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 Objections
Claims 3 and 17 recites “wherein the predictive model trained based on…” which appears to be a typographical error omitting the word “is” and therefore the Examiner believes that the Applicant intended for claims 3 and 17 to recited “wherein the predictive model is trained based on”.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s) 1, 4-8, 15 and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nammi et al. US 2016/0143055 (hereinafter Nammi).
Regarding claim 1, Nammi teaches a user equipment (UE) [Nammi, Fig. 9, UE 110, ¶107],
comprising:
a memory storing instructions; and [See Nammi, Fig. 9, Memory 930, ¶109]
a processor configured to, when executing the instructions stored in the memory, [Nammi, Fig. 9, UE Processor 920 and Memory 930, ¶108-¶109 (UE Processor 920 executing instructions stored in memory 930)]
cause the UE to:
generate user activity data and connectivity context data;
([Nammi, ¶74 and ¶76] According to Nammi, the UE determines UE location/speed data which may be an activity performed by the UE using positioning method such as GPS at multiple intervals. The Examiner considers the UE location/speed data comprising positioning measurements taken by the UE at multiple intervals as being representative of the pattern of positional displacement from the UE’s previous positions over time which meets the definition of the claimed user activity data found in the disclosure provided by the Applicants’ Specification as filed which recites in paragraph 48 that the user context may include pattern UE activity in relation to geographic location. Similarly, Nammi ¶61-¶62 discloses that the UE generates connectivity context data by determining transmission periodicity of transmissions of CSI-RS from the network node (base station), though the UE may alternatively perform CSI measurements according to the transmission periodicity of transmissions of CSI-RS from the network node (base station). The determining of transmission periodicity and/or CSI measurements according to the transmission periodicity is similar to the claimed connectivity context data which is defined in the Applicants’ Specification as including any of the following intensity of network traffic, type, cellular state, network context information comprising information about the state, status and/or operating condition of one or more characteristics of the network, a portion of a network (e.g. a cell), or a particular network device (e.g. a base station). As indicated above the UE determining of transmission periodicity and/or CSI measurements according to the transmission periodicity of transmissions of CSI-RS from the network node (base station) are considered to be connectivity context data as it comprising an operating condition of one or more characteristics of the network or particular network device (e.g. a base station).)
determine, based on the user activity data and the connectivity context data, contextual inference regarding a connection between the UE and a base station;
([Nammi, ¶76] According to Nammi the UE determines, based on the determined UE location/speed data and the UE determined transmission periodicity of transmissions and/or CSI measurements from the network node, contextual inference which is that the transmission periodicity should be made smaller than the current transmission periodicity (interpreted as the claimed contextual inference). This step being determining that the transmission periodicity shown be made smaller involves a comparative analysis because the current transmission periodicity must be known to the UE in order for the UE to make the determination that the transmission periodicity should be smaller (less).)
generate, based on the contextual inference, a connectivity context update; and
([Nammi, ¶76] Generate a recommendation of a specific transmission periodicity regarding reference signals of at least group, wherein specific transmission periodicity is based on the determination that the transmission periodicity should be smaller (interpreted as the claimed contextual inference) as indicated in the previous rationale applied to reject the previous claim limitation.)
communicate the connectivity context update to the base station.
([Nammi, ¶96] Network node receives a recommended value (or range) of transmission periodicity of the CSI-RS, which is interpreted as the claimed connectivity context update.)
Regarding claim 15, Nammi teaches a method performed by a user equipment (UE), the method comprising:
generating user activity data and connectivity context data;
([Nammi, ¶74 and ¶76] According to Nammi, the UE determines UE location/speed data which may be an activity performed by the UE using positioning method such as GPS at multiple intervals. The Examiner considers the UE location/speed data comprising positioning measurements taken by the UE at multiple intervals as being representative of the pattern of positional displacement from the UE’s previous positions over time which meets the definition of the claimed user activity data found in the disclosure provided by the Applicants’ Specification as filed which recites in paragraph 48 that the user context may include pattern UE activity in relation to geographic location. Similarly, Nammi ¶61-¶62 discloses that the UE generates connectivity context data by determining transmission periodicity of transmissions of CSI-RS from the network node (base station), though the UE may alternatively perform CSI measurements according to the transmission periodicity of transmissions of CSI-RS from the network node (base station). The determining of transmission periodicity and/or CSI measurements according to the transmission periodicity is similar to the claimed connectivity context data which is defined in the Applicants’ Specification as including any of the following intensity of network traffic, type, cellular state, network context information comprising information about the state, status and/or operating condition of one or more characteristics of the network, a portion of a network (e.g. a cell), or a particular network device (e.g. a base station). As indicated above the UE determining of transmission periodicity and/or CSI measurements according to the transmission periodicity of transmissions of CSI-RS from the network node (base station) are considered to be connectivity context data as it comprising an operating condition of one or more characteristics of the network or particular network device (e.g. a base station))
determining, based on the user activity data and the connectivity context data, contextual inference regarding a connection between the UE and a base station;
([Nammi, ¶76] According to Nammi the UE determines, based on the determined UE location/speed data and the UE determined transmission periodicity of transmissions and/or CSI measurements from the network node, contextual inference which is that the transmission periodicity should be made smaller than the current transmission periodicity (interpreted as the claimed contextual inference). This step being determining that the transmission periodicity shown be made smaller involves a comparative analysis because the current transmission periodicity must be known to the UE in order for the UE to make the determination that the transmission periodicity should be smaller (less).)
generating, based on the contextual inference, a connectivity context update; and
([Nammi, ¶76] Generate a recommendation of a specific transmission periodicity regarding reference signals of at least group, wherein specific transmission periodicity is based on the determination that the transmission periodicity should be smaller (interpreted as the claimed contextual inference) as indicated in the previous rationale applied to reject the previous claim limitation.)
communicating the connectivity context update to the base station.
([Nammi, ¶96] Network node receives a recommended value (or range) of transmission periodicity of the CSI-RS, which is interpreted as the claimed connectivity context update.)
Regarding claims 4 and 18, Nammi teaches the UE of claim 1 and method of claim 15, wherein the processor is further configured to determine, based on the contextual inference, a configuration recommendation. ([Nammi, Fig. 9, ¶76 and ¶96] The UE using its onboard processor as shown in Fig. 9 of Nammi is configured to determine, based on the determination that the transmission periodicity should be smaller (the claimed contextual inference), a configuration recommendation comprising a value (or range) of a transmission periodicity.)
Regarding claims 5 and 19, Nammi teaches the UE of claim 4 and the method of claim 18, wherein the processor is further configured to communicate the configuration recommendation to the base station.
([Nammi, Fig. 9, ¶76 and ¶96] The UE using its onboard processor as shown in Fig. 9 of Nammi is configured to determine, based on the determination that the transmission periodicity should be smaller (the claimed contextual inference), a configuration recommendation comprising a value (or range) of a transmission periodicity that is to be transmitted to the network node (base station).)
Regarding claim 6, Nammi teaches the UE of claim 1, wherein the connectivity context update does not include the user activity data.
([Nammi ¶76 and ¶96] According to Nammi, the recommendation comprising a value (or range) of a transmission periodicity that is sent to the network node does not include any user activity data such as the UE location/speed.)
Regarding claim 7, Nammi teaches the UE of claim 1, wherein the user activity data is generated based on a user interaction with sensors, applications, and/or operations of the UE.
([Nammi, ¶74 and ¶76] According to Nammi, the determined UE location/speed measured by the UE using positioning methods at multiple intervals which is interpreted as user activity data is based on operations of the UE.)
Regarding claim 8, Nammi teaches the UE of claim 1, wherein the connectivity context data includes information relating to characteristics of the connection between the UE and the base station.
([Nammi, ¶74, ¶76 and ¶93] the UE determining of transmission periodicity and/or CSI measurements according to the transmission periodicity of transmissions of CSI-RS from the network node (base station) are considered to be the claimed connectivity context data, it includes information relating to characteristics of the connection between the UE and the base station as it allows more available transmission power at the network for applying to the transmission of data traffic channels (PDSCH, PMCH, etc.) between the UE and the network node (base station))
Regarding claim 20, Nammi teaches the method of claim 15, wherein the user activity data comprise private user data, and the connectivity context update does not include the private user data.
[Nammi, ¶74 and ¶76] the UE determined UE location/speed data which may be an activity performed by the UE using positioning method such as GPS at multiple intervals. Is considered to be the user activity data as claimed above. Furthermore, based on the disclosure provided in ¶98 of the Applicants’ Specification, the Applicant defines private user data as including an exact location. This disclosure provided by the Applicants regarding private user data corresponds to the disclosure of Nammi which discloses that UE location may be determined using positioning methods such as GPS at multiple intervals which allows for the Examiner to conclude that Nammi does teach wherein the user activity data comprises private user data as claimed. Additionally, according to Nammi [Nammi, ¶76 and ¶96], the claimed connectivity context update disclosed by Nammi as the recommendation comprising a value (or range) of a transmission periodicity that is sent to the network node does not include any user activity data such as the UE location/speed and therefore for since the recommendation does not comprise any user activity data it cannot and does not comprises any user activity data that would be categorized as private.)
Claim(s) 9-12 and 14 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Khafizov et al. US 2022/0239395 (hereinafter Khafizov)
Regarding claim 9, Khafizov teaches a server device, comprising:
([Khafizov, Fig. 1, ¶23] The base station, such as the base station of sector 101 functions as the claimed server as it serves the user equipment connected to it.)
a memory storing instructions;
[Fig. 15, Memory 1530, ¶111-¶112].
and a processor configured to, when executing the instructions stored in the memory,
[Khafizov, Fig. 15, Processor 1520, ¶111-¶112].
cause the processor to:
receive information respectively from a plurality of user equipment (UEs);
([Khafizov, ¶23 and ¶42] The Base Station receives measurement reports from each of the UEs in the sector 101)
process the information to produce a plurality of metrics of a wireless communication network; and ([Khafizov, Fig. 1, Step 102, ¶28-¶30 and ¶42] The base station may process the information to produce a plurality of metrics (channel propagation metrics, QoS metrics, RF metrics) of a wireless network by performing a “push” or “pull” process whereby metrics of the received measurement reports from the UE are further communicated to a Radio Access Network (RAN) Optimization System (ROS).)
provide the plurality of metrics of the wireless communication network to a data-driven network (NW) control unit. ([Khafizov, Fig. 1, 102 ¶40-¶44] The base station may provide the plurality of metrics (channel propagation metrics, QoS metrics or RF metrics) to the ROS as shown in step 102 of Fig. 1)
Regarding claim 10, Khafizov teaches the server device of claim 9, wherein the information from the plurality of UEs correspond to a time window. ([Khafizov, ¶28, ¶33, ¶43-¶44 and ¶55] The channel propagation/QoS/other metrics may be received over time and correspond to different time periods (such as morning or afternoon hours).)
Regarding claim 11, Khafizov teaches the server device of claim 9, wherein the information from the plurality of UEs correspond to a geographic location ([Khafizov, ¶22, ¶30 and ¶41] Channel propagation metrics may be determined as a function of geographical location).
Regarding claim 12, Khafizov teaches the server device of claim 9, wherein the metrics are provided to the data-driven NW control unit according to aggregate coordination cycles. ([Khafizov, ¶22 and ¶28] Step 102 of Fig1 depicts a plurality of metrics from with sectors 101 are received by (provided in an aggregated coordination to) the ROS (data-driven NW control unit) periodically (in cycles).)
Regarding claim 14, Khafizov teaches the server device of claim 9, wherein the information indicating user activity and not containing any confidential information about a specific user. ([Khafizov, Fig. 1, ¶22 and ¶40-¶43] channel propagation metrics as well as RF and QoS metrics which are received by the ROS indicate user activity as it indicates bandwidth, latency, packet loss and other metrics related to the network layer and/or application layer performance for the given UE, but according to Khafizov there is no capability in these metrics for containing any confidential information about a specific user such as the UE location as the location information is not explicitly contained in these metrics.)
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.
Claim(s) 2 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nammi as applied to claims 1 and 15 respectively above, and further in view of WANG et al. US 2024/0291612 (hereinafter WANG)
Regarding claims 2 and 16, Nammi teaches The UE of claim 1 ([Nammi, Fig. 9, UE 110] see the rejection of claim 1) and the method of claim 15 respectively, wherein the UE has the ability to make a contextual interference ([Nammi, ¶76] Generate a recommendation of a specific transmission periodicity regarding reference signals of at least group, wherein specific transmission periodicity is based on the determination that the transmission periodicity should be smaller (interpreted as the claimed contextual inference).)
But it does not teach wherein the inference is based on a predictive model.
However, WANG teaches wherein the inference is based on a predictive model.
([WANG, ¶34, ¶43 and ¶49] WANG teaches a trained AI data processing model for inference, wherein the inference phase refers to a process of using the trained AI data processing model to make a prediction or guide the decision based on collected data and AI data processing model)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Nammi, indicating the ability to determine, based on the user activity data and the connectivity context data, contextual inference regarding, with the teachings of WANG, indicating that the inference is based on a predictive model. The resulting benefit of the combination would have been the ability to make predictions or decisions without being explicitly programmed to do so an improve automatically through experience and be the use of data [WANG, ¶33 and ¶43].
Claim(s) 3 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Nammi, in view of WANG as applied to claims 2 and 16 respectively above, and further in view of Hyder et al. US 11,184,328 (hereinafter Hyder).
Regarding claims 3 and 17, the combination of Nammi, in view of WANG teaches the UE of claim 2 (See the rejection of claim 2 above) and the method of claim 16 respectively, wherein the predictive model trained [WANG, ¶33-¶34 (AI/ML model is trained using training data in a training phase).]
But it does not teach wherein training is based on historical user activity data and historical connectivity context data.
However, Hyder teaches wherein training is based on historical user activity data and historical connectivity context data. ([Hyder, Col 9, Lines 36-52] The training module trains the machine-learned model make predictions using retrieved historical connection parameters (interested as the historical connectivity context data) and different times and client geographic locations (interpreted as the historical user activity) for the connections.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Nammi, in view of WANG, indicating the ability to train a predictive model, with the teachings of Hyder, indicating that the training phase for the predictive model uses training data that comprises historical user activity data and historical connectivity context data. The resulting benefit of the combination would have been the ability to make better performance predictions optimal connections while preventing/reducing lag time [Hyder, Col. 9, Lines 53 and Col. 10, Lines 10].
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khafizov, as applied to claim 9 above, and further in view of Wang et al. US 2024/0381337 (hereinafter Wang2).
Regarding claim 13, Khafizov teaches the server device of claim 9, wherein the plurality of metrics are provided to the data-driven NW control unit. ([See Khafizov, Fig. 1, Step 102] The metrics are provided to the ROS 105 which is interpreted at the claimed data-drive NW control unit.)
But it does not teach wherein plurality of metrics are provided with forecast data.
However, Wang teaches wherein plurality of metrics are provided with forecast data. ([Wang2, ¶52 also see ¶22 and ¶27] The UE 110 periodically computes and sends prediction metrics. Wherein the prediction metrics are considered to be metrics provided with prediction (forecast) data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the teachings of Khafizov, indicating the ability to receive a plurality of metrics from the network UEs, with the teachings of Wang2, indicating that the received metrics comprise forecast data in the form of predictions for the metric data. The resulting benefit of the combination would have been the ability for the UE to provide prediction metrics (e.g., predicted UE operating condition metrics, predicted signal and/or link-quality metrics) to a base station based on various factors observable at the UE which further provides the base station with additional information and time to schedule air interface resources to meet anticipated data requirements and/or mitigate anticipated problems in a corresponding transmission channel, thus allowing the base station to improve the reliability and/or performance (e.g., improved throughput, reduced bit errors) of the services provided by the RAN. [Wang2, ¶22 and ¶32].
Conclusion
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/LONNIE V SWEET/Primary Examiner, Art Unit 2467