DETAILED ACTION
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 .
Status of claims
Claim(s) 1, 2, 5-16, 18, 19, and 21-23 is/are pending.
Wherein, claims were not presented within the most recent received filings of 07/07/2026. Therefore, the presentation of claims are interpreted as corresponding to the amendments received on 03/27/2026. Wherein:
Claim(s) 1, 2, 5-16, 18, 19, and 21-23 is/are pending.
Claim(s) 1, 5, 15, and 18 has/have been amended.
Claim(s) 3, 4, 17, and 20 has/have been cancelled.
The 35 U.S.C § 103 rejection to claim(s) 1, 2, 5-16, 18, 19, and 21-23 have been fully considered in view of the amendments received on 03/27/2026 and are fully addressed in the prior art rejection below.
Response to Arguments
Received 07/07/2026
Applicant's arguments filed 07/07/2026 have been fully considered but they are not persuasive; as expressed below.
Regarding independent claim(s) 1, 15, and 18:
Applicant argues (Remarks, Page 3, ¶ 3), that “The Examiner cites Vargas at Col. 12, lines 6 to 19 and Col. 12, line 49 to Col. 13, line 10 as disclosing the features of ‘determining... a plurality of distance values for word pair combinations’, and at Col. 10, lines 28 to 56, Col. 11, lines 3 to 21, and Col. 12, lines 20 to 43 for the feature of ‘aggregating the plurality of distance values’. Applicant respectfully submits that these citations do not support the Examiner's mapping and that Vargas does not disclose the claimed pipeline.”
The Examiner disagrees. Applicant’s arguments infer a pipeline, wherein steps differ (more or less) than the claimed invention. However, the claim language of the independent claims fails to claim a pipeline. Additionally, the claim language if the independent claims fails to claim an ordered sequence. Thus, the lack of a more explicit sequence fails to provide support for the sought after argument by Applicant. Even further, Applicant fails to view the disclosure, wherein “In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results” (Spec.; [¶ 0087]). Moreover, the Specification is deficient regarding limitations of a pipeline.
Therefore, Applicant’s arguments above fail to be persuasive.
Applicant argues (Remarks, Page 4, ¶ 2), that “This is clearly an aggregate-first pipeline. As Vargas states at Col. 9, line 50 to Col. 10 line 5, the aggregation engine produces an aggregated embedding vector for each document, and the similarity engine then uses those aggregated vectors to determine a similarity measure. The similarity computation in Vargas is performed at the aggregate vector level, not at the individual word pair level.”
The Examiner disagrees. Applicant’s arguments fail to view the teachings within the combination as a whole. Although, Applicant infers a difference based on an “individual word pair level”, the meaning of word pair corresponds to a pair (i.e. two) words. Therefore, to imply other levels regarding pairing of words would be improper. Applicant addresses the taught subject matter of a vector, by Vargas et al. (US Patent No. 11989506 B2), however Applicant fails to view that the vector corresponds to words (plural) further corresponds to word embeddings (Vargas; [Col. 5, lines 15-49]). Thus, Vargas et al. establishes a clear distinction between a vector of words and a word pair.
Additionally, Applicant’s claimed subject matter of “… word pair combinations between each word embedding of words in the textual data for the entity” corresponds to the taught vector by Vargas et al. Wherein, “… the embedding model engine 130 may include a word and/or phrase vectorization model trained to embed each word and/or phrase as a vector encoding the word and/or phrase as well as the context within the document” (Vargas; [Col. 9, lines 36-49]). Moreover, the subject matter of the claimed “aggregating” (i.e. “aggregating the plurality of distance values by computing at least one of a sum or an average of the plurality of distance values”) is silent regarding an individual word pair.
Therefore, Applicant’s arguments above fail to be persuasive.
Applicant argues (Remarks, Page 4, ¶ 3), that “The Examiner's mapping to Col. 5, lines 16 to 49 of Vargas as disclosing ‘a degree of semantic similarity between a pair of words can then be measured by the distance between their embeddings’ does not establish that Vargas computes distances for word pair combinations as part of the process. Col. 5 of Vargas provides description explaining the general concept of word embedding spaces, that semantically similar words have similar vector representations and that similarity can be measured by distance. This passage describes embeddings generally and does not describe or disclose any operational step of computing word pair distances between individual words of two texts. The actual operational pipeline of Vargas is to aggregate word embeddings first and then compute a similarity score between aggregate vectors.”
The Examiner disagrees. Applicant’s arguments fail to view the teachings by the applied prior art. Wherein, Vargas et al., as mentioned by Applicant, recites “… a pair of words can then be measured by the distance between their embeddings”. Therefore, to infer that measuring distances between one or more embeddings of word pairs is silent or otherwise not taught by Vargas et al. is improper given ipso facto. Moreover, Vargas et al. teaches using the embeddings to determine differences between entities, which involves determining a similarity metric. Still further, other than the explicit teaching of distance between word pairs (Vargas; [Col. 5, lines 15-49]), Vargas et al. also teaches a Euclidean similarity implicitly corresponding to a distance (Vargas; [Col. 10, line 15-56, Col. 11, lines 3-17, and Col. 14, line 65 to Col. 15, line 10]). Applicant should note that a vector can comprise just one word pair depending on a given entity.
Additionally, the claim language recites the distance is determined which is broader than one or more calculated steps from using a formula as inferred by Applicant. Even further, Applicant fails to view the “… function to calculate the semantic distance between two entity embeddings” as taught by Wang et al. (US PGPUB No. 20220391690 A1) (Wang; [¶ 0035]).
Therefore, Applicant’s arguments above fail to be persuasive.
Applicant argues (Remarks, Page 4, ¶ 4 to Page 5, ¶ 1), that “Furthermore, the Examiner's citation of Col. 14, lines 35 to 40 of Vargas as disclosing that ‘each (input and stored) document is a set of embeddings corresponding to a matrix/vector(s) of data’ does not disclose word pair distance computation. The fact that documents are represented as sets of embeddings prior to aggregation does not mean that word pair distances between individual word embeddings of two texts are computed. Vargas is explicit that these sets of embeddings are passed through a statistical model to produce an aggregate vector, and similarity is determined from aggregate vectors.”
The Examiner disagrees. Applicant’s arguments fail to view the claimed subject matter, wherein aggregation as claimed (i.e. “aggregating the plurality of distance values by computing at least one of a sum or an average of the plurality of distance values”) is silent regarding a word pair. As mentioned before the determined plurality of distances are of a combination of word pairs, and aggregating the plurality of distance values is no longer narrowed by the source of said distances corresponding to word pair combinations. Still further, the language of the plurality of distance values and word pair combinations corresponds to a data-structure (i.e. vector); wherein, “… the embedding model engine 130 may include a word and/or phrase vectorization model trained to embed each word and/or phrase as a vector encoding the word and/or phrase as well as the context within the document” (Vargas; [Col. 9, lines 36-49]). Additionally, the aggregating (of) the plurality of distance values corresponds to a similarity score. The embeddings of each document create a vector that is associated with a metric that is comparable to other documents as taught by a similarity score (Vargas; “…the similarity engine 150 may use a similarity measure to determine a similarity score between the aggregated embedding vector of the input document 108 and the aggregated embedding vector of each stored document 109. In some embodiments, the similarity measure may include, e.g., an exact match or a predetermined similarity score according to, e.g., Jaccard similarity, Jaro-Winkler similarity, Cosine similarity, Euclidean similarity, Overlap similarity, Pearson similarity, Approximate Nearest Neighbors, K-Nearest Neighbors, among other similarity measure. Thus, the similarity engine 150 may determine a score indicative of a probability of a match between the input document 108 and each stored document 109” [Col. 10, line 15-56 and Col. 11, lines 3-17]; and “… aggregating the word embeddings 203 of each document enables efficient similarity measurement between each pair of documents. Accordingly, in some embodiments, the similarity engine 150 may use the aggregated input embedding i and the aggregated stored embeddings of the pair of the input document 201 and the stored document 202 to determine a similarity measure using the similarity model 251. In some embodiments, the similarity measure may include, e.g., Jaccard similarity, Jaro-Winkler similarity, Cosine similarity, Euclidean similarity, Overlap similarity, Pearson similarity, Approximate Nearest Neighbors, K-Nearest Neighbors, among other similarity measure” [Col. 14, line 65 to Col. 15, line 10]). In other words, Applicant’s arguments mistake aggregation at the document level as the only aggregation of values being processed, consequently Vargas et al. teaches aggregation for a document as well as in comparison between two documents (via a similarity score).
Therefore, Applicant’s arguments above fail to be persuasive.
Applicant argues (Remarks, Page 5, ¶ 3-4), that “In contrast, the claims require a distance-first, then aggregation pipeline including generating word embeddings of a portion of the text in the dashboard search query, determining, for each dashboard, a plurality of distance values for word pair combinations between each word embedding of words in the textual data for the dashboard and each word embedding of words in the dashboard search query, and aggregating the plurality of distance values by computing at least one of a sum or an average of the plurality of distance values to generate a similarity score representing a degree of semantic match between the dashboard search query and the textual data for each dashboard.
These are architecturally distinct computational processes. In Vargas, word-level information is collapsed into a single aggregate vector before any similarity computation occurs, and the similarity value is computed between two aggregate vectors. The claims require that individual word pair distances are computed first across combinations of words from the two texts, producing a plurality of distance values, and those values are then aggregated. Vargas does not disclose and does not suggest a distance-first, then aggregation pipeline.”
The Examiner disagrees. Applicant’s arguments fail to view the broadness of the claim language. Wherein, a specified sequence or order is silent within the claims. As mentioned above Applicant’s Specification discloses performing a different order and still achieving desirable results (Spec.; [¶ 0087]).
Additionally, the distance values are claimed as being between word embedding of words as opposed to word pairs. Even further, aggregating distance values are claimed to generate a similarity score. Thus, the subject matter argued, is silent regarding word pairs in a closely coupled manner to the similarity computation or to the similarity between two texts.
Moreover, Vargas et al. teaches within Fig. 2 first distance measurements (via element 130 and 203) than aggregation (via element 140), prior to calculating similarity (via element 150 and 204) (Vargas; [Col. 11, line 22 to Col. 12, line 43]). Wherein, the aggregation engine (via element 140) processes embeddings from different entities using a statistical model (via element 241) as illustrated within Fig. 3B (Vargas; [Col. 14, lines 41-64 and Col. 15, lines 31 to Col. 16, lines 3]).
Therefore, Applicant’s arguments above fail to be persuasive.
Applicant argues (Remarks, Page 5, ¶ 6 to Page 6, ¶ 1), that “Similarly, Wang does not remedy the deficiencies of Vargas and Basu. As established in the prior response, Wang operates by generating a vector representation of an input text using a neural network and performing a nearest neighbor search to identify entities having vector representations closest in distance to that output vector. Wang consistently operates on single vector representations of the text. Wang does not disclose computing distances between individual word embeddings of two texts, does not disclose generating a plurality of distance values for a given comparison, and does not disclose aggregating such word pair distances to generate a similarity score.”
The Examiner disagrees. Applicant’s arguments fail to view the teachings of the applied prior art, wherein Vargas et al. teaches the agued subject matter. Additionally, Vargas et al. in view of Wang et al. modifies the embeddings of Vargas et al. to explicitly incorporate distance values that can be compared against other embeddings (Wang; [¶ 0043-0046]). Moreover, the word embeddings, aggregation engine, and statistical model of Vargas et al. is modified to incorporate entity embeddings.
Therefore, Applicant’s arguments above fail to be persuasive.
Applicant argues (Remarks, Page 6, ¶ 2), that “Even considering Vargas, Basu, and Wang in combination, the resulting system would at most include generating word embeddings for textual inputs, aggregating those embeddings into aggregate vectors, and performing similarity determination at the aggregate vector level. No combination of these references discloses or suggests modifying Vargas's aggregate-first architecture to instead compute a plurality of word pair distances first and then aggregate those distances. There is no teaching or suggestion in Vargas, Basu, or Wang to invert this pipeline by computing word pair distances prior to aggregation.”
The Examiner disagrees. Applicant’s arguments fail to broadness of the claim language. Wherein, aggregation of distance values is closely coupled in relationship with a similarity score as opposed to the word pair as inferred by Applicant, as addressed in more detail above. Still further, the independent claims are silent regarding a pipeline, as addressed in more detail above. Moreover, the claim language is deficient regarding an explicit ordered sequence and/or closed sequenced order, as addressed in more detail above.
Therefore, Applicant’s arguments above fail to be persuasive.
Applicant’s arguments (Remarks, Page 6, ¶ 3), filed 07/07/2022, with respect to the rejection(s) of claim(s) 15 and 18 under 35 U.S.C § 103 have been fully considered but they are not persuasive due to claim 15’s and claim 18’s similarity to claim 1. Therefore, the rejection is maintained for reasons as addressed above.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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, 2, 8-16, 18, 19, and 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vargas et al., US Patent No. 11989506 B2, hereinafter Vargas, in view of Basu et al., US PGPUB No. 20240004891 A1, hereinafter Basu, and further in view of Wang et al., US PGPUB No. 20220138170 A1, hereinafter Wang.
Regarding claim 15, Vargas discloses a system (Vargas; a system [Col. 6, lines 3-20 and lines 29-43 and Col. 11, lines 18-21], as illustrated within Fig. 1 and Fig. 2) comprising:
at least one memory storing instructions (Vargas; the system [as addressed above] comprises at least one memory storing instructions [Col. 6, lines 21-43]);
a network interface (Vargas; the system [as addressed above] comprises a network interface [Col. 6, lines 6-43], as illustrated within Fig. 1; additional, modeling engine (via a neural network) [Col. 8, line 22 to Col. 9, line 35]); and
at least one hardware processor interoperably coupled with the network interface and the at least one memory (Vargas; the system [as addressed above] comprises at least one hardware processor interoperably coupled with the network interface and the at least one memory [Col. 6, lines 6-43], as illustrated within Fig. 1), wherein execution of the instructions by the at least one hardware processor causes performance of operations (Vargas; the system [as addressed above] comprises execution of the instructions by the at least one hardware processor causes performance of operations [Col. 6, lines 6-43]; additional, software and hardware associated with a modeling engine (via a neural network) [Col. 8, line 22 to Col. 9, line 35]) comprising:
obtaining, for each entity of a plurality of entities, textual data for the entity (Vargas; operations [as addressed above] comprises obtaining textual data for the document (e.g. data entity) for each document (e.g. data entity) of a plurality of documents (i.e. data entities) [Col. 6, line 60 to Col. 7, line 7 and Col. 8, lines 15-48]; moreover, raw collection of documents involving inputting and/or storing documents [Col. 7, lines 8-40], and further involves a variety of formats (which can be processed) [Col. 7, line 41-67]);
for each entity, generating word embeddings of a portion of the textual data for the entity using an artificial intelligence engine (Vargas; operations [as addressed above] comprises generating word embeddings of a portion of the textual data for the document/entity using an AI engine for each document/entity [Col. 8, lines 22-42]; moreover, embedding model engine [Col. 8, line 49 to Col. 9, line 6, Col. 9, line 50 to Col. 10, line 5, and Col. 11, lines 18-46], as illustrated within Fig. 2);
receiving, via the network interface, an entity search query for searching for entities that relate to text in the entity search query (Vargas; operations [as addressed above] comprises receiving a document/entity search query for searching for document/entity that relate to text in the document/entity search query via the network interface [Col. 11, lines 18-46]; moreover, searching stored documents via semantics [Col. 6, lines 44-59 and Col. 8, line 22-48]);
generating word embeddings of a portion of the text in the entity search query by engaging an artificial intelligence engine (Vargas; operations [as addressed above] comprises generating word embeddings of a portion of the text in the document/entity search query by engaging an AI engine [Col. 8, lines 22 to Col. 9, line 5 and Col. 9, lines 50-56]; additionally, embedding model training and/or trained embedding model [Col. 11, lines 22-63 and Col. 12, lines 44-48]);
determining, for each entity, a plurality of distance values for word pair combinations between each word embedding of words in the textual data for the entity and each word embedding of words in the entity search query (Vargas; operations [as addressed above] comprises determining a plurality of implicit distance values (given a determining of similarities) for word pair combinations between each word embedding of words in the textual data for the document/entity and each word embedding of words in the document/entity search query for each document/entity [Col. 12, lines 6-19 and Col. 12, line 49 to Col. 13, line 10]; wherein, a degree of semantic similarity between a pair of words can then be measured by the distance between their embeddings [Col. 5, lines 16-49]; moreover, each (input and stored) document is a set of embeddings corresponding to a matrix/vector(s) of data [Col. 14, lines 35-40]; additionally, a similarity is measure to determine a similarity score (or probability match) between aggregated embedding vectors of documents [Col. 10, lines 28-56 and Col. 11, lines 3-21] associated with relevancy [Col. 12, lines 20-43 and 49-64]);
aggregating the plurality of distance values by computing at least one of a sum or an average of the plurality of distance values to generate a similarity score representing a degree of semantic match between the entity search query and the textual data for each entity (Vargas; operations [as addressed above] comprises aggregating the plurality of implicit distance values (given a determination of similarities) by computing at least one of a sum or an average of the plurality of implicit distance values (given a determination of similarities) to generate a similarity score representing a degree of semantic match between the document/entity search query and the textual data for each document/entity [Col. 10, lines 28-56, Col. 11, lines 3-21, and Col. 12, lines 20-43]; further, a ranked list of documents in response to a search query [Col. 1, lines 39-67]; moreover, aggregating word embeddings of each document enables efficient similarity measurement between each pair of documents [id.]; wherein, a document search employs aggregation technique [Col. 9, line 50 to Col. 10, line 5 and Col. 12, lines 49-64]); and
providing, via the network interface and in response to the entity search query, information about at least one matching entity based on the generated similarity scores for the plurality of entities (Vargas; operations [as addressed above] comprises providing information about at least one matching document/entity based on the generated similarity scores for the plurality of documents/entities [Col. 10, lines 28-56, Col. 11, lines 3-21, and Col. 12, lines 20-43] via the network interface and in response to the document/entity search query [Col. 6, lines 44-59 and Col. 11, lines 18-46]; wherein, a search query triggers a document search [id.]).
Vargas fails to disclose an entity corresponding to a dashboard.
Vargas also fails to explicitly disclose distance values.
However, Basu teaches a data entity corresponding to a dashboard (Bash; data entities corresponding to dashboard(s) [¶ 0037-0038 and ¶ 0051-0052]; moreover, visualizations including one or more dashboards for data assets [id.]).
Vargas and Basu are considered to be analogous art because both pertain to generating and/or managing data in relation with providing media data to a user, wherein one or more computerized units are utilized in order to produce a visualization.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas, to incorporate a data entity corresponding to a dashboard (as taught by Basu), in order to provide an improved user interface for data analytics and optimized data insights associated with a unified catalog of data across an organization (Basu; [¶ 0003-0007]).
Vargas as modified by Basu fails to explicitly disclose distance values.
However, Wang teaches distance values for word pair combinations and distance values to generate a similarity score (Wang; distance values for word pair combinations [¶ 0040-0041, ¶ 0043-0044, and ¶ 0046-0047], as illustrated within Fig. 3 and Fig. 4, and distance values to generate a similarity score [¶ 0029-0031 and ¶ 0033-0036]; additionally, ranking [¶ 0016-0018 and ¶ 0045] in relation with a search engine [¶ 0020-0021 and ¶ 0024] and generating embeddings [¶ 0026-0028, ¶ 0029-0030, and ¶ 0038], as illustrated within Fig. 1 and Fig. 2).
Vargas in view of Basu and Wang are considered to be analogous art because they pertain to generating and/or managing data in relation with providing media data to a user, wherein one or more computerized units are utilized in order to produce a visualization.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu, to incorporate distance values for word pair combinations and distance values to generate similarity score (as taught by Wang), in order to provide improved relational meanings between one or more entities within a logic systems (Wang; [¶ 0002, ¶ 0018, and ¶ 0030]).
Regarding claim 16, Vargas in view of Basu and Wang further discloses the system of claim 15, wherein the similarity score for an entity is based on a determined distance in a vector space between word embeddings of words of the search query and word embeddings of words in the textual data for the entity (Vargas; the similarity score for a document/entity [as addressed within the parent claim(s)] is based on a determination in a vector space between word embeddings of words of the search query and word embeddings of words in the textual data for the document/entity [Col. 10, lines 28-56, Col. 11, lines 3-21, and Col. 12, lines 20-43]; wherein, a degree of semantic similarity between a pair of words can then be measured by the distance between their embeddings [Col. 5, lines 16-49]).
Wang further teaches distance in a vector space between word embeddings of words (Wang; distance in a vector space between word embeddings of words [¶ 0016-0017 and ¶ 0030-0031]; moreover, comparing entity embedding within a vector space [¶ 0040-0041, ¶ 0043-0044 and ¶ 0046-0047], as illustrated within Fig. 3 and Fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate distance in a vector space between word embeddings of words (as taught by Wang), in order to provide improved relational meanings between one or more entities within a logic systems (Wang; [¶ 0002, ¶ 0018, and ¶ 0030]).
Regarding claim 18, the rejection of claim 18 is addressed within the rejection of claim 15, due to the similarities claim 18 and claim 15 share, therefore refer to the rejection of claim 15 regarding the rejection of claim 18. Although, claim 18 and claim 15 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. However, the subject matter/limitations not addressed by claim 15 is/are addressed below.
Vargas discloses a non-transitory, computer-readable medium storing computer- readable instructions, that upon execution by at least one hardware processor, cause performance of operations (Vargas; a non-transitory CRM storing computer- readable instructions that upon execution by at least one hardware processor cause performance of operations [Col. 6, lines 6-43]).
(further refer to the rejection of claim 1)
Regarding claim 19, the rejection of claim 19 is addressed within the rejection of claim 16, due to the similarities claim 19 and claim 16 share, therefore refer to the rejection of claim 16 regarding the rejection of claim 19.
Regarding claim 22, Vargas in view of Basu and Wang further discloses the system of claim 15, wherein execution of the instructions by the at least one hardware processor (Vargas; execution of the instructions by the at least one hardware processor [as addressed within the parent claim(s)]) causes:
receiving feedback on one or more search results of the plurality of entities (Vargas; processor execution [as addressed above] causes receiving feedback on one or more search results of the plurality of documents/entities [Col. 2, lines 32-60 and Col. 3, lines 36-41]; moreover, retuned list of ranked documents [Col. 11, lines 3-21]; moreover, feedback corresponding to user provided interaction [Col. 20, lines 5-51]), the feedback comprising a ranking input (Vargas; the feedback [as addressed above] comprises a ranking input [Col. 11, lines 3-21, Col. 12, lines 32-43, and Col. 12, lines 49-64]; additionally, computing ranking losses [Col. 15, line 26 to Col. 16, line 3]); and
adjusting the similarity score or a ranking of the plurality of entities based on the feedback (Vargas; the feedback [as addressed above] comprises implicitly adjusting the similarity score or a ranking (given user input that affect system learning) of the plurality of documents/entities based on the feedback [Col. 20, lines 5-51]; wherein, confirmation and/or denial may be provided as feedback for training based on the pairing of the input document and the one or more stored documents, thus improving the document search engine based on user feedback [id.]).
Regarding claim 23, the rejection of claim 23 is addressed within the rejection of claim 22, due to the similarities claim 23 and claim 22 share, therefore refer to the rejection of claim 22 regarding the rejection of claim 23.
Regarding claim 1, the rejection of claim 1 is addressed within the rejection of claim 15, due to the similarities claim 1 and claim 15 share, therefore refer to the rejection of claim 15 regarding the rejection of claim 1. Although, claim 1 and claim 15 may not be identical, they are considerably comparable or substantially equivalent given their overlapping subject matter. Thus, it is reasonable to reject claim 1 based on the teachings and rational in relation with the prior art within the rejection of claim 15.
Regarding claim 2, the rejection of claim 2 is addressed within the rejection of claim 16, due to the similarities claim 2 and claim 16 share, therefore refer to the rejection of claim 16 regarding the rejection of claim 2.
Regarding claim 8, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 1, wherein the textual data for a first entity comprises metadata for the first entity (Vargas; the textual data for a 1st document/entity comprises metadata (i.e. tokens) for the 1st document/entity [Col. 8, lines 1-16]; additionally, metadata also correspond to formatting associated with style and/or data-structure of the contents of each document [Col. 7, lines 41-67]; wherein, tokenization correlates generates a set of tokens representing content of a document/entity [Col. 18, lines 25-41]).
Regarding claim 9, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 1, wherein the textual data for a first dashboard comprises user-provided content (Vargas; the textual data for a 1st document/entity comprises user-provided content regarding at least one implicit visual (given display, UI, associated with documents types) included in the 1st document/entity [Col. 6, lines 29-43, Col. 6, line 60 to Col. 7. line 7, Col. 8, lines 1-14, and Col. 11, lines 22-46]; for example, given document types corresponding to HTML, PDF, PPT, PPTM, or VSD and/or presentation document(s) in general it is implicit that document(s) comprise visual content).
Basu further teaches data for a first dashboard comprises user-provided content regarding at least one visual included in the first dashboard (Basu; data for a 1st dashboard comprises user-provided content regarding at least one visual included in the 1st dashboard [¶ 0033-0034, ¶ 0038, and ¶ 0040]; moreover, user interface generation and rendering to provide a BI analyzer for generating dashboard by accessing the data sets, reports, and data model assets [¶ 0043]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate data for a first dashboard comprises user-provided content regarding at least one visual included in the first dashboard (as taught by Basu), in order to provide an improved user interface for data analytics and optimized data insights associated with a unified catalog of data across an organization (Basu; [¶ 0003-0007]).
Regarding claim 10, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 9, wherein the user-provided content for a first visual of the first entity comprises a natural language question that encapsulates content of the first visual (Vargas; the user-provided content for a 1st visual of the 1st document/entity [as addressed within the parent claim(s)] comprises a search query that encapsulates content of the 1st visual [Col. 6, lines 29-43, Col. 6, line 60 to Col. 7. line 7, Col. 8, lines 1-14, and Col. 11, lines 22-46]).
Basu further teaches dashboard(s) (Basu; data entity corresponding to a dashboard [¶ 0033-0034, ¶ 0038, and ¶ 0040]; moreover, user interface data generation [¶ 0043]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate dashboard(s) (as taught by Basu), in order to provide an improved user interface for data analytics and optimized data insights associated with a unified catalog of data across an organization (Basu; [¶ 0003-0007]).
Wang further teaches user-provided content comprises a natural language question (Wang; an implicit natural language question (i.e. UI search query) [¶ 0047] given the skill of one of ordinary skill in the art [¶ 0002], as illustrated within Fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate user-provided content comprises a natural language question (as taught by Wang), in order to provide improved relational meanings between one or more entities within a logic systems (Wang; [¶ 0002, ¶ 0018, and ¶ 0030]).
Regarding claim 11, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 1, further comprising removing stop words from the dashboard search query before generating word embeddings of the portion of the text in the dashboard search query (Vargas; removing stop words from the document/entity search query (within the embedding model) before generating word embeddings of the portion of the text in the document/entity search query [Col. 16, lines 11-21]; moreover, word suppression [id.]; additionally, Fig. 2 illustrates, word embeddings are generated after the embedding model’s processing of documents [Col. 11, lines 22-46]; additionally, document formatting [Col. 7, lines 55-67]).
Basu further teaches dashboard(s) (Bash; one or more entities corresponding to dashboard(s) [¶ 0037-0038 and ¶ 0051-0052]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate dashboard(s) (as taught by Basu), in order to provide an improved user interface for data analytics and optimized data insights associated with a unified catalog of data across an organization (Basu; [¶ 0003-0007]).
Regarding claim 12, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 1, further comprising removing stop words from the textual data of a first entity before generating word embeddings of the portion of the textual data of the first entity (Vargas; removing stop words from the textual data of a 1st entity before generating word embeddings of the portion of the textual data of the 1st entity [Col. 16, lines 11-21], as illustrated by the flow within Fig. 2; moreover, word suppression [id.]; wherein, Fig. 2 illustrates, word embeddings are generated after the embedding model’s processing of documents [Col. 11, lines 22-46]; additionally, formatting [Col. 7, lines 55-67]).
Basu further teaches dashboard(s) (Bash; one or more entities corresponding to dashboard(s) [¶ 0037-0038 and ¶ 0051-0052]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate dashboard(s) (as taught by Basu), in order to provide an improved user interface for data analytics and optimized data insights associated with a unified catalog of data across an organization (Basu; [¶ 0003-0007]).
Regarding claim 13, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 1, wherein providing information about at least one matching entity based on the similarity scores of respective entities comprises ranking entities based on similarity scores and providing information about a set of highest-ranked entities (Vargas; providing information about at least one matching document/entity based on the similarity scores of respective documents/entities [as addressed within parent claim(s)] comprises ranking documents/entities based on similarity scores and providing information about a set of highest-ranked documents/entities [Col. 10, lines 28-56, Col. 11, lines 3-21, Col. 12, lines 32-43, and Col. 12, lines 49-64]; additionally, stored documents are rank ordered according to magnitude of similarity score and a set of the highest scoring stored documents are identified [Col. 15, line 26 to Col. 16, line 3]).
Basu further teaches dashboard(s) (Bash; one or more entities corresponding to dashboard(s) [¶ 0037-0038 and ¶ 0051-0052]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate dashboard(s) (as taught by Basu), in order to provide an improved user interface for data analytics and optimized data insights associated with a unified catalog of data across an organization (Basu; [¶ 0003-0007]).
Regarding claim 14, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 1, wherein providing information about a first matching dashboard comprises providing a link, that when selected, provides access to the first matching dashboard (Basu; providing information about a 1st matching dashboard comprises providing a link, that when selected, provides access to the 1st matching dashboard [¶ 0033-0034 and ¶ 0037-0038]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified Basu and Wang, to incorporate providing information about a first matching dashboard comprises providing a link, that when selected, provides access to the first matching dashboard (as taught by Basu), in order to provide an improved user interface for data analytics and optimized data insights associated with a unified catalog of data across an organization (Basu; [¶ 0003-0007]).
Regarding claim 21, the rejection of claim 21 is addressed within the rejection of claim 22, due to the similarities claim 21 and claim 22 share, therefore refer to the rejection of claim 22 regarding the rejection of claim 21.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vargas in view of Basu and Wang as applied to claim(s) 1 above, and further in view of Pablo et al., US PGPUB No. 20200356627 A1, hereinafter Pablo.
Regarding claim 5, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 1, wherein the distance is a distance determined using a word mover distance algorithm (Wang; the distance is a distance determined using an implicit word mover distance algorithm (given Euclidean distance measure) [¶ 0016-0017 and ¶ 0029-0031], as illustrated within Fig. 2; moreover, calculating distance values [¶ 0043-0044], as illustrated within Fig. 3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate wherein the distance is a distance determined using a word mover distance algorithm (as taught by Wang), in order to provide improved relational meanings between one or more entities within a logic systems (Wang; [¶ 0002, ¶ 0018, and ¶ 0030]).
Vargas in view of Basu and Wang fails to explicitly discloses a distance determined using a word mover distance algorithm.
However, Pablo teaches a distance determined using a word mover distance algorithm (Pablo; a distance determined using a word mover distance algorithm [¶ 0060-0062]).
Vargas in view of Basu and Wang are considered to be analogous art because they pertain to generating and/or managing data in relation with providing data to a user, wherein one or more computerized units are utilized in order to produce relational connections between data.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate a distance determined using a word mover distance algorithm (as taught by Pablo), in order to minimize or omit manual process and increase productivity and reduce errors associated with data management (Pablo; [¶ 0002, ¶ 0007, and ¶ 0009-0012]).
Regarding claim 6, Vargas in view of Basu and Wang further discloses the computer-implemented method of claim 5, wherein the word mover distance algorithm uses a Euclidean distance metric (Wang; wherein the word mover distance algorithm uses a Euclidean distance metric [¶ 0016-0017 and ¶ 0029-0031], as illustrated within Fig. 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate wherein the word mover distance algorithm uses a Euclidean distance metric (as taught by Wang), in order to provide improved relational meanings between one or more entities within a logic systems (Wang; [¶ 0002, ¶ 0018, and ¶ 0030]).
Pablo further teaches the word mover distance algorithm (Pablo; the word mover distance algorithm [¶ 0060-0062]; moreover, Euclidean metric [¶ 0068]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate the word mover distance algorithm (as taught by Pablo), in order to minimize or omit manual process and increase productivity and reduce errors associated with data management (Pablo; [¶ 0002, ¶ 0007, and ¶ 0009-0012]).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vargas in view of Basu, Wang, and Pablo as applied to claim(s) 5 above, and further in view of Kulkarni, US PGPUB No. 20200073953 A1, hereinafter Kulkarni.
Regarding claim 7, Vargas in view of Basu, Wang, and Pablo further discloses the computer-implemented method of claim 5, wherein the word mover distance algorithm uses a Manhattan distance metric (Wang; the implicit word mover distance algorithm uses a distance metric [¶ 0016-0017 and ¶ 0029-0031], as illustrated within Fig. 2, moreover, calculating distance values [¶ 0043-0044], as illustrated within Fig. 3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu and Wang, to incorporate wherein the word mover distance algorithm uses a Manhattan distance metric (as taught by Wang), in order to provide improved relational meanings between one or more entities within a logic systems (Wang; [¶ 0002, ¶ 0018, and ¶ 0030]).
Pablo further teaches the word mover distance algorithm (Pablo; the word mover distance algorithm [¶ 0060-0062]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu, Wang, and Pablo, to incorporate the word mover distance algorithm (as taught by Pablo), in order to minimize or omit manual process and increase productivity and reduce errors associated with data management (Pablo; [¶ 0002, ¶ 0007, and ¶ 0009-0012]).
Vargas in view of Basu, Wang, and Pablo fails to explicitly disclose a Manhattan distance metric.
However, Kulkarni teaches the word mover distance algorithm uses a Manhattan distance metric (Kulkarni; the implicit word mover distance algorithm uses a Manhattan distance metric [¶ 0122]).
Vargas in view of Basu, Wang, and Pablo and Kulkarni are considered to be analogous art because they pertain to generating and/or managing data in relation with providing data to a user, wherein one or more computerized units are utilized in order to produce a visualization.
Therefore, it would have been obvious to one or ordinary skill in the art before the effective filing of the claimed invention was made to modify Vargas as modified by Basu, Wang, and Pablo, to incorporate the word mover distance algorithm uses a Manhattan distance metric (as taught by Kulkarni), in order to provide improved search relevance that further provides better user experience (Kulkarni; [¶ 0002-0003 and ¶ 0018]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(regarding querying content)
Ben Shaul et al. (US PGPUB No. 20240086460 A1); and
Kotwal et al. (US PGPUB No. 20220318099 A1).
(regarding identifying context data of a portal workspace)
Aflalo et al. (US PGPUB No. 20140136531 A1).
(embedding comparison)
Dubey et al. (US Patent No. 11860917 B1);
Collomosse (US PGPUB No. 20210397942 A1);
Mauer et al. (US PGPUB No. 20220141043 A1); and
Wang et al. (US PGPUB No. 20230368268 A1).
(distances between pairs of documents)
Atasu et al. (US Patent No. 11222054 B2); and
Osuala et al. (US PGPUB No. 20240111794 A1).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Refer to PTO-892, Notice of Reference Cited for a listing of analogous art.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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CHARLES LLOYD. BEARD
Primary Examiner
Art Unit 2611
/CHARLES L BEARD/Primary Examiner, Art Unit 2611
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