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
1. The pending claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-10 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis below of the claims’ subject matter eligibility follows the guidance set forth in MPEP 2106 which has incorporated the 2019 PEG.
Regarding to claim 1,
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 1 recites: A method comprising:
“loading a plurality of data sources wherein each data source is comprised of content of interest”. This element reads on a person loads a plurality of data sources wherein each data source is comprised of content of interest which could be considered a mental process of an observation or evaluation.
“scanning the plurality of data sources for the content of interest”. This element reads on a person scans the plurality of data sources for the content of interest which could be considered a mental process of an observation or evaluation.
“detecting the content of interest from scanning the plurality of data sources, wherein detecting is based on a key word search”. This element reads on a person detects the content of interest from scanning the plurality of data sources, wherein detecting is based on a key word search which could be considered a mental process of an observation or evaluation.
“organizing the content of interest as data elements, based on a topic modeling technique”. This element reads on a person organizes the content of interest as data elements, based on a topic modeling technique which could be considered a mental process of an observation or evaluation.
“distributing the data elements into a plurality of topics using a topic modeling algorithm”. This element reads on a person distributes the data elements into a plurality of topics using a topic modeling algorithm which could be considered a mental process of an observation or evaluation.
“merging the data elements within each of the plurality of topics”. This element reads on a person merges the data elements within each of the plurality of topics which could be considered a mental process of an observation or evaluation.
“generating a document with the topics into one or more filtered lists, based on the key word search”. This element reads on a person generates a document with the topics into one or more filtered lists, based on the key word search which could be considered a mental process of an observation or evaluation.
Overall, the limitations directed to organize data sources into a plurality of topics and the various mental process limitations in the context of this claim encompasses limitations that are not only considered to be directed to limitations that could be practically performed in the human mind (including observations and preform an evaluation, judgment, and opinion) aided by the use of pen and paper. If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitation in the mind but for the recitation of generic computer components, then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: In Step 2A Prong 2, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether they integrate the exception into a practical application of the exception.
In particular, the claim only recites the additional elements of “a computer-implemented”
Regarding the computer-implemented,
The processor of a computer system for generating and storing in all steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating and storing). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component(s). 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 additional element “a computer-implemented” is simply applying the abstract idea, and there is nothing done with results. 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, and does not provide any improvement in computer technology (see MPEP2106.05(a)).
Therefore, the additional elements do not integrate the judicial exception into a practical application.
Step 2B Analysis: In Step 2B, we are directed to Identify whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluate those additional elements to determine whether the additional elements, taken individually and in combination, result in the claim as a whole amounting to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, The additional elements “a computer-implemented” is simply applying the abstract idea, and there is nothing done with results.
Accordingly, this additional element(s), taken individually and in combination, do not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 2,
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 2 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 2 recites “classifying one or more data elements within one of the plurality of topics as duplicate; and deleting duplicate data elements within the each of the topics " That is, the claim recites classifying one or more data elements within one of the plurality of topics as duplicate; and deleting duplicate data elements within the each of the topics. The above-noted limitation of claim 2, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 3,
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 3 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 3 recites “wherein the topic modeling algorithm comprises Latent Dirichlet Allocation (LDA)" That is, the claim recites wherein the topic modeling algorithm comprises Latent Dirichlet Allocation (LDA). The above-noted limitation of claim 3, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 4,
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 4 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 4 recites “the data sources are consolidated based on a context." That is, the claim recites the data sources are consolidated based on a context. The above-noted limitation of claim 4, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 5,
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 5 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 5 recites “the data sources are consolidated based on a source." That is, the claim recites the data sources are consolidated based on a source. The above-noted limitation of claim 5, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 6,
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 6 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 6 recites “a user choosing a topic of the content of interest." That is, the claim recites a user choosing a topic of the content of interest. The above-noted limitation of claim 6, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 7,
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 7 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 7 recites “the topic modeling technique comprises dimensionality reduction." That is, the claim recites the topic modeling technique comprises dimensionality reduction. The above-noted limitation of claim 7, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Accordingly, this additional element, taken individually and in combination, does not result in the claim as a whole amounting to significantly more than the judicial exception. The claim is not patent eligible.
Regarding claim 8,
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 8 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 8 recites “the topic modeling technique comprises unsupervised learning." That is, the claim recites the topic modeling technique comprises unsupervised learning. The above-noted limitation of claim 8, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 9,
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 9 is dependent on claim 1, which as indicated in the analysis above, is directed to an abstract idea without significantly more.
Claim 9 recites “the topic modeling technique comprises tagging." That is, the claim recites the topic modeling technique comprises tagging. The above-noted limitation of claim 9, 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, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 10 and 12-16 are rejected under 35 U.S.C. 101 with the same rational of claims 1-3 and 7-9.
Claims 17-20 are rejected under 35 U.S.C. 101 with the same rational of claims 1-3 and 7.
Claim Rejections - 35 USC § 102
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1, 3, 6, 8, 10-11, 13 and 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Asur et al (U.S. 20160063122 A1 hereinafter, “Asur”).
6. With respect to claim 1,
Asur discloses a computer-implemented method comprising:
loading a plurality of data sources (e.g. In a number of examples, content can include a tweet on Twitter, a Facebook post, and/or other social media content associated with an event (e.g., an event of interest)) wherein each data source is comprised of content (e.g. content) of interest (e.g. interest);
scanning (e.g. discovering) the plurality of data sources for the content (e.g. content) of interest (e.g. interest);
detecting the content of interest (e.g. interest) from scanning the plurality of data sources, wherein detecting is based on a key word search (e.g. keyword-based query);
organizing the content of interest as data elements (e.g. subsets), based on a topic modeling technique (e.g. topic model);
distributing the data elements into a plurality of topics using a topic modeling algorithm (e.g. topic model);
merging (e.g. merge) the data elements (e.g. subsets) within each of the plurality of topics (Asur [0027] – [0029], [0068] – [0071] e.g. [0027] The content De2 can be relevant to the event e, but in a number of examples, may not contain the keywords given by the query Q at 216. For example, "top-ranked" (e.g., most relevant) words in each topic z
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Z can give additional keywords that can be used to describe various aspects of the event e. The additional keywords, and in turn additional content sets (e.g., additional set of tweets De2) can be obtained by finding content d
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D that is not present in De1 by selecting those with a high perplexity score (e.g., a perplexity score above a threshold) with respect to the topics, as will be discussed further herein. [0028] At 228, the subsets of content De1 and De2 can be merged, and the merged content DB=De1 U De2 can be used to find additional aspects of the event e. For example, merging subsets De1 and De2 can improve upon topics for the event e. Merging the content can improve the coverage on a content conversation, which can result in a more relevant and informative topic model (e.g., more relevant and informative GDTM). [0029] From each of the topics z
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Z, event e can be summarized (e.g., as a summary within summaries Se at 234) by selecting the content d from each topic z that gives the "best" (e.g., lowest) perplexity score (e.g., the most probably content at 232). At 230, content from unfiltered social media content stream D 214 can be "checked" to see if the content fits any of the topics Z. For example, content from unfiltered social media content stream D 214 can be filtered using topic Z already computed to learn if the content is relevant. [0068] A merge module 457 can include CRI that when executed by the processing resource 442 can merge the first subset of social media content and the second subset of social media content. The merged content can be used to find additional aspects of the event e. [0069] A construction module 459 can include CRI that when executed by the processing resource 442 can construct a summary of the event based on the merged subsets and a perplexity score of social media content within the merged subsets. The constructed event summary can include, for instance, a search extracted representative content from the unfiltered social media content stream for a number of aspects (e.g., topics) of the event. …. [0070] In some instances, the processing resource 442 coupled to the memory resource 448 can execute CRI 458 to extract a first set of social media content relevant to an event from an unfiltered stream of social media content utilizing a keyword-based query; … and construct a summary of the event utilizing the first set of social media content and the second set of social media content. In a number of examples, the second set of social media content can comprise social media content not included in the first set of social media content. For example, the second set of social media content can comprise d
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D, d∉De1. In a number of examples, a third, fourth, and/or any number of sets of social media content relevant to the event can be extracted from the unfiltered stream of social media content. For example, this can be performed multiple times, and a topic model can be continuously refined as a result. [0071] The processing resource 442 coupled to the memory resource 448 can execute CRI 458 in a number of examples to merge the first set of social media content and the second set of social media content, wherein the merged content includes a number of topics associated with the event and summarize the event by selecting social media content from each of the number of topics that results in a lowest perplexity score with respect to each of the number of topics. In a number of examples, the perplexity score utilized in the event summarization comprises a measure of a likelihood that the social media content from each of the number of topics is relevant to the event.); and
generating a document (e.g. merged content - The processing resource 442 coupled to the memory resource 448 can execute CRI 458 in a number of examples to merge the first set of social media content and the second set of social media content, wherein the merged content includes a number of topics associated with the event and summarize the event by selecting social media content from each of the number of topics that results in a lowest perplexity score with respect to each of the number of topics) with the topics into one or more filtered lists (e.g. For example, content from unfiltered social media content stream D 214 can be filtered using topic Z already computed to learn if the content is relevant), based on the key word search (Asur [0022], [0025], [0027], [0053], [0064], [0066], [0070] e.g. [0025] A topic model can be applied to content subset De1 at 220 to obtain topics Z 222 (e.g., aspects, other keywords that describe various aspects of event e, etc.), which can result in an increased understanding of different aspects in the content De1, as compared to an understanding using just the keyword search at 216. The topic model applied can include, for instance, a Decay Topic Model (DTM) and/or a Gaussian Decay Topic Model (GDTM), as will be discussed further herein. The use of the topic model at 220 can be referred to as, for example, "learning an unsupervised topic model.") (Asur [0007], [0010] – [0011], [0016] – [0017], [0020] – [0023], [0027] – [0029], [0064] – [0065] e.g. [0011] For example, event summarization according to the present disclosure can address summarizing a targeted event of interest (e.g., for a human reader) by extracting representative content from an unfiltered social media content stream for the event. …. [0016] At 102, content (e.g., social media content) from an unfiltered social media content stream (e.g., an unfiltered Twitter stream, unfiltered Facebook posts, etc.) associated with an event can be extracted utilizing a topic model. A topic model can include, for instance, a model for discovering topics and/or events that occur in the unfiltered media stream. For example the topic model can include a topic model that considers a decay parameter and/or a temporal correlation parameter, as will be discussed further herein. [0017] In a number of examples, content can include a tweet on Twitter, a Facebook post, and/or other social media content associated with an event (e.g., an event of interest). …. [0020] For example, the constructed summary can include, for example, a single representative content (e.g., a single tweet) that is the most relevant to an event and/or a combination of content (e.g., a number of tweets, words extracted from particular tweets, etc.). The summary can also include a number of different summaries relating to a number of aspects (topics) of the event. For example, an event of interest may include a baseball game with a number of aspects, including a final score, home runs, stolen bases, etc. Each aspect of the baseball game event can have a summary, and/or the overall event can have a summary, for instance. …. [0022] …. To summarize the event of interest a from the unfiltered social media stream D (e.g., unfiltered Tweet stream 214), it can be assumed that there is a set of queries Q, wherein each query q
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Q q is defined by a set of keywords For example, a set of queries for an event "Facebook IPO" may include {{facebook, ipo}, {fb, ipo}, {facebook, initial, public, offer}, {fb, initial, public, offer}, {facebook, initial, offering}, {fb, initial, public, offering}}. [0023] A keyword-based search (e.g., a keyword-based query Q) can be applied at 216 on the unfiltered social media content stream D 214 to obtain an initial subset 218 of relevant content D.sub.e.sup.1 for the event e. For instance, from unfiltered social media stream D, content (e.g., tweets) relevant to an event e can be extracted, such that a relevant piece of content includes content that matches at least one of the queries q
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Q. A piece of content, for example, matches a query q if it contains a number (e.g., all) of the keywords in q. [0027] The content D.sub.e.sup.2 can be relevant to the event e, but in a number of examples, may not contain the keywords given by the query Q at 216. For example, "top-ranked" (e.g., most relevant) words in each topic z
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Z can give additional keywords that can be used to describe various aspects of the event e. The additional keywords, and in turn additional content sets (e.g., additional set of tweets D.sub.e.sup.2) can be obtained by finding content d
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D that is not present in D.sub.e.sup.1 by selecting those with a high perplexity score (e.g., a perplexity score above a threshold) with respect to the topics, as will be discussed further herein. [0028] At 228, the subsets of content De1 and De2 can be merged, and the merged content DB=De1 U De2 can be used to find additional aspects of the event e. For example, merging subsets De1 and De2 can improve upon topics for the event e. Merging the content can improve the coverage on a content conversation, which can result in a more relevant and informative topic model (e.g., more relevant and informative GDTM). [0029] From each of the topics z
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Z, event e can be summarized (e.g., as a summary within summaries Se at 234) by selecting the content d from each topic z that gives the "best" (e.g., lowest) perplexity score (e.g., the most probably content at 232). At 230, content from unfiltered social media content stream D 214 can be "checked" to see if the content fits any of the topics Z. For example, content from unfiltered social media content stream D 214 can be filtered using topic Z already computed to learn if the content is relevant. [0064] In some examples, the system can include a receipt module 450. A receipt module 450 can include CRI that when executed by the processing resource 442 can receive a set of queries, wherein each query in the set of queries is defined by a first set of keywords associated with an event. In a number of examples, the event comprises a concept of interest targeted by a user of the social media (e.g., a user using social media, a user observing social media, etc.). For example, a particular user may choose a targeted topic to summarize. [0065] An extraction module 452 can include CRI that when executed by the processing resource 442 can extract, from an unfiltered social media content stream, a first subset of social media content that matches a first query within the set of queries. Content, for example, matches a query q if it contains a number of (e.g., all) the keywords in q.).
7. With respect to claim 3,
Asur discloses wherein the topic modeling algorithm comprises Latent Dirichlet Allocation (LDA) (Asur [0025] e.g. Latent Dirichlet Allocation Model (NP+LDA), for example.).
8. With respect to claim 6,
Asur discloses a user choosing a topic of the content of interest (Asur [0064] e.g. For example, a particular user may choose a targeted topic to summarize).
9. With respect to claim 8,
Asur discloses wherein the topic modeling technique comprises unsupervised learning (Asur [0025] e.g. The use of the topic model at 220 can be referred to as, for example, "learning an unsupervised topic model").
10. Claims 10, 13 and 16 are same as claims 1, 3 and 8 and are rejected for the same reasons as applied hereinabove.
11. With respect to claim 11,
Asur discloses
12. Claims 17-18 are same as claims 1 and 3 and are rejected for the same reasons as applied hereinabove.
Claim Rejections - 35 USC § 103
13. 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 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.
14. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
15. 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.
16. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
17. Claims 2, 4, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Asur in view of Ozzie et al (U.S. 20110179020 A1 hereinafter, “Ozzie”).
18. With respect to claim 2,
Although Asur substantially teaches the claimed invention, Asur does not explicitly indicate
classifying one or more data elements within one of the plurality of topics as duplicate; and
deleting duplicate data elements within the each of the topics.
Ozzie teaches the limitations by stating
classifying one or more data elements within one of the plurality of topics as duplicate; and
deleting duplicate data elements within the each of the topics (Ozzie [0059] e.g. topic/topical data; de-duplication).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Asur and Ozzie, to provide event summarization can include extracting Content from an unfiltered social media content associated with an event. Event summarization can also include constructing a summary of the event based on the extracted content (Asur [abstract]).
19. With respect to claim 4,
Asur further discloses wherein the data sources are consolidated based on a context (Ozzie [0002], [0026] e.g. [0002] Some applications may be used to consolidate the data items of one or more data feeds, and may notify the user upon receiving a new data item. However, if each data feed comprises many data items, the user might have to review a large volume of data items, of which many might not pertain to topics of interest to the user. Moreover, the user may be reluctant to expand the number of data feeds that are followed, and may therefore miss some topically related data items of data feeds that the user has elected not to follow. [0026] … As a second example, the user 12 may consolidate the date items 18 received from various data sources 14 and data feeds 16 for access through a single user interface. For example, the user 12 may access email messages from many mailing lists through a single email client, or may access a set of RSS feeds through an RSS aggregator (e.g., through an aggregator application that executes locally on the computer of the user 12 to request multiple data feeds 16 and present the data items 18 of these data feeds 16 together, or through an aggregator service that a user 12 may visit to receive an aggregated data feed 16 generated from multiple data feeds 16 by the aggregator service.) By consolidating the data items 18 of many data feeds 16, the user 12 may more easily identify data items associated with a particular topic 20, e.g., by filtering email messages or RSS syndication items for a particular keyword in the subject or body of the message or item.).
20. Claim 12 is same as claim 2 and is rejected for the same reasons as applied hereinabove.
21. Claim 20 is same as claim 2 and is rejected for the same reasons as applied hereinabove.
22. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Asur in view of Simons et al (U.S. 20220399086 A1 hereinafter, “Simons”).
23. With respect to claim 5,
Although Asur substantially teaches the claimed invention, Asur does not explicitly indicate wherein the data sources are consolidated based on a source.
Simons teaches the limitations by stating wherein the data sources are consolidated based on a source (Simons [0021] e.g. Categories under which medical inquiries that can be answered are determined by an analysis of one or more clinical data sources (e.g., CCDA (Consolidated Clinical Document Architecture), FHIR (Fast Healthcare Interoperability Resources) queries, medication histories, fill data, and/or the like) of the patient. Embodiments herein also provide for another ML model that is trained on prior medical inquiries and/or answers to determine if a question is answerable by the systems and devices described herein prior to determining medical inquiry categories. Additionally, an ML category model is configured to determine answers for medical inquiries from the appropriate clinical data source based on the determined category(ies). These answers may be either directly sourced from a data source or may include generated data that is based on a data source (e.g., age of the patient can be generated based on identifying a patient birthdate )).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Asur and Simons, to provide event summarization can include extracting Content from an unfiltered social media content associated with an event. Event summarization can also include constructing a summary of the event based on the extracted content (Asur [abstract])
24. Claims 7, 9, 14-15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Asur in view of Hurwitz et al (U.S. 20220384034 A1 hereinafter, “Hurwitz”).
25. With respect to claim 7,
Although Asur substantially teaches the claimed invention, Asur does not explicitly indicate wherein the topic modeling technique comprises dimensionality reduction.
Hurwitz teaches the limitations by stating wherein the topic modeling technique comprises dimensionality reduction (Hurwitz [0030] – [0031] e.g. dimensionality reduction).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the invention, in view of the teachings of Asur and Hurwitz, to provide event summarization can include extracting Content from an unfiltered social media content associated with an event. Event summarization can also include constructing a summary of the event based on the extracted content (Asur [abstract]).
26. With respect to claim 9,
Hurwitz further discloses wherein the topic modeling technique comprises tagging (Hurwitz [0030] – [0031] e.g. topic tagging).
27. Claims 14-15 are same as claims 7 and 9 and are rejected for the same reasons as applied hereinabove.
28. Claim 19 is same as claim 7 and is rejected for the same reasons as applied hereinabove.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
29. The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
30. When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
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/SYLING YEN/Primary Examiner, Art Unit 2166
September 20, 2026
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