DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a non-final Office action in response to Applicant’s submission filed on 3/2/2026. Currently claims 1-20 are pending and claims 1, 8, 15 are independent. Claims 1, 3, 8, 10, 15, 17 have been amended from the previous claim set dated 8/30/2025. No claims have been added or cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/2/2026 has been entered.
Response to Amendments
Applicant did not amend any claims in this submission, rather Applicant only presented arguments.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Werner et al. (US 20230084257 A1) in view of Joly et al. (US 20220179411 A1)
Regarding claims 1, 8, and 15 (Amended), Werner discloses a computer-implemented method for utilizing autonomous vehicles as mobile edge data nodes for data storage and distribution (Werner ABS - Artificially intelligent and dynamic infrastructure management in edge systems is provided. A number of available autonomous compute, networking, and/or cloud vehicles (ACNCV) is determined), the method comprising: collecting information pertaining to data being generated and used in a smart city to predict data demand in a region of said smart city (Werner ¶61 - the data monitoring module 355 may determine that more ACNCVs should be positioned near a location where local events such as a fair, sporting event, or concert, are regularly scheduled, but those locations are not as populated when no event is in progress. The data monitoring module 355 may monitor various websites, such as those that local magazines and newspapers publish, as well as local community websites, and local radio and news stations); identifying one or more available parking spots in said region of said smart city in response to said predicted data demand exceeding a threshold of capacity to service said predicted data demand (Werner ¶72 - The parking areas 380 may include any locations where an ACNCV 310 is allowed to park. Such locations may include, but are not limited to, public parking lots, college and/or work campuses, street parking, and similar locations. It may be noted that contractual agreements and methods of payment may be negotiated between owners of one or more of the parking areas and the owner/operators of the ACNCVs 310 to provide access to a parking area. In one or more embodiments, a charging facility 340 may also be a parking area 380); identifying an available autonomous vehicle functioning as a mobile edge data node to assist in servicing said predicted data demand in said region of said smart city (Werner ¶74 -At block 405, each ACNCV 310 notifies the AI location optimization module 365 when it enters or leaves a given service area. In this way, the AI location optimization module 365 may determine the number of available ACNCVs 310 that are in service across a given area (e.g., city, campus, etc.)); and instructing said identified available autonomous vehicle to assist in servicing said predicted data demand in said region of said smart city by parking in one of said one or more identified available parking spots in said region of said smart city (Werner ¶85 - If movement is required, at block 430 one or more ACNCVs 310 change position. An example of a change in position may be that the ACNCV 310 moves to the next closest allowable parking area 380 towards other ACNCVs 310 that have higher utilization. An ACNCV 310 may periodically attempt to move to the next closest allowable parking area 380 to determine if utilization increases which would indicate that a more optimal position has been found under the current conditions).
Werner lacks identifying data to be pre-cached in said identified available autonomous vehicle using a machine learning model trained to select said data based on factors comprising a storage capacity of said identified available autonomous vehicle, a processing capacity of said identified available autonomous vehicle, and a location of a client device relative to said identified one or more available parking spots; pre-caching said identified data in a persistent storage device of said identified available autonomous vehicle prior to said identified available autonomous vehicle parking in one of said one or more identified available parking spots, wherein said pre- cached data corresponds to data to be distributed to client devices that cannot be distributed at a designated time by current edge computing servers installed in said region of said smart city.
Joly, from the same field of endeavor, teaches identifying data to be pre-cached (Joly ¶61 - The ECAVC 460 may instruct the computing resources of the autonomous vehicles 430 to host processing and/or precache data for edge computing needs) in said identified available autonomous vehicle (Joly ¶62 - The ECAVC 460 may be configured to instruct, allocate, assign, or otherwise partition the various computing resources of the autonomous vehicles 430, based on the consumption of computing resources from the providers 420 and/or the CRI 440. The ECAVC 460 may assign and re-assign the computing resources of the autonomous vehicles 430 based on changing needs and during transportation operations of autonomous vehicles) using a machine learning model trained to select said data (Joly ¶63 - The ECAVC 460 may be configured to predict a pattern of computer resource usage in a certain location. In some embodiments, the ECAVC 460 may execute machine learning on data) based on factors comprising a storage capacity of said identified available autonomous vehicle, a processing capacity of said identified available autonomous vehicle, and a location of a client device relative to said identified one or more available parking spots (Joly ¶69 - In some embodiments, the system 400 may be configured to manage assignment of computing tasks based on autonomous vehicle capabilities. In detail, the ECAVC 460 (e.g., by request), and/or the VP 450 (e.g., by broadcast) may communicate with each other to identify one or more of the computing resources of each autonomous vehicle 430 that may be adjacent or proximate to a particular location of a computer resource disparity. For example, the resources may include a given autonomous vehicle's 430 data storage capability; the data storage capacity can be decided based on volume of data that is required to provide required service to the different data consumption needs); pre-caching said identified data in a persistent storage device of said identified available autonomous vehicle prior to said identified available autonomous vehicle parking in one of said one or more identified available parking spots, wherein said pre- cached data corresponds to data to be distributed to client devices that cannot be distributed at a designated time by current edge computing servers installed in said region of said smart city (Joly ¶76 - FIG. 4C depicts an example second process 480 of various operations and data movement through system 400, consistent with embodiments of the disclosure. Process 480 may be embodied as a series of steps that are performed by one or more of the entities of system 400 and transmitted through network 405. Process 480 may be a two-step authentication for access to data and decryption of precached data objects (e.g., access to content). The content may be served and replicated from a source (e.g., a provider 420 or CRI 440). The provider 420 and/or CRI 440 may be the ultimate owner of the data being distributed over privately owned devices depending on contracts and rights to access the data. The source may transmit, at 480-1, the precached data for later consumption, to one or more edge computing logical partitions that are hosted by a subset of the autonomous vehicles 430. Each autonomous vehicle 430 may not necessarily have, store, or be configured (or technically able) to decrypt, or otherwise access the encrypted cached data that they are hosting).
It would be It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the dynamic edge computing methodology/system of Werner by including the edge computing techniques of Joly because Joly discloses “The present disclosure relates to autonomous vehicles, and more specifically, to more efficient usage of computing resources of autonomous vehicles (Joly ¶1)”. Additionally, Werner further details that “Embodiments of the present invention generally relate to computer systems, and more specifically to edge computing (Werner ¶1)” so it would be obvious to consider including the additional edge computing techniques that Joly discloses because it would increase the efficiency of the system on Werner.
Regarding claims 2, 9, and 16, Werner in view of Joly discloses assigning an available parking spot of said one or more available parking spots to said identified available autonomous vehicle (Werner ¶80 - For example, FIG. 6 shows a small example where 3 ACNCVs 310 are distributed in a geographic region. The size of the data points indicates the utilization of each end user of hosted applications on the ACNCVs 310. The stars represent the calculated centroids, and the ACNCVs 310 are at the closest available parking area 380 to the centroids).
Regarding claims 3, 10, and 17 (Amended), Werner in view of Joly discloses one or more identified available parking spots is a parking zone managed by a private enterprise, a public enterprise, or a public-private partnership (Werner ¶72 - The parking areas 380 may include any locations where an ACNCV 310 is allowed to park. Such locations may include, but are not limited to, public parking lots, college and/or work campuses, street parking, and similar locations. It may be noted that contractual agreements and methods of payment may be negotiated between owners of one or more of the parking areas and the owner/operators of the ACNCVs 310 to provide access to a parking area. In one or more embodiments, a charging facility 340 may also be a parking area 380.).
Regarding claims 4, 11, and 18, Werner in view of Joly discloses identifying said available autonomous vehicle to assist in servicing said predicted data demand in said region of said smart city based on geolocation of said available autonomous vehicle with respect to said region of said smart city, pre-cached data, storage capacity, computing capacity, available amount of energy in batteries, carbon emission, and amount of energy to be consumed by batteries by traveling to said region of said smart city (Werner ¶74 - At block 405, each ACNCV 310 notifies the AI location optimization module 365 when it enters or leaves a given service area. In this way, the AI location optimization module 365 may determine the number of available ACNCVs 310 that are in service across a given area (e.g., city, campus, etc.). An ACNCV 310 may be out of service if it needs repair, is navigating to a new location, is charging, or taken out of service because it is not needed based on current workload. Additionally, this step may extract data detailing the resources and capabilities available on each ACNCV 310, since the ACNCVs 310 may vary in amount of storage or CPU for example. The AI location optimization module 365 may interrogate a log that each ACNCV 310 maintains to discover the available resources. Alternatively, or in addition, the log may be stored in the database 375).
Regarding claims 5, 12, and 19, Werner in view of Joly discloses consolidating cached data among a plurality of available autonomous vehicles to assist in servicing said predicted data demand in said region of said smart city (Werner ¶79 - FIG. 5 is an exemplary flowchart 500 of the operations the AI location optimization module 365 executes to determine which areas have high utilization. In this way, one or more ACNCVs 310 {i.e. plurality} can be repositioned to provide an improved user experience by moving additional {i.e. consolidated} resources closer to the demand).
Regarding claims 6, 13, and 20, Werner in view of Joly discloses information pertaining to data being generated and used in said smart city to predict said data demand in said region of said smart city is collected from a content delivery network (Werner ¶58 - The data monitoring module {i.e. content delivery network} 355 performs real-time analysis of utilization across all ACNCVs 310 under different conditions. This data is used as input by the AI location optimization module 365. The data monitoring module 355 may extract utilization data that is being tracked on the ACNCV 310 as part of its operation... Condition data may be extracted from external devices not shown in network diagram 100 (e.g., date, time, weather data from sensors and/or servers that list information on local events, traffic data, local news, etc.)).
Regarding claims 7, 14, Werner in view of Joly discloses identified available autonomous vehicle is instructed to be repositioned within a parking area (Werner ¶79 - In this way, one or more ACNCVs 310 can be repositioned to provide an improved user experience by moving additional resources closer to the demand).
Response to Arguments
Applicant's arguments filed 3/2/2026 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
Regarding the 35 USC § 102 and 35 USC § 103 rejections on the previous Office Action, Applicant amended the independent claims to further limit the claims with respect pre-caching information. In light of this amendment, Examiner agrees that the original reference did not clearly teach this, however the amendments necessitated further search and consideration. As a result of this further search and consideration, prior art was found that does teach these limitations (Joly as discussed above). As such, Applicant' s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
Applicant also argues that the prior art fails to disclose “predict data demand.” Examiner interprets [0061] (among others) as predicting data demand based on schedules of upcoming popular/crowded events. For example, when there is a concert or sporting event, the data demand in that area is predicted to be increased and thus additional resources are needed.
Applicant next argues that the prior art fails to disclose “identify parking space” in response to ”predicted data demand.” Examiner interprets Fig 6. And [0080] (among others) as disclosing determining available spot to park in response to determined data needs.
Applicant next argues that the prior art fails to disclose the autonomous vehicle being directed to assist with data demands. Examiner interprets numerous sections of the prior art disclosing this, such as Fig. 4 and Fig. 5 (among others).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624