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) 1-20 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.
Claim(s) 1-4 and 7-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Taneja et al. (US 2021/0076223) (“Taneja”) in view of Piersol (US 2019/0372902) in view of Kennedy et al. (US 2008/0045235) (“Kennedy”).
For claims 1; Taneja discloses: at least one sensor operable to create measured data based on the electromagnetic environment (paragraph 31, 50: The present technology monitors for and detects the interference between nearby access points and uses machine learning in order to dynamically assign/re-assign the access points to different CBRS channels (i.e. different frequencies)), wherein the at least one sensor is in communication with a learning engine, a semantic engine, and at least one server (paragraph 40-41: The DNA-C 310 will be used to further manage each of the access points of an enterprise within the CBRS network 300. In particular, the DNA-C 310 will 1) obtain the assignments of each of the access points provided by the SAS, 2) monitor performance of each of the access points and associated user equipment devices to detect when interference is present, 3) determine different parameters to assign to one or more of the access points when interference is detected, and 4) dynamically re-assign the operational parameters of the access points to minimize and/or eliminate the detected interference. In this way, the DNA-C 310 can resolve issues of interference between access points and/or user equipment that the SAS may not be able to account for); wherein the learning engine is operable to utilize prediction models to predict interference in the electromagnetic environment (paragraph 53, 70: the machine learned models will evaluate the various operational parameters in order to predict what operational parameters should be used. For example, the machine learned model will predict what channel bandwidth and maximum effective isotropic radiated power the SAS may assign to the access point, a level of interference that a user equipment may experience in the CBRS network, and an impact on performance of the user equipment because of the level of interference); wherein the at least one server is operable to create actionable data based on the predicted interference (paragraph 64, 70: the machine learned models will evaluate the various operational parameters in order to predict what operational parameters should be used); wherein the semantic engine is operable to establish a rule or a policy based on a user data input (paragraph 67: if a user associated with user equipment associated with the access point provides feedback that the quality of communication has become poor, the feedback may be used to identify that interference issues may be present at the access point and that the operational parameters of the access point and/or user equipment may need to be modified to address the interference); wherein the survey occupancy application preprocesses at least two signals based on interference between the at least two signals (paragraph 54-55: use machine learning to dynamically adapt the operational parameters of each access point and/or user equipment…For example, re-assigning an access point to use a lower maximum effective isotropic radiated power or reduced channel bandwidth than what is allowed by SAS or a different frequency (if approved by SAS) may be capable of reducing or removing the interference caused by the other access point. The DNA-C, once it is able to identify the updated operational parameters that could be used, will then provide instructions to the affected access points and/or user equipment and modify operation of them accordingly in order to address the interference issue without violating any constraints imposed by SAS for that enterprise); and wherein the rule or the policy based on the user data input is directed towards customer objectives (paragraph 67: if a user associated with user equipment associated with the access point provides feedback that the quality of communication has become poor, the feedback may be used to identify that interference issues may be present at the access point and that the operational parameters of the access point and/or user equipment may need to be modified to address the interference).
Taneja does not expressly disclose, but Piersol from similar fields of endeavor teaches: using natural language processing (NLP) (paragraph 77: user data and models 231 include various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent). Thus it would have been obvious to the person of ordinary skill in the art at the time of the invention to implement the machine learning techniques as described by Piersol in the interference modeling as described by Taneja. The motivation is to improve interference mitigation through user input and processing.
Taneja does not expressly disclose, but Kennedy from similar fields of endeavor teaches: wherein the geolocation engine is operable to utilize time difference of arrival (TDOA), angle of arrival (AOA), and/or frequency difference of arrival (FDOA) to find a location of at least one signal based on the measured data (paragraph 22: method for locating a mobile device may use a network overlay such that one or more wireless location sensors 180 receive a signal from the mobile device 150 including a known sequence or sequences, and compute a location measurement based on an attribute or characteristic of the mobile device's signal. Such attributes may include, among others, time of arrival (TOA), angle of arrival (AOA), time difference of arrival (TDOA), received power level, timing advance, signal strength, signal-to-noise ratio, bit error rate, etc.). Thus it would have been obvious to the person of ordinary skill in the art at the time of the invention to implement the signal locating as described by Kennedy in the interference modeling as described by Taneja. The motivation is to improve interference mitigation.
For claims 2, 13, and 18; Taneja discloses: wherein the learning engine is operable to recommend and/or perform actions based on historical data, external data sources, machine learning (ML), artificial intelligence (Al), neural networks (NNs) and/or other learning techniques (paragraph 26: generating the revised set of operational parameters is performed using a machine learning process. The machine learning process outputs a machine learned model that corresponds to the revised set of operational parameters).
For claims 3 and 14; Taneja discloses: wherein the semantic engine is operable to run autonomously (paragraph 68-70: As new access points are added to the CBRS network or as network conditions associated with the CBRS network change (e.g., traffic load) change for access points already deployed, the machine learned models will evaluate the various operational parameters in order to predict what operational parameters should be used).
For claims 4 and 15; Taneja discloses: an identification engine operable to identify a device or an emitter transmitting at least one signal (paragraph 41: The DNA-C 310 will be used to further manage each of the access points of an enterprise within the CBRS network 300. In particular, the DNA-C 310 will 1) obtain the assignments of each of the access points provided by the SAS, 2) monitor performance of each of the access points and associated user equipment devices to detect when interference is present).
For claims 7, 12, and 20; Taneja discloses: wherein the actionable data relates to interference mitigation or interference prevention (paragraph 75: dynamically assigns access points and/or user equipment of a CBRS network different operational parameters to reduce or remove the effects of interference from other nearby access points and/or user equipment).
For claims 8; Taneja discloses: wherein the at least one server is operable to send at least one notification based on the actionable data in real time or near real time (paragraph 41: dynamically re-assign the operational parameters of the access points to minimize and/or eliminate the detected interference. In this way, the DNA-C 310 can resolve issues of interference between access points and/or user equipment that the SAS may not be able to account for).
For claims 9 and 19; Taneja discloses: wherein the system is operable to provide reconfiguration options relating to time, frequency, and/or spatial settings (paragraph 31: The present technology monitors for and detects the interference between nearby access points and uses machine learning in order to dynamically assign/re-assign the access points to different CBRS channels (i.e. different frequencies)).
For claim 10; Taneja discloses the subject matter in claims 1 and 9 as described above in the office action.
For claims 11; Taneja discloses: at least one server operable to create the actionable data and/or the reconfiguration options (paragraph 78: The DNA-C may utilize machine learning in order to evaluate the possible operational parameters and the constraints on those parameters assigned by the SAS in order to find a revised set of operational parameters that would carry out the reduction in interference with respect to other access points or user equipment).
For claim 16; Taneja discloses the subject matter in claims 1 as described above in the office action. Taneja discloses: wherein the prediction models incorporate descriptive analytics, diagnostic analytics, predictive analytics, and/or prescriptive analytics (paragraph 70: the machine learned models will evaluate the various operational parameters in order to predict what operational parameters should be used. For example, the machine learned model will predict what channel bandwidth and maximum effective isotropic radiated power the SAS may assign to the access point, a level of interference that a user equipment may experience in the CBRS network, and an impact on performance of the user equipment because of the level of interference).
For claim 17; Taneja discloses: the training model includes a ML model, an Al model, a DL model, or a NN model (paragraph 70: As new access points are added to the CBRS network or as network conditions associated with the CBRS network change (e.g., traffic load) change for access points already deployed, the machine learned models will evaluate the various operational parameters in order to predict what operational parameters should be used).
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Taneja in view of Piersol in view of Kennedy as applied to claim 1 above, and further in view of Shalai et al. (US 2023/0050882) (“Shalai”) in view of Ravi et al. (US 2020/0125956) (“Ravi”).
For claim 5; Taneja discloses the subject matter in claim 1 as described above in the office action.
Taneja does not expressly disclose, but Shalai from similar fields of endeavor teaches: wherein a learning engine software development kit (SDK) is operable to convert a training model for operation in another environment (paragraph 5: The method comprises building, by the computing device, the machine learning model, training, by the computing device, the machine learning model, converting, by the computing device, the machine learning model into a web browser compatible format, and uploading, by the computing device, the machine learning model to a server that is arranged to deploy the machine learning model to the plurality of communicating devices). Thus it would have been obvious to the person of ordinary skill in the art at the time of the invention to implement the machine learning techniques as described by Shalai in the interference modeling as described by Taneja. The motivation is to improve interference mitigation through modeling of data sets.
Taneja does not expressly disclose, but Ravi from similar fields of endeavor teaches: a learning engine software development kit (SDK) (paragraph 30: developers can have access to a single SDK for all machine learning services. Thus, developers will have a single set of docs, a common way of getting machine learning products, a single console to visit, and a single initialization call to serve all of the application's different machine learning needs). Thus it would have been obvious to the person of ordinary skill in the art at the time of the invention to implement the software tools as described by Ravi in the interference modeling as described by Taneja. The motivation is to improve distribution and development of machine learning software.
For claim 6; Taneja discloses: the training model includes a ML model, an Al model, a DL model, or a NN model (paragraph 70: As new access points are added to the CBRS network or as network conditions associated with the CBRS network change (e.g., traffic load) change for access points already deployed, the machine learned models will evaluate the various operational parameters in order to predict what operational parameters should be used).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Tedaldi et al. (US 2021/0335505); Tedaldi discloses employ any number of machine learning techniques, to classify the gathered telemetry data and apply a device type label to a device associated with the traffic. In general, machine learning is concerned with the design and the development of techniques that receive empirical data as input (e.g., telemetry data regarding traffic in the network) and recognize complex patterns in the input data. For example, some machine learning techniques use an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN D BLANTON whose telephone number is (571)270-3933. The examiner can normally be reached 7am-6pm EST, Mon-Thu.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Faruk Hamza can be reached at 571-272-7969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN D BLANTON/Primary Examiner, Art Unit 2466
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