee, to have combined Goldenstein, Joy, and Chatterjee. The motivation to combine Goldenstein, Joy, and Chatterjee would be to build an improved and more efficient artificial intelligence model by adjusting and modifying the training data for the model to include relevant portions of given input data (Chatterjee: [0003] and [0004]).
With respect to claim 22, Goldenstein in view of Joy discloses the computer of claim 17, wherein the computer is further configured to segment each piece of content into…a plurality of tokens, wherein the gross filtering and fine filtering are performed …for each piece of content (Joy in [0009] and [0016] discloses pre-processing data to remove stop words and special characters, tokenizing data using term frequency-inverse document frequency, training classification models with the tokenized pre-processed data; Joy in [0037] and [0038] discloses tokenize data by converting the data into numerical format, using the tokenized data to train one or more machine learning models)..
Goldenstein discloses performing gross filtering and fine filtering are performed for each piece of content and Joy discloses pre-processing data by tokenizing the data into tokens and using the tokenized data to train one or more machine learning models, however, Goldenstein and Joy do not explicitly disclose:
segment each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the filtering…performed based on the plurality of tokens for each piece of content;
The Chatterjee reference discloses segmenting each piece of content into at least one sentence, segment each sentence into a plurality of tokens, wherein the filtering is performed based on the plurality of tokens for each piece of content (Chatterjee in [0022] and [0025] discloses a preprocessing module receives input data, input data includes text data such as a natural language sentence, process the input data to prepare the input data, segment the natural language sentence into multiple tokens, filter the tokens to remove irrelevant tokens for a particular natural language processing task, tokens filtered to remove punctuations and stop words which may add noise, receive preprocessed input data, such as multiple tokens or prepared input data without noise, further process the preprocessed data to obtain relevant portions, such as further segmented, evaluated, and filtered to obtain relevant tokens, from the relevant portions or tokens determine a set of influential portions, determine a degree of influence score for a given relevant portion; Chatterjee in [0032] and [0050] discloses input data can be a single sentence or a set of sentences, which is segmented into a number of portions and processed to filter-out irrelevant portions while retaining relevant portions).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Goldenstein, Joy, and Chatterjee, to have combined Goldenstein, Joy, and Chatterjee. The motivation to combine Goldenstein, Joy, and Chatterjee would be to build an improved and more efficient artificial intelligence model by adjusting and modifying the training data for the model to include relevant portions of given input data (Chatterjee: [0003] and [0004]).
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/R.M/Examiner, Art Unit 2159 /MARC S SOMERS/Primary Examiner, Art Unit 2159
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