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 .
This is a Final Office Action in response to the amendment, filed on 07/31/2025.
Claims 1-2, 14 and 15 are amended. Claims 16-20 are news. Claims 1-20 are presented for examination, with claims 1, 14 and 15 being independent.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s argument with regard to rejection of claims 1-20 under 35 U.S.C. 101 abstract idea is acknowledged. However, Examiner is not persuaded. Based upon the consideration of claim 1 and all of the relevant factors with respect to the claim as a whole, it is directed to a judicial exception (i.e., abstract idea) without significantly more. There are no additional limitations recited beyond the judicial exception itself that integrate the exception into a practical application. More particularly, the claim does not recite: (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP §2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP §2106.05(c)); or (iv) any other meaningful limitation (see MPEP §2106.05(e)). See also Guidance, 84 FED. Reg. at 55.
The claim is broadly written. Claim 1, as an exemplary claims is directed to abstract idea of for data integration. Claim does not include any structure and/or a series of steps as how to map concepts between the different ontologies of the datasets, how to score the concepts, how to transform the datasets into a homogenized dataset, what a homogenized data set is … . The claim does not include limitations that are “significantly more” than the abstract idea because the claims do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. No particular machine, no transformation, no other meaningful limitation is persuasively argued by the Applicant. Additionally, the claim fails to recite specific limitations (or a combination of limitations) that are NOT well-understood, routine, and conventional. The steps of: mapping …, scoring …, merging …, generating …; are conventional steps describe an abstract idea, they do not impose any meaningful limits on practicing the abstract idea and thus do not add significantly more to the claimed invention. In particular, the claim recites additional element, transforming …; the limitations do not impose any meaningful limits on practicing the abstract idea and thus do not add significantly more to the claimed invention. The amendment recites: a compute, one or more hardware processors, a non-transitory computer-readable medium; it is generic computer element. The generically recited computer elements do not add a meaningful limitation to the abstract idea.
Viewed as a whole, the claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim amounts to significantly more than the abstract idea itself. Therefore, the claim is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
See also, MPEP 2106.04(a)(2).III.C
“Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01..”
See MPEP 2111 for when and to what extent the specification can be read into claims.
For the above reasons, the Examiner maintains the rejections to claims under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
Applicant’s argument with regard to rejection of claims 1-20 under 35 U.S.C. 103 is acknowledged. New ground of rejections are provided in view of new claims 18-19.
In response to the Applicant’s argument that Otis and Lin, alone or in combination, fail to disclose or suggest the foregoing features of amended claim 1 (similar to claim 14 and 15).
Examiner respectfully submits that Otis discloses features of amended claim 1, i.e.,
wherein the datasets each comprise relational database in a form of a table with rows representing data instances and columns representing the concepts (e.g. Fig. 2 includes two data sets 205 and 225 (“data set 1” and “data set 2,” respectively) as input data sets, and data set 235 (“data set type C”) as an output data set in a form of tables with rows and columns. These tables are stored in a relational database, Otis: [0042] and Fig. 2 );
From Otis disclosure, Fig. 2 describes datasets in a form of a table with rows representing data instances and columns representing the concepts. These datasets are stored in relational database. Otis further discloses the concept of merging rows of datasets based on matching value of column. Therefore, Otis discloses amended limitation in the amended claims 1, 14 and 15.
Otis also discloses amended limitation in claim 2, i.e.,
wherein the template includes a function that uses translation libraries (e.g. The nodes of the hidden layers 224A through 224N [as templates] can transform the information of each input node by applying activation functions to the information, Lin: [0054]-[0055] and Fig. 2. One of the function can be lemmatization; words may be lemmatized in a consistent manner according to a library such as the Natural Language Toolkit (NLTK), Otis: [0028]).
Otis does not disclose, scoring and merging ones of the columns. Jagota reference is cited to teaches the limitation of ‘scoring’ and ‘merging’ in the amended claims (please see below rejection).
In view of at least the foregoing, the Examiner has reconsidered Applicant's remarks. Applicant is invited to further amend the claims to overcome the prior art made of record.
Claim Rejections - 35 USC § 101
35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to the abstract idea for data integration. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1, 14 and 15
Step 1: Claim 1 recites “A computer-implemented method …”; the claim recites a series of steps and therefore are processes. Claim 14 recites “A computer system … “; therefore, the claim is a machine. Claim 15 recites “A tangible, non-transitory computer-readable medium …”’ therefore, the claim is a manufacture.
Independent claims 1, 14 and 15 recite limitations of:
mapping (a mental step that using generic computer component) concepts between the different ontologies of the datasets based on ontology matching, wherein the datasets each comprise a relational dataset in a form of a table with rows representing data instances and columns representing the concepts;
scoring (a mental step that using generic computer component) the concepts based on a relation between the concepts to identify certain ones of the concepts that are more important to improving the performance of the machine learning models;
merging (a mental step that using generic computer component) the different ontologies based on the scoring to generate a merged ontology that merges corresponding ones of the columns and includes the identified concepts;
transforming (insignificant extra-solution activity) the datasets into a homogenized dataset according to the merged ontology; and
generating (a mental step that using generic computer component) a machine learning model based on the homogenized dataset.
Step 2A Prong One: The limitations of: mapping …, scoring …, merging …, generating …; are processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is , other than reciting: a compute, one or more hardware processors, a non-transitory computer-readable medium; are computer components; nothing in the claim elements preclude the step from practically being performed in a human mind or with the aid of pen and paper. Note that the limitations are done by the generically recited computer components under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claims recite the additional limitation: transforming …; the limitations are mere generic analyzing data (see MPEP 2106.05(g)). Further, these additional limitations are recited as being performed by “a computer, one or more hardware processors, a non-transitory computer-readable medium” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation: transforming…, is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) analyzing data, Versata Dev. Group Inc....
As explained with respect to Step 2A, Prong Two, the additional element performing by “a compute, one or more hardware processors, a non-transitory computer-readable medium”; is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
Since, claims 1, 14 and 15 are directed to abstract ideas; thus, the claims are not patent eligible.
Claims 2-13
The limitations as recited in claims 2-13 are simply describe the concepts for data integration. The claims do not include additional element(s) that is sufficient to amount to significantly more than the judicial exceptions. The claims cannot provide an inventive concept. Therefore, claims 2-13 are directed to abstract ideas and are not patent eligible. Analysis of the dependent claims are shown below.
Dependent claim 2 recites the limitations, generating data transformer functions based on a template, wherein the template includes a function that uses translation libraries; is a process, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion), wherein transforming the datasets into the homogenized dataset is based on the data transformer functions, the data transformer functions filtering certain data from the datasets; the limitation is insignificant extra-solution activity of mere generic gathering/collecting and analyzing data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)).
Dependent claim 3 recites the limitations, wherein the datasets are obtained from one or more entities associated with a building system, wherein the machine learning model is configured to predict an action of a component of the building system, the computer-implemented method further comprising generating the action of the component based on the machine learning model and certain data for the building system; are processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 4 recites the limitations, wherein the component of the building system is part of a heating, ventilation, and air conditioned controller (HVAC) system, and wherein the action includes operating the component of the HVAC system; are processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 5 recites the limitation, wherein generating the data transformer functions is further based on a repository of functions, a large language model (LLM) system that generates code, or a machine learning based transformer trained on a sequence of source data to target data; is metal step, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 6 recites the limitations, wherein scoring the concepts based on the relation between the concepts includes calculating a Pearson correlation between each pair of concepts of the concepts, wherein a pair of concepts includes pairs among different primary ontologies of the different ontologies, and pairs within a same primary ontology of the different ontologies, and wherein scores of the concepts represent a strength of a linear relationship between two given concepts of the pair of concepts; are mathematical concepts. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Dependent claim 7 recites the limitations: wherein scoring the concepts based on the relation between the concepts is based on a machine learning routine that builds a machine learning model for each concept of a primary ontology for each dataset of the datasets, wherein each machine learning model generates a feature importance array indicating importance of each of the concepts that is used for scoring the concepts; are mathematical concepts. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Dependent claim 8 recites the limitations, wherein scoring the concepts based on the relation between the concepts includes using a large language model (LLM) system that uses as input the datasets and knowledge, wherein the LLM system and prompt engineering generate a score for each pair of concepts of the concepts; is metal step, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 9 recites the limitation, wherein generating the merged ontology includes implementing a merger system that uses as input primary ontologies of the datasets and the scored concepts; is a metal processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 10 recites the limitation, wherein the merger system generates notes of equality between the concepts in the merged ontology, wherein generating the data transformer functions is further based on the notes of equality; is a metal processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 11 recites the limitations, receiving data including weather information, seasonality information, and current occupancy information for a building; the limitation is insignificant extra-solution activity of mere generic gathering/collecting data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)); generating an indoor temperature prediction for the building based on the machine learning model using the weather information, seasonality information, and the current occupancy information; and generating instructions for actuating a heating, ventilation, and air conditioned controller (HVAC) system of the building in accordance with the indoor temperature prediction; are mathematical concepts. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Dependent claim 12 recites the limitations, receiving data including weather information and seasonality information for a building; the limitation is insignificant extra-solution activity of mere generic gathering/collecting data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)); generating a building occupancy prediction for the building based on the machine learning model using the weather information and the seasonality information; and generating instructions for actuating a heating, ventilation, and air conditioned controller (HVAC) system of the building in accordance with the building occupancy prediction; are mathematical concepts. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Dependent claim 13 recites the limitations, receiving data including coordinates for an area, vegetation density for the area, and facilities in the area; the limitation is insignificant extra-solution activity of mere generic gathering/collecting data (see MPEP 2106.05(g)) and which is well understood routine conventional (see MPEP 2106.05(d)); generating a hazard risk prediction for the area based on the machine learning model using the coordinates, the vegetation density, and the facilities; and generating priorities assigned to certain sub-areas of the area using the hazard risk prediction, wherein the priorities represent a precedence in clearance planning for the area; are mathematical concepts. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Dependent claim 16 recites the limitation, wherein the ontology matching identifies semantic correspondences between the columns; is a metal processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 17 recites the limitation, wherein the ontology matching includes column matching the relational databases by measuring pair-wise attribute correlations in the tables and constructing a dependency graph; is mathematical concept. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Dependent claim 18 recites the limitation, wherein generating the data transformer functions includes using a large language model that uses a prompt engineering example data instance of a first concept and an example data instance of an equivalent concept to generate the data transformer functions; is a metal processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 19 recites the limitation, wherein the machine learning model represents behavior of a digital twin, the method further comprising inferring, by the digital twin, a missing concept from one of the ontologies based on corresponding ones of the concepts from other ones of the ontologies; is a metal processes, that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Dependent claim 20 recites the limitations, wherein the scoring is performed by running a routine that uses AutoML to build a corresponding machine learning model for each of the concepts of a primary ontology of the different ontologies using other ones of the ontologies and to produce a feature importance array in each case as scores, wherein the routine runs in a loop, whereby, for each of the concepts of the primary ontology, the corresponding machine learning model is trained through the AutoML to predict the respective concept using the concepts of the other ones of the ontologies; are mathematical concepts. The courts have found that mathematical relationships fall within the judicial exceptions, grouping of abstract ideas, see MPEP § 2106.04(a)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
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.
Claims 1-2, 5-10, 14-15, 16-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Otis et al., US 2021/0334275 (hereinafter Otis), in view of Jagota et al., US 2013/0297661, and further in view of Lin et al., US 2020/0349464 (hereinafter Lin).
Regarding claim 1, Otis discloses, A computer-implemented method for homogenizing datasets that have different ontologies to optimize performance of machine learning models that use the datasets (e.g. A computer system merges location-based data sets. Each of a plurality of data sets are transformed into a standardized schema, including at least two data sets. The schemas of a first and second data sets are joined to produce a merged data set using a machine learning model to identify corresponding rows of the schemas. The schema of the merged data set is joined with the schemas of the resulting data sets for the data set types to produce a new data set. Independent data sets may be obtained from disparate sources and/or may include different standards, schemas, or other inconsistencies [as different ontologies], Otis: Abstract, [0002], [0004] and [0015]), the computer-implemented method comprising:
mapping concepts between the different ontologies of the datasets based on ontology matching (e.g. Merging module 135 may utilize a machine learning model to identify matches between rows of different data sets. Merging module 135 may utilize a machine learning model to identify matching rows in order to merge data sets. A predictive model of machine learning module 140 calculates a match score for compared rows of different data sets, and identifies rows that should be combined when the match score surpasses a threshold value. For example, the predictive model may use the brand name column 308 [as concept] of data set 302 [as ontology 1] and the place name column 318 [as concept] of data set 312 [as ontology 2] to identify row matches [as mapping concepts between 2 ontologies/datasets]. Similarly, the predictive model may use the polygon name column 330 and/or polygon address column 332 of data set 324 and the place name column 318 and/or place address column 320 of data set 312 to identify row matches, Otis: [0036], [0050]), wherein the datasets each comprise relational database in a form of a table with rows representing data instances and columns representing the concepts (e.g. Fig. 2 includes two data sets 205 and 225 (“data set 1” and “data set 2,” respectively) as input data sets, and data set 235 (“data set type C”) as an output data set in a form of tables with rows and columns. These tables are stored in a relational database, Otis: [0042] and Fig. 2 );
transforming the datasets into a homogenized dataset according to the merged ontology (e.g. FIG. 4 is a flow chart depicting a method 400 of processing multiple data sets to generate a merged location-based data set [as a homogenized dataset]. Data sets are standardized at operation 410. Standardization module 130 may transform data sets into tabular data frames [as transforming data sets into a homogenized dataset] that include rows of values and columns having standardized names, may replace missing values with consistent NULL indicators, may convert values to an appropriate data type (e.g., string, float, integer, etc.), and may perform other standardization operations such as lemmatization, removing suffixes, prefixes, articles (e.g., “the”), and the like. Columns may be reordered as necessary to ensure that all data sets of a same data type have a same ordering of columns, Otis: Fig. 4 and [0054]-[0056); and
Otis does not directly or explicitly disclose:
scoring the concepts based on a relation between the concepts to identify certain ones of the concepts that are more important to improving the performance of the machine learning models;
merging the different ontologies based on the scoring to generate a merged ontology that merges corresponding ones of the columns and includes the identified concepts;
Jagota teaches:
scoring the concepts based on a relation between the concepts to identify certain ones of the concepts that are more important to improving the performance of the machine learning models (e.g. each value in a column is scored against the entity the column represents. For example, if the column has a header that indicates that the column stores "job titles," and one of the values in this column is "XYZ Inc," then this value should get a low score because it is clearly a company name, not a title. The overall score can then be defined to be the average of these posterior probabilities. The score then quantifies how well the data in the column fits the column's entity, Jagota: [0037]. Once all post-processing steps have been considered, then the source columns are transformed in accord with the updated mapping scheme and stored in the database [as improving the performance of the machine learning models], Jagota: [0101]);
merging the different ontologies based on the scoring to generate a merged ontology that merges corresponding ones of the columns and includes the identified concepts (e.g. An example of the former is a pair of source columns named "street1" and "street2," which need to be mapped into a single target column street. The scores returned on the individual calls to the classifier for each cell are then used by a more elaborate probabilistic scoring engine to make the final guess as to column name, Jagota: [0086], [0089]);
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis to include A system and method for mapping columns from a source file to a target file as taught by Jagota to provide techniques for accurately correlating columns in a source file with defined entities of the database model in order to transform the source file for importation into the data model of the MTS.
Otis in view of Jagota does not directly or explicitly disclose:
generating a machine learning model based on the homogenized dataset.
Lin teaches:
generating a machine learning model based on the homogenized dataset (e.g. The output layer 226 [as a machine learning model] provides an output resulting from the processing performed by the hidden layers 224A, 224B, through 224N [as homogenized data set], Lin: [0056]-[0058] and Fig. 2).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Jagota to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Regarding claim 2, Otis and Lin in combination further teaches, generating data transformer functions based on a template, wherein transforming the datasets into the homogenized dataset is based on the data transformer functions, the data transformer functions filtering certain data from the datasets (e.g. The neural network 220 is trained to process the features from the data in the input layer 222 using the different hidden layers 224A, 224B, through 224N [as templates] in order to provide the output through the output layer 226, Lin: [0046], [0058]. Nodes of the input layer 222 can activate a set of nodes in the first hidden layer 224A [as a homogenized dataset]. The nodes of the hidden layers 224A through 224N can transform the information of each input node by applying activation functions to the information, Lin: [0054]-[0055] and Fig. 2. The neural network 220 can adjust the weights of the nodes using backpropagation. In general, backpropagation can include a forward pass, a loss function, a backward pass, and a parameter (e.g., weight, bias, or other parameter) update. A derivative of the loss with respect to the weights (denoted as dL/dW, where W are the weights at a particular layer) can be computed to determine the weights that contributed most to the loss of the network. After the derivative is computed, a weight update can be performed by updating all the weights of the filters, Lin: [0059] and [0063]), wherein the template includes a function that uses translation libraries (e.g. The nodes of the hidden layers 224A through 224N [as templates] can transform the information of each input node by applying activation functions to the information, Lin: [0054]-[0055] and Fig. 2. One of the function can be lemmatization; words may be lemmatized in a consistent manner according to a library such as the Natural Language Toolkit (NLTK), Otis: [0028]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Jagota to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Regarding claim 5, Lin further teaches, wherein generating the data transformer functions is further based on a repository of functions, a large language model (LLM) system that generates code, or a machine learning based transformer trained on a sequence of source data to target data (e.g. The nodes of the hidden layers 224A through 224N can transform the information of each input node by applying activation functions to the information. The information derived from the transformation can then be passed to and can activate the nodes of the next hidden layer 224B, which can perform their own designated functions. Example functions include convolutional operations, up-sampling operations, data transformation operations, feature extraction, phrase parsing, language encoding [as LLM], and/or any other suitable functions, Lin: [0055]. The datasets 102a, 102b, through 102n can be received from another device, obtained from storage, generated by the computing device that includes the machine learning system 100, or can be obtained in any other way [as source data]. The datasets 102a, 102b, through 102n are each individually designed for a specific task, which can be different than or the same as the ultimate target task the machine learning model 106 is trained to perform, Lin: [0027]-[0028], [0035]-[0036], [0027]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Jagota to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Regarding claim 6, Lin further teaches, wherein scoring the concepts based on the relation between the concepts includes calculating a Pearson correlation between each pair of concepts of the concepts, wherein a pair of concepts includes pairs among different primary ontologies of the different ontologies, and pairs within a same primary ontology of the different ontologies, and wherein scores of the concepts represent a strength of a linear relationship between two given concepts of the pair of concepts (e.g. In some cases, the output can include an image and vector pair, which includes an identification of the image in association with the feature vector, Lin: [0071]. The relationship matching 576b can match the relationship embedding 570a with the box features 546a to determine a relationship match score 582a, Lin: [0083]-[0085]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Jagota to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Regarding claim 7, Lin further teaches, wherein scoring the concepts based on the relation between the concepts is based on a machine learning routine that builds a machine learning model for each concept of a primary ontology for each dataset of the datasets, wherein each machine learning model generates a feature importance array indicating importance of each of the concepts that is used for scoring the concepts (e.g. The weights are initially randomized before the neural network 220 is trained. The image can include, for example, an array of numbers representing the pixels of the image. Each number in the array can include a value from 0 to 255 describing the pixel intensity at that position in the array. The learning rate can be set to any suitable value, with a high learning rate including larger weight updates and a lower value indicating smaller weight updates, Lin: [0060]-[0063]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Jagota to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Regarding claim 8, Lin further teaches, wherein scoring the concepts based on the relation between the concepts includes using a large language model (LLM) system that uses as input the datasets and knowledge, wherein the LLM system and prompt engineering generate a score for each pair of concepts of the concepts (see Lin: [0044], [0055], [0083]-[0085]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Jagota to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Regarding claim 9, Otis further discloses, wherein generating the merged ontology includes implementing a merger system that uses as input primary ontologies of the datasets and the scored concepts (e.g. e.g. Merging module 135 may utilize a machine learning model to identify matching rows in order to merge data sets. A predictive model of machine learning module 140 calculates a match score for compared rows of different data sets, and identifies rows that should be combined when the match score surpasses a threshold value, Otis: [0036], [0038], [0050]. FIG. 3B depicts a combine operation 375 to produce a resulting merged data set 366).
Regarding claim 10, Lin further teaches, wherein the merger system generates notes of equality between the concepts in the merged ontology, wherein generating the data transformer functions is further based on the notes of equality (e.g. using cosine distance as an example, the category match score 578a can be equal to the cosine similarity (1-cosine distance), Lin: [0080]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Jagota to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Claim 14 recites, A computer system for homogenizing datasets that have different ontologies to optimize performance of machine learning models that use the datasets, the computer system comprising one or more hardware processors which, alone or in combination, are configured to provide for execution of the following steps are similar to subject matter of claim 1. Therefore, claim 14 is rejected by the same reasons as discussed in claim 1.
Claim 15 recites, A tangible, non-transitory computer-readable medium (e.g. a non-transitory computer useable medium , Otis: [0084]) having instructions thereon which, upon being executed by one or more processors, provide for homogenizing datasets that have different ontologies to optimize performance of machine learning models that use the datasets by execution of the following steps are similar to subject matter of claim 1. Therefore, claim 15 is rejected by the same reasons as discussed in claim 1.
Regarding claim 16, Otis further discloses, wherein the ontology matching identifies semantic correspondences between the columns (e.g. In particular, a row of data set 205 may be joined via a union operation with a row of data set 225 based on a matching date value of column 210 and/or location value of column 215. Thus, each row of data set 230 will include the values of column 220 (“sensor measure 1”) from data set 205 and values of column 230 (“sensor measure 2”) from data set 225, Otis: [0047]).
Regarding claim 17, Jagota further teaches, wherein the ontology matching includes column matching the relational databases by measuring pair-wise attribute correlations in the tables and constructing a dependency graph (e.g. a merge routine may be performed in step 618 to merge a pair of source columns into a single column. A database management system (DBMS) or the equivalent may execute storage and retrieval of information against the database objects or entities, whether the database is relational or graph-oriented, Jagota: [0100], [0101], [0137]) .
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis to include A system and method for mapping columns from a source file to a target file as taught by Jagota to provide techniques for accurately correlating columns in a source file with defined entities of the database model in order to transform the source file for importation into the data model of the MTS.
Regarding claim 20, Otis and Lin in combination further disclose, wherein the scoring is performed by running a routine that uses AutoML (e.g. The gradual adjustment of the percentages of the data used from the different datasets can be controlled manually or can be controlled automatically by the machine learning system, Lin: [0044]) to build a corresponding machine learning model for each of the concepts of a primary ontology of the different ontologies using other ones of the ontologies and to produce a feature importance array in each case as scores, wherein the routine runs in a loop, whereby, for each of the concepts of the primary ontology, the corresponding machine learning model is trained through the AutoML (e.g. The gradual adjustment of the percentages of the data used from the different datasets can be controlled manually or can be controlled automatically by the machine learning system, Lin: [0044]) to predict the respective concept using the concepts of the other ones of the ontologies (e.g. A predictive model of machine learning module 140 calculates a match score for compared rows of different data sets, and identifies rows that should be combined when the match score surpasses a threshold value. For example, the predictive model may use the brand name column 308 [as concept] of data set 302 [as ontology 1] and the place name column 318 [as concept] of data set 312 [as ontology 2] to identify row matches [as mapping concepts between 2 ontologies/datasets]. Similarly, the predictive model may use the polygon name column 330 and/or polygon address column 332 of data set 324 and the place name column 318 and/or place address column 320 of data set 312 to identify row matches, Otis: [0036], [0050]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis to include Techniques and systems for training a machine learning model using different datasets to perform one or more tasks as taught by Lin to provide a model that is fully trained to perform the target task based on any input data.
Claims 3-4 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Otis, in view of Lin, and further in view of Huber et al., US 2023/0168649 (hereinafter Huber).
Regarding claim 3, Lin further teaches, wherein the datasets are obtained from one or more entities associated with a building system (e.g. The image classification sub-module 500C is trained to classify images into different image categories. Examples of image categories include person, table, car, tree, sky, building, among others, Lin: [0092]),
Otis in view of Lin does not directly or explicitly disclose:
wherein the machine learning model is configured to predict an action of a component of the building system, the computer-implemented method further comprising generating the action of the component based on the machine learning model and certain data for the building system.
Huber teaches:
wherein the machine learning model is configured to predict an action of a component of the building system, the computer-implemented method further comprising generating the action of the component based on the machine learning model and certain data for the building system (e.g. generating a prediction model for the control policy, and operating the building management system using the prediction model, Huber: [0012], [0017], Fig. 4).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Lin to include Building control system as taught by Huber to provide services for building.
Regarding claim 4, Huber further teaches, wherein the component of the building system is part of a heating, ventilation, and air conditioned controller (HVAC) system, and wherein the action includes operating the component of the HVAC system (e.g. The BMS that serves building 10 includes an HVAC system 100. HVAC system 100 can include a plurality of HVAC devices (e.g., heaters, chillers, air handling units, pumps, fans, thermal energy storage, etc.) configured to provide heating, cooling, ventilation, or other services for building. Huber: [0045]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Lin to include Building control system as taught by Huber to provide services for building.
Regarding claim 11, Huber further teaches:
receiving data including weather information, seasonality information, and current occupancy information for a building (e.g. two categories of controllers may result in sub-optimal performance due to lack of predictive information on external disturbances such as weather and occupancy, Huber: [0120]);
generating an indoor temperature prediction for the building based on the machine learning model using the weather information, seasonality information, and the current occupancy information (e.g. The system described herein includes a thermal zone served by a local cooling source that may be controlled by a PI controller and a local thermostat. The thermal zone may receive heat directly from solar radiation, plug loads, and internal occupants, while exchanging heat with the ambient due to the indoor/outdoor temperature difference. In order to maintain constant zone temperature, the amount of cooling must be adjusted by a controller to counteract these heat loads. If the measured zone temperature may be above its current setpoint, the controller may increase the magnitude of cooling until the setpoint may be reached, Huber: [0141]); and
generating instructions for actuating a heating, ventilation, and air conditioned controller (HVAC) system of the building in accordance with the indoor temperature prediction (e.g. Huber: [0045],[0073] and [0141]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Lin to include Building control system as taught by Huber to provide services for building.
Regarding claim 12, Huber further teaches:
receiving data including weather information and seasonality information for a building (e.g. two categories of controllers may result in sub-optimal performance due to lack of predictive information on external disturbances such as weather and occupancy, Huber: [0120]. The input neurons are shown to receive values of a zone temperature T.sub.z, a zone relative humidity RH.sub.z, a time of day, an outdoor air temperature T.sub.oa, an outdoor relative humidity RH.sub.oa, and a season. It should be appreciated that said variables are shown for sake of example, Huber: [0227]);
generating a building occupancy prediction for the building based on the machine learning model using the weather information and the seasonality information (e.g. FIG. 16, a block diagram of a neural network model training system 1600. The neural network model 1602 may generate an output 1606 based on the input, e.g. weather, seasonality, Huber: [0226]-[0227]); and
generating instructions for actuating a heating, ventilation, and air conditioned controller (HVAC) system of the building in accordance with the building occupancy prediction (e.g. Huber: [0045],[0073] and [0141]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Lin to include Building control system as taught by Huber to provide services for building.
Regarding claim 13, Huber further teaches:
receiving data including coordinates for an area, vegetation density for the area, and facilities in the area (e.g. Building 10 is served by a BMS. A BMS is, in general, a system of devices configured to control, monitor, and manage equipment in or around a building or building area, Huber: [0044]);
generating a hazard risk prediction for the area based on the machine learning model using the coordinates, the vegetation density, and the facilities (e.g. Integrated control layer 218 can be configured to enhance the effectiveness of demand response layer 214 by enabling building subsystems 228 and their respective control loops to be controlled in coordination with demand response layer 214, Huber: [0064]); and
generating priorities assigned to certain sub-areas of the area using the hazard risk prediction, wherein the priorities represent a precedence in clearance planning for the area (e.g. Automated measurement and validation (AM&V) layer 212 can be configured to verify that control strategies commanded by integrated control layer 218 or demand response layer 214 are working properly (e.g., using data aggregated by AM&V layer 212, integrated control layer 218, building subsystem integration layer 220, FDD layer 216, or otherwise). The calculations made by AM&V layer 212 can be based on building system energy models and/or equipment models for individual BAS devices or subsystems, Huber: [0061], [0066]-[0067]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Lin to include Building control system as taught by Huber to provide services for building.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Otis, in view of Lin, and further in view of Immesoete et al., US 20240378463 (hereinafter Immesoete).
Otis in view of Lin does not directly or explicitly disclose claim 18.
Regarding claim 18, Immesoete teaches, wherein generating the data transformer functions includes using a large language model that uses a prompt engineering example data instance of a first concept and an example data instance of an equivalent concept to generate the data transformer functions (e.g. The method includes: receiving user input; querying the Knowledge Graph to retrieve relevant information based on the user input; potentially updating the Transformer's knowledge, or cache-like implementation of knowledge, or input context, with information retrieved from the Knowledge Graph; and generating a context-aware and accurate response using the updated LLM, Immesoete: [0003]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Lin to include methods, systems, and computer-readable media for the integration of Transformer models as taught by Immesoete to provide enhanced artificial intelligence applications.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Otis, in view of Lin, and further in view of Harvey et al., US 2024/0338602 (hereinafter Harvey).
Otis in view of Lin does not directly or explicitly disclose claim 19.
Regarding claim 19, Harvey teaches, wherein the machine learning model represents behavior of a digital twin, the method further comprising inferring, by the digital twin, a missing concept from one of the ontologies based on corresponding ones of the concepts from other ones of the ontologies (e.g. Using data fusion with the values generated from a learning digital twin can generate many missing data values, Harvey:[0088], [0098], [0103]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify A computer system merges location-based data sets as disclosed by Otis in view of Lin to include digital twin as taught by Harvey to provide values of parameters of the learning model.
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 CECILE H VO whose telephone number is (571)270-3031. The examiner can normally be reached Mon-Fri (9AM-5PM).
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/CECILE H VO/ Examiner, Art Unit 2153 12/13/2025
/KAVITA STANLEY/ Supervisory Patent Examiner, Art Unit 2153
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