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 .
Priority
Applicant claims the benefit of prior-filed U.S. Provisional Patent Application Serial No. 63/477,443, filed December 28, 2022, which is acknowledged.
Drawings
The drawings were received on 09/11/2023. These drawings are acceptable.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on the following date(s): 12/17/2024 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are noted below, where the generic place holder is in bold and the functional language is italicize:
Claim 12:
and a client device configured to interface with the one or more edge computing resource nodes via an application programming interface (API) by: forwarding, via the API, a request for a machine learning inference to one or more hardware devices of the one or more edge computing resource nodes;
Claim 15:
comprising a container orchestrator configured to manage and automate workloads of the one or more edge computing resource nodes.
Claim 16:
a network of edge computing resources configured to: receive, via an application programming interface (API), a request for a machine learning inference from the client device; ...
Claim 17:
wherein the client device comprises a well control system configured to control a wellsite system based on the machine learning inference.
Claim 19:
comprising a server configured to execute a model service, wherein the edge computing resource nodes are configured to generate the machine learning inference based on pre-trained models, model evaluation tools, model templates, data pre-processing methods, additional APIs, or some combination thereof provided by the model service.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 12-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations, in claims 12, 15-17 and 19, as noted in claim interpretation section above, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Specifically, the limitations recite generic place holders where the specification does not appear to clear recite the corresponding hardware for performing the noted claimed functions. Recitation noted that claimed generic place holder may include hardware device was not determined to indicate sufficient disclosure given that the specification also discloses any processing element will be sufficient. There is also no clear indication of the structure linked to the disclosed function and recitations as to how the recited functions are achieved (e.g. an algorithm).Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The claims that depend on claims 12 and 16, fail to resolve the noted deficiency, and thus appropriately rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 12-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Claims 12, 15-17 and 19 contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, in determining whether the specification describes how the claimed function is achieved it is not enough that one skilled in the art could theoretically write a program to achieve the claimed function, rather the specification itself must explain how the claimed function is achieved. See MPEP § 2161.01, subsection I. The specification, in this cases appears to recite claim language does not appear to clearly link the noted generic place holder to a corresponding structure (e.g. hardware component and algorithm) for performing the claimed function.
Thus, the specification fails to meet the written description requirement under 35 USC 112-f claim interpretation.
The claims that depend on claims 12 and 16, fail to resolve the noted deficiency, and thus appropriately rejected.
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 1-11 and 16-20 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.
Claim 1: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
(Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device;. forwarding, via the API, the request to one or more edge computing resources, … and sending, via the API, the machine learning inference to the client device. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
… and wherein the API is configured to interface with one or more hardware devices of the one or more edge computing resource nodes based on the request;… generating, via the one or more edge computing resource nodes, … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… wherein the one or more edge computing resources comprise one or more edge computing resource nodes, … generating, via the one or more edge computing resource nodes, … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
Secondly, the noted additional limitation elements directed to insignificant solution activity, as noted above, the courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 2: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the one or more edge computing resource nodes comprise a plurality of edge computing resource nodes, each edge computing resource node configured to provide a different type of machine learning inference than other edge computing resource nodes of the plurality of edge computing resource nodes. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment and directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 3: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 2.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 4: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 3.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the API comprises a mapping of libraries and tools of the hardware devices to an abstracted set of calls that are exposed to the client device by the API. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 5: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 1
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 6: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 5.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising grouping the one or more edge computing resource nodes into one or more edge clusters using the container orchestrator. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 7: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein generating, via the one or more edge computing resource nodes, the machine learning inference comprises accessing data stored in one or more databases of the edge-based inference service system. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
accessing data stored in one or more databases of the edge-based inference service system. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. Storing and retrieving information in memory,)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
Frist, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
Secondly, the noted additional limitation elements directed to insignificant solution activity, as noted above, the courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
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;
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 8: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 7.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the one or more databases store documentation, licensing information, security information, or some combination thereof. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 9: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea recited in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein receiving, via the API, the request for the machine learning inference from the client device comprises receiving the request from a model service of the edge-based inference service system,… (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
… wherein the model service comprises pre-trained models, model evaluation tools, model templates, data pre-processing methods, additional APIs, or some combination thereof. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
Secondly, the limitations directed to insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity for as noted above.
The courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 10: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 1.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the client device comprises a well control system. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 11: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 10.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising automatically controlling equipment of the well control system based at least in part on the machine learning inference. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 16: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
(Considered directed to a Mental Process: Making evaluations and judgements of observations for formulating observations, evaluations and judgements as claimed; see MPEP § 2106.04(a)(2), subsection III)
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
receive, via an application programming interface (API), a request for a machine learning inference from the client device;, … and send, via the API, the machine learning inference to the client device.. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to insignificant solution activity, e.g. receiving or transmitting data over a network)
a client device; and a network of edge computing resources configured to: … assign, via the API, a workload … generate, via the one or more edge computing resource nodes, … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
… hardware devices associated with the one or more edge computing resource node … (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
First, the additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception; and that generally link the use of a judicial exception to a particular technological environment and/or directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
Secondly, the noted additional limitation elements directed to insignificant solution activity, as noted above, the courts have deemed these types of activity as well-known routine and convectional, see evidences noted below:
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 17: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 16.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the client device comprises a well control system configured to control a wellsite system (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
wherein the client device comprises a well control system configured to control a wellsite system based on the machine learning inference. (Deemed insufficient to transform the judicial exception to a patentable invention because the generically recites an effect of the judicial exception or claims every mode of accomplishing an effect (e.g, control a wellsite system based on the machine learning inference), amounts to a claim that is merely adding the words "apply it" to the judicial exception. See Internet Patents Corporation v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (The recitation of maintaining the state of data in an online form without restriction on how the state is maintained and with no description of the mechanism for maintaining the state describes "the effect or result dissociated from any method by which maintaining the state is accomplished" and does not provide a meaningful limitation because it merely states that the abstract idea should be applied to achieve a desired result (e.g, control a wellsite system based on the machine learning inference)). See also O’Reilly v. Morse, 56 U.S. 62 (1854), see MPEP 2106.05(f) )
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and recites limitation considered mere instructions to implement an abstract idea on a computer.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 18: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 16.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein each hardware device of the one or more respective hardware devices is specialized for a respective type of workload, and the workload is assigned to the one or more edge computing resource nodes based on the respective type of workload of the respective hardware devices of the one or more edge computing resource nodes. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment and directed to invoking computers or other machinery merely as a tool to perform the claimed process/judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 19: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea recited in claim 16.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
comprising a server configured to execute a model service, wherein the edge computing resource nodes are configured to generate the machine learning inference based on pre-trained models, model evaluation tools, model templates, data pre-processing methods, additional APIs, or some combination thereof provided by the model service. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to generally linking the use of a judicial exception to a particular technological environment or field of use. See 2106.05(h).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
Claim 20: Does claim fall within a statutory category? Yes.
Step 2A Prong 1: Evaluate whether the claim recites a judicial exception.
Abstract idea in claim 16.
Step 2A Prong 2: Evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception
The preamble is deemed insufficient to transform the judicial exception to a patentable invention because the preamble generally links the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h).
wherein the API comprises a mapping of libraries and tools of the one or more respective hardware devices to an abstracted set of calls that are exposed to the client device by the API. (Deemed insufficient to transform the judicial exception to a patentable invention because the recitation merely include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea; Thus claim limitations amount to mere instructions to apply the judicial exception using a computer/computing environment as a tool, as discussed in MPEP § 2106.05(f).)
The additional elements do not appear to be sufficient to transform the judicial exception into a practical application at Step 2A as analyzed above.
Step 2B: Evaluates whether the claim as a whole/in combination integrates the recited judicial exception into a practical application of the exception
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception and fail to integrate the abstract into practical application.
The additional limitations are directed to elements that generally link the use of a judicial exception to a particular technological environment or field of use and merely invoke the use of computer technology as a tool for applying the judicial exception.
These types of claimed elements cannot transform the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible.
As shown above, claims 1-11 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more” than the recited judicial exception. The claims are therefore directed to an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1- 9, 12-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Leong et al. (US 20210174952, hereinafter ‘Le’) in view of Koehler et al. (US 12219032, hereinafter ‘Koe’).
Regarding independent claim 1, Le teaches a method comprising:
receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device; forwarding, via the API, the request to one or more edge computing resources, (in[0069] FIG. 6 schematically shows a safety and risk management system 600, in accordance with some embodiments of the invention. As mentioned above, the safety and risk management system 600 may employ an edge intelligence paradigm that data processing and inference is performed at the edge or edge computing server 604 (e.g., on-ship server) while the predictive models may be built, developed and trained on a cloud/data center 610, and run on the edge computing server 604, user device, such as smart form tablet 602 or dashboard tablet 603 (e.g., hardware accelerator), personnel device (e.g., mobile device 601-2) and/or wearable devices 601-1 for inference. And in [0058] The network architecture may comprise interconnect infrastructure or fabric such as purpose-built hardware, herein referred to as “gateways,” which are compatible with a wireless protocol. The local (e.g., on-ship) network may have static configuration or dynamic configuration as described above, and the real-time data may be transmitted [receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device; forwarding, via the API, the request to one or more edge computing resources,] to an edge computing system 115 for analysis [a request for a machine learning inference from a client device; as an analysis request received an edge computing system]… )
wherein the one or more edge computing resources comprise one or more edge computing resource nodes, and wherein the API is configured to interface with one or more hardware devices of the one or more edge computing resource nodes based on the request; (in [0096] The edge computing system 710 onboard the vessel and/or the remote entity 720 may employ any suitable technologies such as container and/or micro-service. For example, the application of the edge computing system (e.g., smart form engine, inference engine, etc.) can be a containerized application [wherein the API is configured to interface with one or more hardware devices of the one or more edge computing resource nodes based on the request],… [0097] In some embodiments, the edge computing system 710 may comprise a plurality of components [wherein the one or more edge computing resources comprise one or more edge computing resource nodes] including a data processing module 711, a form engine 713, an inference engine 715, and a communication module 717. ; And in [0058] The network architecture may comprise interconnect infrastructure or fabric such as purpose-built hardware, herein referred to as “gateways,” which are compatible with a wireless protocol. The local (e.g., on-ship) network may have static configuration or dynamic configuration as described above, and the real-time data may be transmitted to an edge computing system 115 for analysis [wherein the API is configured to interface with one or more hardware devices of the one or more edge computing resource nodes based on the request]. The edge computing system 115 may be onboard the vessel. The edge computing device 115 may be in communication with a remote cloud/data center 117 through the gateways for downloading trained predictive models, and transmitting data such as fleet data (e.g., data from the on-shore operating system, data collected onboard the vessel) and various others)
generating, via the one or more edge computing resource nodes, the machine learning inference; (in [0006] In an aspect, a method for managing safety and risk in a hazardous or remote workplace is provided. The method may comprise: collecting, via a local network, data stream from one or more sensors and a user device; transmitting the data stream to an edge computing device via the local network, wherein the data stream is stored in a local database; processing, at the edge computing device, the data stream to identify a hazardous condition and a health condition [generating, via the one or more edge computing resource nodes, the machine learning inference] of a user associated with the user device…; And in [0073] The edge computing server 604 may analyze the data stream with aid of one or more predictive models [generating, via the one or more edge computing resource nodes, the machine learning inference], the output result may be an alert indicating a detected incident such as fall or trip, a prediction of a impeding adverse event such as a hazardous condition in a work zone, and various other functionalities as described elsewhere herein. The edge computing server 604 may be coupled to a local database 605.)
and sending, via the API, the machine learning inference to the client device. (in [0073] The edge computing server 604 may analyze the data stream with aid of one or more predictive models, the output result may be an alert indicating a detected incident [and sending, via the API, the machine learning inference to the client device] such as fall or trip, a prediction of a impeding adverse event such as a hazardous condition in a work zone, and various other functionalities as described elsewhere herein. The edge computing server 604 may be coupled to a local database 605.)
Le teaches the edge computing system for managing and analyzing data as noted above.
Examiner notes that data source can include a request where the analysis is considered a data processing element based on the request as claimed receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device…
Additionally, Koe teaches a data processing element based on the request as claimed receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device… and wherein the API is configured to interface with one or more hardware devices of the one or more edge computing resource nodes based on the request (in 2:17-48: Examples described herein include a centralized Internet of Things (IoT) manager of an IoT system configured to manage deployment of data pipelines to edge systems for computing within an IoT system via a control plane. Data pipelines are applications that are constructed using a group of containers that each perform various functions within the data pipeline (e.g., subscriber, data processor, publisher, connectors that transform data for consumption by another container within the application, etc.) to transform or process input data to provide output data… In response to receipt of a data pipeline, an edge system may be configured to determine whether to deploy the data pipeline for use [and wherein the API is configured to interface with one or more hardware devices of the one or more edge computing resource nodes based on the request] based on whether relevant data sources and/or other relevant input data for the data pipeline are available to the edge system. In response to receipt of a request [receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device … and wherein the API is configured to interface with one or more hardware devices of the one or more edge computing resource nodes based on the request] for a data pipeline related to a category or some other sensor attribute (e.g., all cameras facing a particular direction or on a particular floor), the centralized IoT manager may identify data sensors within the category and edge systems associated with the identified data sources, and may construct or build a data pipeline application (e.g., serverless code) to be deployed to the identified edge systems.)
Koe and Le are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms in distributed computing environments.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art of developing a data analytics system implemented using a distributed edge system computing, as disclosed by Le with the method of developing information retrieval and processing techniques for distributed computing environments, as disclosed by Le.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Koe and Le as noted above; Doing so allows for processing information by deploying edge systems associated with specific data processing categories, (Le, 2:58-3:3).
Regarding claim 2, the rejection of claim 1 is incorporated and Le in combination with Koe teaches the method of claim 1, wherein the one or more edge computing resource nodes comprise a plurality of edge computing resource nodes, each edge computing resource node configured to provide a different type of machine learning inference than other edge computing resource nodes of the plurality of edge computing resource nodes. (in [0097] In some embodiments, the edge computing system 710 may comprise a plurality of components [wherein the one or more edge computing resource nodes comprise a plurality of edge computing resource nodes] including a data processing module 711, a form engine 713, an inference engine 715, and a communication module 717... [0101] The form engine 713 may be configured to provide a digital form compatible with the existing safety laws or maritime laws [each edge computing resource node configured to provide a different type of machine learning inference than other edge computing resource nodes of the plurality of edge computing resource nodes]. The form engine may automatically generate a smart form based on real-time location data and time data form for the crew to confirm and complete in accordance with laws (e.g., International Maritime Organization guidelines)… [0103] ... The inference engine 715 may perform various functions [each edge computing resource node configured to provide a different type of machine learning inference than other edge computing resource nodes of the plurality of edge computing resource nodes] such as generating preventive procedural alerts and warnings, generating a streamlined safety workflow or operation processes, detecting crew-based metrics (e.g., fatigue level, health condition, under-stress, physiological state, etc.), detecting an incident (e.g., trip, slip or fall detection), predicting an impeding adverse event (e.g., hazardous condition forecasting, identifying a hazardous situation or conditions in a work zone, etc.), and identifying an efficient workflow or tasks assignment for one or more operators and one or more groups… [0104] The communication module 717 may be configured to determine which of the local data or which portion of the local data stays in the local database 731, is to be moved/transmitted to the cloud database 733….)
Additionally, Koe teaches in 3:43-65: The IoT system 100 may include one or more types of edge systems selected from any combination of the edge cluster(s) [each edge computing resource node configured to provide a different type of machine learning inference than other edge computing resource nodes of the plurality of edge computing resource nodes] 110, the edge device(s) 112, and/or the edge VM(s) 115 hosted on the server/cluster 114. Each of the edge cluster(s) (e.g., or tenants) 110 may include a respective cluster of edge nodes or devices that are configured to host a respective edge stack 111… 15:16-22: The centralized IoT manager may maintain configuration information for each of the edge systems, data sources, associated users, including hardware configuration information, installed software version information, connected data source information (e.g., including type, category, identifier, etc.), associated data planes, current operational status, authentication credentials and/or keys, etc… 16:4-12: For example, in response to a request for a new data pipeline or application associated with a particular type or category of data sources and/or a project construct, the centralized IoT manager may identify data sources having the particular type or category (e.g., or attribute, and/or may identify respective edge systems [each edge computing resource node configured to provide a different type of machine learning inference than other edge computing resource nodes of the plurality of edge computing resource nodes] are connected to the identified data sources of the particular type or category and/or are associated with the particular project construct.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding claim 3, the rejection of claim 2 is incorporated and Le in combination with Koe teaches the method of claim 2, wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences. ((in [0097] In some embodiments, the edge computing system 710 may comprise a plurality of components [wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences] including a data processing module 711, a form engine 713, an inference engine 715, and a communication module 717... [0101] The form engine 713 may be configured to provide a digital form compatible with the existing safety laws or maritime laws [wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences]. The form engine may automatically generate a smart form based on real-time location data and time data form for the crew to confirm and complete in accordance with laws (e.g., International Maritime Organization guidelines)… [0103] ... The inference engine 715 may perform various functions [wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences] such as generating preventive procedural alerts and warnings, generating a streamlined safety workflow or operation processes, detecting crew-based metrics (e.g., fatigue level, health condition, under-stress, physiological state, etc.), detecting an incident (e.g., trip, slip or fall detection), predicting an impeding adverse event (e.g., hazardous condition forecasting, identifying a hazardous situation or conditions in a work zone, etc.), and identifying an efficient workflow or tasks assignment for one or more operators and one or more groups… [0104] The communication module 717 may be configured [wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences] to determine which of the local data or which portion of the local data stays in the local database 731, is to be moved/transmitted to the cloud database 733….)
Additionally, Koe teaches in 3:43-65: The IoT system 100 may include one or more types of edge systems selected from any combination of the edge cluster(s) 110, the edge device(s) 112, and/or the edge VM(s) 115 hosted on the server/cluster 114. Each of the edge cluster(s) (e.g., or tenants) 110 may include a respective cluster of edge nodes or devices that are configured to host a respective edge stack 111 [wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences]… 15:16-22: The centralized IoT manager may maintain configuration information for each of the edge systems [wherein each edge computing resource node comprises different sets of hardware devices configured to provide the respective different types of machine learning inferences], data sources, associated users, including hardware configuration information, installed software version information, connected data source information (e.g., including type, category, identifier, etc.), associated data planes, current operational status, authentication credentials and/or keys, etc… 16:4-12: For example, in response to a request for a new data pipeline or application associated with a particular type or category of data sources and/or a project construct, the centralized IoT manager may identify data sources having the particular type or category (e.g., or attribute, and/or may identify respective edge systems are connected to the identified data sources of the particular type or category and/or are associated with the particular project construct.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding claim 4, the rejection of claim 3 is incorporated and Le in combination with Koe teaches the method of claim 3, wherein the API comprises a mapping of libraries and tools of the hardware devices to an abstracted set of calls that are exposed to the client device by the API. (in [0118] User device 702 and personnel device 701-1, 701-2 may include a display. The display may be a screen. The display may or may not be a touchscreen. The display may be a light-emitting diode (LED) screen, OLED screen, liquid crystal display (LCD) screen, plasma screen, or any other type of screen. The display may be configured to show a user interface (UI) or a graphical user interface (GUI) rendered through an application (e.g., via an application programming interface (API) executed on the user device) [wherein the API comprises a mapping of libraries and tools of the hardware devices to an abstracted set of calls that are exposed to the client device by the API]… And in [0096] The edge computing system 710 onboard the vessel and/or the remote entity 720 may employ any suitable technologies such as container and/or micro-service. For example, the application of the edge computing system (e.g., smart form engine, inference engine, etc.) can be a containerized application [wherein the API comprises a mapping of libraries and tools of the hardware devices to an abstracted set of calls that are exposed to the client device by the API]. The edge computing system may deploy a micro-service based architecture in the software infrastructure at the edge such as implementing an application or service in a container…)
Additionally, Koe teaches in 17:59-18:6: The data pipeline 564 can be constructed using computing primitives and building blocks, such as VMs, containers, processes, or any combination thereof. In some examples, the stages of the data pipeline 564 may be described using a user interface or REST API, with data ingestion and movement handled by connector components built into the data pipeline 564. Thus, data may be passed between containers of a data pipeline using API calls [wherein the API comprises a mapping of libraries and tools of the hardware devices to an abstracted set of calls that are exposed to the client device by the API]. As depicted in FIG. 5, the data pipeline 564 may include a group of containers to form a pod that includes a subscriber 582, a first transformation container 583, a pipeline processor 584, a second transformation container 585, and a publisher 586 each configured to perform various functions within the data pipeline 564 to consume, transform, and produce messages or data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding claim 5, the rejection of claim 1 is incorporated and Le in combination with Koe teaches the method of claim 1, comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system. (in [0096] The edge computing system 710 onboard the vessel and/or the remote entity 720 may employ any suitable technologies such as container and/or micro-service. For example, the application of the edge computing system (e.g., smart form engine, inference engine, etc.) can be a containerized application [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system]. The edge computing system may deploy a micro-service based architecture in the software infrastructure at the edge such as implementing an application or service in a container [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system].)
Additionally, Koe teaches in 4:58-62: The one or more data pipelines or applications of the edge stacks may be implemented using a containerized architecture that is managed via a container orchestrator [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system]. The data pipelines and/or applications communicate using application programming interface (API) calls, in some examples; And in 6:47-59: … In some examples, an edge system may provide the transformed data from a data pipeline or an application of the one or more data pipelines or applications of the edge stacks to a respective destination data plane, such as the data plane 152 of the data computing system 150 as edge data. In some examples, the edge systems may be configured to share edge data with other edge systems. The one or more data pipelines or applications of the edge stacks may be implemented using a containerized architecture that is managed via a container orchestrator [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system]. The data pipelines and/or applications communicate using application programming interface (API) calls, in some examples… 11:30-26: Each of the user VMs 330(1)-(N) hosted on the respective computing node includes at least one application and everything the user VM needs to execute (e.g., run) the at least one application (e.g., system binaries, libraries, etc.). Each of the user VMs 330(1)-(N) may generally be configured to execute any type and/or number of applications, such as those requested, specified, or desired by a user…)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding claim 6, the rejection of claim 5 is incorporated and Le in combination with Koe teaches the method of claim 5, comprising grouping the one or more edge computing resource nodes into one or more edge clusters using the container orchestrator. (in [0096] The edge computing system 710 onboard the vessel and/or the remote entity 720 may employ any suitable technologies such as container and/or micro-service. For example, the application of the edge computing system (e.g., smart form engine, inference engine, etc.) can be a containerized application [comprising grouping the one or more edge computing resource nodes into one or more edge clusters using the container orchestrator]. The edge computing system may deploy a micro-service based architecture in the software infrastructure at the edge such as implementing an application or service in a container [comprising grouping the one or more edge computing resource nodes into one or more edge clusters using the container orchestrator].)
Additionally, Koe teaches in 4:58-62: The one or more data pipelines or applications of the edge stacks may be implemented using a containerized architecture that is managed via a container orchestrator [comprising grouping the one or more edge computing resource nodes into one or more edge clusters using the container orchestrator]. The data pipelines and/or applications communicate using application programming interface (API) calls, in some examples; And in 6:47-59: … In some examples, an edge system may provide the transformed data from a data pipeline or an application of the one or more data pipelines or applications of the edge stacks to a respective destination data plane, such as the data plane 152 of the data computing system 150 as edge data. In some examples, the edge systems may be configured to share edge data with other edge systems. The one or more data pipelines or applications of the edge stacks may be implemented using a containerized architecture that is managed via a container orchestrator [comprising grouping the one or more edge computing resource nodes into one or more edge clusters using the container orchestrator]. The data pipelines and/or applications communicate using application programming interface (API) calls, in some examples… 11:30-26: Each of the user VMs 330(1)-(N) hosted on the respective computing node includes at least one application and everything the user VM needs to execute (e.g., run) the at least one application (e.g., system binaries, libraries, etc.). Each of the user VMs 330(1)-(N) may generally be configured to execute any type and/or number of applications, such as those requested, specified, or desired by a user…)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding claim 7, the rejection of claim 1 is incorporated and Le in combination with Koe teaches the method of claim 1, wherein generating, via the one or more edge computing resource nodes, the machine learning inference comprises accessing data stored in one or more databases of the edge-based inference service system. (in [0110] In some embodiments, the edge computing system 710 may construct the local database 731 for fast and efficient data retrieval, query and delivery. For example, the edge computing system may provide customized algorithms to extract, transform, and load (ETL) the data. In some embodiments, the edge computing system may construct the databases using proprietary database architecture or data structures to provide an efficient database model that is adapted to large scale databases [wherein generating, via the one or more edge computing resource nodes, the machine learning inference comprises accessing data stored in one or more databases of the edge-based inference service system], is easily scalable, is efficient in query and data retrieval, or has reduced memory requirements in comparison to using other data structures.)
Regarding claim 8, the rejection of claim 7 is incorporated and Le in combination with Koe teaches the method of claim 7, wherein the one or more databases store documentation, licensing information, security information, or some combination thereof. (in [0109] The local database 731 can be the same as the local database as described in FIG. 6. For example, the local database 731 may comprise storage containing a variety of data consistent with disclosed embodiments. For example, the databases may store [wherein the one or more databases store documentation], for example, raw data collected from the user device, sensors and wearable device, each individual's historical data, data about a predictive model (e.g., parameters, hyper-parameters, model architecture, training dataset, performance metrics, threshold, rules, etc), data generated by a predictive model (e.g., intermediary results, output of a model, latent features, input and output of a component of the model system, etc.), incident report, record, smart form, workflow, safety law or regulatory related data, and user provided information (e.g., confirmation or denial of a prediction result, user information such as name, credential, confirmation of completing a task at different time points, etc), predictive models, algorithms, and the like… One of ordinary skill will recognize that the disclosed embodiments are not limited to the configuration and/or arrangement of the database(s).[0110] In some embodiments, the edge computing system 710 may construct the local database 731 for fast and efficient data retrieval, query and delivery. For example, the edge computing system may provide customized algorithms to extract, transform, and load (ETL) the data. In some embodiments, the edge computing system may construct the databases using proprietary database architecture or data structures to provide an efficient database model that is adapted to large scale databases, is easily scalable, is efficient in query and data retrieval, or has reduced memory requirements in comparison to using other data structures.)
Regarding claim 9, the rejection of claim 1 is incorporated and Le in combination with Koe teaches the method of claim 1, wherein receiving, via the API, the request for the machine learning inference from the client device comprises receiving the request from a model service of the edge-based inference service system, wherein the model service comprises pre-trained models, model evaluation tools, model templates, data pre-processing methods, additional APIs, or some combination thereof. (in [0124] The various functions performed supported by the edge or edge device such as data processing, making inference using a trained model [wherein receiving, via the API, the request for the machine learning inference from the client device comprises receiving the request from a model service of the edge-based inference service system, wherein the model service comprises pre-trained models …] and the like may be implemented in software, hardware, firmware, embedded hardware, standalone hardware, application specific-hardware, or any combination of these…)
Regarding independent claim 12, Le teaches an edge-based inference service system, comprising: one or more edge computing resource nodes; and a client device configured to interface with the one or more edge computing resource nodes via an application programming interface (API) by: forwarding, via the API, a request for a machine learning inference to one or more hardware devices of the one or more edge computing resource nodes; (in[0069] FIG. 6 schematically shows a safety and risk management system 600, in accordance with some embodiments of the invention. As mentioned above, the safety and risk management system 600 may employ an edge intelligence paradigm that data processing and inference is performed at the edge or edge computing server 604 (e.g., on-ship server) while the predictive models may be built, developed and trained on a cloud/data center 610, and run on the edge computing server 604 [an edge-based inference service system, comprising: one or more edge computing resource nodes; and a client device configured to interface with the one or more edge computing resource nodes via an application programming interface (API) by],… And in [0058] The network architecture may comprise interconnect infrastructure or fabric such as purpose-built hardware, herein referred to as “gateways,” which are compatible with a wireless protocol. The local (e.g., on-ship) network may have static configuration or dynamic configuration as described above, and the real-time data may be transmitted [forwarding, via the API, a request for a machine learning inference to one or more hardware devices of the one or more edge computing resource nodes;] to an edge computing system 115 for analysis [a request for a machine learning inference to one or more hardware devices of the one or more edge computing resource nodes; as an analysis request received an edge computing system]…; And in [0096] The edge computing system 710 onboard the vessel and/or the remote entity 720 may employ any suitable technologies such as container and/or micro-service. For example, the application of the edge computing system (e.g., smart form engine, inference engine, etc.) can be a containerized application,… [0097] In some embodiments, the edge computing system 710 may comprise a plurality of components [an edge-based inference service system, comprising: one or more edge computing resource nodes] including a data processing module 711, a form engine 713, an inference engine 715, and a communication module 717. ; And in [0058] The network architecture may comprise interconnect infrastructure or fabric such as purpose-built hardware, herein referred to as “gateways,” which are compatible with a wireless protocol. The local (e.g., on-ship) network may have static configuration or dynamic configuration as described above, and the real-time data may be transmitted to an edge computing system 115 for analysis [an edge-based inference service system, comprising: one or more edge computing resource nodes; and a client device configured to interface with the one or more edge computing resource nodes via an application programming interface (API) by]. The edge computing system 115 may be onboard the vessel. The edge computing device 115 may be in communication with a remote cloud/data center 117 through the gateways for downloading trained predictive models, and transmitting data such as fleet data (e.g., data from the on-shore operating system, data collected onboard the vessel) and various others)
and receiving, via the API, the machine learning inference generated by the one or more edge computing resource nodes. (in [0073] The edge computing server 604 may analyze the data stream with aid of one or more predictive models, the output result may be an alert indicating a detected incident [and receiving, via the API, the machine learning inference generated by the one or more edge computing resource nodes] such as fall or trip, a prediction of a impeding adverse event such as a hazardous condition in a work zone, and various other functionalities as described elsewhere herein. The edge computing server 604 may be coupled to a local database 605. And in 0055: … In some cases, the user device or the sensors may be capable of delivering an alert (e.g., vibration, audio alarm, etc.) in response to a detection of an incident [receiving, via the API, the machine learning inference generated by the one or more edge computing resource nodes] (e.g., trip, fall), an intervention for changing behavior (e.g., fatigue detection or heat exhaustion) or forecasting a hazardous situation (e.g., prediction of an impeding adverse event in a work zone or a physiological condition of the individual). For example, upon the prediction of an impending adverse event (e.g., entering a hazarders work zone, reaching a fatigue level, etc.), intervention such as rhythmic cue, audio, visual, or tactile stimulus may be delivered to the crewmember via the user device, wearable device or sensors.)
Le teaches the edge computing system for managing and analyzing data as noted above.
Examiner notes that data source can include a request where the analysis is considered a data processing element based on the request as claimed receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device;…
Additionally, Koe teaches a data processing element based on the request as claimed receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device; (in 2:17-48: Examples described herein include a centralized Internet of Things (IoT) manager of an IoT system configured to manage deployment of data pipelines to edge systems for computing within an IoT system via a control plane. Data pipelines are applications that are constructed using a group of containers that each perform various functions within the data pipeline (e.g., subscriber, data processor, publisher, connectors that transform data for consumption by another container within the application, etc.) to transform or process input data to provide output data… In response to receipt of a data pipeline, an edge system may be configured to determine whether to deploy the data pipeline for use based on whether relevant data sources and/or other relevant input data for the data pipeline are available to the edge system. In response to receipt of a request [receiving, via an application programming interface (API) of an edge-based inference service system, a request for a machine learning inference from a client device] for a data pipeline related to a category or some other sensor attribute (e.g., all cameras facing a particular direction or on a particular floor), the centralized IoT manager may identify data sensors within the category and edge systems associated with the identified data sources, and may construct or build a data pipeline application (e.g., serverless code) to be deployed to the identified edge systems.)
Koe and Le are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms in distributed computing environments.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art of developing a data analytics system implemented using a distributed edge system computing, as disclosed by Le with the method of developing information retrieval and processing techniques for distributed computing environments, as disclosed by Le.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Koe and Le as noted above; Doing so allows for processing information by deploying edge systems associated with specific data processing categories, (Le, 2:58-3:3).
Regarding claim 13 the rejection of claim 12 is incorporated and Le in combination with Koe teaches the edge-based inference service system of claim 12, wherein each edge computing resource node of the one or more edge computing resource nodes is configured to provide a different type of machine learning inference than other edge computing resource nodes of the one or more edge computing resource nodes. (in [0097] In some embodiments, the edge computing system 710 may comprise a plurality of components [wherein each edge computing resource node of the one or more edge computing resource nodes …] including a data processing module 711, a form engine 713, an inference engine 715, and a communication module 717... [0101] The form engine 713 may be configured to provide a digital form compatible with the existing safety laws or maritime laws […each edge computing resource node of the one or more edge computing resource nodes is configured to provide a different type of machine learning inference than other edge computing resource nodes of the one or more edge computing resource nodes]. The form engine may automatically generate a smart form based on real-time location data and time data form for the crew to confirm and complete in accordance with laws (e.g., International Maritime Organization guidelines)… [0103] ... The inference engine 715 may perform various functions […each edge computing resource node of the one or more edge computing resource nodes is configured to provide a different type of machine learning inference than other edge computing resource nodes of the one or more edge computing resource nodes] such as generating preventive procedural alerts and warnings, generating a streamlined safety workflow or operation processes, detecting crew-based metrics (e.g., fatigue level, health condition, under-stress, physiological state, etc.), detecting an incident (e.g., trip, slip or fall detection), predicting an impeding adverse event (e.g., hazardous condition forecasting, identifying a hazardous situation or conditions in a work zone, etc.), and identifying an efficient workflow or tasks assignment for one or more operators and one or more groups… [0104] The communication module 717 may be configured to determine which of the local data or which portion of the local data stays in the local database 731, is to be moved/transmitted to the cloud database 733….)
Additionally, Koe teaches in 3:43-65: The IoT system 100 may include one or more types of edge systems selected from any combination of the edge cluster(s) […each edge computing resource node of the one or more edge computing resource nodes is configured to provide a different type of machine learning inference than other edge computing resource nodes of the one or more edge computing resource nodes] 110, the edge device(s) 112, and/or the edge VM(s) 115 hosted on the server/cluster 114. Each of the edge cluster(s) (e.g., or tenants) 110 may include a respective cluster of edge nodes or devices that are configured to host a respective edge stack 111… 15:16-22: The centralized IoT manager may maintain configuration information for each of the edge systems, data sources, associated users, including hardware configuration information, installed software version information, connected data source information (e.g., including type, category, identifier, etc.), associated data planes, current operational status, authentication credentials and/or keys, etc… 16:4-12: For example, in response to a request for a new data pipeline or application associated with a particular type or category of data sources and/or a project construct, the centralized IoT manager may identify data sources having the particular type or category (e.g., or attribute, and/or may identify respective edge systems […each edge computing resource node of the one or more edge computing resource nodes is configured to provide a different type of machine learning inference than other edge computing resource nodes of the one or more edge computing resource nodes] are connected to the identified data sources of the particular type or category and/or are associated with the particular project construct.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding claim 14, the rejection of claim 13 is incorporated and the limitation is similar to those in claim 3 and thus rejected under the same rationale.
Regarding claim 15, the rejection of claim 12 is incorporated and the limitation is similar to those in claim 5 and thus rejected under the same rationale.
Regarding claim 5, the rejection of claim 1 is incorporated and Le in combination with Koe teaches the method of claim 1, comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system. (in [0096] The edge computing system 710 onboard the vessel and/or the remote entity 720 may employ any suitable technologies such as container and/or micro-service. For example, the application of the edge computing system (e.g., smart form engine, inference engine, etc.) can be a containerized application [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system]. The edge computing system may deploy a micro-service based architecture in the software infrastructure at the edge such as implementing an application or service in a container [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system].)
Additionally, Koe teaches in 4:58-62: The one or more data pipelines or applications of the edge stacks may be implemented using a containerized architecture that is managed via a container orchestrator [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system]. The data pipelines and/or applications communicate using application programming interface (API) calls, in some examples; And in 6:47-59: … In some examples, an edge system may provide the transformed data from a data pipeline or an application of the one or more data pipelines or applications of the edge stacks to a respective destination data plane, such as the data plane 152 of the data computing system 150 as edge data. In some examples, the edge systems may be configured to share edge data with other edge systems. The one or more data pipelines or applications of the edge stacks may be implemented using a containerized architecture that is managed via a container orchestrator [comprising managing and automating workloads between the one or more one or more edge computing resource nodes using a container orchestrator of the edge-based inference service system]. The data pipelines and/or applications communicate using application programming interface (API) calls, in some examples… 11:30-26: Each of the user VMs 330(1)-(N) hosted on the respective computing node includes at least one application and everything the user VM needs to execute (e.g., run) the at least one application (e.g., system binaries, libraries, etc.). Each of the user VMs 330(1)-(N) may generally be configured to execute any type and/or number of applications, such as those requested, specified, or desired by a user…)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding independent claim 16, Le teaches edge-based inference service system, comprising: a client device; and a network of edge computing resources configured to (in [0069] FIG. 6 schematically shows a safety and risk management system 600, in accordance with some embodiments of the invention. As mentioned above, the safety and risk management system 600 may employ an edge intelligence paradigm that data processing and inference is performed at the edge or edge computing server 604 (e.g., on-ship server) while the predictive models may be built, developed and trained on a cloud/data center 610, and run on the edge computing server 604, user device, such as smart form tablet 602 or dashboard tablet 603 (e.g., hardware accelerator), personnel device (e.g., mobile device 601-2) and/or wearable devices 601-1 for inference.)
assign, via the API, a workload to one or more edge computing resource nodes of the network based on the request and based on one or more respective hardware devices associated with the one or more edge computing resource nodes; (in [0096] The edge computing system 710 onboard the vessel and/or the remote entity 720 may employ any suitable technologies such as container and/or micro-service. For example, the application of the edge computing system (e.g., smart form engine, inference engine, etc.) can be a containerized application [assign, via the API, a workload to one or more edge computing resource nodes of the network based on the request …],… [0097] In some embodiments, the edge computing system 710 may comprise a plurality of components [a workload to one or more edge computing resource nodes of the network based on the request and based on one or more respective hardware devices associated with the one or more edge computing resource nodes for performing analysis task] including a data processing module 711, a form engine 713, an inference engine 715, and a communication module 717. ; And in [0058] The network architecture may comprise interconnect infrastructure or fabric such as purpose-built hardware, herein referred to as “gateways,” which are compatible with a wireless protocol. The local (e.g., on-ship) network may have static configuration or dynamic configuration as described above, and the real-time data may be transmitted to an edge computing system 115 for analysis [and based on one or more respective hardware devices associated with the one or more edge computing resource nodes as local computing system for processing analysis task]. The edge computing system 115 may be onboard the vessel. The edge computing device 115 may be in communication with a remote cloud/data center 117 through the gateways for downloading trained predictive models, and transmitting data such as fleet data (e.g., data from the on-shore operating system, data collected onboard the vessel) [a workload to one or more edge computing resource nodes of the network based on the request and based on one or more respective hardware devices associated with the one or more edge computing resource nodes for processing analysis task of transmitted workload data] and various others)
The remaining limitation are similar to those in claim 1 and thus rejected under the same rationale.
Regarding claim 18, the rejection of claim 16 is incorporated and Le in combination with Koe teaches the edge-based inference service system of claim 16, wherein each hardware device of the one or more respective hardware devices is specialized for a respective type of workload, and the workload is assigned to the one or more edge computing resource nodes based on the respective type of workload of the respective hardware devices of the one or more edge computing resource nodes. (in [0097] In some embodiments, the edge computing system 710 may comprise a plurality of components including a data processing module 711, a form engine 713, an inference engine 715, and a communication module 717... [0101] The form engine 713 may be configured to provide a digital form compatible with the existing safety laws or maritime laws [wherein each hardware device of the one or more respective hardware devices is specialized for a respective type of workload, and the workload is assigned to the one or more edge computing resource nodes based on the respective type of workload of the respective hardware devices of the one or more edge computing resource nodes]. The form engine may automatically generate a smart form based on real-time location data and time data form for the crew to confirm and complete in accordance with laws (e.g., International Maritime Organization guidelines)… [0103] ... The inference engine 715 may perform various functions [wherein each hardware device of the one or more respective hardware devices is specialized for a respective type of workload, and the workload is assigned to the one or more edge computing resource nodes based on the respective type of workload of the respective hardware devices of the one or more edge computing resource nodes] such as generating preventive procedural alerts and warnings, generating a streamlined safety workflow or operation processes, detecting crew-based metrics (e.g., fatigue level, health condition, under-stress, physiological state, etc.), detecting an incident (e.g., trip, slip or fall detection), predicting an impeding adverse event (e.g., hazardous condition forecasting, identifying a hazardous situation or conditions in a work zone, etc.), and identifying an efficient workflow or tasks assignment for one or more operators and one or more groups [based on the respective type of workload of the respective hardware devices of the one or more edge computing resource nodes]… [0104] The communication module 717 may be configured to determine which of the local data or which portion of the local data stays in the local database 731, is to be moved/transmitted to the cloud database 733….)
Additionally, Koe teaches in 3:43-65: The IoT system 100 may include one or more types of edge systems selected from any combination of the edge cluster(s) [wherein each hardware device of the one or more respective hardware devices is specialized for a respective type of workload, and the workload is assigned to the one or more edge computing resource nodes based on the respective type of workload of the respective hardware devices of the one or more edge computing resource nodes] 110, the edge device(s) 112, and/or the edge VM(s) 115 hosted on the server/cluster 114. Each of the edge cluster(s) (e.g., or tenants) 110 may include a respective cluster of edge nodes or devices that are configured to host a respective edge stack 111… 15:16-22: The centralized IoT manager may maintain configuration information for each of the edge systems, data sources, associated users, including hardware configuration information, installed software version information, connected data source information (e.g., including type, category, identifier, etc.), associated data planes, current operational status, authentication credentials and/or keys, etc… 16:4-12: For example, in response to a request for a new data pipeline or application associated with a particular type or category of data sources and/or a project construct, the centralized IoT manager may identify data sources having the particular type or category (e.g., or attribute, and/or may identify respective edge systems [wherein each hardware device of the one or more respective hardware devices is specialized for a respective type of workload, and the workload is assigned to the one or more edge computing resource nodes based on the respective type of workload of the respective hardware devices of the one or more edge computing resource nodes] are connected to the identified data sources of the particular type or category and/or are associated with the particular project construct.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Koe and Le for the same reasons disclosed above.
Regarding claim 19, the rejection of claim 16 is incorporated and Le in combination with Koe teaches the edge-based inference service system of claim 16, comprising a server configured to execute a model service, wherein the edge computing resource nodes are configured to generate the machine learning inference based on pre-trained models, model evaluation tools, model templates, data pre-processing methods, additional APIs, or some combination thereof provided by the model service. (in [0124] The various functions performed supported by the edge or edge device such as data processing, making inference using a trained model [comprising a server configured to execute a model service, wherein the edge computing resource nodes are configured to generate the machine learning inference based on pre-trained models …] and the like may be implemented in software, hardware, firmware, embedded hardware, standalone hardware, application specific-hardware, or any combination of these…)
Regarding claim 20, the rejection of claim 16 is incorporated and the limitation is similar to those in claim 4 and thus rejected under the same rationale.
Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Leong et al. (US 20210174952, hereinafter ‘Le’) in view of Koehler et al. (US 12219032, hereinafter ‘Koe’) in further view of Al-Mohsen et al. (US 20220389809, hereinafter ‘Al’).
Regarding claim 10 , the rejection of claim 1 is incorporated and Le in combination with Koe teaches the method of claim 1, wherein the client device comprises a (in [0004] Systems and methods of the present disclosure provide an integrated platform for controlling work flow [wherein the client device comprises a ], detecting, predicting and managing risks in the maritime industry… [0005] Example embodiments are described with reference to the tracking of personnel or operators on vessels. However, it is to be understood that the invention itself is more broadly applicable, and other example embodiments may be applied to the tracking of persons and objects generally, including, for example, on other kinds of vessels, in a workplace such as buildings, and on other properties [wherein the client device comprises a ].)
While, Le in combination with Koe teaches the distributed computing environment for processing data. The references do not expressly disclose the control system operations as well control system operations.
Al does expressly disclose the control system operations as well control system operations. in [0016] In some embodiments, the well system (106) includes a wellbore (120), a well sub-surface system (122), a well surface system (124), and a well control system (126). The control system (126) may control various operations of the well system (106) [wherein the client device comprises a well control system], such as well production operations, well completion operations, well maintenance operations, and reservoir monitoring, assessment and development operations. In some embodiments, the control system (126) includes a computer system that is the same as or similar to that of computer system (502) described below in FIG. 5 and the accompanying description.
Al, Koe and Le are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms in distributed computing environments.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art of processing well data information using a distributed control system, as disclosed by Al with the method of developing information retrieval and processing techniques for distributed computing environments, as collectively disclosed by Koe and Le.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Al, Koe and Le as noted above; Doing so allows for processing information in a distributed control system to implement virtual flow sensing that can help avoid piping modifications to a gas plant as well as shutting down the facility, which can result in lost plant production, (Al, 0013-0014).
Claims 10-11 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Leong et al. (US 20210174952, hereinafter ‘Le’) in view of Koehler et al. (US 12219032, hereinafter ‘Koe’) in further view of Li et al. (US 20230193751, hereinafter ‘Li’).
Regarding claim 10 , the rejection of claim 1 is incorporated and Le in combination with Koe teaches the method of claim 1, wherein the client device comprises a (in [0004] Systems and methods of the present disclosure provide an integrated platform for controlling work flow [wherein the client device comprises a ], detecting, predicting and managing risks in the maritime industry… [0005] Example embodiments are described with reference to the tracking of personnel or operators on vessels. However, it is to be understood that the invention itself is more broadly applicable, and other example embodiments may be applied to the tracking of persons and objects generally, including, for example, on other kinds of vessels, in a workplace such as buildings, and on other properties [wherein the client device comprises a ].)
While, Le in combination with Koe teaches the distributed computing environment for processing data. The references do not expressly disclose the control system operations as well control system operations.
Li does expressly disclose the control system operations as well control system operations. in [0019] Keeping with FIG. 1, the well environment (100) [wherein the client device comprises a well control system] may include a reservoir simulator (160) and various well systems, such as a drilling system (110), a logging system (112), a control system (114), and a well completion system (not shown). The drilling system (110) may include a drill string, drill bit, a mud circulation system and/or the like for use in boring the wellbore (104) into the formation (106). The control system (114) may include hardware and/or software for managing drilling operations and/or maintenance operations [wherein the client device comprises a well control system]... Without loss of generality, the term “control system” may refer to a drilling operation control system that is used to operate and control the equipment, a data acquisition and monitoring system that is used to acquire equipment data and to monitor one or more well operations, or a well interpretation software system that is used to analyze and understand well events, such as drilling progress. A logging system may be similar to a control system with a specific focus on managing one or more logging tools.
Li, Koe and Le are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms in distributed computing environments.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art of processing well data information using a distributed control system, as disclosed by Li with the method of developing information retrieval and processing techniques for distributed computing environments, as collectively disclosed by Koe and Le.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Li, Koe and Le as noted above; Doing so allows for processing well properties and spatial information using a machine-learning model in order to make spatial predictions, (Li, 0052).
Regarding claim 11 , the rejection of claim 10 is incorporated and Li further teaches the method of claim 10, comprising automatically controlling equipment of the well control system based at least in part on the machine learning inference. (in [0038] Keeping with FIG. 1, geosteering may be used to position the drill bit or drill string of the drilling system (110) relative to a boundary between different subsurface layers (e.g., overlying, underlying, and lateral layers of a pay zone) during drilling operations. In particular, a formation property volume (e.g., formation property volume (175)) may be used by the drilling system (110) for steering the drill bit in the direction of desired hydrocarbon concentrations. In some embodiments, a well path of a wellbore (104) may be updated by the control system (114) using the formation property volume. For example, a control system (114) may communicate geosteering commands to the drilling system (110) [comprising automatically controlling equipment of the well control system based at least in part on the machine learning inference] based on well log data updates that are further adjusted by the reservoir simulator (160) using a formation property volume. As such, the control system (114) may generate one or more control signals for drilling equipment [comprising automatically controlling equipment of the well control system based at least in part on the machine learning inference] (or a logging system may generate for logging equipment) based on an updated well path design and/or an updated formation property volume. As such, a geosteering system may use various sensors located inside or adjacent to the drill string to determine different rock formations within a well path. In some geosteering systems, drilling tools may use resistivity or acoustic measurements to guide the drill bit during horizontal or lateral drilling..)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Li, Koe and Le for the same reasons disclosed above.
Regarding claim 17 , the rejection of claim 16 is incorporated and Le in combination with Koe teaches the edge-based inference service system of claim 16, wherein the client device comprises a (in [0004] Systems and methods of the present disclosure provide an integrated platform for controlling work flow [wherein the client device comprises a ], detecting, predicting and managing risks in the maritime industry… [0005] Example embodiments are described with reference to the tracking of personnel or operators on vessels. However, it is to be understood that the invention itself is more broadly applicable, and other example embodiments may be applied to the tracking of persons and objects generally, including, for example, on other kinds of vessels, in a workplace such as buildings, and on other properties [wherein the client device comprises a ].)
While, Le in combination with Koe teaches the distributed computing environment for processing data. The references do not expressly disclose the control system operations as well control system operations.
Li does expressly disclose the control system operations as well control system operations. in [0019] Keeping with FIG. 1, the well environment (100) [wherein the client device comprises a well control system configured to control a wellsite system based on the machine learning inference] may include a reservoir simulator (160) and various well systems, such as a drilling system (110), a logging system (112), a control system (114), and a well completion system (not shown). The drilling system (110) may include a drill string, drill bit, a mud circulation system and/or the like for use in boring the wellbore (104) into the formation (106). The control system (114) may include hardware and/or software for managing drilling operations and/or maintenance operations [wherein the client device comprises a well control system configured to control a wellsite system based on the machine learning inference]... Without loss of generality, the term “control system” may refer to a drilling operation control system that is used to operate and control the equipment, a data acquisition and monitoring system that is used to acquire equipment data and to monitor one or more well operations, or a well interpretation software system that is used to analyze and understand well events, such as drilling progress. A logging system may be similar to a control system with a specific focus on managing one or more logging tools.
Li, Koe and Le are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms in distributed computing environments.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art of processing well data information using a distributed control system, as disclosed by Li with the method of developing information retrieval and processing techniques for distributed computing environments, as collectively disclosed by Koe and Le.
One of ordinary skill in the arts would have been motivated to combine the methods disclosed by Li, Koe and Le as noted above; Doing so allows for processing well properties and spatial information using a machine-learning model in order to make spatial predictions, (Li, 0052).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Balakrishnan et al. (US 20230177349): teaches an edge computing system may be described to encompass any number of deployments at the previously discussed layers operating in the edge cloud, which provide coordination from client and distributed computing devices.
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/OLUWATOSIN ALABI/Primary Examiner, Art Unit 2129
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