DETAILED ACTION
Claims 1-20 are pending in the present application and are under examination on the merits. This communication is the first action on the merits (FAOM).
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
Applicant filed an Information Disclosure Statement (IDS) on 11/18/2024. This filing is in compliance with 37 C.F.R. 1.97.
As required by M.P.E.P. 609(C), the applicant's submission of the Information Disclosure Statement is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609(C), a copy of the PTOL -1449 form, initialed and dated by the examiner, is attached to the instant office action.
Drawings
The drawings filed on 11/18/2024 are acceptable as filed.
Claim Rejections - 35 USC§ 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Here, under considerations of the broadest reasonable interpretation of the claimed invention, Examiner finds that the Applicant invented a method and system for gathering data and creating graph models with the data. Examiner formulates an abstract idea analysis, following the framework described in the MPEP as follows:
Step 1: The claims are directed to a statutory category, namely a "method" (claims 1-8) and "system" (claims 9-20).
Step 2A - Prong 1: The claims are found to recite limitations that set forth the abstract idea(s), namely, regarding claim 1:
receiving, … sensor data, wherein each piece of the sensor data comprises information associated with an exchange;
parsing… the sensor data to identify components of each piece of the sensor data, resulting in parsed sensor data;
resolving… missing data within the parsed sensor data, resulting in parsed, resolved sensor data;
mapping … the parsed, resolved sensor data to a graph data structure, the graph data structure comprising nodes and edges, wherein each node and each edge of the graph data structure comprises metadata associated with the exchange;
storing the graph data
Independent claims 9 and 17 recites substantially similar claim language.
Dependent claims 2-8, 10-16, and 18-20 recite the same or similar abstract idea(s) as independent claims 1, 9, and 17 with merely a further narrowing of the abstract idea(s) to particular data characterization and/or additional data analyses performed as part of the abstract idea.
The limitations in claims 1-20 above falling well-within the groupings of subject matter identified by the courts as being abstract concepts, specifically the claims are found to correspond to the category of:
"Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)" as the limitations identified above are directed to gathering data and creating graph models with the data and thus is a method of organizing human activity including at least commercial or business interactions or relations and/or a management of user personal behavior; and/or
"Mental processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)" as the limitations identified above include mere data observations, evaluations, judgements, and/or opinions, e.g. including user observation and evaluation by gathering data and creating graph models with the data, which is capable of being performed mentally and/or using pen and paper.
Step 2A - Prong 2: Claims 1-20 are found to clearly be directed to the abstract idea identified above because the claims, as a whole, fail to integrate the claimed judicial exception into a practical application, specifically the claims recite the additional elements of:
" receiving, from a plurality of sources at a computer system… storing the graph data structure in a graph database / A system comprising: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: / A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising" (claims 1, 9, and 17), “further comprising: executing, via the at least one processor, an Artificial Intelligence (AI) algorithm using the graph data structure as an input, wherein the AI algorithm comprises at least one of: Centrality, Community Analysis, Graph Machine Learning and Embeddings, Path Optimization, Classification Analysis, Similarity Analysis, Topological Link Prediction, and Frequent Pattern Mining,” (claim 2); “wherein the plurality of sources comprise: at least one database; and at least one physical sensor,” (claims 4, 12, and 20) however the aforementioned elements merely amount to generic components of a general purpose computer used to "apply" the abstract idea (MPEP 2106.0S(f)) and thus fails to integrate the recited abstract idea into a practical application, furthermore the high-level recitation of receiving data from a generic "computer system" is at most an attempt to limit the abstract to a particular field of use (MPEP 2106.0S(h), e.g.: "For instance, a data gathering step that is limited to a particular data source (such as the Internet) or a particular type of data (such as power grid data or XML tags) could be considered to be both insignificant extra-solution activity and a field of use limitation. See, e.g., Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie lndem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).") and/or merely insignificant extra-solution activity (MPE 2106.05(g)) and thus further fails to integrate the abstract idea into a practical application;
Step 2B: Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 merely amount to a general purpose computer that attempts to apply the abstract idea in a technological environment (MPEP 2106.0S(f)), including merely limiting the abstract idea to a particular field of use of KPI analysis of “sensor data” via a "computer system", as explained above, and/or performs insignificant extra-solution activity, e.g. data gathering or output, (MPEP 2106.0S(g)), as identified above, which is further found under step 2B to be merely well-understood, routine, and conventional activities as evidenced by MPEP 2106.0S(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser's back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to gathering data and creating graph models with the data.
Claims 1-20 are accordingly rejected under 35 USC§ 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Note: The analysis above applies to all statutory categories of invention. As such, the presentment of any claim otherwise styled as a machine or manufacture, for example, would be subject to the same analysis
For further authority and guidance, see:
MPEP § 2106
https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over WIPO Patent Application Publication Number WO 2021/222384 to Strong Force Intellectual Capital LLC (hereafter referred to as Strong Force) in view of U.S. Patent Application Publication Number 2017/0289187 to Noel et al. (hereafter referred to as Noel).
As per claim 1, Strong Force teaches:
A method comprising: receiving, from a plurality of sources at a computer system, sensor data, wherein each piece of the sensor data comprises information associated with an exchange (Paragraph Number [0867] teaches the digital twin update module 266 receives sensor data from a sensor system 25 of a transportation system and updates the status of the digital twin of the transportation system and/or digital twins of any affected systems, subsystems, devices, workers, processes).
[determining] via at least one processor of the computer system, the sensor data to identify components of each piece of the sensor data, resulting in parsed sensor data (Paragraph Number [0867] teaches the digital twin 1/0 system 204 may receive the sensor data in one or more sensor packets. The digital twin 1/0 system 204 may provide the sensor data to the digital twin update module 266 and may identify the environment from which the sensor packets were received and the sensor that provided the sensor packet. In response to the sensor data, the digital twin update module 266 may update a state of one or more digital twins based on the sensor data. In response to the sensor data, the digital twin update module 266 may update a state of one or more digital twins based on the sensor data).
resolving, via the at least one processor, missing data within the parsed sensor data, resulting in parsed, resolved sensor data (Paragraph Number [0867] teaches the digital twin update module 266 may identify certain areas within the environment that are monitored by the sensor).
mapping, via the at least one processor of the computer system, the parsed, resolved sensor data to a graph data structure, the graph data structure comprising nodes and edges, wherein each node and each edge of the graph data structure comprises metadata associated with the exchange (Paragraph Number [0867] teaches the digital twin update module 266 may identify certain areas within the environment that are monitored by the sensor and may update a record (e.g., a node in a graph database) to reflect the current sensor data. Paragraph Number [0866] teaches "the digital twin creation module 264 may create a node for the respective entity and may include any relevant data. For example, the digital twin creation module 264 may create a node representing a machine in the environment. In this example, the digital twin creation module 264 may include the dimensions, behaviors, properties, location, and/or any other suitable data relating to the machine in the node representing the machine. The digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge. In representing a process, the digital twin creation module 264 may create a node for the entire process or may create a node for each step in the process. In some of these embodiments, the digital twin creation module 264 may relate the process nodes to the nodes that represent the machinery/devices that perform the steps in the process. In embodiments where an edge connects the process step nodes to the machinery /device that performs the process step, the edge or one of the nodes may contain information that indicates the input to the step, the output of the step, the amount of time the step takes, the nature of processing of inputs to produce outputs, a set of states or modes the process can undergo)
storing the graph data structure in a graph database (Paragraph Number [0867] teaches the digital twin update module 266 may update a record (e.g., a node in a graph database) corresponding to the sensor that provided the sensor data to reflect the current sensor data. Paragraph Number [0865] teaches the digital twin creation module 264 may create a graph database that defines the relationships between a set of digital twins. In these embodiments, the digital twin creation module 264 may create nodes for the environment, systems and subsystems of the transportation system, devices in the environment, sensors in the environment, workers that work in the environment, processes that are performed in the environment, and the like. In embodiments, the digital twin creation module 264 may write the graph database representing a set of digital twins to the digital twin datastore 269).
Strong Force teaches gathering data and creating graph models with the data but does not explicitly teach parsing sensor data to determine associations as described by the following citations from Noel:
parsing, via at least one processor of the computer system, the sensor data (Paragraph Number [0068] teaches with a priori knowledge of all of the possible sensor inputs (relevant for example to the data model of FIG. 2), the system 100 of FIG. 1 can implement one or more algorithms to parse the received data and convert it into a common data format that is compatible with a graph database. As an example, the ingested data 1002 can be parsed so as to provide data to build a multitude of nodes 1004 and relationships 1006 that will eventually be converted into nodes and edges of a graph database. The ingested data 1002 from the sensor can be parsed for data related to the nodes 1004 and relationships 1006. As example, the ingested data 1002 can be parsed to provide information about an instance of a node 1004 including attributes such as: unique identified (UID) 1008, time 1010, name 1012, type 104, as well as various properties 1016, defined by key 1010 and value 1020. An algorithm can be programmed such that the ingested data is parsed to provide the above values).
Both Strong Force and Noel are directed to generating graphical models. Strong Force discloses gathering data and creating graph models with the data. Noel improves upon Strong Force by disclosing parsing sensor data to determine associations. One of ordinary skill in the art would be motivated to further include parsing sensor data to determine associations, to efficiently determine and utilize associations between gathered data so as to provide that information in a graphical visualization. Accordingly, 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 system and method of gathering data and creating graph models with the data in Strong Force to further utilize parsing sensor data to determine associations as disclosed in Noel, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9, Strong Force teaches:
A system comprising: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: (Paragraph Number [0997] teaches the term system may define any combination of one or more computing devices, processors, modules, software, firmware, or circuits that operate either independently or in a distributed manner to perform one or more functions. A system may include one or more subsystems. Paragraph Number [0998] teaches various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above. Paragraph Number [0999] teaches the computer readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various ones of the aspects described above. In some embodiments, computer readable media may be non-transitory media).
The remainer of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 17, Strong Force teaches:
A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising: (Paragraph Number [0997] teaches the term system may define any combination of one or more computing devices, processors, modules, software, firmware, or circuits that operate either independently or in a distributed manner to perform one or more functions. A system may include one or more subsystems. Paragraph Number [0998] teaches various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above. Paragraph Number [0999] teaches the computer readable medium or media may be transportable, such that the program or programs stored thereon may be loaded onto one or more different computers or other processors to implement various ones of the aspects described above. In some embodiments, computer readable media may be non-transitory media.).
The remainer of the claim limitations are substantially similar to those found in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claim 2, the combination of Strong Force and Noel teaches each of the limitations of claim 1.
In addition, Strong Force teaches:
further comprising: executing, via the at least one processor, an Artificial Intelligence (AI) algorithm using the graph data structure as an input, wherein the AI algorithm comprises at least one of: Centrality, Community Analysis, Graph Machine Learning and Embeddings, Path Optimization, Classification Analysis, Similarity Analysis, Topological Link Prediction, and Frequent Pattern Mining (Paragraph Number [0845] teaches in scenarios where the digital twin system 200 is providing data representations of digital twins (e.g., for dynamic modeling, simulations, machine learning), the digital twin system 200 may traverse a graph database and may determine a configuration of the transportation system to be depicted based on the nodes in the graph database that are related (either directly or through a lower level node) to the transportation system node of the transportation system and the edges that define the relationships between the related nodes. In some scenarios, the digital twin system 200 may receive real-time sensor data from a sensor system 25 of a transportation system 11 and may apply one or more dynamic models to the digital twin based on the sensor data. Paragraph Number [0854] teaches a machine learned prediction model may be used to predict the cause of irregular vibrational patterns (e.g., a suboptimal, critical, or alarm vibration fault state) for a bearing of an engine in a transportation system).
As per claims 10 and 18, the combination of Strong Force and Noel teaches each of the limitations of claims 9 and 17 respectively. Additionally, the claims 10 and 18 recite claim language that is substantially similar to language contained in claim 1 and are rejected for the same reasons put forth in regard to claim 1.
As per claims 3, 11, and 19, the combination of Strong Force and Noel teaches each of the limitations of claims 1, 9, and 17 respectively.
In addition, Strong Force teaches:
wherein the resolving of the missing data further comprises: identifying missing data within the parsed sensor data, filling in the missing data within the parsed sensor data (Paragraph Number [0867] teaches the digital twin update module 266 may identify certain areas within the environment that are monitored by the sensor).
resolving timing differences between pieces of the parsed sensor data (Paragraph Number [0668] teaches an aspect provided herein includes a system for transportation 5211 comprising: a vehicle interface 52188 for gathering hormonal state data of a rider (e.g., user 5290) in the vehicle 5210; and an artificial intelligence-based circuit 52189 that is trained on a set of outcomes related to rider in-vehicle experience and that induces, responsive to the sensed rider hormonal state data, variation in one or more of the user experience parameters to achieve at least one desired outcome in the set of outcomes, the set of outcomes including a least one outcome that promotes rider safety, the inducing variation including control of timing and extent of the variation).
As per claims 4, 12, and 20, the combination of Strong Force and Noel teaches each of the limitations of claims 1, 9, and 17 respectively.
In addition, Strong Force teaches:
wherein the plurality of sources comprise: at least one database; and at least one physical sensor (Paragraph Number [0865] teaches depending on the type of transportation system, the types of objects, devices, and sensors that are found in the environments will differ. Non-limiting examples of physical objects 222 include raw materials, manufactured products, excavated materials, containers (e.g., boxes, dumpsters, cooling towers, ship funnels, vats, pallets, barrels, palates, bins, and the like), furniture (e.g., tables, counters, workstations, shelving, etc.), and the like. Non-limiting examples of devices 265 include robots, computers, vehicles (e.g., cars, trucks, tankers, trains, forklifts, cranes, etc.), machinery /equipment (e.g., tractors, tillers, drills, presses, assembly lines, conveyor belts, etc.), and the like. The sensors 227 may be any sensor devices and/or sensor aggregation devices that are found in a sensor system 25 within a transportation system. Non-limiting examples of sensors 227 that may be implemented in a sensor system 25 may include temperature sensors 231, humidity sensors 233, vibration sensors 235, LIDAR sensors 238, motion sensors 239, chemical sensors 241, audio sensors 243, pressure sensors 253, weight sensors 254, radiation sensors 255, video sensors 270, wearable devices 257, relays 275, edge devices 277, switches 278, infrared sensors 297, radio frequency (RF) Sensors 215, Extraordinary Magnetoresistive (EMR) sensors 280, and/or any other suitable sensors" §816; "the digital twin creation module 264 may create a graph database).
As per claims 5 and 13, the combination of Strong Force and Noel teaches each of the limitations of claims 1 and 9, respectively.
In addition, Strong Force teaches:
wherein the nodes comprise: a supplier node; a product node; a customer node; an exchange location node; an exchange node; and a sales contract and terms node (Paragraph Number [0837] teaches an enterprise node. Paragraph Number [0836] teaches a node representing the manufacturing environment ... nodes representing an HV AC system, a lighting system, a manufacturing system ... subsystem node representing a heating system of the facility, a third subsystem node representing the fan system of the facility, and one or more nodes representing a thermostat of the facility (or multiple thermostats) ... nodes representing various sensors (e.g., temperature sensors, humidity sensors, and the like)" §836; Paragraph Number [0845] teaches "a node representing a robot. Paragraph Number [0845] teaches the transportation system node. Paragraph Number [0866] teaches the digital twin creation module 264 may create a node for the entire process or may create a node for each step in the process. (Examiner asserts that a skilled person could create any kind of node which derives from the business they want to represent in a digital manner)).
As per claims 6 and 14, the combination of Strong Force and Noel teaches each of the limitations of claims 1 and 5, and 9 and 13 respectively.
In addition, Strong Force teaches:
wherein the edges identify relationships between the nodes defined by the exchange for each piece of the parsed, resolved sensor data. (Paragraph Number [0866] teaches the digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge).
As per claims 7 and 15, the combination of Strong Force and Noel teaches each of the limitations of claims 1, 5, and 6, and 9, 13, and 14 respectively.
In addition, Strong Force teaches:
wherein the edges further identify at least one self-referencing relationship (Paragraph Number [0866] teaches the digital twin creation module 264 may connect nodes of related entities with an edge, thereby creating a relationship between the entities. In doing so, the created relationship between the entities may define the type of relationship characterized by the edge. (Examiner notes that the self-reference edges in the graph theory was part of the common general knowledge of the skilled person in the art of data processing. They would use the common general knowledge if the business they wants to represent in a digital manner has self-reference relationships)):
As per claims 8 and 16, the combination of Strong Force and Noel teaches each of the limitations of claims 1 and 9, respectively.
In addition, Strong Force teaches:
further comprising: retrieving, at the computer system from the graph database, the graph data structure and a plurality of additional graph data structures, resulting in graph data (Paragraph Number [0873] teaches it is noted that while a graph database is discussed, the digital twin system 200 may employ other suitable data structures to store information relating to a set of digital twins. In these embodiments, the data structures, and any related storage system, may be implemented such that the data structures provide for some degree of feedback loops and/or recursion when representing iteration of flows).
executing, via the at least one processor, a machine learning algorithm using the graph data, wherein output of the machine learning model comprises a pattern between relationships of nodes and edges within the graph data (Paragraph Number [0845] teaches in scenarios where the digital twin system 200 is providing data representations of digital twins (e.g., for dynamic modeling, simulations, machine learning), the digital twin system 200 may traverse a graph database and may determine a configuration of the transportation system to be depicted based on the nodes in the graph database that are related (either directly or through a lower level node) to the transportation system node of the transportation system and the edges that define the relationships between the related nodes. In some scenarios, the digital twin system 200 may receive real-time sensor data from a sensor system 25 of a transportation system 11 and may apply one or more dynamic models to the digital twin based on the sensor data. Paragraph Number [0854] teaches a machine learned prediction model may be used to predict the cause of irregular vibrational patterns (e.g., a suboptimal, critical, or alarm vibration fault state) for a bearing of an engine in a transportation system).
communicating, from the computer system to a remote computing device, the pattern (Paragraph Number [1010] teaches the server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, social networks, and the like. Additionally, this coupling and/or connection may facilitate remote execution of programs across the network. The networking of some or all of these devices may facilitate parallel processing of a program or method at one or more locations without deviating from the scope of the disclosure. In addition, any of the devices attached to the server through an interface may include at least one storage medium capable of storing methods, programs, code and/or instructions. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for program code, instructions, and programs).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW H. DIVELBISS whose telephone number is (571) 270-0166. The fax phone number is 571-483-7110. The examiner can normally be reached on M-Th, 7:00 - 5:00. 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, Jerry O'Connor can be reached on (571) 272-6787.
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/M.H.D/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624