CTNF 18/189,716 CTNF 90432 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 an abstract idea without significantly more. Regarding claim 1 , Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a method/process. Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? The limitations of: determining, with the processor, a plurality of event sequences from the event data; (mental evaluation, a person can look at events a determine sequences mentally) determining, with the processor, a plurality of event patterns from the plurality of event sequences; (mental evaluation, once a person has determined sequences, they can look at that data and pick out patterns) generating, with the processor, at least one graph based on the plurality of event patterns, the at least one graph including nodes connected by edges to form a tree, each node of the least one graph representing a respective event in at least one event pattern in the plurality of event patterns, each edge of the least one graph connecting a first respective node to a second respective node and indicating that a second event represented by the second respective node follows a first event represented by the first respective node in the at least one event pattern of the plurality of event patterns (mental evaluation, after looking at the data, a human can for example, using pen and paper, draw out a graph by drawing the nodes and connecting them based on the data that was looked at earlier) Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? The limitations of: with a processor (applying the abstract idea on generic computer components, MPEP 2106.05(f)) receiving, with a processor, event data from the system, the event data indicating events that occurred in the system and times at which the events occurred; (receiving data, insignificant extra-solution activity MPEP 2106.05(d)) wherein the at least one graph is used to predict at least one possible future event in the system (applying the abstract idea to a particular field of use MPEP 2106.05(h)) Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? The limitations of: with a processor (applying the abstract idea on generic computer components, MPEP 2106.05(f)) receiving, with a processor, event data from the system, the event data indicating events that occurred in the system and times at which the events occurred; (receiving data, insignificant extra-solution activity MPEP 2106.05(d), receiving data is well-understood routine and conventional, MPEP 2106.05(d)(II)(i)) wherein the at least one graph is used to predict at least one possible future event in the system (applying the abstract idea to a particular field of use MPEP 2106.05(h)) Dependent claim 2 recites determining a series of events, mental evaluation. Dependent claim 3 recites combining multiple sets of data, combining data, MPEP 2106.05(d)(II). Dependent claim 4 recites labeling each event, mental observation. Dependent claim 5 recites, first and second type of events, particular field of use, MPEP 2106.05(h). Dependent claim 6 recites forming sequences, mental evaluation. Dependent claim 7 recites forming sequences sequentially, mental evaluation. Dependent claim 8 recites forming sequences based on time, mental evaluation. Dependent claim 9 recites patterns being subset of events, MPEP 2106.05(h). Dependent claim 10 recites determining a frequency of events, mental evaluation. Dependent claim 11 recites determining two events overlap and combining them, mental observation. Dependent claim 12 recites augmenting data, MPEP 2106.05(h). Dependent claim 13 recites an average of events, time, rating, or probability, MPEP 2106.05(h). Regarding claim 14 , Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes, the claim is directed to a method/process. Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? The limitations of: extracting, with the processor, a partial event sequence from the event data; (mental evaluation, a human can look at event data and “extract” sequences by making a mental determination) predicting, with the processor, a possible future event based on the partial event sequence and using at least one graph, (mental evaluation, given the data, a human can look at it and make a prediction of something to happen in the future) the at least one graph including nodes connected by edges to form a tree, each node of the least one graph representing a respective event in at least one event pattern, each edge of the least one graph connecting a first respective node to a second respective node and indicating that a second event represented by the second respective node follows a first event represented by the first respective node in the at least one event pattern (mental evaluation, a human can draw the tree using pen and paper) Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? The limitations of: receiving, with a processor, event data from the system, the event data indicating events that occurred in the system and times at which the events occurred; (generic computer to carry out the abstract idea, MPEP 2106.05(f) and receiving data, insignificant extra-solution activity MPEP 2106.05(g)) Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? receiving, with a processor, event data from the system, the event data indicating events that occurred in the system and times at which the events occurred; (generic computer to carry out the abstract idea, MPEP 2106.05(f) and receiving data, insignificant extra-solution activity MPEP 2106.05(g), receiving data is well-understood, routine and conventional in the art, MPEP 2106.05(d)(II)(i)) Dependent claim 15 recites mapping the sequences and identifying events, mental evaluation. Dependent claim 16 recites displaying on a screen, MPEP 2106.05(f). Dependent claim 17 recites determining a time series and displaying, mental evaluation and MPEP 2106.05(f). Dependent claim 18 recites displaying metadata, MPEP 2106.05(f). Dependent claim 19 recites displaying a graph, MPEP 2106.05(f). Dependent claim 20 recites displaying statistical information, MPEP 2106.05(f). Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 1-7, 9-10, and 13-15 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Li, Yanhao, and Wei Liu. "Sudden event prediction based on event knowledge graph." Regarding claim 1 , Li teaches “a method for generating a knowledge graph representation of patterns of events in a system, the method comprising: receiving, with a processor, event data from the system, the event data indicating events that occurred in the system and times at which the events occurred” (pg. 7 §4.1 “Based on the event knowledge graph structure given in Section 3.2, this section manually constructs an event knowledge graph for the transportation domain. The construction process firstly collected the traffic news corpus and obtained coarse-grained event classes from the news under the guidance of experts. Secondly, the event granularity was refined layer by layer to get the event classes applicable to event prediction”, figure 3 shows that time is included in this data) ; “determining, with the processor, a plurality of event sequences from the event data” (pg. 2 ¶2 “an event knowledge graph in a specific domain provided event scenario models to represent occurrence patterns of different events, which are described by a set of subevents and logical relations (including cause, sequence, concurrence, and so on) between them”) ; “determining, with the processor, a plurality of event patterns from the plurality of event sequences” (previous citation, and pg. 5 “The event scenario model is formed by associating the event classes through logical relations. Furthermore, it reflects the pattern about the occurrence of an initial event and a set of subsequent events triggered by it”) ; and “generating, with the processor, at least one graph based on the plurality of event patterns, the at least one graph including nodes connected by edges to form a tree, each node of the least one graph representing a respective event in at least one event pattern in the plurality of event patterns, each edge of the least one graph connecting a first respective node to a second respective node and indicating that a second event represented by the second respective node follows a first event represented by the first respective node in the at least one event pattern of the plurality of event patterns” (pg. 5 fig. 1 PNG media_image1.png 360 674 media_image1.png Greyscale which is the event graph structure which is generated from the gathered data) , “wherein the at least one graph is used to predict at least one possible future event in the system” (abstract “We improve the prediction ability of the HGEP model through prior knowledge provided by scenario models in the event knowledge graph. To obtain multiple prediction outcomes, we design a scoring function to calculate the score of the occurrence probability of each event class.”) Regarding claim 2 ¸Li teaches “determining, with the processor, a chronological time series of events from the event data, wherein the plurality of event sequences is determined from the time series of events” (pg. 7 §4.1 “In the event instance, we marked its arguments with <Denoter>, <Object>, <Participant>, <Location>, and <Time> tags. <Relations> brought together all the <eRelation> tags” i.e. time tags, events depend on each other) Regarding claim 3 , Li teaches “wherein the event data includes multiple sets of event data from multiple sources of event data, the method further comprising: combining the multiple sets of event data into the chronological time series of events” (pg. 7 §4.1 “Based on the event knowledge graph structure given in Section 3.2, this section manually constructs an event knowledge graph for the transportation domain. The construction process firstly collected the traffic news corpus and obtained coarse-grained event classes from the news under the guidance of experts. Secondly, the event granularity was re fined layer by layer to get the event classes applicable to event prediction”, figure 3 shows that time is included in this data) Regarding claim 4 , Li teaches “labeling, with the processor, each event from the event data as a respective event type from a predetermined set of event types” (pg. 6 PNG media_image2.png 274 730 media_image2.png Greyscale event classes i.e. types) Regarding clam 5 , Li teaches “wherein the events of the event data include events of: a first event type indicating that a measurable parameter of the system has a value that is outside of a predetermined or expected range; and a second event type indicating that a process performed by the system is halted” (previous citation shows the type/class that is measurable) Regarding claim 6 , Li teaches “the determining the plurality of event sequences further comprising: forming each respective event sequence in the plurality of event sequences as a subset of sequential events from the event data” (pg. 7 fig. 2 PNG media_image3.png 348 774 media_image3.png Greyscale which shows each event having its own sequence) Regarding claim 7 , Li teaches “the determining the plurality of event sequences further comprising: forming each respective event sequence in the plurality of event sequences such that the respective event sequence begins with at least one sequential event of a first event type and ends with at least one sequential event of a second event type” (pg. 8 fig. 4 PNG media_image4.png 480 1232 media_image4.png Greyscale shows the events leading to events) Regarding claim 9 , Li further teaches “wherein each event pattern is a subset of events with that occurs, in a particular chronological order, within at least one event sequence in the plurality of event sequences” (pg. 8 fig. 4 shows patterns/subsets PNG media_image4.png 480 1232 media_image4.png Greyscale ) Regarding claim 10 , Li further teaches “determining, for each respective event pattern in the plurality of event patterns, a frequency with which the respective event pattern is found within the plurality of event sequences” (pg. 2 ¶2 “On this basis, an event knowledge graph in a specific domain provided event scenario models to represent occurrence patterns of different events, which are described by a set of subevents and logical relations (including cause, sequence, concurrence, and so on) between them.”) Regarding claim 13 , Li teaches “the augmenting the at least one graph further comprising determining statistical information including at least one: an average number of events in the plurality of event sequences that occur between sequential events in the plurality of event patterns; an average amount of time between sequential events in the plurality of event patterns; an average impact rating of a particular events in a particular event pattern in the plurality of event patterns; and a probability of occurrence for respective events in the plurality of event patterns” (pg. 6 ¶ above §4 “We designed prediction functions to parse the scenario information to obtain the probability of occurrence for each event category. First, a linear function was used as the projection function to project the scenario information into different result spaces to obtain the result vector. Then, we designed an analytic function to obtain the probability of occurrence of each event class corresponding to the result vector”) Regarding claim 14 , Li teaches “a method for predicting possible future events in a system, the method comprising: receiving, with a processor, event data from the system, the event data indicating events that occurred in the system and times at which the events occurred” (pg. 7 §4.1 “Based on the event knowledge graph structure given in Section 3.2, this section manually constructs an event knowledge graph for the transportation domain. The construction process firstly collected the traffic news corpus and obtained coarse-grained event classes from the news under the guidance of experts. Secondly, the event granularity was re fined layer by layer to get the event classes applicable to event prediction”, figure 3 shows that time is included in this data) ; “extracting, with the processor, a partial event sequence from the event data” (pg. 2 ¶2 “an event knowledge graph in a specific domain provided event scenario models to represent occurrence patterns of different events, which are described by a set of subevents and logical relations (including cause, sequence, concurrence, and so on) between them”) ; and “predicting, with the processor, a possible future event based on the partial event sequence and using at least one graph” (abstract “We improve the prediction ability of the HGEP model through prior knowledge provided by scenario models in the event knowledge graph. To obtain multiple prediction outcomes, we design a scoring function to calculate the score of the occurrence probability of each event class.”) , “the at least one graph including nodes connected by edges to form a tree, each node of the least one graph representing a respective event in at least one event pattern, each edge of the least one graph connecting a first respective node to a second respective node and indicating that a second event represented by the second respective node follows a first event represented by the first respective node in the at least one event pattern” (pg. 5 fig. 1 PNG media_image1.png 360 674 media_image1.png Greyscale which is the event graph structure which is generated from the gathered data) Regarding claim 15 , Li teaches “the predicting the possible future event comprising: mapping the partial event sequence onto the at least one graph; and identifying events represented in the at least one graph that follow the mapped partial event sequence” (pg. 5 “The event scenario model is formed by associating the event classes through logical relations. Furthermore, it reflects the pattern about the occurrence of an initial event and a set of subsequent events triggered by it”) Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 8, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li further in view of Rospocher, Marco, et al. "Building event-centric knowledge graphs from news." [herein Rosp] Regarding claim 8 , the Li reference has been addressed above. While Li generally teaches sequence events with time, Rosp more specifically teaches “the determining the plurality of event sequences further comprising: forming each respective event sequence in the plurality of event sequences such that a time between a last event and a first event in the respective event sequence is less than a predetermined maximum amount of time” (Ros pg. 7 “the Time and Date Recognizer detects temporal expressions so that events can be organized on a timeline by the Temporal Relation Detection [36] module and causally linked by the Causal Relation Detection module” and pg. 10 §Event linking “As for entities and time, we need to create instances for events. In the case of events however, we (usually) do not have an external URI. Events are less tangible and establishing identity across mentions is a difficult task. We follow an approach that takes the compositionally of events as a starting point [51]. The compositionality principle dictates that events are not just defined by the action (or the relation or property) but also by the time, place and participants. For that, we use an algorithm that compares events for all these properties [52].”) It would have been obvious to one having ordinary skill in the art at the time that the invention was effectively filed to combine the teachings of Li with that of Rosp since a combination of known methods would yield predictable results. As shown in Rosp, it is known in the art to determine sequences based on time. Therefore these techniques when applied to the event knowledge graph of Li would operate in a similar and predictable manner. This allows for better knowledge graph modeling. Regarding claim 12 , the Li and Rosp references have been addressed above. Rosp further teache “augmenting, with the processor, the at least one graph with at least one of statistical information and metadata” (Rosp pg. 19 ¶1 “Furthermore, we are looking into utilizing additional metadata about the news sources such as the type of publication and the author, or even additional markup such as found in Wikinews, to enrich and improve the quality of the extracted information.”) 07-21-aia AIA Claim (s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li further in view of Liang et al. US 2024/0144033 . Regarding claim 11 , the Li reference has been addressed above. While Li generally teaches data fusion, Liang more specifically teaches “the generating the at least one graph further comprising: determining that at least two event patterns in the plurality of event patterns overlap with one another” (Liang [0053] “For example, the fused knowledge graph is the fused knowledge graph 230. In some embodiments, the instance data of the entity to be fused and the instance data of the child entity may also overlap, that is, the entity to be fused and the child entity have instance data of a same instance”) ; and “combining the at least two event patterns to form the at least one graph, at least one overlapping event in the at least two event patterns being represented by at least one first node in the at least one graph, at least one non-overlapping event in the at least two event patterns being represented by at least one branch extending from the at least one first node in the at least one graph, the at least one branch including at least one second node” ([0053] “The knowledge fusion method can further combine instance data belonging to a same instance to remove redundant instance data from the fused entity. In some embodiments, the above-mentioned knowledge fusion method can be implemented by using a graph operator (for example, an entity fusion operator). The graph fusion operator can correspond to program code, and is invoked when the fused knowledge graph is generated based on the instance data.”) It would have been obvious to one having ordinary skill in the art at the time that the invention was effectively filed to combine the teachings of Li with that of Liang since by combining the techniques of Liang which allow for redundant data to be combined together, one would have better knowledge graph representation . 07-21-aia AIA Claim (s) 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Hao, Xuejie, et al. "Construction and application of a knowledge graph." Regarding claim 16 , the Li reference has been addressed above. While Li generally displays data, Hao more particularly teaches “displaying, on a display screen, the possible future event” (Hao pg. 2 “Conversely, knowledge graphs display the structured knowledge of classification and arrangement to users through the grid graphic information display interface.”) It would have been obvious to one having ordinary skill in the art at the time that the invention was effectively filed to combine the teachings of Li with that of Hao since it is known in the art to display the results of data analyzed which Li touches upon but Hao explicitly discloses. Regarding claim 17 , the Li and Hao references have been addressed above. Li further teaches determining, with the processor, a chronological time series of events from the event data” (pg. 8 fig. 4 shows patterns/subsets PNG media_image4.png 480 1232 media_image4.png Greyscale ) ; and Hao teaches “displaying, on a display screen, a timeline representing the chronological time series of events” (Hao pg. 7 “The purpose of using Neo4j is to update and search data. Neo4j can directly display the query results in the form of graphs to realize the function of knowledge visualization.”) Regarding claim 18 , the Li and Hao references have been addressed above. Hao further teaches “displaying, on a display screen, metadata associated with at least one event represented in the timeline” (Hao pg. 9 PNG media_image5.png 622 1026 media_image5.png Greyscale ) Regarding claim 19 , the Li and Hao references have been addressed above. Hao further teaches “displaying, on a display screen, a graphical representation of the at least one graph” (Hao pg. 12 figures 10 and 11 show visualization of knowledge graphs) Regarding claim 20 , the Li and Hao references have been addressed above. Li further teaches “displaying, on a display screen, statistical information associated with at least one event represented in the at least one graph” (pg. 11 “The final event classes and the number of event instances they contained are shown in Table 2.” which is interpreted as statistical information) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Wei et al. US 2023/0035121 Guan, Saiping, et al. "What is event knowledge graph: A survey." IEEE Transactions on Knowledge and Data Engineering 35.7 (2022): 7569-7589. Jia, Bin, et al. "Pattern discovery and anomaly detection via knowledge graph." 2018 21st International Conference on Information Fusion (FUSION) . IEEE, 2018. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN W FIGUEROA whose telephone number is (571)272-4623. The examiner can normally be reached Monday-Friday, 10AM-6PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MIRANDA HUANG can be reached at (571)270-7092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. KEVIN W FIGUEROA Primary Examiner Art Unit 2124 /Kevin W Figueroa/Primary Examiner, Art Unit 2124 Application/Control Number: 18/189,716 Page 2 Art Unit: 2124 Application/Control Number: 18/189,716 Page 3 Art Unit: 2124 Application/Control Number: 18/189,716 Page 4 Art Unit: 2124 Application/Control Number: 18/189,716 Page 5 Art Unit: 2124 Application/Control Number: 18/189,716 Page 6 Art Unit: 2124 Application/Control Number: 18/189,716 Page 7 Art Unit: 2124 Application/Control Number: 18/189,716 Page 8 Art Unit: 2124 Application/Control Number: 18/189,716 Page 9 Art Unit: 2124 Application/Control Number: 18/189,716 Page 10 Art Unit: 2124 Application/Control Number: 18/189,716 Page 11 Art Unit: 2124 Application/Control Number: 18/189,716 Page 12 Art Unit: 2124 Application/Control Number: 18/189,716 Page 13 Art Unit: 2124 Application/Control Number: 18/189,716 Page 14 Art Unit: 2124 Application/Control Number: 18/189,716 Page 15 Art Unit: 2124 Application/Control Number: 18/189,716 Page 16 Art Unit: 2124 Application/Control Number: 18/189,716 Page 17 Art Unit: 2124 Application/Control Number: 18/189,716 Page 18 Art Unit: 2124
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