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
Based on new ground of rejection, applicant’s argument submitted on 05/19/2026 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 Delhave in view of Yamada et al. (US 2024/0019872, hereinafter Yamada).
Regarding claims 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 local information acquired by the selected one or more mobile bodies ( para 22; 0058 and para 0085-0086 – machine learning model using a set of spatiotemporal trajectory data comprising, past and current speed, acceleration, position, and /or direction component or combination thereof and para 89; 0126– environment specific data).
Delhave does not explicitly disclose wherein, in a case where the controller selects multiple mobile bodies, the controller selects the multiple mobile bodies having different traveling directions, based on positions and traveling directions of the plurality of mobile bodies.
In an analogous art, Yamada discloses wherein in a case where the controller selects multiple mobile bodies, the controller selects the multiple mobile bodies having different traveling directions, based on positions and traveling directions of the plurality of mobile bodies (para 0007; 0051; 0082; 0091; 0097; and 0115; the movement directions of respective moving bodies are opposite). 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 Yamada ’s disclosure in order to collect more reliable data based on the current positions to have more accurate predictions.
Regarding claim 9, Delhave discloses an information processing method (Fig. 7), that causes an information processing apparatus (page 43; para 01 – controller) 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 local information acquired by the selected one or more mobile bodies (para 22; 0058 and para 0085-0086 – machine learning model using a set of spatiotemporal trajectory data comprising, past and current speed, acceleration, position, and /or direction component or combination thereof and para 89; 0126– environment specific data).
Delhave does not explicitly disclose wherein, in a case where the controller selects multiple mobile bodies, the controller selects the multiple mobile bodies having different traveling directions, based on positions and traveling directions of the plurality of mobile bodies.
In an analogous art, Yamada discloses wherein in a case where the controller selects multiple mobile bodies, the controller selects the multiple mobile bodies having different traveling directions, based on positions and traveling directions of the plurality of mobile bodies (para 0007; 0051; 0082; 0091; 0097; and 0115; the movement directions of respective moving bodies are opposite). 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 Yamada ’s disclosure in order to collect more reliable data based on the current positions to have more accurate predictions.
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, in a case where 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 local information for the specified machine learning, the machine learning being executed based on the acquired local information (page 26; para 02 and page 44; para o2– environment specific data).
Delhave does not explicitly disclose that the controller is configured to transmit information for specifying the position and travelling direction of the mobile body; wherein, in a case where the controller selects multiple mobile bodies, the controller selects the multiple mobile bodies having different traveling directions, based on positions and traveling directions of the plurality of mobile bodies.
In an analogous art, Yamada discloses that the controller is configured to transmit information for specifying the position and travelling direction of the mobile body (para 0006-0007; 0097; positions and directions), wherein in a case where the controller selects multiple mobile bodies, the controller selects the multiple mobile bodies having different traveling directions, based on positions and traveling directions of the plurality of mobile bodies (para 0007; 0051; 0082; 0091; 0097; and 0115; the movement directions of respective moving bodies are opposite). 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 Yamada ’s disclosure in order to collect more reliable data based on the current positions to have more accurate predictions.
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 analogous art, Yamada discloses that the mobile bodies are selected based on information specifying the position (para 0079-0080; moving body position) and traveling direction of the plurality of mobile bodies (para 0047; 0051; and 0097; movement directions of the respective moving bodies). 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 Yamada’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 analogous art, Yamada discloses that the information specifying the positions (para 0079-0080; moving body position) and traveling directions of the plurality of mobile bodies (para 0047; 0051; and 0097; movement directions of the respective moving bodies). 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 Yamada’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, Yamada discloses that the information specifying the positions (para 0079-0080; moving body position) and traveling directions of the plurality of mobile bodies (para 0047; 0051; and 0097; movement directions of the respective moving bodies). 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 Yamada’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/Yamada 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, Yamada discloses that the information specifying the positions (para 0079-0080; moving body position) and traveling directions of the plurality of mobile bodies (para 0047; 0051; and 0097; movement directions of the respective moving bodies). 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 Yamada’s disclosure in order to improve the reliability and usefulness of the data.
Delhave/Yamada 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/Yamada’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, Yamada discloses that the information specifying the positions (para 0079-0080; moving body position) and traveling directions of the plurality of mobile bodies (para 0047; 0051; and 0097; movement directions of the respective moving bodies). 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 Yamada’s disclosure in order to improve the reliability and usefulness of the data.
Delhave/Yamada 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/Yamada’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/Yamada in view of Ali (WO 2021256978, hereinafter Ali).
Regarding claims 23, 26, 29 and 38, Delhaye/Yamada 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/Yamada’s method/device by adding Ali’s disclosure in order to provide seamless service.
Conclusion
6. Applicant's amendment has necessitated the new ground of rejection. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).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.
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/SAMINA F CHOUDHRY/Primary Examiner, Art Unit 2462
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