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 .
The present application was filed on 06/26/2023. Preliminary amendment filed on 6/26/2023, that amended claims 1-5, 7-9, 11, 14-15 and 17-19 and cancelled claims 21-40. Claims 1-20 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The present application is a national stage application under 35 U.S.C. 371 of International Application No. PCT/EP2020/087918, filed on 12/28/2020.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/26/2023 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1:
Step 1: Claim 1 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
for each feature of the first set of measurable features, determining whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature - In the context of the claim limitation, this encompasses a mental process of evaluating features based on observed data.
if there is at least one sensor of the one or more sensors satisfying the associated measurement specification, estimating a resource usage by each of the at least one sensor for collecting data corresponding to the feature - In the context of the claim limitation, this encompasses a mental process of evaluation/judgement/opinion to estimate resource usage based on observed data and the measurement specification.
determining a first subset of the first set of measurable features, wherein the at least one sensor of the one or more sensors is selected for collecting data corresponding to the first subset of the first set of measurable features based on the estimated resource usage - In the context of the claim limitation, this encompasses a mental process of evaluating a subset of the features based on the estimated resource usage.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “perform training of a machine learning model”; “the client computing device comprising one or more sensors”; “from a coordinating computing device of the plurality of computing devices”; “if the client computing device belongs to the first group of computing devices, performing training of the machine learning model using the first group of computing devices” – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Regarding the “machine learning model”, no details of the model are recited, and the model is recited at a high level of generality and the model can be constructed by hand with pen and paper. Thus, the claimed “machine learning model”, under the broadest reasonable interpretation (BRI), in light of the specification, could be any learning model, which could be constructed by hand with pen and paper. That is, the “machine learning model” limitation gives the indication that the model can be constructed by hand with pen and paper.
The machine learning model is recited at a high level of generality and therefore is being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
The claim also recites “collecting data”; “obtaining information identifying a first set of measurable features…each feature of the first set of measurable features being associated with a measurement specification for collecting data corresponding to the feature”; “sending information identifying the first subset of the first set of measurable features to the coordinating computing device”; “obtaining information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instructions to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitation of “collecting…”; “obtaining…” and “sending…” are directed to an insignificant extra-solution activities that are well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 2:
Step 1: Claim 2 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of an independent claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application.
The claim recites “wherein the obtaining information identifying a first set of measurable features further comprises obtaining the measurement specification for each feature of the first set of features”, which recites the insignificant extra-solution activities of mere data gathering. MPEP 2106.05(g). Accordingly, the additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of “wherein the obtaining information … further comprises obtaining the measurement specification” is directed to an insignificant extra-solution activities that are well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 3:
Step 1: Claim 3 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
wherein the measurement specification for each feature of the first set of features is at least one of: data sampling frequency, data resolution, data accuracy, data measurement unit, and a value range of the feature - In the context of the claim limitation, this encompasses a mental process of evaluating feature using data sampling frequency, data resolution, data accuracy, data measurement unit, and a value range of the feature.
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 4:
Step 1: Claim 4 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “wherein the plurality of computing devices is heterogeneous in terms of at least one of: sensor configuration, sensor availability, radio communication capabilities, network capabilities, execution environment, software version, systematic noise and interferences, existence of stochastic noise and interferences, measurement capabilities, storage capabilities, battery capacities, and compute capabilities” – this is a mere instruction to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 5:
Step 1: Claim 5 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
wherein each feature of the first set of measurable features is a feature representing a property of a physical environment - In the context of the claim limitation, this encompasses a mental process of evaluating feature that representing a property of a physical environment.
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 6:
Step 1: Claim 6 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the claim 5.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “wherein a feature representing a property of a physical environment is at least one of: temperature, light, acceleration, sound intensity, altitude, humidity, moisture, weather data, and positioning information” – this is a mere instruction to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 7:
Step 1: Claim 7 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
wherein the step of determining whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature further comprises adjusting a configuration of the at least one sensor of the one or more sensors to satisfy the feature's measurement specification - In the context of the claim limitation, this encompasses a mental process of evaluating sensor based on the feature’s measurement specification.
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 8:
Step 1: Claim 8 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
wherein the determining a first subset of the first set of measurable features further comprises at least one of: determining that the number of features in the first subset of the first set of measurable features is maximized, with a constraint that a sum of the corresponding estimated resource usage is below a threshold value; each feature of the first set of measurable features having a weight value indicating an importance of the feature, and determining that a sum of the corresponding estimated resource usage is weighted by the importance of each feature, with a constraint that the sum is below a threshold value - In the context of the claim limitation, this encompasses a mental process of evaluating a subset of feature and compare threshold.
Step 2A Prong 2: Please see analysis of the independent claim 1.
Step 2B Analysis: Please see analysis of the independent claim 1.
Claim 9:
Step 1: Claim 9 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim recites “wherein the obtaining information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices further comprises: if the client computing device belongs to the first group of computing devices, obtaining information identifying a second subset of the first set of measurable features, wherein the second subset of the first set of measurable features have associated measurement specifications for collecting data corresponding to the features, and wherein the measurement specifications are satisfied by each of the first group of computing devices”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of “wherein the obtaining…” are directed to an insignificant extra-solution activities that are well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 10:
Step 1: Claim 10 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim recites “collecting data corresponding to the features of the second subset of the first set of measurable features based on the features' measurement specifications”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of “collecting…” are directed to an insignificant extra-solution activities that are well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 11:
Step 1: Claim 11 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim recites “wherein the obtaining information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices further comprises: if the client computing device does not belong to the first group of computing devices, obtaining information identifying a second set of measurable features, wherein the second set of measurable features have associated measurement specifications for collecting data corresponding to the features, and wherein the measurement specifications are satisfied by a second group of computing devices”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of “wherein the obtaining…” are directed to an insignificant extra-solution activities that are well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 12:
Step 1: Claim 12 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
for each feature of the second set of measurable features, determining whether there is at least one sensor satisfying the feature's measurement specification for collecting data corresponding to the feature - In the context of the claim limitation, this encompasses a mental process of evaluating features based on the observed data.
if there is at least one sensor satisfying the feature's measurement specification for collecting data corresponding to the feature, estimating a resource usage by each of the at least one sensor for collecting data corresponding to the feature - In the context of the claim limitation, this encompasses a mental process of evaluating sensor based on the collecting data.
determining whether to collect data corresponding to the features of the second set of measurable features based on the estimated resource usage for collecting data corresponding to features of the second set of measurable features - In the context of the claim limitation, this encompasses a mental process of evaluating a subset of the features observed data for the sensor.
Step 2A Prong 2: Please see analysis of claim 11.
Step 2B Analysis: Please see analysis of claim 11.
Claim 13:
Step 1: Claim 13 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
wherein the determining whether to collect data corresponding to the features of the second set of measurable features further comprises: if a sum of the corresponding estimated resource usage is below a threshold value, collecting data corresponding to the features of the second set of measurable features based on the features' measurement specifications - In the context of the claim limitation, this encompasses a mental process of evaluating a subset of features and comparing to threshold.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “performing training of the machine learning model by the second group of computing devices” – this is a mere instruction to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 14:
Step 1: Claim 14 is directed to a method performed by a client computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “wherein the machine learning model is at least one of: a federated learning model, and a distributed collaborative learning model” – this is a mere instruction to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 15:
Step 1: Claim 15 is directed to a method performed by a coordinating computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
determining if the client computing device belongs to a first group of computing devices based on the first subset of the first set of measurable features - In the context of the claim limitation, this encompasses a mental process of evaluating/observing features in the subset.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “perform training of a machine learning model”; “the client computing device, wherein the client computing device comprises one or more sensors” – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Regarding the “machine learning model”, no details of the model are recited, and the model is recited at a high level of generality and the model can be constructed by hand with pen and paper. Thus, the claimed “machine learning model”, under the broadest reasonable interpretation (BRI), in light of the specification, could be any learning model, which could be constructed by hand with pen and paper. That is, the “machine learning model” limitation gives the indication that the model can be constructed by hand with pen and paper.
The machine learning model is recited at a high level of generality and therefore is being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
The claim recites “sending information identifying a first set of measurable features to a client computing device of the plurality of computing devices”; “obtaining information identifying a first subset of the first set of measurable features… the first subset of the first set of measurable features have associated measurement specifications for collecting data corresponding to the features, which measurement specifications are satisfied by at least one of the one or more sensors based on an estimated resource usage associated with the at least one of the one or more sensors” which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitation of “sending…” and “obtaining…” are directed to an insignificant extra-solution activities that are well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 16:
Step 1: Claim 16 is directed to a method performed by a coordinating computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of an independent claim 15.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim recites “wherein the sending information identifying a first set of measurable features to a client computing device further comprises sending a measurement specification for each feature of the first set of features” which recites the insignificant extra-solution activities of mere data transmission. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of “wherein the sending…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 17:
Step 1: Claim 17 is directed to a method performed by a coordinating computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of an independent claim 15.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “if the computing device does not belong to the first group of computing devices” – this is a mere instruction to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
The claim recites “sending information identifying a second set of measurable features, wherein the second set of measurable features have associated measurement specifications, which measurement specifications are satisfied by a second group of computing devices” which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of “sending…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 18:
Step 1: Claim 18 is directed to a method performed by a coordinating computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of the independent claim 15.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “if no group can be found for the client computing device, notifying the client computing device that it is not able to participate in training of the machine learning model” – this is a mere instruction to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 19:
Step 1: Claim 19 is directed to a method performed by a coordinating computing device of a plurality of computing devices, which is directed to a process, one of the statutory categories.
Step 2A Prong 1: Please see analysis of an independent claim 15.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “if the client computing device belongs to the first group of computing devices” – this is a mere instruction to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
The claim recites “sending information identifying a second subset of the first set of measurable features wherein the second subset of the first set of measurable features have associated measurement specifications, which measurement specifications are satisfied by each of the first group of computing devices” which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of “sending…” is directed to an insignificant extra-solution activity that is well known, routine and conventional because the limitation is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
Claim 20:
Step 1: Claim 20 is directed to a client computing device of a plurality of computing devices , which is directed to a an article of manufacture, one of the statutory categories.
Step 2A Prong 1: The claim recites the limitations:
for each feature of the first set of measurable features, determine whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature - In the context of the claim limitation, this encompasses a mental process of evaluation/judgement/opinion features based on the observed data.
if there is at least one sensor of the one or more sensors satisfying the associated measurement specification, estimate a resource usage by each of the at least one sensor for collecting data corresponding to the feature - this encompasses a mental process of evaluation/judgement/opinion to estimate resource usage based on observed data and the measurement specification.
determine a first subset of the first set of measurable features, wherein the at least one sensor of the one or more sensors is selected for collecting data corresponding to the first subset of the first set of measurable features based on the estimated resource usage - In the context of the claim limitation, this encompasses a mental process of evaluating a subset of the features observed data for the sensor.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. The claim further recites “perform training of a machine learning model, the client computing device comprising one or more sensors for collecting data, the client computing device comprising processing circuitry causing the computing device to be operative”; “if the client computing device belongs to the first group of computing devices, perform training of the machine learning model using the first group of computing devices” – these are mere instructions to apply an exception using a generic computer component or are merely asserting that a judicial exception is to be carried out on a generic computer. Merely asserting that a judicial exception is to be carried out on a generic computer cannot meaningfully integrate the judicial exception into a practical application, and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See MPEP § 2106.05(f).
Regarding the “machine learning model”, no details of the model are recited, and the model is recited at a high level of generality and the model can be constructed by hand with pen and paper. Thus, the claimed “machine learning model”, under the broadest reasonable interpretation (BRI), in light of the specification, could be any learning model, which could be constructed by hand with pen and paper. That is, the “machine learning model” limitation gives the indication that the model can be constructed by hand with pen and paper.
The machine learning model is recited at a high level of generality and therefore is being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
The claim recites “obtain information identifying a first set of measurable features from a coordinating computing device of the plurality of computing devices, each feature of the first set of measurable features being associated with a measurement specification for collecting data corresponding to the feature”; “send information identifying the first subset of the first set of measurable features to the coordinating computing device”; “obtain information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices”, which recites the insignificant extra-solution activities of mere data gathering and output. MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element is directed to a mere instruction to apply the judicial exception. Mere instruction to apply a judicial exception does not amount to significantly more. See MPEP 2106.05(f). The recitations of “obtain…”; “send…”; “obtain…” are directed to an insignificant extra-solution activities that are well known, routine and conventional because the limitations are directed to receiving or transmitting data over a network, e.g., using the Internet to gather data. See MPEP 2106.05(d)(II), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). Therefore, the claim does not include additional elements which provide an inventive concept nor represent significantly more than the abstract idea, and the claim is not patent eligible. Nothing in the claim provides significantly more than this. As such, the claim is not patent eligible.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bonawitz (“US 11488054 B2”) in view of Dey (“US 10031973 B2”).
Claim 1.
Bonawitz teaches a method performed by a client computing device of a plurality of computing devices configured to perform training of a machine learning model, the client computing device comprising one or more sensors for collecting data, the method comprising (Column 6 “FIG. 1A depicts a block diagram of an example computing system 100 that can perform distributed machine learning model training according to example embodiments of the present disclosure. The system 100 includes a user computing device 102, a server computing system 130, and a training computing system 150 that are communicatively coupled over a network 180… The memory 114 can store data 116 and instructions 118 which are executed by the processor 112 to cause the user computing device 102 to perform operations” and Column 9 “one or more sensors, a context manager, a device state component, and/or additional components” teaches computing device to perform training machine learning model and sensor for collecting data):
obtaining information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices (Column 10 “ At 204, the computing system can select a plurality of available user devices within a region, such that the plurality of selected user devices within the region can be tasked with training a machine-learned model… At 206, the computing system can provide a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region” and Fig. 2 204-206 shows obtaining information from computing devices and coordinating);
and if the client computing device belongs to the first group of computing devices, performing training of the machine learning model using the first group of computing devices (Column 10 “At 208, the selected user devices within the region, such as user computing device 102 of FIG. 1, can perform training of the received current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices. For example, in some implementations, the current version of the machine-learned model associated with the region can be trained at each of the selected user devices within the region using federated learning techniques. For example, each of the devices selected for a training iteration can be tasked with using the locally-generated and locally-stored data on the device to compute an update to the machine-learned model” teaches performing training machine learning model using computing device).
Bonawitz does not explicitly teach obtaining information identifying a first set of measurable features from a coordinating computing device of the plurality of computing devices, each feature of the first set of measurable features being associated with a measurement specification for collecting data corresponding to the feature; for each feature of the first set of measurable features, determining whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature; if there is at least one sensor of the one or more sensors satisfying the associated measurement specification, estimating a resource usage by each of the at least one sensor for collecting data corresponding to the feature; determining a first subset of the first set of measurable features, wherein the at least one sensor of the one or more sensors is selected for collecting data corresponding to the first subset of the first set of measurable features based on the estimated resource usage; sending information identifying the first subset of the first set of measurable features to the coordinating computing device.
However, in the same field, analogous art Dey teaches obtaining information identifying a first set of measurable features from a coordinating computing device of the plurality of computing devices, each feature of the first set of measurable features being associated with a measurement specification for collecting data corresponding to the feature (Column 5 “Referring now to FIG. 1, a network implementation 100 of a system 102 for identifying a sensor, from a plurality of sensors, to be deployed in a physical environment is illustrated, in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may be configured to store sensor data and capture metadata of the plurality of sensors in a data store” and Column 7 “The sensor data 304 may indicate measurement values captured by the sensors 302. Further, the data capturing module 212 may be configured to capture annotated specification and metadata 306…The metadata may comprise at least one of a measurable range, a feature, a communication capability, a model name, a model number, a manufacturer detail, and the like. The sensor data 304 and the metadata 306 may be stored in the data store 220” teaches obtaining data identifying features from a computing device, measurement value captured by the sensor (specification for collecting data));
for each feature of the first set of measurable features, determining whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature (Column 8 “The sensor and sensing service discovery service may help the user 104 to identify the right sensors and their capability using the knowledge repository 308 storing interrelations among entities associated with the sensors and using a defined set of rules for quantitative reasoning for a given use case requirement of the user 104… Thus, the data capturing module 212 creates sensor ontology in form of the knowledge repository 308 comprising hierarchical information of the sensors 302, properties of the sensors 302, feature of interest of the sensors 302, measuring capabilities of the sensors 302, communication capabilities of the sensors 302, and all related context information in either concept form or annotation form” and Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities” teaches feature of the data, determining sensor satisfying the associated measurement specification for collected data);
if there is at least one sensor of the one or more sensors satisfying the associated measurement specification, estimating a resource usage by each of the at least one sensor for collecting data corresponding to the feature (Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the associated measurement specification for collected data, resources comprises data related to sensor);
determining a first subset of the first set of measurable features, wherein the at least one sensor of the one or more sensors is selected for collecting data corresponding to the first subset of the first set of measurable features based on the estimated resource usage (Column 5 “the data store and a knowledge repository present in the data store may be reasoned in order to identify a subset of the sensor data and a subset of the sensor information respectively. In one aspect, the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component” teaches determining subset of the sensor information Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the associated measurement specification for collected data, resources comprises data related to sensor);
sending information identifying the first subset of the first set of measurable features to the coordinating computing device (Column 5 “the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component…identifying a sensor to be deployed in a physical environment may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system” teaches identifying the subset of feature to the computing device).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 2.
Bonawitz in view of Dey teaches the method according to claim 1,
Dey further teaches wherein the obtaining information identifying a first set of measurable features further comprises obtaining the measurement specification for each feature of the first set of features (Column 2 “The thematic information may indicate feature and measurement capabilities of the plurality of sensors. The temporal information may indicate time of the receipt of the sensor data. The spatial information indicates location of the plurality of sensors. Further, the method may comprise creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” teaches obtaining information comprising the measurement for the feature).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 3.
Bonawitz in view of Dey teaches the method according to claim 1,
Dey further teaches wherein the measurement specification for each feature of the first set of features is at least one of: data sampling frequency, data resolution, data accuracy, data measurement unit, and a value range of the feature (Column “The sensor ontology comprises detail knowledge of sensor type…accuracy, data communication protocols and their interdependencies among sensor parameters…from the sensor hierarchy and select sensor features, via a feature selection service, and capabilities or categories, via a sensor capability selection service, relevant to a sensor and hence enable crafting a new sensor into the system 102” teaches measurement feature comprising data measurement unit and Column 7 “The sensor data 304 may indicate measurement values captured by the sensors 302…The metadata may comprise at least one of a measurable range, a feature, a communication capability, a model name, a model number, a manufacturer detail, and the like” teaches measurement values comprising data accuracy).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 4.
Bonawitz in view of Dey teaches the method according to claim 1,
Dey further teaches wherein the plurality of computing devices is heterogeneous in terms of at least one of: sensor configuration, sensor availability, radio communication capabilities, network capabilities, execution environment, software version, systematic noise and interferences, existence of stochastic noise and interferences, measurement capabilities, storage capabilities, battery capacities, and compute capabilities (Column 1 “These sensors are of distinct types and are capable of generating heterogeneous data” teaches computing devices is heterogeneous of sensor availability and Column 2 “The thematic information may indicate feature and measurement capabilities of the plurality of sensors” teaches measurement capabilities and Column 6 “Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like” teaches storage and computing devices).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 5.
Bonawitz in view of Dey teaches the method according to claim 1,
Dey further teaches wherein each feature of the first set of measurable features is a feature representing a property of a physical environment (Column 2 “a method for identifying a sensor, from a plurality of sensors, to be deployed in a physical environment is disclosed” teaches physical environment).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 6.
Bonawitz in view of Dey teaches the method according to claim 5,
Dey further teaches wherein a feature representing a property of a physical environment is at least one of: temperature, light, acceleration, sound intensity, altitude, humidity, moisture, weather data, and positioning information (Column 9 “the spatial query component may be like “Find all temperature sensors near to the north block of the building”. Similarly, the temporal query component may be like” teaches physical environment comprising temperature).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 7.
Bonawitz in view of Dey teaches the method according to claim 1,
Dey further teaches wherein the step of determining whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature further comprises adjusting a configuration of the at least one sensor of the one or more sensors to satisfy the feature's measurement specification (Column 10 “the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities” and Column 11 “This enables in faster processing of searching and thereby retrieval of search results in form of the required sensor data. In one embodiment, the data store 220 comprising the sensor data 304, the metadata 306, and the knowledge repository 308 may be frequently updated, via a data enriching module 216” teaches determining the sensors that satisfying the measurement for collecting data).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 8.
Bonawitz in view of Dey teaches the method according to claim 1,
Dey further teaches wherein the determining a first subset of the first set of measurable features further comprises at least one of: determining that the number of features in the first subset of the first set of measurable features is maximized, with a constraint that a sum of the corresponding estimated resource usage is below a threshold value; each feature of the first set of measurable features having a weight value indicating an importance of the feature, and determining that a sum of the corresponding estimated resource usage is weighted by the importance of each feature, with a constraint that the sum is below a threshold value (Column 9 and 10 “the thematic query component may be like “find the entire local manufactured high precession accelerometer sensor having capability of sending alert crosses the threshold”. The thematic concepts, the temporal concepts and spatial concepts that may be related to the thematic query component, the temporal query component, and the spatial query component may be included into the knowledge repository 308” teaches measurable feature comprises that determining the features resources below threshold) .
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 9.
Bonawitz in view of Dey teaches the method according to claim 1,
Dey further teaches wherein the obtaining information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices further comprises: if the client computing device belongs to the first group of computing devices, obtaining information identifying a second subset of the first set of measurable features, wherein the second subset of the first set of measurable features have associated measurement specifications for collecting data corresponding to the features, and wherein the measurement specifications are satisfied by each of the first group of computing devices (Column 5 “the data store and a knowledge repository present in the data store may be reasoned in order to identify a subset of the sensor data and a subset of the sensor information respectively. In one aspect, the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component” teaches determining subset of the sensor information Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the associated measurement specification for collected data, resources comprises data related to sensor).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 10.
Bonawitz in view of Dey teaches the method according to claim 9,
Dey further teaches wherein the method further comprises collecting data corresponding to the features of the second subset of the first set of measurable features based on the features' measurement specifications (Column 5 “the data store and a knowledge repository present in the data store may be reasoned in order to identify a subset of the sensor data and a subset of the sensor information respectively. In one aspect, the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component” teaches determining subset of the sensor information Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the measurement capabilities, resources comprises data related to sensor).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 11.
Bonawitz in view of Dey teaches the method according to claim 1,
Bonawitz further teaches wherein the obtaining information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices further comprises (Column 10 “ At 204, the computing system can select a plurality of available user devices within a region, such that the plurality of selected user devices within the region can be tasked with training a machine-learned model… At 206, the computing system can provide a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region” and Fig. 2 teaches 204-206 shows obtaining information from computing devices):
Dey further teaches if the client computing device does not belong to the first group of computing devices, obtaining information identifying a second set of measurable features, wherein the second set of measurable features have associated measurement specifications for collecting data corresponding to the features, and wherein the measurement specifications are satisfied by a second group of computing devices (Column 1 “one or more generic terms or the related terms does not match with the information present in the data store, there is a challenge of retrieving sensor information from the data store against such queries, and hence such queries may remain unresolved” teaches retrieving sensor information does not match, Column 10 “The sensor data 304 and the metadata may be annotated with thematic concepts, the temporal concepts and spatial concepts or may be inferred during resolving of the search query so that queries are satisfied by the query interpreter module 318 and the quantity reasoning module 322” obtaining information of the features and collecting data).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 12.
Bonawitz in view of Dey teaches the method according to claim 11,
Dey further teaches wherein the method further comprises: for each feature of the second set of measurable features, determining whether there is at least one sensor satisfying the feature's measurement specification for collecting data corresponding to the feature (Column 8 “The sensor and sensing service discovery service may help the user 104 to identify the right sensors and their capability using the knowledge repository 308 storing interrelations among entities associated with the sensors and using a defined set of rules for quantitative reasoning for a given use case requirement of the user 104… Thus, the data capturing module 212 creates sensor ontology in form of the knowledge repository 308 comprising hierarchical information of the sensors 302, properties of the sensors 302, feature of interest of the sensors 302, measuring capabilities of the sensors 302, communication capabilities of the sensors 302, and all related context information in either concept form or annotation form” and Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities” teaches feature of the data, determining sensor satisfying for collected data);
if there is at least one sensor satisfying the feature's measurement specification for collecting data corresponding to the feature, estimating a resource usage by each of the at least one sensor for collecting data corresponding to the feature (Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the measurement capabilities, resources comprises data related to sensor); and
determining whether to collect data corresponding to the features of the second set of measurable features based on the estimated resource usage for collecting data corresponding to features of the second set of measurable features (Column 5 “the data store and a knowledge repository present in the data store may be reasoned in order to identify a subset of the sensor data and a subset of the sensor information respectively. In one aspect, the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component” teaches determining subset of the sensor information Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the measurement capabilities, resources comprises data related to sensor).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 13.
Bonawitz in view of Dey teaches the method according to claim 12,
Dey further teaches wherein the determining whether to collect data corresponding to the features of the second set of measurable features further comprises: if a sum of the corresponding estimated resource usage is below a threshold value, collecting data corresponding to the features of the second set of measurable features based on the features' measurement specifications; and performing training of the machine learning model by the second group of computing devices (Column 9 and 10 “the thematic query component may be like “find the entire local manufactured high precession accelerometer sensor having capability of sending alert crosses the threshold”. The thematic concepts, the temporal concepts and spatial concepts that may be related to the thematic query component, the temporal query component, and the spatial query component may be included into the knowledge repository 308” teaches measurable feature comprises that determining the features resources below threshold).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 14.
Bonawitz in view of Dey teaches the method according to claim 1,
Bonawitz further teaches wherein the machine learning model is at least one of: a federated learning model, and a distributed collaborative learning model (Column 3 “each copy of the machine learning model can be trained using federated learning based on users within that region” teaches the machine learning comprising a federated learning model).
Claim 15.
Bonawitz teaches a method performed by a coordinating computing device of a plurality of computing devices configured to perform training of a machine learning model, the method comprising (Column 6 “FIG. 1A depicts a block diagram of an example computing system 100 that can perform distributed machine learning model training according to example embodiments of the present disclosure. The system 100 includes a user computing device 102, a server computing system 130, and a training computing system 150 that are communicatively coupled over a network 180… The memory 114 can store data 116 and instructions 118 which are executed by the processor 112 to cause the user computing device 102 to perform operations” and Column 9 “one or more sensors, a context manager, a device state component, and/or additional components” teaches coordinating computing devices to perform training machine learning model and sensor for collecting data):
obtaining information identifying a first subset of the first set of measurable features from the client computing device (Column 10 “ At 204, the computing system can select a plurality of available user devices within a region, such that the plurality of selected user devices within the region can be tasked with training a machine-learned model… At 206, the computing system can provide a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region” and Fig. 2 teaches 204-206 shows obtaining information from computing devices),
determining if the client computing device belongs to a first group of computing devices based on the first subset of the first set of measurable features (Column 10 “At 208, the selected user devices within the region, such as user computing device 102 of FIG. 1, can perform training of the received current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices. For example, in some implementations, the current version of the machine-learned model associated with the region can be trained at each of the selected user devices within the region using federated learning techniques. For example, each of the devices selected for a training iteration can be tasked with using the locally-generated and locally-stored data on the device to compute an update to the machine-learned model” teaches client computing device performing machine learning model).
Bonawitz does not explicitly teach sending information identifying a first set of measurable features to a client computing device of the plurality of computing devices…wherein the client computing device comprises one or more sensors, and the first subset of the first set of measurable features have associated measurement specifications for collecting data corresponding to the features, which measurement specifications are satisfied by at least one of the one or more sensors based on an estimated resource usage associated with the at least one of the one or more sensors.
However, in the same field, analogous art, Dey teaches sending information identifying a first set of measurable features to a client computing device of the plurality of computing devices (Column 5 “the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component…identifying a sensor to be deployed in a physical environment may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system” teaches identifying the subset of the feature to the computing device);
wherein the client computing device comprises one or more sensors, and the first subset of the first set of measurable features have associated measurement specifications for collecting data corresponding to the features, which measurement specifications are satisfied by at least one of the one or more sensors based on an estimated resource usage associated with the at least one of the one or more sensors (Column 5 “the data store and a knowledge repository present in the data store may be reasoned in order to identify a subset of the sensor data and a subset of the sensor information respectively. In one aspect, the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component” teaches determining subset of the sensor information Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches satisfying the associated measurement specification for collected data, resources comprises data related to sensor).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 16.
Bonawitz in view of Dey teaches the method according to claim 15,
Dey further teaches wherein the sending information identifying a first set of measurable features to a client computing device further comprises sending a measurement specification for each feature of the first set of features (Column 5 “the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component…identifying a sensor to be deployed in a physical environment may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system” teaches identifying the subset of the feature to the computing device).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 17.
Bonawitz in view of Dey teaches the method according to claim 15,
Dey further teaches wherein the method further comprises: if the computing device does not belong to the first group of computing devices, sending information identifying a second set of measurable features, wherein the second set of measurable features have associated measurement specifications, which measurement specifications are satisfied by a second group of computing devices (Column 1 “one or more generic terms or the related terms does not match with the information present in the data store, there is a challenge of retrieving sensor information from the data store against such queries, and hence such queries may remain unresolved” teaches retrieving sensor information does not match, Column 10 “The sensor data 304 and the metadata may be annotated with thematic concepts, the temporal concepts and spatial concepts or may be inferred during resolving of the search query so that queries are satisfied by the query interpreter module 318 and the quantity reasoning module 322” obtaining information of the features and collecting data).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 18.
Bonawitz in view of Dey teaches the method according to claim 15,
Bonawitz further teaches wherein the method further comprises: if no group can be found for the client computing device, notifying the client computing device that it is not able to participate in training of the machine learning model (Column 3-4 “region specific models can be generated and trained; however, regional models may lose out on a significant amount of training data (e.g., data from the user population outside the specific region) that may benefit the model. For instance, if model interactions across the world are not identical but are related, discarding all of this additional available data (e.g., users outside the region) for training the model may reduce the effectiveness of the model” teaches discarding data if not identical for training the model).
Claim 19.
Bonawitz in view of Dey teaches the method according to claim 15,
Dey further teaches wherein the method further comprises: if the client computing device belongs to the first group of computing devices, sending information identifying a second subset of the first set of measurable features wherein the second subset of the first set of measurable features have associated measurement specifications, which measurement specifications are satisfied by each of the first group of computing devices (Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the measurement capabilities, resources comprises data related to sensor).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Claim 20.
Bonawitz teaches a client computing device of a plurality of computing devices configured to perform training of a machine learning model, the client computing device comprising one or more sensors for collecting data, the client computing device comprising processing circuitry causing the computing device to be operative to (Column 6 “FIG. 1A depicts a block diagram of an example computing system 100 that can perform distributed machine learning model training according to example embodiments of the present disclosure. The system 100 includes a user computing device 102, a server computing system 130, and a training computing system 150 that are communicatively coupled over a network 180… The memory 114 can store data 116 and instructions 118 which are executed by the processor 112 to cause the user computing device 102 to perform operations” and Column 9 “one or more sensors, a context manager, a device state component, and/or additional components” teaches computing device to perform training machine learning model and sensor for collecting data):
obtain information from the coordinating computing device whether the client computing device belongs to a first group of computing devices of the plurality of computing devices (Column 10 “ At 204, the computing system can select a plurality of available user devices within a region, such that the plurality of selected user devices within the region can be tasked with training a machine-learned model… At 206, the computing system can provide a current version of a machine-learned model associated with the region to the plurality of selected user devices within the region” and Fig. 2 teaches 204-206 shows obtaining information from computing devices); and
if the client computing device belongs to the first group of computing devices, perform training of the machine learning model using the first group of computing devices (Column 10 “At 208, the selected user devices within the region, such as user computing device 102 of FIG. 1, can perform training of the received current version of the machine-learned model associated with the region using data local to each of the plurality of selected user devices. For example, in some implementations, the current version of the machine-learned model associated with the region can be trained at each of the selected user devices within the region using federated learning techniques. For example, each of the devices selected for a training iteration can be tasked with using the locally-generated and locally-stored data on the device to compute an update to the machine-learned model” teaches client computing device performing machine learning model).
Bonawitz does not explicitly teach obtain information identifying a first set of measurable features from a coordinating computing device of the plurality of computing devices, each feature of the first set of measurable features being associated with a measurement specification for collecting data corresponding to the feature; for each feature of the first set of measurable features, determine whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature; if there is at least one sensor of the one or more sensors satisfying the associated measurement specification, estimate a resource usage by each of the at least one sensor for collecting data corresponding to the feature; determine a first subset of the first set of measurable features, wherein the at least one sensor of the one or more sensors is selected for collecting data corresponding to the first subset of the first set of measurable features based on the estimated resource usage; send information identifying the first subset of the first set of measurable features to the coordinating computing device.
However, in the same field, analogous art, Dey teaches obtain information identifying a first set of measurable features from a coordinating computing device of the plurality of computing devices, each feature of the first set of measurable features being associated with a measurement specification for collecting data corresponding to the feature (Column 5 “Referring now to FIG. 1, a network implementation 100 of a system 102 for identifying a sensor, from a plurality of sensors, to be deployed in a physical environment is illustrated, in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may be configured to store sensor data and capture metadata of the plurality of sensors in a data store” and Column 7 “The sensor data 304 may indicate measurement values captured by the sensors 302. Further, the data capturing module 212 may be configured to capture annotated specification and metadata 306…The metadata may comprise at least one of a measurable range, a feature, a communication capability, a model name, a model number, a manufacturer detail, and the like. The sensor data 304 and the metadata 306 may be stored in the data store 220” teaches obtaining data identifying features from a computing device, measurement value capture by the sensor (specification collecting data));
for each feature of the first set of measurable features, determine whether there is at least one sensor of the one or more sensors satisfying the associated measurement specification for collecting data corresponding to the feature (Column 8 “The sensor and sensing service discovery service may help the user 104 to identify the right sensors and their capability using the knowledge repository 308 storing interrelations among entities associated with the sensors and using a defined set of rules for quantitative reasoning for a given use case requirement of the user 104… Thus, the data capturing module 212 creates sensor ontology in form of the knowledge repository 308 comprising hierarchical information of the sensors 302, properties of the sensors 302, feature of interest of the sensors 302, measuring capabilities of the sensors 302, communication capabilities of the sensors 302, and all related context information in either concept form or annotation form” and Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities” teaches feature of the data, determining sensor satisfying the associated measurement specification for collected data);
if there is at least one sensor of the one or more sensors satisfying the associated measurement specification, estimate a resource usage by each of the at least one sensor for collecting data corresponding to the feature (Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the measurement capabilities, resources comprises data related to sensor);
determine a first subset of the first set of measurable features, wherein the at least one sensor of the one or more sensors is selected for collecting data corresponding to the first subset of the first set of measurable features based on the estimated resource usage (Column 5 “the data store and a knowledge repository present in the data store may be reasoned in order to identify a subset of the sensor data and a subset of the sensor information respectively. In one aspect, the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component” teaches determining subset of the sensor information Column 10 “Specifically, the “MeasureConsumption” concept may be associated with a tuple of energy and power that is indicative of sensors satisfying consumption measurement capabilities. Further, after the identification of the few set of sensors, the quantity reasoning module 322 may be configured to execute the temporal query “measurement done during peak hour” on the knowledge repository 308 in order to identify at least one sensor of the few set of energy sensors that is capable of sensing energy consumption during peak hour and does not sleep in the peak hour” and Column 12 “The external resource 334 may include an external database capable of storing structured and/or unstructured data related to the sensors 302” teaches sensor satisfying the measurement capabilities, resources comprises data related to sensor);
send information identifying the first subset of the first set of measurable features to the coordinating computing device (Column 5 “the subset of the sensor data and subset of the sensor information may be matching with the at least one of the basic query component and the inferred query component…identifying a sensor to be deployed in a physical environment may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system” teaches identifying the subset of the feature to the computing device).
Bonawitz and Dey are analogous art because they are both directed to selecting resources such as sensors and processing associated data within a distributed computing environment.
It would have been obvious for one of ordinary skill in the arts before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey into the disclosed invention of Bonawitz.
One of ordinary skill in the arts would have been motivated to make this modification because of the following, “creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information” to “enable effective and efficient searching technique of identifying the sensor”, as suggested by Dey (Dey, Column 2 and Column 4).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Lokesha Patel whose telephone number is (571)272-6267. The examiner can normally be reached 8 AM - 4 PM.
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/LOKESHA PATEL/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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