DETAILED ACTION
This rejection is in response to application filed 03/25/2024.
Claims 1-20 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claims 9-10 and 19 are 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.
Claims 9 and 19 recite “a user interface,” rendering said claims indefinite because it is unclear whether “a user interface” in dependent claims 9 and 19 are the same or different from a user interface recited in independent claims 1 and 11. Appropriate correction or clarification is required.
Claim 10 recites “a lineage node of a lineage graph” rendering said claims indefinite because it is unclear whether “a lineage node of a lineage graph” in dependent claim 10 is the same or different from a lineage node of a lineage graph recited in independent claim 1. 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 1-20 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 1-10 are directed to a method, claims 11-19 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;
mapping, …, the knowledge node of the knowledge graph to a lineage node of a 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;
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, 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 via a semantic relevance learning engine, ….; …, 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);
… the semantic relevance learning engine is configured to...(Claims 6 and 16);
…in a 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,… in a user interface (Claim 11);
…via DHP (Claim 15);
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: …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 via a semantic relevance learning engine, ….; …, 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);
… the semantic relevance learning engine is configured to...(Claims 6 and 16);
…in a 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,… in a user interface (Claim 11);
…via DHP (Claim 15);
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: …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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 6, 9-11, 16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lei et al. (US Pub. No. 20220207343 A1, hereinafter “Lei”).
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));
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; 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; 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, [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 claims 6 and 16
Lei discloses the computer-implemented method of claim 1, wherein the semantic relevance learning engine is configured to identify contextual links between the knowledge graph and the lineage graph (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).
Regarding claims 9 and 19
Lei discloses 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 a 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
Lei discloses 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 a lineage node of a 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)).
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) 2-5, 7-8, 12-15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lei as applied to claim 1 above, and further in view of Wang et al. (US Pub. No. 20170270222 A1, hereinafter “Wang”).
Regarding claims 2 and 12
Lei discloses the computer-implemented method of claim 1, further comprising generating the 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).
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 at the time the invention was made 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 2, 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 2.
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 2.
Regarding claim 5
The combination of Lei and Wang teaches the computer-implemented method of claim 2, 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 a data 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 2.
Regarding claims 7 and 17
Lei discloses the computer-implemented method of claim 6, 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 2.
Regarding claims 8 and 18
Lei discloses the computer-implemented method of claim 6, 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 a marketplace.
However, Wang teaches:
… including user query interpretation,… including users and datasets in a 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 2.
Regarding claim 15
The combination of Lei and Wang teaches the computer program product of claim 12, 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 a data marketplace, and the historical query via 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 2.
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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A. Smith can be reached at 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688