DETAILED ACTION
This communication is in response to Application No. 18/838,933 filed on August 15th, 2024 in which claims 1-20 are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statements submitted on 08/15/2024 and 11/19/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements were considered by the examiner.
Specification
The contents of the specification are sufficient for examination purposes.
Claim Objections
Claims 7 and 17 objected to because of the following informalities:
“semantic embedding similarity on topic names . . . semantic embedding similarity on topic names” appears twice in the list of “one or more” (Claim 7, ln. 1-6; Claim 17, ln. 1-6).
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 1, the claim recites the term “related” (ln. 6, 8, 15-16, and 18), which is a relative term that renders the claim indefinite. The term “related” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As a result, it is not clear what “content” qualifies as “related content” (ln. 6), what “topics” qualify as “related topics” (ln. 8), or what “topic node” qualifies as “a related topic node” (ln. 15-19). Therefore, the scope of the claim is indefinite. As a result, the claim is rejected. The claim should be amended to provide a standard for ascertaining the requisite degree of the term “related”.
Regarding Claim 2, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 3, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 4, the claim recites the term “related” (ln. 2-3), which is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As a result, it is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 5, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 6, the claim recites the term “related” (ln. 2), which is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As a result, it is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 7, the claim recites the term “related” (ln. 6-8), which is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As a result, it is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 8, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 9, the claim recites the term “related” (ln. 1), which is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As a result, it is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 10, the claim recites the term “related” (ln. 3), which is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As a result, it is similarly rejected and should be amended in a similar manner.
Additionally, the limitation “a text box containing information and/or links to other topic pages associated with the connecting line” (ln. 1-2) is indefinite because it is not clear whether “associated with the connecting line” applies to the “information” or only the “links to other topic pages”. Therefore, the scope of the claim is indefinite. As a result, the claim is rejected. The claim should be amended to resolve this ambiguity.
Furthermore, the claim is rejected because it is dependent on an indefinite claim.
Regarding Claim 11, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 1. As a result, it is similarly rejected and should be amended in a similar manner.
Regarding Claim 12, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 2. As a result, it is similarly rejected.
Regarding Claim 13, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 3. As a result, it is similarly rejected.
Regarding Claim 14, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 4. As a result, it is similarly rejected and should be amended in a similar manner.
Regarding Claim 15, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 5. As a result, it is similarly rejected.
Regarding Claim 16, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 6. As a result, it is similarly rejected and should be amended in a similar manner.
Regarding Claim 17, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 7. As a result, it is similarly rejected and should be amended in a similar manner.
Regarding Claim 18, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 8. As a result, it is similarly rejected.
Regarding Claim 19, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 9. As a result, it is similarly rejected and should be amended in a similar manner.
Regarding Claim 20, the claim is indefinite for substantially the same reasoning as discussed in regard to the rejection of Claim 10. As a result, it is similarly rejected and should be amended in a similar manner.
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 abstract ideas without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a machine claim. Therefore, claims 1-10 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, steps of the claimed subject matter are mental processes. Specifically, the claim recites
“perform a set of operations, the set of operations comprising . . . categorizing related content into a topic” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“determining attributes of the topic” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“generating a knowledge graph of related topics” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may memorialized as a knowledge graph with the aid of pen and paper);
“ranking the knowledge graph for importance by a relationship type” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“filtering the knowledge graph based on a filtering parameter” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper); and
“ranking the knowledge graph for relevance based on a relevance feature” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, causes the system to” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea);
“receiving content” (amounts to insignificant extra-solution activity because receiving content amounts to the transmission of data, which is incidental to the claimed subject matter);
“from a knowledge base . . . based on the ranked and filtered knowledge graph . . . wherein the visualization display comprises one or more of: a root topic node; a related topic node; a connecting line between the root topic node and the related topic node; and an interactive element accessible through the root topic node, related topic node and/or connecting line” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea); and
“generating . . . a visualization display” (amounts to insignificant extra-solution activity because generating a visualization display amounts to presenting offers and gathering statistics, which is incidental to the claimed subject matter).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“A system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, causes the system to” (mere instructions to apply the exception using generic computer components does not provide an inventive concept);
“receiving content” (transmission of data, such as through a network, see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), or by accessing information in memory, see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, is well‐understood, routine, and conventional; which is recited here with a high level of generality, and remains insignificant extra-solution activity even upon reconsideration);
“from a knowledge base . . . based on the ranked and filtered knowledge graph . . . wherein the visualization display comprises one or more of: a root topic node; a related topic node; a connecting line between the root topic node and the related topic node; and an interactive element accessible through the root topic node, related topic node and/or connecting line” (merely reciting a particular technological environment or field of use does not provide an inventive concept); and
“generating . . . a visualization display” (presenting offers and gathering statistics are well‐understood, routine, and conventional, see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; which are recited here with a high level of generality, and remains insignificant extra-solution activity even upon reconsideration).
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-10. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 2 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein content comprises one or more of: a document, email, online chat, meeting mentioning a topic or user, presentation, an address or location where a meeting or event will take place, a video recording of a meeting that happened online, the information of all users who participated in a meeting, phone number, email address, user contact information, organization contact information, team contact information, metadata, individual user, teams of users, top contacts for a user or who a user communicates with regularly” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein content comprises one or more of: a document, email, online chat, meeting mentioning a topic or user, presentation, an address or location where a meeting or event will take place, a video recording of a meeting that happened online, the information of all users who participated in a meeting, phone number, email address, user contact information, organization contact information, team contact information, metadata, individual user, teams of users, top contacts for a user or who a user communicates with regularly” (merely reciting a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 2 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 3:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 3 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein a knowledge base comprises an accumulation of content across a distributed network” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein a knowledge base comprises an accumulation of content across a distributed network” (merely reciting a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 3 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 4 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein an attribute comprises one or more of: a name of the topic, alternate names for the topic, a description of the topic, topic definitions, related people, related documents, related sites, related groups, related webpages or specific attributes for each type of topic” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein an attribute comprises one or more of: a name of the topic, alternate names for the topic, a description of the topic, topic definitions, related people, related documents, related sites, related groups, related webpages or specific attributes for each type of topic” (merely reciting a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 4 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 5:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 5 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein a relationship type comprises one or more of a topic to topic relationship, topic to document relationship, topic to user relationship, user to user relationship, user to document relationship and document to document relationship” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein a relationship type comprises one or more of a topic to topic relationship, topic to document relationship, topic to user relationship, user to user relationship, user to document relationship and document to document relationship” (merely reciting a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 5 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 6:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 6 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites:
“wherein a filtering parameter comprises one or more of filtering out related topic candidates if they do not co-occur in any document from a topic within n-levels of the root topic, filtering out documents if they have not been accessed within a certain time period, filtering out users if there has been no communication within a certain time period and/or filtering out users based on location” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 6 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 6 depends on. As discussed above, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Whereas if a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulas or equations, or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Here, the claim recites additional elements that are mental processes or mathematical concepts. Specifically, the claim recites:
“wherein a relevance feature comprises one or more of a Jaccard overlap ratio between associated people and document sets for topic pairs” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper; mathematical concept – alternatively, amounts to a mathematical calculation based on a formula);
“number of descriptions available for topic pairs” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“cosine similarity between topic embeddings produced on semantic content associated with topics” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper; mathematical concept – alternatively, amounts to a mathematical calculation based on a formula);
“semantic embedding similarity on topic names, overlap ratio among established people for topics” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“semantic embedding similarity on topic names” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“count of established people for related topics” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“count of established documents for related topics” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“an overlap ratio among established documents for topics” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“count of definitions for related topics” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“semantic embedding similarity on top document titles” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper);
“a count of definitions of source topic” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper); and
“and/or the pre-trained knowledge graph directly to produce topic embeddings and cosine similarity” (mental process – amounts to exercising judgment to form opinions, with reference to observed information, which may be aided by pen and paper; mathematical concept – alternatively, amounts to a mathematical calculation based on a formula).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 7 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 8:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 8 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the visualization display is a graph visualization web component of n-levels” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein the visualization display is a graph visualization web component of n-levels” (merely reciting a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 8 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 9:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 9 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein a related topic node comprises one or more of a discovered node, confirmed node and/or rejected node” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein a related topic node comprises one or more of a discovered node, confirmed node and/or rejected node” (merely reciting a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 9 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 10:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 10 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein an interactive element comprises one or more of a text box containing information and/or links to other topic pages associated with the connecting line, root topic node, related topic node, a topic legend and/or a search function” (amounts to merely reciting a particular technological environment or field of use, which does not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional element:
“wherein an interactive element comprises one or more of a text box containing information and/or links to other topic pages associated with the connecting line, root topic node, related topic node, a topic legend and/or a search function” (merely reciting a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 10 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 11, the claim recites limitations that are all substantially the same as limitations of Claim 1, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 11 is rejected under the same rationale.
Regarding Claim 12, the claim recites limitations that are all substantially the same as limitations of Claim 2, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 12 is rejected under the same rationale.
Regarding Claim 13, the claim recites limitations that are all substantially the same as limitations of Claim 3, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 13 is rejected under the same rationale.
Regarding Claim 14, the claim recites limitations that are all substantially the same as limitations of Claim 4, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 14 is rejected under the same rationale.
Regarding Claim 15, the claim recites limitations that are all substantially the same as limitations of Claim 5, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 15 is rejected under the same rationale.
Regarding Claim 16, the claim recites limitations that are all substantially the same as limitations of Claim 6, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 16 is rejected under the same rationale.
Regarding Claim 17, the claim recites limitations that are all substantially the same as limitations of Claim 7, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes and mathematical concepts without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 17 is rejected under the same rationale.
Regarding Claim 18, the claim recites limitations that are all substantially the same as limitations of Claim 8, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 18 is rejected under the same rationale.
Regarding Claim 19, the claim recites limitations that are all substantially the same as limitations of Claim 9, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 19 is rejected under the same rationale.
Regarding Claim 20, the claim recites limitations that are all substantially the same as limitations of Claim 10, in the form of a method. For substantially the same reasoning, the claim is also directed to performing mental processes without integration into a practical application or amounting to either significantly more or an inventive concept.
Accordingly, Claim 20 is rejected under the same rationale.
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.
Claims 1-5, 8-15, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bly et al. (hereinafter Bly) (Pat. App. Pub. No. US 2020/0250562 A1).
Regarding Claim 1, Bly teaches a system comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, causes the system to perform a set of operations, the set of operations comprising (Para. [0027], “the present invention may be embodied in whole or in part as a system . . . For example, in some embodiments, one or more of the operations, functions, processes, or methods described herein may be implemented by one or more suitable processing elements (such as a processor, microprocessor, CPU, GPU, controller, etc.) . . . The processing element or elements are programmed with a set of executable instructions (e.g., software instructions), where the instructions may be stored in a suitable data storage element”, where “a system” comprises at least one processor, “one or more suitable processing elements (such as a processor, microprocessor, CPU, GPU, controller, etc.)”; and memory storing instructions, “the instructions may be stored in a suitable data storage element”, that, when executed by the at least one processor, causes the system to perform a set of operations, “one or more of the operations, functions, processes, or methods described herein may be implemented by one or more suitable processing elements”; see also Para. [0193], “Any of the software components, processes or functions described in this application may be implemented as software code to be executed by a processor using any suitable computer language such as, for example, Python, Java, JavaScript, C++ or Perl using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions, or commands in (or on) a non-transitory computer-readable medium, such as a random-access memory (RAM), a read only memory (ROM), a magnetic medium such as a hard-drive or a floppy disk, or an optical medium such as a CD-ROM”):
receiving content from a knowledge base (Fig. 2(a); Para. [0082], “identifying and accessing a set of sources that contain information and data regarding statistical associations between variables or factors used in a study (as suggested by step or stage 202)”; and Para. [0052], “Information/data about or demonstrating statistical associations between topics, factors, or variables may be retrieved (i.e., accessed and obtained) from a number of sources. These may include (but are not limited to) journal articles, technical and scientific publications and databases, digital “notebooks” for research and data science, experimentation platforms (for example for A/B testing), data science and machine learning platforms, and/or a public website (element/website 116)”, where content, “sources that contain information and data”, is received, “identifying and accessing”, from a knowledge base, a collection of knowledge “include[ing] (but are not limited to) journal articles, technical and scientific publications and databases, digital “notebooks” for research and data science, experimentation platforms (for example for A/B testing), data science and machine learning platforms, and/or a public website (element/website 116)”; see also Fig. 1(a) and Para. [0059], “A central database (“SystemDB” 108) stores the information/data that has been retrieved and its associated data structures (i.e., nodes, edges, values), as described herein. An instance or projection of the central database containing all or a subset of the information/data stored in SystemDB is made available to a defined customer, business or organization 104 (or group thereof) for their own use”, where the content, “the information/data” is retrieved, “made available to . . . for their own use”, from a knowledge base, “A central database (“SystemDB” 108)”, for use by “a defined customer, business or organization 104 (or group thereof)”);
categorizing related content into a topic (Fig. 2(a) and Para. [0084], “The results of processing the accessed information and data are then structured or represented . . . it generally includes the elements used to construct a Feature Graph (i.e., nodes representing a topic or variable, edges representing a statistical association, measures including a metric or evaluation of a statistical association)”, where related content, “the accessed information and data”, are categorized, “are then structured or represented”, into “a topic”);
determining attributes of the topic (Fig. 3 and Para. [0099] – [100], “This allows a user of the Feature Graph to retrieve datasets based on the previously demonstrated or determined predictive power of that data with regards to a specified target/topic (rather than the potentially less relevant or irrelevant datasets about topics semantically related to a specified target/topic, as in a knowledge graph); For example, using an embodiment of the system and methods described herein, if a data scientist searches for “vandalism” as a target topic or goal of a study, they will retrieve datasets for topics that have been shown to predict that target/topic—for example, “household income,” “luminosity,” and “traffic density”” where the “topic[s]” have name attributes, such as ““vandalism” . . . “household income,” “luminosity,” and “traffic density””, which must be determined to be associated with the categorized topics; see also Para. [0116], “The variable “skin problems in grades 7-12” may additionally be semantically grounded/linked to the concept “Acne vulgaris” and the variable “personal earnings” may be semantically grounded to the concept “Personal Income”, with both concept names sourced from an ontology such as Wikidata”; see generally Para. [0057] – [0058], “variable names (e.g., “aerobic exercise”) are stored as retrieved and may then be semantically grounded to (i.e., linked or associated with) public domain ontologies (e.g., Wikidata) to facilitate clustering of variables (and the associated statistical association) based on common or typically synonymous or closely related terms and concepts; For example, a variable labeled as “log_house_sale_price” by a given user might be semantically associated by the system (and further affirmed by the user) with “Real Estate Price,” a topic in Wikidata with the unique ID, Q58081362”);
generating a knowledge graph of related topics (Fig. 2(a); Fig. 2(b), Para. [0081] - [0085], “FIG. 2(a) is a flow chart or flow diagram illustrating a process, method, function or operation for constructing a Feature Graph 200 . . . the process or operations described with reference to FIG. 2(a) enable the construction of a graph containing nodes and edges linking certain of the nodes (an example of which is illustrated in FIG. 3). The nodes represent topics, targets or variables of a study or observation and the edges represent a statistical association between a node and one or more other nodes”, where “a Feature Graph” of related topics, “The nodes represent topics” is generated, “constructing”, and where, despite the author’s distinction between a “Knowledge graph” and a “Feature Graph”, see Para. [0088], the “Feature Graph” is with the broadest reasonable interpretation in light of the specification of a knowledge graph because it captures topic relatedness, “The nodes represent topics, targets or variables of a study or observation and the edges represent a statistical association between a node and one or more other nodes”, based on a plurality of topic nodes associated with a network, “a graph containing nodes and edges linking certain of the nodes”; see also Fig. 3 and Claim 1, “constructing a feature graph based on the stored results of processing the accessed source or sources, the feature graph including a set of nodes and a set of edges, wherein each edge in the set of edges connects a node in the set of nodes to one or more other nodes, and further, wherein each node represents a variable found to be statistically associated with a topic of a study described in a source and each edge represents a statistical association between a node and the topic of the study described in the source or between a first node and a second node”);
ranking the knowledge graph for importance by a relationship type (Para. [0134] – [0136], “a Data Recommender application may be used to leverage the benefits of a Feature Graph . . . a Data Recommender application may perform the parameter tuning work for the user and return variables and datasets that are expected to be of highest relevance to the user. To produce a dataset recommendation, the application may take into account a number of characteristics or signals, including, for example: Hops to Target: Evidence of a direct association between a Variable and the Target is of greater weight than evidence of an indirect association between a Variable and another Variable that is directly associated to the Target”, where the “the knowledge graph” is ranked, “weight[ed]” components to “return variables and datasets”, for importance, “highest relevance to the user”, by a relationship type, “direct association” or “indirect association”; see generally Fig. 3; Para. [0085], “The nodes represent topics, targets or variables of a study or observation and the edges represent a statistical association between a node and one or more other nodes”; and Para. [0031], “An edge may be associated with one or more values; such values may represent a characteristic of the connected nodes, a metric or measure of the relationship between a node or nodes”, where various relationship types are disclosed);
filtering the knowledge graph based on a filtering parameter; ranking the knowledge graph for relevance based on a relevance feature (Fig. 1; Fig. 2(b); and Para. [0090], “A Data Recommender application (such as 112 in FIG. 1) then traverses the Feature Graph to identify datasets that are expected to be of relevance and useful to training the model (step or stage 226). The identified datasets may then be ranked, filtered or otherwise ordered (step or stage 228”, where the knowledge graph, “The identified datasets” of “the Feature Graph”, is “filtered” and “ranked”; see also Para. [0134], “the Data Recommender algorithm/process traverses the Feature Graph . . . [and] filters the results based on certain data usability factors (e.g., keys required for data joins) and/or based on the specified purpose of the model (for example, the model requires interpretable/explainable features, or the model must not use protected class information, etc.)”, where the knowledge graph, “Feature Graph”, is filtered, “filters”, based on filtering parameters, “certain data usability factors . . . and/or based on the specified purpose”; see also Para. [0134], “the Data Recommender algorithm/process traverses the Feature Graph, ranks the most predictive relationships based on the statistical information and metadata stored in the Feature Graph” and Para. [0079] – [0080], “Variables and/or concepts statistically associated to the Input . . . the ranking of the output results may take into account the value and quality of the association”, where the “ranking” is for relevance, “most predictive” based on “the value and quality of the association”, based on a relevance feature, “statistical information and metadata stored in the Feature Graph”); and
generating, based on the ranked and filtered knowledge graph, a visualization display (Fig. 1(b); Para. [0068], “FIG. 1(b) is a screenshot illustrating a user interface icon 150 (also shown in FIG. 1(d)) that may be used in an implementation of an embodiment of the system and methods described herein to differentiate a Statistical Search (the name or label given by the inventor to the type of search described herein), to more easily enable a user to trigger and control a Statistical Search, and to identify a location (the outlined query input “box”) into which to insert a Statistical Search query 160”; and Para. [0157] – [0164], “Generate a user interface to enable a user to input a search . . . and Present to the user a ranking or listing of the identified datasets, with such ranking or listing being subject to filtering by one or more user specified criteria (if desired)”, where a visualization display, the data displayed to “a user interface”, is generated, in part, based on the ranked and filtered knowledge graph, “Present to the user a ranking or listing of the identified datasets, with such ranking or listing being subject to filtering”; see also Para. [0090], “A Data Recommender application (such as 112 in FIG. 1) then traverses the Feature Graph to identify datasets that are expected to be of relevance and useful to training the model (step or stage 226). The identified datasets may then be ranked, filtered or otherwise ordered (step or stage 228, which will be described in greater detail) prior to presentation to a user (step or stage 230)”),
wherein the visualization display comprises one or more of: a root topic node; a related topic node; a connecting line between the root topic node and the related topic node (Fig. 1(b); Para. [0068], “FIG. 1(b) is a screenshot illustrating a user interface icon 150 . . . that may be used . . . to differentiate a Statistical Search (the name or label given by the inventor to the type of search described herein), to more easily enable a user to trigger and control a Statistical Search, and to identify a location (the outlined query input “box”) into which to insert a Statistical Search query 160”; and Para. [0069], “an embodiment may instead employ a “micro-graph” 150 comprising two nodes and one edge connecting the nodes, signaling to the user that a Statistical Search is implemented in a broader sense (i.e., looking for statistical associations) than a standard semantic search, and giving the user control over aspects of the search. By selecting the source node 151, the target node 152, or both nodes, a user may specify her intent with respect to traversal of a Feature Graph”; the visualization display, the data displayed to “a user interface”, includes a graph visualization component, “a user interface icon 150”, with a connecting line between a root topic node, “the source node 151”, and a related topic node, “the target node 152”; see also Para. [0157] – [0164], “Generate a user interface to enable a user to input a search term or concept C1 (e.g., a topic of interest or variable related to the topic) for initiating a statistical search and/or a semantic search, and/or one or more controls for a search; note that an example of such a user interface is described with reference to FIGS. 1(b), 1(c) and 1(d); Determine a concept (C2) that is semantically associated with C1 . . . Determine variables . . . by executing a search over a Feature Graph . . . and Present to the user a ranking or listing of the identified datasets, with such ranking or listing being subject to filtering by one or more user specified criteria (if desired)”, where visualization display comprises a connecting line, “semantically associated”, a root topic node, “a user to input a search term or concept C1”, a related topic node, “Determine a concept (C2) that is semantically associated with C1” in the form of “identified datasets”);
and an interactive element accessible through the root topic node, related topic node and/or connecting line (Fig. 1(b); Fig. 1(d); Para. [0068], “FIG. 1(b) is a screenshot illustrating a user interface icon 150 (also shown in FIG. 1(d)) that may be used in an implementation of an embodiment of the system and methods described herein to differentiate a Statistical Search (the name or label given by the inventor to the type of search described herein), to more easily enable a user to trigger and control a Statistical Search, and to identify a location (the outlined query input “box”) into which to insert a Statistical Search query 160”; and Para. [0157] – [0164], “Generate a user interface to enable a user to input a search term or concept C1 (e.g., a topic of interest or variable related to the topic) for initiating a statistical search and/or a semantic search, and/or one or more controls for a search; note that an example of such a user interface is described with reference to FIGS. 1(b), 1(c) and 1(d); Determine a concept (C2) that is semantically associated with C1 . . . Determine variables . . . by executing a search over a Feature Graph . . . and Present to the user a ranking or listing of the identified datasets, with such ranking or listing being subject to filtering by one or more user specified criteria (if desired)”, where a related topic node, “a concept (C2)”, which is a discovered and confirmed to be associated with the root node, “Determine a concept (C2) that is semantically associated with C1”, makes the interactive element, “a user interface” populated with “the identified datasets”, accessible, “Present to the user”).
Regarding Claim 2, Bly teaches the system of claim 1, wherein content comprises one or more of: a document, email, online chat, meeting mentioning a topic or user, presentation, an address or location where a meeting or event will take place, a video recording of a meeting that happened online, the information of all users who participated in a meeting, phone number, email address, user contact information, organization contact information, team contact information, metadata, individual user, teams of users, top contacts for a user or who a user communicates with regularly (Fig. 2(a) and Para. [0082], “identifying and accessing a set of sources that contain information and data regarding statistical associations between variables or factors used in a study (as suggested by step or stage 202)”, where content, “sources that contain information and data”, comprises, at the least, documents, see Para. [0052], “Information/data . . . from a number of sources. These may include (but are not limited to) journal articles, technical and scientific publications”, and metadata, see Para. [0035], “using conventional approaches data is organized to be searchable primarily based on language. For example, this form of organization might be based on metadata”).
Regarding Claim 3, Bly teaches the system of claim 1, wherein a knowledge base comprises an accumulation of content across a distributed network (Para. [0052], “Information/data about or demonstrating statistical associations between topics, factors, or variables may be retrieved (i.e., accessed and obtained) from a number of sources. These may include (but are not limited to) journal articles, technical and scientific publications and databases, digital “notebooks” for research and data science, experimentation platforms (for example for A/B testing), data science and machine learning platforms, and/or a public website (element/website 116)”, where the knowledge base comprises an accumulation of content, “Information/data . . . retrieved (i.e., accessed and obtained) from a number of sources”, across a distributed network, “These may include (but are not limited to) journal articles, technical and scientific publications and databases, digital “notebooks” for research and data science, experimentation platforms (for example for A/B testing), data science and machine learning platforms, and/or a public website (element/website 116)”; see also Para. [0055], “information/data retrieval is generally happening on a regular or continuing basis, providing the system with new information to store and structure and thereby expose to users”; see generally Para. [190], “computing environments in which an embodiment of the invention may be implemented include . . . a data storage element (e.g., a database) that can be accessed remotely over a network . . . networks, or other configurable components that may be used by multiple users for data entry, data processing, application execution, data review”).
Regarding Claim 4, Bly teaches the system of claim 1, wherein an attribute comprises one or more of: a name of the topic, alternate names for the topic, a description of the topic, topic definitions, related people, related documents, related sites, related groups, related webpages or specific attributes for each type of topic (Fig. 3 and Para. [0099] – [100], “This allows a user of the Feature Graph to retrieve datasets based on the previously demonstrated or determined predictive power of that data with regards to a specified target/topic (rather than the potentially less relevant or irrelevant datasets about topics semantically related to a specified target/topic, as in a knowledge graph); For example, using an embodiment of the system and methods described herein, if a data scientist searches for “vandalism” as a target topic or goal of a study, they will retrieve datasets for topics that have been shown to predict that target/topic—for example, “household income,” “luminosity,” and “traffic density”” where the “topic[s]” have attributes comprising, at the least, a name of the topic, such as ““vandalism” . . . “household income,” “luminosity,” and “traffic density””; see also Para. [0116], “The variable “skin problems in grades 7-12” may additionally be semantically grounded/linked to the concept “Acne vulgaris” and the variable “personal earnings” may be semantically grounded to the concept “Personal Income”, with both concept names sourced from an ontology such as Wikidata”).
Regarding Claim 5, Bly teaches the system of claim 1, wherein a relationship type comprises one or more of a topic to topic relationship, topic to document relationship, topic to user relationship, user to user relationship, user to document relationship and document to document relationship (Fig. 3; Para. [0085], “The nodes represent topics, targets or variables of a study or observation and the edges represent a statistical association between a node and one or more other nodes”; and Para. [0031], “An edge may be associated with one or more values; such values may represent a characteristic of the connected nodes, a metric or measure of the relationship between a node or nodes”, where a relationship type, “An edge . . . may represent . . . the relationship between a node or nodes”, comprises, at the least, a topic to topic relationship, “The nodes represent topics . . . edges represent a statistical association between a node and one or more other nodes”).
Regarding Claim 8, Bly teaches the system of claim 1, wherein the visualization display is a graph visualization web component of n-levels (Fig. 1(b); Para. [0068], “FIG. 1(b) is a screenshot illustrating a user interface icon 150 . . . that may be used . . . to differentiate a Statistical Search (the name or label given by the inventor to the type of search described herein), to more easily enable a user to trigger and control a Statistical Search, and to identify a location (the outlined query input “box”) into which to insert a Statistical Search query 160”; and Para. [0069], “an embodiment may instead employ a “micro-graph” 150 comprising two nodes and one edge connecting the nodes, signaling to the user that a Statistical Search is implemented in a broader sense (i.e., looking for statistical associations) than a standard semantic search, and giving the user control over aspects of the search. By selecting the source node 151, the target node 152, or both nodes, a user may specify her intent with respect to traversal of a Feature Graph”; the visualization display, the data displayed to “a user interface”, includes a graph visualization component, “a user interface icon 150”, of n-levels, “comprising two nodes and one edge connecting the nodes”; see also Para. [0169], “a service platform that may be used in implementing an embodiment of the systems and methods described herein. In some embodiments, a service platform (a multi-tenant or other “cloud-based” system) which provides access to one or more of data, applications, and data processing capabilities includes a website (e.g., ServicePlatform.com)”, where the “a service platform that may be used in implementing an embodiment of the systems and methods described herein” is a “website”, thus the above discussed graph visualization component is a web component; see also Para. [0092], where further details of the n-levels are discussed).
Regarding Claim 9, Bly teaches the system of claim 1, wherein a related topic node comprises one or more of a discovered node, confirmed node and/or rejected node (Fig. 1(b); Fig. 1(d); Para. [0068], “FIG. 1(b) is a screenshot illustrating a user interface icon 150 (also shown in FIG. 1(d)) that may be used in an implementation of an embodiment of the system and methods described herein to differentiate a Statistical Search (the name or label given by the inventor to the type of search described herein), to more easily enable a user to trigger and control a Statistical Search, and to identify a location (the outlined query input “box”) into which to insert a Statistical Search query 160”; and Para. [0157] – [0164], “Generate a user interface to enable a user to input a search term or concept C1 (e.g., a topic of interest or variable related to the topic) for initiating a statistical search and/or a semantic search, and/or one or more controls for a search; note that an example of such a user interface is described with reference to FIGS. 1(b), 1(c) and 1(d); Determine a concept (C2) that is semantically associated with C1 . . . Determine variables . . . by executing a search over a Feature Graph . . . and Present to the user a ranking or listing of the identified datasets, with such ranking or listing being subject to filtering by one or more user specified criteria (if desired)”, where a related topic node, “a concept (C2)”, which is a discovered and confirmed to be associated with the root node, “Determine a concept (C2) that is semantically associated with C1”, makes the interactive element, “a user interface” populated with “the identified datasets”, accessible, “Present to the user”)
Regarding Claim 10, Bly teaches the system of claim 1, wherein an interactive element comprises one or more of a text box containing information and/or links to other topic pages associated with the connecting line, root topic node, related topic node, a topic legend and/or a search function (Fig. 1(b); Fig. 1(d); Para. [0068], “FIG. 1(b) is a screenshot illustrating a user interface icon 150 (also shown in FIG. 1(d)) that may be used in an implementation of an embodiment of the system and methods described herein to differentiate a Statistical Search (the name or label given by the inventor to the type of search described herein), to more easily enable a user to trigger and control a Statistical Search, and to identify a location (the outlined query input “box”) into which to insert a Statistical Search query 160”; and Para. [0157] – [0164], “Generate a user interface to enable a user to input a search term or concept C1 (e.g., a topic of interest or variable related to the topic) for initiating a statistical search and/or a semantic search, and/or one or more controls for a search; note that an example of such a user interface is described with reference to FIGS. 1(b), 1(c) and 1(d); Determine a concept (C2) that is semantically associated with C1 . . . Determine variables . . . by executing a search over a Feature Graph . . . and Present to the user a ranking or listing of the identified datasets, with such ranking or listing being subject to filtering by one or more user specified criteria (if desired)”, where an interactive element, “a user interface” populated with “the identified datasets”, comprises, at the least, a text box containing information, “Present to the user a ranking or listing of the identified datasets”, associated with the connecting line, “semantically associated”, a root topic node, “a user to input a search term or concept C1”, a related topic node, “Determine a concept (C2) that is semantically associated with C1” in the form of “identified datasets”, and a search function, “the outlined query input “box”) into which to insert a Statistical Search query 160”).
Regarding Claim 11, Bly teaches a method comprising (Abstract, “A system and associated methods for organizing, representing, finding, discovering, and accessing data. Embodiments represent information and data in the form of a data structure termed a “Feature Graph””): . . .
filtering the knowledge graph based on filtering parameters . . . (Para. [0134], “the Data Recommender algorithm/process traverses the Feature Graph . . . [and] filters the results based on certain data usability factors (e.g., keys required for data joins) and/or based on the specified purpose of the model (for example, the model requires interpretable/explainable features, or the model must not use protected class information, etc.)”, where the knowledge graph, “Feature Graph”, is filtered, “filters”, based on filtering parameters, “certain data usability factors . . . and/or based on the specified purpose”).
The remaining limitations are substantially the same as limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 12, the additional elements of the dependent claim are substantially the same as limitations of Claim 2, therefore it is rejected under the same rationale.
Regarding Claim 13, the additional elements of the dependent claim are substantially the same as limitations of Claim 3, therefore it is rejected under the same rationale.
Regarding Claim 14, the additional elements of the dependent claim are substantially the same as limitations of Claim 4, therefore it is rejected under the same rationale.
Regarding Claim 15, the additional elements of the dependent claim are substantially the same as limitations of Claim 5, therefore it is rejected under the same rationale.
Regarding Claim 18, the additional elements of the dependent claim are substantially the same as limitations of Claim 8, therefore it is rejected under the same rationale.
Regarding Claim 19, the additional elements of the dependent claim are substantially the same as limitations of Claim 9, therefore it is rejected under the same rationale.
Regarding Claim 20, the additional elements of the dependent claim are substantially the same as limitations of Claim 10, therefore it is rejected under the same rationale.
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.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bly in view of Zhao et al. (hereinafter Zhao) (Pat. App. Pub. No. US 2023/0186120 A1).
Regarding Claim 6, Bly teaches the system of claim 1, wherein a filtering parameter . . . (Para. [0134], “the Data Recommender algorithm/process traverses the Feature Graph . . . [and] filters the results based on certain data usability factors (e.g., keys required for data joins) and/or based on the specified purpose of the model (for example, the model requires interpretable/explainable features, or the model must not use protected class information, etc.)”, where the knowledge graph, “Feature Graph”, is filtered, “filters”, based on filtering parameters, “certain data usability factors . . . and/or based on the specified purpose”).
Bly does not explicitly disclose . . . comprises one or more of filtering out related topic candidates if they do not co-occur in any document from a topic within n-levels of the root topic, filtering out documents if they have not been accessed within a certain time period, filtering out users if there has been no communication within a certain time period and/or filtering out users based on location.
However, Zhao teaches . . . [filtering that] comprises one or more of filtering out related topic candidates if they do not co-occur in any document from a topic within n-levels of the root topic, filtering out documents if they have not been accessed within a certain time period, filtering out users if there has been no communication within a certain time period and/or filtering out users based on location (Para. [0083] – [0084], “As an example, on Jun. 12, 2020, the total number of tweets collected was 60,000. The 1,000 most active users were selected for further analysis. For seven days of historical tweets from these most active users, a total of 309,644 tweets were collected, 310 tweets per user on average. By counting the number of interactions (retweets/mentions) between users over the seven days, a social network analysis graph was developed”, where filtering comprises, at the least, filtering out “users” if there has been no communication within a certain time period, where users without communication “on Jun. 12, 2020” will not be part of the “1,000 most active users were selected for further analysis” and users without communication for “seven days of historical tweets” will not contribute to the “a total of 309,644 tweets were collected, 310 tweets per user on average”; see also Para. [0086], “For behavior pattern analysis, drawing from these 24,000+ tweets over the 14 days (168 time frames, 2 hour each) between Oct. 22, 2020 and Nov. 04, 2020, 42 patterns are obtained”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the filtering parameter of Bly with the filtering that comprises one or more of filtering out related topic candidates if they do not co-occur in any document from a topic within n-levels of the root topic, filtering out documents if they have not been accessed within a certain time period, filtering out users if there has been no communication within a certain time period and/or filtering out users based on location of Zhao in order to filter search results to only return data of only the most recently active users (Bly, Para. [0157] – [0164], “Generate a user interface to enable a user to input a search . . . and Present to the user a ranking or listing of the identified datasets, with such ranking or listing being subject to filtering by one or more user specified criteria (if desired)”; Zhao, Para. [0083], “To narrow down the search scope and reduce computational complexity, only the most active users and those with abnormal behavior are selected and added into a designated database to be further tracked and analyzed; (2) historical social network analysis: the historical behavior of users in the designated database is collected via Twitter API by querying the most recent tweets of each users. These tweets are used to construct a Social Knowledge Graph for social network analysis and pattern analysis”), which will allow for scalable analysis of impactful data in data science applications (Zhao, Para. [0081], “to capture events of interest in the ever-changing world, there is a need for a scalable, automated process to discover potentially influential individuals or social networks” and Bly, Para. [0134], “a Data Recommender application may be used to leverage the benefits of a Feature Graph. In a typical use case, a user (a data scientist), inputs a desired target or topic (a “Target”) and model purpose, and the Data Recommender retrieves the “best” datasets for her to use for training the model”; see generally Zhao, Para. [0003], “The era of big data provides a great opportunity for latent anomaly detection at a large scale and in real time. There is an increasing need for both governments (e.g., first responders) and businesses (e.g., security personnel) to discover latent anomalous activities in unstructured publicly available data produced by professional agencies and the general public, for safety and protection”).
Regarding Claim 16, the additional elements of the dependent claim are substantially the same as limitations of Claim 6, therefore it is rejected under the same rationale.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bly in view of Phan et al. (hereinafter Phan) (“Robust Representation Learning of Biomedical Names”).
Regarding Claim 7, Bly teaches the system of claim 1, wherein a relevance feature . . . (Para. [0079] – [0080], “Variables and/or concepts statistically associated to the Input . . . the ranking of the output results may take into account the value and quality of the association”, where the “ranking” is for relevance, “most predictive” based on “the value and quality of the association”, based on a relevance feature, “statistical information and metadata stored in the Feature Graph”).
Bly does not explicitly disclose . . . comprises one or more of a Jaccard overlap ratio between associated people and document sets for topic pairs, number of descriptions available for topic pairs, cosine similarity between topic embeddings produced on semantic content associated with topics, semantic embedding similarity on topic names, overlap ratio among established people for topics, semantic embedding similarity on topic names, count of established people for related topics, count of established documents for related topics, an overlap ratio among established documents for topics, count of definitions for related topics, semantic embedding similarity on top document titles, a count of definitions of source topic and/or the pre-trained knowledge graph directly to produce topic embeddings and cosine similarity.
However, Phan teaches . . . [a relevance feature] comprises one or more of a Jaccard overlap ratio between associated people and document sets for topic pairs, number of descriptions available for topic pairs, cosine similarity between topic embeddings produced on semantic content associated with topics, semantic embedding similarity on topic names, overlap ratio among established people for topics, semantic embedding similarity on topic names, count of established people for related topics, count of established documents for related topics, an overlap ratio among established documents for topics, count of definitions for related topics, semantic embedding similarity on top document titles, a count of definitions of source topic and/or the pre-trained knowledge graph directly to produce topic embeddings and cosine similarity (Pg. 3275, Col. 1, Abstract, “This paper proposes a new framework for learning robust representations of biomedical names . . . Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks”; Pg. 3282, Col. 1, Para. 1, “We collect all names of the top-20 retrieved concepts as a synonym candidate set. Cosine similarity is then used to rank the candidates”; Pg. 3283, Col. 2, Tab. 4, “cosine similarly scores of name embeddings”; and Pg. 3283, Col. 1, Para. 2, “We evaluate the correlation between embedding cosine similarity and human judgments, regarding semantic similarity and relatedness”, where a relevance feature, “method outperforms other baselines on . . . semantic similarity and relatedness”, comprises, at the least, “cosine similarity” between topic embeddings produced on semantic content associated with topics, “cosine similarly scores of name embeddings”, which is also semantic embedding similarity on topic names).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the relevance feature of Bly with the relevance feature comprises one or more of a Jaccard overlap ratio between associated people and document sets for topic pairs, number of descriptions available for topic pairs, cosine similarity between topic embeddings produced on semantic content associated with topics, semantic embedding similarity on topic names, overlap ratio among established people for topics, semantic embedding similarity on topic names, count of established people for related topics, count of established documents for related topics, an overlap ratio among established documents for topics, count of definitions for related topics, semantic embedding similarity on top document titles, a count of definitions of source topic and/or the pre-trained knowledge graph directly to produce topic embeddings and cosine similarity of Phan in order to supplement Bly’s dataset retrieval methods (Bly, Para. [0100], For example, using an embodiment of the system and methods described herein, if a data scientist searches for “vandalism” as a target topic or goal of a study, they will retrieve datasets for topics that have been shown to predict that target/topic—for example, “household income,” “luminosity,” and “traffic density””) with semantically grounded topic attributes (compare Bly, Para. [0116], “The variable “skin problems in grades 7-12” may additionally be semantically grounded/linked to the concept “Acne vulgaris” and the variable “personal earnings” may be semantically grounded to the concept “Personal Income”, with both concept names sourced from an ontology such as Wikidata” with Phan, Pg. 3275, Col. 2, Para. 2, “Representations of the names are also expected to be well clustered in their distributional space, i.e., names of the same concepts are close to each other and distant from those of other concepts. Learning such conceptually grounded representations is highly desired for a wide range of applications, e.g., synonym retrieval/discovery, biomedical name normalization, and query expansion”), using a method with high performance on retrieval, similarity, and relatedness benchmarks as well as high practical utility on real-world applications (Phan, Pg. 3275, Col. 1, Abstract, “Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks. Moreover, our proposed method is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications”), which overcome shortcomings with semantic similarity metrics (Phan, Pg. 3275, Col. 1, Abstract, “Biomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering down stream applications inefficacious and/or potentially unreliable” and Bly, Para. [0099], “the potentially less relevant or irrelevant datasets about topics semantically related to a specified target/topic”).
Regarding Claim 17, the additional elements of the dependent claim are substantially the same as limitations of Claim 7, therefore it is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tal et al. (Pat. No. US 11,264,140 B1) discloses methods for construction and analysis of knowledge graphs.
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/MATTHEW BRYCE GOLAN/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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