CTNF 18/266,021 CTNF 99377 DETAILED ACTION This action is responsive to the Application filed on 06/08/2023. Claims 1-9, 11-12, 14, 17-22 and 25-26 are pending in the case. Domestic Benefit Domestic Benefit dated 12/08/2020 is acknowledged Information Disclosure Statement The information disclosure statement (IDS) submitted on 06/08/2023 and 10/11/2024 in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-9, 11-12, 14, 17-22 and 25-26 are rejected under 35 U.S.C. 101 as claims are directed towards judicial exceptions without significantly more. Regarding claim 1: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites determining, for each feature, one or more target measurement configurations indicating how data for the feature can be generated in the target domain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine how a feature could be measured or generated. See 2106.04.(a)(2).III.C. The claim recites for each feature, performing, for each of a plurality of candidate source domains, the steps of: determining one or more source measurement configurations indicating how data for the feature can be generated in the candidate source domain, which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine how a feature could be measured or generated. See 2106.04.(a)(2).III.C. The claim recites and determining a similarity metric indicative of a similarity between the one or more source measurement configurations and the one or more target measurement configurations which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine a similarity. See 2106.04.(a)(2).III.C. The claim recites based on the similarity metrics determined for each feature for the plurality of candidate source domains, selecting one or more selected source domains from the plurality of candidate source domains which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine an opinion to make a selection from multiple choices. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the machine learning model is trained with one or more features recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f)) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding claim 2: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: transmitting an indication of the one or more selected source domains to the target domain recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) Subject Matter Eligibility Analysis Step 2B: Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 3: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites obtaining an ontology for the feature, wherein the ontology describes possible measurement configurations that can be used to generate data for the feature in the plurality of candidate source domains and the target domain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass evaluating to identify a relationship (an ontology) that describes measurement configuration. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 4: The rejection of claim 3 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein the one or more source measurement configurations and the one or more target measurement configurations form paths through the ontology which is specifying the type of information the mental process of using judgement to determine how a feature could be measured or generated to determine a similarity. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 5: The rejection of claim 4 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein, for each feature, the step of determining the similarity metric comprises comparing the paths through the ontology taken by the one or more source measurement configurations and the one or more target measurement configurations which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass comparing paths in a relationship and evaluating the paths to determine similarities of each path. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 6: The rejection of claim 5 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein, for each feature, the similarity metric comprises a sum of hops in the paths through the ontology taken by the one or more source measurement configurations and the one or more target measurement configurations that overlap which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 7: The rejection of claim 6 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein the sum is a weighted sum wherein each hop is associated with a weighting which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 8: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites further comprising calculating an overall similarity based on the similarity metrics for each of the one or more features which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 9: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites further comprising calculating the overall similarity by summing the similarity metrics for each of the one or more features, wherein the overall similarity is a weighted sum of the similarity metrics for the each of the one or more features which is an abstract idea (Mathematical Calculations (see MPEP 2106.04(a)(2)(I)(C))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding claim 11: The rejection of claim 8 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites determining that the candidate source domain is suitable for use in the target domain based on a value of the overall similarity being above a predetermined threshold which is an abstract idea (Mathematical Relationships (see MPEP 2106.04(a)(2)(I)(A)))). Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 12: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites ranking the candidate source domains based on the value of the overall similarities, wherein candidate source domains with a higher overall similarities are ranked higher which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to compare values and evaluating to choose an order by comparing values. See 2106.04.(a)(2).III.C The claim recites selecting the one or more selected source domains as the highest ranked candidate source domains which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select from a set of options. See 2106.04.(a)(2).III.C Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 14: The rejection of claim 1 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites wherein the target measurement configurations and source measurement configurations comprise one or more of measurement protocol…measurement frequency, a sampling interval… which is specifying the type of information the mental process of using judgement to determine how a feature could be measured or generated to determine a similarity. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the target measurement configurations and source measurement configurations comprise one or more of…a sensor type, a measurement application and a network layer (merely specifies a particular technological environment in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation merely specifies a field of use in which the abstract idea is to take place, i.e. a field of use (see MPEP 2106.05(h)). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 17: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites selecting a first source domain from the one or more selected source domains which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine an opinion to make a selection from multiple choices. See 2106.04.(a)(2).III.C. The claim recites utilizing the model based on the model definition in the target domain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using a set of rules and applying it to information. See 2106.04.(a)(2).III.C Subject Matter Eligibility Analysis Step 2A Prong 2: obtaining a one or more selected source domain recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) transmitting a request to a model store for a model definition associated with the first source domain recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) receiving the model definition recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) Subject Matter Eligibility Analysis Step 2B: Additional element (a) (b) and (c) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 18: The rejection of claim 17 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites updating weights in the model based on data collected in the target domain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select new numerical values based on data and adjusting the weights of a model. See 2106.04.(a)(2).III.C Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible. Regarding Claim 19: The rejection of claim 17 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites updating an ontology…with changes to one or more target measurement configurations indicating how data for the one or more features can be generated in the target domain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select new numerical values based on data and adjusting the weights of a model. See 2106.04.(a)(2).III.C Subject Matter Eligibility Analysis Step 2A Prong 2: updating an ontology store recites the well-understood, routing and conventional activity of storing data (see MPEP 2106.05(g)) Subject Matter Eligibility Analysis Step 2B: Further, additional element (a) updating an ontology store is well understood, routine, and conventional activity of “storing and retrieving information in memory " (see MPEP 2106.05(d)(II)(iv), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 20: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: a non-transitory computer readable medium storing computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of claim 1 (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 21: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites determine, for each feature, one or more target measurement configurations indicating how data for the feature can be generated in the target domain; which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine how a feature could be measured or generated. See 2106.04.(a)(2).III.C. The claim recites for each feature, perform, for each of a plurality of candidate source domains, the steps of: determining one or more source measurement configurations indicating how data for the feature can be generated in the candidate source domain, which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine how a feature could be measured or generated. See 2106.04.(a)(2).III.C. The claim recites and determining a similarity metric indicative of a similarity between the one or more source measurement configurations and the one or more target measurement configurations; which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine a similarity. See 2106.04.(a)(2).III.C. The claim recites based on the similarity metrics determined for each feature for the plurality of candidate source domains, select one or more selected source domains from the plurality of candidate source domains. which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine an opinion to make a selection from multiple choices. See 2106.04.(a)(2).III.C. Subject Matter Eligibility Analysis Step 2A Prong 2: wherein the machine learning model is trained with one or more features (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))) the apparatus comprising processing circuitry configured to cause the apparatus to (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) and (b) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). The additional element(s) (a) (b) and (c) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 22: The rejection of claim 21 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim does not contain elements that would warrant a Step 2A Prong 1 analysis. Subject Matter Eligibility Analysis Step 2A Prong 2: transmit an indication of the one or more selected source domains to the target domain recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)) Subject Matter Eligibility Analysis Step 2B: Additional element (a) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). The additional element(s) (a) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible. Regarding Claim 25: Subject Matter Eligibility Analysis Step 2A Prong 1: The claim recites select a first source domain from the one or more selected source domains which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to determine an opinion to make a selection from multiple choices. See 2106.04.(a)(2).III.C. The claim recites utilize the model based on the model definition in the target domain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using a set of rules and applying it to information. See 2106.04.(a)(2).III.C Subject Matter Eligibility Analysis Step 2A Prong 2: processing circuitry configured to (merely recites a generic computer on which to perform the abstract idea, e.g. "apply it on a computer" (see MPEP 2106.05(f))) obtain a one or more selected source domains which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) transmit a request to a model store for a model definition associated with the first source domain which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) receive the model definition which amount to mere extra solution activity of obtaining and/or gathering data over a network, see MPEP §2106.05(g) Subject Matter Eligibility Analysis Step 2B: Additional elements (a) do not integrate the abstract idea into a practical application nor do the additional limitation provide significantly more than the abstract idea because the limitation amount to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP §2106.05(f). Additional element (b) (c) and (d) obtaining a network input is well understood, routine, and conventional activity of “transmitting or receiving data over a network" (see MPEP 2106.05(d)(II)(i) using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 ). The additional element(s) (a) (b) (c) and (d) in the claim do/does not include any additional elements , when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor significantly more than the judicial exception for the reasons set forth in step 2A prong 2 analysis above. The claim is not patent eligible Regarding Claim 26: The rejection of claim 25 is incorporated and further claim recites further additional elements/limitations: Subject Matter Eligibility Analysis Step 2A Prong 1: The claims recites update weights in the model based on data collected in the target domain which, under the broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations encompass using judgement to select new numerical values based on data and adjusting the weights of a model. See 2106.04.(a)(2).III.C Subject Matter Eligibility Analysis Step 2A Prong 2: The claim does not contain elements that would warrant a Step 2A Prong 2 analysis. Subject Matter Eligibility Analysis Step 2B: The claim does not include any additional element, when considered separately and in combination, that amount to an integration of the judicial exception into a practical application, nor to significantly more than the judicial exception. The claim is not patent eligible . Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-2, 8-9, 12, 14 , 20-22 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ruder et al.(“Learning to select data for transfer learning with Bayesian Optimization”, henceforth known as Ruder ) . Regarding claim 1: Ruder discloses determining, for each feature (Ruder, Page 373, Col. 1, Paragraph 1, “For document classification with a bag-of-words, X is the space of all document vectors, x i is the i-th document vector, and X is a sample of documents” where each feature corresponds to a word in the vocabulary terms that define the dimensions of the feature space which corresponds to each feature as all the words are considered in X sample of document ) , one or more target measurement configurations indicating how data for the feature can be generated in the target domain (Ruder, Page 375, Col. 2, Paragraph 2, “For each dataset…we chose to use small number (100) target domain examples… Unlabeled data is used in addition to calculate the representation of the target domain” where the dataset examples and unlabeled data of the target domain correspond to target measurement configuration as it identifies the types of data that occur in or are compatible with the target domain and indicates how the data can be generated in the target domain ) Ruder discloses for each feature, performing, for each of a plurality of candidate source domains, the steps of: determining one or more source measurement configurations indicating how data for the feature can be generated in the candidate source domain (Ruder, Page 373, Col. 2, Paragraph 6, “the training examples of all source domains are then scored and sorted according to Equation 1” where the training examples of the target domain correspond to target measurement configuration as the examples identifies the types of data that occur in or are compatible with the target domain and indicates how the data can be generated in the source domain ) and determining a similarity metric indicative of a similarity between the one or more source measurement configurations and the one or more target measurement configurations (Ruder, Page 373, Col. 1, Paragraph 5, “rank the available n training examples X = {x1,x2,··· ,xn} of k source domains D = {D1,D2,··· ,Dk} according to a domain similarity measure S and choose the top m samples for training their algorithm” where the domain similarity measure S corresponds to determining a similarity metric between source and target measurement configurations ) Ruder discloses based on the similarity metrics determined for each feature for the plurality of candidate source domains, selecting one or more selected source domains from the plurality of candidate source domains( Ruder, Page 373, Col. 1, Paragraph 5, “rank the available n training examples X = {x1,x2,··· ,xn} of k source domains D = {D1,D2,··· ,Dk} according to a domain similarity measure S and choose the top m samples for training their algorithm ” where choosing the top m samples for training corresponds to selecting one or more source domains ) Regarding claim 2: The rejection of claim 1 with prior art is incorporated and further: Ruder discloses further comprising transmitting an indication of the one or more selected source domains to the target domain (Ruder, Page 373, Col. 2, Paragraph 6, “the training examples of all source domains are then scored and sorted…the model for the respective task T is trained on the top n samples” where the model used for the target domain being trained on the top n samples from source domains corresponds to transmitting an indication of one or more selecting source domains to the target domain with the indication being the ranking and use of the top n samples with the target domain model ) Regarding claim 8: The rejection of claim 1 with prior art is incorporated and further: Ruder discloses further comprising calculating an overall similarity based on the similarity metrics for each of the one or more features (Ruder, Page 373, Col. 2, Equation 1, where S is an overall similar similarity score of the vector of similarity feature values, φ(X) ) Regarding claim 9: The rejection of claim 1 with prior art is incorporated and further: Ruder discloses f urther comprising calculating the overall similarity by summing the similarity metrics for each of the one or more features, wherein the overall similarity is a weighted sum of the similarity metrics for the each of the one or more features (Ruder, Page 373, Col. 2, Equation 1, where the vector of similarity feature values, φ(X) is dot product with the vector of weights w, which when the dot product is expanded creates the summing of similarity metrics with the overall similarity being a weighted sum(wf1+ wf2 +wf3+…) ) Regarding claim 12: The rejection of claim 9 with prior art is incorporated and further: Ruder discloses ranking the candidate source domains based on the value of the overall similarities, wherein candidate source domains with a higher overall similarities are ranked higher and selecting the one or more selected source domains as the highest ranked candidate source domains (Ruder, Page 373, Col. 2, Paragraph 6, “the training examples of all source domains are then scored and sorted according to Equation 1…the model for the respective task T is trained on the top n samples” where ranking, sorting and training based on top n samples corresponds to ranking and selecting highest ranked candidate ) Regarding claim 14: The rejection of claim 1 with prior art is incorporated and further: Ruder discloses wherein the target measurement configurations and source measurement configurations comprise one or more of:a measurement protocol, a sensor type, a measurement frequency, a measurement application, a sampling interval, and a network layer (Ruder, Page 373, Col. 1, Paragraph 5, “In order to select training data for adaptation for a task T , existing approaches rank the available n training examples X = {x1,x2,··· ,xn} of k source domains D = {D1,D2,··· ,Dk} according to a domain similarity measure S and choose the top m samples for training their algorithm” where the examples/samples corresponding to the measure configurations are sampled intervals of an entire dataset Regarding claim 20: Ruder discloses computer program product comprising a non-transitory computer readable medium, storing computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of claim 1 (Page 375, Col. 1, Paragraph 1, “We use the n examples with the highest score as determined by the learned data selection measure for training our models 2 ” and footnote 2 reading “all code is available at (github link)” where the code to replicate the method and experiments being included at a github corresponds to a code using a processor and memory ) Regarding claim 21: Ruder discloses determine, for each feature (Ruder, Page 373, Col. 1, Paragraph 1, “For document classification with a bag-of-words, X is the space of all document vectors, x i is the i-th document vector, and X is a sample of documents” where each feature corresponds to a word in the vocabulary terms that define the dimensions of the feature space which corresponds to each feature as all the words are considered in X sample of document ) , one or more target measurement configurations indicating how data for the feature can be generated in the target domain (Ruder, Page 375, Col. 2, Paragraph 2, “For each dataset…we chose to use small number (100) target domain examples… Unlabeled data is used in addition to calculate the representation of the target domain” where the dataset examples and unlabeled data of the target domain correspond to target measurement configuration as it identifies the types of data that occur in or are compatible with the target domain and indicates how the data can be generated in the target domain ) Ruder discloses for each feature, perform, for each of a plurality of candidate source domains, the steps of:determining one or more source measurement configurations indicating how data for the feature can be generated in the candidate source domain( Ruder, Page 373, Col. 2, Paragraph 6, “the training examples of all source domains are then scored and sorted according to Equation 1” where the training examples of the target domain correspond to target measurement configuration as the examples identifies the types of data that occur in or are compatible with the target domain and indicates how the data can be generated in the source domain ) Ruder discloses determining a similarity metric indicative of a similarity between the one or more source measurement configurations and the one or more target measurement configurations( Ruder, Page 373, Col. 1, Paragraph 5, “rank the available n training examples X = {x1,x2,··· ,xn} of k source domains D = {D1,D2,··· ,Dk} according to a domain similarity measure S and choose the top m samples for training their algorithm” where the domain similarity measure S corresponds to determining a similarity metric between source and target measurement configurations ) Ruder discloses based on the similarity metrics determined for each feature for the plurality of candidate source domains, select one or more selected source domains from the plurality of candidate source domains( Ruder, Page 373, Col. 1, Paragraph 5, “rank the available n training examples X = {x1,x2,··· ,xn} of k source domains D = {D1,D2,··· ,Dk} according to a domain similarity measure S and choose the top m samples for training their algorithm ” where choosing the top m samples for training corresponds to selecting one or more source domains ) Regarding claim 22: The rejection of claim 21 with prior art is incorporated and further: Ruder discloses transmit an indication of the one or more selected source domains to the target domain( Ruder, Page 373, Col. 2, Paragraph 6, “ the training examples of all source domains are then scored and sorted…the model for the respective task T is trained on the top n samples” where the model used for the target domain being trained on the top n samples from source domains corresponds to transmitting an indication of one or more selecting source domains to the target domain with the indication being the ranking and use of the top n samples with the target domain model ) 07-15-03-aia AIA Claim(s) 17-19 and 25-26 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US20190180199A1, henceforth known as Bobroff . Regarding claim 17: Bobroff discloses obtaining a one or more selected source domains (Bobroff, [0048], “As shown by FIG. 6, training data 610 is provided to a distance estimator 620, which can compute distances between the provided training data 610 and training data associated with respective models stored in one or more model databases” where training data are considered source domains and comparing incoming training data with training data associated with multiple models corresponds to obtaining one or more selected source domains as the models include training data (See also Bobroff, [0035], “…properties of a model can include training data 216 associated with the model.”)) Bobroff discloses selecting a first source domain from the one or more selected source domains (Bobroff, [0047], “In an aspect, system 600 can provide suggestions and/or other guidance based on a full or partial set of training data 610 to select a model that can be used in connection with the training data” where selecting a model based on the different and partial training data corresponds to selecting a first source domain from the one or more source domains ) Bobroff discloses transmitting a request to a model store for a model definition associated with the first source domain( Bobroff, [0048], “In another aspect, distances computed by the distance estimator 620 can be provided to a model query/search module 630 to facilitate suggestion of one or more models from the model database 640” where the suggesting from the model database corresponds to transmitting a request to a model ) Bobroff discloses receiving the model definition (Bobroff, [0047], “Accordingly, a user of system 600 can leverage an existing set of models and/or model components, prepared by the user and/or others, to obtain useful model information without manual review of large sets of model data.”) Bobroff discloses and utilizing the model based on the model definition in the target domain( Bobroff, [0036], “In an aspect, the model components for a given model can further include an identity of a base (parent) model 220 from which the model is derived. For instance, a machine learning model trained to operate on a given type of data can be used as the base model for another machine learning model to be trained to operate on a similar type of data through techniques such as transfer learning. Other techniques for deriving a new model from a base model 220 could also be used” where training a base model for to be trained to operate on a similar type of data corresponds to utilizing the model based on the model definition a target domain ) Regarding claim 18: The rejection of claim 17 with prior art is incorporated and further: Bobroff discloses updating weights in the model based on data collected in the target domain (Bobroff, [0053], “Subsequent to suggesting models and/or model components, a suggested model can be trained and deployed”) Regarding claim 19: The rejection of claim 17 with prior art is incorporated and further: Bobroff discloses updating an ontology store ([0055], “The system 800 shown in FIG. 8 further includes an intake component 820 that can identify an incoming machine learning model…the incoming model provided to the intake component 820 and/or supporting information provided with the model can include respective model components” where updating the model database with the intake component corresponds to updating ontology store ) with changes to one or more target measurement configurations indicating how data for the one or more features can be generated in the target domain ([0054] “…The set of models identified by the identification component…can include respective model components, such as model configurations, model program code, model training data, model feedback, deployment data, parent model information, etc., in a similar manner to that described above”) where the identification component identifying the training data of the target domain corresponds to updating the ontology store with changes to one or more measurement configurations indicating how data for the one or more features can be generated in the target domain as it as identifies the types of data that occur in or are compatible with the target domain and indicates how the data can be generated in the target domain ) Regarding claim 25: The rejection of claim 17 with prior art is incorporated and further: Bobroff discloses obtain a one or more selected source domains (Bobroff, [0048], “As shown by FIG. 6, training data 610 is provided to a distance estimator 620, which can compute distances between the provided training data 610 and training data associated with respective models stored in one or more model databases” where training data are considered source domains and comparing incoming training data with training data associated with multiple models corresponds to obtaining one or more selected source domains as the models include training data (See also Bobroff, [0035], “…properties of a model can include training data 216 associated with the model.”)) Bobroff discloses select a first source domain from the one or more selected source domains (Bobroff, [0047], “In an aspect, system 600 can provide suggestions and/or other guidance based on a full or partial set of training data 610 to select a model that can be used in connection with the training data” where selecting a model based on the different and partial training data corresponds to selecting a first source domain from the one or more source domains ) Bobroff discloses transmit a request to a model store for a model definition associated with the first source domain( Bobroff, [0048], “In another aspect, distances computed by the distance estimator 620 can be provided to a model query/search module 630 to facilitate suggestion of one or more models from the model database 640” where the suggesting from the model database corresponds to transmitting a request to a model ) Bobroff discloses receive the model definition (Bobroff, [0047], “Accordingly, a user of system 600 can leverage an existing set of models and/or model components, prepared by the user and/or others, to obtain useful model information without manual review of large sets of model data.”) Bobroff discloses utilize the model based on the model definition in the target domain( Bobroff, [0036], “In an aspect, the model components for a given model can further include an identity of a base (parent) model 220 from which the model is derived. For instance, a machine learning model trained to operate on a given type of data can be used as the base model for another machine learning model to be trained to operate on a similar type of data through techniques such as transfer learning. Other techniques for deriving a new model from a base model 220 could also be used” where training a base model for to be trained to operate on a similar type of data corresponds to utilizing the model based on the model definition a target domain ) Regarding claim 26: The rejection of claim 25 with prior art is incorporated and further: Bobroff discloses wherein the target domain is further configured to update weights in the model based on data collected in the target domain (Bobroff, [0053], “Subsequent to suggesting models and/or model components, a suggested model can be trained and deployed”) Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 3-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ruder et al.(“Learning to select data for transfer learning with Bayesian Optimization”, henceforth known as Ruder ) and Ye et al.(“XLearn: Learning Activity Labels across Heterogeneous Datasets”, henceforth known as Ye) Regarding claim 3: The rejection of claim 1 with prior art is incorporated and further: Ye discloses for each feature: obtaining an ontology for the feature (Ye, Page 6, Paragraph 1, “To make the learning effective, XLearn builds on an assumption that there exist simple ontologies that semantically relate the feature spaces across different datasets and as well as their label spaces.”) , wherein the ontology describes possible measurement configurations that can be used to generate data for the feature in the plurality of candidate source domains and the target domain (Ye, Page 10, Figure 2, “An ensemble is employed to learn the correlation of feature space in each dataset and the combined activity labels from all the datasets.”) References Ruder and Ye are analogous art because they are from the same field of endeavor of using machine learning to identify and interpret source and target domain similarities. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ruder and Ye before him or her, to modify the similarity metric of Ruder to include the ontology of Ye to assist human activity cognition in real-world applications. The suggestion/motivation for doing so would have been Page 5, Paragraph 5, “Thus, this technique is not subject to heterogeneous sensor deployments and activity sets in different environments, which addresses the key challenge in transfer learning of human activity recognition in the real-world applications. To make the learning effective, XLearn builds on an assumption that there exist simple ontologies that semantically relate the feature spaces across different datasets and as well as their label spaces.” Regarding claim 4: The rejection of claim 3 with prior art is incorporated and further: Ye discloses wherein the one or more source measurement configurations and the one or more target measurement configurations form paths through the ontology (Ye, Page 11, Paragraph 2, “Figure 3 presents part of object and location ontologies. For example, Door PNG media_image1.png 41 37 media_image1.png Greyscale MovableBarrier and Bedroom PNG media_image1.png 41 37 media_image1.png Greyscale SleepingArea” where the is-a relationships form a path through the ontology ) Regarding claim 5: The rejection of claim 4 with prior art is incorporated and further: Ye discloses wherein, for each feature, the step of determining the similarity metric comprises comparing the paths through the ontology taken by the one or more source measurement configurations and the one or more target measurement configurations( Ye, Page 11, Paragraph 3, “Based on the hierarchy, we can measure the similarity between domain concepts [35]. The approach works by finding the least common subsumer (LCS) of the two input concepts and computing the path length from LCS up to the root node” where computing the path length from LCS to the root node to find the similarity between domain concepts corresponds determining the similarity metric by comparing paths through the ontology ) Regarding claim 6: The rejection of claim 5 with prior art is incorporated and further: Ye discloses wherein, for each feature, the similarity metric comprises a sum of hops in the paths through the ontology taken by the one or more source measurement configurations and the one or more target measurement configurations that overlap (Ye, Page 11, Paragraph 4, “Definition 3. Let c1 and c2 be two concepts organised in a hierarchy. The conceptual similarity measure between them is: PNG media_image2.png 46 251 media_image2.png Greyscale where N1(N2) are the paths length between c1 (c2) and the LCS node of c1 and c2, and N3 is the path length between LCS and the root” where path lengths corresponds to a sum of hops in the paths ) Regarding claim 7: The rejection of claim 6 with prior art is incorporated and further: Ye discloses wherein the sum is a weighted sum wherein each hop is associated with a weighting (Ye, Page 13, PNG media_image3.png 82 775 media_image3.png Greyscale where each hop is associated with a weight as ω C is a weight that is assigned to the similarity type and the computed similarity that the weight is being applied to is the path lengths simC(ci,cj)) . 07-21-aia AIA Claim (s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ruder et al.(“Learning to select data for transfer learning with Bayesian Optimization”, henceforth known as Ruder ) and Ruder et al.(“Learning to select data for transfer learning with Bayesian Optimization”, henceforth known as Ruder ) and US20200184256A1, henceforth known as Ye2) Regarding claim 11: The rejection of claim 8 with prior art is incorporated and further: Ye2 discloses further comprising, determining that the candidate source domain is suitable for use in the target domain based on a value of the overall similarity being above a predetermined threshold (Ye2, [0069], “In one arrangement, a threshold may be used to determine whether two domains are similar”) References Ruder and Ye2 are analogous art because they are from the same field of endeavor of using machine learning to identify and interpret source and target domain similarities. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ruder and Ye2 before him or her, to modify the similarity metric of Ruder to include the similarity metric of Ye2 to guarantee a minimum level of similarity between source and target domain. The suggestion/motivation for doing so would have been Ye2, [0069], “If the similarity value determined by the domain gap measure is smaller than the threshold, the source domain 160 and the target domain 170 are considered as dissimilar”. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES JEFFREY JONES JR whose telephone number is (703)756-1414. The examiner can normally be reached Monday - Friday 8:00 - 5:00 EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /C.J.J./Examiner, Art Unit 2122 /KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122 Application/Control Number: 18/266,021 Page 2 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 3 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 4 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 5 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 6 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 7 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 8 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 9 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 10 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 11 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 12 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 13 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 14 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 15 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 16 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 17 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 18 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 19 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 20 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 21 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 22 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 23 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 24 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 25 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 26 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 27 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 28 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 29 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 30 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 31 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 32 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 33 Art Unit: 2122 ` Application/Control Number: 18/266,021 Page 34 Art Unit: 2122 `
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