DETAILED ACTION
Claim(s) 1-9 and 11-21 are presented for examination.
Claim(s) 1-9 and 11-18 are amended.
Claim 10 is canceled.
Claim(s) 19-21 are new.
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 .
Priority
As required by M.P.E.P.201.14(c), acknowledgement is made to applicant’s claim for priority based on application(s) PCT/SE2022/050228 submitted on March 8th, 2022.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on September 4th, 2024 and March 10th, 2026 follow the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
Applicant’s amendment(s) to the specification of the disclosure filed August 30th, 2024 is/are considered.
Applicant’s amendment(s) to the abstract of the disclosure filed August 30th, 2024 is/are considered.
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed (i.e., “DATA TRANSMISSION USING QUALITY OF SERVICE (QoS) AND MACHINE LEARNING (ML)”.
Claim Objections
Claim(s) 1-9 and 11-21 are objected to because of the following informalities:
Claim 1 recites “User Equipment, UE …” in line 2 and “quality of service, QoS” in line 4.
Since the limitation(s) is/are introduced for a first time, it is suggested to put the acronym in parenthesis (i.e., user equipment (UE), quality of service (QoS) … etc.)
Claims 9 and 11 recite a similar limitation.
Claim(s) 2-8 and 12-21 are also being objected for being dependent on an objected base claim as set forth above.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim(s) is are directed to a signal per se.
Claim 9 recites, “A computer storage medium storing a computer program comprising instructions, which, when executed by at least one processor, cause the at least one processor to perform a method, the method comprising: obtaining …, using …, determining …, and transmitting ...”.
In the specification, fig. 5A, pg. 13, ¶160-¶162 recites as follows:
[0160] The network node 110 may further comprise a memory 570 comprising one or more memory units. The memory 570 comprises instructions executable by the processor in network node 110. The memory 570 is arranged to be used to store e.g. information, indications, data, configurations, and applications to perform the methods herein when being executed in the network node 110.
[0162] In some embodiments, a respective carrier 590 comprises the respective computer program 580, wherein the carrier 590 is one of an electronic signal, an optical signal, an electromagnetic signal, a magnetic signal, an electric signal, a radio signal, a microwave signal, or a computer-readable storage medium.
Analysis:
First, neither the claim nor specification as whole provide what is the storage medium. For instance, the specification describes the term “storage medium …” by providing different types of carrier signals [emphasis added].
Since the Applicant fails to inclusively and explicitly provide antecedent basis to limit the specific statutory embodiments of: “storage memory …”, it belongs to the intrinsic non-statutory embodiments such as carrier signal, radio wave, light wave, and transmission medium/media.
A claim whose broadest reasonable interpretation (BRI) covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and is therefore directed to non-statutory subject matter.
The BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007).
Since the BRI encompasses transitory forms of signal transmission, in view of the above analysis, claim 9 is ineligible for patent protection as failing to be limited to embodiments which fall within a statutory category.
Claim Rejections - 35 U.S.C. § 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.
Claims 5 and 15 are 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 pre-AIA the applicant regards as the invention.
Claim 5 recites “the location of the UE …” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites a similar limitation.
For the purpose of examination, examiner will interpret as best understood.
Claim Rejections - 35 U.S.C. § 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 7, 9, 11-13, 17 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over FEKI et al. (US 2022/0353637 A1) hereinafter “Feki” in view of Pateromichelakis et al. (US 2023/0328580 A1) hereinafter “Pateromichelakis”.
Regarding Claims 1 and 11,
Feki discloses a network node configured for handling operation of a User Equipment, UE, in a wireless communications network [see fig. 7, pg. 7, ¶178 lines 1-2, an LMF (Location Management Function) “505” identifying required User Equipment (UE) “525” assistance data], the network node being configured to [see fig. 7, pg. 7, ¶178 lines 1-2, the LMF “505” implemented to]:
obtain a first value of a quality of service, QoS, characteristic for a service that is associated with a task performed by the UE [see fig. 7: Step “700”, pg. 7, ¶179 lines 1-8, a message is sent from the LCS client “530” to the LMF “505” requesting a determined location comprising the identity of a user equipment and the required location quality of service (QoS) or accuracy];
obtain a set of second values of the QoS characteristic for the service [see fig. 7: Step “702”/ ”704”, pg. 7, ¶180 lines 1-5; ¶181 lines 1-7, the LMF “505” determines or identifies the required UE assistance data based on the quality of service (QoS) or accuracy; and interacts with the 5G-RAN and the UE to obtain the required assistance data];
use the obtained set of second values and the obtained first value in a machine learning model to determine a value of an operating parameter of the UE [see fig. 7: Step(s) “706”/ “708”, pg. 7, ¶182 lines 1-4; ¶183 lines 1-3, the LMF “505” derives the accuracy of the location estimate provided using the trained neural network model; and determines if the positioning accuracy meets the required threshold]; and
transmit an indication of the determined value of the operating parameter for controlling operation of the UE in the wireless communications network based on the determined value of the operating parameter [see fig. 7: Step(s) “712”/ “714”, pg. 7, ¶185 lines 1-10, when the positioning accuracy meets the required threshold, the LMF “505” is configured to cause a response to be sent to the LCS client “530” to provide the UE identity and the determined location of the UE with an indication of the accuracy of the determined location].
Although Feki discloses using the obtained set of second values and the obtained first value in a machine learning model, Feki does not explicitly teach using the obtained first and second values “for performance of the task by the UE”.
However discloses Pateromichelakis discloses obtaining a first value of a quality of service, QoS, characteristic for a service that is associated with a task performed by the UE [see fig. 7: Step “705”, pg. 13, ¶190 lines 1-15, receiving a QoS parameter for at least one QoS flow, the at least QoS flow corresponding to at least one UE];
obtaining a set of second values of the QoS characteristic for the service [see fig. 7: Step “710”, pg. 13, ¶190 lines 1-15, obtaining a data analytics model, the data analytics model describing at least one expected condition for the at least one UE and/or at least one serving RAN node];
using the obtained set of second values and the obtained first value in a machine learning model to determine a value of an operating parameter of the UE for performance of the task by the UE [see fig. 7: Step “715”, pg. 13, ¶190 lines 1-15, determining an expected QoS profile adaptation pattern based on the data analytics model for a first time interval, wherein the expected QoS profile adaptation pattern comprises at least one QoS profile to be associated with the at least one QoS flow during the first time interval]; and
transmitting an indication of the determined value of the operating parameter for controlling operation of the UE in the wireless communications network based on the determined value of the operating parameter [see fig. 7: Step “720”, pg. 13, ¶190 lines 1-15, transmitting the expected QoS profile adaptation pattern to at least one network node associated with the QoS flow].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide using the obtained first and second values “for performance of the task by the UE” as taught by Pateromichelakis in the system of Feki for reducing complexity and/or processing burden at RAN nodes, which grows significantly as the number of QoS flows and the QoS alternatives increase [see Pateromichelakis, pg. 4, ¶38 lines 1-7].
Regarding Claims 2 and 12,
The combined system of Feki and Pateromichelakis discloses the network node according to claim 11.
Feki further discloses wherein the operating parameter is associated with:
a location of the UE in the wireless communications network [see fig. 7: Step “700”, pg. 7, ¶179 lines 1-8, the request comprises the identity of a user equipment and the required location quality of service or accuracy].
Regarding Claims 3 and 13,
The combined system of Feki and Pateromichelakis discloses the network node according to claim 11.
Feki further discloses wherein the QoS characteristic comprises of a packet delay [see pg. 3, ¶85 lines 1-3, the information indicating the associated location quality of service comprises one or more of: a quality of service class; and a required latency].
Regarding Claims 7 and 17,
The combined system of Feki and Pateromichelakis discloses the network node according to claim 11.
Feki further discloses wherein the network node is further configured to:
determine a value of the operating parameter for at least one other UE in the wireless communications network [see fig. 7: Step(s) “706”/ “708”, pg. 7, ¶182 lines 1-4; ¶183 lines 1-3, the LMF “505” derives the accuracy of the location estimate provided using the trained neural network model], wherein:
the machine learning model is further taking the value of the operating parameter for the at least one other UE into account [see fig. 7: Step(s) “706”/ “708”, pg. 7, ¶182 lines 1-4; ¶183 lines 1-3, the LMF “505” determines if the positioning accuracy meets the required threshold].
Regarding Claim 9,
Feki discloses a computer storage medium storing a computer program comprising instructions [see fig(s). 4 & 7, pg. 6, ¶138 lines 1-6, at least one memory “452” including computer code for one or more programs], which, when executed by at least one processor [see fig(s). 4 & 7, pg. 6, ¶138 lines 1-6, during an implementation by at the least processor “450”], cause the at least one processor to perform a method [see fig(s). 4 & 7, pg. 6, ¶138 lines 1-6, triggers the at least processor “450” to execute a procedure], the method [see fig(s). 4 & 7, pg. 6, ¶138 lines 1-6, the procedure] comprising:
obtain a first value of a quality of service, QoS, characteristic for a service that is associated with a task performed by the UE [see fig. 7: Step “700”, pg. 7, ¶179 lines 1-8, a message is sent from the LCS client “530” to the LMF “505” requesting a determined location comprising the identity of a user equipment and the required location quality of service (QoS) or accuracy];
obtain a set of second values of the QoS characteristic for the service [see fig. 7: Step “702”/ ”704”, pg. 7, ¶180 lines 1-5; ¶181 lines 1-7, the LMF “505” determines or identifies the required UE assistance data based on the quality of service (QoS) or accuracy; and interacts with the 5G-RAN and the UE to obtain the required assistance data];
use the obtained set of second values and the obtained first value in a machine learning model to determine a value of an operating parameter of the UE [see fig. 7: Step(s) “706”/ “708”, pg. 7, ¶182 lines 1-4; ¶183 lines 1-3, the LMF “505” derives the accuracy of the location estimate provided using the trained neural network model; and determines if the positioning accuracy meets the required threshold]; and
transmit an indication of the determined value of the operating parameter for controlling operation of the UE in the wireless communications network based on the determined value of the operating parameter [see fig. 7: Step(s) “712”/ “714”, pg. 7, ¶185 lines 1-10, when the positioning accuracy meets the required threshold, the LMF “505” is configured to cause a response to be sent to the LCS client “530” to provide the UE identity and the determined location of the UE with an indication of the accuracy of the determined location].
Although Feki discloses using the obtained set of second values and the obtained first value in a machine learning model, Feki does not explicitly teach using the obtained first and second values “for performance of the task by the UE”.
However discloses Pateromichelakis discloses obtaining a first value of a quality of service, QoS, characteristic for a service that is associated with a task performed by the UE [see fig. 7: Step “705”, pg. 13, ¶190 lines 1-15, receiving a QoS parameter for at least one QoS flow, the at least QoS flow corresponding to at least one UE];
obtaining a set of second values of the QoS characteristic for the service [see fig. 7: Step “710”, pg. 13, ¶190 lines 1-15, obtaining a data analytics model, the data analytics model describing at least one expected condition for the at least one UE and/or at least one serving RAN node];
using the obtained set of second values and the obtained first value in a machine learning model to determine a value of an operating parameter of the UE for performance of the task by the UE [see fig. 7: Step “715”, pg. 13, ¶190 lines 1-15, determining an expected QoS profile adaptation pattern based on the data analytics model for a first time interval, wherein the expected QoS profile adaptation pattern comprises at least one QoS profile to be associated with the at least one QoS flow during the first time interval]; and
transmitting an indication of the determined value of the operating parameter for controlling operation of the UE in the wireless communications network based on the determined value of the operating parameter [see fig. 7: Step “720”, pg. 13, ¶190 lines 1-15, transmitting the expected QoS profile adaptation pattern to at least one network node associated with the QoS flow].
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide using the obtained first and second values “for performance of the task by the UE” as taught by Pateromichelakis in the system of Feki for reducing complexity and/or processing burden at RAN nodes, which grows significantly as the number of QoS flows and the QoS alternatives increase [see Pateromichelakis, pg. 4, ¶38 lines 1-7].
Regarding Claim 19,
The combined system of Feki and Pateromichelakis discloses the method according to claim 2.
Feki further discloses wherein the QoS characteristic comprises a packet delay [see pg. 3, ¶85 lines 1-3, the information indicating the associated location quality of service comprises one or more of: a quality of service class; and a required latency].
Allowable Subject Matter
Claim(s) 4, 6, 8, 14, 16, 18, 20 and 21 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all the limitations of the base claim and any intervening claims.
Claims 5 and 15 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include 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.
United States Patent Application Publication: ZHU et al. (US 2022/0400373 A1);
see fig. 8, pgs. 10-11, ¶113-¶126.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUSHIL P SAMPAT whose telephone number is (469) 295-9141. The examiner can normally be reached on Mon-Fri (8 AM - 5 PM).
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/RUSHIL P. SAMPAT/Primary Examiner- TC 2400, Art Unit 2469
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