DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 2/9/2024. Acknowledgment is made with respect to a claim of priority to PCT Application PCT/US2022/040805 filed on 8/18/2022 and Provisional Application 63/235,015 filed on 8/19/2021.
Claim Objections
Claims 5 and 14 are objected to because of the following informalities:
Claims 5 and 14 recite the limitation “the first device remote from the second device” (emphasis added) which should read as “wherein the first device is remote from the second device” (emphasis added) for better grammatical clarity. Appropriate correction is required.
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 6, 7, 15, and 16 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.
Claims 6 and 15 recite the limitation “transmit, to a server operatively coupled with the first device, the aggregation of encoded masks” (emphasis added). There is insufficient antecedent basis for the claimed “aggregation of encoded masks”. For examination purposes, the limitation will be interpreted to mean “transmit, to a server operatively coupled with the first device, the aggregation of encoded [[masks]] shares” (emphasis added). Dependent claims 7 and 16 depend on indefinite claims 6 and 15, respectively, and are also rejected under 35 USC § 112(b) by virtue of this dependency. Appropriate correction is required.
Claims 7 and 16 recite the limitation “based on the first plurality of encoded shares and the first aggregation of encoded shares” (emphasis added). There is insufficient antecedent basis for the claimed “first aggregation of encoded shares”. For examination purposes, the limitation will be interpreted to mean “based on the first plurality of encoded shares and [[the]] a first aggregation of encoded shares” (emphasis added). Appropriate correction is required.
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 19-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed towards non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subjected matter because the claimed invention is directed towards signals per se.
With respect to independent claim 19, the originally filed specification fails to disavow any transitory signals as part of the claimed computer readable medium. Under a broadest reasonable interpretation of the claim language, the “computer readable medium” of claim 19 and its dependents may encompass transitory signals, and is thus directed towards signals per se. Examiner suggests amending claim 19 and its dependents to recite "A non-transitory computer readable medium". Dependent claim 20 depends on rejected claim 19, and is also rejected under 35 USC § 101 by virtue of this dependency. Appropriate correction is required.
Claims 1-20 are further rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a system; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
partition the first model into a plurality of local mask shares each including a distinct portion of the first model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of partitioning a model, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally divide information that forms part of a generic model into portions.
encode one or more of the plurality of local mask shares into a corresponding first plurality of encoded shares: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of encoding mask share information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally compress or encode mask share information. Alternatively, the limitation encompasses a mathematical concept of encoding masks, which is performed through mathematical computation as evidenced by equation 10 of the originally filed specification.
generate an aggregation of encoded shares including a first encoded share having a first index among the first plurality of encoded shares and a second encoded share having the first index among a second plurality of encoded shares, the second encoded share including a distinct portion of a second model generated by a second device via machine learning: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of aggregating encoded mask share information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally combine encoded mask share information. Alternatively, the limitation encompasses a mathematical concept of aggregating or combining encoded masks, which is performed through mathematical computation as evidenced by paragraph [0100] of the originally filed specification.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “a first device operatively coupled with a second device, the first device including a processor and memory to” and “generate, based on a model parameter and data restricted to the first device, a first model via machine learning”.
The additional elements of “a first device operatively coupled with a second device, the first device including a processor and memory to” amount to generic computer components used as a tool to perform an existing process. The additional element of “generate, based on a model parameter and data restricted to the first device, a first model via machine learning” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic first model is broadly generated using machine learning. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “a first device operatively coupled with a second device, the first device including a processor and memory to” amount to generic computer components used as a tool to perform an existing process. The additional element of “generate, based on a model parameter and data restricted to the first device, a first model via machine learning” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic first model is broadly generated using machine learning. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element “transmit, to the second device and based on a device index of the second device, the first plurality of encoded shares” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element “receive, from the second device and based on a device index of the first device, the second plurality of encoded shares” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
generate a plurality of random masks each corresponding to one or more of the distinct portions of the first model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating masks corresponding to a model, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
partition, based on the plurality of random masks, the plurality of local mask shares: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of partitioning masks, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element “the first device remote from the second device” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional elements “transmit, to a server operatively coupled with the first device, the aggregation of encoded masks; and transmit, to the server, the first plurality of encoded shares” are insignificant extra-solution activities required for any uses of the mental processes (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
generate, in response to a determination that the second device satisfies a dropout condition and based on the first plurality of encoded shares and the first aggregation of encoded shares, an aggregate model corresponding to a machine learning model comprising the first model and the second model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of generating an aggregate model, which is performed through mathematical computation as evidenced by paragraph [0100] of the originally filed specification.
Step 2A Prong 2, Step 2B: The additional element “cause, in response to the transmission to the server, the server to” amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
determine that the second device satisfies the dropout condition by a determination that an absence of transmission, from the second device, of second plurality of encoded shares each including a distinct portion of the second model generated by the second device: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that a device satisfies a dropout condition, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element “cause, in response to the transmission to the server, the server to” amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element “generate via machine learning the first model based on the model parameter and the data restricted to the first device” amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional element “receive, from a server operatively coupled with the first device, an instruction to” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claims 10-18
Claims 10-18 recite a method (step 1: a process) to perform the steps of claims 1-9, respectively, without any additional elements that integrate the abstract ideas into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-9, respectively.
Claims 19-20
Claims 19-20 recite a computer readable medium (step 1: a manufacture if it was directed towards a statutory category) using a processor and instructions to perform the steps of claims 1-2, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-2, respectively.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5, 6, 9-12, 14, 15, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Choudhury et al. (US 20210150269 A1, hereinafter “Choudhury”).
Regarding claim 1, Choudhury discloses [a] system to generate a model based on a subset of models generated at remote devices, the system comprising: a first device operatively coupled with a second device, the first device including a processor and memory to: ([0072]; “The computer system 800 can be an electronic, computer framework comprising and/or employing any number and combination of computing devices and networks utilizing various communication technologies, as described herein”; and [0097]; “ As shown, cloud computing environment 50 includes one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate”; and [0104])
generate, based on a model parameter and data restricted to the first device, a first model via machine learning; ([0022]; “Hence, federated learning is used to leverage the data from multiple such sites to construct one or more accurate machine learning models”; and [0029]; “Additionally, each local node 120A, 120B, 120C, can have accumulated different (more, fewer) types of parameters”; and [0103]; “Through experimental evaluation using at least two real-world datasets and varying parameter settings, the implementation has shown that embodiments of the present invention provide high model performance, while offering an acceptable level of privacy”)
partition the first model into a plurality of local mask shares each including a distinct portion of the first model; ([0040]; “DIs are not used as part of learning the FL model; these attributes can be removed from the dataset, or be masked, or otherwise perturbed so that they are no longer a threat to individuals' privacy”, which discloses masking local shared information like DIs; and [0049]; “This can include, after using the k-anonymity, and/or the (k, km)-anonymity algorithms, generating a counterpart of the data values using algorithms like… partitioning methods (e.g., Mondrian), or a combination thereof”, wherein Dis can include private or restricted data such as a user’s name or social security number)
encode one or more of the plurality of local mask shares into a corresponding first plurality of encoded shares; and ([0040 and 0049])
generate an aggregation of encoded shares including a first encoded share having a first index among the first plurality of encoded shares and a second encoded share having the first index among a second plurality of encoded shares, ([0059]; “The updates to the parameters computed from the local models 122A, 122B, 122C are sent to the aggregator server 110 for training the federated model 112, at block 240”; and [0063]; “Alternatively, in one or more embodiments of the present invention a first local node 120A sends its mapping M1 to a second local node 120B. The second local node 120B computes a union of M1 and M2, which is the mapping at the second local node 120B”. The mappings of the data from the first and second node may correspond to the masked data that is sent to the aggregation server. This is functionally equivalent to providing the partitions an index, as the nodes communicate the correspond mappings to the server)
the second encoded share including a distinct portion of a second model generated by a second device via machine learning ([0040 and 0049]).
Regarding claim 10, it is a method claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claim 19, it is a computer readable medium claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claims 2, 11, and 20, the rejection of claims 1, 10, and 19 are incorporated and Choudhury further discloses transmit, to the second device and based on a device index of the second device, the first plurality of encoded shares ([0040]; [0049]; [0059]; [0063]).
Regarding claims 3 and 12, the rejection of claims 1 and 10 are incorporated and Choudhury further discloses transmit, to the second device and based on a device index of the second device, the first plurality of encoded shares ([0064]; “Similarly, the mapping information can also be shared across the local nodes 120A, 120B, 120C, through a secure protocol”; and [0066]; “The data samples that are received as part of the request are in the form of the original data, while the federated learning model has been trained on anonymized data.”; and [0040]; [0049]; [0059]; [0063]).
Regarding claims 5 and 14, the rejection of claims 1 and 10 are incorporated and Choudhury further discloses the first device remote from the second device ([0072]; “Computer system 800 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network”).
Regarding claims 6 and 15, the rejection of claims 1 and 10 are incorporated and Choudhury further discloses transmit, to a server operatively coupled with the first device, the aggregation of encoded masks; and transmit, to the server, the first plurality of encoded shares ([0040]; [0049]; [0059]; [0063-0064]).
Regarding claims 9 and 18, the rejection of claims 1 and 10 are incorporated and Choudhury further discloses receive, from a server operatively coupled with the first device, an instruction to generate via machine learning the first model based on the model parameter and the data restricted to the first device ([0040]; [0049]; [0059]; [0063-0064]; and [0103]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4 and 13 are rejected under 35 USC § 103 as being obvious over Choudhury in view of Konecny et al., (Konecny et al., “FEDERATED LEARNING: STRATEGIES FOR IMPROVING COMMUNICATION EFFICIENCY”, Oct. 30, 2017, arXiv:1610.05492v2, pp. 1-10, hereinafter “Konecny”).
Regarding claims 4 and 13, the rejection of claims 1 and 10 are incorporated and Choudhury further discloses the first device to: ([0040 and 0049]); and partition ([0040 and 0049]), the plurality of local mask shares ([0040]; [0049]; [0059]; [0063-0064]).
Choudhury fails to explicitly disclose but Konecny discloses generate a plurality of random masks… based on the plurality of random masks (Page 3, ¶6; “We restrict the update Hi t to be a sparse matrix, following a pre-defined random sparsity pattern (i.e., a random mask)”).
Choudhury and Konecny are analogous art because both are concerned with distributed learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in distributed learning to combine the random masks of Konecny with the device, partitioning, and mask shares of Choudhury to yield to the predictable result of generate a plurality of random masks each corresponding to one or more of the distinct portions of the first model; and partition, based on the plurality of random masks, the plurality of local mask shares. The motivation for doing so would be to provide the aggregation system a plurality of anonymized mask data for processing (Konecny; Page 3, ¶6).
Claims 7, 8, 16, and 17 are rejected under 35 USC § 103 as being obvious over Choudhury in view of Milton (US 20200017117 A1, hereinafter “Milton”).
Regarding claims 7 and 16, the rejection of claims 1, 6, 10, and 15 are incorporated and Choudhury further discloses cause, in response to the transmission to the server, the server to generate … based on the first plurality of encoded shares and the first aggregation of encoded shares, an aggregate model corresponding to a machine learning model comprising the first model and the second model ([0040]; [0049]; [0059]; [0063-0064]).
Choudhury fails to explicitly disclose but Milton discloses in response to a determination that the second device satisfies a dropout condition ([0092]; “Various regularization schemes can be applied when using the CNN, such as introducing a dropout operation, applying a dropConnect operation, stochastic pooling, using artificial data, or adding a weight decay term, or applying a max norm constraint by limiting a magnitude of the weight vector for every perceptron”; and [0107]; “Alternatively, transmission of the one or more control-system adjustment values to a vehicle may be modified, delayed, or stopped by further operations executing on a top-view computing layer, as further described below”).
Choudhury and Milton are analogous art because both are concerned with machine learning systems. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning to combine the dropout condition of Milton with the encoded shares and aggregate model of Choudhury to yield to the predictable result of cause, in response to the transmission to the server, the server to generate, in response to a determination that the second device satisfies a dropout condition and based on the first plurality of encoded shares and the first aggregation of encoded shares, an aggregate model corresponding to a machine learning model comprising the first model and the second model. The motivation for doing so would be to determine if a device transmission has been dropped from a server (Milton; [0092 and 0107]).
Regarding claims 8 and 17, the rejection of claims 1, 6, 7, 10, 15, and 16 are incorporated and Choudhury further discloses cause, in response to the transmission to the server, the server to …, from the second device, of second plurality of encoded shares each including a distinct portion of the second model generated by the second device ([0040]; [0049]; [0059]; [0063-0064]).
Choudhury fails to explicitly disclose but Milton discloses determine that the second device satisfies the dropout condition by a determination that an absence of transmission ([0092]; “Various regularization schemes can be applied when using the CNN, such as introducing a dropout operation, applying a dropConnect operation, stochastic pooling, using artificial data, or adding a weight decay term, or applying a max norm constraint by limiting a magnitude of the weight vector for every perceptron”; and [0107]; “Alternatively, transmission of the one or more control-system adjustment values to a vehicle may be modified, delayed, or stopped by further operations executing on a top-view computing layer, as further described below”).
The motivation to combine Choudhury and Milton is the same as discussed above with respect to claim 7.
Conclusion
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/BRENT JOHNSTON HOOVER/ Primary Examiner, Art Unit 2127
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