DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/01/2026 has been entered. The application contains claims 1, 2, 5-18 and 20, all examined and rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
It is acknowledged that claims 1, 7, 14 and 17 were amended. Claims 3, 4 and 19 were canceled.
Response to Arguments
Applicant’s arguments with respect to amended portions of claim(s) 1, 14 and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5, 13, 14, 15, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over JOKIOINEN (WO 2017129863 A1) in view of Ferilli, Stefano. “Integration Strategy and Tool between Formal Ontology and Graph Database Technology.” Electronics (26 October 2021) further in view of Xu et al. (CN 107870621 A).
Regarding claim 1, JOKIOINEN discloses:
a computer-implemented method of controlling an aquatic vessel, at least by (Pg. 15 line 10 “autonomous operation for a vessel”)
the method comprising: receiving input data comprising a plurality of observations from a respective plurality of sensors associated with the aquatic vessel, at least by (Pg. 15 lines 25-37, which describe monitoring vessel surroundings, objects and obstacles (e.g. a plurality of observations) by receiving information from via multiple sensor(s) of the vessel “radar devices, image capture devices, vibration capture devices and/or audio capture devices of the vessel”)
populating a graph database with the plurality of observations of the input data, wherein the graph database is based on a formal ontology that defines concepts and relationships relating to the plurality of sensors, at least by (Pg. 16 lines 1-20, describes forming a route plan for the vessel based on obstacle data (e.g. plurality of observations of the input data) and generated route plan by utilizing at least one graph search (Pg. 15 lines 15-16), which describes the use of a graph)
and performing a query on the graph database to generate information comprising a control signal configured to control at least by (Pg. 16 lines 1-20, describes forming a route plan for the vessel based on obstacle data (e.g. plurality of observations of the input data) and generated route plan by utilizing at least one graph search (Pg. 15 lines 15-16) to avoid collision, “if any obstacles require a change in any of current route parameters of the vessel. If change(s) are required, the current route parameters of the vessel are changed accordingly, step 412. The route parameters of the vessel may comprise route offset and/or speed of the vessel. At step 414, routing instructions are generated. The generation of the routing instructions may comprise generating the routing instructions for use by a control computer of the vessel (e.g. a control signal)”, which describes querying a graph to generate a route plan to control the vessel according to the route plan (e.g. control signal), (in one interpretation) by controlling the vessel, controlling an engine, a propeller, and/or a warning device of the aquatic vessel would be incorporated.)
But JOKIOINEN fails to specifically describe: encoding a formal ontology in Resource Description Format (RDF), the formal ontology defining how observations of concepts and relationships between the concepts are stored in a graph database; producing a database schema of the graph database by generating a knowledge graph structure based on the formal ontology encoded in RDF, wherein database schema of the graph database includes nodes of the knowledge graph structure representing the concepts of the formal ontology encoded in RDF and edges of the knowledge graph structure representing relationships of the formal ontology encoded in RDF; populating the graph database by adding nodes comprising the plurality of observations of the input data onto the database schema; and obtain a fix of the aquatic vessel using a Global Positioning System (GPS) device
However, Ferilli describes encoding a formal ontology in Resource Description Format (RDF), the formal ontology defining how observations of concepts and relationships between the concepts are stored in a graph database, at least by (Sec. 2 para 1, describes “formal ontology… inferences on the available knowledge (concerning both the concepts and their instances) expressed according to the ontology… ontology requires a conceptualization step, by which: (1) the relevant entities, relationships and their attributes in a domain of interest are identified; (2) names are defined for them; (3) possibly (in the case of formal ontologies) axioms are stated expressing what is mandatory, permitted or prohibited in that domain”, Sec. 1 further describes “Ontologies represent the backbone of the formal semantics of a knowledge graph. They can be seen as the data schema of the graph” (e.g. defining how observations of concepts and relationships between the concepts are stored in a graph database) and further describe encoding the formal ontology in RDF based on OWL, Sec. 2 para. 2, “standard formalism for expressing ontologies and KGs is the Web Ontology Language (OWL) … OWL is based on the Resource Definition Framework (RDF), originally developed for describing resources on the Web but amenable to knowledge representation in general. RDF graphs are based on a directed graph data model in which nodes are Uniform Resource Identifiers (URIs)”)
Ferilli describes producing a database schema of the graph database by generating a knowledge graph structure based on the formal ontology encoded in RDF, wherein database schema of the graph database includes nodes of the knowledge graph structure representing the concepts of the formal ontology encoded in RDF and edges of the knowledge graph structure representing relationships of the formal ontology encoded in RDF, at least by (Sec. 1 para. 2, “A graph is a data structure consisting of nodes (usually representing things) and arcs connecting these nodes (usually representing relationships between things)”, Sec. 1 para. 3, “formal ontologies and graph DBs… GraphBRAIN, aimed at bridging the gap between them… Defining a formalism for expressing graph DB schemas… Defining the mapping between the graph DB model expressed by this formalism and the standard ontological model” Sec. 1 para. 2, “Ontologies represent the backbone of the formal semantics of a knowledge graph. They can be seen as the data schema of the graph” where edges in the RDF graph are predicates of the RDF triple representing relationships of the formal ontology encoded in RDF, see Sec. 2 para. 2.)
Ferilli describes populating the graph database by adding nodes comprising the plurality of observations of the input data onto the database schema, at least by (Sec. 1 para. 2, “the terminological part of an ontology (Tbox, reporting definitions and axioms) is considered in conjunction with the assertional part (Abox, specifying individuals or instances) the result is a so-called Knowledge Graph …A knowledge graph is created when you apply an ontology (the data model) to a dataset of individual data points (the [. . . ] data). In other words: ontology + data = knowledge graph” where + data or assertional part are the nodes comprising the plurality of observations added to the ontology/database schema)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN with Ferilli ability to bridge the gap between formal ontologies and graph DBs to improve the effectiveness of reasoning at the ontological level (Ferilli: Sec 5, Conclusions).
Also, JOKIOINEN and Ferilli fails to specifically describe: cause the aquatic vessel to rise or dive, cause the aquatic vessel to maintain a minimum distance from a vessel or an object detected by at least one of the sensors.
However, Xu describes the above limitation at least by (Claims 4-6, which describes threshold hold value related to distance from different locations of the vessel to an obstacle that when exceeded, collision avoidance action is triggered that adjusts the vessel vertically (rise or dive) to maintain a minimum distance between the vessel and the obstacle.
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN and Ferilli with Xu ability to control a vessel autonomously in aquatic environments by improving the accuracy of the obstacle recognition (Xu, abstract).
As per claim 3, 4 and 19, canceled.
As per claim 5, claim 1 is incorporated and JOKIOINEN fails to describe:
wherein populating the graph database comprises adding a plurality of nodes containing the respective plurality of observations to the graph database
However, Ferilli the above limitation at least by (Sec. 1 para. 2, “the terminological part of an ontology (Tbox, reporting definitions and axioms) is considered in conjunction with the assertional part (Abox, specifying individuals or instances) the result is a so-called Knowledge Graph …A knowledge graph is created when you apply an ontology (the data model) to a dataset of individual data points (the [. . . ] data). In other words: ontology + data = knowledge graph” where + data or assertional part are the nodes comprising the plurality of observations added to the ontology/database schema)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN with Ferilli ability to bridge the gap between formal ontologies and graph DBs to improve the effectiveness of reasoning at the ontological level (Ferilli: Sec 5, Conclusions).
As per claim 13, claim 1 is incorporated and JOKIOINEN further describes:
wherein a result of the query performed on the graph database is used to determine a situation of the aquatic vessel, and the method further comprises outputting the information configured to control the aquatic vessel, the information configured to control the aquatic vessel comprising a signal for directly or indirectly controlling the aquatic vessel in response to the situation, at least by (see Pg. 8 lines 4-8, discloses collision avoidance instructions provided to the control computer of the vessel).
As per claim 15, claim 14 is incorporated and JOKIOINEN further describes:
An aquatic vessel control system comprising the computer program product according to claim 14, and at least one processor configured to execute the instructions, at least by (see Pg. 8 lines 4-8, discloses collision avoidance instructions provided to the control computer of the vessel).
Claim(s) {14, 17} recite equivalent claim limitations as claim(s) 1 above, except that they set forth the claimed invention as a computer program product including one or more non- transitory computer readable medium, as such they are rejected for the same reasons as applied hereinabove.
Claim(s) 2, 7, 8, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over JOKIOINEN, Ferilli and Xu in view of Zhao et al. (“Ontology-Based Driving Decision Making: A Feasibility Study at Uncontrolled Intersections”, 2017).
As per claim 2, claim 1 is incorporated and JOKIOINEN fails to describe:
wherein performing the query comprises performing a plurality of queries on the graph database and generating the information based on a result of a final query of the plurality of queries
However, Zhao the above limitation at least by (Sec 4.2 which describes performing multiple SPARQL queries on the knowledge base (e.g. graph database) and forming “decision result inferred using the SWRL rule reasoner” (e.g. generating the information based on a result of a final query of the plurality of queries))
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN with Zhao’s ontology-based knowledge base to improve the decision-making system to deal with more complicated obstacles (Zhao, Sec. 6 Conclusion).
As per claim 7, claim 4 is incorporated and JOKIOINEN fails to describe:
wherein populating the graph database comprises converting the input data to RDF
However, Zhao the above limitation at least by (Sec. 1 Paragraph 2 and Sec. 4, #1. “sensor data are converted into RDF stream data format with the ontology” where the ontology is further describes as “consists of concepts (classes) and the relationships (properties) among them… An instance is described by a collection of RDF triples in the form of <subject, property, object>, where property is also called predicate” as such the added nodes are the concepts and/or subject and object).
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN with Zhao’s ontology-based knowledge base to improve the decision-making system to deal with more complicated obstacles (Zhao, Sec. 6 Conclusion).
As per claim 8, claim 7 is incorporated and JOKIOINEN fails to describe:
wherein converting the input data comprises adjusting at least part of a data structure of the input data to match at least part of a data structure of the graph database based on the formal ontology,
However, Zhao the above limitation at least by (Sec. 1 Paragraph 2 and Sec. 4, #1. “sensor data are converted into RDF stream data format with the ontology”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN with Zhao’s ontology-based knowledge base to improve the decision-making system to deal with more complicated obstacles (Zhao, Sec. 6 Conclusion).
Claim(s) 18 recite equivalent claim limitations as claim(s) 2 above, except that they set forth the claimed invention as a computer program product including one or more non- transitory computer readable medium, as such they are rejected for the same reasons as applied hereinabove.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over JOKIOINEN, Ferilli and Xu further in view of Zhao and Slepian et al. (US 20180211730 A1).
As per claim 6, claim 5 is incorporated and JOKIOINEN fails to describe:
wherein receiving the input data comprises receiving a data stream comprising the input data, and wherein populating the graph database comprises periodically populating the graph database with the observations of the input data,
However, Zhao the above limitation at least by (Sec. 1 Paragraph 2 and Sec. 4, #1. “sensor data are converted into RDF stream data format with the ontology” where the ontology is further describes as “consists of concepts (classes) and the relationships (properties) among them”.
Furthermore Slepian, discloses: periodically populating the graph database, at least by (paragraph [0096] “analytic engine capable of receiving and processing the … data to … periodically update the knowledgebase… comprising at least one sensor”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN with Zhao’s ontology-based knowledge base to improve the decision-making system to deal with more complicated obstacles (Zhao, Sec. 6 Conclusion), and also Slepian abilty to periodically update the knowledgebase to improve the accuracy of the system (Slepian, Para. 0080).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over JOKIOINEN, Ferilli and Xu further in view of Cardasis et al. (US 20220414613 A1).
As per claim 9, claim 1 is incorporated and JOKIOINEN further describes:
wherein the ontology is based on a Sensor, Observations, Sample and Actuator (SOSA) framework and further includes at least one additional class not included in the SOSA framework, at least by (Sec. 4.1 describes ontology based on Subject, Property, Object, Timestamp, but does not specifically describe SOSA.)
However, Cardasis et al. (US 20220414613 A1) teaches the above limitations at least by (paragraph [0012, 0067], “Many ontology frameworks are available for adaptation and use with the present teachings. For example, the “Sensor, Observation, Sample, and Actuator” (SOSA) ontologies have been developed by the World Wide Web Consortium (W3C) to provide flexible but coherent perspectives for representing the entities, relations, and activities involved in sensing, sampling and actuation.”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN, Ferilli and Xu with Cardasis the ability to provide flexible but coherent perspectives for representing the entities, relations, and activities involved in sensing, sampling and actuation).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over JOKIOINEN, Ferilli, Xu and Cardasis further in view of Baughman (US 12093293 B2).
As per claim 10, claim 9 is incorporated and JOKIOINEN, Zhao, Xu and Cardasis fails to describes:
wherein the at least one additional class comprises one or more classes representing uncertainty of the observations
However, Baughman teaches the above limitations at least by (col. 8 lines 43-65, which describes confidence level related to the sensor data feeds associated concepts (e.g. one or more classes) where the confidence level represents uncertainty of the observations)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN, Ferilli, Xu and Cardasis with the ability to determine a confidence level associated with the detected correlations provided by Baughman to assure that the ontology adequately represent high correlated concepts (Baughman, col. 9 lines 1-3).
Claim(s) 11, 12, 16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over JOKIOINEN, Ferilli and Xu and Massarella (US 20160140153 A1).
As per claim 11, claim 1 is incorporated and JOKIOINEN, Ferilli and Xu fails to describes:
further comprising building a time tree for the graph database that splits the observations into pre-determined periods of time, at least by (Pg. 3 Para. 2, where time class provides the continuous time of the observed behavior, but fails to specifically describe time tree for the graph database that splits the observations into pre-determined periods of time)
However, Massarella teaches the above limitations at least by (at least by (paragraph [0069-0070] which describes a time tree index, with a set of time tree nodes representing a particular time period among the series of time pointing to corresponding data record that includes measured data, “time tree branch nodes T.sub.2 . . . T.sub.m−1 and time tree leaf nodes T.sub.M. Each time tree node T.sub.m corresponds to a time period such as, for example, a year, a day, an hour, a minute, a second, a milliseconds, a microsecond, a nanosecond, a picosecond or ranges thereof” describes time tree for the graph database that splits the observations into pre-determined periods of time)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN, Ferilli and Xu with Massarella time tree index to improve data access efficiency (Massarella, para. 0183).
As per claim 12, claim 11 is incorporated and JOKIOINEN, Ferilli and Xu fails to describes:
wherein a timestamp included in the input data for each said observation is used to generate a new branch for each observation in the time tree,
However, Massarella (US 20160140153 A1) teaches the above limitations at least by (at least by (paragraph [0069-0070] which describes a time tree index, with a set of time tree nodes representing a particular time period among the series of time pointing to corresponding data record that includes measured data, “time tree branch nodes T.sub.2 . . . T.sub.m−1 and time tree leaf nodes T.sub.M. Each time tree node T.sub.m corresponds to a time period such as, for example, a year, a day, an hour, a minute, a second, a milliseconds, a microsecond, a nanosecond, a picosecond or ranges thereof” describes time tree for the graph database that splits the observations into pre-determined periods of time)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to modify JOKIOINEN, Zhao and Xu with Massarella time tree index to improve data access efficiency (Massarella, para. 0183).
Claim(s) 16 and 20 recite equivalent claim limitations as claim(s) 11 above, except that they set forth the claimed invention as a computer program product including one or more non- transitory computer readable medium, as such they are rejected for the same reasons as applied hereinabove.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HOMOCEANU (US 20200166947 A1), Abstract.
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/DENNIS TRUONG/Primary Examiner, Art Unit 2152 06/02/2026
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