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 .
Claim Rejections - 35 USC § 102
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.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-8, 10, and 13-22 are rejected under 35 U.S.C. 102(a)(1)/102(a)(2) as being anticipated by Ash et al. (US 2022/0292423).
Regarding Claims 1, 20, and 21, Ash discloses a system comprising:
one or more memory devices storing processor-executable instructions ([0203], Ash); and
one or more processors configured to execute instructions to cause the system to perform operations comprising ([0203], Ash):
extracting information associated with a first instance of content associated with a first person accessed from a data source ([0086], “a “core topic” 106 or main subject for a promotional or marketing effort, related to one or more topics, phrases, or the like extracted based on the methods and systems described herein from a primary online content object 102,” [0138], “These include changes in management, especially in “C-level” executives like the CEO, CTO, CFO, CMO, CIO, CSO or the like and officer-level executives like the President or the VP of engineering, marketing or finance, which may indicate directional changes that tend to lead to increased or decreased purchasing,” [0144], “a press release issued by Company B that states in the body text “Company B has recently added Person X as its new CTO,” the information extraction system 204 may extract the entities Person X, Company B, Company C, and CTO. The name of Person X and Company C may be extracted from the resume based on language patterns associated with resumes, while the names of Company B and Person A, and the role CTO may be extracted from the press release based on a parsing and natural language processing of the sentence quoted above,” wherein “name of Person X and Company C may be extracted from the resume” is an example of the information claimed; Ash);
cleaning the extracted information based on contextual information associated with the first instance of the content by analyzing relationships using the contextual information between a first element and a second element distinct from first element in the extracted information ([0088], “Within the platform 100, key phrases 112 are extracted from the primary online content object 102 and are processed, such as using a variety of models 118, resulting in one or more content clusters 130 that are stored in a content cluster data store 132. The clusters may comprise the topic clusters 168 that are semantically relevant to core topics reflected in the primary online content object 102, as indicated by the key phrases. The models 118, which may access a corpus of content extracted by crawling a relevant set of pages on the Internet, are applied to the key phrases 112 to establish the clusters, which arrange topics around a core topic based on semantic similarity,” Ash);
classifying the cleaned information into a first set of categories in the database ([0088], “which arrange topics around a core topic based on semantic similarity. From the content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise,” Fig. 2, Topics, Core topic, and Suggested topic, Ash);
determining a second set of categories based on information associated with the first person, wherein the information associated with the first person includes the cleaned information associated with the first instance of the content ([0088], Fig. 2, Topics, Core topic, and Suggested topic, [0138], and [0144], Ash);
aggregating the cleaned information associated with the first person using the second set of categories, wherein one or more keywords of the cleaned information are grouped into categories ([0088], Fig. 6, Topic Cluster 168, [0138], and [0144], Ash);
determining a third set of categories of information associated with a group of people including the first person, wherein the information includes the information associated with the first person ([0088], Fig. 2, Topics, Core topic, and Suggested topic, [0138], and [0144], Ash); and
determining a fourth set of categories based on the third set of categories ([0128], [0500], and [0565], Ash).
Regarding Claim 2, Ash discloses a system, wherein determining the fourth set of categories includes:
generating a graph representation of relationships between content instances ([0160] and [0180], Ash); and
training at least one model using the graph representation ([0160] and [0180], Ash).
Regarding Claim 3, Ash discloses a system, wherein the operations further comprise:
using the at least one trained model to generate embeddings ([0154], Ash); and
identifying nearest k-embeddings based on the generated embeddings ([0154], Ash).
Regarding Claims 6 and 22, Ash discloses a system, wherein classifying the cleaned information into the first set of categories in the database further comprises:
processing the first instance of the content by accessing text associated with the first instance of the content to determine one or more keywords ([0088], “which arrange topics around a core topic based on semantic similarity. From the content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise,” Fig. 2, Topics, Core topic, and Suggested topic, Ash); and
classifying the one or more keywords based on a second instance of the content associated with the first person ([0088], “which arrange topics around a core topic based on semantic similarity. From the content clusters 130 a suggestion generator 134 may generate one or more suggested topics 138, which may be presented in a user interface 152 of a content development management application 150 within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevant information about it (such as indicators of its similarity or relevancy as described elsewhere herein) and create content, such as web pages, emails, customer chats, and other generated online presence content 160 on behalf of the enterprise,” Fig. 2, Topics, Core topic, and Suggested topic, Ash).
Regarding Claim 7, Ash discloses a system, wherein classifying the one or more keywords based on a second instance of the content associated with the first person further comprises:
tokenizing the one or more keywords ([0180], Ash);
generating embeddings of the tokenized one or more keywords based on similarity to keywords associated with a content corpus including the first instance of the content ([0180], [0345], [0462], Ash); and
extracting features of the embeddings ([0180], [0345], [0462], Ash).
Regarding Claim 8, Ash discloses a system, wherein the one or more keywords are tokenized into one or more single words or short sentences, wherein the one or more keywords are tokenized based on the first instance of the content and the second instance of the content ([0180], [0187], and [0239], Ash).
Regarding Claim 10, Ash discloses a system, wherein the text associated with the first instance of the content is accessed by converting audio or speech content within the first instance of the content to text using natural language processing ([0239], Ash).
Regarding Claim 13, Ash discloses a system, wherein aggregating the cleaned information associated with the first person using the second set of categories, wherein one or more keywords of the cleaned information are grouped into categories further comprises:
generating embeddings of the one or more keywords of the cleaned information grouped into a category based on context similarity and semantic similarity ([0180], [0345], [0462], Ash);
aggregating nearest-k embeddings of the embeddings into a single embedding representing the group ([0154], Ash); and
extracting features for each of category of the second set of categories ([0154], Ash).
Regarding Claim 14, Ash discloses a system, wherein the context similarity is based on the first instance of the content and second instance of the content associated with the first person ([0106] and [0575], Ash).
Regarding Claim 15, Ash discloses a system, wherein the context similarity is based on similarity of the first person associated with the cleaned information and a second person associated with the cleaned information ([0106] and [0575], Ash).
Regarding Claim 16, Ash discloses a system, wherein extracting features for each category is performed using term frequency-inverse documentary frequency measure ([0500], [0561], and [0565], Ash).
Regarding Claims 17 and 23, Ash discloses a system, wherein the operations further comprise:
generating data for the information associated with the group of people by determining frequency patterns associated with the information by ([0500], [0561], and [0565], Ash);:
deleting one or more keywords of the information with low usability, wherein the low usability is determined based on context information associated with the information, frequency analysis within a category associated with the one or more keywords, and the frequency of the one or more keywords in the content ([0500], [0561], and [0565], Ash);
generating embeddings of similar keywords across the third set of categories ([0500], [0561], and [0565], Ash); and
determining frequency patterns associated with a keyword associated with the information, wherein the frequency patterns associated with the keyword associated with the information indicates frequency of the keyword within a category ([0500], [0561], and [0565], Ash).
Regarding Claim 18, Ash discloses a system, wherein the frequency patterns associated with the keyword is determined using term-frequency-inverse document frequency measure of a keyword with frequency of usage of the keyword in content associated with the first person, and frequency of usage of the keyword in the content ([0500], [0561], and [0565], Ash).
Regarding Claim 19, Ash discloses a system, wherein the contextual information associated with the instance of the content includes selected subset of categories from a predefined set of categories ([0108], [0116], and [0118], Subtopic, Ash).
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 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.
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.
Claims 4 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Ash et al. (US 2022/0292423) in view of Abuammar et al. (US 2020/0372217).
Regarding Claim 4, Ash discloses all the limitations as discussed above but does not expressly disclose resolving grammatical and typographic errors, and wherein the grammatical and typographic errors are resolved by calculating relation distances between the first element and the second element in the extracted information. Abuammar discloses: resolving grammatical and typographic errors, and wherein the grammatical and typographic errors are resolved by calculating relation distances between the first element and the second element in the extracted information ([0123]-[0124], Abuammar). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Ash by incorporating resolving grammatical and typographic errors, and wherein the grammatical and typographic errors are resolved by calculating relation distances between the first element and the second element in the extracted information, as disclosed by Abuammar, in order to provide accurate extracted data free of errors. See: KSR International Co. v. Teleflex Inc., 82 USPQ 1385, 1396 (US 2007); MPEP § 2143.
Regarding Claim 11, Ash/Abuammar discloses a system of claim 6, wherein the text associated with the first instance of the content is the subtitles or captions of a video content ([0123], Fig. 10, Abuammar).
Regarding Claim 12, Ash/Abuammar discloses a system, wherein the text associated with the first instance of the content is a textual description of the first instance of the content.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ash et al. (US 2022/0292423) in view of Lee et al. (US 8,589,399).
Regarding Claim 5, Ash discloses all the limitations as discussed above but does not expressly disclose: removing stop words and removing common words. Lee discloses: wherein cleaning the extracted information based on contextual information further comprises: removing one or more stop words from a corpus of keywords (Col. 6, lines 43-62, Lee), and removing one or more common words from the corpus of keywords (Col. 6, lines 43-62, Lee). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Ash by incorporating removing stop words and removing common words, as disclosed by Lee, in order to reduce data size without eliminating important words. See: KSR International Co. v. Teleflex Inc., 82 USPQ 1385, 1396 (US 2007); MPEP § 2143.
Furthermore, Ash/Lee discloses: wherein the corpus of keywords includes the cleaned information associated with the first person ([0128], [0500], and [0565], Ash; and Col. 6, lines 43-62, Lee).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ash et al. (US 2022/0292423) in view of Enuka et al. (US 2021/0256156).
Regarding Claim 9, Ash discloses all the limitations as discussed above but does not expressly disclose a bag of words model. Enuka discloses: extracting features of the embeddings is performed using a bag of words model. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the system of Ash by incorporating a bag of words model, as disclosed by Enuka, in order to easyly and quickly preprocess data for document classification. See: KSR International Co. v. Teleflex Inc., 82 USPQ 1385, 1396 (US 2007); MPEP § 2143.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIOVANNA B COLAN whose telephone number is (571)272-2752. The examiner can normally be reached Mon - Fri 8:30-5:00.
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/GIOVANNA B COLAN/Primary Examiner, Art Unit 2165 December 3, 2025