DETAILED ACTION
This action is responsive to claims filed on 4 March 2024.
Claims 1-18, 35-36 are pending for examination.
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 .
Claim Objections
Claim 1 and analogous claims 18, 35 are objected to because of the following informalities: “Machine Learning, ML, models” in lines 2, 1, 1, respectively, should be “Machine Learning (ML) models”. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: “a User Equipment, UE” in line 3 should be “a User Equipment (UE)”. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: “an Internet of Things, IOT, device” in line 4 should be “an Internet of Things (IOT) device”. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: “a next generation base station, gNB, or” in line 5 should be “a next generation base station (gNB) or”. Appropriate correction is required.
Claim 14 is objected to because of the following informalities: “a Human Robot Collaboration, HRC, area” in line 2 should be “a Human Robot Collaboration (HRC) area”. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: “an Unmanned Autonomous Vehicle, UAV” in line 2 should be “an Unmanned Autonomous Vehicle (UAV)”. Appropriate correction is required.
Claim 35 is objected to because of the following informalities: “analyzer” in line 6 should be “analyser”, as described in Specification [0025]. Appropriate correction is required.
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 limitations are:
“A Machine Learning, ML, agent configured to dynamically select” in claim 35.
“a receiver configured to receive” in claim 35.
“an analyzer (analyser) configured to analyze” in claim 35.
“a selector configured to select” in claim 35.
“a generator configured to generate” in claim 35.
Examiner notes, for the record, the generic placeholder listed above, “A Machine Learning, ML, agent configured to dynamically select”, is listed in the Specification as embodiments of the “ML agent 20B in accordance with the aspect of an embodiment shown in Figure 2B”, “shown in Figures 2A and 2B”, which “may be incorporated into a system, for example, where the system is all or part of a telecommunications network” as described in Specification [0023]-[0024] and Drawings [Fig. 2B].
Examiner further notes the written description fails to disclose the corresponding structure of the generic placeholders, “receiver”, “analyzer”, “selector”, “generator”, listed above, and thus, are further rejected below for indefiniteness.
Claim limitations,
“a receiver configured to receive” in claim 35.
“an analyzer (analyser) configured to analyze” in claim 35.
“a selector configured to select” in claim 35.
“a generator configured to generate” in claim 35.
invoke 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. Although the generic placeholders listed above are described in Specification [0024], [0025], [0032], [0037], respectively, the disclosure is devoid of any structure that performs the function in the claim.
Therefore, Claim 35 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.
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 2-5, 10 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 2 recites the limitation "the same task" in line 2. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the same task" has been construed to be “a same task”.
Claim 3 recites the limitation "the resource requirements for the high complexity ML model" in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the resource requirements for the high complexity ML model" has been construed to be “resource requirements for the high complexity ML model”. Claims 4-5, which are dependent on claim 3, are similarly rejected.
Claim 3 recites the limitation "the resource requirements for the low complexity ML model" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the resource requirements for the low complexity ML model" has been construed to be “resource requirements for the low complexity ML model”. Claims 4-5, which are dependent on claim 3, are similarly rejected.
Claim 4 recites the limitation "the resource requirements for the medium complexity ML model" in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the resource requirements for the medium complexity ML model" has been construed to be “resource requirements for the medium complexity ML model”.
Claim 5 recites the limitation "the resource requirements of each of the plurality of ML models" in line 3. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the resource requirements of each of the plurality of ML models" has been construed to be “resource requirements of each of the plurality of ML models”.
Claim 5 recites the limitation "the results of the analysis" in lines 3-4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the results of the analysis" has been construed to be “results of the analysis”.
Claim 10 recites the limitation "the suggested action generation" in line 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the term "the suggested action generation" has been construed to be “a suggested action generation”.
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 1-18, 35-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 1-17), a machine/system/product (Claims 18, 35-36), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a
judicial exception.
Regarding independent claims 1, 18, 35, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 18, 35:
“analyzing the safety information to determine a risk value for the state of the environment” (mental process of judgement and evaluation)
“selecting one of the plurality of ML models, wherein the selection is based on the determined risk value” (mental process of judgement and evaluation)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“receiving, at a ML agent hosting the plurality of ML models, information about a state of the environment”
“processing the information about the state of the environment using the selected ML model to generate a state prediction”
“generating one or more suggested actions to be performed on the environment using the state prediction”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“wherein the information about the state of the environment comprises safety information”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. 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. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 18, 35 do not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2-17, 36, which depend from claim 1, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claim 2:
Incorporates the rejection of claim 1.
“wherein the plurality of ML models has been trained to perform the same task”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 3:
Incorporates the rejection of claim 1.
“wherein the plurality of ML models comprises a high complexity ML model and a low complexity ML model, and wherein the resource requirements for the high complexity ML model are higher than the resource requirements for the low complexity ML model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 4:
Incorporates the rejection of claim 3.
“wherein the plurality of ML models further comprises a medium complexity ML model, and wherein the resource requirements for the medium complexity ML model are higher than the resource requirements for the low complexity ML model and lower than the resource requirements for the high complexity ML model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 5:
Incorporates the rejection of claim 3.
“prior to the step of receiving the information about the state of the environment, analyzing the resource requirements of each of the plurality of ML models” (mental process of judgement and evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“storing the results of the analysis for use in the step of selecting of one of the plurality of ML models”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 6:
Incorporates the rejection of claim 1.
“wherein the selection of one of the plurality of ML models is based on a comparison of the determined risk value with one or more predetermined safety thresholds” (mental process of judgement and evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 7:
Incorporates the rejection of claim 1.
“wherein the selection of one of the plurality of ML models is further based on a power status of the apparatus” (mental process of judgement and evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“wherein the ML agent is or forms part of an apparatus”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 8:
Incorporates the rejection of claim 7.
“wherein the selection of one of the plurality of ML models utilizes a plurality of selection rules”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 9:
Incorporates the rejection of claim 7.
“wherein the selection of one of the plurality of ML models utilizes a model selection ML model”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 10:
Incorporates the rejection of claim 1.
“further comprising performing feature adaptation on the state prediction, prior to the step of generating one or more suggested actions, wherein the feature adaptation ensures that the state prediction is in a suitable format for use in the suggested action generation”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 11:
Incorporates the rejection of claim 1.
“further comprising selecting an action from the one or more suggested actions, and modifying the environment based on the selected action” (mental process of judgement and evaluation)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 12:
Incorporates the rejection of claim 1.
“wherein the environment is at least a portion of a communications network”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 13:
Incorporates the rejection of claim 12.
“wherein the method is performed by a node in the communications network, wherein the node is: a User Equipment, UE; an Internet of Things, IoT, device; a next generation base station, gNB; or a Core Network Node”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 14:
Incorporates the rejection of claim 1.
“wherein the environment is a Human Robot Collaboration, HRC, area”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 15:
Incorporates the rejection of claim 14.
“wherein the method is performed by a robot or robot controller in the HRC area”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 16:
Incorporates the rejection of claim 1.
“wherein the environment is an autonomous agent operation area”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 17:
Incorporates the rejection of claim 16.
“wherein the method is performed by an Unmanned Autonomous Vehicle, UAV”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 36:
Incorporates the rejection of claim 1.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2-17, 36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 6, 14-18, 35 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (NPL: “Safe Path Planning with Multi-Model Risk Level Sets”, hereinafter ‘Huang’), in view of Gohari et al. (NPL: "Blending Controllers via Multi-Objective Bandits", hereinafter 'Gohari').
Regarding claim 1 and analogous claims 18, 35, Huang teaches A method for dynamically selecting an action to be taken in an environment using one of a plurality of Machine Learning, ML, models, the method comprising: receiving, at a ML agent hosting the plurality of ML models, information about a state of the environment ([IV. EXPERIMENTS AND ANALYSIS, pg. 6271-6272] We implemented our safe planner from Algorithm 1 together with the hybrid risk map calculation method on an autonomous buggy testbed. More details on our autonomous buggy platform can be found in [35]. A Sick LMS 151 2D Lidar is the primary sensor for object localization and multi-model risk map assessment. The buggy navigates itself within a 2D map of the environment constructed through laser-based SLAM (Simultaneous Localization and Mapping), and the ego autonomous testbed’s localization is through an adaptive Monte-Carlo localization (AMCL) method [36].; [A. GP Regulated Risk Map, pg. 6269] For detected and tracked objects in the environment, we generate a risk map that computes the probability that a point in the environment is occupied or in the direct path of an object. For an environment Q ⊂ R2, we define points in Q as q = (x,y) , where x and y are the coordinates of the 2D environment. We model our object detection module by a Gaussian process, which receiving information about a state of the environment returns a probability p(x,y) that a point in the environment is occupied by the tracked object or its path.; As taught, Huang describes the at a ML agent hosting the plurality of ML models implementation of the safe planner from Algorithm 1 for multi-model risk map assessment on an autonomous buggy testbed.),
wherein the information about the state of the environment comprises safety information ([I. Introduction, pg. 6268] To complement this risk map, we also incorporate the Dynamic Risk Density (DRD) [2], which creates a comprises safety information “safety net” for the autonomous system, based on the wherein the information about the state of the environment occupancy density and velocity field of the environment.);
analyzing the safety information to determine a risk value for the state of the environment ([II. Models of Risk, pg. 6269] Figure 2 illustrates the two types of risk maps we will combine in this paper, as well as the resulting combined map. The GP regulated risk creates a risk map based on object-tracking and behavior prediction, while Dynamic Risk Density combines the occupancy density of the environment with a velocity field estimate.; As taught by Huang, risk maps are generated by analyzing safety information object-tracking and behavior prediction and occupancy density of the environment with a velocity field estimate.);
processing the information about the state of the environment using the selected ML model to generate a state prediction; and generating one or more suggested actions to be performed on the environment using the state prediction ([A. Multi-Model Risk Map Construction, pg. 6270-6271] Here, we present four possible methods for combining these risk maps, which we refer to as the “Maximum Safety” model (rm), the “Joint-Minimum” model (rj), the “Aggressive” model (ra), and the “Convex Combination” model (rc).; [B. Safe Path Planner, pg. 6271] processing the information about the state of the environment using the selected ML model Once the combined risk map r(x,y) has been calculated, we input the map into our safe path planner, to generate a state prediction summarized in Algorithm 1. The planner generating one or more suggested actions to be performed on the environment using the state prediction calculates the shortest safe path connecting the ego vehicle from its origin location, denoted as O, to a desired destination point, denoted as D in the 2D environment. We define the path on a discretized grid gt of the environment, where sO and sd are the origin and destination in the grid, respectively. Safety is specified by some threshold, which can be tuned to the given task and application. The following defines the-safe node and-safe path.; As taught by Huang, Algorithm 1 describes using the combined risk with one of the four models to determine and predict the next state prediction safe action, which is used to calculate a shortest safe path for the vehicle.).
Huang fails to teach selecting one of the plurality of ML models, wherein the selection is based on the determined risk value;
Gohari teaches selecting one of the plurality of ML models, wherein the selection is based on the determined risk value ([1 Introduction, pg. 2] We define “blending controllers” as learning a switching strategy between a given safe and a given performant controller using the observations and feedback from the environment. We assume that the environment dynamics are unknown, and that, upon taking an action, the environment issues the agent with a feedback vector containing a one-step reward and an auxiliary risk value cost that measures the safety of the action. For example, the environment might issue a cost whenever the agent at the proximity of an obstacle. The framework of auxiliary costs for measuring safety is conventional in reinforcement learning algorithms that respect safety [4, 12, 14], and is not limited to this work.; The main contributions of this work are as follows: Propose a novel contextual multi-armed multi-objective bandit algorithm for blending controllers. The algorithm maintains an optimistic estimate of the next-step feedback for every arm. These estimations, on which the algorithm bases its choice of arms, become more accurate as time progresses. The algorithm then selecting one of the plurality of ML models picks the arm with the smallest estimated loss in individual objectives, and we show that such a decision rule leads to picking an arm wherein the selection is based on the determined risk value whose estimated next-step feedback vector is not Pareto dominated by any other arm.; We construct the performant and the safe controllers using deep reinforcement learning methods [27]. We generate the context for the proposed bandit algorithm using the action values estimated by the underlying neural networks.; As taught by Gohari, a contextual multi-armed, multi-objective bandit selects an arm, a controller constructed using deep reinforcement learning, determined by the next-step feedback vector, described to contain an auxiliary risk value cost that measures the safety of the action.);
Huang and Gohari are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Huang, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Gohari to Huang before the effective filing date of the claimed invention in order to blend a performant and a safe controller to generate a single controller that is safer than the performant and accumulates higher rewards than the safe controller (cf. Gohari, [Abstract, pg. 1] Abstract Safety and performance are often two competing objectives in sequential decision making problems. Existing performant controllers, such as controllers derived from reinforcement learning algorithms, often fall short of safety guarantees. On the contrary, controllers that guarantee safety, such as those derived from classical control theory, require restrictive assumptions and are often conservative in performance. Our goal is to blend a performant and a safe controller to generate a single controller that is safer than the performant and accumulates higher rewards than the safe controller. To this end, we propose a blending algorithm using the framework of contextual multi-armed multi-objective bandits. At each stage, the algorithm observes the environment’s current context alongside an immediate reward and cost, which is the underlying safety measure. The algorithm then decides which controller to employ based on its observations. We demonstrate that the algorithm achieves sublinear Pareto regret, a performance measure that models coherence with an expert that always avoids picking the controller with both inferior safety and performance. We derive an upper bound on the loss in individual objectives, which imposes no additional computational complexity. We empirically demonstrate the algorithm’s success in blending a safe and a performant controller in a safety-focused testbed, the Safety Gym environment. A statistical analysis of the blended controller’s total reward and cost reflects two key takeaways: The blended controller shows a strict improvement in performance compared to the safe controller, and it is safer than the performant controller.).
Regarding claim 2, Huang, as modified by Gohari, teaches The method of claim 1.
Huang teaches wherein the plurality of ML models has been trained to perform the same task ([A. Multi-Model Risk Map Construction, pg. 6270-6271] Here, we present four possible methods for combining these risk maps, which we refer to as the “Maximum Safety” model (rm), the “Joint-Minimum” model (rj), the “Aggressive” model (ra), and the “Convex Combination” model (rc).; [B. Safe Path Planner, pg. 6271] Once the combined risk map r(x,y) has been calculated, we input the map into our safe path planner, summarized in Algorithm 1. The planner calculates the shortest safe path connecting the ego vehicle from its origin location, denoted as O, to a desired destination point, denoted as D in the 2D environment. We define the path on a discretized grid gt of the environment, where sO and sd are the origin and destination in the grid, respectively. Safety is specified by some threshold, which can be tuned to the given task and application. The following defines the-safe node and-safe path.; As taught by Huang, Algorithm 1 describes wherein the plurality of ML models has been trained to perform the same task using the combined risk with one of the four models to determine and predict the next safe action, which is used to calculate a shortest safe path for the vehicle.).
Huang and Gohari are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 6, Huang, as modified by Gohari, teaches The method of claim 1.
Huang teaches wherein the selection of one of the plurality of ML models is based on a comparison of the determined risk value with one or more predetermined safety thresholds ([B. Safe Path Planner, pg. 6271] Once the combined risk map r(x,y) has been calculated, we input the map into our safe path planner, wherein the selection of one of the plurality of ML models summarized in Algorithm 1. The planner calculates the shortest safe path connecting the ego vehicle from its origin location, denoted as O, to a desired destination point, denoted as D in the 2D environment. We define the path on a discretized grid gt of the environment, where sO and sd are the origin and destination in the grid, respectively. with one or more predetermined safety thresholds Safety is specified by some threshold, which can be tuned to the given task and application. The following defines the-safe node and-safe path.; In highly cluttered environments, there may be no feasible path between the origin and destination, especially is based on a comparison of the determined risk value if the chosen risk model is too conservative and threshold is too small.).
Huang and Gohari are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 14, Huang, as modified by Gohari, teaches The method of claim 1.
Huang teaches wherein the environment is a Human Robot Collaboration, HRC, area ([B. Safe Path Planning in Semi-Structured Environment, pg. 6272] We tested the safe path planner together with the multi model risk map in a wherein the environment is a Human Robot Collaboration, HRC, area semi-structured environment, i.e. a tunnel environment, where the flow of pedestrians and bicycles is, more or less, in a fixed direction, by design of the environment. In this testing scenario, the buggy encountered both the pedestrians and bicyclists going through the tunnel.).
Huang and Gohari are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 15, Huang, as modified by Gohari, teaches The method of claim 14.
Huang teaches wherein the method is performed by a robot or robot controller in the HRC area ([B. Safe Path Planning in Semi-Structured Environment, pg. 6272] We tested the safe path planner together with the multi model risk map in a semi-structured environment, i.e. a tunnel environment, where the flow of pedestrians and bicycles is, more or less, in a fixed direction, by design of the environment. In this testing scenario, wherein the method is performed by a robot or robot controller in the HRC area the buggy encountered both the pedestrians and bicyclists going through the tunnel.).
Huang and Gohari are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 16, Huang, as modified by Gohari, teaches The method of claim 1.
Huang teaches wherein the environment is an autonomous agent operation area ([IV. EXPERIMENTS AND ANALYSIS, pg. 6271-6272] We implemented our safe planner from Algorithm 1 together with the hybrid risk map calculation method on an autonomous buggy testbed. More details on our autonomous buggy platform can be found in [35]. A Sick LMS 151 2D Lidar is the primary sensor for object localization and multi-model risk map assessment. The buggy navigates itself wherein the environment is an autonomous agent operation area within a 2D map of the environment constructed through laser-based SLAM (Simultaneous Localization and Mapping), and the ego autonomous testbed’s localization is through an adaptive Monte-Carlo localization (AMCL) method [36].).
Huang and Gohari are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 17, Huang, as modified by Gohari, teaches The method of claim 16.
Huang teaches wherein the method is performed by an Unmanned Autonomous Vehicle, UAV ([IV. EXPERIMENTS AND ANALYSIS, pg. 6271-6272] We implemented our safe planner from Algorithm 1 together with the hybrid risk map calculation method on an wherein the method is performed by an Unmanned Autonomous Vehicle, UAV autonomous buggy testbed. More details on our autonomous buggy platform can be found in [35]. A Sick LMS 151 2D Lidar is the primary sensor for object localization and multi-model risk map assessment. The buggy navigates itself within a 2D map of the environment constructed through laser-based SLAM (Simultaneous Localization and Mapping), and the ego autonomous testbed’s localization is through an adaptive Monte-Carlo localization (AMCL) method [36].).
Huang and Gohari are combinable for the same rationale as set forth above with respect to claim 1.
Claims 3-5, 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, Gohari, and further in view of Xu et al. (NPL: "DiReCt: Resource-Aware Dynamic Model Reconfiguration for Convolutional Neural Network in Mobile Systems", hereinafter 'Xu').
Regarding claim 3, Huang, as modified by Gohari, teaches The method of claim 1.
Huang, as modified by Gohari, fails to teach wherein the plurality of ML models comprises a high complexity ML model and a low complexity ML model, and wherein the resource requirements for the high complexity ML model are higher than the resource requirements for the low complexity ML model.
Xu teaches wherein the plurality of ML models comprises a high complexity ML model and a low complexity ML model, and wherein the resource requirements for the high complexity ML model are higher than the resource requirements for the low complexity ML model ([3.1 Overall Energy Consumption Model] Due to the limited mobile battery capacity, the energy consumption is always considered as the primary resource constraint [18]. Here, we firstly propose the energy consumption model to describe the overall cost regarding the CNN computation workload, and evaluate the computation feasibility with certain battery budget [19]. In mobile systems, the CNN is mainly computed in the CPU, the CPU energy consumption can be modeled as Eq. 1: ECPU = WCNN Bη CPU ×Pη CPU = WCNN BPeak CPU ×η) =WCNN×PPeak CPU×η×(PPeak CPU BPeak , CPU (1) where WCNN (FLOP) is the total computation workload of a CNN task; BCPU (FLOPS) and PCPU (mW) are the accessible CPU computation bandwidth and power consumption respectively; η is the current CPU utilization rate. Since the peak CPU bandwidth and power consumption are device-specific constants, the energy consumption is mainly subject to the CNN workload.; As taught by Xu, energy consumption model to describe the overall cost regarding the CNN computation workload is modeled using WCNN (FLOP) (total computation workload of a CNN task), BCPU (FLOPS) and PCPU (mW) (CPU computation bandwidth and power consumption, respectively), and current CPU utilization rate. Xu further describes peak CPU bandwidth and power consumption as device-specific constants and the energy consumption is subject to the CNN workload, similar to the claimed model complexities relative to the resource requirements.).
Huang, Gohari, and Xu are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Huang and Gohari, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Xu to Huang before the effective filing date of the claimed invention in order to reconfigure a CNN with different accuracy and resource consumption levels to adapt to various mobile computation scenarios (cf. Xu, [Abstract] Although Convolutional Neural Networks (CNNs) have been widely applied in various applications, their deployment in resource-constrained mobile systems remains a significant concern. To overcome the computation resource constraints, such as limited memory and energy capacity, many works are proposed for mobile CNN optimization. However, most of them lack a comprehensive modeling analysis of the CNN computation consumption and merely focus on static optimization schemes regardless of different mobile computation scenarios. In this work, we proposed DiReCt– a resource-aware CNN reconfiguration system. Leveraging accurate CNN computation consumption modeling and mobile resource constraint analysis, DiReCt can reconfigure a CNN with different accuracy and resource consumption levels to adapt to various mobile computation scenarios.).
Regarding claim 4, Huang, as modified by Gohari and Xu, teaches The method of claim 3.
Xu teaches wherein the plurality of ML models further comprises a medium complexity ML model, and wherein the resource requirements for the medium complexity ML model are higher than the resource requirements for the low complexity ML model and lower than the resource requirements for the high complexity ML model ([3.1 Overall Energy Consumption Model] Due to the limited mobile battery capacity, the energy consumption is always considered as the primary resource constraint [18]. Here, we firstly propose the energy consumption model to describe the overall cost regarding the CNN computation workload, and evaluate the computation feasibility with certain battery budget [19]. In mobile systems, the CNN is mainly computed in the CPU, the CPU energy consumption can be modeled as Eq. 1: ECPU = WCNN Bη CPU ×Pη CPU = WCNN BPeak CPU ×η) =WCNN×PPeak CPU×η×(PPeak CPU BPeak , CPU (1) where WCNN (FLOP) is the total computation workload of a CNN task; BCPU (FLOPS) and PCPU (mW) are the accessible CPU computation bandwidth and power consumption respectively; η is the current CPU utilization rate. Since the peak CPU bandwidth and power consumption are device-specific constants, the energy consumption is mainly subject to the CNN workload.; As taught by Xu, energy consumption model to describe the overall cost regarding the CNN computation workload is modeled using WCNN (FLOP) (total computation workload of a CNN task), BCPU (FLOPS) and PCPU (mW) (CPU computation bandwidth and power consumption, respectively), and current CPU utilization rate. Xu further describes peak CPU bandwidth and power consumption as device-specific constants and the energy consumption is subject to the CNN workload, similar to the claimed model complexities relative to the resource requirements.).
Huang, Gohari, and Xu are combinable for the same rationale as set forth above with respect to claim 3.
Regarding claim 5, Huang, as modified by Gohari and Xu, teaches The method of claim 3.
Xu teaches further comprising, prior to the step of receiving the information about the state of the environment, analyzing the resource requirements of each of the plurality of ML models, and storing the results of the analysis for use in the step of selecting of one of the plurality of ML models ([5.2.2 Adaptive Scheduling Algorithm] After analyzing the mobile system resource constraints, an adaptive dynamic scheduling algorithm is further proposed to reconfigure a CNN model for different computation scenarios. Assuming the original CNN model is Mod0 and applying RLUT, the Mod0 can be reconfigured into different models (Mod1, ...,Modn) with multiple computation consumption levels and accuracy levels: Ei, Mi,Ti, and Ai.; [Fig. 6.] The proposed analyzing the resource requirements of each of the plurality of ML models, and storing the results of the analysis for use in the step of selecting of one of the plurality of ML models RLUT stores the reconfiguration schemes for multiple CNN models including their corresponding accuracy level Ai, energy consumption Ei, memory usage Mi, execution time Ti, filter pruning amount F−i and sparsity rate SRi. prior to the step of receiving the information about the state of the environment With these configuration info stored in RLUT, the original CNN can be directly reconfigured to generate a new adaptive model.).
Huang, Gohari, and Xu are combinable for the same rationale as set forth above with respect to claim 3.
Regarding claim 7, Huang, as modified by Gohari, teaches The method of claim 1.
Huang, as modified by Gohari, fails to teach wherein the ML agent is or forms part of an apparatus, and wherein the selection of one of the plurality of ML models is further based on a power status of the apparatus.
Xu teaches wherein the ML agent is or forms part of an apparatus, and wherein the selection of one of the plurality of ML models is further based on a power status of the apparatus ([3.1 Overall Energy Consumption Model] Due to wherein the selection of one of the plurality of ML models is further based on a power status of the apparatus the limited mobile battery capacity, the energy consumption is always considered as the primary resource constraint [18]. Here, we firstly propose the energy consumption model to describe the overall cost regarding the CNN computation workload, and evaluate the computation feasibility with certain battery budget [19]. wherein the ML agent is or forms part of an apparatus In mobile systems, the CNN is mainly computed in the CPU, the CPU energy consumption can be modeled as Eq. 1: ECPU = WCNN Bη CPU ×Pη CPU = WCNN BPeak CPU ×η) =WCNN×PPeak CPU×η×(PPeak CPU BPeak , CPU (1) where WCNN (FLOP) is the total computation workload of a CNN task; BCPU (FLOPS) and PCPU (mW) are the accessible CPU computation bandwidth and power consumption respectively; η is the current CPU utilization rate. Since the peak CPU bandwidth and power consumption are device-specific constants, the energy consumption is mainly subject to the CNN workload.).
Huang, Gohari, and Xu are combinable for the same rationale as set forth above with respect to claim 3.
Regarding claim 8, Huang, as modified by Gohari and Xu, teaches The method of claim 7.
Huang teaches wherein the selection of one of the plurality of ML models utilizes a plurality of selection rules ([A. Multi-Model Risk Map Construction, pg. 6271] 1) Maximum-Safety Model (rm): This model constructs the most conservative estimate of the combined risk by multiplying both risk maps, and rm is calculated as: rm(x,y) = 1−(1−p(x,y))(1−H(x,y)). (11) We consider this model the most conservative, as any amount of risk in either map will be enlarged in this map. 2) Joint-Minimum Model (rj): For this model, we choose the minimum between the two models as the risk value. For a node (x,y) to be considered “safe” under this construction, the node must be considered “safe” by both models. We calculate rj as: rj(x,y) = 1 −min{1−p(x,y),1−H(x,y)}. (12) The Joint-Minimum model is appropriate in situations where we are worried about missed detections, and the probability of false positives is low for either map. 3) The Aggressive Model (ra): In contrast to the Joint Minimum model, the Aggressive model takes the maximum value between the two risk maps. Thus, a location in r(x,y) is deemed safe when considered safe by either one of the two models. ra(x,y) = 1−max{1−p(x,y),1−H(x,y)}, (13) The Aggressive model is useful when both maps may (in dependently) produce a large number of false-positive risk detections, and we tend to ignore the falsely detected risk values. 4) The Convex Combination Model (rc): Our final model finds the convex combination of the two individual risk maps, calculates rc as: rc(x,y) = 1−θ(1−p(x,y))+(1−θ)(1−H(x,y) , (14) where θ ∈ (0,1) is a scaling factor. Here, we can tune θ to bias the weight of one map over another. For instance, a larger value of θ biases the belief of the GP regulated risk model more than the Dynamic Risk Density one.; As taught by Huang, each of the four models are described with rules for proper selection of each model.).
Huang, Gohari, and Xu are combinable for the same rationale as set forth above with respect to claim 3.
Regarding claim 9, Huang, as modified by Gohari and Xu, teaches The method of claim 7.
Huang teaches wherein the selection of one of the plurality of ML models utilizes a model selection ML model ([III. NAVIGATION WITH MULTI-MODEL RISK, pg. 6270] In this section, we first present our method for combining the risk maps into a single unified metric, and then describe our safe path planner given the combined risk map. wherein the selection of one of the plurality of ML models utilizes a model selection ML model Algorithm 1 summarizes our planning process, which is implemented in the experiments in Section IV.).
Huang, Gohari, and Xu are combinable for the same rationale as set forth above with respect to claim 3.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, Gohari, and further in view of Ha et al. (NPL: "World Models", hereinafter 'Ha').
Regarding claim 10, Huang, as modified by Gohari, teaches The method of claim 1.
Huang, as modified by Gohari, fails to teach further comprising performing feature adaptation on the state prediction, prior to the step of generating one or more suggested actions, wherein the feature adaptation ensures that the state prediction is in a suitable format for use in the suggested action generation.
Ha teaches further comprising performing feature adaptation on the state prediction, prior to the step of generating one or more suggested actions, wherein the feature adaptation ensures that the state prediction is in a suitable format for use in the suggested action generation ([Figure 8., pg. Flow diagram of our Agent model. The raw observation is first processed by V at each time step t to produce zt. The input into C is this performing feature adaptation on the state prediction latent vector zt concatenated with M’s hidden state ht at each time step. C will then prior to the step of generating one or more suggested actions, wherein the feature adaptation ensures that the state prediction is in a suitable format for use in the suggested action generation output an action vector at for motor control, and will affect the environment. M will then take the current zt and action at as an input to update its own hidden state to produce ht+1 to be used at time t + 1.).
Huang, Gohari, and Ha are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Huang and Gohari, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Ha to Huang before the effective filing date of the claimed invention in order to quickly train in an unsupervised manner to learn a compressed spatial and temporal representation of the environment (cf. Ha, [Abstract] We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.).
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Huang, Gohari, and further in view of Chergui et al. (NPL: "Offline SLA-Constrained Deep Learning for 5G Networks Reliable and Dynamic End-to-End Slicing", hereinafter 'Chergui').
Regarding claim 11, Huang, as modified by Gohari, teaches The method of claim 1.
Huang, as modified by Gohari, fails to teach further comprising selecting an action from the one or more suggested actions, and modifying the environment based on the selected action.
Chergui teaches further comprising selecting an action from the one or more suggested actions, and modifying the environment based on the selected action ([B. Contributions, pg. 351] Unlike existing online DNN optimization strategies, we introduce a new dataset-based training approach where the constrained DNN models are optimized for each slice to respect two types of SLA, namely, violation rate-based SLA and resource bound-based SLA. This is achieved by imposing dataset-dependent custom non convex constraints to the DNN output and using a two player non-zero sum game strategy to solve the resulting offline optimization task. In this intent, the SLA thresholds act as hyperparameters that can be further comprising selecting an action from the one or more suggested actions, and modifying the environment based on the selected action
fine-tuned by the infrastructure operator according to the SLAs with the slices’ tenants. Note that we have adopted deep learning since it enables automatic discovery of important features from raw datasets, as well as yields generalized models, which is suitable for heterogeneous resources allocation.).
Huang, Gohari, and Chergui are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Huang and Gohari, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Chergui to Huang before the effective filing date of the claimed invention in order to address resource provisioning as an enabler for end-to-end dynamic slicing in software defined networking/network function virtualization (SDN/NFV) based fifth generation (5G) networks (cf. Chergui, [Abstract, pg. 350] In this paper, we address the issue of resource provisioning as an enabler for end-to-end dynamic slicing in software defined networking/network function virtualization (SDN/NFV) based fifth generation (5G) networks. The different slices’ tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources, backhaul capacity as well as data forwarding elements (DFE) and SDN controller connections. By invoking massive key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes, we first introduce a low-complexity slices’ traffics predictor based on a soft gated recurrent unit (GRU). We then build—at each virtual network function—joint multi-slice deep neural networks (DNNs) and train them to estimate the required resources based on the traffic per slice, while not violating two service level agreement (SLA), namely, violation rate-based SLA and resource bounds based SLA. This is achieved by integrating dataset-dependent generalized non-convex constraints into the DNN offline optimization tasks that are solved via a non-zero sum two-player game strategy. In this respect, we highlight the role of the underlying hyperparameters in the trade-off between overprovisioning and slices’ isolation. Finally, using reliability theory, we provide a closed-form analysis for the lower bound of the so-called reliable convergence probability and showcase the effect of the violation rate on it.).
Regarding claim 12, Huang, as modified by Gohari, teaches The method of claim 1.
Huang, as modified by Gohari, fails to wherein the environment is at least a portion of a communications network.
Chergui teaches wherein the environment is at least a portion of a communications network ([Abstract, pg. 350] In this paper, we address the issue of resource pro visioning as an enabler for end-to-end dynamic slicing in software defined networking/network function virtualization wherein the environment is at least a portion of a communications network (SDN/NFV) based fifth generation (5G) networks. The different slices’ ten ants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources, backhaul capacity as well as data forwarding elements (DFE) and SDN controller connections. By invoking massive key performance indicators (KPIs) datasets stemming from a live cellular network endowed with traffic probes, we first introduce a low-complexity slices’ traffics predictor based on a soft gated recurrent unit (GRU). We then build—at each virtual network function—joint multi-slice deep neural networks (DNNs) and train them to estimate the required resources based on the traffic per slice, while not violating two service level agreement (SLA), namely, violation rate-based SLA and resource bounds based SLA.).
Huang, Gohari, and Chergui are combinable for the same rationale as set forth above with respect to claim 11.
Regarding claim 13, Huang, as modified by Gohari and Chergui, teaches The method of claim 12.
Chergui teaches wherein the method is performed by a node in the communications network, wherein the node is: a User Equipment, UE; an Internet of Things, IoT, device; a next generation base station, gNB; or a Core Network Node ([Abstract, pg. 350] In this paper, we address the issue of resource provisioning as an wherein the method is performed by a node in the communications network , wherein the node is enabler for end-to-end dynamic slicing in software defined networking/network function virtualization (SDN/NFV) based fifth generation (5G) networks. The different slices’ tenants (i.e. logical operators) are dynamically allocated isolated portions of physical resource blocks (PRBs), baseband processing resources, backhaul capacity as well as data forwarding elements (DFE) and SDN controller connections.; [II. NETWORK ARCHITECTURE AND DATASETS, pg. 351-352] As depicted in Fig. 2, we consider a fully SDN/NFV architecture [15] wherein the baseband processing units run as a next generation base station, gNB softwarized virtual entities called vBBUs on datacenters close to the transmission/reception points (TRPs). On the other hand, all conventional enhanced packet core (EPC) entities no longer exist or are collapsed. Instead, the user plane packet gateways (PGWs) are replaced by virtualized data forwarding entities (vDFEs), while control plane serving gateway (SGW) and mobility management entity (MME) are replaced by a set of software applications implemented on top of a virtualized SDN controller (vSDNC) as suggested by many scientific research papers, e.g., [16], [17].).
Huang, Gohari, and Chergui are combinable for the same rationale as set forth above with respect to claim 11.
Claim 36 is rejected under 35 U.S.C. 103 as being unpatentable over Huang, Gohari, and further in view of Thornton et al. (U.S. Pre-Grant Publication No. 20190156178, hereinafter 'Thornton').
Regarding claim 36, Huang, as modified by Gohari, teaches A method in accordance with any of claim 1.
Huang, as modified by Gohari, fails to teach A computer program product comprising a non-transitory computer-readable medium comprising instructions which, when executed on a computer processing circuitry, cause the computer processing circuitry to perform
Thornton teaches A computer program product comprising a non-transitory computer-readable medium comprising instructions which, when executed on a computer processing circuitry, cause the computer processing circuitry to perform ([0093] FIG. 6 illustrates an exemplary computing device 150 that may be utilized in an exemplary embodiment to perform the methods taught herein. The computing device 150 includes one or more non-transitory computer-readable media for storing one or more computer-executable instructions, such as but not limited to software or firmware modules of system 100 according to the principles taught herein.).
Huang, Gohari, and Thornton are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Huang and Gohari, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Thornton to Huang before the effective filing date of the claimed invention in order to provide optimization of data classification model architectures or analysis model architectures for applications that are subject to deployment resource constraints (cf. Thornton, [0016] Systems and methods taught herein provide optimization of data classification model architectures or analysis model architectures for applications that are subject to deployment resource constraints. Unlike conventional model optimization techniques that focus solely on improving accuracy, the present systems and methods can optimize data model architectures for use in real-world environments that are subject to resource constraints including limited computing, memory, or other resources. As a result, the optimized data models are selected based on both accuracy and other factors related to deployment such as model size or speed. The data models selected by embodiments of the present invention can be lightweight yet still highly functional according to the needs of the particular environment.).
Conclusion
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/MM/Examiner, Art Unit 2129
/IMAD KASSIM/Primary Examiner, Art Unit 2129
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