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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 Jan 29, 2026 has been entered.
Response to Arguments
Based on new ground of rejection, applicant’s argument submitted on 12/26/2025 are moot.
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 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 20claimed 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 1, 3-5, 8-9, 11-13, 16-17, 19-22, 24-25, 27-28, 30-32 and 35-37 are rejected under 35 U.S.C. 103 as being unpatentable over Wugedele Bao (“ Edge Computing-Based Joint Client Selection and Networking Scheme for Federed Learning in Vehicular IoT”).
Regarding claim 1, Delhave discloses an information processing apparatus (Fig. 7), comprising: a controller (page 43; para 01 – controller) configured to select one or more mobile bodies that provide computing resources related to a specified machine learning, from among a plurality of mobile bodies connected to a mobile communication network (Page 5; Para 01, and first selecting, at a communications server, a first number of the UE terminals, wherein the first selection comprises receiving past spatiotemporal trajectory data from one or more sensors associated with each of the selected UE terminals; and storing the past spatiotemporal trajectory of each of the selected UE terminals; and first determining a machine learning model for predicting the future spatiotemporal trajectory of any one of the selected UE terminals, wherein the communications server comprises computer-executable instructions configured to perform spatiotemporal trajectory prediction and spatiotemporal crowd behavior prediction based on machine learning training; and sending, to each of the selected UE terminals, the machine learning model configuration and machine learning model parameters; and executing, at each of the selected UE terminals, the machine learning model, wherein the executing comprises receiving the machine learning model configuration and machine learning model parameters; and inputting, into the machine learning model, present spatiotemporal trajectory data from one or more sensors associated with each of the selected UE terminals; and obtaining, at the processor of each of the selected UE terminals, the predicted spatiotemporal trajectory of the selected UE terminal, wherein each of the selected UE terminals comprises computer- executable instructions configured to perform spatiotemporal trajectory prediction based on the received machine learning model configuration and parameters; and sending, to the communications serve. Then performing second selection of UEs from the first selected group of UEs); wherein the specified machine learning is executed based on information acquired from surroundings by the selected one or more mobile bodies (page 26; para 02 and page 44; para o2– environment specific data).
Delhave does not explicitly disclose wherein the controller selects multiple bodies, the controller selects the multiple mobile bodies based on positions and traveling directions of the plurality of mobile bodies so as to avoid selecting the multiple mobile bodies traveling in the same direction.
In an analogous art, Wugedele discloses wherein the controller selects multiple bodies (Page 0039; Abstract; assign some vehicles as edge vehicles and uses the edge vehicle FL clients to conduct the training of local models), the controller selects the multiple mobile bodies based on traveling directions of the plurality of mobile bodies (Page 0039; Abstract; the client selection takes into account the vehicle velocity (wherein velocity includes traveling speed and direction), vehicle distribution and the wireless link connectivity between vehicles ) the client selection so as to avoid selecting the multiple mobile bodies traveling in the same direction (Page 40; col. 1; para 03; and col. 2; para 01; neighboring vehicles in close proximity could have similar sensing capability and sensor data, so it is unnecessary to select both vehicles due to limited communication resources…. Select one vehicle in each cluster. It’s obvious that selecting the two vehicles in the same direction is unnecessary because both vehicles could have similar sensor data, therefore only one vehicle is selected in on cluster that can be in one direction).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the controller selects the mobile bodies based on positions.
In an analogous art, Viswanathan discloses that the controller selects the mobile bodies based on positions (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Regarding claim 9, Delhave discloses an information processing method that cause an information processing apparatus (Fig. 7) to select one or more mobile bodies that provide computing resources related to a specified machine learning, from among a plurality of mobile bodies connected to a mobile communication network (Page 5; Para 01, and first selecting, at a communications server, a first number of the UE terminals, wherein the first selection comprises receiving past spatiotemporal trajectory data from one or more sensors associated with each of the selected UE terminals; and storing the past spatiotemporal trajectory of each of the selected UE terminals; and first determining a machine learning model for predicting the future spatiotemporal trajectory of any one of the selected UE terminals, wherein the communications server comprises computer-executable instructions configured to perform spatiotemporal trajectory prediction and spatiotemporal crowd behavior prediction based on machine learning training; and sending, to each of the selected UE terminals, the machine learning model configuration and machine learning model parameters; and executing, at each of the selected UE terminals, the machine learning model, wherein the executing comprises receiving the machine learning model configuration and machine learning model parameters; and inputting, into the machine learning model, present spatiotemporal trajectory data from one or more sensors associated with each of the selected UE terminals; and obtaining, at the processor of each of the selected UE terminals, the predicted spatiotemporal trajectory of the selected UE terminal, wherein each of the selected UE terminals comprises computer- executable instructions configured to perform spatiotemporal trajectory prediction based on the received machine learning model configuration and parameters; and sending, to the communications serve. Then performing second selection of UEs from the first selected group of UEs); wherein the specified machine learning is executed based on information acquired from surroundings by the selected one or more mobile bodies (page 26; para 02 and page 44; para o2– environment specific data).
Delhave does not explicitly disclose wherein the controller selects multiple bodies, the controller selects the multiple mobile bodies based on positions and traveling directions of the plurality of mobile bodies so as to avoid selecting the multiple mobile bodies traveling in the same direction.
In an analogous art, Wugedele discloses wherein the controller selects multiple bodies (Page 0039; Abstract; assign some vehicles as edge vehicles and uses the edge vehicle FL clients to conduct the training of local models), the controller selects the multiple mobile bodies based on traveling directions of the plurality of mobile bodies (Page 0039; Abstract; the client selection takes into account the vehicle velocity (wherein velocity includes traveling speed and direction), vehicle distribution and the wireless link connectivity between vehicles ) the client selection so as to avoid selecting the multiple mobile bodies traveling in the same direction (Page 40; col. 1; para 03; and col. 2; para 01; neighboring vehicles in close proximity could have similar sensing capability and sensor data, so it is unnecessary to select both vehicles due to limited communication resources…. Select one vehicle in each cluster .. so it’s obvious that selecting the two vehicles in the same direction is unnecessary because both vehicles could have similar sensor data, therefore only one vehicle is selected in on cluster that can be in one direction).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the controller selects the mobile bodies based on positions.
In an analogous art, Viswanathan discloses that the controller selects the mobile bodies based on positions (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Regarding claim 17, Delhaye discloses a non-transitory storage medium storing a program
that causes a computer to execute the method steps of claim 9 (para 0063; non-transitory computer readable medium storing instructions to perform the method steps).
Regarding claim 30, Delhave discloses a mobile body connected to a mobile communication network (Fig. 7), comprising: a controller configured to transmit (page 43; para 01 – controller), to an information processing apparatus, the information being used by the information processing apparatus to select one or more mobile bodies that provide computing resources related to a specified machine learning, from among a plurality of mobile bodies connected to the mobile communication network (Page 5; Para 01, and first selecting, at a communications server, a first number of the UE terminals, wherein the first selection comprises receiving past spatiotemporal trajectory data from one or more sensors associated with each of the selected UE terminals; and storing the past spatiotemporal trajectory of each of the selected UE terminals; and first determining a machine learning model for predicting the future spatiotemporal trajectory of any one of the selected UE terminals, wherein the communications server comprises computer-executable instructions configured to perform spatiotemporal trajectory prediction and spatiotemporal crowd behavior prediction based on machine learning training; and sending, to each of the selected UE terminals, the machine learning model configuration and machine learning model parameters; and executing, at each of the selected UE terminals, the machine learning model, wherein the executing comprises receiving the machine learning model configuration and machine learning model parameters; and inputting, into the machine learning model, present spatiotemporal trajectory data from one or more sensors associated with each of the selected UE terminals; and obtaining, at the processor of each of the selected UE terminals, the predicted spatiotemporal trajectory of the selected UE terminal, wherein each of the selected UE terminals comprises computer- executable instructions configured to perform spatiotemporal trajectory prediction based on the received machine learning model configuration and parameters; and sending, to the communications serve. Then performing second selection of UEs from the first selected group of UEs); wherein, when the mobile body is selected as one of the one or more mobile bodies by the selection performed by the information processing apparatus, the mobile body acquires information from its surroundings for the specified machine learning, the machine learning being executed based on the acquired information (page 26; para 02 and page 44; para o2– environment specific data).
Delhave does not explicitly disclose wherein the controller selects multiple bodies, the controller selects the multiple mobile bodies based on positions and traveling directions of the plurality of mobile bodies so as to avoid selecting the multiple mobile bodies traveling in the same direction.
In an analogous art, Wugedele discloses wherein the controller selects multiple bodies (Page 0039; Abstract; assign some vehicles as edge vehicles and uses the edge vehicle FL clients to conduct the training of local models), the controller selects the multiple mobile bodies based on traveling directions of the plurality of mobile bodies (Page 0039; Abstract; the client selection takes into account the vehicle velocity (wherein velocity includes traveling speed and direction), vehicle distribution and the wireless link connectivity between vehicles ) the client selection so as to avoid selecting the multiple mobile bodies traveling in the same direction (Page 40; col. 1; para 03; and col. 2; para 01; neighboring vehicles in close proximity could have similar sensing capability and sensor data, so it is unnecessary to select both vehicles due to limited communication resources…. Select one vehicle in each cluster .. so it’s obvious that selecting the two vehicles in the same direction is unnecessary because both vehicles could have similar sensor data, therefore only one vehicle is selected in on cluster that can be in one direction).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the controller selects the mobile bodies based on positions.
In an analogous art, Viswanathan discloses that the controller selects the mobile bodies based on positions (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Regarding claims 3, 11, 19, and 31, Delhaye discloses wherein the controller selects mobile bodies to be used for machine learning based on a present time/current time (para 0082-0083; current spatiotemporal positioning data).
Delhave does not explicitly disclose that the mobile bodies are selected based on information specifying the position and traveling direction of the plurality of mobile bodies.
In an analogus art, Wugedele discloses that the mobile bodies are selected based on information specifying traveling direction of the plurality of mobile bodies (Page 34; Abstract; selection based on velocity). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the controller selects the mobile bodies based on positions.
In an analogous art, Viswanathan discloses that the controller selects the mobile bodies based on positions (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Regarding claims 4, 12, and 20, Delhaye discloses wherein the controller acquires information received from a network (para 0082; 0153; spatiotemporal positioning is determined by the base station and communicated to the controller).
Delhave does not explicitly disclose that the information specifying the positions and traveling directions of the plurality of mobile bodies.
In an analogus art, Wugedele discloses that the information specifying traveling directions of the plurality of mobile bodies (Page 34; Abstract; selection based on velocity). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the information specifying the positions of the plurality of mobile bodies.
In an analogous art, Viswanathan discloses that the information specifying the positions of the plurality of mobile bodies (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Regarding claims 5, and 13, Delhaye discloses wherein the controller acquires information from a cellular network (para 0069; cellular network).
Delhave does not explicitly disclose that the information specifying the positions and traveling directions of the plurality of mobile bodies.
In an analogous art, Wugedele discloses that the information specifying traveling directions of the plurality of mobile bodies (Page 34; Abstract; selection based on velocity). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the information specifying the positions of the plurality of mobile bodies.
In an analogous art, Viswanathan discloses that the information specifying the positions of the plurality of mobile bodies (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Regarding claims 8, and 16, Delhaye discloses wherein the controller selects mobile bodies to be used for federated learning (para 0024 and 0121; federated learning model).
Regarding claims 21, 24, 27 and 36, Delhave discloses wherein the controller supplies the selected one or more mobile bodies with a learning model and wherein the specified machine learning is executed on the supplied learning model based on the information acquired from the surroundings by the selected one or more mobile bodies. (page 26; para 02 and page 44; para o2– environment specific data).
Regarding claims 22, 25, 28 and 37, Delhave discloses wherein the controller receives the learning model that has been trained by the specified machine learning executed on the selected one or more mobile bodies (page 26; para 02 and page 44; para o2– environment specific data).
Regarding claim 32, Delhaye discloses wherein the mobile communication network is a cellular network (para 0069; cellular network).
Regarding claim 35, Delhaye discloses wherein the machine learning is federated learning (para 0024 and 0121; federated learning model).
4. Claims 6-7 and 14-15, 33-34 are rejected under 35 U.S.C. 103 as being unpatentable over Delhaye/Wugedele/Viswanath in view of Merwaday et al. (US 2022/0038554, hereinafter Merwaday).
Regarding claims 6, 14, and 33, Delhaye discloses wherein the controller acquires the information from a 5G (para 0093; 5G).
Delhave does not explicitly disclose that the information specifying the positions and traveling directions of the plurality of mobile bodies.
In an analogous art, Wugedele discloses that the information specifying traveling directions of the plurality of mobile bodies (Page 34; Abstract; selection based on velocity). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the information specifying the positions of the plurality of mobile bodies.
In an analogous art, Viswanathan discloses that the information specifying the positions of the plurality of mobile bodies (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Delhave/Wugedele/Viswanath does not explicitly disclose that 5GC is part of 5G.
In an analogous art, Merwaday discloses wherein the controller acquires the mobile body information from a 5G (para 0046; 5G core network 5GC).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele/Viswanath’s method/device by adding Merwaday’s disclosure in order to provide faster connection and reduces network latency.
Regarding claims 7, 15, and 34, Delhave does not explicitly disclose that the information specifying the positions and traveling directions of the plurality of mobile bodies.
In an analogous art, Wugedele discloses that the information specifying traveling directions of the plurality of mobile bodies (Page 34; Abstract; selection based on velocity). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave’s method/system by having Wugedele ’s disclosure in order to improve resource allocation.
Delhave/Wugedele does not explicitly disclose that the information specifying the positions of the plurality of mobile bodies.
In an analogous art, Viswanathan discloses that the information specifying the positions of the plurality of mobile bodies (page 004; para 01 & page 45; last para – selecting a vehicle based on the direction and position). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele’s method/system by having Viswanathan’s disclosure in order to improve the reliability and usefulness of the data.
Delhave/Wugedele/Viswanath does not expclitly discloses wherein the controller acquires the information via an NEF from an NWDAF, an AMF, or an LMF in the SGC.
In an analogous art, Merwaday discloses wherein the controller acquires the mobile body information via an NEF (para 0061-0062 - NEF) from an NWDAF, an AMF, or an LMF in the SGC (para 0047; AMF).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to modify Delhave/Wugedele/Viswanath’s method/device by adding Merwaday’s disclosure in order to provide faster connection and reduces network latency.
5. Claims 23, 26, 29 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Delhaye/Wugedele/Viswanath in view of Ali (WO 2021256978, hereinafter Ali).
Regarding claims 23, 26, 29 and 38, Delhaye/Wugedele/Viswanath does not explicitly disclose wherein the controller selects the one or more mobile bodies so as to avoid selecting the multiple mobile bodies traveling in the same direction, based on handover information including handover history of the plurality of mobile bodies.
In an analogous art, Ali discloses disclose wherein the controller selects the one or more mobile bodies so as to avoid selecting the multiple mobile bodies traveling in the same direction, based on handover information including handover history of the plurality of mobile bodies (page 04; para 03; page 23; para 01; page 28; para 02; page 33; para 02). It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention to modify Delhaye/Wugedele/Viswanthan’s method/device by adding Ali’s disclosure in order to provide seamless service.
Conclusion
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMINA CHOUDHRY whose telephone number is (571)270-7102. The examiner can normally be reached on Monday to Thursday (7:30 a.m. to 5.00p.m.).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yemane Mesfin can be reached on (571)272-3927. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SAMINA F CHOUDHRY/ Primary Examiner, Art Unit 2462