DETAILED ACTION
This rejection is in response to Amendments filed 05/18/2026.
Claims 1, 3-5, 7-11, 13-15, and 17-20 are currently pending and have been examined.
Claims 2, 6, 12, and 16 are cancelled.
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 .
Response to Arguments
Applicant’s arguments, see page 7, filed 05/18/2026, with respect to 35 U.S.C. 112(b) to claims 9-10 and 19 have been fully considered and are persuasive. The 35 U.S.C. 112(b) to claims 9-10 and 19 has been withdrawn.
Applicant's arguments filed 05/18/2026 have been fully considered but they are not persuasive.
With respect to Applicant’s arguments on pages 7-9 of remarks filed 05/18/2026 that the claims are not directed to certain methods of organizing human activity because the claims recite specific graph-processing techniques performed using specialized data structures that are implemented by a processor, specific machine learning architecture (e.g. graph convolutional neural network), and specific operations, Examiner respectfully disagrees.
One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Under step 2A (prong 1) of the Subject Matter Eligibility Test, the processor, graph convolutional network model, and semantic relevance learning engine are not analyzed as being directed towards the abstract idea. Under step 2A (prong 2) of the Subject Matter Eligibility Test, the processor, graph convolutional network model, and semantic relevance learning engine are analyzed as additional elements. The claims are directed towards certain methods of organizing human activity because the claims recite commercial interactions such as sales activities (e.g. a ranked dataset recommendation in a marketplace based on a user query).
With respect to Applicant’s arguments on pages 9-10 of remarks filed 05/18/2026 that the claims integrated the abstract idea into a practical application because the claims recite a specific technical implementation that improves computer based semantic search that generates improved user-relevant search query results by improving the ranking and recommendation of query results, Examiner respectfully disagrees.
In addition, a specific way of achieving a result is not a stand-alone consideration in Step 2A Prong Two. However, the specificity of the claim limitations is relevant to the evaluation of several considerations including the use of a particular machine, particular transformation and whether the limitations are mere instructions to apply an exception. See MPEP §§ 2106.05(b), 2106.05(c), 2106.04(d)(I), and 2106.05(f).
In determining patent eligibility, examiners should consider whether the claim "purport(s) to improve the functioning of the computer itself" or "any other technology or technical field." If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. See MPEP § 2106.05(a).
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP §§ 2106.05(a)(II) and 2106.05(f).
A specific way of achieving a result to rank and recommend datasets is not a stand-alone consideration in Step 2A Prong Two. Applicant’s specification in paragraph [0018] states that implementations of the invention improve the process of ranking and recommending search query results and datasets by popularity beyond simple metrics such as user “likes,” reviews, views, or downloads. It is not apparent to one of ordinary skill in the art how improving the process of ranking and recommendation of datasets improves technology. Improving the process of ranking and recommendation of datasets appears to be directed to improving a commercial problem rather than an improving technology. Thus, the claims do not include more than mere instructions to perform the method of ranking and recommending datasets on a generic component or machinery to qualify as an improvement to an existing technology.
With respect to Applicant’s arguments on pages 10-11 of remarks filed 05/18/2026 that the claims are patent eligible as concluded at Step 2A Prong 1 and 2 because the patent eligibility does not proceed to Step 2B, Examiner respectfully disagrees.
The second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Step 2B determines whether: the claim as a whole does not amount to significantly more than the exception itself (there is no inventive concept in the claim) (Step 2B: NO) and thus is not eligible, warranting a rejection for lack of subject matter eligibility and concluding the eligibility analysis; or the claim as a whole does amount to significantly more than the exception (there is an inventive concept in the claim) (Step 2B: YES), and thus is eligible at Pathway C, thereby concluding the eligibility analysis. See MPEP §§ 2106.05(I) and 2106.05(II).
After evaluating the additional elements (e.g. processor set, semantic relevance learning engine, and relational graph convolutional network model) both individually and in combination, the claim as a whole does not amount to significantly more than the exception itself. The additional elements do not amount to significantly more than the judicial exception because the claims do not include more than mere instructions to perform the method of ranking and recommending datasets on a generic component or machinery to qualify as an improvement to technology.
With respect to Applicant’s arguments on pages 11-12 of remarks filed 05/18/2026 that Lei does not teach a lineage graph, lineage node, or mapping the knowledge node of the knowledge graph to a lineage node in the lineage graph because Applicant’s specification in paragraph [0019] states lineage graphs may include concepts including data products and users as lineage nodes and edges, Examiner respectfully disagrees.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., lineage graphs may include concepts including data products and users as lineage nodes and edges) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
With respect to Applicant’s arguments on pages 11-13 of remarks filed 05/18/2026 that Lei does not teach “generating, by the processor set, a lineage graph based on the user profile, user access details in a marketplace, and a historical query,” Examiner respectfully disagrees.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant’s arguments are unpersuasive because Lei is being attacked individually where the rejections are based on the combination of Lei and Wang.
With respect to Applicant’s arguments on pages12 of remarks filed 05/18/2026 that Lei does not teach “identify contextual links between the knowledge graph and the lineage graph using a semantic relevance learning engine” because Lei describes constructing the query graph from extracted entities in text snippet using relationships defined in the knowledge graph, Examiner respectfully disagrees.
Lei teaches identify contextual links between the knowledge graph and the lineage graph using a semantic relevance learning engine because this reference teaches determining the relationships between the entities for the query graph based on a defined graph schema for the knowledge graph (KG) to generate connections between the nodes based on the relationships. The nodes of the query graph are compared to the nodes of the KG to match entities. (Lei, [0057]; [0045]; [0046]; [0002]; [0090]).
With respect to Applicant’s arguments on pages 11-12 of remarks filed 05/18/2026 that Wang does not teach “generating, by the processor set, a lineage graph based on the user profile, user access details in a marketplace, and a historical query,” Examiner respectfully disagrees.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant’s arguments are unpersuasive because Wang is being attacked individually where the rejections are based on combination of Lei and Wang.
With respect to Applicant’s arguments on pages 13 of remarks filed 05/18/2026 that Examiner’s rationale on page 16 of Non-Final Rejection (02/18/2026) that Lei would continue to teach the lineage graph based on the query is unsupported because Lei does not teach a lineage graph and the proposed modification requires impermissible hindsight and combining Wang’s user generated content with Lei’s query graph would not teach generating a lineage graph from such data or using the lineage graph for semantic dataset recommendation and ranking, Examiner respectfully disagrees.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
The combination of Lei and Wang does not represent impermissible hindsight. Lei teaches a lineage graph based on the query because Lei teaches modeling a text snippet associated with an unstructured text query into a query graph with nodes and edges based on extracting entities from the text snippet as shown in FIG. 6. (Lei, FIG. 6, [0058]; [0059]; [0094]). Lei teaches the lineage graph based on the query but does not teach being based on the user profile, user access details in a marketplace, and a historical query. Wang is applied as a secondary reference to teach the user profile, user access details in a marketplace, and a historical query. (Wang, [0021]; [0022]; [0030]).
With respect to Applicant’s arguments on pages 13 of remarks filed 05/18/2026 that dependent claims 3-5, 7-10, 13-15, and 17-19 are patentable due to their dependence on independent claims 1 or 11, Examiner respectfully disagrees.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
There is insufficient antecedent basis for the following limitation in
Claim 20 recites: the semantic relevance learning engine.
Appropriate correction or clarification is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, 7-11, 13-15, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1, 3-5, and 7-10 are directed to a method, claims 11, 13-15, and 17-19 are directed to a computer program product comprising one or more computer readable storage media (e.g. Applicant’s specification [0044]: computer readable storage medium/media is not to be construed as storage in the form of transitory signals per se), claim 20 is directed to a system which falls within one of the four statutory categories of invention(process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 1, recites the abstract idea of:
identifying,…, an entity in a user query or a user profile;
mapping, …, the entity to a knowledge node in a knowledge graph;
generating, …, a lineage graph based on the user profile, user access details in a marketplace, and a historical query;
mapping, …, the knowledge node of the knowledge graph to the lineage node of a lineage graph, wherein… identify contextual links between the knowledge graph and the lineage graph;
generating, …., a list of matched nodes from the mapping of the knowledge node of the knowledge graph to the lineage node of the lineage graph;
generating, …, an interestingness score for a dataset associated with the list of matched nodes based on the user query and the mapping of the knowledge node of the knowledge graph to the lineage node of the lineage graph;
identifying, …, a ranked dataset recommendation based on the interestingness score; and
communicating, …., instructions to communicate the interestingness score and the ranked dataset recommendation ....
The above-recited limitations amounts to certain methods of organizing human activity associated with sales activities and commercial interaction such as communicating a ranked dataset recommendation and score based on identifying an entity in a user query, mapping the entity and knowledge node, mapping the knowledge node to the lineage node, generating a list of matching nodes, and generating a score associated with the matched nodes. Such concepts have been considered ineligible certain methods of organizing human activity by the Courts. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
A computer-implemented method, comprising:…, by a processor set, …; …, by the processor set via a relational graph convolutional network model, …; …by the processor set …; …, by the processor set via a semantic relevance learning engine, … the semantic relevance learning engine is configured to…; …, by the processor set, …;…, by the processor set, …; …, by the processor set, …; and …, by the processor set, … in a user interface (Claim 1);
…via Dirichlet-Hawkes processing (DHP) (Claims 3 & 13);
…in the user interface (Claims 9 and 19);
A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:…, via a semantic relevance learning engine,… the semantic relevance learning engine… in a user interface (Claim 11);
A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: …the semantic relevance learning engine…; …in a user interface (Claim 20).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
A computer-implemented method, comprising:…, by a processor set, …; …, by the processor set via a relational graph convolutional network model, …; …by the processor set …; …, by the processor set via a semantic relevance learning engine, … the semantic relevance learning engine is configured to…; …, by the processor set, …;…, by the processor set, …; …, by the processor set, …; and …, by the processor set, … in a user interface (Claim 1);
…via Dirichlet-Hawkes processing (DHP) (Claims 3 & 13);
…in the user interface (Claims 9 and 19);
A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:…, via a semantic relevance learning engine,… the semantic relevance learning engine… in a user interface (Claim 11);
A system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: …the semantic relevance learning engine…; …in a user interface (Claim 20).
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-5, 7-11, 13-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lei et al. (US Pub. No. 20220207343 A1, hereinafter “Lei”) in view of Wang et al. (US Pub. No. 20170270222 A1, hereinafter “Wang”).
Regarding claims 1, 11, and 20
Lei discloses a computer-implemented method, comprising:
identifying, by a processor set, an entity in a user query or a user profile; mapping, by the processor set via a relational graph convolutional network model, the entity to a knowledge node in a knowledge graph (Lei, [0057]: extract relevant entities from text snippets; [0094]: text snippet associated with unstructured text query; [0092]: employ GNN trained on knowledge graph (KG) associates with text snippet to identify similar terms included within KG; [0001]: graph neural networks (GNNs); [0043]: the KG are modeled where nodes correspond to entities; [0045]: GNN 308′ likewise learns/generates the low-dimensional reference node vector representations of the node embeddings for the nodes in the KG; [0002]: knowledge graphs (KGs));
generating, by the processor set, a lineage graph based on…query (Lei, [0058]: modeling a text snippet 602 as a query graph; [0059]: extracting entities from text snippet by employ existing named entity recognition techniques; [0094]: text snippet associated with unstructured text query);
mapping, by the processor set via a semantic relevance learning engine, the knowledge node of the knowledge graph to a lineage node of a lineage graph, wherein the semantic relevance learning engine is configured to identify contextual links between the knowledge graph and the lineage graph; generating, by the processor set, a list of matched nodes from the mapping of the knowledge node of the knowledge graph to the lineage node of the lineage graph; generating, by the processor set, an interestingness score for a dataset associated with the list of matched nodes based on the user query and the mapping of the knowledge node of the knowledge graph to the lineage node of the lineage graph; identifying, by the processor set, a ranked dataset recommendation based on the interestingness score; and communicating, by the processor set, instructions to communicate the interestingness score and the ranked dataset recommendation in a user interface (Lei, [0057]: extract the relevant entities from the text snippet 502 as the nodes for the query graph and assigns types to the entities and determines the relationships between the entities based on a defined graph schema for the KG; [0044]: align the query graph 302 and with the KG 304 as closely as possible to find one or more corresponding node in the KG for the unknown/ambiguous node in the query graph 302; [0045]: maps query graph to generate node and generate nodes for KG; [0046]: matching network compares query graph node with each node for KG and generate and return a ranked list of top matches and matching score; [0002]: knowledge graphs (KGs); [0090]: display results of matching process via device display).
Lei does not teach:
…the user profile, user access details in a marketplace, and a historical query.
However, Wang teaches:
…the user profile, user access details in a marketplace, and a historical query (Wang, [0021]: user generated content includes user profile, access data, and search queries; [0022]: user retrieve user generated content from plurality of sources (e.g. companies); [0030]: user generated content retrieved from search query related to historical data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the lineage graph based on the query of Lei with data such as the user profile, user access details in a marketplace, and a historical query as taught by Wang because the results of such a modification would be predictable. Specifically, Lei would continue to teach the lineage graph based on the query except that now data such as the user profile, user access details in a marketplace, and a historical query is taught according to the teachings of Wang in order to organize data. This is a predictable result of the combination. (Wang, [0001-0003]).
Regarding claims 3 and 13
The combination of Lei and Wang teaches the computer-implemented method of claim 1, wherein the generating the lineage graph …(Lei, [0058]: modeling a text snippet 602 as a query graph; [0059]: extracting entities from text snippet by employ existing named entity recognition techniques; [0094]: text snippet associated with unstructured text query).
However, Wang teaches:
…comprises clustering the user profile and the historical query via Dirichlet-Hawkes processing (DHP) (Wang, [0026]: A multimodal Dirichlet Process Mixture Sets model may be used to group the user content into the clusters. For example, user content having features related to a defined cluster may be grouped into the cluster. A descriptive name may be assigned to a cluster based upon features of the cluster; [0027]: a multimodal Dirichlet Process Mixture Sets model may be executed upon the search results having features similar to topics assigned to the one or more cluster and/or features similar to features of user generated content using the one or more clusters; [0021]: user generated content includes user profile, access data, and search queries; [0022]: user retrieve user generated content from plurality of sources (e.g. companies); [0030]: user generated content retrieved from search query related to historical data).
The motivation to combine Lei and Wang is the same as set forth above in claim 1.
Regarding claims 4 and 14
The combination of Lei and Wang teaches the computer-implemented method of claim 3, wherein the clustering comprises generating textual clusters, temporal clusters, or both (Wang, [0026]: clusters derived from textual features; .
The motivation to combine Lei and Wang is the same as set forth above in claim 1.
Regarding claim 5
The combination of Lei and Wang teaches the computer-implemented method of claim 1, wherein the generating the lineage graph…(Lei, [0058]: modeling a text snippet 602 as a query graph; [0059]: extracting entities from text snippet by employ existing named entity recognition techniques; [0094]: text snippet associated with unstructured text query).
However, Wang teaches:
comprises clustering the user profile, the user access details in the marketplace, and the historical query (Wang, [0026]: A multimodal Dirichlet Process Mixture Sets model may be used to group the user content into the clusters. For example, user content having features related to a defined cluster may be grouped into the cluster. A descriptive name may be assigned to a cluster based upon features of the cluster; [0027]: a multimodal Dirichlet Process Mixture Sets model may be executed upon the search results having features similar to topics assigned to the one or more cluster and/or features similar to features of user generated content using the one or more clusters; [0021]: user generated content includes user profile, access data, and search queries; [0022]: user retrieve user generated content from plurality of sources (e.g. companies); [0030]: user generated content retrieved from search query related to historical data).
The motivation to combine Lei and Wang is the same as set forth above in claim 1.
Regarding claims 7 and 17
The combination of Lei and Wang teaches the computer-implemented method of claim 1, wherein the contextual links comprise knowledge graph nodes including user queries, and lineage graph nodes …(Lei, [0057]: extract relevant entities from text snippets; [0094]: text snippet associated with unstructured text query; [0043]: the KG are modeled where nodes correspond to entities; [0045]: GNN 308′ likewise learns/generates the low-dimensional reference node vector representations of the node embeddings for the nodes in the KG; [0002]: knowledge graphs (KGs); [0008]: the unstructured text snippet as a query graph comprising nodes for terms included in the text snippet).
Lei does not teach:
including user profiles.
However, Wang teaches:
including user profiles (Wang, [0021]: user generated content includes user profile, access data, and search queries).
The motivation to combine Lei and Wang is the same as set forth above in claim 1.
Regarding claims 8 and 18
The combination of Lei and Wang teaches the computer-implemented method of claim 1, wherein the contextual links comprise knowledge graph nodes…, and lineage graph nodes … (Lei, [0057]: extract relevant entities from text snippets; [0094]: text snippet associated with unstructured text query; [0043]: the KG are modeled where nodes correspond to entities; [0045]: GNN 308′ likewise learns/generates the low-dimensional reference node vector representations of the node embeddings for the nodes in the KG; [0002]: knowledge graphs (KGs); [0008]: the unstructured text snippet as a query graph comprising nodes for terms included in the text snippet; [0044]: align the query graph 302 and with the KG 304 as closely as possible to find one or more corresponding node in the KG; [0045]: maps query graph to generate node and generate nodes for KG; [0046]: matching network compares query graph node with each node for KG).
Lei does not teach:
… including user query interpretation,… including users and datasets in the marketplace.
However, Wang teaches:
… including user query interpretation,… including users and datasets in the marketplace (Wang, [0020]: user generated content may be leveraged to organize search results based on topics associated with clusters; [0021]: user generated content includes user profile, access data, and search queries; [0022]: user retrieve user generated content from plurality of sources (e.g. companies); [0030]: user generated content retrieved from search query related to historical data).
The motivation to combine Lei and Wang is the same as set forth above in claim 1.
Regarding claims 9 and 19
The combination of Lei and Wang teaches the computer-implemented method of claim 1, further comprising: generating a semantic relevance score for the dataset associated with the list of matched nodes; identifying the ranked dataset recommendation based on the semantic relevance score; and communicating instructions to communicate the semantic relevance score and the ranked dataset recommendation in the user interface (Lei, [0044]: align the query graph 302 and with the KG 304 as closely as possible to find one or more corresponding node in the KG for the unknown/ambiguous node in the query graph 302; [0045]: maps query graph to generate node and generate nodes for KG; [0046]: matching network compares query graph node with each node for KG and generate and return a ranked list of top matches and matching score; [0002]: knowledge graphs (KGs); [0090]: display results of matching process via device display).
Regarding claim 10
The combination of Lei and Wang teaches the computer-implemented method of claim 1, wherein the generating the interestingness score is based on the user query and the mapping the knowledge node of the knowledge graph to the lineage node of the lineage graph (Lei, [0044]: align the query graph 302 and with the KG 304 as closely as possible to find one or more corresponding node in the KG for the unknown/ambiguous node in the query graph 302; [0045]: maps query graph to generate node and generate nodes for KG; [0046]: matching network compares query graph node with each node for KG and generate and return a ranked list of top matches and matching score; [0002]: knowledge graphs (KGs)).
Regarding claim 15
The combination of Lei and Wang teaches the computer program product of claim 11, wherein the generating the lineage graph… (Lei, [0058]: modeling a text snippet 602 as a query graph; [0059]: extracting entities from text snippet by employ existing named entity recognition techniques; [0094]: text snippet associated with unstructured text query).
However, Wang teaches:
…comprises clustering the user profile, the user access details in the marketplace, and the historical query (Wang, [0026]: A multimodal Dirichlet Process Mixture Sets model may be used to group the user content into the clusters. For example, user content having features related to a defined cluster may be grouped into the cluster. A descriptive name may be assigned to a cluster based upon features of the cluster; [0027]: a multimodal Dirichlet Process Mixture Sets model may be executed upon the search results having features similar to topics assigned to the one or more cluster and/or features similar to features of user generated content using the one or more clusters; [0021]: user generated content includes user profile, access data, and search queries; [0022]: user retrieve user generated content from plurality of sources (e.g. companies); [0030]: user generated content retrieved from search query related to historical data).
The motivation to combine Lei and Wang is the same as set forth above in claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Scanlan et al. (US Pub. No. 20240248765 A1) related to applying end user data is applied to a deployed AI model to generate a listing of the resources and a probability score indicating a likelihood the user will be interested and Coutinho et al. (US Pub. No. 20230145199 A1) related to knowledge graphs which map the aspects of entities, then transforming the graphs into vectors for a similarity comparison. The non-patent literature, Graph Visualization Techniques for Web Clustering Engines, is related to graph-based user interface for Web clustering engines that makes it possible for the user to explore and visualize the different semantic categories and their relationships at the desired level of detail.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
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