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 .
Claims 1-20 are presented for examination.
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 May 27, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because (a) the “I” in “information” in reference character S201 should not be capitalized; and (b) in reference character S502, “measuremnt” should be “measurement”. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (p. 1, l. 23 of the specification as originally filed). Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The use of the term BLUETOOTH (p. 23, l. 4 of the specification as originally filed), which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The abstract of the disclosure is objected to because every sentence except the first one is a sentence fragment. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 1 and 20 are objected to because of the following informalities: “responsive to … belongs” should be “responsive to … belonging”. Claims 2-14 are objected to for dependency on claim 1. Appropriate correction is required.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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”; “responsive to the client computing device belong[ing] to the first group of computing devices, performing training of the local machine learning model on the client computing device, wherein the local machine learning model is local to the client computing device” – 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”; and “sending, to the coordinating computing device, one or more model weight values for the local machine learning model based on the training of the local machine learning model”, which recite 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: 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 recitations of “collecting”, “obtaining”, “sending” 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.
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 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 the 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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”; “sending information, to the client computing device, whether the client computing device belongs to the first group of computing devices for performing training of a local machine learning model, the local machine learning model being local to the client computing device”; and “receiving, from the client computing device, one or more model weight values for the local machine learning model based on the client computing device training of the local machine learning model”, which recite 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: 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 “sending”, “obtaining”, “sending”, and “receiving” 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: 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: 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: 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: 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 an article of manufacture, one of the statutory categories.
Step 2A Prong 1: The claim recites the same judicial exceptions as in claim 1.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The claim further recites a “client computing device of a plurality of computing devices configured to perform training of a local 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 [perform the method]”. This is a mere instruction to apply an exception using a generic computer component or merely asserts 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).
Otherwise, the analysis at this step mirrors that of claim 1.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim further recites a “client computing device of a plurality of computing devices configured to perform training of a local 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 [perform the method]”. This is a mere instruction to apply an exception using a generic computer component or merely asserts 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).
Otherwise, the analysis at this step mirrors that of claim 1.
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.
Claim Rejections - 35 USC § 103
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bonawitz (US 11488054) (“Bonawitz”) in view of Dey et al. (US 10031973) (“Dey”) and further in view of Akdeniz et al. (WO 2021158313) (“Akdeniz”).
Claim 1.
Bonawitz teaches a method performed by a client computing device of a plurality of computing devices configured to perform training of a local 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);
responsive to the client computing device belong[ing] to the first group of computing devices, performing training of the …machine learning model on the … computing device (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 of a machine learning model using a 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).
Dey and the instant application 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 art 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 art 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).
Neither Bonawitz nor Dey appears to disclose explicitly the further limitations of the claim. However, Akdeniz discloses performing training of the local machine learning model on the client computing device, wherein the local machine learning model is local to the client computing device (a MEC server may request that the clients share their respective compute rates and communication times in order to estimate the total update time from each client; based on this, the MEC server may perform a client set selection procedure by grouping the clients into sets for each training round – Akdeniz, paragraph 258; a candidate set of clients is chosen based on this grouping, and the global model is sent to the candidate set of clients [thereby making the global model a local model on each device] – id. at paragraph 560; see also paragraph 186 (indicating that the clients perform training locally, i.e., train a local machine learning model)); and
sending, to the coordinating computing device, one or more model weight values for the local machine learning model based on the training of the local machine learning model (each client computing node obtains a global model from a central server [thereby turning it into a local model], updates [trains] aspects of the global model (e.g., NN node weights), and communicates [sends] the updates [including the model weight values] to the global model to the central server – Akdeniz, paragraph 128).
Akdeniz and the instant application both relate to federated learning and are analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Bonawitz and Dey to perform training of the weights locally and send the weights to the coordinating device, as disclosed by Akdeniz, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase privacy by transferring results instead of raw data. See Akdeniz, paragraph 36.
Claim 20 is a client computing device claim corresponding to method claim 1 and is rejected for the same reasons as given in the rejection of that claim.
Claim 2.
Bonawitz in view of Dey/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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) .
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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 comprise data related to a sensor).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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).
Dey and the instant application 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 art 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 art 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).
Neither Bonawitz nor Dey appears to disclose explicitly the further limitations of the claim. However, Akdeniz discloses sending information, to the client computing device, whether the client computing device belongs to the first group of computing devices for performing training of a local machine learning model, the local machine learning model being local to the client computing device (a MEC server may request that the clients share their respective compute rates and communication times in order to estimate the total update time from each client; based on this, the MEC server may perform a client set selection procedure by grouping the clients into sets for each training round – Akdeniz, paragraph 258; a candidate set of clients is chosen based on this grouping, and the global model is sent to the candidate set of clients [note that the sending of this model informs the clients that they are members of the selected group] – id. at paragraph 560; see also paragraph 186 (indicating that the clients perform training locally, i.e., train a local machine learning model)); and
receiving, from the client computing device, one or more model weight values for the local machine learning model based on the client computing device training of the local machine learning model (each client computing node obtains a global model from a central server [thereby turning it into a local model], updates [trains] aspects of the global model (e.g., NN node weights), and communicates the updates to the global model to the central server [which thereby receives the weights from the clients] – Akdeniz, paragraph 128). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Bonawitz and Dey to group the client devices and train weights on each of them locally, as disclosed by Akdeniz, and an ordinary artisan could reasonably expect to have done so successfully. Doing so would increase the efficiency of the training by ensuring that client devices with similar capabilities receive similar workloads. See Akdeniz, paragraph 258.
Claim 16.
Bonawitz in view of Dey/Akdeniz 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).
Dey and the instant application 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 art 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 art 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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/Akdeniz 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/Akdeniz 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).
Dey and the instant application 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 art before the effective filing date of the claimed invention to incorporate the limitation(s) above as taught by Dey/Akdeniz into the disclosed invention of Bonawitz.
One of ordinary skill in the art 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).
Response to Arguments
Applicant's arguments filed July 22, 2026 (“Remarks”) have been fully considered but they are, except insofar as rendered moot by the introduction of a new ground of rejection, not persuasive.
Applicant argues that the claims as amended are eligible because (a) training a machine learning model and providing model weight values cannot be practically mentally performed; (b) the instant claims as a whole are directed to a technical solution to training machine learning models on heterogeneous IoT devices that is reflected in the claims themselves; and (c) the claims as a whole recite a specific and unconventional arrangement for improving distributed machine learning technology itself. Remarks at 14-16. However, regarding (a), the rejection does not assert that those steps are mentally performable, but rather that they either merely instruct the reader to apply the judicial exception on a computer or amount to insignificant extra-solution activity that is well-understood, routine, and conventional. Regarding (b), even assuming arguendo that the specification recites an improvement to machine learning, which Examiner does not concede, Applicant does not indicate which specific limitations of the claims allegedly reflect that improvement. Regarding (c), the claims, when read as an ordered whole, are directed to the abstract idea of determining whether there are sensors that satisfy certain conditions, estimating their resource usage, determining a subset of measurable features, and/or determining whether a client computing device belongs to a group. The additional elements, beyond those constituting the judicial exception itself, have either been shown to be conventional in the rejection itself or merely instruct the reader to apply the judicial exception using a computer programmed with generically recited machine learning models.
Regarding the art rejection, Applicant argues that the Bonawitz/Dey combination does not teach (a) determining whether a sensor satisfies a measurement specification for collecting data; (b) estimating a resource usage by the sensors; (c) determining a subset of measurable features; (d) sending information identifying a subset of measurable features to a coordinating computing device; and (e) sending the model weight values to the coordinating computing device. Remarks at 9-14.
As an initial matter, many of these arguments implicitly argue that Bonawitz is not physically combinable with Dey. See, e.g., Remarks at 6-7 (“[T]he system 102 of Dey when combined with Bonawitz would appear to map to the server computing system 130 which is alleged to correspond to the coordinating computing device of Claim 1 and not the user computing device 102 which is alleged to coordinate to the client computing device of Claim 1…. Thus, combining Dey with Bonawitz appears to teach that the server computing system 130 of Bonawitz includes a data capturing module that creates a sensor ontology in form of the knowledge repository 308 comprising hierarchical information of the sensors.”). However, that is not the test. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
To the extent that Applicant directly argues that the references do not teach the claims rather than vaguely alleging the impropriety of the combination, these arguments appear to be as follows: (a) that Dey does not teach estimating resource usage by a sensor for collecting data because Dey merely describes determining whether the sensor is awake or asleep during peak hours; (b) that Dey does not teach sending information identifying a subset of measurable features from a client device to a coordinating device because Dey merely identifies a subset of sensor information that is in a data store; and (c) that neither Dey nor Bonawitz discloses the amended features of the independent claims. However, regarding (a), it is noted that determining whether a sensor device is asleep or awake is a form of estimating its resource usage, namely, estimating whether its resource usage is zero or near-zero or whether it is significantly higher than zero. Regarding (b), the bottom of column 4 and the top of column 5 of Dey disclose that a query (information used to identify a subset of data) received from a user (client device) is executed on a data store (coordinating device) to identify the subset of sensor data and sensor information (including measurable features thereof). Argument (c) is moot by virtue of the use of newly cited reference Akdeniz to teach these limitations.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN C VAUGHN whose telephone number is (571)272-4849. The examiner can normally be reached M-R 7:00a-5:00p ET.
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/RYAN C VAUGHN/Primary Examiner, Art Unit 2125
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