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) 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.
The Examiner notes in paragraph [0156] Ramos de Azevedo teaches various aspects of the present disclosure provide a self-healing and distributed routing functionality that can be present on every node belonging to each network (e.g., Cloud Network(s), Backbone/Core Network(s), Fixed AP (or Fixed Access) Network(s), Mobile AP (or Mobile Access) Network(s), User (or End-User Devices) Network(s), Sensor Network(s), etc.) that is part of the Network of Moving Things. Such routing functionality may, for example, be driven by any of a variety of models (e.g., a traffic-based model, network-based model, vehicular/mobility-based model, QoS-based model, client-based model, energy conservation-based model, any combination thereof, etc.) and/or complex functions. In other words, the prior art actually teaches that a combination of models may be used. Application scenario is given the broadest reasonable interpretation such as the action of putting something into operation. If the Applicant’s intent is otherwise the Examiner respectfully suggest adding explicit language to the claim.
Regarding Arguments for claims 3-4 and 16-17, Applicant argues Ramos de Azevedo does not disclose the first model that uses a specific historical first parameter set as input and a historical application scenario as output to train the mapping relationship between the first parameter set and the application scenario. This enables the first model to predict a corresponding current application scenario when a current first parameter set is input.
The Examiner respectfully disagrees further reading of the prior art paragraph 0026 discloses Various aspects of the platform may, for example, establish thresholds to avoid any decision that is to be constantly or repeatedly performed without any significant advantage (e.g., technology change, certificate change, IP change, etc.). Various aspects of the platform may also, for example, learn locally (e.g., with the decisions performed) and dynamically update the decisions. A PHOSITA would appreciate that learning involves training in this context. See learning in at least 0278.
Claims 6 and 19: The Examiner respectfully disagree. 0278 teaches the existence of a prediction model provided not only with real-time information, but also with the overall network view and historical network utilization and/or performance information may provide for overall optimization and incremental learning (or model adaptation) over time. 0026 teaches various aspects of the platform may gather context parameters that can influence any or all decisions. Such parameters may, for example, be derived locally, gathered from a neighborhood, fixed APs, the Cloud, etc. Various aspects of the platform may also, for example, ask for historical information to feed any of the decisions, where such information can be derived from historical data, from surveys, from simulators, etc. Various aspects of the platform may additionally, for example, probe or monitor decisions made throughout the network, for example to evaluate the network and/or the decisions themselves in real-time. Various aspects of the platform may further, for example, enforce the decisions in the network (e.g., after evaluating the probing results). Various aspects of the platform may, for example, establish thresholds to avoid any decision that is to be constantly or repeatedly performed without any significant advantage (e.g., technology change, certificate change, IP change, etc.). Various aspects of the platform may also, for example, learn locally (e.g., with the decisions performed) and dynamically update the decisions.
Regarding Claim 8: The Applicant’s argument regarding claim 8 is untenable. A PHOSITA would appreciate that a cell for the communication/residence of the terminal could not be determined without at least a first indication message (e.g., a signal, more specific – registration message, signal strength message etc.)
Regarding Claim 9: The applicant respectfully disagrees with the Examiner's comment that claim 9 is disclosed by Ramos de Azevedo. The third model in the present application is trained based on a historical third parameter set and a corresponding first indication message to determine the cell for communication/residence by the terminal. The historical parameter set serves as input of the third model, and the corresponding first indication message serves as the output of the third model. Ramos de Azevedo does not disclose such the third model at all.
However, the Examiner respectfully disagree. As noted above it appears Ramos de Azevedo teaches a third model based on the reading of at least paragraph 0156 which stipulates a combination of models thereof. Assuming arguendo this would not suggest an inclusive third model. The Examiner has present additional evidence on the record that suggest such a feature would have been obvious.
Regarding Claim 11: The Examiner respectfully disagrees because depending on the type of parameters or information received, a second or another message is synonymous with receiving more data. Based on the data or parameter a model is configured or adjusted. Similar rationale applies to the argument of claim 12.
In order to overcome such rejections, the Examiner respectfully suggest that the Applicant specify generic or catch-all terminology such as Application – what is the application specifically ? 1st and 2nd indications – what are the indicators? a specific protocol message or indication (e.g., NAS signaling, JAVA command etc.) ?
The Applicant is further reminded that the Examiner is required to give the claims the broadest reasonable interpretation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-4, 6, 8-14, 16-17, 19, and 31, is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramos de Azevedo US Patent Pub. No.: 2018/0124631 and further in view of Liu US Patent Pub. No.: 2003/0224804 and further in view of Li et al. US Patent Pub. No.: 2021/0329522 A1, hereinafter, ‘Li’
Consider Claims 1 (as applied to Claims 14 and 31), Ramos de Azevedo teaches a communication method, performed by a terminal (e.g., see context of paragraphs 0156 and 0277 for hardware and device architecture )and comprising: determining at least one model for mobility management, wherein the model for the mobility management comprises one or more of a first model, a second model, and a third model; and the mobility management comprises one or a combination of: predicting an application scenario, configuring a measurement parameter, and determining a cell for communication/residence(e.g., see at least 0216 – “determining an optimized (or predicted optimized) set of connections for the network of moving things. As discussed herein, block 710 may comprise predicting such connections based on an optimization model comprising any of a variety of parameters (e.g., communication network parameters, node context parameters, etc.).” – see also 0217, 0235-0239, 0245, 0278, and claims 1 and 7) ;wherein the first model is configured to predict the application scenario (e.g., see at least 0216) wherein the second model (note: Such routing functionality may, for example, be driven by any of a variety of models (e.g., a traffic-based model, network-based model, vehicular/mobility-based model, QoS-based model, client-based model, energy conservation-based model, any combination thereof, etc.) and/or complex functions.- 0156) is configured to configure the measurement parameter, and the measurement parameter is configured for the terminal to perform channel measurement (e.g., see at least 0204 measuring the operating environment. See environmental characteristics relating to the channel 0181, 0210, 0221, 0233, 0250-0257 and 0264); the measurement parameter comprises at least one of a channel measurement period and/or a measurement port (e.g., see historical data/information – 0026, 0188, 0198-0201, 0221, and 0278).
However, assuming arguendo a PHOSITA could not determine based on the reading of Ramos de Azevedo, the suggestion of wherein the model for the mobility management would further comprises a third model configured for determining a cell for communication/residence (i.e., the multiple models/all three models).
Liu teaches in paragraph 0011 teaches mobile stations each comprising: a transceiver for transmitting or receiving location management information to/from the mobile network; mobility model storage means for storing one or more mobility models corresponding to the activities of the mobile station paragraph 0034 teaches -the, transceiver 100 in the mobile station 1 downloads the mobility models from the mobile network 2 and stores them in the mobility model storage unit 105. The downloading procedure is not necessary if the mobility models are previously stored in the mobile stations 1. It will be understandable for persons skilled in the art that the downloading and the storing procedure may be executed once for all without being executed every time the mobile station is powered on, so that the downloaded models are permanently stored in the mobile station for being used in the future.
Li teaches and claims in claim 1 “a mobile user device, comprising: a processor; and a memory unit operatively connected to the processor and including computer code that when executed, causes the processor to: infer at least one of A1 and A2-related handover event information from operational information available on the mobile user device, the operational information being obtained from an operating system application programming interface (API) running on the mobile user device; download a local handover prediction model from one of an edge server or a cloud server, wherein the local handover prediction model is a per-cell handover prediction model associated only with a serving cell providing communications services to the mobile user device derived from a global handover prediction model; predict occurrence of a handover based on the local handover prediction model and the inferred A1 and A2-related handover event information; initiate a handover from the serving base station to a target cell; and mask a disruption in the communication services due to the handover.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the model for the mobility management would further comprises a third model configured for determining a cell for communication/residence (i.e., the multiple models/all three models) for the purpose of mobility management.
Consider Claims 3 and 16, Ramos de Azevedo teaches wherein the first model is obtained by training according to a historical first parameter set and a corresponding application scenario(e.g., see at least 0278 – “ The existence of a prediction model provided not only with real-time information, but also with the overall network view and historical network utilization and/or performance information may provide for overall optimization and incremental learning (or model adaptation) over time”); wherein the first parameter set comprises at least one of following parameters: location information, application usage information, cell selection information, and channel measurement information; and wherein the first parameter set is an input of the first model, and the application scenario is an output of the first model (“various aspects of the platform may gather context parameters that can influence any or all decisions. Such parameters may, for example, be derived locally, gathered from a neighborhood, fixed APs, the Cloud, etc. Various aspects of the platform may also, for example, ask for historical information to feed any of the decisions, where such information can be derived from historical data, from surveys, from simulators, etc.” – 0026 – see also 0216—0218, 0221, and 0225-0239) (see also remarks above).
Consider Claims 4 and 17, Ramos de Azevedo teaches wherein predicting the application scenario comprises: determining a current first parameter set; and determining a current application scenario according to the first parameter set and the first model (see 0026, 0216—0218, 0221, and 0225-0239) (see also remarks above).
Consider Claims 6 and 19, Ramos de Azevedo teaches wherein the second model (note: Such routing functionality may, for example, be driven by any of a variety of models (e.g., a traffic-based model, network-based model, vehicular/mobility-based model, QoS-based model, client-based model, energy conservation-based model, any combination thereof, etc.) and/or complex functions.- 0156) is obtained by training according to a historical second parameter set and a corresponding measurement parameter (e.g., see historical data/information – 0026, 0188, 0198-0201, 0221, and 0278); wherein the second parameter set comprises at least one of following parameters: channel measurement information, time information, location information, a cell identity (ID), and scenario information; the measurement parameter comprises at least one of a channel measurement period and/or a measurement port; and wherein the second parameter set is an input of the second model, and the measurement parameter is an output of the second model(e.g., see historical data/information – 0026, 0188, 0198-0201, 0221, and 0278 ) (see also remarks above).
Consider Claim 8, Ramos de Azevedo teaches wherein the cell for the communication/residence of the terminal is determined based on a first indication message (e.g., this is met based on connection information figure 7).
Consider Claim 9, Ramos de Azevedo teaches wherein the third model is obtained by training according to a historical third parameter set and a corresponding first indication message(e.g., see at least 0278 – “ The existence of a prediction model provided not only with real-time information, but also with the overall network view and historical network utilization and/or performance information may provide for overall optimization and incremental learning (or model adaptation) over time”); wherein the third parameter set comprises at least one of following parameters: time, a channel measurement value, location information, a cell ID, and scenario information; and wherein the historical parameter set is an input of the third model, and the corresponding first indication message is an output of the third model(e.g., see at least 0204 measuring the operating environment. See environmental characteristics relating to the channel 0181, 0210, 0221, 0233, 0250-0257 and 0264). see historical data/information – 0026, 0188, 0198-0201, 0221, and 0278).
However, assuming arguendo a PHOSITA could not determine based on the reading of Ramos de Azevedo, the suggestion of wherein the model for the mobility management would further comprises a third model configured for determining a cell for communication/residence (i.e., the multiple models/all three models).
Liu teaches in paragraph 0011 teaches mobile stations each comprising: a transceiver for transmitting or receiving location management information to/from the mobile network; mobility model storage means for storing one or more mobility models corresponding to the activities of the mobile station paragraph 0034 teaches -the, transceiver 100 in the mobile station 1 downloads the mobility models from the mobile network 2 and stores them in the mobility model storage unit 105. The downloading procedure is not necessary if the mobility models are previously stored in the mobile stations 1. It will be understandable for persons skilled in the art that the downloading and the storing procedure may be executed once for all without being executed every time the mobile station is powered on, so that the downloaded models are permanently stored in the mobile station for being used in the future.
Li teaches and claims in claim 1 “a mobile user device, comprising: a processor; and a memory unit operatively connected to the processor and including computer code that when executed, causes the processor to: infer at least one of A1 and A2-related handover event information from operational information available on the mobile user device, the operational information being obtained from an operating system application programming interface (API) running on the mobile user device; download a local handover prediction model from one of an edge server or a cloud server, wherein the local handover prediction model is a per-cell handover prediction model associated only with a serving cell providing communications services to the mobile user device derived from a global handover prediction model; predict occurrence of a handover based on the local handover prediction model and the inferred A1 and A2-related handover event information; initiate a handover from the serving base station to a target cell; and mask a disruption in the communication services due to the handover.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the model for the mobility management would further comprises a third model configured for determining a cell for communication/residence (i.e., the multiple models/all three models) for the purpose of mobility management.
Consider Claim 10, Ramos de Azevedo teaches wherein the model for the mobility management is determined among a plurality of the first models, the second models and the third models(note: Such routing functionality may, for example, be driven by any of a variety of models (e.g., a traffic-based model, network-based model, vehicular/mobility-based model, QoS-based model, client-based model, energy conservation-based model, any combination thereof, etc.) and/or complex functions.- 0156 -0278 – “ The existence of a prediction model provided not only with real-time information, but also with the overall network view and historical network utilization and/or performance information may provide for overall optimization and incremental learning (or model adaptation) over time”).
see historical data/information – 0026, 0188, 0198-0201, 0221, and 0278).
However, assuming arguendo a PHOSITA could not determine based on the reading of Ramos de Azevedo, the suggestion of wherein the model for the mobility management would further comprises a third model configured for determining a cell for communication/residence (i.e., the multiple models/all three models).
Liu teaches in paragraph 0011 teaches mobile stations each comprising: a transceiver for transmitting or receiving location management information to/from the mobile network; mobility model storage means for storing one or more mobility models corresponding to the activities of the mobile station paragraph 0034 teaches -the, transceiver 100 in the mobile station 1 downloads the mobility models from the mobile network 2 and stores them in the mobility model storage unit 105. The downloading procedure is not necessary if the mobility models are previously stored in the mobile stations 1. It will be understandable for persons skilled in the art that the downloading and the storing procedure may be executed once for all without being executed every time the mobile station is powered on, so that the downloaded models are permanently stored in the mobile station for being used in the future.
Li teaches and claims in claim 1 “a mobile user device, comprising: a processor; and a memory unit operatively connected to the processor and including computer code that when executed, causes the processor to: infer at least one of A1 and A2-related handover event information from operational information available on the mobile user device, the operational information being obtained from an operating system application programming interface (API) running on the mobile user device; download a local handover prediction model from one of an edge server or a cloud server, wherein the local handover prediction model is a per-cell handover prediction model associated only with a serving cell providing communications services to the mobile user device derived from a global handover prediction model; predict occurrence of a handover based on the local handover prediction model and the inferred A1 and A2-related handover event information; initiate a handover from the serving base station to a target cell; and mask a disruption in the communication services due to the handover.”
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the model for the mobility management would further comprises a third model configured for determining a cell for communication/residence (i.e., the multiple models/all three models) for the purpose of mobility management.
Consider Claims 11, Ramos de Azevedo teaches the claimed invention further comprising: receiving a second indication message, wherein the second indication message is configured to instruct the terminal to configure multiple models(e.g., this is met based on connection information figure 7).
Consider Claim 12, Ramos de Azevedo teaches wherein configuring the multiple models comprises: configuring the multiple models based on acquisition locations of the multiple models comprised in the second indication message; or configuring the multiple models based on predefined information(note: Such routing functionality may, for example, be driven by any of a variety of models (e.g., a traffic-based model, network-based model, vehicular/mobility-based model, QoS-based model, client-based model, energy conservation-based model, any combination thereof, etc.) and/or complex functions.- 0156 -0278 – “ The existence of a prediction model provided not only with real-time information, but also with the overall network view and historical network utilization and/or performance information may provide for overall optimization and incremental learning (or model adaptation) over time”).
Consider Claim 13, Ramos de Azevedo teaches the claimed invention further comprising: receiving a third indication message, wherein the third indication message is configured to instruct the terminal to reconfigure one or more models; and determining to reconfigure one or more models according to the third indication message (e.g., see model adaptation and reconfiguration – 0237-238).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20070177655 A1 teaches a piece of software that can be used to model a multiple-cell environment in order to obtain the performance characteristics of a network in terms of the bit rate within a cell, the distribution of the average bit rates of the users within a cell or the distribution of the average packet transmission delays – para 0201.
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.
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/CHARLES T SHEDRICK/Primary Examiner, Art Unit 2646