DETAILED ACTION
Status of Claims
The following is a Non-Final Office action in response to the reply received 2/19/2025.
Claims 1-2, 10, 13-17 and 19 have been amended.
Claims 1-20 are currently pending and have been examined.
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 .
Response to Arguments
Applicant’s amendments and associated arguments, filed 2/19/2025, with respect to the rejection of the claims under 35 U.S.C. §101 have been considered but they are not persuasive.
Applicant argues that the claim amendments amount to more than a human selecting a best way to predict routing. Examiner acknowledges that the claims recite both an abstract concept (summarized as the selection of a best way to predict routing) and additional elements (characterized as the elements for applying the abstract idea, and the performance of extra-solution activity, such as sending/receiving information, which are well-understood computing functions; see rejection below). However, those additional elements, as currently recited, do not act to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Applicant further argues that the improvement for building a plurality of offline models by processing the filtered training data set using machine learning; and selecting a best performing propagation model from the plurality of offline models for the route to be taken by the autonomous vehicle meets the test under Step 2B. Examiner respectfully disagrees. Building models by processing filtered data, and selecting the best performing propagation model from the plurality of models for the route to be taken by an autonomous vehicle, are processes that can be performed in the human mind or by a human using pen and paper. The recitation of “using machine learning” amounts to apply it, the characterization of the model as being an offline model and the vehicle as being autonomous characterizes the field of use, neither of which rise to an integration of the abstract idea into a practical application, or amount to significantly more than the abstract idea itself.
Applicant’s amendments and associated arguments, filed 2/19/2025, with respect to the rejection of the claims under 35 U.S.C. §102 and §103 have been considered but are but are moot because the arguments do not apply to all the references being used in the current rejection.
Claim Objections
Claims 2 is/are objected to because of the following informalities:
The claim 2 limitation “wherein the filtering removes data that is not informative to determining quality of service for autonomous vehicle along the route” should likely read “wherein the filtering removes data that is not informative to determining quality of service for the autonomous vehicle along the route.”
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.
Claim 12 is 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.
The term “non-walkable” in claim 12 is a relative term which renders the claim indefinite. The term “non-walkable” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because they recite an abstract idea without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claims 1-13 recite a series of steps, therefore claims 1-13 are a method/process which is within at least one of the four statutory categories.
Claims 14-20 recite a system/ machine, therefore claims 14-20 are a system/ machine which is within at least one of the four statutory categories.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Independent claim 14 (and similarly, claim 1) includes limitations that recite an abstract idea (emphasized below). Claim 14 recites:
An electronic device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the electronic device comprising:
a non-transitory machine-readable storage medium having stored therein a route prediction block; and
a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, wherein the route prediction block, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, aggregate the real-time mobile communication network data with the historical data to form a training data set, filter the training data set for relevance to servicing route prediction for a route to be taken by the autonomous vehicle, build a plurality of offline models by processing the filtered training data set using machine learning, and select a best performing propagation model from the plurality of offline models for the route to be taken by the autonomous vehicle.
Independent claim 15 includes limitations that recite an abstract idea (emphasized below). Claim 15 recites:
A network device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, wherein the network device is to execute a plurality of virtual machines, the plurality of virtual machines implementing network function virtualization (NFV), the network device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, wherein the route prediction block, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, aggregate the real-time mobile communication network data with the historical data to form a training data set, filter the training data set for relevance to servicing route prediction for a route to be taken by the autonomous vehicle, build a plurality of offline models by processing the filtered training data set using machine learning, and select a best performing propagation model from the plurality of offline models for the route to be taken by the autonomous vehicle.
Independent claim 16 includes limitations that recite an abstract idea (emphasized below). Claim 16 recites:
A control plane device in a software defined networking (SDN) network including a plurality of data plane devices, wherein the control plane device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the control plane device comprising: a non-transitory machine-readable storage medium having stored therein a prediction service; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the prediction service, wherein the prediction service, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, aggregate the real-time mobile communication network data with the historical data to form a training data set, filter the training data set for relevance to servicing route prediction for a route to be taken by the autonomous vehicle, build a plurality of offline models by processing the filtered training data set using machine learning, and select a best performing propagation model from the plurality of offline models for the route to be taken by the autonomous vehicle.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, other than reciting that the method is performed by a device, nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of a generic computer does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
If the claim recites a judicial exception in step 2A Prong One, the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Regarding claim 14, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An electronic device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the electronic device comprising:
a non-transitory machine-readable storage medium having stored therein a route prediction block; and
a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, wherein the route prediction block, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, aggregate the real-time mobile communication network data with the historical data to form a training data set, filter the training data set for relevance to servicing route prediction for a route to be taken by the autonomous vehicle, build a plurality of offline models by processing the filtered training data set using machine learning, and select a best performing propagation model from the plurality of offline models for the route to be taken by the autonomous vehicle.
Regarding claim 15, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A network device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, wherein the network device is to execute a plurality of virtual machines, the plurality of virtual machines implementing network function virtualization (NFV), the network device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, wherein the route prediction block, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, aggregate the real-time mobile communication network data with the historical data to form a training data set, filter the training data set for relevance to servicing route prediction for a route to be taken by the autonomous vehicle, build a plurality of offline models by processing the filtered training data set using machine learning, and select a best performing propagation model from the plurality of offline models for the route to be taken by the autonomous vehicle.
Regarding claim 16 the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A control plane device in a software defined networking (SDN) network including a plurality of data plane devices, wherein the control plane device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the control plane device comprising: a non-transitory machine-readable storage medium having stored therein a prediction service; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the prediction service, wherein the prediction service, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, aggregate the real-time mobile communication network data with the historical data to form a training data set, filter the training data set for relevance to servicing route prediction for a route to be taken by the autonomous vehicle, build a plurality of offline models by processing the filtered training data set using machine learning, and select a best performing propagation model from the plurality of offline models for the route to be taken by the autonomous vehicle.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional elements of (i) An electronic device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the electronic device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, (ii) A network device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, wherein the network device is to execute a plurality of virtual machines, the plurality of virtual machines implementing network function virtualization (NFV), the network device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, wherein the route prediction block, under control of the processor, (iii) A control plane device in a software defined networking (SDN) network including a plurality of data plane devices, wherein the control plane device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the control plane device comprising: a non-transitory machine-readable storage medium having stored therein a prediction service; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the prediction service, wherein the prediction service, under control of the processor, the examiner submits that these limitations are mere instructions to apply the above-noted abstract idea by merely using a general processor to perform the process (MPEP § 2106.05). In particular, the functions of the device is recited at a high-level of generality (i.e., as a generic processor processing selecting a best way to predict route based on received current and historical data) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Regarding the additional elements of wherein the route prediction block, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, and wherein the prediction service, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, the examiner submits that these limitations of sending and receiving signal and data are recited at a high level of generality (i.e. as a general means of gathering data), and amounts to mere data gathering, which is a form of insignificant extra-solution activities merely using a general computer (a non-transitory machine-readable storage medium, a processor, etc.) to perform the process (MPEP § 2106.05). In particular, the devices recited at a high-level of generality (i.e., as a generic means of gathering and sending data information) such that it amounts no more than mere data gathering, which is a form of insignificant extra-solution activity.
Regarding the additional element of using machine learning, this function is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and cannot integrate the judicial exception into a practical application.
Regarding the characterization of the model as an offline model, and the vehicle as an autonomous vehicle, and the real-time data as mobile communication network data, these characterizations amount to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application.
Accordingly, in combination, 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.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of (i) An electronic device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the electronic device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, (ii) A network device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, wherein the network device is to execute a plurality of virtual machines, the plurality of virtual machines implementing network function virtualization (NFV), the network device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block, wherein the route prediction block, under control of the processor, (iii) A control plane device in a software defined networking (SDN) network including a plurality of data plane devices, wherein the control plane device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the control plane device comprising: a non-transitory machine-readable storage medium having stored therein a prediction service; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the prediction service, wherein the prediction service, under control of the processor, and using machine learning to perform selecting a best way to predict route based on received current and historical data amounts to nothing more than applying the exception using a generic computer component. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
And as discussed above, the additional limitations of wherein the route prediction block, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, and wherein the prediction service, under control of the processor, to receive the real-time mobile communication network data pertaining to a mobile communication network, for selecting a best way to predict route based on received current and historical data, the examiner submits that these limitations are insignificant extra-solution activities. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The additional limitation of “receive …,” is a well-understood, routine, and conventional activity because the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere collection or receipt of data over a network is a well understood, routine, and conventional function. Hence, the claim is not patent eligible.
As discussed above, the characterization of the model as an offline model, and the vehicle as an autonomous vehicle, and the real-time data as mobile communication network data, these characterizations amount to merely indicating a field of use or technological environment in which to apply a judicial exception which does not amount to significantly more than the exception itself.
Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. Therefore, claims 14, 15 and 16 are ineligible under 35 USC §101.
Regarding claim 1, the claim(s) recite analogous limitations to claim 14, above, and is/are therefore rejected on the same premise.
Dependent claims 2-13, 17-18, and 19-20 specifies limitations that elaborate on the abstract idea of claims 1, 14, and 15 (e.g., further characterizing data filtering, the network communication data, the models and their respective outputs, etc.) and recites further abstract concepts such as validating model performance/accuracy and mapping (which are mental processes than can be performed in the human mind or by a human using pen and paper) and thus is directed to an abstract idea, and do not recite any further additional limitations that integrate the claim into a practical application or amount to “significantly more” for similar reasons.
Claim 12 further recites the function of storing data, which amounts to extra-solution of activity that well-known in the prior art. The Versata and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that storing and retrieving data in memory is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
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.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Svennebring et al. (US 20190319868, hereinafter Svennebring, already of record from IDS) in view of Li (US 20190303197, hereinafter Li).
Regarding claim 1, Svennebring discloses a method of a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the method comprising (See at least Svennebring: Para. 0057, 0059, 0061 (real-time traffic data), Para 0061 (“service provider provides interactive map and navigation services, which provides to UEs 121a turn-by-turn directions for a selected route. In this example, the service provider may use the LPP ( Link Performance Prediction) notifications to optimize the travel routes based on physical traffic (e.g., volume of vehicles travelling in a certain road or highway) and/or predicted network connectivity, which may be useful for semi-autonomous or fully-autonomous vehicle systems), Para 0018 (However, these UEs 121 may comprise any mobile or non-mobile computing device, such as tablet computers, wearable devices, PDAs, pagers, desktop computers, laptop computers, wireless handsets, unmanned vehicles or drones, and/or any type of computing device including a wireless communication interface.), Para 0050 (disclosing navigation applications used by UE 121)
receiving, by a processor, the real-time mobile communication network data pertaining to a mobile communications network (See at least Svennebring: Fig. 1, Para. 0026-0032 (disclosing collected data and collection pathways), 0178, 0179);
aggregating the real-time mobile communication network data with the historical data to form a training data set (See at least Svennebring: Para. 0034-0040 (disclosing processing of collected data) Para 0043 (In embodiments, the LPPS 200 uses spatial and temporal (spatio-temporal) historical data and/or real-time data to predict link quality. The spatio-temporal historical data is data related to the performance experienced over multiple locations (e.g., space) and at multiple time instances (e.g., temporal)), Para. 0179 the spatial-temporal-history data 722 and the real-time data 744 (or subsets thereof) are supplied to the cell load model 710, which is a machine learning (ML) model used to predict one or more cell characteristics, such as an expected cell performance at a given location and time instance);
filtering the training data set for relevance to servicing route prediction for a route to be taken by an autonomous vehicle (See at least Svennebring: Para 0043-0044 (disclosing associating data with space and time, i.e. filtering) Para. 0050-0051, 0163 (disclosing data filtering process, When the UE 111, 121 is moving using a navigation application, the cell transition prediction layer predicts the cell transitions using a cell movement/mobility pattern determined from obtained route/journey data, navigation settings, and/or other information from the navigation application,…), 0061 (application for semi-autonomous or fully autonomous vehicle systems));
building a plurality of … models by processing the filtered training data set using machine learning (See at least Svennebring: Para. 0044-0051 (building models using machine learning), Fig. 11, 0043, 0179, 0181 (training with filtered (i.e., time/location) data); Para. 0050-0051, 0163 (disclosing data filtering process, When the UE 111, 121 is moving using a navigation application, the cell transition prediction layer predicts the cell transitions using a cell movement/mobility pattern determined from obtained route/journey data, navigation settings, and/or other information from the navigation application, and each cell predicted to be visited by the UE 111, 121 is combined (fused) with respective predicted cell behaviors and respective predicted cell load(s).),); and
selecting a best performing propagation model from the plurality of … models for the route to be taken by the autonomous vehicle. (See at least Svennebring: Fig. 11, Para 0061 (a service provider provides interactive map and navigation services, which provides to UEs 121a turn-by-turn directions for a selected route. In this example, the service provider may use the LPP notifications to optimize the travel routes based on physical traffic (e.g., volume of vehicles travelling in a certain road or highway) and/or predicted network connectivity, which may be useful for semi-autonomous or fully-autonomous vehicle systems). Para 0191-0193, 0204, 0209-0211 (cell sequencing based on predictions), Para 0237)
Svennebring strongly suggests that models are generated or trained offline (see 0282, specifically the concept of offloading resource intensive tasks). Li more explicitly discloses that models can be online, offline, or a combination thereof (seat least Li, Fig. 3, Para 0017, 0027-0029, 0034, 0087, 0088)
One of ordinary skill in the art at the time of filing would have recognized that applying offline modeling of Li to the method in Svennebring would have yielded predictable results and resulted in an improved system capable of executing resource intensive modeling in an offline mode to facilitate improved/adaptive implementation of online models.
Regarding claims 14-16, the claim limitations are analogous to claim 1 (see the rejection of claim 1 above), and Svennebring further discloses:
An electronic device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the electronic device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the route prediction block (see at least Svennebring: Fig. 1, Para. 0018, 0034-0035, 0050, 0133, 0271-0290)
A network device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, wherein the network device is to execute a plurality of virtual machines, the plurality of virtual machines implementing network function virtualization (NFV), the network device comprising: a non-transitory machine-readable storage medium having stored therein a route prediction block; and a processor coupled to the non-transitory machine-readable storage medium, the processor to at least one of the plurality of virtual machines, the at least one of the plurality of virtual machines to execute the route prediction block (see at least Svennebring: Fig. 1, Para. 0018, 0034-0035, 0050, 0133, 0271-0290)
A control plane device in a software defined networking (SDN) network including a plurality of data plane devices, wherein the control plane device to implement a route prediction system for an autonomous vehicle utilizing real-time mobile communication network data in conjunction with historical data, the control plane device comprising: a non-transitory machine-readable storage medium having stored therein a prediction service; and a processor coupled to the non-transitory machine-readable storage medium, the processor to execute the prediction service (see at least Svennebring: Fig. 1, Para. 0018, 0034-0035, 0050, 0133, 0271-0290)
Regarding claim 2, Svennebring in combination with Li teaches the method of claim 1. Svennebring further discloses:
wherein the filtering removes data that is not informative to determining quality of service for autonomous vehicle along the route (See at least Svennebring: Fig. 2, 7-11, Para. 0050-0051, 0163-0165).
Regarding claim 3, Svennebring in combination with Li teaches the method of claim 1.
Svennebring and Li further discloses
further comprising: validating performance of at least one trained offline model from the plurality of offline models (see at least Svennebring 0045, 0063, 0179, 0181-0182, 0185, 0191, 0196, 0210, 0213, 0245, 282 (continuous training/updating of multiple models, offloading resource based intensive computational tasks) ; Li, Fig. 3, Para 0017, 0027-0029, 0034, 0059, 0087, 0088 (models can be online/offline)
See claim 1 for rationale to combine.
Regarding claim 4, Svennebring in combination with Li teaches the method of claim 1.
Svennebring and Li further discloses
wherein the validating determines accuracy of each of the plurality of offline models (see at least Svennebring 0045, 0063, 0179, 0181-0182, 0185, 0191, 0196, 0210, 0213, 0245, 282 (continuous training/updating of multiple models, offloading resource based intensive computational tasks) ; Li, Fig. 3, Para 0017, 0027-0029, 0034, 0059, 0087, 0088 (models can be online/offline)
See claim 1 for rationale to combine.
Regarding claim 5, Svennebring in combination with Li teaches the method of claim 1. Svennebring further discloses:
wherein the real-time mobile communication network data includes key performance indicators for cells of the mobile communication network organized as time series data classified by predictability and statistical characteristics of each cell (See at least Svennebring: Fig. 9-11 Para. 0117, 0187).
Regarding claim 6, Svennebring in combination with Li teaches the method of claim 1. Svennebring and Li further discloses:
further comprising: maintaining continuous tracking accuracy of each of the plurality of offline models (see at least Svennebring 0045, 0063, 0179, 0181-0182, 0185, 0191, 0196, 0210, 0213, 0245, 282 (continuous training/updating of multiple models based of tracked spatio-temporal data, offloading resource based intensive computational tasks) ; Li, Fig. 3, Para 0017, 0027-0029, 0034, 0059, 0087, 0088 (models can be online/offline)
See claim 1 for rationale to combine.
Regarding claim 7, Svennebring in combination with Li teaches the method of claim 1. Svennebring and Li further discloses:
wherein each of the plurality of offline models can output key performance indicators for measuring quality of service (See at least Svennebring: Para. 0057; See at least Li: Fig. 3, Para 0017, 0027-0029, 0034, 0057-0059, 0087, 0088 (disclosing online, offline, combination modeling))
See claim 1 for rationale to combine.
Regarding claim 8, Svennebring in combination with Li teaches the method of claim 7. Svennebring further discloses:
wherein the output key performance indicators include a received signal received power (RSRP) or received signal received quality (RSRQ) (See at least Svennebring: Para. 0186).
Regarding claim 9, Svennebring in combination with Li teaches the method of claim 1. Svennebring and Li further discloses:
wherein separate offline models can be maintained per mobile network operator (See at least Svennebring: Para. 0062; See at least Li: Fig. 3, Para 0017, 0027-0029, 0034, 0057-0059, 0087, 0088 (disclosing online, offline, combination modeling))
See claim 1 for rationale to combine.
Regarding claim 10, Svennebring in combination with Li teaches the method of claim 1. Svennebring and Li further discloses:
wherein a combination of offline models can be utilized to generate a measure of predicted quality of service for a location in the mobile communication network (See at least Svennebring:Fig. 9-11, Para. 0012, 0061; See at least Li: Fig. 3, Para 0017, 0027-0029, 0034, 0057-0059, 0087, 0088 (disclosing online, offline, combination modeling))).
See claim 1 for rationale to combine.
Regarding claim 11, Svennebring in combination with Li teaches the method of claim 1. Svennebring and Li further discloses:
wherein input to the offline models can include digital terrain model, cluster classes, and antenna properties for a cell of a mobile network operator (See at least Svennebring: Para. 0032, 0168, 0233; See at least Li: Fig. 3, Para 0017, 0027-0029, 0034, 0057-0059, 0087, 0088 (disclosing online, offline, combination modeling)).
See claim 1 for rationale to combine.
Regarding claim 12, Svennebring in combination with Li teaches the method of claim 1. Svennebring further discloses:
storing data responsive to identifying the data is from cell tower handovers, correlated with a non-walkable distance, and along a route (See at least Svennebring: Fig. 7-11, Para. 0190).
Regarding claim 13, Svennebring in combination with Liteaches the method of claim 1. Svennebring further discloses:
mapping the real-time mobile communication data the route by: merging route information with mobile communication network location information; determining whether mobile communication network components are geo-located proximate to the route; and determining whether mobile communication network component signal coverage covers the route. (See at least Svennebring: Fig. 11, Para 0061 (a service provider provides interactive map and navigation services, which provides to UEs 121a turn-by-turn directions for a selected route. In this example, the service provider may use the LPP notifications to optimize the travel routes based on physical traffic (e.g., volume of vehicles travelling in a certain road or highway) and/or predicted network connectivity, which may be useful for semi-autonomous or fully-autonomous vehicle systems). Para 0191-0193, 0204, 0209-0211 (cell sequencing based on predictions), Para 0237)
Regarding claims 17 and 19, Svennebring in combination with Li teaches the electronic device and a network device of claims 14 and 15. Svennebring further discloses:
the filter removes data that is not informative to determining quality of service for the autonomous vehicle along the route (See at least Svennebring: Fig. 2, 7-11, Para. 0050-0051, 0163-0165).
Regarding claims 18 and 20, Svennebring in combination with Li teaches the electronic device and a network device of claims 14 and 15. Svennebring and Li further discloses:
the route prediction block is further to validate performance of at least one trained offline model from the plurality of offline models (See at least Svennebring: Para. 0044-0051 (building models using machine learning, increasing accuracy), Fig. 11, 0043, 0179, 0181 (training with filtered (i.e., time/location) data), 0185 (training and refining model to improve accuracy; Li: Fig. 3, Para 0017, 0027-0029, 0034, 0057-0059, 0073, 0087, 0088 (disclosing online, offline, combination modeling and determining accuracy of predictions)).
See claim 1 for rationale to combine.
Conclusion
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/ABBY J FLYNN/Examiner, Art Unit 3663
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