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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/13/2025 has been entered.
Claim status
This action is in response to applicant filed on 11/24/2025
Claims 1-58 have been previously cancelled.
Claims 59, 68, 69 and 77 have been amended.
Claims 59-78 are pending for examination.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 78 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SARAIVA ET AL("A priori selection of high accuracy mobile station position estimates", 2014 INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS), IEEE, 17 August 2014 (2014-08-17), pages 1-5, XP032677535, DOI: 10.1109/ITS.2014.6947960 [retrieved on 2014-11-05])
Regarding claim 78: Saraiva disclose an apparatus, comprising:
at least one processor (Fig. 1, item 20), and at least one memory including computer code (Fig. 1, item 40,42), the at least one memory and the computer code configured, with the at least one processor, to cause the apparatus at least to:
use a trained neural network model to determine an accuracy of a determined position for a communication device using a location method of a plurality of different location methods wherein input data to the trained neural network comprises the determined position (Abstract; paragraph bridging columns 1 and 2 of page 1; first paragraph of section "Ill. PROCEDURE FOR A PRIORI ACCURACY CLASSIFICATION OF MS POSITION ESTIMATES"; section "Ill-C. ANN Accuracy Classification"; last page, from Table III until the end of the article; These passages disclose using an Artificial Neural Network, ANN, for classifying the accuracy of MS position estimates produced (i.e. determined position) by the method RF-FING+RTD-PRED, which is a network based DCM positioning technique. The ANN classifies determined positions into three accuracy classes: high, medium and low), wherein input data to the trained neural network comprises the determined position (section "IV-C. ANN Training and Topology”)
wherein said neural network model is trained offline using at least one training set of data for the location method of the plurality of different location methods. (section "IV-C. ANN Training and Topology”).
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.
Claim(s) 59-63, 67-69 and 73-76 is/are rejected under 35 U.S.C. 103 as being unpatentable over Niemenmaa et al. (US 2014/0155096) in view of Tenny et al. (US 2020/0367022).
Regarding claim 59: Niemenmaa disclose an apparatus, comprising:
at least one processor (Fig. 1, item 20), and
at least one memory storing instructions that (Fig. 1, item 40,42), that, when executed by the at least one processor, cause the apparatus at least to:
receive a request for a location of a communication device with information indicating a target location quality of service associated with said location (¶0044-0045);
based on the information, determine a location method from a plurality of location methods to use and one or more parameters for said determined location method (¶0044-0047 & ¶0051).
Niemenmaa does not explicitly disclose wherein the one or more parameters comprise assistance data from an NG-RAN, and the NG-RAN is configured to provide access to a core network.
In analogous art regarding location services, Tenny disclose wherein the one or more parameters comprise assistance data from an NG-RAN (Fig. 1, 8 and ¶0100:The assistance data may include the position of several NG-RAN 650 nodes used in triangulating the UE 640 position. The assistance data is transmitted to the UE 640 at step 4. At step 5, the UE 640 measures positioning reference signals (PRSs) transmitted from the NG-RAN 650 nodes), and the NG-RAN is configured to provide access to a core network (Fig. 1, ¶0050- ¶0051)).
Before the effective filing date, it would have been obvious to the one of the ordinary skill in the art to include the feature of wherein the one or more parameters comprise assistance data from an NG-RAN, and the NG-RAN is configured to provide access to a core network, as disclose by Tenny, to the system of Niemenmaa. The motivation is to increase the accuracy of the determination.
Regarding claim 60: The combination of Niemenmaa and Tenny disclose the apparatus of claim 59, wherein the location quality of service of said location comprises a location accuracy (Niemenmaa: ¶0042).
Regarding claim 61: The combination of Niemenmaa and Tenny disclose the apparatus of claim 59, wherein the instructions, executed by the at least one processor, cause the apparatus at least to: use the determined location method with the one or more parameters to determine said location for said communication device (Niemenmaa: ¶0047).
Regarding claim 62: The combination of Niemenmaa and Tenny disclose the apparatus of claim 61, wherein the instructions, executed by the at least one processor, cause the apparatus at least to: to cause the apparatus at least to: determine a location quality of service of said determined location for said communication device (¶0047).
Regarding claim 63: The combination of Niemenmaa and Tenny disclose the apparatus of claim 62, , wherein the instructions, executed by the at least one processor, cause the apparatus at least to: use a different location method and a different parameter to determine the location for said communication device when said determined location quality of service of said determined location does not meet the target location quality of service (Niemenmaa: ¶0045).
Regarding claim 67: The combination of Niemenmaa, Tenny and Saraiva disclose the apparatus of claim 59, wherein the information indicating the target location quality of service comprises at least one of: a quality of service (QoS) class, or a required latency (Niemenmaa: ¶0045).
Regarding claim 68: The combination of Niemenmaa and Tenny disclose the apparatus of claim 59, the assistance data from the radio access network is assistance data for said communication device (Tenny: Fig. 1, 8 and ¶0100:The assistance data may include the position of several NG-RAN 650 nodes used in triangulating the UE 640 position. The assistance data is transmitted to the UE 640 at step 4. At step 5, the UE 640 measures positioning reference signals (PRSs) transmitted from the NG-RAN 650 nodes).
Regarding claim 69: Niemenmaa disclose an apparatus, comprising:
at least one processor (Fig. 1, item 20), at least one memory storing instructions that (Fig. 1, item 40,42), that, when executed by the at least one processor, cause the apparatus at least to:
use a location method with one or more parameters to determine a location for a communication device (¶0044-0047 & ¶0051);
determine a location quality of service of said location (¶0044-0045); and if said determined location quality of service does not meet a target location quality of service for said position, use a different location method and a different parameter (¶0045).
Niemenmaa does not explicitly disclose wherein the one or more parameters comprise assistance data from an NG-RAN, and the NG-RAN is configured to provide access to a core network.
In analogous art regarding location services, Tenny disclose wherein the one or more parameters comprise assistance data from an NG-RAN (Fig. 1, 8 and ¶0100:The assistance data may include the position of several NG-RAN 650 nodes used in triangulating the UE 640 position. The assistance data is transmitted to the UE 640 at step 4. At step 5, the UE 640 measures positioning reference signals (PRSs) transmitted from the NG-RAN 650 nodes), and the NG-RAN is configured to provide access to a core network (Fig. 1, ¶0050- ¶0051)).
Before the effective filing date, it would have been obvious to the one of the ordinary skill in the art to include the feature of wherein the one or more parameters comprise assistance data from an NG-RAN, and the NG-RAN is configured to provide access to a core network, as disclose by Tenny, to the system of Niemenmaa. The motivation is to increase the accuracy of the determination.
Regarding claim 73: The combination of Niemenmaa and Tenny disclose the apparatus of claim 49, wherein the at least one memory and the computer code are further configured, with the at least one processor, to cause the apparatus at least to: receive a request for said location of the communication device with information indicating the target location quality of service associated with said location (Niemenmaa: ¶0044-0045).
Regarding claim 74: The combination of Niemenmaa and Tenny disclose the apparatus of claim 53, wherein the at least one memory and the computer code are further configured, with the at least one processor, to cause the apparatus at least to: determining which of a plurality of location methods to use and one or more parameters for said determined location method based on said information (Niemenmaa: ¶0045).
Regarding claim 75: The combination of Niemenmaa and Tenny disclose the apparatus of claim 49, wherein the determined location quality of service of said location comprises a location accuracy (Niemenmaa: ¶0042).
Regarding claim 76: The combination of Niemenmaa and Tenny disclose the apparatus of claim 53, wherein the information indicating the target location quality of service comprises at least one of: a quality of service (QoS) class, or a required latency (Niemenmaa: ¶0045).
Regarding claim 77: The combination of Niemenmaa and Tenny disclose the apparatus of claim 74, wherein the assistance data from the radio access network is assistance data for said communication device (Tenny: Fig. 1, 8 and ¶0100:The assistance data may include the position of several NG-RAN 650 nodes used in triangulating the UE 640 position. The assistance data is transmitted to the UE 640 at step 4. At step 5, the UE 640 measures positioning reference signals (PRSs) transmitted from the NG-RAN 650 nodes),
Claim(s) 64-66 & 70-72 is/are rejected under 35 U.S.C. 103 as being unpatentable over Niemenmaa et al. (US 2014/0155096) in view of Tenny et al. (US 2020/0367022) in view of SARAIVA ET AL("A priori selection of high accuracy mobile station position estimates", 2014 INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS), IEEE, 17 August 2014 (2014-08-17), pages 1-5, XP032677535, DOI: 10.1109/ITS.2014.6947960 [retrieved on 2014-11-05])
Regarding claim 64: The combination of Niemenmaa and Tenny disclose the apparatus of claim 62, but does not explicitly disclose wherein the at least one memory and the computer code are further configured, with the at least one processor, to cause the apparatus at least to: determine the location quality of service of said determined location based on a trained neural network.
In analogous art regarding location accuracy systems, Saraiva disclose a system that determine the location quality of service of said determined location based on a trained neural network.(Abstract; paragraph bridging columns 1 and 2 of page 1; first paragraph of section "Ill. PROCEDURE FOR A PRIORI ACCURACY CLASSIFICATION OF MS POSITION ESTIMATES"; section "Ill-C. ANN Accuracy Classification"; last page, from Table III until the end of the article; These passages disclose using an Artificial Neural Network, ANN, for classifying the accuracy of MS position estimates produced by the method RF-FING+RTD-PRED, which is a network based DCM positioning technique. The ANN classifies determined positions into three accuracy classes: high, medium and low).
Before the effective filing date, it would have been obvious to the one of the ordinary skill in the art to include the feature of determine the location quality of service of said determined location based on a trained neural network, as disclose by Saraiva, to the system of the combination of Niemenmaa and Tenny. The motivation is to increase accuracy reducing human interaction.
Regarding claim 65: The combination of Niemenmaa, Tenny and Saraiva disclose the apparatus of claim 64, wherein said trained neural network is trained with respect to at least one of the location methods (Abstract; paragraph bridging columns 1 and 2 of page 1; first paragraph of section "Ill. PROCEDURE FOR A PRIORI ACCURACY CLASSIFICATION OF MS POSITION ESTIMATES"; section "Ill-C. ANN Accuracy Classification"; last page, from Table III until the end of the article; These passages disclose using an Artificial Neural Network, ANN, for classifying the accuracy of MS position estimates produced by the method RF-FING+RTD-PRED, which is a network based DCM positioning technique. The ANN classifies determined positions into three accuracy classes: high, medium and low).
Regarding claim 66: The combination of Niemenmaa, Tenny and Saraiva disclose the apparatus of claim 64, wherein said trained neural network is trained offline methods (section "IV-C. ANN Training and Topology”).
Regarding claim 70: Claim 70 is rejected for the same reason of claim 64.
Regarding claim 71: Claim 71 is rejected for the same reason of claim 65.
Regarding claim 72: Claim 72 is rejected for the same reason of claim 66.
Response to Arguments
Applicant's arguments filed 11/24/2025 have been fully considered but they are not persuasive.
Applicant’s arguments with respect to claim(s) 59-63, and 73-76 under 35 USC 102 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments in regards to claim 78 have been fully considered but they are not persuasive. Applicant argue that the prior art does not teach “wherein input data to the trained neural network comprises the determined position”
Examiner respectfully disagrees: as the prior art does teach (section "IV-C. ANN Training and Topology”) where it teaches the location measurement taken and using the same measurement to train the neural network.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR CASILLASHERNANDEZ whose telephone number is (571)270-5432. The examiner can normally be reached Monday-Friday, 8:30AM-4:30PM.
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/OMAR CASILLASHERNANDEZ/Primary Examiner, Art Unit 2689