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 .
DETAILED ACTION
Response to Arguments
1. Applicant's arguments, filed on 2/23/2026 with respect to claims 1-15 and 17-20 in the remarks, have been considered but are moot in view of the new ground(s) of rejection necessitated by the new limitations added to claims 1, 11 and 19. See the rejection below of claims 1, 11 and 19 for relevant citations found in Pezeshki disclosing the newly added limitations.
Claim Rejections - 35 USC § 102
2. 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 8-15 and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pezeshki et al., (US 2021/0336683), (hereinafter, Pezeshki).
Regarding claim 1, Pezeshki discloses a method for information processing, comprising:
receiving, by a terminal (= UE), first information sent by a first device entity
(= gNB/network entity can transmit measurement configuration information to the UE, see [0086, 0074 and 0013]; whereby the measurement configuration information is being associated with the “first information”); and
performing, by the terminal, a positioning and related operation about surrounding environment sensing based on the first information (= network entity can select beams based on UE position/orientation, see [0033 and 0074]; measurement configuration may include information configuring the UE to report measurement for one or more proposed beams determined by the network entity, see [0086]; and the UE may receive the SSBs and may measure a signal quality metric for proposed beams and one or more other beams, see [0087 and 0090]), wherein the first information comprises at least one of the following:
a first request, wherein the first request is used to indicate the terminal to perform the positioning and related operation about surrounding environment sensing based on a first processing method (= position determination may be performed by UE; and training model using data sets and sharing parameter for the trained model with UE, see [0059]; input machine learning model predicts a set of beams, see, [0060-61 and 0067-68]);
first sub-information for indicating the first processing method (= UE may receive information including conditions for reporting measurements for other beams in the configuration information received from the network entity, see [0088]);
first assistance information used for the first processing method;
result information obtained by processing based on a second processing;
an indication for requesting second sub-information, wherein the second sub- information is terminal related information or information obtained by hiding an information feature of the terminal related information, the terminal related information comprises at least one of the following: positioning related information, service information, beam management (BM) related information, channel state information (CSI) related information, mobility related information, or resource scheduling information, a manner for hiding the information feature comprises any one of the following: encryption processing or performing feature abstraction processing by using a preset neural network; or
an indication for requesting third sub-information, wherein the third sub-information is used to update a third processing method, wherein the first assistance information meets one of the following: the first assistance information is obtained by performing first preset processing on first target information; the first assistance information is the first target information; or when the terminal and the first device entity are authenticated through a preset interactive authentication process, the first assistance information is the first target information, wherein: the first target information comprises second device entity related information or information obtained by performing second preset processing on the second device entity related information; and the first preset processing or the second preset processing comprises at least one of the following: encryption processing or feature abstraction processing performed by using the preset neural network.
Regarding claim 2, as mentioned in claim 1, Pezeshki discloses the method, wherein the performing, by the terminal, the positioning and related operation about surrounding environment sensing based on the first information comprises at least one of the following:
performing, by the terminal, positioning and surrounding environment sensing based on the first processing method;
sending, by the terminal, the second sub-information or the third sub-information to a target device, wherein the target device is the first device entity or a second device entity (= UE may perform measurement for each beam, selects beam with highest measured signal quality metrics and report the selected beams to the network entity, see [0087]); or
performing, by the terminal, positioning and related processing about surrounding environment sensing based on at least part of the result information.
Regarding claim 3, as mentioned in claim 1, Pezeshki discloses the method, wherein the second sub-information further comprises measurement information of a target cell (= network 100 includes a number of base stations covering each cell, see [0041]; and proposed beam/additional information include cell id, see [0012 and 0079]), and the measurement information comprises at least one of beam measurement information(= beam measurement, see, [0075 and 0087]), channel measurement information, or RRM measurement information; or the second sub-information comprises information obtained by hiding an information feature of at least one measurement information of a target cell, and the target cell comprises at least one of a serving cell and a neighboring cell.
Regarding claim 4, as mentioned in claim 1, Pezeshki discloses the method, wherein when the first information comprises an indication for requesting the second sub-information, the first information further comprises at least one of the following: third sub-indication information that is used to indicate a manner for hiding the information feature; or fourth sub-indication information that is used to determine information content of the terminal related information (= configuration information including the proposes beams of highest signal quality; and for the UE to measure beam with highest signal quality, see [0075]; whereby the beams information are being associated with the “terminal related information”).
Regarding claim 8, as mentioned in claim 1, Pezeshki discloses the method, wherein the first processing method comprises a processing method performed by using a first neural network, the first sub-information is used to indicate neural network related information of the first neural network, and the neural network related information comprises at least one of a structure of the neural network or a parameter of the neural network (= (= using data sets to train machine learning model to prediction best beam, see [0059-61 and 0067-68]).
Regarding claim 9, as mentioned in claim 1, Pezeshki discloses the method, wherein the first device entity comprises a set comprising one or at least two of the following: a base station, a location management function (LMF), a network data analytics function (NWDAF), a first network element, or a second network element, wherein the first network element is used to execute a network element of the second processing method, and the second network element is used for at least one of the following: collecting information required for performing the second processing method; or performing information calculation on information required for performing the second processing method (= machine learning based algorithm may be trained and deploy to the network entity, see, [0060, 0070 and 0092]).
Regarding claim 10, as mentioned in claim 1, Pezeshki discloses the method, wherein when the first information is result information obtained by processing based on the second processing method (= trained ML techniques), before the receiving, by the terminal, the first information sent by the first device entity, the method further comprises: sending, by the terminal, the second sub-information to the first device entity, wherein the second sub-information is used to determine the result information
(= given an input/location of UE, ML trained with various techniques to learn optimal beam prediction and selection for the beam measurement configuration, see [0060-61]; whereby the output from the training is being associated with the “result information”).
Regarding claim 11, Pezeshki discloses a method for information processing, comprising:
sending, by a first device entity, first information to a terminal (= gNB/network entity can transmit measurement configuration information to the UE, see [0086, 0074 and 0013]; whereby the measurement configuration information is being associated with the “first information”), wherein the first information is used to trigger the terminal to perform a positioning and related operation about surrounding environment sensing (= network entity can select beams based on UE position/orientation, see [0033]; measurement configuration may include information configuring the UE to report measurement for one or more proposed beams determined by the network entity, see [0086]; and the UE may receive the SSBs and may measure a signal quality metric for proposed beams and one or more other beams, see [0087 and 0090]),
wherein the first information comprises at least one of the following:
a first request, wherein the first request is used to indicate the terminal to perform the positioning and related operation about surrounding environment sensing based on a first processing method (= position determination may be performed by UE; and training model using data sets and sharing parameter for the trained model with UE, see [0059]; input machine learning model predicts a set of beams, see, [0060-61 and 0067-68]);
first sub-information for indicating the first processing method (= UE may receive information including conditions for reporting measurements for other beams in the configuration information received from the network entity, see [0088]);
first assistance information used for the first processing method;
result information obtained by processing based on a second processing method;
an indication for requesting second sub-information, wherein the second sub-information is terminal related information or information obtained by hiding an information feature of the terminal related information, wherein the terminal related information comprises at least one of the following:
positioning related information, service information, beam management (BM) related information, channel state information (CSI) related information, mobility related information, or resource scheduling information, and a manner for hiding the information feature comprises any one of the following:
encryption processing or performing feature abstraction processing by using a preset neural network; or an indication for requesting third sub-information, wherein the third sub-information is used to update a third processing method, wherein the first assistance information meets one of the following: the first assistance information is obtained by performing first preset processing on first target information; the first assistance information is the first target information; or when the terminal and the first device entity are authenticated through a preset interactive authentication process, the first assistance information is the first target information, wherein the first target information comprises second device entity related information or information obtained by performing second preset processing on the second device entity related information.
Regarding claim 12, as mentioned in claim 11, Pezeshki discloses the method, wherein the positioning and related operation about surrounding environment sensing comprises at least one of the following: performing positioning and surrounding environment sensing based on the first processing method; sending the second sub-information or the third sub-information to a target device, wherein the target device is the first device entity or a second device entity(= UE may perform measurement for each beam, selects beam with highest measured signal quality metrics and report the selected beams to the network entity, see [0087]); or performing positioning and related processing about surrounding environment sensing based on at least part of the result information.
Regarding claim 13, as mentioned in claim 11, Pezeshki discloses the method, wherein the second sub-information further comprises measurement information of a target cell (= network 100 includes a number of base stations covering each cell, see [0041]; and proposed beam/additional information include cell id, see [0012 and 0079]), and the measurement information comprises at least one of beam measurement information (= beam measurement, see, [0075 and 0087]), channel measurement information, or RRM measurement information; or the second sub-information comprises information obtained by hiding an information feature of at least one measurement information of a target cell, and the target cell comprises at least one of a serving cell or a neighboring cell.
Regarding claim 14, as mentioned in claim 11, Pezeshki discloses the method, wherein when the first information comprises an indication for requesting the second sub-information, the first information further comprises at least one of the following: third sub-indication information that is used to indicate a manner for hiding the information feature; or fourth sub-indication information that is used to determine information content of the terminal related information (= configuration information including the proposes beams of highest signal quality; and for the UE to measure beam with highest signal quality, see [0075]; whereby the beams information are being associated with the “terminal related information”).
Regarding claim 15, as mentioned in claim 11, Pezeshki discloses the method, wherein the first processing method comprises a processing method performed by using a first neural network, and the first sub-information is used to indicate neural network related information of the first neural network (= using data sets to train machine learning model to prediction best beam, see [0059-61]).
Regarding claim 18, as mentioned in claim 11, Pezeshki discloses the method, wherein the first device entity comprises a set comprising one or at least two of the following: a base station, a location management function (LMF), a network data analytics function (NWDAF), a first network element, and a second network element, wherein the first network element is used to execute a network element of the second processing method, and the second network element is used for at least one of the following: collecting information required for performing the second processing method; or performing information calculation on information required for performing the second processing method. (= machine learning based algorithm may be trained and deploy to the network entity, see, [0060, 0070 and 0092]).
Regarding claim 19, Pezeshki disclose method for information processing, comprising:
sending, by a second device entity (= plurality of network entities), first target information to a first device entity (= gNB may initially generate a proposed set of beams based on position information derived from signals received from plurality of network entities, see [0084]; whereby the position information is being associated with the “first targe information”), wherein the first target information comprises second device entity related information (= proposed set of beams based on position information derived from signals received from plurality of network entities, see [0084]), or information obtained by performing second preset processing on the second device entity related information, and the first target information is used to determine first information sent by the first device entity to a terminal (= gNB can input the received UE position information into a machine learning model trained to generate a proposed set of beams, see [0085]; after generating the proposed set of beams, the gNB can transmit measurement configuration information to the UE, see [0086]; whereby the measurement configuration information is being associated with the “first information”),
wherein the first information is used to trigger the terminal to perform a positioning and related operation about surrounding environment sensing (= network entity can select beams based on UE position/orientation, see [0033 and 0074]; measurement configuration may include information configuring the UE to report measurement for one or more proposed beams determined by the network entity, see [0086]; and the UE may receive the SSBs and may measure a signal quality metric for proposed beams and one or more other beams, see [0087 and 0090]), and the first information comprises at least one of the following:
a first request, wherein the first request is used to indicate the terminal to perform the positioning and related operation about surrounding environment sensing based on a first processing method (= position determination may be performed by UE; and training model using data sets and sharing parameter for the trained model with UE, see [0059]; input machine learning model predicts a set of beams, see, [0060-61 and 0067-68]);
first sub-information for indicating the first processing method (= UE may receive information including conditions for reporting measurements for other beams in the configuration information received from the network entity, see [0088]);
first assistance information used for the first processing method; or
result information obtained by processing based on a second processing method, wherein the first assistance information meets one of the following: the first assistance information is obtained by performing first preset processing on the first target information; the first assistance information is the first target information; or when the terminal and the first device entity are authenticated through a preset interactive authentication process, the first assistance information is the first target information, wherein the first target information comprises second device entity related information or information obtained by performing second preset processing on the second device entity related information.
Regarding claim 20, as mentioned in claim 19, Pezeshki discloses the method, wherein the positioning and related operation about surrounding environment sensing comprises at least one of the following:
performing positioning and surrounding environment sensing based on the first processing method;
sending second sub-information or third sub-information to a target device, wherein the target device is the first device entity or a second device entity (= UE may perform measurement for each beam, selects beam with highest measured signal quality metrics and report the selected beams to the network entity, see [0087]); or
performing positioning and related processing about surrounding environment sensing based on at least part of the result information, wherein the second sub-information is terminal related information or information obtained by hiding an information feature of the terminal related information; and the third sub- information is used to update a third processing method.
Allowable Subject Matter
3. Claims 1-4, 8-15 and18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
CONCLUSION
4. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of 33the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kwasi Karikari whose telephone number is
571-272-8566.The examiner can normally be reached on M-Sat (6am – 10pm).
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Charles Appiah can be reached on 571-272-7904.
The fax phone number for the organization where this application or proceeding is assigned is 571-273-8566.
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/Kwasi Karikari/
Primary Examiner: Art Unit 2641.