DETAILED ACTION
Election/Restrictions
Applicant’s election without traverse of Group I claims 1-13 in the reply filed on 5/28/2026 is acknowledged.
Claims 14-20 are canceled from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 5/28/2026. Claims 21-27 are newly added.
Claim Rejections - 35 USC § 103
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, 9, 11-12, 21, 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crossley et al (US 10496752 B1) in view of Morris (US 20200342016).
Regarding claim 1, Crossley discloses a computing system (Fig. 20 system 2000), comprising:
one or more processors (Fig. 20 processor 2002);
a memory storing program (Fig. 20 memory 2004, storage 2006) instructions that, when executed by the one or more processors (col 38 lines 63-67 to col 39 lines 1-32), cause the one or more processors to at least:
determine, from a corpus of documents, a plurality of documents that include an unmapped target content item (col 4 lines 20-25 the social-networking system 1860 may construct a corpus of text by collecting text content from content objects created by online social network users that satisfy the one or more conditions. In particular embodiments, the social-networking system 1860 may collect text content from only content objects created during a pre-determined period of time; col 5 lines 60-67 word vectors in the table 101 may properly reflect insights of the contemporary public; col 9 lines 40-67 looking up the two input n-grams in the table 10; col 11 lines 44-67 social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900);
identify a plurality of mapped content items included in the plurality of documents (col 9 lines 40-67 looking up the two input n-grams in the table 10; col 11 lines 44-67 social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900; social-networking system 1860 may identify that the selected word vectors correspond to ‘motivation,’ ‘awesomeness,’ and ‘enthusiasm’ by looking up the word vectors 410, 420, 430, and 440 in the table 101);
aggregate at least some of the plurality of mapped content items into an aggregated set of mapped content items (col 9 lines 40-67 social-networking system 1860 may look up word vectors corresponding to each of the two input n-grams by looking up the two input n-grams in the table 101; col 10 lines 1-5 The social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900.);
Crossley fails to specifically teach where Morris teaches aggregate a plurality of embedding vectors associated with the aggregated set of mapped content items to generate an inferred embedding vector for the unmapped target content item (¶47-48 to create query vectors (operation 2.4) phrases (or key phrases) that occur in a certain local sample of returned or found documents (e.g. “query hits”) may be analyzed to find phrases in those local samples, and the embedding vectors associated with those phrases (e.g. calculated in operation 2.2) may be averaged across all query hits, or across all vectors associated with phrase found, to produce a vector for the query).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of aggregate a plurality of embedding vectors associated with the aggregated set of mapped content items to generate an inferred embedding vector for the unmapped target content item from Morris into the system as disclosed by Crossley. The motivation for doing this is to improve systems and methods for assigning queries to topics.
Regarding claim 2, the combination of Crossley and Morris disclose the computing system of claim 1, wherein: the program instructions that, when executed by the one or more processors, further cause the one or more processors to at least determine, from the plurality of mapped content items, a plurality of key content items (Crossley col 11 lines 44-67 social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900; social-networking system 1860 may identify that the selected word vectors correspond to ‘motivation,’ ‘awesomeness,’ and ‘enthusiasm’ by looking up the word vectors 410, 420, 430, and 440 in the table 101); and
aggregating the at least some of the plurality of mapped content items into the aggregated set of mapped content items includes aggregating the plurality of key content items into the aggregated set of mapped content items (Crossley col 9 lines 40-67 The social-networking system 1860 may calculate an average vector by taking a weighted average of the word vectors corresponding to the two input n-grams; col 10 lines 1-5 The social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900).
Regarding claim 3, the combination of Crossley and Morris disclose the computing system of claim 2, wherein the plurality of key content items are determined based at least in part on a frequency of each of the plurality of key content items (Crossley col 20 lines 45-50 The social-networking system 1860 may determine a Term Frequency-Inverse Document Frequency (TF-IDF) score for each n-gram in the first table).
Regarding claim 4, the combination of Crossley and Morris disclose the computing system of claim 2, wherein determining the plurality of key content items from the plurality of mapped content items includes determining that a respective term frequency/inverse document frequency (TF/IDF) score associated with each of the plurality of key content items exceeds a threshold (Crossley col 20 lines 60-67 to col 21 lines 1-15 the social-networking system 1860 may identify the most relevant k clusters to the particular subject by calculating an average TD-IDF score for the cluster by taking an average of determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster for each cluster in the plurality of clusters, and selecting k clusters with highest average TD-IDF scores from the plurality of clusters. In particular embodiments, the social-networking system 1860 may identify the most relevant k clusters to the particular subject by determining a maximum TD-IDF score for the cluster by comparing determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster for each cluster in the plurality of clusters, and selecting k clusters with highest maximum TD-IDF scores from the plurality of clusters).
Regarding claim 9, Crossley discloses a computer-implemented method, comprising:
determining, from a corpus of documents, a plurality of documents that include an unmapped target content item (col 4 lines 20-25 the social-networking system 1860 may construct a corpus of text by collecting text content from content objects created by online social network users that satisfy the one or more conditions. In particular embodiments, the social-networking system 1860 may collect text content from only content objects created during a pre-determined period of time; col 5 lines 60-67 word vectors in the table 101 may properly reflect insights of the contemporary public; col 9 lines 40-67 looking up the two input n-grams in the table 10; col 11 lines 44-67 social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900);
for each document of the plurality of documents:
identifying a plurality of mapped content items included in the document (col 9 lines 40-67 looking up the two input n-grams in the table 10; col 11 lines 44-67 social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900; social-networking system 1860 may identify that the selected word vectors correspond to ‘motivation,’ ‘awesomeness,’ and ‘enthusiasm’ by looking up the word vectors 410, 420, 430, and 440 in the table 101); and
aggregating a plurality of embedding vectors associated with at least some of the plurality of mapped content items to generate a respective first averaged embedding vector for the document (col 9 lines 40-67 The social-networking system 1860 may calculate an average vector by taking a weighted average of the word vectors corresponding to the two input n-grams; col 10 lines 1-5 The social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900); and
Crossley fails to specifically teach where Morris teaches aggregating the respective first averaged embedding vectors generated for each document of the plurality of documents to generate an overall averaged embedding vector as an inferred embedding vector for the unmapped target content item (¶47-48 to create query vectors (operation 2.4) phrases (or key phrases) that occur in a certain local sample of returned or found documents (e.g. “query hits”) may be analyzed to find phrases in those local samples, and the embedding vectors associated with those phrases (e.g. calculated in operation 2.2) may be averaged across all query hits, or across all vectors associated with phrase found, to produce a vector for the query).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of aggregating the respective first averaged embedding vectors generated for each document of the plurality of documents to generate an overall averaged embedding vector as an inferred embedding vector for the unmapped target content item from Morris into the method as disclosed by Crossley. The motivation for doing this is to improve systems and methods for assigning queries to topics.
Regarding claim 11, the combination of Crossley and Morris disclose the computer-implemented method of claim 9, wherein the corpus of documents include documents associated with a plurality of content item types (Crossley col 5 lines 1-15 the social-networking system 1860 may construct a corpus of text by collecting text content from content objects created by users of the online social network. An online social network may have a large number of users. The users may generate content objects to express themselves. User-generated content may include anything a user can add, upload, send, or “post” to social-networking system 1860. As an example and not by way of limitation, a user communicates posts to social-networking system 1860 from a client system 1830. Posts may include data such as status updates or other textual data, location information, photos, videos, links, music or other similar data or media); .
Regarding claim 12, the combination of Crossley and Morris disclose the computer-implemented method of claim 9, wherein the unmapped target content item includes a text-based content item (Crossley col 5 lines 1-15 the social-networking system 1860 may construct a corpus of text by collecting text content from content objects created by users of the online social network. An online social network may have a large number of users. The users may generate content objects to express themselves. User-generated content may include anything a user can add, upload, send, or “post” to social-networking system 1860. As an example and not by way of limitation, a user communicates posts to social-networking system 1860 from a client system 1830. Posts may include data such as status updates or other textual data, location information, photos, videos, links, music or other similar data or media).
Regarding claim 21, the combination of Crossley and Morris disclose the computer-implemented method of claim 9, further comprising:
determining, from the plurality of mapped content items, a plurality of key content items (Crossley col 11 lines 44-67 social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900; social-networking system 1860 may identify that the selected word vectors correspond to ‘motivation,’ ‘awesomeness,’ and ‘enthusiasm’ by looking up the word vectors 410, 420, 430, and 440 in the table 101); and
aggregating the at least some of the plurality of mapped content items into an aggregated set of mapped content items including aggregating the plurality of key content items into the aggregated set of mapped content items (Crossley col 9 lines 40-67 The social-networking system 1860 may calculate an average vector by taking a weighted average of the word vectors corresponding to the two input n-grams; col 10 lines 1-5 The social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900).
Regarding claim(s) 24-26 (drawn to a CRM):
The rejection/proposed combination of Crossley and Morris, explained in the rejection of system claim(s) 1-2 and 4, anticipates/renders obvious the steps of the computer readable medium of claim(s) 24-26 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 1-2 and 4 is/are equally applicable to claim(s) 24-26. See Crossley col 41 lines 30-46.
Claim 5, 10, 13, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Crossley and Morris as applied to claim 4, 9, and 26 above, and further in view of Arfa et al (US Patent 11182806 B1).
Regarding claim 5, the combination of Crossley and Morris disclose the computing system of claim 4, but fail to teach where Arfa teaches wherein: the program instructions that, when executed by the one or more processors, further cause the one or more processors to at least:
determine, for each of the plurality of key content items, a respective weight based at least in part on the respective TF/IDF score (col 9 lines 55-67 The social-networking system 1860 may assign a weight to each of the two word vectors for calculating the weighted average. The weight assigned to a word vector may be an Inverse Document Frequency (IDF) score for the corresponding n-gram. The IDF score may be based on a number of documents containing the corresponding n-gram in a corpus of text); and
apply, for each of the plurality of key content items, the respective weight to a respective embedding vector of the plurality of embedding vectors for the key content item to generate a plurality of weighted embedding vectors (col 9 lines 55-67 The social-networking system 1860 may assign a weight to each of the two word vectors for calculating the weighted average. The weight assigned to a word vector may be an Inverse Document Frequency (IDF) score for the corresponding n-gram. The IDF score may be based on a number of documents containing the corresponding n-gram in a corpus of text); and
aggregating the plurality of embedding vectors includes aggregating the plurality of weighted embedding vectors to generate the inferred embedding vector for the unmapped target content item (col 9 lines 55-67 The social-networking system 1860 may calculate an average vector by taking a weighted average of the word vectors corresponding to the two input n-grams).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of determine, for each of the plurality of key content items, a respective weight based at least in part on the respective TF/IDF score, and apply, for each of the plurality of key content items, the respective weight to a respective embedding vector of the plurality of embedding vectors for the key content item to generate a plurality of weighted embedding vectors, and aggregating the plurality of embedding vectors includes aggregating the plurality of weighted embedding vectors to generate the inferred embedding vector for the unmapped target content item from Arfa into the system as disclosed by the combination of Crossley and Morris. The motivation for doing this is to improve word embedding models.
Regarding claim 10, the combination of Crossley and Morris disclose the computer-implemented method of claim 9,
aggregating the plurality of embedding vectors associated with at least some of the plurality of mapped content items includes:
determining a respective term frequency/inverse document frequency (TF/IDF) score for each of the at least some of the plurality of mapped content items (Crossley col 20 lines 60-67 to col 21 lines 1-15 the social-networking system 1860 may identify the most relevant k clusters to the particular subject by calculating an average TD-IDF score for the cluster by taking an average of determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster for each cluster in the plurality of clusters, and selecting k clusters with highest average TD-IDF scores from the plurality of clusters. In particular embodiments, the social-networking system 1860 may identify the most relevant k clusters to the particular subject by determining a maximum TD-IDF score for the cluster by comparing determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster for each cluster in the plurality of clusters, and selecting k clusters with highest maximum TD-IDF scores from the plurality of clusters),
The combination of Crossley and Morris fail to teach where Arfa teaches wherein: determining a respective weight for each of the at least some of the plurality of mapped content items based on the respective TF/IDF score (col 9 lines 55-67 The social-networking system 1860 may assign a weight to each of the two word vectors for calculating the weighted average. The weight assigned to a word vector may be an Inverse Document Frequency (IDF) score for the corresponding n-gram. The IDF score may be based on a number of documents containing the corresponding n-gram in a corpus of text);
applying, for each of the at least some of the plurality of mapped content items, the respective weight to a respective embedding vector of the plurality of embedding vectors for the at least some of the plurality of mapped content items to generate a plurality of weighted embedding vectors (col 9 lines 55-67 The social-networking system 1860 may assign a weight to each of the two word vectors for calculating the weighted average. The weight assigned to a word vector may be an Inverse Document Frequency (IDF) score for the corresponding n-gram. The IDF score may be based on a number of documents containing the corresponding n-gram in a corpus of text); and
aggregating the plurality of weighted embedding vectors to generate the respective first averaged embedding vector for the document (col 9 lines 55-67 The social-networking system 1860 may calculate an average vector by taking a weighted average of the word vectors corresponding to the two input n-grams).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein: determining a respective weight for each of the at least some of the plurality of mapped content items based on the respective TF/IDF score; applying, for each of the at least some of the plurality of mapped content items, the respective weight to a respective embedding vector of the plurality of embedding vectors for the at least some of the plurality of mapped content items to generate a plurality of weighted embedding vectors; and aggregating the plurality of weighted embedding vectors to generate the respective first averaged embedding vector for the document from Arfa into the method as disclosed by the combination of Crossley and Morris. The motivation for doing this is to improve word embedding models.
Regarding claim 13, the combination of Crossley and Morris disclose the computer-implemented method of claim 9, wherein:
aggregating the plurality of embedding vectors associated with at least some of the plurality of mapped content items includes:
determining a respective importance for each of the at least some of the plurality of mapped content items (Crossley col 20 lines 60-67 to col 21 lines 1-15 the social-networking system 1860 may identify the most relevant k clusters to the particular subject by calculating an average TD-IDF score for the cluster by taking an average of determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster for each cluster in the plurality of clusters, and selecting k clusters with highest average TD-IDF scores from the plurality of clusters. In particular embodiments, the social-networking system 1860 may identify the most relevant k clusters to the particular subject by determining a maximum TD-IDF score for the cluster by comparing determined TD-IDF scores for n-grams corresponding to word vectors that belong to the cluster for each cluster in the plurality of clusters, and selecting k clusters with highest maximum TD-IDF scores from the plurality of clusters);
The combination of Crossley and Morris fail to teach where Arfa teaches determining, based at least in part on the respective importances, a respective weight for each of the at least some of the plurality of mapped content items (col 9 lines 55-67 The social-networking system 1860 may assign a weight to each of the two word vectors for calculating the weighted average. The weight assigned to a word vector may be an Inverse Document Frequency (IDF) score for the corresponding n-gram. The IDF score may be based on a number of documents containing the corresponding n-gram in a corpus of text);
applying, for each of the at least some of the plurality of mapped content items, the respective weight to a respective embedding vector of the plurality of embedding vectors for the at least some of the plurality of mapped content items to generate a plurality of weighted embedding vectors (col 9 lines 55-67 The social-networking system 1860 may assign a weight to each of the two word vectors for calculating the weighted average. The weight assigned to a word vector may be an Inverse Document Frequency (IDF) score for the corresponding n-gram. The IDF score may be based on a number of documents containing the corresponding n-gram in a corpus of text); and
aggregating the plurality of weighted embedding vectors to generate the respective first averaged embedding vector for the document (col 9 lines 55-67 The social-networking system 1860 may calculate an average vector by taking a weighted average of the word vectors corresponding to the two input n-grams).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of determining, based at least in part on the respective importances, a respective weight for each of the at least some of the plurality of mapped content items; applying, for each of the at least some of the plurality of mapped content items, the respective weight to a respective embedding vector of the plurality of embedding vectors for the at least some of the plurality of mapped content items to generate a plurality of weighted embedding vectors; and aggregating the plurality of weighted embedding vectors to generate the respective first averaged embedding vector for the document from Arfa into the method as disclosed by the combination of Crossley and Morris. The motivation for doing this is to improve word embedding models.
Regarding claim(s) 27 (drawn to a CRM):
The rejection/proposed combination of Crossley, Morris, and Arfa, explained in the rejection of system claim(s) 5, anticipates/renders obvious the steps of the computer readable medium of claim(s) 27 because these steps occur in the operation of the proposed combination as discussed above. Thus, the arguments similar to that presented above for claim(s) 5 is/are equally applicable to claim(s) 27.
Claim 6-7 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Crossley and Morris as applied to claim 2 and 21 above, and further in view of Satyavarta et al (US 20170017638).
Regarding claim 6, the combination of Crossley and Morris discloses the computing system of claim 2, but fail to teach where Satyavarta teaches wherein determining the plurality of key content items from the plurality of mapped content items (¶72 can use only a single feature to measure contextual relevance (e.g., NPMI)) includes determining that a pointwise mutual information (PMI) score associated with each of the plurality of key content items exceeds a threshold (¶96 step 712 can include sub-step 718 where the meme analysis engine removes, from the key terms, one or more terms having a normalized pointwise mutual information (NPMI) score below a pre-determined threshold).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein determining the plurality of key content items from the plurality of mapped content items includes determining that a pointwise mutual information (PMI) score associated with each of the plurality of key content items exceeds a threshold from Satyavarta into the system as disclosed by the combination of Crossley and Morris. The motivation for doing this is to methods for gaining insights to a large quantity of data.
Regarding claim 7, the combination of Crossley, Morris, and Satyavarta discloses the computing system of claim 6, wherein the PMI score associated with each of the plurality of key content items is a function of a co-occurrence of the unmapped target content item and each of the plurality of key content items (Satyavarta ¶72 In some embodiments, the relevance rank engine 218 can use only a single feature to measure contextual relevance (e.g., NPMI). NPMI is a co-occurrence measure that scores higher for words that mostly occur together e.g., “New York”, “Red Sox” vs. low for key terms where each word can occurs with several others, e.g., “of the”, but both “of” and “the” occur with many other terms). The motivation to combine the references is discussed above in the rejection for claim 6.
Regarding claim 22, the combination of Crossley and Morris discloses the computer-implemented method of claim 21, but fail to teach where Satyavarta teaches wherein determining the plurality of key content items from the plurality of mapped content items (¶72 can use only a single feature to measure contextual relevance (e.g., NPMI)) includes determining that a pointwise mutual information (PMI) score associated with each of the plurality of key content items exceeds a threshold (¶96 step 712 can include sub-step 718 where the meme analysis engine removes, from the key terms, one or more terms having a normalized pointwise mutual information (NPMI) score below a pre-determined threshold).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to have implemented the teaching of wherein determining the plurality of key content items from the plurality of mapped content items includes determining that a pointwise mutual information (PMI) score associated with each of the plurality of key content items exceeds a threshold from Satyavarta into the method as disclosed by the combination of Crossley and Morris. The motivation for doing this is to methods for gaining insights to a large quantity of data.
Allowable Subject Matter
Claim 8 and 23 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 8, and similarly regarding claim 23, the prior art of record, alone or in combination, fails to teach at least “determine, for each of the plurality of key content items, a respective weight based at least in part on the respective PMI score; and apply, for each of the plurality of key content items, the respective weight to a respective embedding vector of the plurality of embedding vectors for the key content item to generate a plurality of weighted embedding vectors; and aggregating the plurality of embedding vectors includes aggregating the plurality of weighted embedding vectors to generate the inferred embedding vector for the unmapped target content item.”
At best, Satyavarta et al (US 20170017638) teaches in ¶96 step 712 can include sub-step 718 where the meme analysis engine removes, from the key terms, one or more terms having a normalized pointwise mutual information (NPMI) score below a pre-determined threshold.
At best, Crossley et al (US 10496752 B1) teaches in col 9 lines 40-67 looking up the two input n-grams in the table 10, and in col 11 lines 44-67 social-networking system 1860 may select k word vectors from the word vectors in the table 101 closest to the average vector in the embedding space 1900.
Conclusion
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/KEVIN KY/ Primary Examiner, Art Unit 2671
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