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 .
Information Disclosure Statement
Items 7, 8, and 10 under Non-Patent Literature on the information disclosure statement filed on 4/8/2024 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the listing do not show a date and place of publication of the references and it is unclear whether the listing is qualify as a prior art (see MPEP 309.01(B)(1)(e)(v)). It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
Claim Objections
Claim 11 is objected to because of the following informalities:
Regarding claim 11:
Lines 7-8 recite “determining second feature information”, the examiner suggests changing to “determining the second feature information” since the “second feature information” is mentioned on line 3.
Line 16 recites “a second AI model”, the examiner suggests changing to “the second AI model” since it is mentioned on line 8.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 20 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 20 recites the limitation "the time of arrival", “the multipath”, in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites the limitation "the energy of the channel” in line 4. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1, 11-12, 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tripathi et al. (US 2021/0099942 A1)
(1) Regarding claim 1:
Tripathi discloses a method performed by a first node in a wireless communication system, the method comprising:
obtaining information related to a channel between a user equipment (UE) and a base station (measurements 710 can be from signals of the UE such as an RRC measurement report and CSL The measurements can include RSRPs, beam IDs (from the serving cell and a number of neighboring cells), para. 0110); and
extracting first feature information based on the information related to the channel between the UE and the base station (There are at least two quantities of measurements (i) RSRP and (ii) beam ID. RSRP can be used to estimate the relative distance between the UE and the gNB, para. 0108), the first feature information being used to determine information of a location of the UE using an artificial intelligence (AI) model (The reverse engineered propagation model 730 can be an AI driven propagation model. The reverse engineered propagation model 730 can be trained oflline using a supervised learning of a neural network. The identified UE pseudo location 740 can be in cartesian or polar coordinates relative to the gNB, para. 0110).
(2) Regarding claim 12:
Tripathi discloses a node in a wireless communication system (system as shown in figure 4), the node comprising:
Memory (storage device 415 in figure 4, para. 0063-0064) storing one or more computer programs (para. 0012); and
one or more processors (processor(s) 410 as shown in figure 4) communicatively coupled to the memory (para. 0064),
wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors (para. 0012), cause the node to:
obtain information related to a channel between a user equipment (UE) and a base station (measurements 710 can be from signals of the UE such as an RRC measurement report and CSL The measurements can include RSRPs, beam IDs (from the serving cell and a number of neighboring cells), para. 0110), and
extract first feature information based on the information related to the channel between the UE and the base station (There are at least two quantities of measurements (i) RSRP and (ii) beam ID. RSRP can be used to estimate the relative distance between the UE and the gNB, para. 0108), the first feature information being used to determine information of a location of the UE using an artificial intelligence (AI) model (The reverse engineered propagation model 730 can be an AI driven propagation model. The reverse engineered propagation model 730 can be trained oflline using a supervised learning of a neural network. The identified UE pseudo location 740 can be in cartesian or polar coordinates relative to the gNB, para. 0110).
(3) Regarding claim 11:
A method performed by a second node in a wireless communication system, the method comprising:
receiving first feature information (There are at least two quantities of measurements (i) RSRP and (ii) beam ID. RSRP can be used to estimate the relative distance between the UE and the gNB, para. 0108; the examiner interprets first feature information as the relative distance between the UE and the gNB) or second feature information transmitted by a first node;
when the first feature information is received, performing at least one of determining information of a location of a user device (UE) using a first artificial intelligence (AI) model based on the first feature information (The reverse engineered propagation model 730 can be an AI driven propagation model. The reverse engineered propagation model 730 can be trained oflline using a supervised learning of a neural network. The identified UE pseudo location 740 can be in cartesian or polar coordinates relative to the gNB, para. 0110), or determining second feature information using a second AI model based on the first feature information and determining the information of the location of the UE based on the second feature information; and
when the second feature information is received, determining the information of the location of the UE based on the received second feature information (since the option of receiving the second feature information is not selected, therefore, this limitation that further limiting a not selected option does not carrier weight),
wherein the received first feature information is extracted based on information related to a channel between the UE and a base station by the first node (measurements 710 can be from signals of the UE such as an RRC measurement report and CSL The measurements can include RSRPs, beam IDs (from the serving cell and a number of neighboring cells), para. 0110), and/or the received second feature information is determined based on the extracted first feature information using a second AI model by the first node.
(4) Regarding claim 18:
Tripathi discloses one or more non-transitory computer readable storage media storing computer-executable instructions that, when executed by one or more processors of a node in a wireless communication system, cause the node to perform operations (Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from computer readable program code and embodied in a computer readable medium, para. 0012; server 400 according to various embodiments of this disclosure. The server 400 includes a bus system 405, which supports communication between at least one processing device such as the processor 410, at least one storage device 415, para. 0063), the operations comprising:
obtaining information related to a channel between a user equipment (UE) and a base station; and
extracting first feature information based on the information related to the channel between the UE and the base station (There are at least two quantities of measurements (i) RSRP and (ii) beam ID. RSRP can be used to estimate the relative distance between the UE and the gNB, para. 0108), the first feature information being used to determine information of a location of the UE using an artificial intelligence (AI) model (The reverse engineered propagation model 730 can be an AI driven propagation model. The reverse engineered propagation model 730 can be trained oflline using a supervised learning of a neural network. The identified UE pseudo location 740 can be in cartesian or polar coordinates relative to the gNB, para. 0110).
Claims are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tian et al. (US 2026/0025699 A1 with effective filing date of CN202310387459.0 on 3/31/2023).
(1) Regarding claim 1:
Tian discloses a method performed by a first node in a wireless communication system, the method comprising:
obtaining information related to a channel between a user equipment (UE) and a base station (step 704 in figure 7, measure the PRS to obtain in a channel measurement result, para. 0203); and
extracting first feature information based on the information related to the channel between the UE and the base station (as describe in step 420 in figure 4, step 705 involves a step of determine a bit quantity corresponding to each of the carried one or more paths and the number of paths, para. 0136-0138), the first feature information being used to determine information of a location of the UE using an artificial intelligence (AI) model (step 705 determine based on the positioning accuracy requirement and a feature of the channel measurement result, information related to a payload size of a channel measurement report and step 706, the UE forward channel measurement report and first indication information to LMF for select an AI positioning model based on the channel measurement report, para. 0205-0209, 0206).
(2) Regarding claim 2:
Tian further discloses the first feature information is related to energy and/or time of arrival of a multipath of the channel between the UE and the base station (In Table 2, the Rician K-factor (KF) is defined as a ratio of a sum of power of LOS paths to a sum of power of NLOS paths in multiple paths. Same as a shadow fading factor (shadow fading, SF), the KF is also related to a geometric position on a map. When the UE moves to different locations, the KF of the UE changes accordingly. In this case, the multipath power also changes with the location, para. 0133, the examiner interprets the NLOS path as the claimed multipath).
(3) Regarding claim 11:
Tian discloses a method performed by a second node in a wireless communication system, the method comprising:
receiving first feature information or second feature information transmitted by a first node (as describe in step 420 in figure 4, step 705 involves a step of determine a bit quantity corresponding to each of the carried one or more paths and the number of paths, para. 0136-0138; the channel condition may be determined based on the feature of the channel measurement result, para. 0140);
when the first feature information is received, performing at least one of determining information of a location of a user device (UE) using a first artificial intelligence (AI) model based on the first feature information (step 705 determine based on the positioning accuracy requirement and a feature of the channel measurement result, information related to a payload size of a channel measurement report and step 706, the UE forward channel measurement report and first indication information to LMF for select an AI positioning model based on the channel measurement report, para. 0205-0209), or determining second feature information using a second AI model based on the first feature information and determining the information of the location of the UE based on the second feature information; and
when the second feature information is received, determining the information of the location of the UE based on the received second feature information (since the option of receiving the second feature information is not selected, therefore, this limitation that further limiting a not selected option does not carrier weight),
wherein the received first feature information is extracted based on information related to a channel between the UE and a base station by the first node (as describe in step 420 in figure 4, step 705 involves a step of determine a bit quantity corresponding to each of the carried one or more paths and the number of paths, para. 0136-0138, 0206), and/or the received second feature information is determined based on the extracted first feature information using a second AI model by the first node.
(4) Regarding claim 12:
Tian discloses a node in a wireless communication system (communication apparatus 1100 as shown in figure 11), the node comprising:
memory storing one or more computer programs (memory 1120 in figure 11, The steps of the methods disclosed with reference to this application may be directly performed and completed by a hardware processor, or may be performed and completed by using a combination of hardware in the processor and a software module, para. 0247); and
one or more processors communicatively coupled to the memory (processor 1110 in figure 11, para. 0247),
wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors, cause the node to:
obtain information related to a channel between a user equipment (UE) and a base station (step 704 in figure 7, measure the PRS to obtain in a channel measurement result, para. 0203), and
extracting first feature information based on the information related to the channel between the UE and the base station (as describe in step 420 in figure 4, step 705 involves a step of determine a bit quantity corresponding to each of the carried one or more paths and the number of paths, para. 0136-0138), the first feature information being used to determine information of a location of the UE using an artificial intelligence (AI) model (step 705 determine based on the positioning accuracy requirement and a feature of the channel measurement result, information related to a payload size of a channel measurement report and step 706, the UE forward channel measurement report and first indication information to LMF for select an AI positioning model based on the channel measurement report, para. 0205-0209, 0206).
(5) Regarding claim 13:
Tian further discloses the first feature information is related to energy and/or time of arrival of a multipath of the channel between the UE and the base station (In Table 2, the Rician K-factor (KF) is defined as a ratio of a sum of power of LOS paths to a sum of power of NLOS paths in multiple paths. Same as a shadow fading factor (shadow fading, SF), the KF is also related to a geometric position on a map. When the UE moves to different locations, the KF of the UE changes accordingly. In this case, the multipath power also changes with the location, para. 0133, the examiner interprets the NLOS path as the claimed multipath).
(6) Regarding claim 18:
Tian disclose one or more non-transitory computer readable storage media (memory 1120 as shown in figure 11) storing computer-executable instructions that, when executed by one or more processors of a node in a wireless communication system (The steps of the methods disclosed with reference to this application may be directly performed and completed by a hardware processor, or may be performed and completed by using a combination of hardware in the processor and a software module, para. 0247), cause the node to perform operations, the operations comprising:
obtain information related to a channel between a user equipment (UE) and a base station (step 704 in figure 7, measure the PRS to obtain in a channel measurement result, para. 0203), and
extracting first feature information based on the information related to the channel between the UE and the base station (as describe in step 420 in figure 4, step 705 involves a step of determine a bit quantity corresponding to each of the carried one or more paths and the number of paths, para. 0136-0138), the first feature information being used to determine information of a location of the UE using an artificial intelligence (AI) model (step 705 determine based on the positioning accuracy requirement and a feature of the channel measurement result, information related to a payload size of a channel measurement report and step 706, the UE forward channel measurement report and first indication information to LMF for select an AI positioning model based on the channel measurement report, para. 0205-0209, 0206).
(7) Regarding claim 19:
Tian further discloses the first feature information is related to energy and/or time of arrival of a multipath of the channel between the UE and the base station (In Table 2, the Rician K-factor (KF) is defined as a ratio of a sum of power of LOS paths to a sum of power of NLOS paths in multiple paths. Same as a shadow fading factor (shadow fading, SF), the KF is also related to a geometric position on a map. When the UE moves to different locations, the KF of the UE changes accordingly. In this case, the multipath power also changes with the location, para. 0133, the examiner interprets the NLOS path as the claimed multipath).
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tian et al. (US 2026/0025699 A1).
Tain discloses all subject matter of claims 1 and 12, but fails to explicitly disclose extracting the first feature information by using a signature transform based on the information related to the channel.
However, Tain discloses the channel measurement result (or the feature of the channel measurement result) may reflect an NLOS degree of an environmental channel. It can be learned from the foregoing description of the inventive concept that if NLOS degrees (that is, different channel conditions) are different, degrees of impact of the information related to the payload size of the channel measurement report on inference accuracy of an AI positioning model are different. Therefore, the first network element may learn of an NLOS degree of a channel based on the feature of the channel measurement result, to learn of a current channel condition. The first network element may determine information related to different payload sizes under different channel conditions, provided that the positioning accuracy requirement is ensured (para. 0130); It can be learned from the foregoing description of the information related to the payload size that, determining the information related to the payload size of the channel measurement report by the first network element may be specifically determining the path quantity corresponding to the one or more paths carried in a payload of the channel measurement report, for example, information about 256 paths is carried or information about 32 paths is carried. Further, the first network element may further determine a bit quantity corresponding to each of the carried one or more paths. For example, assuming that it is determined that the payload of the channel measurement report carries information about 32 paths, information about each of the 32 paths is represented by using a quantity of bits. For example, information about each path may be represented by using 16 bits, 32 bits, or 64 bits. When more bits are used to represent information about a path, accuracy of the represented information about the path is higher. For example, accuracy of information about a path represented by 32 bits is higher than accuracy of information about a path represented by 16 bits (para. 0138) (the examiner takes a broadest reasonable interprets that the decision of the channel payload size based on the channel condition as the claimed signature transform based on the information related to the channel).
It is desirable to extract he first feature information by using a signature transform based on the information related to the channel because it reduces overhead in reporting of the channel condition (para. 0004).
Allowable Subject Matter
Claims 3, 5-10, 14, 16-17 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hasegawa et al. (US 2026/0019982 A1) discloses positioning with estimated measurements.
Chai et al. (US 2024/0211770 A1) discloses communication method and apparatus.
Yerramalli et al. (US 2024/0114477 A1) discloses positioning model performance monitoring.
Li (US 2026/0059268 A1) discloses positioning method, and base station device, and storage medium.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIU M LEE whose telephone number is (571)270-1083. The examiner can normally be reached M-T 8:30-7:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chieh M Fan can be reached at 571-272-3042. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SIU M LEE/Primary Examiner, Art Unit 2632 3/31/2026