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 .
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 USC 101 because the claimed invention is directed to an abstract idea without adding significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to either a practical application of the abstract idea or significantly more than the abstract idea itself. Groupings of abstract ideas include: Mathematical Concepts, Mental Processes and Certain Methods of Organizing Human Activity.
Certain Methods of Organizing Human Activity include:
Fundamental economic principles or practices,
Commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), and
Managing personal behavior or relationships or interaction between people (including social activities, teaching and following rules or instructions).
Mathematical Concepts
Mathematical relationships
Mathematical formulas
Mathematical calculations
Mental Processes
Concepts performed in the human mind (including an observation, evaluation, judgement, opinion)
Step 1
In the instant case, claim 1 is directed to a process. Analysis of claim 1 applies to analysis of claims 2-20.
Step 2A Revised (First Prong)
Determine whether claim 1 is directed to a judicial exception. Elements of an abstract idea are underlined. See Analysis.
Step 2A Revised (Second Prong)
Determine whether claim 1 has additional elements (in italics) integrated into a practical application:
a) requires an additional element or a combination of elements in the claim to apply, rely on, or use the judicial exception in a manger that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception; and
b) uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
See Analysis.
Step 2B (Revised)
In Step 2B, evaluate whether claim 1 recites additional elements that amount to an inventive concept that adds significantly more than the recited judicial exception. See Analysis.
Analysis
In Claim 1:
A method for management of resources associated with an entity, the method comprising:
monitoring, by a processing unit, real-time resource status associated with each of one or more resources associated with an entity;
identifying, by the processing unit, at least one action to be performed on at least one resource amongst the one or more resources, based on corresponding real- time resource status;
generating, by the processing unit, a context-aware search query related to the at least one action;
executing, by the processing unit, the context-aware search query to retrieve one or more contents associated with the at least one resource;
creating, by the processing unit, a personalized recommendation based on the one or more contents; and
providing, by the processing unit, the personalized recommendation to a user associated with the entity, for initiating the at least one action.
Claim 1 executes methods that are directed to abstract ideas comprising processes that can be executed by a human while following a procedure that organizes human activity related to commercial interactions using conventional computing elements.
No evidence of an improvement to the functioning of a computer, or to any other technology or technical field.
No evidence exists in the instant specification or claims of a particular machine.
No evidence exists of a transformation or reduction of a particular article to a different state or thing.
The claim does not go beyond generally linking the use of the judicial exception to a particular technological environment, e.g. processor, device.
Claim 1 does not recite additional elements that amount to inventive concepts that are “significantly more” than the recited judicial exception. Courts have routinely found conventional computer processing functions (e.g. sending/receiving data, formatting data, storing data, retrieving data, manipulating data, calculating, searching data, displaying data, organizing data) insignificant to transform an abstract idea into a patent-eligible invention. See Alice, 134 S. Ct. at 2360. As such, the claims amount to nothing significantly more than an instruction to implement the abstract idea across a generic computer network which is not enough to transform an abstract idea into a patent-eligible invention.
The elements of the instant process, when taken in combination, together do not offer substantially more than the sum of the functions of the steps when each is taken alone. That is, the steps involved in the recited process undertake their roles in performance of their activities according to their generic functionalities which are well-understood, routine and conventional. The elements together execute in routinely and conventionally accepted coordinated manners and interact with their partner elements to achieve an overall outcome which, similarly, is merely the combined and coordinated execution of generic computer functionalities which are well-understood, routine and conventional activities previously known to the industry.
Conclusion
Accordingly, the examiner concludes there are no meaningful limitations in claims 1-20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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, 2, 5-9, 12-16 and 18-20 are rejected under 35 USC 103 as being unpatentable over Milev, US 2019/0258747, in view of Pan et al., US 2007/0299824 “Pan.”
In Milev see at least (underlined text is for emphasis):
Regarding claim 1: A method for management of resources associated with an entity, the method comprising:
monitoring, by a processing unit, real-time resource status associated with each of one or more resources associated with an entity;
[Milev: 0003] In an industrial operating environment, a digital representation of an asset, referred to as a digital twin, can be made up of a variety of operational technology (OT) and information technology (IT) data management systems. Examples of OT data systems include data historian services which maintain a history of sensor data streams from sensors attached to an asset and monitoring systems that detect and store alerts and alarms related to potential fault conditions of an asset. Examples of IT data systems include enterprise resource planning (ERP) systems, maintenance record databases, and the like. Each of these systems operates in a narrow information silo with its own semantics, concerns and data storage models.
[Milev: 0023] Assets may be outfitted with one or more sensors (e.g., physical sensors, virtual sensors, etc.) configured to monitor respective operations or conditions of the asset and the environment in which the asset operates. Data from the sensors can be recorded or transmitted to a cloud-based or other remote computing environment.
[Milev: 0029] … A domain event may refer to a particular type of knowledge artifact which models state or status of an entity in time, and which has event specific contextualizing semantics such as “this Actor took this Action with respect to this Entity in accordance with this Business Process at this Time.”
identifying, by the processing unit, at least one action to be performed on at least one resource amongst the one or more resources, based on corresponding real-time resource status;
[Milev: 0017] … In addition, the interactive digital twin is capable of communicating with other digital twins to therefore learn from and identify similar operating patterns and issues in other assets, as well as steps taken to resolve those issues.
[Milev: 0022] … As another non-limiting example, the generated context can provide suggestions about actions to take to resolve the current issue.
generating, by the processing unit, a context-aware search query related to the at least one action; executing, by the processing unit, the context-aware search query to retrieve one or more contents associated with the at least one resource;
Rejection is based in part upon the teachings applied to claim 1 by Milev and further upon the combination of Milev-Liu.
In Milev see at least:
[Milev: 0017] … The interactive digital twin is configured to simulate operation of an asset such as a machine, an equipment, a software process, an actor, an information resource, a system of assets, and the like. In addition to generate a simulated representation, the interactive digital twin is also configured to communicate (i.e., converse) with a user via multiple communication channels such as speech (audio), text, gestures (images/video), and the like. For example, the interactive digital twin may receive queries and requests from a user, and generate and output responses within the context of the queries/requests. As one example, the interactive digital twin may receive a query about an issue of an asset being modeled by the interactive digital twin and generate an answer based on context modeled by the digital twin.
[Milev: 0018] … Through the dashboard, a user can ask a digital twin a question and it will provide an answer/data in a consumable form that is user friendly along with additional information and metrics. Please note: Providing an answer, data and additional information and metrics in a consumable form that is user friendly that includes steps/actions to take to resolve a problem with an asset qualifies as content.
[Milev: 0019] Some non-limiting examples of queries that can be submitted to the interactive digital twin include questions about current status of various components of an asset, queries about occurrences to and with the asset over a predetermined period of time (e.g., the past 24 hours, etc.), questions about similar issues that have occurred in the asset or other assets, queries about possible solutions to an issue, and the like. In response, the interactive digital twin can provide declarative responses such as words and/or phrases answering the question asked by the user, information about other issues and conditions that have occurred previously with the asset, similar issues in other assets, and the like.
[Milev: 0030] Context may refer to an accumulation of knowledge related to a subject (e.g., an asset, component of the asset, a case involving the asset, an event, etc.) which can be reasoned over to provide subject-specific insight. Context may be generated by acquiring knowledge with an intent to provide a specific solution or set of solutions for a particular problem or issue. As a non-limiting example, context about an asset provided with a digital twin may include insight such as similarly matching events and operations that have previously occurred to the asset (or similar type assets) as well as suggestions about how to handle a current event, and the like.
[Milev: 0038] … The interactive digital twin 222 may traverse the knowledge graph 224 to generate a response to a query from the user system 210.
[Milev: 0042] In the communication sequence 300 shown in FIG. 3, the interactive digital twin 320 generates and transmit an alert to the user system 310 indicating that there is a warning or an error associated with an underlying asset being modeled by the interactive digital twin 320. In response, the user submits a query “What is causing the alert?” In response, the interactive digital twin 320 can provide the specific component (or components) that are overheating based on the query submitted by the user. Here, the interactive digital twin 320 can identify a subject of the query (i.e., context of the query), process the query for an answer, and return the answer in the form of text or speech “The air compressor is overheating.” The interactive digital twin 320 knows to return an answer based on context included in the query.
Although Milev’s system a) receives user queries in the form of questions, b) interprets the user’s query, c) converses with the user, and d) identifies actions/steps to take in order to resolve a problem with a resource, Milev does not expressly mention techniques that assist the user in forming a query. Pan on the other hand would have taught Milev such techniques.
In Pan see at least:
[Pan: 0001] The present invention generally relates to a method for query recommendation in conversation systems and, more particularly, to a hybrid method that combines natural language generation with query retrieval for context appropriate query recommendation.
[Pan: 0016] Principles of the invention provide a hybrid query recommendation framework that facilitates robust natural language interaction for systems with imperfect interpretation. When receiving a problematic user query that can not be understood by the natural language interpreter of a conversation system, the method dynamically recommends valid queries that are most relevant to the current user request. Based on the recommendations, the user can revise his request accordingly so that the next round of conversation will be more successful.
[Pan: 0019] Still further, another exemplary embodiment of the invention provides accurate and context-sensitive recommendations that help users revise problematic queries so that the revised queries have a better chance to be understood by the system.
[Pan: 0020] Accordingly, principles of the invention provide a hybrid query recommendation framework that is executable within a multimodal conversation application. An example of a particular multimodal conversation application is in the real-estate domain in which potential home buyers interact with the system using multiple modalities, such as speech and gesture, to request residential real-estate information. The hybrid query recommendation framework executing within the application takes a problematic user query, associated system interpretation results, and the current conversation context as input and formulates query recommendations that are most relevant to the current user query.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Pan, which provide accurate context-sensitive recommendations that help users revise problematic queries so that the revised queries have a better chance to be understood by the system, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Pan to the teachings of Milev would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Rejection is based upon the teachings and rationale applied to claim 1 by Milev-Pan and further upon the combination of Milev-Pan:
creating, by the processing unit, a personalized recommendation based on the one or more contents; and
[Milev: 0022] … As another non-limiting example, the generated context can provide suggestions about actions to take to resolve the current issue.
providing, by the processing unit, the personalized recommendation to a user associated with the entity, for initiating the at least one action.
[Milev: 0026] … The context can be used to trigger actions, …
[Milev: 0030] … Context may be generated by acquiring knowledge with an intent to provide a specific solution or set of solutions for a particular problem or issue.
[Milev: 0044] … In addition, the interactive digital twin 320 may output similar problems and how they were resolved via the display of the user system 310.
Regarding claims 8 and 15: Rejections are based upon the teachings and rationale applied to claim 1 by Milev-Pan and further upon the combination of Milev-Pan pertaining to system computing elements, e.g. central database, user devices, one or more processors and memory, see [Milev: Fig. 1; Fig. 5; 0036; 0051-0056]
Regarding claims 2, 9 and 16: Rejections are based upon the teachings and rationale applied to claims 1, 8 and 15 by Milev-Pan pertaining to a) a central database and b) entity type:
[Milev: 0021] … As an example, an asset may include a physical asset such as a turbine, jet engine, windmill, oil rig, healthcare machine, or the like. As additional examples, an asset may include a software asset (e.g., an application, an analytic, a service, etc.), a system of hardware and/or software (also referred to as a system of things), a physical process, an actor such as a human operator, weather, and the like. …
[Milev: Fig. 1: 0031] … Assets 110 may include hardware/structural assets such as machine and equipment used in industry, healthcare, manufacturing, energy, transportation, and that like….
[Milev: Fig. 1: 0034] … For example, the assets 110 may be an asset community (e.g., turbines, healthcare, power, industrial, manufacturing, mining, oil and gas, elevator, etc.) which may be communicatively coupled to the cloud platform 120 via one or more intermediate devices such as a stream data transfer platform, database, or the like.
[Milev: 0036] In some embodiments, the cloud platform 120 may include a local, system, enterprise, or global computing infrastructure that can be optimized for industrial data workloads, secure data communication, and compliance with regulatory requirements. The cloud platform 120 may include a database management system (DBMS) for creating, monitoring, and controlling access to data in a database coupled to or included within the cloud platform 120. The cloud platform 120 can also include services that developers can use to build or test industrial or manufacturing-based applications and services to implement IIoT applications that interact with assets 110.
Regarding claims 5, 12 and 18: Rejections are based upon the teachings and rationale applied to claims 1, 8 and 15 by Milev-Pan pertaining to resolving an issue with a resource.
Claims 6, 7, 13, 14, 19 and 20: Rejections are based upon the teachings and rationale applied to claims 1, 8 and 15 by Milev-Pan regarding keywords:
[Pan: 0038] FIG. 3 illustrates a preferred embodiment in which the system uses a cascade model in case-based sentence generation. In the cascade model, the sentence generator 304 first retrieves words, phrases and sentence segments that can convey some of the content in a recommendation from the current user query. Then, the sentence generator retrieves words, phrases and sentence segments from the legal query corpus 306 (part of query corpus 106) to convey the rest of the content in a recommendation. Finally, the system applies adaptation rules to combine all the words, phrases and sentence segments to form a fluent and grammatical sentence.
Claims 3, 4, 10, 11 and 17 are rejected under 35 USC 103 as being unpatentable over Milev, US 2019/0258747, and Pan, US 2007/0299824, as applied to claims 1 and 8 further in view of Barton et al., US 2019/0102735 “Barton.”
Regarding claims 3 and 10: Rejections are based in part upon the teachings and rationale applied to claims 1 and 8 by Milev-Pan and further upon the combination of Milev-Pan-Barton.
In Milev-Pan see at least:
[Milev:0023] Assets may be outfitted with one or more sensors (e.g., physical sensors, virtual sensors, etc.) configured to monitor respective operations or conditions of the asset and the environment in which the asset operates. Data from the sensors can be recorded or transmitted to a cloud-based or other remote computing environment. …
[Milev: 0035] Information from the assets 110 may be communicated to the cloud platform 120. For example, external sensors can be used to sense information about a function of an asset, or to sense information about an environment condition at or around an asset, a worker, a downtime, a machine or equipment maintenance, and the like. The external sensor can be configured for data communication with the cloud platform 120 which can be configured to store the raw sensor information and transfer the raw sensor information to the user devices 130 where it can be accessed by users, applications, systems, and the like, for further processing.
Although Milev-Pan do not expressly mention techniques applied to types of sensors, Barton on the other hand would have taught Milev-Pan such techniques.
In Barton see at least:
[Barton: 0005] Embodiments of the invention include systems, methods and devices for tracking the location an asset (e.g., package) that is in transit (rail, air, truck, etc.) and monitoring environmental conditions that the asset is subjected to during transit. For example, one embodiment includes a package label, which comprises a flexible substrate comprising a plurality of components disposed on the flexible substrate. The components comprise a processor to control operations of the package label, a memory device, a global positioning system (GPS) device configured to determine a geolocation of the package label, a plurality of sensors configured to determine sensor information, and a wireless communications device. The processor is configured to periodically collect real-time location data from the GPS device and sensor information from the plurality of sensors, at a first predetermined time interval, and store the collected information in the memory device. The wireless communications device is configured to transmit the collected real-time location data and sensor information to a remote service provider for access by registered user of the package label, under control of the processor. The processor is configured to periodically access the real-time location data and sensor information in the memory device and command the wireless communication device to transmit the collected location data and sensor information to the remote service provider at a second predetermined time interval. The plurality of sensors comprises one or more of: a temperature sensor to capture real-time temperature information which comprises at least one of an external ambient temperature surrounding the asset, and a temperature of the asset; a humidity sensor to capture real-time humidity information; an altimeter sensor to capture real-time barometric pressure information; a light sensor to capture real-time light exposure information; and an x-y-z accelerometer to capture real-time motion information.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Barton, which include various types of sensors monitoring assets in transit, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Barton to the teachings of Milev-Pan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
Regarding claim 4, 11 and 17: Rejections are based upon the teachings and rationale applied above to the combination of Milev-Pan-Barton pertaining to automatically initiating, by the processing unit, the monitoring of the real-time resource status of each of the one or more resource, at predefined intervals of time:
[Barton: 0005] … The processor is configured to periodically collect real-time location data from the GPS device and sensor information from the plurality of sensors, at a first predetermined time interval, and store the collected information in the memory device.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2016/0092511 (Liu et al.) “Interactive Construction of Queries,” discloses: [Abstract] Method and system to assist a user in formulating a search query is described. The system may provide suggested entities, entity types, and relationship operators to a user and interactively build a complex structured query. Complex structured queries may include a combination of one or more entities and entity types, together with one or more relationship operators.
US 2019/0135318 (Kilaru et al.) “Rail Fleet Maintenance Management System and Method,” discloses: [0033] The fleet data management and risk analysis module includes a computer-implemented method and associated system for managing maintenance of one or more railcars in a fleet, comprising identifying a geographical location of a railcar; receiving sensor data associated with said railcar; retrieving, from a database, a maintenance report for said railcar, wherein said maintenance report includes at least a first maintenance history for a first hardware component of said railcar and a first maintenance schedule for said first hardware component; applying, with a processor, a risk rules engine to determine a first risk score for said first hardware component, wherein said first risk score is based at least in part on said geographical location, said sensor data, said first maintenance history, and said first maintenance schedule; and displaying said first risk score via a graphical user interface.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 June 18, 2026
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