DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/26/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because the text is ineligible i.e., cannot be determined for figures 5b, 5c and 6. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities: para. 0030 of the Spec recites “system 104 may be configured to generate the knowledge graph 106” when it should recite “system 104 may be configured to generate the knowledge graph 110.”
Appropriate correction is required.
Claim Objections
Claim 5 is objected to because of the following informalities: the claim recites the acronym of LLM but does not prefix Language Models with the word Large. Appropriate correction 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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. While the independent claims 1 and 12 are directed to statutory subject matter under Step 1, i.e., a method and machine, the independent claims recite the following judicial exceptions:
determining a causal chain of events... based on a causal expression, wherein the causal chain of events indicates a cause-and-effect relationship among one or more entities... using a Natural Language Processing (NLP) technique, wherein the one or more attributes indicate a topic label, a sentiment label, and a temporal label; creating a plurality of nodes based on the causal chain of events and the assigned one or more attribute labels, wherein each of the plurality of nodes indicates a collection of one or more entities having the assigned one or more attribute labels; and generating a knowledge graph based on clustering the plurality of nodes, wherein the knowledge graph indicates a visual depiction of the causal chain of events among the one or more entities such that each of the plurality of nodes is interlinked through at least one directional edge representing the causal chain of events
When viewing these claim limitations under the Broadest Reasonable Interpretation, these claim limitations can be performed in the human mind through the use of observations, evaluations, judgements and opinion and thus fall under the mental process grouping under Step 2A, Prong One.
These judicial exceptions are not integrated into a practical application under Step 2A, Prong two because the additional claim elements of:
corresponding to an input data; within the input data; assigning one or more attribute labels to the one or more entities within the input data; wherein the generated knowledge graph along with the assigned one or more attribute labels is retrieved based on at least one of a user-query input or parameter filters amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., retrieving and/or manipulating input data)
a memory; at least one processor m communication with the memory, and the at least one processor are recited at a high-level of generality using generic computer components (i.e., using a generic processor and generic memory to perform generic computer functions) such that it does not amount to a particular machine.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B because as stated above the additional claim elements of:
corresponding to an input data; within the input data; assigning one or more attribute labels to the one or more entities within the input data; wherein the generated knowledge graph along with the assigned one or more attribute labels is retrieved based on at least one of a user-query input or parameter filters are well-understood, routine, conventional activity that court decisions, such as Versata Dev. Group and OIP Techs cited in MPEP 2106.05(d)(II) have indicated that the merely storing and retrieving information from a computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here)
a memory; at least one processor m communication with the memory, and the at least one processor are recited at a high-level of generality using generic computer components (i.e., using a generic processor and generic memory to perform generic computer functions) such that it does not amount to a particular machine.
Dependent claims 2-11 and 13-22 are directed to statutory subject matter under Step 1, but when viewed under the Broadest Reasonable Interpretation, do not contain additional claim limitations that transform the judicial exception into a practical application under Step 2A, Prong Two because the additional claim elements of
wherein the input data includes one of a forecast, related news associated with a domain, user-input text articles, predictions pre-stored in a memory, and a set of keywords, as recited in claims 3 and 14, amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., retrieving and/or manipulating input data)
retrieving the related news articles from one of the memory and online networks, as recited in claims 4 and 15, amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., retrieving data)
wherein the causal expression is inferred using Natural Language Processing (NLP) techniques including Language Model (LLM) and a relation extraction model for determining the causal chain of events in the input data, as recited in claims 5 and 16, recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model(s) and/or configuration(s) used and the training and/or finetuning steps used to generate/output the causal expression and causal chain of event in the input data.
enabling a user search of the generated knowledge graph based on one or more attribute labels associated with each of the plurality of nodes upon receiving the user-query input; providing an explanation of the generated knowledge graph based on the causal chain of events and the user-query input; and tracing a root node among the plurality of nodes in the generated knowledge graph based on the causal chain of events and the user-query input such that a reverse traversal trajectory corresponding to the at least one directional edge is created in the generated knowledge graph, as recited in claims 8 and 19, amount to mere insignificant extra-solution activity in which the limitations amount to general data gathering, manipulation and/or outputting of data (i.e., retrieving and/or manipulating input data)
And do not include additional elements that amount to significantly more than the judicial exception under Step 2B because the additional claim elements of
wherein the input data includes one of a forecast, related news associated with a domain, user-input text articles, predictions pre-stored in a memory, and a set of keywords, as recited in claims 3 and 14, are well-understood, routine, conventional activity that court decisions, such as buySAFE, Versata Dev. Group and OIP Techs cited in MPEP 2106.05(d)(II) have indicated that the merely storing and retrieving information and receiving and/or sending of data over a network using a generic computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here)
retrieving the related news articles from one of the memory and online networks, as recited in claims 4 and 15, are well-understood, routine, conventional activity that court decisions, such as buySAFE, Versata Dev. Group and OIP Techs cited in MPEP 2106.05(d)(II) have indicated that the merely storing and retrieving information and receiving and/or sending of data over a network using a generic computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here)
wherein the causal expression is inferred using Natural Language Processing (NLP) techniques including Language Model (LLM) and a relation extraction model for determining the causal chain of events in the input data, as recited in claims 5 and 16, recites only the idea of a solution or outcome and fails to recite the details of how the solution is accomplished since no description is given as to the type of machine learning model(s) and/or configuration(s) used and the training and/or finetuning steps used to generate/output the causal expression and causal chain of event in the input data.
enabling a user search of the generated knowledge graph based on one or more attribute labels associated with each of the plurality of nodes upon receiving the user-query input; providing an explanation of the generated knowledge graph based on the causal chain of events and the user-query input; and tracing a root node among the plurality of nodes in the generated knowledge graph based on the causal chain of events and the user-query input such that a reverse traversal trajectory corresponding to the at least one directional edge is created in the generated knowledge graph, as recited in claims 8 and 19, are well-understood, routine, conventional activity that court decisions, such as buySAFE, Versata Dev. Group and OIP Techs cited in MPEP 2106.05(d)(II) have indicated that the merely storing and retrieving information and receiving and/or sending of data over a network using a generic computer are well- understood, routine, and conventional functions when claimed in a merely generic manner (as it is here)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7, 9-18, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Friedman et al. US 2023/0316003 Al(“Friedman”) in view of Sipple US 2014/0095425 Al(“Sipple”).
Regarding claim 1, Friedman teaches a method for generating a knowledge graph, the method comprising:
determining a causal chain of events corresponding to an input data based on a causal expression, wherein the causal chain of events indicates a cause-and-effect relationship among one or more entities within the input data(Friedman, paras., [0160-0187] see also figs., 13-16, “[A] transformer based NLP architecture that jointly extracts knowledge graphs including (1) variables or factors described in language, (2) qualitative causal relationships over these variables, (3) qualifiers and magnitudes that constrain these causal relationships, and ( 4) word senses to localize each extracted node within a large ontology... automatically extracting (1) entities that are the subject of causal relationships, (2) causal relationships describing mechanisms, intentions, monotonicity, and temporal priority, (3) multi-label attributes to further characterize the causal structure, and ( 4) ontologically-grounded word senses for applicable nodes in the causal graph.”);
assigning one or more attribute labels to the one or more entities within the input data using a Natural Language Processing (NLP) technique(Friedman, para. 0174, see also figs. 13A-13B, “Attributes are Boolean labels, and each entity (i.e., graph node) may have zero or more associated attributes. Attribute inference is therefore a multi-label classification problem. In FIGS. 13A-13B, attributes are rendered as parenthetical labels inside the nodes[assigning one or more attribute labels to the one or more entities within the input data using a Natural Language Processing (NLP) technique], e.g., Correlation and Sign+ in the FIG. 13A nodes for []associated with[] and []higher,[] respectively. The multi-label nature allows the FIG. 13A []higher[] node to be categorized simultaneously as Sign+ and Comparison.”), [wherein the one or more attributes indicate a topic label, a sentiment label, and a temporal label]1;
creating a plurality of nodes based on the causal chain of events and the assigned one or more attribute labels, wherein each of the plurality of nodes indicates a collection of one or more entities having the assigned one or more attribute labels(Friedman, paras. [0169-0175], see also figs. 13, “[C]ausal knowledge graphs using advances in transformer based models such as SpERT to learn graph-based representations from examples. The resulting knowledge graphs are ontologically-grounded and support graph-based reasoning...the SpEAR knowledge graph format includes the following three types of elements: entities, attributes, and relations. These are described below. Entities are labeled spans within a textual example. These are the nodes in the knowledge graph... entity nodes are associated with a token sequence (e.g., "smoking rate" in FIG. 13A) and a corresponding entity class (e.g., Factor)... [a]ttributes are Boolean labels, and each entity (i.e., graph node) may have zero or more associated attributes[creating a plurality of nodes based on the causal chain of events and the assigned one or more attribute labels wherein each of the plurality of nodes indicates a collection of one or more entities having the assigned one or more attribute labels]. Attribute inference is therefore a multi-label classification problem. In FIGS. 13A-13B, attributes are rendered as parenthetical labels inside the nodes, e.g., Correlation and Sign+ in the FIG. 13A”);
[and generating a knowledge graph based on clustering the plurality of nodes]2, wherein the knowledge graph indicates a visual depiction of the causal chain of events among the one or more entities such that each of the plurality of nodes is interlinked through at least one directional edge representing the causal chain of events(Friedman, paras. [0160-0175], see also figs., 13-16, “NLP architecture that jointly extracts knowledge graphs including (1) variables or factors described in language, (2) qualitative causal relationships over these variables, (3) qualifiers and magnitudes that constrain these causal relationships, and ( 4) word senses to localize each extracted node within a large ontology... [q]ualitative proportionalities describe how one quantity impacts another, in a directional, monotonic fashion. According to some implementations, [a, q+, b] (and respectively, [ a, q-, b]) are designated as qualitative proportionalities from a to b, such that increasing a would increase (and respectively, decrease) b... [t]he SpEAR knowledge graph format includes the following three types of elements: entities, attributes, and relations... [r]elations are directed edges between labeled entities, representing semantic relationships. These are critical for expressing what-goes-with-what over the set of entities[wherein the knowledge graph indicates a visual depiction of the causal chain of events among the one or more entities such that each of the plurality of nodes is interlinked through at least one directional edge representing the causal chain of events]. For example in the sentence in FIG. 13A, the relations (i.e., edges) indicate that the "higher" association asserts the antecedent ( arg0) "men" against ( comp_to) "women" for the consequent (argl) "smoking rate." In FIGS. 13A-13B, the modifier relations link nodes to others that semantically modify them.”);
wherein the generated knowledge graph along with the assigned one or more attribute labels is retrieved based on at least one of a user-query input or parameter filters(Friedman, para. 0177, see also fig. 14, “FIG. 14 illustrates a SpEAR knowledge graph output for []Obese patients have a higher mean IOP (intraocular pressure) and lower flow velocity than non-obese patients.[] The two qualitative comparisons "higher" and "lower" support qualitative Sign+ and Sign- attributes, and q+ and q- relations, respectively[wherein the generated knowledge graph along with the assigned one or more attribute labels is retrieved based on at least one of parameter filters].”).3
Friedman does not teach: wherein the one or more attributes indicate a topic label, a sentiment label, and a temporal label; and generating a knowledge graph based on clustering the plurality of nodes.
However, Sipple teaches:
wherein the one or more attributes indicate a topic label, a sentiment label, and a temporal label(Sipple, paras. [0041-0053], “Source Label (S'): Hierarchical Label of Media
Taxonomy (Type, Sub-Type, Media Name)[ wherein the one or more attributes indicate a topic label]. Sentiment Label (St): Label associated with the effect of the language in the context (Fear, Anxiety, Anger, etc.)[a sentiment label]... [t]imestamp of Event (t): Time stamp estimate of the event (not publication time stamp)[ and a temporal label].”);
and generating a knowledge graph based on clustering the plurality of nodes(Sipple, paras. [0104-0122], see also figs. 24-25, “Correlation module 20 identifies the highly anomalous
event types in clusters, i.e. event types that generate significantly more anomalies than the average number of anomalies with respect to its cluster (local scope) and all event types within all clusters (global scope)... [t]he anomaly template contains both event type clusters and the
contexts from all anomalous and cohort event types...[g]iven an anomaly template At, and the causal relationship constraints between the event types in At, a complete graph can be reduced into a BBN[and generating a knowledge graph based on clustering the plurality of nodes].”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Friedman with the teachings of Sipple the motivation to do so would be to detect events within data streams to those found in Friedman for further analysis(Sipple, para., 0037, “[E]vent information harvested from the data streams is transformed into an atomic message format. Attributes of the messages are mapped to feature dimensions in a multidimensional histogram. The histogram is queried to determine co-occurrence of event types, and correlated event types are clustered based on the corresponding event type attributes using a multiple assignment hierarchal clustering algorithm.”).
Regarding claim 2, Friedman in view of Sipple teaches the method as claimed in claim 1, wherein the user-query is based on a domain expertise of a user(Friedman, paras. [0177-0178], see also fig. 14, “[F]or our two use-cases: (1) the SciClaim dataset of scientific claims and (2) ethnographic mental models. These two schemas share some qualitative causal representations but vary in other domain-specific descriptions[is based on a domain expertise of a user].”) and the parameter filters include a timeline(Sipple, para. 0122, see also fig. 26, “The appropriate anomaly template is recalled, Riot is placed on the current timeline, correlated event types that exceed the probability threshold are extracted and are placed on the timeline with the appropriate lag (positive lag indicates a future event, negative lag indicates a previous event)[ and the parameter filters include a timeline].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Friedman with the above teachings of Sipple for the same rationale stated at Claim 1.
Regarding claim 3, Friedman in view of Sipple teaches the method as claimed in claim 1, wherein the input data includes one of a forecast, related news associated with a domain, user-input text articles, predictions pre-stored in a memory, and a set of keywords(Friedman, para., 0160, “This approach may include results in use cases of processing textual inputs from academic publications, news articles[wherein the input data includes one of related news associated with a domain], social media, or the like.”).4
Regarding claim 4, Friedman in view of Sipple teaches the method as claimed in claim 3, wherein when the input data is the set of keywords, the method comprises: retrieving the related news articles from one of the memory and online networks(Sipple, para., 0039, “[A] crawler crawls web sites and other on-line sources, such as social media sources, news feeds, and the like to retrieve data in a known manner[retrieving the related news articles from one of online networks].”).5
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Friedman with the above teachings of Sipple for the same rationale stated at Claim 1.
Regarding claim 5, Friedman in view of Sipple teaches the method as claimed in claim 1, wherein the causal expression is inferred using Natural Language Processing (NLP) techniques including Language Model (LLM) and a relation extraction model for determining the causal chain of events in the input data(Friedman, para. 0163, “Context-sensitive language models may detect and characterize the qualitative causal structure of everyday and scientific language in a representation that is usable by cognitive systems. As evidence, one present our SpEAR (Span-based Joint Entity and Relation) transformer-based NLP model based on BERT (Bidirectional Encoder Representations from Transformers)[ wherein the causal expression is inferred using Natural Language Processing (NLP) techniques including Language Model (LLM)] and SpERT that extracts causal structure from text as knowledge graphs[and a relation extraction model for determining the causal chain of events in the input data]....”).
Regarding claim 6, Friedman in view of Sipple teaches the method as claimed in claim 1, wherein determining the causal chain of events indicating the cause-and-effect relationship among one or more entities based on at least one of a predefined threshold, a relation extraction model, and a customized user-interaction for adjusting the knowledge graph(Friedman, para. 0163, “[A]utomatically extracting (1) entities that are the subject of causal relationships, (2) causal relationships describing mechanisms, intentions, monotonicity, and temporal priority, (3) multi-label attributes to further characterize the causal structure, and ( 4) ontologically-grounded word senses for applicable nodes in the causal graph. Context-sensitive language models may detect and characterize the qualitative causal structure of everyday and scientific language in a representation that is usable by cognitive systems... SpERT that extracts causal structure from text as knowledge graphs[wherein determining the causal chain of events indicating the cause-and-effect relationship among one or more entities based on at least one of a relation extraction model]....”).6
Regarding claim 7, Friedman in view of Sipple teaches the method as claimed in claim 6, further comprising: determining a causal intensity for the cause-and-effect relationship of each of the one or more entities based on a correlation value, a latency value, and a directness value of a causal link, wherein the causal intensity indicates a degree of influence that each of the one or more entities has on another within the knowledge graph(Sipple, paras. [0113-0121], see also fig. 24, “Granger Causality and Mill's Methods to estimate causal relationship between every pair of correlated event types in the anomaly template[determining a causal intensity for the cause-and-effect relationship of each of the one or more entities based on a correlation value]... [t]he BBN will provide a set of predicted event types with probabilities. Then the temporal sequence is applied to estimate the lead time distribution for each predicted event type[a latency value]... [t]he edges of the graph indicate the causal dependency of the parent to the child node. At each node the conditional probabilities are annotated[a directness value of a causal link, wherein the causal intensity indicates a degree of influence that each of the one or more entities has on another within the knowledge graph].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Friedman with the above teachings of Sipple for the same rationale stated at Claim 1.
Regarding claim 9, Friedman in view of Sipple teaches the method as claimed in claim 1, wherein clustering the plurality of nodes is based on a semantic similarity to identify higher-level causal structures in the generated knowledge graph(Friedman, para. 0164, “The nodes within the causal, semantic graphs produced by SpEAR link to the WordNet word sense hierarchy to facilitate subsequent reasoning[is based on a semantic similarity to identify higher-level causal structures in the generated knowledge graph].”).
Regarding claim 10, Friedman in view of Sipple teaches the method as claimed in claim 1, wherein:
clustering the plurality of nodes in the generated knowledge graph is based on extracting temporal information indicative of time or temporal order from the input data(Sipple, paras. [0110-0122], see also figs. 1 and 24-25 “Causation module 30 of FIG. 1 uses a novel method that employs Granger Causality to build a causality structure for Bayesian Belief Networks (BBN). The Causal Analysis leverages temporal sequencing methods that support causal inference... [g]iven an Anomaly Template, At, with correlated event types {A=Damage, B=Evacuate Victims, C=Earthquake,} formed by observing multiple realizations of one or more event types over time ( e.g. Earthquake in 2010 and Earthquake in 2008), a temporal sequence of all event types in At, with respect to one reference event type in At can be established[clustering the plurality of nodes in the generated knowledge graph is based on extracting temporal information indicative of time or temporal order from the input data].”);
and assigning the temporal label to each of the plurality of nodes based on the extracted temporal information such that a chronological relationship is created in the causal chain of events providing a temporal context within the knowledge graph(Sipple, paras. [0110-0122], see also figs. 1 and 24-25 “Causation module 30 of FIG. 1 uses a novel method that employs Granger Causality to build a causality structure for Bayesian Belief Networks (BBN). The Causal Analysis leverages temporal sequencing methods that support causal inference... [g]iven an Anomaly Template, At... [a]ny event in At will have one or more contexts with time intervals defined by start time
t
s
and end time
t
e
[and assigning the temporal label to each of the plurality of nodes based on the extracted temporal information]... [t]emporal Sequence Constraint:
P
Y
→
X
1
⋀
X
2
⋀
…
<
α
Event Y is unlikely to precede any of its causal event types {
X
1
,
X
2
... }...[u]sing the above constraints, a table of causal relationships can be constructed for each pair of event types in At, which form the edges of a BBN[such that a chronological relationship is created in the causal chain of events providing a temporal context within the knowledge graph].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Friedman with the above teachings of Sipple for the same rationale stated at Claim 1.
Regarding claim 11, Friedman in view of Sipple teaches the method as claimed in claim 9, wherein: clustering the plurality of nodes enables one of, density increase of the generated knowledge graph, the user to manually label the cause- and-effect relationship into explainable groups, and capture domain expertise with customization(Sipple, para. 0109, “The Resolution, Source Filtering and Reduction components of Correlation module 20 of FIG.1 provide users of the embodiment the ability to adjust and bias source inputs to tune the system to the desired precision and accuracy of the output[clustering the plurality of nodes enables one of capture domain expertise with customization].”).7
Regarding claim 12, Friedman teaches a system for generating a knowledge graph, the system comprising: a memory; at least one processor in communication with the memory, and the at least one processor(Friedman, para. 0070, “The computing machine 500 may include a hardware processor 502 (e.g., a central processing unit (CPU), a GPU, a hardware processor core, or any combination thereof), a main memory 504 and a static memory 506, some
or all of which may communicate with each other via an interlink (e.g., bus) 508.”) and for all of the other claim limitations they are rejected on the same basis as independent claim 1 since they are analogous claims.
Referring to dependent claims, 13-18 and 20-22 they are rejected on the same basis as
dependent claims 2-7 and 9-11 since they are analogous claims.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Friedman et al. US 2023/0316003 Al(“Friedman”) in view of Sipple US 2014/0095425 Al(“Sipple”) and in view of Dubyak et al., US 20180373785 A1(“Dubyak”).
Regarding claim 8, Friedman in view of Sipple teaches the method as claimed in claim 1 but do not teach: comprising:
enabling a user search of the generated knowledge graph based on one or more attribute labels associated with each of the plurality of nodes upon receiving the user-query input;
providing an explanation of the generated knowledge graph based on the causal chain of events and the user-query input;
and tracing a root node among the plurality of nodes in the generated knowledge graph based on the causal chain of events and the user-query input such that a reverse traversal trajectory corresponding to the at least one directional edge is created in the generated knowledge graph.
However, Dubyak teaches:
enabling a user search of the generated knowledge graph based on one or more attribute labels associated with each of the plurality of nodes upon receiving the user-query input; providing an explanation of the generated knowledge graph based on the causal chain of events and the user-query input (Dubyak, paras. [0049-0055], see also fig. 3, “At 302, the graph-based knowledge recommendation program 110a, 110b receives a query from a user... [t]he query may include the user identifying problems or symptoms of the problems, which corresponds with a set of query nodes of the system components in the graph-based knowledge resource 206... the pilot enters into the graph-based knowledge recommendation program 110a, 110b a query on the rudder limit failure node, one of the symptoms of the problem, in the pressurized aircraft[enabling a user search of the generated knowledge graph based on one or more attribute labels associated with each of the plurality of nodes upon receiving the user-query input]...[t]hen, the graph-based knowledge recommendation program 110a, 110b may, using the query nodes as the origin of a graph traversal, traverse the causal and functional links of the graph-based knowledge resource 206 until a set of recommended responses are retrieved... the graph-based knowledge recommendation program 110a, 110b retrieves two recommended responses to the query[providing an explanation of the generated knowledge graph based on the causal chain of events and the user-query input].”);
and tracing a root node among the plurality of nodes in the generated knowledge graph based on the causal chain of events and the user-query input such that a reverse traversal trajectory corresponding to the at least one directional edge is created in the generated knowledge graph(Dubyak, paras. [0035-0036], “[T]he identified entities and relationships within the corpus of technical documents may be extracted. Then, a graphical representation (i.e., knowledge graph) of the causal and functional relations may be generated based on the extracted entities and relationships to represent the causal and function structure of the complex system described in the technical documents. The graphical representation (i.e., knowledge graph) may be stored in the graph-based knowledge resource.... querying the graph-based knowledge resource and identifying root causes[and tracing a root node among the plurality of nodes in the generated knowledge graph] and remedial actions. Initially, the previously built graph-based knowledge resource may be queried by the user[based on the causal chain of events and the user-query input] to solve a particular problem. Then, the graph-based knowledge recommendation program may determine whether the graph-based knowledge resource may produce more than one recommended response and, identifying through reverse graph traversal, whether additional relevant symptoms may be dispositive[such that a reverse traversal trajectory corresponding to the at least one directional edge is created in the generated knowledge graph].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Friedman in view of Sipple with the teachings of Dubyak the motivation to do so would be to allow users to interact with generated causal knowledge graphs to obtain explanations in the form of recommendations(Dubyak, para. 0003, “[P]resent invention discloses a method for recommending responses to emergent conditions. The present invention may include receiving a query from a user. The present invention may also include retrieving a plurality of recommended responses for the received query from a plurality of entities and a plurality of relations stored in a graph-based knowledge resource. The present invention may further include presenting the retrieved plurality of recommended responses to the user.”).
Referring to dependent claim 19 it is rejected on the same basis as dependent claim 8 since they are analogous claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250292107 A1(details constructing a temporal knowledge graph that can be queried for predicting subjects within a temporal domain )
US 20240045896 A1(details developing a dynamic knowledge graph based on streaming updates and clustering )
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Friedman.
2 Examiner Notes: The claim limitations that are not in bold and contained within square brackets (i.e., [ ]) are
claim limitations that are not taught by the prior art of Friedman.
3 Examiner Notes: According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
4 Examiner Notes: According to the broadest reasonable interpretation (BRI), and in line with the decision in
SuperGuide Corp. v. DirecTV Enters.,Inc., 358 F.3d 870 (Fed. Cir. 2004), Examiner is interpreting the claim elements as individual items that may serve as replacements for each other and thus Examiner is interpreting the claim as requiring one or more elements but not all.
5 Examiner Notes: According to the broadest reasonable interpretation (BRI), and in line with the decision in
SuperGuide Corp. v. DirecTV Enters.,Inc., 358 F.3d 870 (Fed. Cir. 2004), Examiner is interpreting the claim elements as individual items that may serve as replacements for each other and thus Examiner is interpreting the claim as requiring one or more elements but not all.
6 Examiner Notes: According to the broadest reasonable interpretation (BRI), and in line with the decision in
SuperGuide Corp. v. DirecTV Enters.,Inc., 358 F.3d 870 (Fed. Cir. 2004), Examiner is interpreting the claim elements as individual items that may serve as replacements for each other and thus Examiner is interpreting the claim as requiring one or more elements but not all.
7 Examiner Notes: According to the broadest reasonable interpretation (BRI), and in line with the decision in
SuperGuide Corp. v. DirecTV Enters.,Inc., 358 F.3d 870 (Fed. Cir. 2004), Examiner is interpreting the claim elements as individual items that may serve as replacements for each other and thus Examiner is interpreting the claim as requiring one or more elements but not all.
Prosecution Insights