And 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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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, 6-10, 12, and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. US Patent Pub. No.: 2025/0071731, hereinafter, ‘Kim’ in view of Roy et al. US Patent Pub. No.: 2024/0107597 A1, hereinafter, ‘Roy’.
Consider Claim 12, and as applied to the method of Claim 1, Kim teaches a system (e.g., see data analysis device including first data analysis device and a second data analysis device- noted in 0010), comprising: one or more processors (see “at least one processor” line 6 of paragraph 0010) configured to: receive log data identifying user equipment (UE) mobility information for a UE (i.e., the referenced UE is applicable to more than 1 which would meet a set) (e.g., see at least 0082 – “The mobility data may also be referred to as “log” in that the mobility data includes location and time information that occurs in a network.”); train a model of UE mobility based on the log data (e.g., see at least 0073 – “a neural network-based prediction model to train the prediction model to predict base stations serving a target location to which the UE is expected to move. The second data analysis device may train the prediction model to predict the base stations serving the target location using mobility data”- training also discussed in 0008, 0010, 0155, 0188, and 0205); analyze, using the model of UE mobility, the UE mobility information for the UE to predict a location of the UE(e.g., see at least 0010 – “a neural network-based prediction model to train the prediction model to predict base stations serving a target location to which the UE is expected to move – at least 0055 – “The mobility of the UE for paging may be determined by predicting a location of the UE at the time of paging by a movement path”); generate, based on predicting the location of the UE, a set of recommended cells for paging the UE; and transmit, one or more paging messages toward the UE in one or more cells of the set of recommended cells (e.g., see at least 0056-predict the mobility of the UE suitable for paging, thereby also predicting the base stations serving the corresponding UE at the time of paging. – see also 0104).
However, Kim does not specifically teach wherein the model of UE mobility is selectively enabled based on the particular UE operating in one or more cells associated with a virtualized radio access network (VRAN) with a split central unit (CU) and distributed unit (DU) architecture.
In analogous art, Roy teaches methods leveraging artificial intelligence and machine learning (AI/ML) models to enhance wireless communications efficiency in 5G/6G networks. The processes involve storing, configuring, and transferring AI/ML models within base stations and user equipment devices (UE), allowing for localized decision-making and improved network performance. Features include dynamic model activation/deactivation.
Roy further teaches “the gNB can configure, activate, deactivate the AI/ML models, and therefore is able to enable or disable the AI/ML model executing on UE 301.”- 0053.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try to missing features of Kim to arrive at the predictable result wherein the model of UE mobility is selectively enabled based on the particular UE operating in one or more cells associated with a virtualized radio access network (VRAN) with a split central unit (CU) and distributed unit (DU) architecture for the purpose of allowing for localized decision-making and improved network performance.
Consider Claim 2, Kim teaches the claimed invention further comprising: receiving, by the first network device, updated mobility information for the set of UEs (i.e., “the prediction model may be trained using a movement path tracked” – 0089 ); and updating, by the first network device, the model of UE mobility based on receiving the updated mobility information for the set of UEs (e.g., this is met because the mobility data is based on the mobility of a UE being tracked and thus the model is being updated with new batch data for training a prediction model based on constantly changing/updating mobility data as noted in at least 0089-0092).
Consider Claim 3, Kim teaches wherein the set of recommended cells is based on timing information, the timing information relating to at least one of a time of day, or a day of a week (i.e., this is met based on data samples at different fixed interval and movement period which would reflect a relationship based on the time by adjusting those time intervals depending on the time of day- e.g., shorter or longer periods depending on daily load/scheduling needs – 0083-0084).
Consider Claim 6, Kim teaches receiving, by the first network device, a request for the set of recommended cells; and wherein transmitting the information identifying the set of recommended cells comprises: transmitting, by the first network device, the information identifying the set of recommended cells based on receiving the request for the set of recommended cells (i.e., the teachings of the above method is met by context of flow chart of figure 8, a POSITA would appreciate the explicit request and reply inherent in the underlying algorithm to achieve the end goal – the predicted/ recommended cells are based on a model that is designed(implied request) for an explicit output(recommendations based on the model).
Consider Claim 7, Kim teaches wherein the particular UE is included in the set of UEs (i.e., this is met based on the implication the UE of at least 0005 is applicable to multiple UEs).
Consider Claim 8, Kim teaches wherein analyzing the UE mobility information for the particular UE comprises: calculating, by the first network device, a movement path of the particular UE (e.g., see movement path as claimed in claims 3-4 of the referenced prior art); and predicting, by the first network device, the location of the particular UE based on the movement path (e.g., see movement path as claimed in claims 3-4 of the referenced prior art).
Consider Claim 9, Kim teaches wherein the model of UE mobility is a machine learning probability model (e.g., see at least 0103 – “An output layer 750 of the prediction model 700 may output a probability”).
Consider Claim 10, Kim teaches wherein analyzing the UE mobility information for the particular UE comprises: determining, by the first network device and using the model of UE mobility, a probability of the particular UE being in a particular cell (e.g., see at least 0103 – “An output layer 750 of the prediction model 700 may output a probability that the UE is located at each base station (gNB) using the fully connected layers 730.” ); and determining, by the first network device, whether to include the particular cell in the set of recommended cells based on the probability of the UE being in the particular cell.
Consider claim 14, Kim teaches wherein the one or more processors are further configured to: receive handover logs indicating active state transitions of the UE during a sampled period interval (i.e., 0082- “mobility data including location information and/or time information that the UE has moved in an active mode to predict the mobility of the UE. The mobility data may also be referred to as “log” in that the mobility data includes location and time information that occurs in a network. The mobility data initially collected by the mobility management device may include, for example, identification information of the UE (UE ID), source gNB and destination gNB, and travel time.”); and wherein the one or more processors, to train the model of UE mobility, are configured to: train the model of UE mobility based on the handover logs and the active state transitions of the UE during the sampled period interval(e.g., see at least 0073 – “a neural network-based prediction model to train the prediction model to predict base stations serving a target location to which the UE is expected to move. The second data analysis device may train the prediction model to predict the base stations serving the target location using mobility data”- training also discussed in 0008, 0010, 0155, 0188, and 0205).
Consider Claim 15, Kim teaches wherein the one or more processors are further configured to: receive updated UE mobility information after training the model of UE mobility(i.e., “the prediction model may be trained using a movement path tracked” – 0089 ); and wherein the one or more processors, when analyzing the UE mobility information to predict the location of the UE, are configured to: process the updated UE mobility information to calculate a movement path; and predict the location of the UE based on the movement path(e.g., this is met because the mobility data is based on the mobility of a UE being tracked and thus the model is being updated with new batch data for training a prediction model based on constantly changing/updating mobility data as noted in at least 0089-0092).
Claim(s) 16-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. US Patent Pub. No.: 2025/0071731, hereinafter, ‘Kim’ in view of Roy et al. US Patent Pub. No.: 2024/0107597 A1, hereinafter, ‘Roy’ and further in view of Lee et al. US Patent Pub. No.: 2015/0156743, hereinafter, ‘Lee’.
Consider Claim 16, Kim teaches a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a network device(e.g., see data analysis device including first data analysis device and a second data analysis device- noted in 0010), cause the network device to: receive, from a network device, information identifying a set of recommended cells for paging(e.g., see at least 0056-predict the mobility of the UE suitable for paging, thereby also predicting the base stations serving the corresponding UE at the time of paging. – see also 0104), wherein the set of recommended cells for paging is based on an output of a machine learning model analyzing the log data identifying the UE mobility information for the UE (e.g., see at least 0010 – “a neural network-based prediction model to train the prediction model to predict base stations serving a target location to which the UE is expected to move – at least 0055 – “The mobility of the UE for paging may be determined by predicting a location of the UE at the time of paging by a movement path”).
However, Kim does not specify a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a second network device, cause the second network device to: receive, from a user equipment (UE), a request for paging; transmit, to a first network device, log data identifying UE mobility information for the UE; and transmit, to the UE, one or more paging messages in one or more cells associated with the set of recommended cells for paging.
In analogous art, Lee teaches a paging procedure (e.g., see figure 6) receiving an incoming call… “Paging requests are transmitted to the relevant eNBs according to mobility information kept in UE's MM context in the serving MME. The MME initiates the paging procedure by transmitting a paging message to each eNB with cells belonging to the tracking area(s) in which the UE is registered.” In other words, Lee teaches the steps that would warrant the use of a prediction model taught by Kim. A page is received, gather mobility information from the User and then based Kim model and recommend/predict a bases station.
Therefore, it would have been obvious to a person ordinary skill in the art before the effective filing date to try to: receive, from a user equipment (UE), a request for paging; transmit, to a first network device, log data identifying UE mobility information for the UE; and transmit, to the UE, one or more paging messages in one or more cells associated with the set of recommended cells for paging for the purpose of efficiently utilizing network resources.
Consider Claim 17, Kim teaches wherein the request for paging is associated with a handover request or a tracking area update (In 0050 -Kim teaches the association of TA and the paging. It is known that tracking area or the location is essential for paging the UE ).
Consider Claim 18, Kim teaches wherein the set of recommended cells is based on timing information or movement pattern information of the log data(e.g., see at least 0073 – “a neural network-based prediction model to train the prediction model to predict base stations serving a target location to which the UE is expected to move. The second data analysis device may train the prediction model to predict the base stations serving the target location using mobility data”- training also discussed in 0008, 0010, 0155, 0188, and 0205).
Consider Claim 20, Kim teaches wherein the one or more instructions further cause the second network device to: evaluate the set of recommended cells using one or more evaluation criteria; and select the one or more cells for paging based on evaluating the set of recommended cells using the one or more evaluation criteria (e.g., see at least 0148 – “The one second data analysis device 1001 or the first data analysis devices 1003 distributed within the network may generate a base station list by aligning, for example, in descending order, the probability that the UE is located at each base station output through the fully connected layers, and selecting base stations corresponding to a predetermined percentage (e.g., top 20%) of the aligned probabilities. The top 20% may be determined through experimentation to balance prediction accuracy with signaling overhead, but is not necessarily limited thereto”)
Claim(s) 4-5, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. US Patent Pub. No.: 2025/0071731, hereinafter, ‘Kim’ in view of Lee et al. US Patent Pub. No.: 2015/0156743, hereinafter, ‘Lee’ and further in view of Da Silva et al. US Patent Pub. No.: 2025/0294517, hereinafter, ‘Da Silva’.
Consider Claim 4, Kim as modified by Lee teaches the claimed invention except receiving, by the first network device, a request to enable use of the model of UE mobility; and wherein analyzing the UE mobility information for the particular UE to predict the location of the particular UE comprises: analyzing, by the first network device, the UE mobility information for the particular UE using the model of UE mobility based on receiving the request to enable use of the model of UE mobility.
It is understood that model updating may refer to a procedure where one model is replaced by another model for prediction. (e.g., Da Silva teaches in 0387 prediction model may be obtained for a first time, or the prediction model may be an updated prediction model, see step 504 in FIG. 5, that may replace a previously obtained prediction model.)(i.e., this would read on receiving the request to enable use of the model of UE mobility).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try receiving, by the first network device, a request to enable use of the model of UE mobility; and wherein analyzing the UE mobility information for the particular UE to predict the location of the particular UE comprises: analyzing, by the first network device, the UE mobility information for the particular UE using the model of UE mobility based on receiving the request to enable use of the model of UE mobility for the purpose of handling the mobility information in a network.
Consider Claim 5, Kim as modified by Lee teaches the claimed invention except wherein a default paging scheme is configured for a subsequent paging cycle in which use of the model of UE mobility is disabled.
It is understood that model updating may refer to a procedure where one model is replaced by another model for prediction. (e.g., Da Silva teaches in 0387 prediction model may be obtained for a first time, or the prediction model may be an updated prediction model, see step 504 in FIG. 5, that may replace a previously obtained prediction model.)(i.e., this would read effectively disabling the previous model by replacing it to use the updated model).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein a default paging scheme is configured for a subsequent paging cycle in which use of the model of UE mobility is disabled for the purpose of handling the mobility information in a network.
Consider Claim 13, Kim as modified by Lee teaches the claimed invention except wherein the one or more processors are further configured to: disable use of the model of UE mobility; and revert to paging using a default paging scheme for one or more subsequent paging messages based on disabling use of the model of UE mobility.
It is understood that model updating may refer to a procedure where one model is replaced by another model for prediction. (e.g., Da Silva teaches in 0387 prediction model may be obtained for a first time, or the prediction model may be an updated prediction model, see step 504 in FIG. 5, that may replace a previously obtained prediction model.)(i.e., this would read on effectively disabling the previous model by replacing it to use the updated model or reverting back to a model previously used (default)).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the one or more processors are further configured to: disable use of the model of UE mobility; and revert to paging using a default paging scheme for one or more subsequent paging messages based on disabling use of the model of UE mobility for the purpose of handling the mobility information in a network.
Consider Claim 19, Kim as modified by Lee teaches the claimed invention except wherein the one or more instructions further cause the second network device to: transmit a request to deactivate the machine learning model; and revert to a static paging procedure based on transmitting the request to deactivate the machine learning model.
It is understood that model updating may refer to a procedure where one model is replaced by another model for prediction. (e.g., Da Silva teaches in 0387 prediction model may be obtained for a first time, or the prediction model may be an updated prediction model, see step 504 in FIG. 5, that may replace a previously obtained prediction model.)(i.e., this would read on effectively disabling the previous model by replacing it to use the updated model or reverting back to a model previously used (default)- a request to update is the equivalent of the request to deactivate the previous model).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the one or more instructions further cause the second network device to: transmit a request to deactivate the machine learning model; and revert to a static paging procedure based on transmitting the request to deactivate the machine learning model for the purpose of handling the mobility information in a network.
Allowable Subject Matter
Claim 21 is 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.
Consider Claim 21, Kim as modified by Roy as modified Lee teaches the claimed invention except wherein the one or more instructions further cause the second network device to: disable the machine learning model based on determining that the machine learning model is inaccurately trained.
Roy teaches enabling and disabling the model, but this enabling and disabling is not based on inaccurate training.
In analogous art, Mahler teaches receiving a plurality of model versions from respective teams within the organization. Each received model version is processed to generate a respective predictive model corresponding to the model version, including identifying one or more collections of training data specified by the model version, identifying one or more training engines specified by the model version, training, by the identified one or more training engines, a predictive model using the identified one or more collections of training data, computing one or more performance metrics for the predictive model, and associating the one or more performance metrics for the predictive model with the corresponding model version in the version repository. The system can disable the corresponding trained models so that the teams are not utilizing models that were trained on obsolete data.-0063 In other words, Mahler suggests disabling models that were trained on obsolete data or data that is no longer used. The suggestion of Mahler does not specifically determine that the model was inaccurately trained. The training of Mahler could have been very well been accurate and simply out of date. The Examiner’s understanding is that inaccurate means an item or piece of information contains errors or fails to reflect the truth. Obsolete means something is no longer useful, relevant, or in active use—even if it is factually correct.
Therefore, the subject matter of claim 21 appears inventive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Pub. No.: 20220386209 teaches User Equipment (UE) assisted data collection for mobility prediction. In one embodiment, a method performed by a UE for UE-assisted data collection for mobility prediction comprises receiving, from a network node of a cellular communications system, a UE trajectory prediction model for predicting a UE trajectory. The method further comprises executing the UE trajectory prediction model to generate a predicted trajectory for the UE, comparing the actual UE trajectory to the predicted UE trajectory, and sending, to a network node of the cellular communications system, a result of the comparison of the actual UE trajectory to the predicted UE trajectory. In this manner, mobility prediction performance is improved
With respect to Claim 3, see US Patent Pub. No.: 20120115515 – “As illustrated in FIG. 5, smart paging list 500 may include information associated with UE (e.g., UE 110) that may be used to identify a particular base station likely to be serving the UE at particular times. For example, smart paging list 500 may include a profile identification (ID) field 510, a day field 520, a time field 530, a paging base station/sector field 540, and a variety of records or entries 550 associated with fields 510-540.”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES TERRELL SHEDRICK whose telephone number is (571)272-8621. The examiner can normally be reached 8A-5P.
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, Matthew D Anderson can be reached at 571 272 4177. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES T SHEDRICK/Primary Examiner, Art Unit 2646
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