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 .
Status of Application
This office action is in response to the most recent filings filed by applicants on 12/16/24.
No claims are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
Note:
Regarding dependent claims 6-7, 13-14, and 20, the claims recite “at least one of” as well as “and” in the claim, which makes the claim scope ambiguous. For instance, claim 6 recites: “wherein triggering the intervention event includes at least one of: adjusting a difficulty level of the simulation scenario; and triggering an agent behavior in the simulation scenario.” At least one of is reasonably understood to mean one of the items listed, on the other hand “and” is reasonably understood to mean all the items in the list need to be included. As such the scope of the claim is unclear. For the purposes of this office action, the claims are reasonably understood as reciting “at least one of”. The “and” in the claim is reasonably understood to be a typo. The “and” is understood as being an “or”. Appropriate corrections or clarifications are requested.
This note is intended as a conversation starter to help applicants understand the examiner’s perspective. Applicants are welcome to call the examiner to discuss this further.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/983243.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the current application are broader than the copending application.
For instance, independent claim 1 of the current application recites:
A computer-implemented method comprising: executing, by a computing device, a simulation scenario; receiving input data from a user participating in the simulation scenario; receiving input data from the simulation scenario; analyzing the input data from the user participating in the simulation scenario and the input data from the simulation scenario; and providing feedback to the user participating in the simulation scenario based upon, at least in part, analyzing the input data from the user participating in the simulation scenario and the input data from the simulation scenario.
Claim 1 of the copending application recites:
A computer-implemented method comprising: receiving, by a computing device, input data from a user participating in a simulation scenario; receiving input data from the simulation scenario, wherein the input data from the user participating in the simulation scenario and the input data from the simulation scenario is processed using feature engineering data; and predicting performance of the user in the simulation scenario to generate a predicted performance of the user based upon, at least in part, the input data from the user participating in the simulation scenario and the input data from the simulation scenario processed using the artificial intelligence feature engineering model.
Claims of the instant application recite substantially similar limitations and are obvious variants of each other when comparing Independent Claims of co-pending Application No. 18/983243. The claims of the instant application are broader and would read on the narrower version of the claims in the referenced co-pending. The claims at issue are not patentably distinct from each other:
Examiner notes that elimination of an element or its functions is deemed to be obvious in light of prior art teachings of at least the recited element or its functions (see in re Karlson, 136 USPQ 184, 186; 311 F2d 581 (CCPA 1963)), thereby rendering the elimination of any elements recited in the claims of the referenced copending application (that are not recited in the instant claims) obvious.
Accordingly, one of ordinary skill in the art would have recognized the slight differences between the claim language / limitations of the corresponding claims as being directed towards intention, slight variations in terminology, or obvious variants of similar claim elements, and therefore these claims are not patentably distinct from one another despite these slight differences.
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 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-7 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 8-14 is/are directed to a computer program product which is not a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 15-20 is/are directed to a system which is a statutory category.
Computer Readable Storage Medium
Before we dive into the two-prong inquiry, the independent claim 8 recites “A computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising:”. In applicants originally submitted specification describes “computer program product residing on a computer readable storage medium having a plurality of instructions” in: [008] In another example implementation, a computer program product may reside on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, may cause at least a portion of the one or more processors to perform operations that may include but are not limited to executing a simulation scenario. Input data from a user participating in the simulation scenario may be received. Input data from the simulation scenario may be received. The input data from the user participating in the simulation scenario and the input data from the simulation scenario may be analyzed. Feedback may be provided to the user participating in the simulation scenario based upon, at least in part, analyzing the input data from the user participating in the simulation scenario and the input data from the simulation scenario.
Here, the description is simply a mention of the terms “computer readable storage medium”. There is no further discussion or description of the structure discussed in claim 8. Since, the claimed features appear to not be restricted to any type of hardware implementation, and could be considered to be implemented through software components alone. Thus, the claim is considered to be directed to a computer program, per se. A claim that covers both statutory and non-statutory embodiments (under the broadest reasonable interpretation of the claim when read in light of the specification and in view of one skilled in the art) embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter.
Software per se
Claims 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Based on a reading of the original disclosure, including the other claims, Examiner interprets the claim language under a broadest reasonable interpretation standard.
The “computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which” may refer to software or modules. Accordingly, the broadest reasonable interpretation of the claims reveals that the claims are not directed to a system, but rather to software per se. Software is not a statutory class of invention. For the reasons noted, the claims are rejected because they are not directed to statutory subject matter.
Although claims 8-14 are failing to fall under one of the four statutory categories, the claim could potentially be amended. When a claim fails to fall under at least one of the four statutory categories and it appears from Applicant' s disclosure that the claim could be amended to be directed to a statutory category, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception. See MPEP 2106 I and II.
Assuming that independent claim 8 is amended to recite a statutory category, then claims 8-14 would be 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.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, 8 and 15, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Method Claim 1 is directed to an abstract idea, as evidenced by claim limitations “executing, a simulation scenario; receiving input data from a user participating in the simulation scenario; receiving input data from the simulation scenario; analyzing the input data from the user participating in the simulation scenario and the input data from the simulation scenario; and providing feedback to the user participating in the simulation scenario based upon, at least in part, analyzing the input data from the user participating in the simulation scenario and the input data from the simulation scenario”
In the originally submitted specification (PGPub) [0005]: Providing feedback to the user participating in the simulation scenario may include triggering an intervention event in the simulation scenario based upon, at least in part the input data from the user participating in the simulation scenario and the input data from the simulation scenario. Triggering the intervention event may include at least one of adjusting a difficulty level of the simulation scenario and triggering an agent behavior in the simulation scenario. Triggering the intervention event may include providing at least one of a visual cue, an audio cue, a virtual instructor cue, and a virtual instructor intervention.
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing the predictions of performance for the user for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —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).
Independent Claims 8 and 15 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A computer-implemented method comprising: A computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising: A computing system including one or more processors and one or more memories configured to perform operations comprising: by a computing device,” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 8 and 15 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
In the originally submitted specification, [0025] Software may include artificial intelligence systems, which may include machine learning or other computational intelligence. For example, artificial intelligence (AI) may include one or more models used for one or more problem domains. When presented with many data features, identification of a subset of features that are relevant to a problem domain may improve prediction accuracy, reduce storage space, and increase processing speed. This identification may be referred to as feature engineering. Feature engineering may be performed by users or may only be guided by users. In various implementations, a machine learning system may computationally identify relevant features, such as by performing singular value decomposition on the contributions of different features to outputs.
The additional elements of “using the artificial intelligence feature engineering model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “using the artificial intelligence feature engineering model” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Similarly dependent claims 2-7, 9-14 and 16-20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “wherein the input data from the user participating in the simulation scenario includes user biometrics”. Dependent claims 3 recite “wherein the input data from the user participating in the simulation scenario includes user performance data”. Dependent claims 4 recite “wherein the input data from the simulation scenario includes game state data.” Dependent claims 5 recites “wherein providing feedback to the user participating in the simulation scenario includes triggering an intervention event in the simulation scenario based upon, at least in part the input data from the user participating in the simulation scenario and the input data from the simulation scenario”. Dependent claims 6 recites “wherein triggering the intervention event includes at least one of: adjusting a difficulty level of the simulation scenario; and triggering an agent behavior in the simulation scenario”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 7 recites “wherein triggering the intervention event includes providing at least one of a visual cue, an audio cue, a virtual instructor cue, and a virtual instructor intervention”. In this claim, “a visual cue, an audio cue, a virtual instructor cue, and a virtual instructor intervention” is an additional element, but it is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-14 and 16-20 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising: A computer-implemented method comprising: A computing system including one or more processors and one or more memories configured to perform operations comprising: by a computing device, using the artificial intelligence feature engineering model” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0025]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 8 and 15 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-7, 9-14 and 16-20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 8, and 15. As a result, Examiner asserts that dependent claims, such as dependent claims 2-7, 9-14 and 16-20 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Al Zahrani et al. (US 2024/0338639 A1), and further in view of Crabtree et al. (US 2018/0232807 A1).
As per claims 1, 8 and 15: Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
A computer-implemented method comprising (see Al Zahrani, Fig. 2, [0005] receive input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. [0006] In another embodiment, a system includes memory to store computer executable instructions, and one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement an analyzer having an input for receiving discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and for receiving live data relating to a worker real-time physiological condition. The analyzer also has an AI engine that uses an AI model applying natural language and decision tree processing to the input received by the analyzer and a report generator for generating a report of a worker fitness assessment that relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0029] FIG. 2 is an example of a system 200 for assessing worker job performance fitness in accordance with certain embodiments. The system 200 can be implemented using one or more modules, shown in block form. The one or more modules can be in software or hardware form, or a combination thereof. In some examples, one or more portions of the system 200 can be implemented as machine readable instructions for execution on a computing platform 202 having a processor 204 and a memory 206. [0031] The computing platform 202 can include one or more computing devices selected from, for example, a desktop computer, a server, a controller, a blade, a mobile phone, a tablet, a laptop, a personal digital assistant (PDA), and the like. By way of example, the memory 206 can be implemented, for example, as a non-transitory computer storage medium, such as volatile memory (e.g., random access memory), non-volatile memory (e.g., a hard disk drive, a solid-state drive, a flash memory, or the like), or a combination thereof. The processor 204 can be implemented, for example, as one or more processor cores. The memory 206 can store machine-readable instructions (e.g., which can include the analyzer 208) that can be retrieved and executed by the processor 204. Each of the processor 204 and the memory 206 can be implemented on a similar or a different computing platform. The computing platform 202 can be implemented in a cloud computing environment. In such a situation, features of the computing platform 202 can be representative of a single instance of hardware or multiple instances of hardware executing across the multiple of instances (e.g., distributed) of hardware (e.g., computers, routers, memory, processors, or a combination thereof). Alternatively, the computing platform 202 can be implemented on a single dedicated server or workstation.):
Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
A computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising: (see Al Zahrani, [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0006] In another embodiment, a system includes memory to store computer executable instructions, and one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement an analyzer having an input for receiving discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and for receiving live data relating to a worker real-time physiological condition. The analyzer also has an AI engine that uses an AI model applying natural language and decision tree processing to the input received by the analyzer and a report generator for generating a report of a worker fitness assessment that relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0029] FIG. 2 is an example of a system 200 for assessing worker job performance fitness in accordance with certain embodiments. The system 200 can be implemented using one or more modules, shown in block form. The one or more modules can be in software or hardware form, or a combination thereof. In some examples, one or more portions of the system 200 can be implemented as machine readable instructions for execution on a computing platform 202 having a processor 204 and a memory 206. [0031] The computing platform 202 can include one or more computing devices selected from, for example, a desktop computer, a server, a controller, a blade, a mobile phone, a tablet, a laptop, a personal digital assistant (PDA), and the like. By way of example, the memory 206 can be implemented, for example, as a non-transitory computer storage medium, such as volatile memory (e.g., random access memory), non-volatile memory (e.g., a hard disk drive, a solid-state drive, a flash memory, or the like), or a combination thereof. The processor 204 can be implemented, for example, as one or more processor cores. The memory 206 can store machine-readable instructions (e.g., which can include the analyzer 208) that can be retrieved and executed by the processor 204. Each of the processor 204 and the memory 206 can be implemented on a similar or a different computing platform. The computing platform 202 can be implemented in a cloud computing environment. In such a situation, features of the computing platform 202 can be representative of a single instance of hardware or multiple instances of hardware executing across the multiple of instances (e.g., distributed) of hardware (e.g., computers, routers, memory, processors, or a combination thereof). Alternatively, the computing platform 202 can be implemented on a single dedicated server or workstation.):
Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
A computing system including one or more processors and one or more memories configured to perform operations comprising: by a computing device (see Al Zahrani, [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0006] In another embodiment, a system includes memory to store computer executable instructions, and one or more processors, operatively coupled to the memory, that execute the computer executable instructions to implement an analyzer having an input for receiving discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and for receiving live data relating to a worker real-time physiological condition. The analyzer also has an AI engine that uses an AI model applying natural language and decision tree processing to the input received by the analyzer and a report generator for generating a report of a worker fitness assessment that relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0029] FIG. 2 is an example of a system 200 for assessing worker job performance fitness in accordance with certain embodiments. The system 200 can be implemented using one or more modules, shown in block form. The one or more modules can be in software or hardware form, or a combination thereof. In some examples, one or more portions of the system 200 can be implemented as machine readable instructions for execution on a computing platform 202 having a processor 204 and a memory 206. [0031] The computing platform 202 can include one or more computing devices selected from, for example, a desktop computer, a server, a controller, a blade, a mobile phone, a tablet, a laptop, a personal digital assistant (PDA), and the like. By way of example, the memory 206 can be implemented, for example, as a non-transitory computer storage medium, such as volatile memory (e.g., random access memory), non-volatile memory (e.g., a hard disk drive, a solid-state drive, a flash memory, or the like), or a combination thereof. The processor 204 can be implemented, for example, as one or more processor cores. The memory 206 can store machine-readable instructions (e.g., which can include the analyzer 208) that can be retrieved and executed by the processor 204. Each of the processor 204 and the memory 206 can be implemented on a similar or a different computing platform. The computing platform 202 can be implemented in a cloud computing environment. In such a situation, features of the computing platform 202 can be representative of a single instance of hardware or multiple instances of hardware executing across the multiple of instances (e.g., distributed) of hardware (e.g., computers, routers, memory, processors, or a combination thereof). Alternatively, the computing platform 202 can be implemented on a single dedicated server or workstation.):
Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
executing, by a computing device, a simulation scenario
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on;
Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
receiving input data from a user participating in the simulation scenario
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on;
Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
receiving input data from the simulation scenario
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on;
Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
analyzing the input data from the user participating in the simulation scenario and the input data from the simulation scenario
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on. This is reasonably understood to read on “analyzing the input data”.
However, Al Zahrani does not explicitly show “analyzing the input data”. Reference Crabtree shows the above limitation at least in [0041]: Within directed computational graph module 155, data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data may be then transferred to a general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis. Directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. [0047] Additionally, within the large amounts of data gathered and stored, a substantial amount of the stored data may require frequent updating, for instance, stock symbols and corresponding prices, which may prove to be time-consuming. Business operating system 100 may be configured to autonomously and continuously gather data in a background process, for example, using subroutines of connector module 135, such as email reader 238 or market plugins 236; using subroutines of automated planning service module 130, such as financial markets function library 251; using web crawler module 115 to scour news financial news sites; or using time series data store 120 to receive updated stock pricing at regular intervals. The data may then be processed and used by business operating system 100 to improve and update stored data. These operations may include, but not limited to, semantic extraction from corporate news and macro data; cross-linking to GraphStack entries; and automated time series feature engineering through the use of libraries like TSFresh, or using dimensionality reduction. Additionally, the high-bandwidth capabilities of business operating system 100 enables low-latency links to market data providers and venues to provide a nearly real-time channel to market data for the user using a ticker plant module 233 shown in FIG. 2C. The data that may be provided by market data providers and venues may include, but is not limited to, stock symbols and pricing, order book information, fill reports, news, and fundamentals. Business operating system 100 may also be configured to perform error-checking and self-heal the data as it is received.
Reference Al Zahrani and Reference Crabtree are analogous prior art to the claimed invention because the references generally relate to field of analyzing data using simulations to make better future predictions. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Crabtree, particularly the ability to employ feature engineering model (Crabtree: [0041]), in the disclosure of Reference Al Zahrani, particularly in the ability to transform data collected for analysis (Al Zahrani: [0027]), in order to provide for an advanced decentralized financial decision platform 100 according to an embodiment of the invention. Client access 105 to system or platform 100 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, as taught by Reference Crabtree (see at least in [0027]), where upon the execution of the method and system of Reference Crabtree for creating a first dataset comprising at least a user-defined set of computing instructions comprising at least instructions regarding data flow locality, a parametric evaluator configured to retrieve the first dataset, and process the first dataset by performing at least a plurality of transformations and predictive analysis on the first dataset and specifying at least an intended focus on financial trading, and an optimizer configured to retrieve the processed first dataset from the parametric evaluator and determine an optimal locality for executing a trade (Abstract) so that the process of analyzing data using simulations to make better future predictions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data using simulations to make better future predictions field of endeavor, and in the 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, given the existing technical ability to combine the elements as evidenced by Reference Al Zahrani in view of Reference Crabtree, the results of the combination were predictable (MPEP 2143 A); and
Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
providing feedback to the user participating in the simulation scenario based upon, at least in part, analyzing the input data from the user participating in the simulation scenario and the input data from the simulation scenario.
Reference Al Zahrani shows in [0013]: a system 100 for assessing worker job performance fitness, for example for performing tasks in a particular environment such as an oil and gas plant. Inputs to the system 100 comprise discrete data 102 and live (e.g. wearable or real-time) data 104. This is reasonably understood to read on “providing feedback”. However, Crabtree below shows “providing feedback” as discussed below.
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on. This is reasonably understood to read on “analyzing the input data”.
However, Al Zahrani does not explicitly show “analyzing the input data”. Al Zahrani also does not explicitly show “providing feedback”. Reference Crabtree shows “analyzing the input data” the above limitation at least in [0041]: Within directed computational graph module 155, data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data may be then transferred to a general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis. Directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. [0047] Additionally, within the large amounts of data gathered and stored, a substantial amount of the stored data may require frequent updating, for instance, stock symbols and corresponding prices, which may prove to be time-consuming. Business operating system 100 may be configured to autonomously and continuously gather data in a background process, for example, using subroutines of connector module 135, such as email reader 238 or market plugins 236; using subroutines of automated planning service module 130, such as financial markets function library 251; using web crawler module 115 to scour news financial news sites; or using time series data store 120 to receive updated stock pricing at regular intervals. The data may then be processed and used by business operating system 100 to improve and update stored data. These operations may include, but not limited to, semantic extraction from corporate news and macro data; cross-linking to GraphStack entries; and automated time series feature engineering through the use of libraries like TSFresh, or using dimensionality reduction. Additionally, the high-bandwidth capabilities of business operating system 100 enables low-latency links to market data providers and venues to provide a nearly real-time channel to market data for the user using a ticker plant module 233 shown in FIG. 2C. The data that may be provided by market data providers and venues may include, but is not limited to, stock symbols and pricing, order book information, fill reports, news, and fundamentals. Business operating system 100 may also be configured to perform error-checking and self-heal the data as it is received.
Reference Crabtree shows “providing feedback” at least in [0077] Model definition language service 840 may be configured to use functions of advanced decentralized financial decision platform 100, such as DCG module 155 with associated transformer modules, graph stack service 145, and automated planning service 130, to allow user management of models, and defining of vectors using a declarative specification language (DSL). The use of a DSL for vectorizing the compute environment and data flow descriptions may enable linking of search processes to the rules engine 830, parametric evaluator 810, and feedback loop processes during ongoing operational-use based on the ability to encode appropriateness when combined with rules engine 830, serving as a basis for deep and reinforcement learning to support ongoing improvement to functions of optimizer 820. Model definition language service 840 may also enable a user or an autonomous trading system to initiate evaluation of specific pipelines, activities, overall system health, and the like of a specific instance of system 800.
Reference Al Zahrani and Reference Crabtree are analogous prior art to the claimed invention because the references generally relate to field of analyzing data using simulations to make better future predictions. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Crabtree, particularly the ability to employ feature engineering model (Crabtree: [0041]), in the disclosure of Reference Al Zahrani, particularly in the ability to transform data collected for analysis (Al Zahrani: [0027]), in order to provide for an advanced decentralized financial decision platform 100 according to an embodiment of the invention. Client access 105 to system or platform 100 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, as taught by Reference Crabtree (see at least in [0027]), where upon the execution of the method and system of Reference Crabtree for creating a first dataset comprising at least a user-defined set of computing instructions comprising at least instructions regarding data flow locality, a parametric evaluator configured to retrieve the first dataset, and process the first dataset by performing at least a plurality of transformations and predictive analysis on the first dataset and specifying at least an intended focus on financial trading, and an optimizer configured to retrieve the processed first dataset from the parametric evaluator and determine an optimal locality for executing a trade (Abstract) so that the process of analyzing data using simulations to make better future predictions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data using simulations to make better future predictions field of endeavor, and in the 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, given the existing technical ability to combine the elements as evidenced by Reference Al Zahrani in view of Reference Crabtree, the results of the combination were predictable (MPEP 2143 A).
As per claims 2, 9 and 16: Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
wherein the input data from the user participating in the simulation scenario includes user biometrics.
Reference Al Zahrani shows [0014] Discrete data 102 that is input into system 100 includes such information as worker experience 106, worker physical fitness and/or medical condition 108, and worker workload 110. Live data 104 relates to the worker's real-time physiological condition, as gleaned for example from a wearable device (not shown) or the like that can for instance provide pulse rate and cardio rhythms and patterns and other heart information, worker temperature, blood glucose level, skin moisture and hydration, sleep time, exercise time and level, and any other parameters that may be monitored in real-time and that may be relevant for determining that particular worker's physical and/or mental state, and fitness for a particular task or tasks. [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0035] FIG. 3 is a flow diagram of a method 300 for assessing worker job performance fitness in accordance with certain embodiments. The method includes receiving, at 302, input about a worker. The input can include discrete data (e.g. discrete data 102, FIG. 1) relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data (e.g. live data 104, FIG. 1) relating to a worker real-time physiological condition. At 304, an AI model (e.g. AI model 216, FIG. 2) using natural language and decision tree processing is applied to the received input. At 306, a worker fitness assessment based on the AI model is reported, wherein the worker assessment (e.g. assessment 210, FIG. 2) relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task.
As per claims 3, 10 and 17: Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
wherein the input data from the user participating in the simulation scenario includes user performance data.
Reference Al Zahrani shows in [0012]: use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0013] FIG. 1 is a schematic diagram of a system 100 for assessing worker job performance fitness, for example for performing tasks in a particular environment such as an oil and gas plant. Inputs to the system 100 comprise discrete data 102 and live (e.g. wearable or real-time) data 104. Outputs 105 of the system 100 include one or more of indicators or classifications of worker stress level, overall fitness of the worker and/or fitness for a particular task or tasks, advisories on recommended actions, warnings, or remediations, or indicators of qualification or disqualification of the worker from one or more tasks, shifts, etc. [0022] Worker physical fitness condition 108 can relate to factors such as individual and family medical history, chronic disease, hearing or vision or other sensory deficits or impairments, body mass and general physical fitness, physical disabilities, and any other relevant personal or family medical history conditions or the like. [0032]: The AI model 216 can provide a recommendation to improve the work environment like more training, operator simulation training (OTS), and more frequent drills. In certain embodiments, model 216 can perform perspective analytics, whereby similarities are drawn between current trends and past incidents to give insights about the future; which worker is more susceptible to what type of risk and how were similar cases were addressed. It will be appreciated that perspective analytics is a type of data analytics that identifies the root cause of an issue or recommendations to fix it based on past historic data of similar issues/incidents. [0043] A user may enter commands and information into computer system 400 through one or more input devices 440, such as a pointing device (e.g., a mouse, touch screen), keyboard, microphone, joystick, game pad, scanner, and the like. These can correspond to user interface 220 (FIG. 2) for instance. These and other input devices 440 are often connected to processing unit 402 through a corresponding port interface 442 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, serial port, or universal serial bus (USB). One or more output devices 444 (e.g., display, a monitor, printer, projector, or other type of displaying device) is also connected to system bus 406 via interface 446, such as a video adapter.
As per claims 4, 11 and 18: Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
wherein the input data from the simulation scenario includes game state data.
Reference Al Zahrani shows in [0043] A user may enter commands and information into computer system 400 through one or more input devices 440, such as a pointing device (e.g., a mouse, touch screen), keyboard, microphone, joystick, game pad, scanner, and the like. These can correspond to user interface 220 (FIG. 2) for instance. These and other input devices 440 are often connected to processing unit 402 through a corresponding port interface 442 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, serial port, or universal serial bus (USB). One or more output devices 444 (e.g., display, a monitor, printer, projector, or other type of displaying device) is also connected to system bus 406 via interface 446, such as a video adapter.
However, Al Zahrani does not explicitly show “game state data”. Reference Crabtree shows: [0042] Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the data already available in automated planning service module 130, which also runs powerful information theory-based predictive statistics functions and machine learning algorithms 130a to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. Then, using all or most available data, automated planning service module 130 may propose business decisions most likely to result in favorable business outcomes with a usably high level of certainty. Closely related to the automated planning service module 130 in the use of system-derived results in conjunction with possible externally supplied additional information in the assistance of end user business decision making, action outcome simulation module 125 with a discrete event simulator programming module 125a coupled with an end user-facing observation and state estimation service 140, which is highly scriptable 140b as circumstances require and has a game engine 140a to more realistically stage possible outcomes of business decisions under consideration, allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data. [0045]: Both possibly during and following predictive analyses carried out by the system, results may be presented to clients 105 in formats best suited to convey the both important results for analysts to make highly informed decisions and, when needed, interim or final data in summary and potentially raw for direct human analysis. Simulations which may use data from a plurality of field spanning sources to predict future trade conditions these are accomplished within the action outcome simulation module 125. Data and simulation formatting may be completed or performed by the observation and state estimation service 140 using its ease of scripting and gaming engine to produce optimal presentation results. [0045]: Both possibly during and following predictive analyses carried out by the system, results may be presented to clients 105 in formats best suited to convey the both important results for analysts to make highly informed decisions and, when needed, interim or final data in summary and potentially raw for direct human analysis. Simulations which may use data from a plurality of field spanning sources to predict future trade conditions these are accomplished within the action outcome simulation module 125. Data and simulation formatting may be completed or performed by the observation and state estimation service 140 using its ease of scripting and gaming engine to produce optimal presentation results.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Crabtree, particularly the ability to employ feature engineering model (Crabtree: [0041]), in the disclosure of Reference Al Zahrani, particularly in the ability to transform data collected for analysis (Al Zahrani: [0027]), in order to provide for an advanced decentralized financial decision platform 100 according to an embodiment of the invention. Client access 105 to system or platform 100 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, as taught by Reference Crabtree (see at least in [0027]), where upon the execution of the method and system of Reference Crabtree for creating a first dataset comprising at least a user-defined set of computing instructions comprising at least instructions regarding data flow locality, a parametric evaluator configured to retrieve the first dataset, and process the first dataset by performing at least a plurality of transformations and predictive analysis on the first dataset and specifying at least an intended focus on financial trading, and an optimizer configured to retrieve the processed first dataset from the parametric evaluator and determine an optimal locality for executing a trade (Abstract) so that the process of analyzing data using simulations to make better future predictions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data using simulations to make better future predictions field of endeavor, and in the 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, given the existing technical ability to combine the elements as evidenced by Reference Al Zahrani in view of Reference Crabtree, the results of the combination were predictable (MPEP 2143 A).
As per claims 5, 12 and 19: Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
wherein providing feedback to the user participating in the simulation scenario includes triggering an intervention event in the simulation scenario based upon, at least in part the input data from the user participating in the simulation scenario and the input data from the simulation scenario.
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on. This is reasonably understood to read on “analyzing the input data”.
However, Al Zahrani does not explicitly show “analyzing the input data”. Al Zahrani also does not explicitly show “providing feedback”. Al Zahrani does not explicitly show “triggering an intervention event”.
Reference Crabtree shows “analyzing the input data” the above limitation at least in [0041]: Within directed computational graph module 155, data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data may be then transferred to a general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis. Directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. [0047] Additionally, within the large amounts of data gathered and stored, a substantial amount of the stored data may require frequent updating, for instance, stock symbols and corresponding prices, which may prove to be time-consuming. Business operating system 100 may be configured to autonomously and continuously gather data in a background process, for example, using subroutines of connector module 135, such as email reader 238 or market plugins 236; using subroutines of automated planning service module 130, such as financial markets function library 251; using web crawler module 115 to scour news financial news sites; or using time series data store 120 to receive updated stock pricing at regular intervals. The data may then be processed and used by business operating system 100 to improve and update stored data. These operations may include, but not limited to, semantic extraction from corporate news and macro data; cross-linking to GraphStack entries; and automated time series feature engineering through the use of libraries like TSFresh, or using dimensionality reduction. Additionally, the high-bandwidth capabilities of business operating system 100 enables low-latency links to market data providers and venues to provide a nearly real-time channel to market data for the user using a ticker plant module 233 shown in FIG. 2C. The data that may be provided by market data providers and venues may include, but is not limited to, stock symbols and pricing, order book information, fill reports, news, and fundamentals. Business operating system 100 may also be configured to perform error-checking and self-heal the data as it is received.
Reference Crabtree shows “providing feedback” at least in [0077] Model definition language service 840 may be configured to use functions of advanced decentralized financial decision platform 100, such as DCG module 155 with associated transformer modules, graph stack service 145, and automated planning service 130, to allow user management of models, and defining of vectors using a declarative specification language (DSL). The use of a DSL for vectorizing the compute environment and data flow descriptions may enable linking of search processes to the rules engine 830, parametric evaluator 810, and feedback loop processes during ongoing operational-use based on the ability to encode appropriateness when combined with rules engine 830, serving as a basis for deep and reinforcement learning to support ongoing improvement to functions of optimizer 820. Model definition language service 840 may also enable a user or an autonomous trading system to initiate evaluation of specific pipelines, activities, overall system health, and the like of a specific instance of system 800.
Reference Crabtree shows “triggering an intervention event” in [0058]: Some pre-programmed rules that may be applied are whether the sending individual, Dr. Kean in our example, may legally access and send the payload. A failure to pass this test or other tests, individually or in combination (where the ‘AND’ conjunctive is implicitly in effect by default but ‘OR’ disjunctive may also be used) may stop the transaction. Here, stopping the transaction reads on the claim limitation above.
Reference Al Zahrani and Reference Crabtree are analogous prior art to the claimed invention because the references generally relate to field of analyzing data using simulations to make better future predictions. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Crabtree, particularly the ability to employ feature engineering model (Crabtree: [0041]), in the disclosure of Reference Al Zahrani, particularly in the ability to transform data collected for analysis (Al Zahrani: [0027]), in order to provide for an advanced decentralized financial decision platform 100 according to an embodiment of the invention. Client access 105 to system or platform 100 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, as taught by Reference Crabtree (see at least in [0027]), where upon the execution of the method and system of Reference Crabtree for creating a first dataset comprising at least a user-defined set of computing instructions comprising at least instructions regarding data flow locality, a parametric evaluator configured to retrieve the first dataset, and process the first dataset by performing at least a plurality of transformations and predictive analysis on the first dataset and specifying at least an intended focus on financial trading, and an optimizer configured to retrieve the processed first dataset from the parametric evaluator and determine an optimal locality for executing a trade (Abstract) so that the process of analyzing data using simulations to make better future predictions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data using simulations to make better future predictions field of endeavor, and in the 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, given the existing technical ability to combine the elements as evidenced by Reference Al Zahrani in view of Reference Crabtree, the results of the combination were predictable (MPEP 2143 A).
As per claims 6, 13 and 20: Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
wherein triggering the intervention event includes at least one of:
adjusting a difficulty level of the simulation scenario; and
triggering an agent behavior in the simulation scenario.
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Reference Al Zahrani shows “an agent behavior in the simulation scenario” at least in [0032] Analyzer 208 includes an AI engine 214 implementing for example AI model 216 using the decision tree (DT) algorithm 114, natural language processing (NLP) algorithm 116, and advanced pattern recognition (APR) algorithm 118 as described above in connection with FIG. 1. Input data 212 can be processed as inputs through AI model 216 to monitor the health and stress level of the worker and so on as described above. The AI model 216 can provide the optimum manpower requirements to execute critical activities, considering the human error factor and response rate such as emergency shutdown, system isolation, respond to emergency and Test & Inspection activities. The AI model 216 can provide a recommendation to improve the work environment like more training, operator simulation training (OTS), and more frequent drills. In certain embodiments, model 216 can perform perspective analytics, whereby similarities are drawn between current trends and past incidents to give insights about the future; which worker is more susceptible to what type of risk and how were similar cases were addressed. It will be appreciated that perspective analytics is a type of data analytics that identifies the root cause of an issue or recommendations to fix it based on past historic data of similar issues/incidents.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on. This is reasonably understood to read on “analyzing the input data”.
However, Al Zahrani does not explicitly show “analyzing the input data”. Al Zahrani also does not explicitly show “providing feedback”. Al Zahrani does not explicitly show “triggering an intervention event”.
Reference Crabtree shows “analyzing the input data” the above limitation at least in [0041]: Within directed computational graph module 155, data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data may be then transferred to a general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis. Directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. [0047] Additionally, within the large amounts of data gathered and stored, a substantial amount of the stored data may require frequent updating, for instance, stock symbols and corresponding prices, which may prove to be time-consuming. Business operating system 100 may be configured to autonomously and continuously gather data in a background process, for example, using subroutines of connector module 135, such as email reader 238 or market plugins 236; using subroutines of automated planning service module 130, such as financial markets function library 251; using web crawler module 115 to scour news financial news sites; or using time series data store 120 to receive updated stock pricing at regular intervals. The data may then be processed and used by business operating system 100 to improve and update stored data. These operations may include, but not limited to, semantic extraction from corporate news and macro data; cross-linking to GraphStack entries; and automated time series feature engineering through the use of libraries like TSFresh, or using dimensionality reduction. Additionally, the high-bandwidth capabilities of business operating system 100 enables low-latency links to market data providers and venues to provide a nearly real-time channel to market data for the user using a ticker plant module 233 shown in FIG. 2C. The data that may be provided by market data providers and venues may include, but is not limited to, stock symbols and pricing, order book information, fill reports, news, and fundamentals. Business operating system 100 may also be configured to perform error-checking and self-heal the data as it is received.
Reference Crabtree shows “providing feedback” at least in [0077] Model definition language service 840 may be configured to use functions of advanced decentralized financial decision platform 100, such as DCG module 155 with associated transformer modules, graph stack service 145, and automated planning service 130, to allow user management of models, and defining of vectors using a declarative specification language (DSL). The use of a DSL for vectorizing the compute environment and data flow descriptions may enable linking of search processes to the rules engine 830, parametric evaluator 810, and feedback loop processes during ongoing operational-use based on the ability to encode appropriateness when combined with rules engine 830, serving as a basis for deep and reinforcement learning to support ongoing improvement to functions of optimizer 820. Model definition language service 840 may also enable a user or an autonomous trading system to initiate evaluation of specific pipelines, activities, overall system health, and the like of a specific instance of system 800.
Reference Crabtree shows “triggering an intervention event” in [0058]: Some pre-programmed rules that may be applied are whether the sending individual, Dr. Kean in our example, may legally access and send the payload. A failure to pass this test or other tests, individually or in combination (where the ‘AND’ conjunctive is implicitly in effect by default but ‘OR’ disjunctive may also be used) may stop the transaction. Here, stopping the transaction reads on the claim limitation above.
Reference Al Zahrani and Reference Crabtree are analogous prior art to the claimed invention because the references generally relate to field of analyzing data using simulations to make better future predictions. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Crabtree, particularly the ability to employ feature engineering model (Crabtree: [0041]), in the disclosure of Reference Al Zahrani, particularly in the ability to transform data collected for analysis (Al Zahrani: [0027]), in order to provide for an advanced decentralized financial decision platform 100 according to an embodiment of the invention. Client access 105 to system or platform 100 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, as taught by Reference Crabtree (see at least in [0027]), where upon the execution of the method and system of Reference Crabtree for creating a first dataset comprising at least a user-defined set of computing instructions comprising at least instructions regarding data flow locality, a parametric evaluator configured to retrieve the first dataset, and process the first dataset by performing at least a plurality of transformations and predictive analysis on the first dataset and specifying at least an intended focus on financial trading, and an optimizer configured to retrieve the processed first dataset from the parametric evaluator and determine an optimal locality for executing a trade (Abstract) so that the process of analyzing data using simulations to make better future predictions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data using simulations to make better future predictions field of endeavor, and in the 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, given the existing technical ability to combine the elements as evidenced by Reference Al Zahrani in view of Reference Crabtree, the results of the combination were predictable (MPEP 2143 A).
As per claims 7 and 14: Regarding claim limitations below, Reference Al Zahrani in view of Crabtree shows:
wherein triggering the intervention event includes providing at least one of a visual cue, an audio cue, a virtual instructor cue, and a virtual instructor intervention.
Al Zahrani shows “simulation scenario”: [0030] The system 200 includes an analyzer 208 that is operable to provide an assessment report 210 based on received data inputs 212. The inputs 212 may correspond to the discrete data 102 and live (real-time or wearable) data 104 described above, and the assessment report 210 may correspond to the output 105 relating to the one or more of classification of worker stress level, overall fitness of the worker and/or fitness for particular tasks, and advisories on recommended actions, warnings, or remediations also described above. The advisories can be qualifications or disqualifications from tasks or work shifts, and in certain embodiments may include indications of stress level and risk level. In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on.
Regarding the claim limitations above, Al Zahrani shows use of artificial intelligence in [0005] According to an embodiment consistent with the present disclosure, method for assessing worker job performance fitness includes receiving input about a worker, the input including discrete data relating to one or more of worker experience, worker physical fitness condition, and worker workload, and live data relating to a worker real-time physiological condition. The method further includes applying an AI model using natural language and decision tree processing to the received input, and reporting a worker fitness assessment based on the AI model, wherein the worker fitness assessment relates to one or more of classification of worker stress level, overall fitness of the worker, and fitness for a particular task. [0012] Embodiments in accordance with the present disclosure generally relate to the use of an artificial intelligence (AI) model that can, in real time, provide a worker job performance indicator. The worker job performance indicator can relate to worker fitness or healthiness, including for example worker risk/stress level and preparedness for performance of one or more of various tasks in a plant such as an oil and gas facility. The model can be fed live data as well as discrete data. The live data can be from a wearable component worn by each plant worker involved in demanding jobs (example plant Turnaround and Inspection). The live data can include input including but not limited to, sleep hours, heart rate, exercise hours, etc. The discrete data can include results from medical health checks, medical history, workload, years of experience, etc. The output of the model can include a stress level indicator and a risk level that can be used for example to qualify or disqualify the worker from conducting certain high risk and extremely demanding tasks. [0025] In certain embodiments, analysis module 112 can build an AI (artificial intelligence) model implementing an AI classification algorithm such as a decision tree algorithm or other AI techniques for facilitating the decision process about the worker's stress level, fitness and remediation recommendations as functions of system inputs 102 (discrete data) and 104 (live data).
Also, Al Zahrani shows transforming data in [0027]: On the other hand, a natural language generation (NLG) operation may simulate the human ability to create natural language text to identify labels for one or more plant hazards associated with text from an unstructured data source. NLP operations 116 may transform internal and external document formats (e.g. HTML, Word, PowerPoint, Excel, PDF text, PDF image) into a standardized searchable format for use by for example a plant health manager. A plant health manager may have the ability to identify, tag and search specific document sections to identify meaningful portions within text, and may include various semantic tools that identify health concepts within the text such as chemical elements, biological elements, and physical injuries and their respective causes. [0030]: In certain embodiments, they can include work environment improvement suggestions, such as more training, operator simulation training (OTS), which is an operations simulator that includes various what-if scenarios and measures how the worker addresses each and is based on process simulation models, or conducting more or frequent drills. In certain embodiments, worker health related alerts can be generated, such as may be necessary in the presence of increased pulse rate, potential heart attack symptoms, onset of dehydration, and so on. This is reasonably understood to read on “analyzing the input data”.
However, Al Zahrani does not explicitly show “analyzing the input data”. Al Zahrani also does not explicitly show “providing feedback”. Al Zahrani does not explicitly show “triggering an intervention event”. Also, Al Zahrani does not explicitly show “a visual cue, an audio cue”.
Reference Crabtree shows “analyzing the input data” the above limitation at least in [0041]: Within directed computational graph module 155, data may be split into two identical streams in a specialized pre-programmed data pipeline 155a, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data may be then transferred to a general transformer service module 160 for linear data transformation as part of analysis or the decomposable transformer service module 150 for branching or iterative transformations that are part of analysis. Directed computational graph module 155 represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. [0047] Additionally, within the large amounts of data gathered and stored, a substantial amount of the stored data may require frequent updating, for instance, stock symbols and corresponding prices, which may prove to be time-consuming. Business operating system 100 may be configured to autonomously and continuously gather data in a background process, for example, using subroutines of connector module 135, such as email reader 238 or market plugins 236; using subroutines of automated planning service module 130, such as financial markets function library 251; using web crawler module 115 to scour news financial news sites; or using time series data store 120 to receive updated stock pricing at regular intervals. The data may then be processed and used by business operating system 100 to improve and update stored data. These operations may include, but not limited to, semantic extraction from corporate news and macro data; cross-linking to GraphStack entries; and automated time series feature engineering through the use of libraries like TSFresh, or using dimensionality reduction. Additionally, the high-bandwidth capabilities of business operating system 100 enables low-latency links to market data providers and venues to provide a nearly real-time channel to market data for the user using a ticker plant module 233 shown in FIG. 2C. The data that may be provided by market data providers and venues may include, but is not limited to, stock symbols and pricing, order book information, fill reports, news, and fundamentals. Business operating system 100 may also be configured to perform error-checking and self-heal the data as it is received.
Reference Crabtree shows “providing feedback” at least in [0077] Model definition language service 840 may be configured to use functions of advanced decentralized financial decision platform 100, such as DCG module 155 with associated transformer modules, graph stack service 145, and automated planning service 130, to allow user management of models, and defining of vectors using a declarative specification language (DSL). The use of a DSL for vectorizing the compute environment and data flow descriptions may enable linking of search processes to the rules engine 830, parametric evaluator 810, and feedback loop processes during ongoing operational-use based on the ability to encode appropriateness when combined with rules engine 830, serving as a basis for deep and reinforcement learning to support ongoing improvement to functions of optimizer 820. Model definition language service 840 may also enable a user or an autonomous trading system to initiate evaluation of specific pipelines, activities, overall system health, and the like of a specific instance of system 800.
Reference Crabtree shows “triggering an intervention event” in [0058]: Some pre-programmed rules that may be applied are whether the sending individual, Dr. Kean in our example, may legally access and send the payload. A failure to pass this test or other tests, individually or in combination (where the ‘AND’ conjunctive is implicitly in effect by default but ‘OR’ disjunctive may also be used) may stop the transaction. Here, stopping the transaction reads on the claim limitation above.
Reference Crabtree shows “a visual cue, an audio cue” [0089]: Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radio frequency (RF), BLUETOOTH™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) or external SATA (ESATA) interfaces, high-definition multimedia interface (HDMI), digital visual interface (DVI), analog or digital audio interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces 15 may include physical ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor (such as a dedicated audio or video processor, as is common in the art for high-fidelity AN hardware interfaces) and, in some instances, volatile and/or non-volatile memory (e.g., RAM). Services 23 may for example be WINDOWS™ services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 21. Input devices 28 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof. Output devices 27 may be of any type suitable for providing output to one or more users, whether remote or local to system 20, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof. Memory 25 may be random-access memory having any structure and architecture known in the art, for use by processors 21, for example to run software. Storage devices 26 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form (such as those described above, referring to FIG. 12). Examples of storage devices 26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.
Reference Al Zahrani and Reference Crabtree are analogous prior art to the claimed invention because the references generally relate to field of analyzing data using simulations to make better future predictions. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Crabtree, particularly the ability to employ feature engineering model (Crabtree: [0041]), in the disclosure of Reference Al Zahrani, particularly in the ability to transform data collected for analysis (Al Zahrani: [0027]), in order to provide for an advanced decentralized financial decision platform 100 according to an embodiment of the invention. Client access 105 to system or platform 100 for specific data entry, system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, as taught by Reference Crabtree (see at least in [0027]), where upon the execution of the method and system of Reference Crabtree for creating a first dataset comprising at least a user-defined set of computing instructions comprising at least instructions regarding data flow locality, a parametric evaluator configured to retrieve the first dataset, and process the first dataset by performing at least a plurality of transformations and predictive analysis on the first dataset and specifying at least an intended focus on financial trading, and an optimizer configured to retrieve the processed first dataset from the parametric evaluator and determine an optimal locality for executing a trade (Abstract) so that the process of analyzing data using simulations to make better future predictions can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar analyzing data using simulations to make better future predictions field of endeavor, and in the 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, given the existing technical ability to combine the elements as evidenced by Reference Al Zahrani in view of Reference Crabtree, the results of the combination were predictable (MPEP 2143 A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
A. Tolk et al., "Defense and security applications of modeling and simulation — Grand challenges and current efforts," Proceedings of the 2012 Winter Simulation Conference (WSC), Berlin, Germany, 2012, pp. 1-15, doi: 10.1109/WSC.2012.6465262.
This reference discloses the positions of seven international experts regarding current and future grand challenges for modeling and simulation (M&S) supporting the defense and security domain. Topics addressed include new interoperability issues, real-time analysis challenges, evolving military and training exercises, the future role and importance of Operations Research and M&S, modeling human teams and cultural behavior challenges, how to support successful co-evolving of research and academic programs, and the implications of enterprise postures and operational concepts of future M&S. In summary, all contributions focus on a particular facet that in summary help to understand the conceptual, technical, and organizational challenges we are currently facing.
Foreign Reference:
(CN 115619540 A) Jiang et al. Information Processing Method, Device, Device And Storage Medium. This reference discloses an information processing method, device, device and storage medium. The information processing method comprises: according to the first resource amount of the first mechanism to be allocated resource in the first time interval, the data prediction model obtained by training the historical actual payment resource corresponding to the first mechanism and the second mechanism, calculating the first prediction resource amount and the first confidence interval of the first mechanism in the first time interval, when the first resource amount is not in the first confidence interval, prompting the user to perform risk checking before paying the resource to be paid. Thus, before the resource of each clearing day is allocated, comparing the resource amount of the resource to be paid on the same day with the calculated confidence interval, if the resource amount of the resource to be allocated on the same day is not in the confidence interval, then determining that the resource amount of the resource to be allocated is abnormal, so, it can carry out resource monitoring process related to resource transfer, reduce manual intervention, improve the accuracy of resource amount to be paid resource.
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/N.N.P/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624