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 .
DETAILED ACTION
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 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/14/23. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-7, 11, 14-20, 24, 26-30 are rejected under 35 U.S.C. 102 (a) (2) as being anticipated by Abraham et al. (US 11032665).
As per claim 1, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“at least one of a task or a condition of at least one procedure”, Abraham teaches:
An apparatus (Fig. 10: 1002) for wireless communication, comprising: a memory (Fig. 10: 1006); and
at least one processor (Fig. 10: 1004) coupled to the memory (Fig. 10: 1006), the memory and the at least one processor configured to:
receive a configuration for one or more machine learning (ML) models (Fig. 5: 500; col. 11, 45-48, disclosed as the inevitable step of receiving the trained ML model form the memory of the device), the configuration associated with at least one of a task or a condition of at least one procedure of an user equipment UE (Fig. 7: 712, col. 13, 48-64, Fig. 9, col. 15, 27-44 and 60-66, the condition of one procedure is, also in accordance with the present application (description, par. 0043), location estimation of an UE ); and
allocate the one or more ML models to at least one of a baseline model group (BMG) or a specific model group (SMG) (Fig. 4; col. 10, 40 - col. 11, 5) for switching between the one or more ML models based on the at least one of the tasks or the condition of the at least one procedure of the UE (Fig. 8: 810; col. 9, 65 - col. 10, 3; col. 14, 50-col. 15, 16; selecting the machine learning model of the multiple machine learning models 401 can be based at least in part on an accuracy, also referred to herein as precision, of the intermittent estimation of the other locations of the other user equipment devices; the terms "baseline model group (BMG)" and "specific model group (SMG)" have not well defined technical meanings and are also not defined in claim 1, according to Abraham the ML models are selected based on their accuracy or speed, hence very accurate or very speedy ML models can be considered as belonging for instance to specific model group; Abraham also defines balanced ML models, they can be considered as belonging for instance to baseline model group).
As per claim 2, Abraham teaches:
The apparatus of claim 1, wherein the one or more ML models allocated to the BMG correspond to first ML models of a first complexity and a first performance, and wherein the one or more ML models allocated to the SMG correspond to second ML models of a second complexity and a second performance that are higher than the first complexity and the first performance. (i.e. Precision of the estimated UE locations 304 is represented in FIG. 3 by the radii of the estimated UE locations 304. In general, the calculations of the machine learning model can strike a balance between speed and precision. It is computationally easier for the machine learning model 200 to determine estimated UE locations 304 at a lower degree of precision (larger radius), and computationally harder for the machine learning model 200 to determine estimated UE locations 304 at a higher degree of precision (smaller radius). In an aspect of this disclosure, any desired level of precision can be selected for estimated UE location data 231, understanding that higher precision estimates come at the cost of either slower estimation speeds or more compute resources; col. 10, 29-39 and 61-64).
As per claim 3, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“at least one of a task or a condition of at least one procedure”, Abraham teaches:
The apparatus of claim 1, wherein the one or more ML models are switchable at the UE based on the at least one of the task or the condition of the at least one procedure of the UE. (i.e. A desired machine learning model can be selected for real-time deployment in a communication network, based at least on part on the performance metrics (accuracy, precision and recall, etc.) of the various different machine learning models; col. 9, 65 - col. 10, 3 and col. 14, 40 - col. 15, 16).
As per claim 4, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“at least one of the BMG based on an ML inference or the SMG based”, Abraham teaches:
The apparatus of claim 1, wherein the one or more ML models are allocated to at least one of the BMG based on an ML inference or the SMG based on a UE capability. (i.e. FIG. 4 illustrates operation and evaluation of multiple machine learning models in order to select a machine learning model, in accordance with various aspects and embodiments of the subject disclosure. FIG. 4 includes multiple machine learning models 401, binary classifier 402, (X,Y) estimator 403, and output combiner 405. FIG. 4 further includes accuracy evaluation 410, model performance information 420, and known UE location data 211. In general, with regard to FIG. 4, a current machine learning model of multiple machine learning models 401, along with binary classifier 402, (X,Y) estimator 403, and output combiner 405, can use inputs such as illustrated in FIG. 2 to determine final estimates 407. The final estimates 407 comprise estimated UE location data 231. Accuracy evaluation 410 can then compare the final estimates 407 to the known UE location data 211, and accuracy evaluation 410 can provide feedback 411 to the current machine learning model 401. The current machine learning model 401 can use feedback 411 to refine its estimate calculations, and the illustrated cycle can be repeated to train the current machine learning model 401. Accuracy evaluation 410 can also output model performance information 420 which can include, e.g., precision and processing time information associated with the current machine learning model 401. After a current machine learning model of machine learning models 401 has been trained and evaluated, the illustrated training and evaluation process can be repeated with a next machine learning model of machine learning models 401. The model performance information 420 for each of machine learning models 401 can be used to select a desired machine learning model for deployment, e.g., for use in real-time UE geolocation estimates; col. 10, 40-64).
As per claim 5, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“at least one of the BMG or the SMG”, Abraham teaches:
The apparatus of claim 1, wherein the memory and the at least one processor are further configured to sub-allocate the one or more ML models allocated to the at least one of the BMG or the SMG into at least one of a BMG subgroup or an SMG subgroup, the BMG subgroup corresponding to at least one of a common function subgroup, a downlink/uplink subgroup, or an advanced function subgroup, the SMG subgroup corresponding to at least one of a positioning subgroup, a channel state feedback (CSF) subgroup, or a decoding subgroup. (i.e. FIG. 4 illustrates operation and evaluation of multiple machine learning models in order to select a machine learning model, in accordance with various aspects and embodiments of the subject disclosure. FIG. 4 includes multiple machine learning models 401, binary classifier 402, (X,Y) estimator 403, and output combiner 405. FIG. 4 further includes accuracy evaluation 410, model performance information 420, and known UE location data 211; col. 10, 40-64).
As per claim 6, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“at least one of the BMG or the SMG”, Abraham teaches:
The apparatus of claim 1, wherein the one or more ML models are allocated to the at least one of the BMG or the SMG based on a performance associated with the one or more ML models, the one or more ML models allocated to the BMG based on a first performance associated with a plurality of tasks, the one or more ML models allocated to the SMG based on a second performance associated with a single task. ((i.e. based on a performance; col. 10, 40-64).
As per claim 7, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“at least one of the BMG or the SMG”, Abraham teaches:
The apparatus of claim 1, wherein the one or more ML models are allocated to the at least one of the BMG or the SMG based on a complexity of the one or more ML models, the one or more ML models allocated to the BMG based on a first complexity, the one or more ML models allocated to the SMG based on a second complexity that is higher than the first complexity. (i.e. higher precision estimation; col. 10, 29-39 and 61-64).
As per claim 11, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“the BMG or the SMG”, Abraham teaches:
The apparatus of claim 1, wherein the memory and the at least one processor are further configured to receive a second configuration for one or more second ML models, the configuration for the one or more ML models corresponding to the BMG or the SMG, the second configuration for the one or more second ML models corresponding to an opposite one of the BMG or the SMG from the configuration of the one or more ML models. (i.e. based on a second configuration; col. 10, 29-39 and 61-64).
As per claim 14, Abraham teaches:
Since this appears to be MARKUSH type language requiring at a minimum just one
“at least one of a task or a condition of at least one procedure”, Abraham teaches:
The apparatus of claim 1, wherein the memory and the at least one processor are further configured to switch between the one or more ML models based on the allocation of the one or more ML models and the at least one of the task or the condition of the at least one procedure of the UE. (i.e. based on the allocation of the one or more ML models; col. 10, 40-64).
Claims 15-20, 24 are the apparatus claims corresponding to apparatus claims 1-2, 4-7, 11 (i.e. operation performed by network device col. 15, 27-44) respectively, and rejected under the same rational set forth in connection with the rejection of the above claims.
Claims 26-28 are the method claims corresponding to apparatus claims 1-3 respectively, and rejected under the same rational set forth in connection with the rejection of the above claims.
Claims 29-30 are the method claims corresponding to apparatus claims 1-2 respectively, and rejected under the same rational set forth in connection with the rejection of the above claims.
Allowable Subject Matter
Claims 8-10, 12-13, 21-23, 25 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smith et al. (US 20100052991) discloses:
Techniques for accurate position location and tracking suitable for a wide range of facilities in variable environments are disclosed. In one aspect, a system for position location comprises a plurality of sensors (e.g. a network monitor, an environment sensor) for generating measurements of a plurality of sources, a plurality of objects or tags, each object generating measurements of the plurality of sources, and a processor for receiving the measurements and generating a position location for one or more objects in accordance with the received measurements. In another aspect, a position engine comprises a mapped space of a physical environment, and a processor for updating the mapped space in response to received measurements. The position engine may receive second measurements from an object within the physical environment, and generate a position location estimate for the object from the received second measurements and the mapped space.
Duggan et al. (US 20150248797) discloses:
A system and method for real-time location detection consists of a scalable real time location system (RTLS). It provides revised real time object location determinations. It includes a tag within a location environment, a processor to calculate a location of the tag, and at least one exclusion zone in the environment. Processing includes an original location determination of the tag and a revised location determination of the tag. The revised location determination is calculated by applying attributes of at least one exclusion zone to the original location determination of the tag. Some exclusion zones are defined by no-fly exclusion zones. The revised location determination improves the operation of the RTLS by correcting for impossible and improbable original location determinations. For embodiments, system deployment consists of three phases: collection of training and testing data, network training and testing, and network adaptive maintenance.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAD RAMPURIA whose telephone number is (571)272-7870 and e-mail address is sharad.rampuria@uspto.gov. The examiner can normally be reached on Mon.-Thurs.: 8 AM-6 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Appiah can be reached on 571-272-7904. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHARAD RAMPURIA/
Primary Patent Examiner
Art Unit 2641
Prosecution Insights