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 .
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)(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.
1. Claims 1, 11, 14 and 18-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Xu (2022/0167124).
Regarding claim 1. Xu teaches a user equipment information prediction method, comprising:
obtaining, by a first network element, user equipment location data of a first time period using a location service (LCS) architecture (0069 – the server may determine the one or more expected UE behaviour patterns having timing information of one or more or the group of UEs based on activity of the one or more or the group of UEs. For example, the server may collect the activity of the one or more or the group of UEs for example from the one or more or the group of UEs and/or from a network device such as LMF (e.g., LCS) and/or from and other server, etc. and then learn the expected UE behaviour parameters based on activity of the one or more or the group of UEs for example by using machine learning); and
inputting, by the first network element, the user equipment location data of the first time period into a traffic model or a supervised learning model, and obtaining user equipment prediction information of a second time period output by the traffic model or the supervised learning model (0069 – the server may determine the one or more expected UE behaviour patterns having timing information of one or more or the group of UEs based on activity of the one or more or the group of UEs. For example, the server may collect the activity of the one or more or the group of UEs for example from the one or more or the group of UEs and/or from a network device such as LMF and/or from and other server, etc. and then learn the expected UE behaviour parameters based on activity of the one or more or the group of UEs for example by using machine learning, 0065, 0080-0089 – the one or more expected UE behaviour parameters comprises expected UE moving trajectory).
Regarding claims 14 and 19. Xu teaches a program and a first network element, comprising a processor and a memory, wherein a program or instructions capable of running on the processor are stored in the memory, wherein the program or the instructions, when executed by the processor, cause the first network element to perform (0120 – server comprises memory, processor and program):
obtaining user equipment location data of a first time period using a location service (LCS) architecture (0069 – the server may determine the one or more expected UE behaviour patterns having timing information of one or more or the group of UEs based on activity of the one or more or the group of UEs. For example, the server may collect the activity of the one or more or the group of UEs for example from the one or more or the group of UEs and/or from a network device such as LMF (e.g., LCS) and/or from and other server, etc. and then learn the expected UE behaviour parameters based on activity of the one or more or the group of UEs for example by using machine learning); and
inputting the user equipment location data of the first time period into a traffic model or a supervised learning model, and obtaining user equipment prediction information of a second time period output by the traffic model or the supervised learning model (0069 – the server may determine the one or more expected UE behaviour patterns having timing information of one or more or the group of UEs based on activity of the one or more or the group of UEs. For example, the server may collect the activity of the one or more or the group of UEs for example from the one or more or the group of UEs and/or from a network device such as LMF and/or from and other server, etc. and then learn the expected UE behaviour parameters based on activity of the one or more or the group of UEs for example by using machine learning, 0065, 0080-0089 – the one or more expected UE behaviour parameters comprises expected UE moving trajectory).
Regarding claims 11 and 18. Xu teaches wherein the user equipment location data of the first time period comprises at least one of the following: identification information of the user equipment; geographic location information of the user equipment; a moving speed of the user equipment at a current geographical location; a moving direction of the user equipment at the current geographical location; location accuracy information; a time stamp; an age of location data; or first indication information indicating that the user equipment is located in an external area or an internal area (0065, 0069, 0080 – UE moving trajectory, 0081 – location and timing information, 0082 – geographic information of the UE, 0083 – coordinate of the UE, 0084 – velocity and/or acceleration , 0085 – parameters have respective associated validity time).
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.
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.
2. Claims 2, 4, 12, 15, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Paredes Cabrera (2023/0140473).
Regarding claims 2, 15 and 20. Xu does not explicitly teach wherein the user equipment prediction information of the second time period comprises at least one of the following: the total number of user equipment located in the second area within the second time period; an average moving speed of user equipment located in the first traffic environment in the second area within the second time period; the number of user equipment located in the second area within the second time period and whose moving speed exceeds a preset value; a proportion of the number of user equipment located in the second area within the second time period and whose moving speed exceeds the preset value; the number of user equipment with a first direction in the second area within the second time period; or a proportion of the number of user equipment with the first direction in the second area within the second time period.
Paredes Cabrera teaches the data to collect for different days and times that may assist the AI algorithm to predict future UE and/or network behaviour may include one or more of: number of UEs connecting to the access network at specific days and/or times, the average number of UEs moving from a non-neighboring cell and a non-neighboring node, routes followed by all UEs at specific days and times on average for all UEs in the network, routes followed at specific days and times for specific UE(s), average speeds of UE paths (0041-0052) and the output of the AI algorithm may include: improved allocation of resources to match the expected demand at different days and/or times, predicting routes for specific users at specific times and/or days of the week to optimize resources and to optimize mobility procedures, optimize paging procedures by allowing the AI algorithms predict location of the UE at any given time (0041-0070).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to include the number of UEs connecting to the access network at specific days and/or times, the average number of UEs moving from one location to another, routes followed by all UEs at specific days and times on average for all the UEs in the network, routes followed at specific days and times for specific UE(s) average speeds of the UE paths, etc. as taught by Paredes Cabrera in order to improve allocation of resources to match the expected demand at different days and/or times.
Regarding claims 4 and 17. Xu does not teach wherein the user equipment prediction information of the second time period output by the supervised learning model further comprises:
a regional hotspot map within the second time period; wherein the regional hotspot map is used for indicating at least one of the following:
a probability that the user equipment is located in a first area within the second time period;
a change trend of a speed of the user equipment within the second time period; the number of user equipment located in the first area within the second time period; or
a change trend of the number of user equipment located in the first area within the second time period.
Paredes Cabrera teaches the data to collect for different days and times that may assist the AI algorithm to predict future UE and/or network behaviour may include one or more of: number of UEs connecting to the access network at specific days and/or times, the average number of UEs moving from a non-neighboring cell and a non-neighboring node, routes followed by all UEs at specific days and times on average for all UEs in the network, routes followed at specific days and times for specific UE(s), average speeds of UE paths (0041-0052) and the output of the AI algorithm may include: improved allocation of resources to match the expected demand at different days and/or times, predicting routes for specific users at specific times and/or days of the week to optimize resources and to optimize mobility procedures, optimize paging procedures by allowing the AI algorithms predict location of the UE at any given time (0041-0070).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to include the number of UEs connecting to the access network at specific days and/or times, the average number of UEs moving from one location to another, routes followed by all UEs at specific days and times on average for all the UEs in the network, routes followed at specific days and times for specific UE(s) average speeds of the UE paths, etc. as taught by Paredes Cabrera in order to improve allocation of resources to match the expected demand at different days and/or times.
Regarding claim 12. Xu does not explicitly teach wherein the method further comprises: collecting, by the first network element, statistics on statistical information of the first time period based on the user equipment location data of the first time period, wherein the statistical information of the first time period comprises at least one of the following: the total number of user located in the second area within the first time period; the total number of user equipment located in the second area within the first time period; the number of user equipment located in a first traffic environment in the second area within the first time period; a proportion of the number of user equipment located in the first traffic environment in the second area within the first time period; an average moving speed of user equipment located in the first traffic environment in the second area within the first time period; a direction of the first transportation means located in the second area within the first time period; the number of user equipment with a same direction in the first transportation means located in the second area within the first time period; the number of user equipment located in the second area within the first time period and whose moving speed exceeds a preset value; a proportion of the number of user equipment located in the second area within the first time period and whose moving speed exceeds the preset value; the number of user equipment with a first direction in the second area within the first time period; or
a proportion of the number of user equipment with the first direction in the second area within the first time period; wherein the first transportation means is one of the total vehicles located in the second area within the first time period.
Paredes Cabrera teaches the data to collect for different days and times that may assist the AI algorithm to predict future UE and/or network behaviour may include one or more of: number of UEs connecting to the access network at specific days and/or times, the average number of UEs moving from a non-neighboring cell and a non-neighboring node, routes followed by all UEs at specific days and times on average for all UEs in the network, routes followed at specific days and times for specific UE(s), average speeds of UE paths (0041-0052) and the output of the AI algorithm may include: improved allocation of resources to match the expected demand at different days and/or times, predicting routes for specific users at specific times and/or days of the week to optimize resources and to optimize mobility procedures, optimize paging procedures by allowing the AI algorithms predict location of the UE at any given time (0041-0070).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to include the number of UEs connecting to the access network at specific days and/or times, the average number of UEs moving from one location to another, routes followed by all UEs at specific days and times on average for all the UEs in the network, routes followed at specific days and times for specific UE(s) average speeds of the UE paths, etc. as taught by Paredes Cabrera in order to improve allocation of resources to match the expected demand at different days and/or times.
3. Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Paredes Cabrera further in view of Chin et al (2021/0329514).
Regarding claims 3 and 16. Xu in view of Paredes Cabrera do not teach wherein the user equipment prediction information of the second time period output by the traffic model further comprises: a predicted vehicle of the user equipment within the second time period.
Chin teaches using machine learning (ML) and artificial intelligence (AI) engine which is trained with historical UE mobility information to predict the type of transport, such as a car, subway, bicycle, train, elevator, etc. (e.g., a predicted vehicle of the UE within the second time period) wherein the mobility information may be used to predict the location, mobility information, and usage of the UE at certain times during the user’s daily schedule and used to reduce the rates of handoff and to select a RAT to camp on (0074). For example, based on historical contextual awareness information, such as the historical user patterns and user behaviors … predict that from 8 a.m. to 8:20 am the user will commute to work in a car (0075) … use real-time and historical contextual awareness to predict or infer other contextual awareness information, such as if the if the user of the UE is inside a car, subway, airplane, driving, running, walking or sitting (0082) … contextual information indicates the user typically commutes to work in a car using a route A in a highway A (0083) … commutes back home in a car (0085) … and historical context information indicates that starting at 7:10 pm the user typically passes the coverage hole and begins walking or running in an area (0086).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu in view of Paredes Cabrera to train the AI/ML as taught by Chin in order to predict the user’s mode of travel (e.g., predict the vehicle of the user equipment) at certain times and/or days which is used to select the best RAT to service the user at certain times/locations that reduces the number of handovers.
4. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Paredes Cabrera further in view of Chin et al (2021/0329514).
Regarding claim 5. Xu does not teach wherein before the inputting, by the first network element, the user equipment location data of the first time period into a traffic model, the method further comprises:
performing, by the first network element, data filtering on the user equipment location data of the first time period to obtain location data meeting a preset condition;
performing, by the first network element, environment judgment for the user equipment based on the location data meeting the preset condition and a historical usage model of the user equipment, and determining a current traffic environment of the user equipment, wherein the traffic environment comprises at least one of walking, riding a bicycle, taking a bus, taking a subway, and taking a car; and
determining, by the first network element based on the current traffic environment of the user equipment, a traffic model corresponding to the current traffic environment of the user equipment; wherein the inputting, by the first network element, the user equipment location data of the first time period into a traffic model comprises:
inputting, by the first network element, the user equipment location data meeting the preset condition within the first time period into the traffic model corresponding to the current traffic environment of the user equipment.
Chin teaches using machine learning (ML) and artificial intelligence (AI) engine which is trained with historical UE mobility information to predict the type of transport, such as a car, subway, bicycle, train, elevator, etc. wherein the mobility information may be used to predict the location, mobility information, and usage of the UE at certain times during the user’s daily schedule and used to reduce the rates of handoff and to select a RAT to camp on (0074). For example, based on historical contextual awareness information, such as the historical user patterns and user behaviors … predict that from 8 a.m. to 8:20 am the user will commute to work in a car (0075) … use real-time and historical contextual awareness to predict or infer other contextual awareness information, such as if the if the user of the UE is inside a car, subway, airplane, driving, running, walking or sitting (0082) … contextual information indicates the user typically commutes to work in a car using a route A in a highway A (0083) … commutes back home in a car (0085) … and historical context information indicates that starting at 7:10 pm the user typically passes the coverage hole and begins walking or running in an area (0086).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to train the AI/ML as taught by Chin in order to predict the user’s traffic model/environment (e.g., walking, running, driving, subway, airplane) at certain times and/or days which is used to select the best RAT to service the user at certain times/locations that reduces the number of handovers.
5. Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Kumar et al (2023/0093963).
Regarding claim 6. Xu does not teach wherein before the inputting, by the first network element, the user equipment location data of the first time period into a traffic model, the method further comprises: performing, by the first network element, training and correction on the traffic model based on the location data of the first time period.
Kumar teaches training and updating AI/ML models (0127, 0166) to more precisely determine UE mobility patterns (e.g., walking , in a car, etc.) (0072, 0082) providing for more efficient cell selection and/or reselection (0083).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to train and update AI/ML models as taught by Kumar in order to more precisely determine UE mobility patterns which provides for more efficient cell selection and/or reselection.
Regarding claim 7. Xu does not teach wherein the performing, by the first network element, training and correction on the traffic model based on the user equipment location data of the first time period comprises:
performing, by the first network element, data filtering on the user equipment location data of the first time period to obtain location data meeting a preset condition;
performing, by the first network element, environment judgment for the user equipment based on the location data meeting the preset condition and a historical usage model of the user equipment, and determining a current traffic environment of the user equipment, wherein the traffic environment comprises at least one of walking, riding a bicycle, taking a bus, taking a subway, and taking a car;
inputting, by the first network element, the location data meeting the preset condition into a traffic model corresponding to the current traffic environment of the user equipment, and obtaining user equipment prediction information of a third time period output by the traffic model; and comparing, by the first network element, the user equipment prediction information of the third time period with location data of the third time period, and performing training and correction on the traffic model based on a comparison result; wherein the third time period is before the second time period.
Kumar teaches training and updating AI/ML models (0127, 0166) to more precisely determine UE mobility patterns (e.g., walking , in a car, etc.) (0072, 0082) providing for more efficient cell selection and/or reselection (0083).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to train and update AI/ML models as taught by Kumar in order to more precisely determine UE mobility patterns which provides for more efficient cell selection and/or reselection.
6. Claims 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Ly et al (2025/0310214).
Regarding claim 8. Xu does not teach wherein the inputting, by the first network element, the user equipment location data of the first time period into a supervised learning model comprises: inputting, by the first network element, user equipment location data meeting a timeliness requirement into the supervised learning model.
Ly teaches using timeliness parameters in conjunction with AI/ML in order to provide more accurate location predictions (0124, 0131).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to use timeliness parameters as taught by Ly in order to provide for more accurate location predictions.
Regarding claim 9. Xu does not explicitly teach wherein before the inputting, by the first network element, user equipment location data meeting a time and efficiency requirement into the supervised learning model, the method further comprises: performing, by the first network element, training by using historical location data of the user equipment to obtain the supervised learning model; and
verifying, by the first network element, output accuracy of the supervised learning model based on the user equipment location data meeting the timeliness requirement.
However, Xu teaches using historical information (0004).
Ly teaches using timeliness parameters in conjunction with AI/ML in order to provide more accurate location predictions (0124, 0131).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to use timeliness parameters as taught by Ly in order to provide for more accurate location predictions.
Regarding claim 10. Xu teaches wherein the user equipment location data
Xu does not use the term timeliness.
Ly teaches using timeliness parameters in conjunction with AI/ML in order to provide more accurate location predictions (0124, 0131).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to use timeliness parameters as taught by Ly in order to provide for more accurate location predictions.
7. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Fiorese et al (2020/0288296).
Regarding claim 13. Xu teaches wherein the first network element is a network element with a network data analytics function (NWDAF) (0049 – NWDAF).
Xu does not explicitly teach sending, by the first network element, the user equipment prediction information of the second time period or the statistical information of the first time period to a user of the NWDAF.
Fiorese taches receiving a request for UE location and sending to the requesting entity, the calculated UE mobility trajectory prediction (0061, 0063) and enables the NWDAF to propose alternative routes that are cheaper and/or have better QoS (0062).
It would have been obvious for one of ordinary skill in the art before the effective filing date to modify Xu to enable a user of the NWDAF to request location as taught by Fiorese in order to enable the NWDAF to propose alternative routes that are cheaper and/or have better QoS.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
---(2023/0179309) Liu et al teaches training AI/ML algorithms which are then used to predict the location of at least one UE at one or more time points in the future (0047).
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BARRY W TAYLOR whose telephone number is (571)272-7509. The examiner can normally be reached Monday-Thursday: 7-5.
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/BARRY W TAYLOR/Primary Examiner, Art Unit 2646
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