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 .
35 USC § 101
The claims are not being rejected under 35 U.S.C. 101 because the independent claims contain an analogous limitation to, “detecting at least one cellular-related disruption corresponding to a cell of a plurality of cells associated with a marshaling environment, wherein the detection of the at least one cellular-related disruption is associated with the one or more marshaling-related characteristics”. This limitation does not represent a judicial exception as it contains actions that the human mind is not equipped to perform and therefore cannot be practically performed by the human mind resulting in this limitation not representing an abstract idea. This limitation at least represents an additional element that it sufficient to cause the claim as-a-whole to be integrated into a practical application and therefore the claims are eligible via Step 2A Prong Two of the Subject Matter Eligibility Test.
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 1-8, 10-15, 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cohn (US 9,260,244).
As per Claim 1:
Cohn discloses the following limitations:
“A method comprising: monitoring, by a vehicle-marshaling algorithm of a vehicle, one or more marshaling-related characteristics based on an exchange of one or more messages with an infrastructure system”
Cohn Column 8 Line 63-Column 9 Line 38 describes Figure 2 and discloses an autonomous vehicle operating via an algorithm (which represents a vehicle-marshaling algorithm as said algorithm allows for the movement of said vehicle) that travels about a space monitoring and transmitting network traffic data representative of network performance for various areas of the space.
“detecting at least one cellular-related disruption corresponding to a cell of a plurality of cells associated with a marshaling environment, wherein the detection of the at least one cellular-related disruption is associated with the one or more marshaling-related characteristics”
Cohn Column 8 Line 63-Column 9 Line 38 discloses identifying the performance of a network at various locations in a space by monitoring the network traffic data. Any discrepancies in the network performance would be identified by said monitoring.
“ analyzing the one or more marshaling-related characteristics associated with the at least one cellular-related disruption based on one or more marshaling commands”
Cohn Column 9 Line 53-Column 10 Line 8 discloses analyzing said network traffic data 204 in order to suggest connectivity improvements.
“and receiving an adjustment to the one or more marshaling commands based on the analysis, wherein a traverse of the vehicle is adjusted based on the receipt of the adjustment to the one or more marshaling commands.”
Cohn Column 2 Lines 25-54 discloses adjusting the paths of autonomous vehicles based on analysis of connectivity performance throughout a space.
With regards to Claim 2, Cohn discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the one or more marshaling-related characteristics include unicast wireless frequencies associated with the vehicle, a plurality of cell identifiers associated with the plurality of cells, radio frequency-related performance metrics, or a combination thereof.”
Cohn Column 3 Lin 59-Column 4 Line 45 discloses analyzing data including characteristics of unicast wireless frequencies (evidenced by the unicast wireless communications between autonomous vehicles and infrastructure.
With regards to Claim 3, Cohn discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the at least one cellular-related disruption includes degradation of a live communication link between the vehicle and the infrastructure system, an interference associated with the live communication link, or a combination thereof.”
Cohn Column 8 Line 63-Column 9 Line 38 discloses real-time communication link degradation reporting (network outages reported in real-time).
With regards to Claim 4, Cohn discloses all of the limitations of Claim 1 and further discloses the following limitations:
“further comprising: transmitting, from the vehicle via a live communication link, the analysis of the one or more marshaling-related characteristics to the infrastructure system, wherein the transmission of the analysis is simultaneous to the traverse of the vehicle across the marshaling environment.”
Cohn Column 8 Line 63-Column 9 Line 38 discloses real-time communication link degradation reporting (network outages reported in real-time) as the vehicle traverses the environment.
With regards to Claim 5, Cohn discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the analysis of the one or more marshaling-related characteristics further comprises: verifying a location of the vehicle based on coordinates of the location of the vehicle matching snap-shot data associated with the location of the vehicle, wherein the snap-shot data is obtained from one or more vehicle sensors; and causing, in response to the verification of the location of the vehicle, a time-stamp and a virtual dynamic radio-frequency coverage heat map of the at least one cellular-related disruption to be generated.”
Cohn Column 3 Lines 20-58 discloses including timestamp and positional coordinate data for the purposes of building a map of network performance in a physical space.
With regards to Claim 6, Cohn discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the adjustment to the one or more marshaling commands is received in response to a disruption-related threshold being exceeded.”
Cohn Column 2 Line 55 - Column 3 Line 20 discloses automatic instructions being presented to an autonomous vehicle based on the network performance information. (33) specifically discloses "outages" which trigger network performance analysis which would represent events with thresholds being exceeded.
With regards to Claim 7, Cohn discloses all of the limitations of Claim 1 and further discloses the following limitations:
“wherein the adjustment to the one or more marshaling commands includes control-time-based marshaling, waypoints-based marshaling, message transmission rates, a velocity of the vehicle, a curvature of the traverse of the vehicle, a spacing buffer associated with the vehicle or a combination thereof.”
Cohn Column 2 Lines 25-54 discloses adjusting the paths of autonomous vehicles.
As per Claim 8:
Cohn discloses the following limitations:
“ A system comprising: a vehicle system configured to: monitor, by a vehicle-marshaling algorithm of a vehicle, one or more marshaling-related characteristics based on an exchange of one or more messages with an infrastructure system, detect at least one cellular-related disruption corresponding to a cell of a plurality of cells associated with a marshaling environment, wherein the detection of the at least one cellular-related disruption is associated with the one or more marshaling-related characteristics, analyze the one or more marshaling-related characteristics associated with the at least one cellular-related disruption based on one or more marshaling commands, and receive an adjustment to the one or more marshaling commands based on the analysis, wherein a traverse of the vehicle is adjusted based on the receipt of the adjustment to the one or more marshaling commands;”
Cohn Column 8 Line 63-Column 9 Line 38 describes Figure 2 and discloses an autonomous vehicle operating via an algorithm (which represents a vehicle-marshaling algorithm as said algorithm allows for the movement of said vehicle) that travels about a space monitoring and transmitting network traffic data representative of network performance for various areas of the space. Column 8 Line 63-Column 9 Line 38 discloses identifying the performance of a network at various locations in a space by monitoring the network traffic data. Any discrepancies in the network performance would be identified by said monitoring. Column 9 Line 53-Column 10 Line 8 discloses analyzing said network traffic data 204 in order to suggest connectivity improvements. Column 2 Lines 25-54 discloses adjusting the paths of autonomous vehicles based on analysis of connectivity performance throughout a space.
“the infrastructure system configured to: receive the analysis of the one or more marshaling-related characteristics, generate a time-stamp and a virtual dynamic radio-frequency coverage heat map of the at least one cellular-related disruption, and transmit the one or more adjustments to the one or more marshaling commands to the vehicle system”
Cohn Column 3 Lines 20-58 discloses including timestamp and positional coordinate data to transmissions to infrastructure for the purposes of building a map of network performance in a physical space.
“and a mobile network operator configured to: receive the analysis of the one or more marshaling-related characteristics via an original equipment manufacturing cloud system, and repair the at least one cellular-related disruption.”
Cohn Column 3 Lines 20-58 discloses improving network connectivity in response to analysis of corresponding performance data.
With regards to Claim 10, this claim is substantially similar to Claim 3 and is therefore rejected using the same references and rationale.
With regards to Claim 11, this claim is substantially similar to Claim 4 and is therefore rejected using the same references and rationale.
With regards to Claim 12, this claim is substantially similar to Claim 5 and is therefore rejected using the same references and rationale.
With regards to Claim 13, this claim is substantially similar to Claim 6 and is therefore rejected using the same references and rationale.
With regards to Claim 14, this claim is substantially similar to Claim 7 and is therefore rejected using the same references and rationale.
As per Claim 15: this claim is substantially similar to Claim 1 and is therefore rejected using the same references and rationale.
With regards to Claim 17, this claim is substantially similar to Claim 3 and is therefore rejected using the same references and rationale.
With regards to Claim 18, this claim is substantially similar to Claim 4 and is therefore rejected using the same references and rationale.
With regards to Claim 19, this claim is substantially similar to Claim 5 and is therefore rejected using the same references and rationale.
With regards to Claim 20, Cohn discloses all of the limitations of Claim 15 and further discloses the following limitations:
“wherein the adjustment to the one or more marshaling commands is received in response to a disruption-related threshold being exceeded”
Cohn Column 2 Line 55 - Column 3 Line 20 discloses automatic instructions being presented to an autonomous vehicle based on the network performance information. (33) specifically discloses "outages" which trigger network performance analysis which would represent events with thresholds being exceeded.
“and wherein the adjustment to the one or more marshaling commands includes control-time-based marshaling, waypoints-based marshaling, message transmission rates, a velocity of the vehicle, a curvature of the traverse of the vehicle, a spacing buffer associated with the vehicle or a combination thereof.”
Cohn Column 2 Lines 25-54 discloses adjusting the paths of autonomous vehicles.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Cohn in view of Weil (US 2008/0247327) and is directed similarly to wireless communications (See Paragraph [0001]).
With regards to Claim 9, Cohn discloses all of the limitations of Claim 8 and further discloses the following limitations:
“…and wherein the one or more marshaling-related characteristics include unicast wireless frequencies associated with the vehicle, a plurality of cell identifiers associated with the plurality of cells, radio frequency-related performance metrics, or a combination thereof.”
Cohn Column 3 Line 59-Column 4 Line 46 discloses analyzing data including characteristics of unicast wireless frequencies (evidenced by the unicast wireless communications between autonomous vehicles and infrastructure.
Cohn does not explicitly disclose the following limitations that Weil does disclose:
“wherein the one or more marshaling-related characteristics are analyzed via one or more of latency one-way, round-trip time, inter-packet gap, congestion, reference-signal-received-power, reference-signal-received-quality, signal-to-interference-plus-noise-ratio, interference, packet-loss, through-put, start-and-end associated with physical cell identifiers, frequency channels and bands monitoring indoors and outdoors, cell identifiers, start-and-end evolved-node-Bs,”
Weil Paragraph [0038] discloses a latency test measuring round-trip travel time in a network.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to modify the system disclosed by Cohn with the latency determination disclosed by Weil. One of ordinary skill in the art would have been motivated to make this modification, with a reasonable expectation of success, in order to make the system more accurate by utilizing well-known performance metrics for network performance assessment.
With regards to Claim 16, this claim is substantially similar to Claim 9 and is therefore rejected using the same references and rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Godfrey Maciorowski, whose telephone number is (571) 272-4652. The examiner can normally be reached on Monday-Friday from 7:30am to 5:00pm EST.
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/GODFREY ALEKSANDER MACIOROWSKI/Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658