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 .
The instant office communication is in response to communication filed on 02/13/2024.
Claims 1-20 are pending of which claims 1, 17 and 20 are independent.
The IDS(s) submitted on 10/24/2024 has been considered.
Internet Communications
Applicant is encouraged to submit a written authorization for Internet communications (PTO/SB/439, http://www.uspto.gov/sites/default/files/documents/sb0439.pdf) in the instant patent application to authorize the examiner to communicate with the applicant via email. The authorization will allow the examiner to better practice compact prosecution. The written authorization can be submitted via one of the following methods only: (1) Central Fax which can be found in the Conclusion section of this Office action; (2) regular postal mail; (3) EFS WEB; or (4) the service window on the Alexandria campus. EFS web is the recommended way to submit the form since this allows the form to be entered into the file wrapper within the same day (system dependent). Written authorization submitted via other methods, such as direct fax to the examiner or email, will not be accepted. See MPEP § 502.03.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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)(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.
Claim(s) 1, 2, 3, 4, 5, 12, 14, 15, 17, 20, and 21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dwivedi et al (US 20220229143 A1).
Regarding claim 1, Dwivedi discloses an apparatus (i.e. BS of Fig. 5 is the apparatus and its details is shown in Fig. 8) , comprising: at least one processor (i.e. Fig. 8 processor 803); and at least one memory (i.e. Fig. 8 Memory 805) storing instructions that, when executed by the at least one processor, cause the apparatus at least to (See Figs. 5, 7-9 and at least paragraphs 5, 28, and 30):
receive from a network entity (i.e. Fig. 5 LS/Location Server is the network entity) or transmit to a user device (i.e. UE of Fig. 5 with details in the UE of Fig.8), one or more feature set lists (i.e. Applicant defines in paragraph 79 of the specification that a feature set is one or more features of a radio channel and any characteristic of the radio channel and therefor measurements of the radio channel can be a feature set and in paragraph 79 channel response and delay measurements are given as an example of feature set. Dwivedi teaches in paragraphs 26-27 the UE sends to the BS/Apparatus channel impulse response measurements and power delay measurements of the radio channel as a feature set and herein after referred to as LOS detection measurements. In other words Dwivedi’ s LOS detection measurements are the claimed feature set lists. Dwivedi in paragraphs 41-53 provides a feature set lists includes peak value of the power delay profile of the radio channel, time difference of arrival, dynamic range of path, doppler spread of multipath, Rician distribution, Angle of Arrival ( AoA), Time of Arrival (ToA) etc…), each comprising a plurality of feature sets in a priority order or rank order associated with the apparatus or the user device (See Paragraph 65 indicating determination/classification of the path as LOS or NLOS based on a weighted average of the LOS detection measurements/methods based on priority of the loss detection ten different methods described in paragraphs 41-53. See paragraph 69 for instance First Loss Detection Method described in paragraph 44 and its associated First Loss Detection Measurement/1st Feature set has a weight of 49% and second through ninth Loss Detection Measurements/Feature Sets have a 7 to 8% weight. In paragraph 67, Dwivedi further explains priority order or rank order as “ For sequential decisions using a subset of the LOS detection methods, the methods for detecting LOS can be used together to make probability of detecting LOS very high. Every LOS detection method has a probability of an accurate LOS detection measure. To achieve higher probability of LOS detection, a subset of the methods can be used sequentially. These methods can be used sequentially in order of descending probabilities, where the method providing highest probability can be used first, in order to reduce the time it takes to arrive at a final decision.”), wherein the one or more feature set lists (i.e. Loss Detection Measurements of the different LOS detection methods are the feature set lists described in paragraphs 41-53) are based on at least one of the following:
one or more bandwidth parts configured for the apparatus or the user device, one or more bandwidth capabilities of the apparatus or the user device, or one or more radio environment scenarios associated with the apparatus or the user device (i.e. each of the Loss Detection Methods described in paragraphs 41-53 generating the Loss Detection Measurements/feature set lists are based on environment scenarios associated with the apparatus/BS or UE such as the strength/power of the received signal by the BS/UE per paragraph 44 or Time of Arrival of the signal at the UE/BS per paragraph 46, etc.…. Note limitation is in the alternative) ; and
classify or select channel measurements of the one or more received signals per measurement by the apparatus or by the user device, to estimate a likelihood of a propagation condition classification comprising one of: line-of-sight (LOS), non-line-of-sight (NLOS) and obstructed line-of-sight, based on the feature sets to determine positioning of the apparatus or the user device.(See Paragraphs 67, 68 and 69 indicating classifying LOS or NLOS path based on weight/priority/rank of the Loss Detection Measurements/feature set priority/rank weight. See Fig. 6 steps 605 and 607, Fig. 10 steps 1007 and 1008 and Fig. 11 steps 1105 and 1107 and see associated descriptions ).
Regarding claim 17, Dwivedi discloses an apparatus (i.e. BS of Fig. 5 is the apparatus and its details is shown in Fig. 8) comprising at least one processor (i.e. Fig. 8 processor 803), and at least one memory (i.e. Fig. 8 Memory 805) storing instructions which, when executed by the at least one processor, cause the apparatus at least to:
determine one or more feature set lists (i.e. The BS determines a feature set lists by conducting Loss Detection Measurements. Applicant defines in paragraph 79 of the specification that a feature set is one or more features of a radio channel and any characteristic of the radio channel and therefor measurements of the radio channel can be a feature set and in paragraph 79 channel response and delay measurements are given as an example of feature set. Per Dwivedi’s paragraphs 26-27 the Loss Detection Measurements include radio channel characteristics in the form of channel impulse response measurements and power delay and peak measurements and match Applicant’s feature set. In other words Dwivedi’ s LOS detection measurements are the claimed feature set lists. Dwivedi in paragraphs 41-53 provides a feature set lists includes peak value of the power delay profile of the radio channel, time difference of arrival, dynamic range of path, doppler spread of multipath, Rician distribution, Angle of Arrival ( AoA), Time of Arrival (ToA) etc… In Paragraph 75 Dwivedi discloses the apparatus/BS/gNB of Fig. 5 determining the Loss Detection Measurements/feature set lists and sending to the UE. ) based on at least one of the following: one or more bandwidth parts configured for one or more network entity or user device, one or more bandwidth capabilities of the one or more network entity or user device, or one or more radio environment scenarios associated with the one or more network entity or the user device(i.e. each of the Loss Detection Methods described in paragraphs 41-53 generating the Loss Detection Measurements/feature set lists are based on environment scenarios associated with the apparatus/BS or UE such as the strength/power of the received signal by the BS/UE per paragraph 44 or Time of Arrival of the signal at the UE/BS per paragraph 46, etc.…. Note limitation is in the alternative), wherein the one or more feature set lists comprise a plurality of feature sets in a priority order or rank order(See Paragraph 65 indicating determination/classification of the path as LOS or NLOS based on a weighted average of the LOS detection measurements/methods based on priority of the loss detection ten different methods described in paragraphs 41-53. See paragraph 69 for instance First Loss Detection Method described in paragraph 44 and its associated First Loss Detection Measurement/1st Feature set has a weight of 49% and second through ninth Loss Detection Measurements/Feature Sets have a 7 to 8% weight. In paragraph 67, Dwivedi further explains priority order or rank order as “ For sequential decisions using a subset of the LOS detection methods, the methods for detecting LOS can be used together to make probability of detecting LOS very high. Every LOS detection method has a probability of an accurate LOS detection measure. To achieve higher probability of LOS detection, a subset of the methods can be used sequentially. These methods can be used sequentially in order of descending probabilities, where the method providing highest probability can be used first, in order to reduce the time it takes to arrive at a final decision.”),; and
transmit the one or more feature set lists to the one or more network entity or the
user device (See paragraph 75 wherein the apparatus/BS/gNB of Fig. 5 sends the Loss Detection Measurement/feature set list to the UE and/or network entity/Location Server and Core Network) , wherein a feature set of the plurality of feature sets comprises one or more radio channel features. (Dwivedi in paragraphs 41-53 provides a feature set lists includes peak value of the power delay profile of the radio channel, time difference of arrival, dynamic range of path, doppler spread of multipath, Rician distribution, Angle of Arrival ( AoA), Time of Arrival (ToA) etc… In Paragraph 75 Dwivedi discloses the apparatus/BS/gNB of Fig. 5 determining the Loss Detection Measurements/feature set lists and sending to the UE. )
Regarding claim 17, Dwivedi discloses a user device (i.e. Fig. 5 UE and detail of UE in Fig. 7), comprising: at least one processor (Fig. 7 processor 703); and at least one memory (Fig. 7 memory 705) storing instructions that, when executed by the at least one processor, cause the user device at least to:
receive from a network entity (i.e. per paragraph 75 Network Entity being either the BS/gNB or LS/Local Server transmit to the UE of Fig. 5 LOS Detection measurements including Channel Impulse Response measurements and peak power measurements per Dwivedi’s paragraph’s 26-27 and corresponds to paragraph 79 in Applicant’s specification feature set lists), one or more feature set lists (Dwivedi in paragraphs 41-53 provides a feature set lists includes peak value of the power delay profile of the radio channel, time difference of arrival, dynamic range of path, doppler spread of multipath, Rician distribution, Angle of Arrival ( AoA), Time of Arrival (ToA) etc…, each comprising a plurality of feature sets in a priority order or rank order(See Paragraph 65 indicating determination/classification of the path as LOS or NLOS based on a weighted average of the LOS detection measurements/methods based on priority of the loss detection ten different methods described in paragraphs 41-53. See paragraph 69 for instance First Loss Detection Method described in paragraph 44 and its associated First Loss Detection Measurement/1st Feature set has a weight of 49% and second through ninth Loss Detection Measurements/Feature Sets have a 7 to 8% weight. In paragraph 67, Dwivedi further explains priority order or rank order as “ For sequential decisions using a subset of the LOS detection methods, the methods for detecting LOS can be used together to make probability of detecting LOS very high. Every LOS detection method has a probability of an accurate LOS detection measure. To achieve higher probability of LOS detection, a subset of the methods can be used sequentially. These methods can be used sequentially in order of descending probabilities, where the method providing highest probability can be used first, in order to reduce the time it takes to arrive at a final decision.”), associated with the user device (i.e. Loss Detection Measurements are UE-BS paired – paragraph 75), wherein the one or more feature set lists are based on at least one of the following: one or more bandwidth parts configured for the user device, one or more bandwidth capabilities of the user device, or one or more radio environment scenarios associated with the user device(i.e. each of the Loss Detection Methods described in paragraphs 41-53 generating the Loss Detection Measurements/feature set lists are based on environment scenarios associated with the apparatus/BS or UE such as the strength/power of the received signal by the BS/UE per paragraph 44 or Time of Arrival of the signal at the UE/BS per paragraph 46, etc.…. Note limitation is in the alternative),; and
classify or select channel measurements of the one or more received signals per measurement to estimate a likelihood of a propagation condition classification comprising one of: line-of-sight (LOS), non-line-of-sight (NLOS) and obstructed line-of-sight, based on the feature sets to determine positioning of the user device. .(See Paragraphs 67, 68 and 69 indicating classifying LOS or NLOS path based on weight/priority/rank of the Loss Detection Measurements/feature set priority/rank weight. See Fig. 6 steps 605 and 607, Fig. 10 steps 1007 and 1008 and Fig. 11 steps 1105 and 1107 and see associated descriptions ).
Regarding claim 2, Dwivedi discloses the apparatus according to claim 1, wherein the apparatus is further caused to: estimate intermediary features comprising one or more of: time of arrival (TOA), angle of arrival (AoA), time of flight (ToF), and range to be used for positioning of the apparatus or the user device. (Dwivedi discloses a LOS Detection Measurements/ set features based on ToA (Time of Arrival ) in paragraph 58 and based on distance range in paragraph 59. See also paragraphs 46and 47 and Figs. 2 and 3 )
Regarding claim 3, Dwivedi discloses the apparatus according to claim 1, wherein the priority order or rank order of the plurality of feature sets is based on one or a combination of: a classification accuracy using each of the plurality of feature sets (i.e. per paragraph 67 classification accuracy of each LOS detection method is being confirmed by explicitly stating that “Every LOS detection method has a probability of an accurate LOS detection measure.”), a positioning accuracy using each of the plurality of feature sets, or a separation or separability of anyone of the propagation condition classification in a feature domain.
Regarding claim 4, Dwivedi discloses the apparatus according to claim 1, wherein the apparatus is further caused to:
indicate to the network entity (i.e. Location Server of Fig. 5) or receive from the user device, an outcome of the channel measurement classification per measurement pertaining to the likelihood of the propagation condition classification and/or the estimated intermediatory feature.(i.e. per paragraph 97 and operation 11105 of Fig. 11 the BS/gNB processor 803 transmits outcome of the channel measurement classification per measurement pertaining to the likelihood of the propagation condition classification as a LOS or NLOS. See also paragraphs 75 and 89)
Regarding claim 5, Dwivedi discloses the apparatus according to claim 1, wherein the apparatus is further caused to: indicate to the network entity or receive from the user device, uplink channel measurements comprising at least: channel impulse response (CIR), power delay profile (PDP) and Reference Signal Received Power (RSRP). (See Paragraphs 26, 27, 42 and 50 wherein the BS/gNB receives channel impulse response (CIR), power delay profile (PDP) and Reference Signal Received Power (RSRP).)
Regarding claim 12, Dwivedi discloses the apparatus according to claim 1, further being caused to: report, to the network entity (i.e. Location Server of Fig. 5 is the network entity and BS of Fig. 5 is the apparatus) or receive a report from the user device, the feature set selected by the apparatus. (Per paragraphs 75 and 89 Fig. 5 BS transmitting to Fig. 5 LS the feature set selected by the BS as Loss Detection Measurement and result)
Regarding claim 14, Dwivedi discloses the apparatus according to claim 1, wherein the apparatus relays the plurality of feature sets from the network entity to the user device, and the apparatus comprises one of:
a network node (gNB),
a RAN/edge network entity,
a RAN intelligent controller (RIC),
a C-RAN node, and a NG-RAN node with AI/ML capability. (See paragraphs 30, 75, and 77 and Figs. 5, 8 and 9 where the apparatus can be a BS/gNB or RAN Network Entity/LS)
Regarding claim 15, Dwivedi discloses the apparatus according to claim 1, wherein the network entity comprises one of: a location management function, a core network node, a network data analytics function (NWDAF), and a RAN location management component. (See paragraphs 75 and 77 and Figs. 5 and 9 wherein a location server of Fig. 5 corresponds to a location management function and core network node are disclosed.)
Regarding claim 21, claim 21 is rejected in the same scope as claim 15.
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 (i.e., changing from AIA to pre-AIA ) 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 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) 6, 9, 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dwivedi in view of Alawieh et al (US 20250056488 A1)
Regarding claim 6, Dwivedi discloses the apparatus according to claim 1, however, Dwivedi fails to disclose wherein the apparatus is further caused to:
indicate to the network entity or receive from the user device, one or more of: the
bandwidth part, the bandwidth capability and computational capabilities within which signal measurements are performed by either the apparatus or by the user device.
Alawieh, in the same endeavor, discloses indicating to the network entity or receive from the user device, one or more of: the bandwidth part, the bandwidth capability and computational capabilities within which signal measurements are performed by either the apparatus or by the user device. (See paragraph 391 where the network has UE computational capabilities to run trained model to estimate position. See paragraphs 398-399 where the UE sends the network/LMF its capability to run Machine Learned model.)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Alawieh ‘s technique of indicating AI/ML support capability, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Alawieh ‘s technique of indicating AI/ML support capability, since Alawieh states in paragraph 399, 400 and 622 hat the modification results in allowing the UE to use AI/ML models in particular compressed models saving cost and reducing network traffic while maintaining the accuracy of the uncompressed models.
Regarding claim 18, Dwivedi discloses the apparatus according to claim 17, but fails to disclose wherein the one or more feature set lists are determined based further on a computational capability of the one or more network entity or the user device.
Alawieh, in the same endeavor, discloses wherein the one or more feature set lists are determined based further on a computational capability of the one or more network entity or the user device. (See paragraph 391 where the network has UE computational capabilities to run trained model to estimate position. See paragraphs 398-399 where the UE sends the network/LMF its capability to run Machine Learned model.)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Alawieh ‘s technique of indicating AI/ML support capability, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Alawieh ‘s technique of indicating AI/ML support capability, since Alawieh states in paragraphs 399, 400 and 622 that the modification results in allowing the UE to use AI/ML models in particular compressed models saving cost and reducing network traffic while maintaining the accuracy of the uncompressed models.
Regarding claim 9, Dwivedi discloses the apparatus according to claim 1, but fails to disclose further being caused to:
transmit, to the network entity or receive from the user device, a request for the one
or more feature set lists; and
receive from the network entity or transmit to the user device, the one or more
feature set lists in response to transmitting or receiving the request.
Alawieh, in the same endeavor, discloses transmit, to the network entity or receive from the user device, a request for the one or more feature set lists (i.e. paragraphs 398-400 indicate the BS/LMF receives from a UE a request in a RequestAssistanceData message asking for ML model for LOS classification); and receive from the network entity or transmit to the user device, the one or more
feature set lists in response to transmitting or receiving the request. (i.e. paragraphs 398-400 indicate the BS/LMF transmits to a UE the requested ML model for LOS classification using ProvideAssistanceData)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Alawieh ‘s technique of indicating AI/ML support capability, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Alawieh ‘s technique of indicating AI/ML support capability, since Alawieh states in paragraphs 399, 400 and 622 that the modification results in allowing the UE to use AI/ML models in particular compressed models saving cost and reducing network traffic while maintaining the accuracy of the uncompressed models.
Regarding claim 10, Dwivedi discloses the apparatus according to claim 1, but fails to disclose further being caused to:
transmit, to the network entity or receive from the user device, an indication for
updating the one or more feature set lists.
Alawieh, in the same endeavor, discloses being caused to:
transmit, to the network entity or receive from the user device, an indication for
updating the one or more feature set lists. (In paragraph 191 Alawieh discloses configuring to receive update information on the machine-learning model or on parameters of the machine-learning model from a network entity 200 of the wireless communication system. The user equipment 100 may, e.g., be configured to update the machine-learning model or the parameters of the machine-learning model using the update information. See paragraphs 98 and 100)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Alawieh ‘s technique of indicating AI/ML support capability, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Alawieh ‘s technique of indicating AI/ML support capability, since Alawieh states in paragraphs 399, 400 and 622 that the modification results in allowing the UE to use AI/ML models in particular compressed models saving cost and reducing network traffic while maintaining the accuracy of the uncompressed models.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dwivedi in view of Hasegawa et al (US 20250142516 A1)
Regarding claim 11, Dwivedi discloses the apparatus according to claim 1, but fails to disclose further being caused to: wherein the one or more feature set lists are associated with a validity time possibly based on a mobility condition of the apparatus, wherein the validity time indicates how long the one or more feature set lists are valid.
Hasegawa, in the same endeavor discloses wherein the one or more feature set lists are associated with a validity time possibly based on a mobility condition of the apparatus, wherein the validity time indicates how long the one or more feature set lists are valid. (See paragraphs 166 and 199 disclose validity time of a feature set associated AI/ML with Position Reference Signal (PRS))
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Hasegawa ‘s technique of using validity time, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Hasegawa ‘s technique of using validity time, since Hasegawa states in paragraph 178 that the modification allows the WTRU to perform weight validation allowing the WRTU to determine the weights associated with certain PRS parameters for an AI/ML algorithm, past measurements, and/or pre-configured data.
Claim(s) 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dwivedi in view of Shafin et al (US 20220095267 A1).
Regarding claim 7, Dwivedi discloses the apparatus according to claim 1, but fails to disclose wherein the apparatus is further caused to: indicate to the network entity or receive from the user device, conditions associated with the radio environment scenarios the apparatus is serving or the user device is located in, wherein the environmental scenarios comprising one or more of: user density, clutter density, sidelink (SL) channel conditions, sidelink reference signal received power (SL RSRP), sidelink channel busy ratio (SL CBR), and contextual environment type or information.
Shafin, in the same endeavor, discloses indicate to the network entity or receive from the user device, conditions associated with the radio environment scenarios the apparatus is serving or the user device is located in, wherein the environmental scenarios comprising one or more of: user density, clutter density, sidelink (SL) channel conditions, sidelink reference signal received power (SL RSRP), sidelink channel busy ratio (SL CBR), and contextual environment type or information. (See Shafin Fig. 5 and paragraph 51 – triggering condition to collect Loss Detection measurements/feature set includes user density/evidence of a threshold number of UEs from which data can be collected in the service zone of the base station.)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Shafin ‘s technique of triggering new measurement, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Shafin ‘s technique of triggering new measurement, since Shaffin states in paragraphs 5-6 and 59 that the modification results in performing accurate classification of LoS/NLoS point identification in wireless communication networks that uses artificial intelligence.
Regarding claim 19, Dwivedi discloses the apparatus according to claim 17, but fails to disclose, wherein the transmission of the one or more feature set lists is triggered based on changes in a radio environment.
Shafin, in the same endeavor, discloses wherein the transmission of the one or more feature set lists is triggered based on changes in a radio environment. (See Shafin Fig. 5 and paragraph 51 – triggering condition to collect Loss Detection measurements/feature set includes conditions indicating a poor mapping of base station coverage to usage demand, such as a high incidence of dropped calls or utilization of a particular base station dropping below a threshold value relative to neighbor base stations. Further examples of predetermined conditions which may trigger collection include evidence of a threshold number of UEs from which data can be collected in the service zone of the base station..)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Shafin ‘s technique of triggering new measurement, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Shafin ‘s technique of triggering new measurement, since Shafin states in paragraphs 5-6 and 59 that the modification results in performing accurate classification of LoS/NLoS point identification in wireless communication networks that uses artificial intelligence.
Claim(s) 8 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dwivedi in view of Khan Beigi et al (US 20260019124 A1, hereinafter referred to as Khan).
Regarding claim 8, Dwivedi discloses the apparatus according to claim 1, but fails to disclose wherein the one or more feature set lists are based further on one or more frequency ranges, and wherein the feature set is selected based further on a frequency range used by the apparatus or the user device.
Khan, in the same endeavor, discloses wherein the one or more feature set lists are based further on one or more frequency ranges, and wherein the feature set is selected based further on a frequency range used by the apparatus or the user device. (In paragraph 153 frequency range, e.g. FR1, for channel measurement and reporting for CSI-RS and the associated probability/indication of LOS/NLOS. frequency range, e.g. FR2 , for channel measurement and reporting for CSI-RS and the associated probability/indication of LOS/NLOS.)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Khan’s ‘s technique of reporting feature set, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Khan’s ‘s technique of reporting feature set, since Khan states in paragraphs 155-156 that the modification results in allowing a configuration of cross-frequency-region measurement reporting such that a WRTU may be configured to perform measurements in a first frequency region in order to support functions in a second frequency region.
Regarding claim 13, Dwivedi discloses the apparatus according to claim 1, but fails to disclose, wherein the one or more feature set lists comprise one or more identifiers per feature set of the plurality of feature sets and information on how to retrieve the plurality of feature sets.
Khan, in the same endeavor, discloses wherein the one or more feature set lists comprise one or more identifiers per feature set of the plurality of feature sets and information on how to retrieve the plurality of feature sets.(In Paragraph 195 the feature ser lists can be easily retrieved as it is associated with a particular frequency range)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Khan’s ‘s technique of reporting feature set, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Khan’s ‘s technique of reporting feature set, since Khan states in paragraphs 155-156 that the modification results in allowing a configuration of cross-frequency-region measurement reporting such that a WRTU may be configured to perform measurements in a first frequency region in order to support functions in a second frequency region.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dwivedi in view of Mohammad Soleymani (US 20250063575 A1, hereinafter referred to as Mohammad).
Regarding claim 16, Dwivedi discloses the apparatus according to claim 1, but fails to disclose wherein, the user device transmits a message indicative of the one or more feature set lists via sidelink to another user device, or receive a message indicative of the one or more feature set lists via sidelink from another user device.
Mohammad, in the same endeavor, discloses wherein, the user device transmits a message indicative of the one or more feature set lists via sidelink to another user device, or receive a message indicative of the one or more feature set lists via sidelink from another user device. (See paragraph 181 indicating RSs may be used to enable sidelink-based positioning relying on the measurements on the sidelink or sidelink-assisted positioning. See paragraphs 354 and 357 detailing sidelink measurement reports for LOS/NLOS determination)
In view of the above, having Dwivedi ’s LOS detection based on CIR and then given the well- established teaching of Mohammad ’s ‘s technique of sidelink measurement reporting, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to modify Dwivedi ’s LOS detection based on CIR as taught by Mohammad ’s ‘s technique of sidelink measurement reporting, since Mohammad states in paragraphs 44-45 that the modification results in providing means for high precision distance and angle measurements based on PC5 sidelink and the corresponding waveform which may be OFDM.
Conclusion
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/HABTE MERED/Primary Examiner, Art Unit 2474