DETAILED ACTION
Claims 1-5, 7-9, 11, 13-23 are pending.
RCE of 01/02/2026 is/are entered.
Response to Arguments
The claims have been to amended to state wherein the at least one local condition is associated with at least one machine learning model for selecting a wireless access point type, and wherein the at least one local condition comprises at least one of: a presence at a particular type of transit location or a presence within an enclosed structure. As such new search/consideration and a new ground of rejection is established as below. Arguments pertaining the previous ground of rejections are thus considered moot.
Applicant also indicated all previous arguments pertaining Cili and Brisebois are maintained. However, no specific rebuttal to the examiner’s response to arguments in the previous actions are provided. Therefore the arguments are not persuasive.
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) 1-5, 7, 9, 11, 13-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cili (US 2016/0316425) in view of Brisebois et al. (US 2018/0270677) and further in view of Suzaki et al. (US 2021/0051678) and in further view of Kotzin (US 2007/0153701).
As to claim 1:
Cili discloses a method comprising: initiating, by a processing system of a user endpoint device (Fig. 3, processing system of user device), the processing system including at least one processor (340), an audio call for the user endpoint device (¶0009, 0088, 0090, 0099, establishing an real time service, including an audio call) at a location (¶0087, location such as shops, airport, residence of user); selecting, by the processing system, a wireless access point type from among a plurality of wireless access point types for the audio call based on at least one of: performance metrics collected by the user endpoint device at the location or performance metrics for the location obtained by the user endpoint device from a remote database; (¶0097-0100, mobile device to select among base station types, namely WLAN access point or different cellular base station, based on one or more criteria. See ¶0102, criteria for selection of base station includes quality of links of each connections such as RSSI, reliability, etc. collected by the mobile device at location of the mobile device)
and implementing, by the processing system, the audio call via the wireless access point type that is selected. (Fig. 7, 706, ¶0009, 0088, 0090, 0099, establishing a real time service, including an audio call using the chosen access point type)
Cili discloses all limitations of claim 1, however is silent on the selecting a wireless access point type is by a machine learning model.
Brisebois in a related field of endeavor discloses the selecting is in accordance with at least one machine learning model. (See Brisebois, ¶0146, 0059, using machine learning to facilitating selection) and applying the performance metrics collected by the user endpoint device at the location or the performance metrics for the location obtained by the user endpoint device from the remote database as inputs to the at least one machine learning model. (Brisebois, ¶0071, measurement of quality metric are inputted to build learning model, ¶0057, 0059, uses measurements and machine learning to facilitate selection)
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the metrics under consideration for Cili’s network selection to include the selecting is in accordance with at least one machine learning model. This implementation advantageously automatically adjust technology selection thresholds (in this case signal strength) to prevent or minimize repeated attempts to access technologies (¶0059 of Brisebois).
The combination of Cili does not explicitly discloses detecting at least one local condition of a location that is specific to the location, wherein the at least one local condition is associated with at least one machine learning model, and selecting at least one machine learning based on the at least one condition that is detected.
Suzaki in a related field of RF measurement using learning models discloses in Fig.3, ¶0046-0049, 0053-0062, a communication device with a model selection unit to select a learning model among a plurality of learning model, wherein quality measurement unit’s input and position acquisition to measure RF condition tied to the position, which is used as basis for selection processing unit, for example: “The selection unit 220 acquires the quality environment position supervised data from the external device. The selection unit 220 acquires the radio wave environment information, the terminal identification information, and the request signal or the error information from the base station side generation unit 20. The selection unit 220 generates the communication quality information on the basis of the radio wave environment information. The selection unit 220 acquires the terminal position information from the position estimation unit 2, (…) The selection unit 220 selects the learning model data according to the terminal position information acquired from the position estimation unit 21”, i.e. the model is associated with the condition(s) because the condition(s) such as location and/or environment condition match criteria to single out the selected model.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Cili to further include local-based selection of machine learning model as disclosed by Suzaki. Suzaki in ¶0010-0012, 0046 discusses such implementation allows for flexible adaptation of network conditioning and resource use for and solving problems unique to each specific position/cell where a terminal is located.
Regarding: wherein the at least one local condition comprises at least one of: a presence at a particular type of transit location or a presence within an enclosed structure
Suzaki discloses the condition as detected being a specific location information of the terminal for selecting the ML model, however is silent on what the specific locations are.
Kotzin, in a related field of wireless sensing, discloses a system/method for reselection for secondary networks, wherein specific enclosed location of the wireless terminal is based on (See Abstract). Specifically, per ¶0031, 0026-0028, 0036, the system is configured to detect environment condition and whether the user is inside a building. Upon detecting the user is inside the building (enclosed structure), the system employs a different strategy for cell search in accordance with the current location. Since Suzaki in combination with Cili uses location of the mobile to determine an AI model for cell search (i.e. each AI model represent a different cell search strategy). A combination arrives at the claimed invention.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the combination of Cili to incorporate Kotzin’s building interior detection for wireless network searching strategy as applied above. The implementation of Kotzin’s system/method for reselection for secondary networks, wherein specific enclosed location of the wireless terminal is based on advantageously allow for better decisions concerning when to trigger a network search, improving chance for successful connection/detection. (¶0007 of Kotzin)
As to claim 19:
Cili discloses a non-transitory computer-readable medium storing instructions which, when executed by a processing system of a user endpoint device (¶0075, processor/CRM) including at least one processor, cause the processing system to perform operations, the operations comprising: initiating an audio call for the user endpoint device (¶0009, 0088, 0090, 0099, establishing an real time service, including an audio call) at a location (¶0087, location such as shops, airport, residence of user); selecting a wireless access point type from among a plurality of wireless access point types for the audio call based on at least one of: performance metrics collected by the user endpoint device at the location or performance metrics for the location obtained by the user endpoint device from a remote database; ; (¶0097-0100, mobile device to select among base station types, namely WLAN access point or different cellular base station, based on one or more criteria. See ¶0102, criteria for selection of base station includes quality of links of each connections such as RSSI, reliability, etc. collected by the mobile device at location of the mobile device)
and implementing the audio call via the wireless access point type that is selected. (Fig. 7, 706, ¶0009, 0088, 0090, 0099, establishing a real time service, including an audio call using the chosen access point type)
Cili discloses all limitations of claim 1, however is silent on the selecting a wireless access point type is by a machine learning model.
Brisebois in a related field of endeavor discloses the selecting is in accordance with at least one machine learning model. (See Brisebois, ¶0146, 0059, using machine learning to facilitating selection) and applying the performance metrics collected by the user endpoint device at the location or the performance metrics for the location obtained by the user endpoint device from the remote database as inputs to the at least one machine learning model. (Brisebois, ¶0071, measurement of quality metric are inputted to build learning model, ¶0057, 0059, uses measurements and machine learning to facilitate selection)
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the metrics under consideration for Cili’s network selection to include the selecting is in accordance with at least one machine learning model. This implementation advantageously automatically adjust technology selection thresholds (in this case signal strength) to prevent or minimize repeated attempts to access technologies (¶0059 of Brisebois).
The combination of Cili does not explicitly discloses detecting at least one local condition of a location that is specific to the location, wherein the at least one local condition is associated with at least one machine learning model, and selecting at least one machine learning based on the at least one condition that is detected.
Suzaki in a related field of RF measurement using learning models discloses in Fig.3, ¶0046-0049, 0053-0062, a communication device with a model selection unit to select a learning model among a plurality of learning model, wherein quality measurement unit’s input and position acquisition to measure RF condition tied to the position, which is used as basis for selection processing unit, for example: “The selection unit 220 acquires the quality environment position supervised data from the external device. The selection unit 220 acquires the radio wave environment information, the terminal identification information, and the request signal or the error information from the base station side generation unit 20. The selection unit 220 generates the communication quality information on the basis of the radio wave environment information. The selection unit 220 acquires the terminal position information from the position estimation unit 2, (…) The selection unit 220 selects the learning model data according to the terminal position information acquired from the position estimation unit 21”, i.e. the model is associated with the condition(s) because the condition(s) such as location and/or environment condition match criteria to single out the selected model.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Cili to further include local-based selection of machine learning model as disclosed by Suzaki. Suzaki in ¶0010-0012, 0046 discusses such implementation allows for flexible adaptation of network conditioning and resource use for and solving problems unique to each specific position/cell where a terminal is located.
Regarding: wherein the at least one local condition comprises at least one of: a presence at a particular type of transit location or a presence within an enclosed structure
Suzaki discloses the condition as detected being a specific location information of the terminal for selecting the ML model, however is silent on what the specific locations are.
Kotzin, in a related field of wireless sensing, discloses a system/method for reselection for secondary networks, wherein specific enclosed location of the wireless terminal is based on (See Abstract). Specifically, per ¶0031, 0026-0028, 0036, the system is configured to detect environment condition and whether the user is inside a building. Upon detecting the user is inside the building (enclosed structure), the system employs a different strategy for cell search in accordance with the current location. Since Suzaki in combination with Cili uses location of the mobile to determine an AI model for cell search (i.e. each AI model represent a different cell search strategy). A combination arrives at the claimed invention.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the combination of Cili to incorporate Kotzin’s building interior detection for wireless network searching strategy as applied above. The implementation of Kotzin’s system/method for reselection for secondary networks, wherein specific enclosed location of the wireless terminal is based on advantageously allow for better decisions concerning when to trigger a network search, improving chance for successful connection/detection. (¶0007 of Kotzin)
As to claim 20:
Cili discloses an apparatus comprising: a processing system including at least one processor of a user endpoint device; and a computer-readable medium storing instructions which, when executed by the processing system (Fig. 3, ¶0075, processing system of user device), cause the processing system to perform operations, the operations comprising: initiating an audio call for the user endpoint device (¶0009, 0088, 0090, 0099, establishing an real time service, including an audio call) at a location (¶0087, location such as shops, airport, residence of user); selecting a wireless access point type from among a plurality of wireless access point types for the audio call based on at least one of: performance metrics collected by the user endpoint device at the location or performance metrics for the location obtained by the user endpoint device from a remote database; ; (¶0097-0100, mobile device to select among base station types, namely WLAN access point or different cellular base station, based on one or more criteria. See ¶0102, criteria for selection of base station includes quality of links of each connections such as RSSI, reliability, etc. collected by the mobile device at location of the mobile device)
and implementing the audio call via the wireless access point type that is selected. (Fig. 7, 706, ¶0009, 0088, 0090, 0099, establishing a real time service, including an audio call using the chosen access point type).
Cili discloses all limitations of claim 1, however is silent on the selecting a wireless access point type is by a machine learning model.
Brisebois in a related field of endeavor discloses the selecting is in accordance with at least one machine learning model. (See Brisebois, ¶0146, 0059, using machine learning to facilitating selection) and applying the performance metrics collected by the user endpoint device at the location or the performance metrics for the location obtained by the user endpoint device from the remote database as inputs to the at least one machine learning model. (Brisebois, ¶0071, measurement of quality metric are inputted to build learning model, ¶0057, 0059, uses measurements and machine learning to facilitate selection)
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the metrics under consideration for Cili’s network selection to include the selecting is in accordance with at least one machine learning model. This implementation advantageously automatically adjust technology selection thresholds (in this case signal strength) to prevent or minimize repeated attempts to access technologies (¶0059 of Brisebois).
The combination of Cili does not explicitly discloses detecting at least one local condition of a location that is specific to the location, wherein the at least one local condition is associated with at least one machine learning model, and selecting at least one machine learning based on the at least one condition that is detected.
Suzaki in a related field of RF measurement using learning models discloses in Fig.3, ¶0046-0049, 0053-0062, a communication device with a model selection unit to select a learning model among a plurality of learning model, wherein quality measurement unit’s input and position acquisition to measure RF condition tied to the position, which is used as basis for selection processing unit, for example: “The selection unit 220 acquires the quality environment position supervised data from the external device. The selection unit 220 acquires the radio wave environment information, the terminal identification information, and the request signal or the error information from the base station side generation unit 20. The selection unit 220 generates the communication quality information on the basis of the radio wave environment information. The selection unit 220 acquires the terminal position information from the position estimation unit 2, (…) The selection unit 220 selects the learning model data according to the terminal position information acquired from the position estimation unit 21”, i.e. the model is associated with the condition(s) because the condition(s) such as location and/or environment condition match criteria to single out the selected model.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Cili to further include local-based selection of machine learning model as disclosed by Suzaki. Suzaki in ¶0010-0012, 0046 discusses such implementation allows for flexible adaptation of network conditioning and resource use for and solving problems unique to each specific position/cell where a terminal is located.
Regarding: wherein the at least one local condition comprises at least one of: a presence at a particular type of transit location or a presence within an enclosed structure
Suzaki discloses the condition as detected being a specific location information of the terminal for selecting the ML model, however is silent on what the specific locations are.
Kotzin, in a related field of wireless sensing, discloses a system/method for reselection for secondary networks, wherein specific enclosed location of the wireless terminal is based on (See Abstract). Specifically, per ¶0031, 0026-0028, 0036, the system is configured to detect environment condition and whether the user is inside a building. Upon detecting the user is inside the building (enclosed structure), the system employs a different strategy for cell search in accordance with the current location. Since Suzaki in combination with Cili uses location of the mobile to determine an AI model for cell search (i.e. each AI model represent a different cell search strategy). A combination arrives at the claimed invention.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the combination of Cili to incorporate Kotzin’s building interior detection for wireless network searching strategy as applied above. The implementation of Kotzin’s system/method for reselection for secondary networks, wherein specific enclosed location of the wireless terminal is based on advantageously allow for better decisions concerning when to trigger a network search, improving chance for successful connection/detection. (¶0007 of Kotzin)
As to claims 2, 21:
Cili’s combination discloses all limitations of claim 1/20, wherein the performance metrics collected by the user endpoint device at the location include at least one of: first performance metrics for a first wireless access point type at the location or second performance metrics for a second wireless access point type at the location, and wherein the performance metrics for the location obtained by the user endpoint device from the remote database include at least one of: third performance metrics for the first wireless access point type at the location or fourth performance metrics for the second wireless access point type at the location. (See Cili, ¶0102, measuring at mobile device, “SNR, RSRQ, BLER, and/or any desirable metrics, e.g., that measure the quality or reliability of the radio links of each connection”)
As to claims 3, 22:
Cili’s combination discloses all limitations of claim 1/20, wherein the plurality of wireless access point types includes at least: a cellular access point type and a non-cellular wireless access point type. (Cili, ¶0097-0100, mobile device to select among base station types, namely WLAN access point or different cellular base station, based on one or more criteria)
As to claim 11:
Cili’s combination discloses all limitations of claim 1, wherein the at least one local condition comprises further a presence at a foreign jurisdiction of the user endpoint device. (See Cili, ¶0102, determining the condition of whether the mobile is roaming, i.e. roaming is a condition in which the mobile device travels in coverage of networks foreign to the home networks covered the user’s subscription)
As to claim 13:
Cili’s combination discloses all limitations of claim 1, further comprising: performing at least one configuration task to configure at least one aspect of the user endpoint device based upon the at least one local condition that is detected. (See Cili, at least ¶0101-0102, upon detecting a particular condition and/or metrics, performing one configuration task such as configuring the device to subject to preference of a particular type of network connection over others. For example, ¶0095, “if the mobile device is switching between base stations often (e.g., based on the location of the mobile device or the mobility of the mobile device) and/or the mobile device is already performing data communication using WLAN, it may be more efficient to simply use the WLAN RAT rather than cellular RAT”)
As to claim 14:
Cili’s combination discloses all limitations of claim 1, further comprising: performing at least one registration task to register the user endpoint device with at least one communication network based upon the at least one local condition that is detected. (¶0104 - 0106, upon detecting a local condition, for example the condition being, the mobile device to switch connection to a different RAT, i.e. network registration is intrinsic to establishing any network (per ¶0083).)
As to claim 15:
Cili’s combination discloses all limitations of claim 1, further comprising: collecting performance quality measurements for the audio call via the wireless access point type that is selected. (Cili, ¶0109, the method can be performed for network switching while the voice call is on-going, i.e. collection of network metrics are performed for the current on-going call per ¶0102)
As to claim 17:
Cili’s combination discloses all limitations of claim 15, further comprising: selecting a different wireless access point type from among the plurality of wireless access point types for the audio call in accordance with the performance quality measurements; and migrating the audio call to the different wireless access point type. (Cili, ¶0109, the method can be performed for network switching while the voice call is on-going, i.e. collection of network metrics are performed for the current on-going call per ¶0102, namely the on-going call is to be switched to another selected access point type while it is ongoing, i.e. being migrated, responsive the same evaluation of metrics collected)
As to claim 4, 23:
Cili’s combination discloses all limitations of claim 2/21, and Cili discloses the performance metrics collected by the user endpoint device at the location include measurements for at least one of: the first wireless access point type or the second wireless access point type. . (See ¶0102, “SNR, RSRQ, BLER, and/or any desirable metrics, e.g., that measure the quality or reliability of the radio links of each connection”)
Cili however does not explicitly mention specific metrics being at least one of: a packet loss, a latency, or a jitter.
Brisebois, however in a related field of endeavor for RAT selection based on metrics, discloses in at least ¶0070, 0073, wherein metrics collected for consideration of access point selection include among other things jitter, signal strength, packet loss etc.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the metrics under consideration for Cili’s network selection to include other metrics such as packet loss/jitter. Cili in ¶0102 makes clear that any desirable metrics are acceptable, thus opens up possibility for preferences and design constraints. Furthermore, Brisebois in ¶0019 expresses desirable metrics include jitter as voice data is highly susceptible to jitter. Thus this implementation provides critical consistency for voice service by considering jitter metrics or losses of package.
As to claim 5:
Cili’s combination discloses all limitations of claim 2, however Cili is silent on the performance metrics for the location obtained by the user endpoint device from the remote database include measurements of at least one of: a packet loss, a latency, or a jitter for at least one of: the first wireless access point type or the second wireless access point type.
Brisebois, however in a related field of endeavor for RAT selection based on metrics, discloses in at least ¶0070, 0073, wherein metrics collected for consideration of access point selection include among other things jitter, signal strength, packet loss etc. In ¶0033-0037, the metric information can be stored at repository of network control node 112 and to be communicated to mobile devices to aid in selection.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the metrics under consideration for Cili’s network selection to include other metrics such as packet loss/jitter and distributed in a centralized manner. Cili in ¶0102 makes clear that any desirable metrics are acceptable, thus opens up possibility for preferences and design constraints. Furthermore, Brisebois in ¶0019 expresses desirable metrics include jitter as voice data is highly susceptible to jitter. Thus this implementation provides critical consistency for voice service by considering jitter metrics or losses of package easily accessible from a network server, i.e. avoid individual measurement costs.
As to claim 7:
Cili’s combination discloses all limitations of claim 1, wherein Brisebois discloses the selecting is in accordance with at least one machine learning model. (See Brisebois, ¶0146, 0059, using machine learning to facilitating selection), the selecting comprises: applying the at least one of: the performance metrics collected by the user endpoint device at the location or the performance metrics for the location obtained by the user endpoint device from the remote database as inputs to the at least one machine learning model. (Brisebois, ¶0071, measurement of quality metric are inputted to build learning model, ¶0057, 0059, uses measurements and machine learning to facilitate selection)
As to claim 9:
Cili’s combination discloses all limitations of claim 7, wherein the selecting further comprises: obtaining at least one output of the at least one machine learning model, wherein the at least one output comprises a selection of the wireless access point type from among the plurality of wireless access point types. (Brisebois,¶0025, 0146, mobile device self-optimizing technology selection as a self-learning method for the mobile device to tune thresholds used for the selection of wireless transport technologies, making a selection of a cell site that benefits more customers)
As to claim 16:
Cili’s combination discloses all limitations of claim 15, wherein Brisebois, however in a related field of endeavor for RAT selections, discloses in transmitting the performance quality measurements to the remote database, wherein the performance quality measurements are stored in the remote database as additional performance metrics for the wireless access point type at the location. (¶0030, 0033, 0037 that metrics collected by the mobile device stored in its repository 104 is then transmitted for storage at network control device 112.)
As to claim 18:
Cili’s combination discloses all limitations of claim 17, wherein Brisebois discloses the selecting is in accordance with at least one machine learning model. (See Brisebois, ¶0146, 0059, using machine learning to facilitating selection)
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cili (US 2016/0316425) in view of Brisebois et al. (US 2018/0270677) and further in view of Suzaki et al. (US 2021/0051678) in further view of Kotzin (US 2007/0153701) and in further view of Thiagarajan et al. (US 2020/0092181).
As to claim 8:
Cili’s combination discloses all limitations of claim 7, however is silent on the selecting further comprises: obtaining at least one output of the at least one machine learning model, wherein the at least one output comprises at least one of: a predicted packet loss, a predicted latency, or a predicted jitter for at least one of: the first wireless access point type or the second wireless access point type.
Thiagarajan, in a related field of endeavor of network selection using AI, discloses in at least ¶0134, 0139, 0028 to use a machine learning model to generate predicted estimates of latency and other traffic metrics to aid in network selection at the geographical area of the mobile device.
It would have been obvious to one of ordinary skill in the art before the effective filing time of the invention that the system of Cili and Brisebois to incorporate use of a machine learning model to generate predicted estimates of latency. This implementation, with the aid of machine learning, advantageously produce more accuracy prediction of network traffic condition, thus more effective at network decision (¶0028 of Thiagarajan).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cronin (US 2015/0358834) - Methods and systems for signal strength management are provided. A first (mobile) communication device may provide a server with information relating to the signal strength, and such information may be used to determine future signal strength (e.g., at a different location). The first user may see the signal strength of a second communication device with which it may be communicating. The communication device may also communicate with a system that maintains a real-time database of signal strengths throughout an area, such as a state. A user's handheld device may receive an indications, warnings, and recommended courses of action that the user may take to maintain communications when a signal strength of a wireless communication network is predicted to change. Users of the present invention may take actions to insure that the quality of their call is maintained.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUAN M HUA whose telephone number is (571)270-7232. The examiner can normally be reached 10:30-6:30.
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/QUAN M HUA/Primary Examiner, Art Unit 2645