CTNF 18/436,799 CTNF 88930 DETAILED ACTION 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. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult the 2019 PEG for more details of the analysis. Step 1 According to the first part of the analysis, in the instant case, claims 1-12, 13-20, are directed to a method, system of training a ML model. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A, Step 2A, Prong 1 Following the determination of whether or not the claims fall within one of the four categories (Step 1), it must be determined if the claims recite a judicial exception (e.g. mathematical concepts, mental processes, certain methods of organizing human activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial exception as explained below. Regarding Claims 1, 13 these claims recite identifying, by one or more processors coupled to memory, a plurality of unstructured object names each associated with a respective object tag, the plurality of unstructured object names corresponding to a plurality of building devices of a building; determining, by the one or more processors, a plurality of embeddings based on the plurality of unstructured object names, the plurality of embeddings comprising a position embedding; and training, by the one or more processors, a machine-learning model based on (i) the plurality of embeddings including the position embedding, and (ii) the respective object tag of the plurality of unstructured object names, the machine-learning model trained to generate corrected object tags for the plurality of building devices. The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they could be performed mentally, and they are also disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0019]-[0044] Fig. 1, etc. Thus, the claim recites abstract ideas. Step 2A, Prong 2 Following the determination that the claims recite a judicial exception, it must be determined if the claims recite additional elements that integrate the exception into a practical application of the exception (Step 2A, Prong 2). In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that integrate the exception into a practical application of the exception as explained below. In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). Regarding Claims 1, 13 these claims This limitation recites using one or more neural networks as a tool to perform an abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).) This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f)) Step 2B Based on the determination in Step 2A of the analysis that the claims are directed to a judicial exception, it must be determined if the claims contain any element or combination of elements sufficient to ensure that the claim amounts to significantly more than the judicial exception (Step 2B). In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons given above in the Step 2A, Prong 2 analysis. Furthermore, each additional element identified above as being insignificant extra-solution activity is also well-known, routine, conventional as described below. Claims 1, 13 : The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group , collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource , obtaining and comparing intangible data; see Digitech , organizing information through mathematical correlations; see Grams , diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone , using categories to organize store and transmit information; see Smartgene , comparing new and stored information and using rules to identify options). Further the claimed invention appears to be something that can be performed by head and hand (Gottschalk v. Benson). The claimed solution is not necessarily rooted in computer technology in order to overcome a problem (DDR v. Hotels.com). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites the additional elements of “identifying…”, “determining…”, ”training…” These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself. Step 2A/2B Prong 2 Dependent Claims Regarding to claim 2, 14 Claim 2, 14 merely recite other additional elements that executing the ML model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 3, 15 Claim 3, 15 merely recite other additional elements that generating tags which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 4, 16 Claim 4, 16 merely recite other additional elements that analyzing the data to generate control signals which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 5, 17 Claim 5, 17 merely recite other additional elements that generating alerts which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 6, 18 Claim 6, 18 merely recite other additional elements that the model is a transformer which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 7, 19 Claim 7, 19 merely recite other additional elements that the model is a tokenizer which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 8, 20 Claim 8, 20 merely recite other additional elements that performing table lookup which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 9 Claim 9 merely recite other additional elements that calculating a focal loss which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 10 Claim 10 merely recite other additional elements that pre-processing data to generate names which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 11 Claim 11 merely recite other additional elements that ML model is trained to generate keywords which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Regarding to claim 12 Claim 12 merely recite other additional elements that ML model is trained to generate classifications which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible. Claim Rejections - 35 USC § 102 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. 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. 07-15 AIA Claim s 1-4, 10-16 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Park US 2020/0125043 . In regard to claim 1, Park disclose A method for training machine-learning models for building data conversion, comprising: ([0055]-[0067] [0074]-[0078] training classifiers for building data translation) identifying, by one or more processors coupled to memory, a plurality of unstructured object names each associated with a respective object tag, the plurality of unstructured object names corresponding to a plurality of building devices of a building; ([0028]-[0040] [0059]-[0067] identify, object identifiers and object names associated with labels corresponding to the devices of a building) determining, by the one or more processors, a plurality of embeddings based on the plurality of unstructured object names, the plurality of embeddings comprising a position embedding; ([0028]-[0040][0047] [0053]-[0067] [0073]-[0090] determine, vectors based on the object identifiers and object names and the vectors include physical location (substring in relevant symbol list) and training, by the one or more processors, a machine-learning model based on (i) the plurality of embeddings including the position embedding, and (ii) the respective object tag of the plurality of unstructured object names, the machine-learning model trained to generate corrected object tags for the plurality of building devices. ([0047][0050]-[0067] [0073]-[0091] training, a classifier based on the vectors including the location vector and object name label of the object names and to output the corrected object labels (symbol list using the subset of labeled point descriptors) for the devices of the building) In regard to claim 2, Park disclose The method of claim 1, Park disclose further comprising executing, by the one or more processors, the machine-learning model using a second plurality of unstructured object names as input to generate a respective plurality of structured object names. ([0047][0050]-[0067] [0073]-[0091] retraining, a classifier with different set of training data as input to output the new relevant symbol list using the subset of labeled point descriptors for the devices of the building) In regard to claim 3, Park disclose The method of claim 2, Park disclose further comprising generating, by the one or more processors, a plurality of building automation and control network (BACnet) object type tags based on unstructured data. ([0047][0050]-[0067] [0073]-[0091] output the symbol list using the various subset of labeled point descriptors and indicate the devices attributes of the building) In regard to claim 4, Park disclose The method of claim 2, Park disclose further comprising analyzing, by the one or more processors, data associated with the plurality of building devices based on the respective plurality of structured object names to generate control signals for controlling one or more of the plurality of building devices. ([0046][0047][0050]-[0067] [0073]-[0091] analyzing the data based on the identified devices and user can add, remove or modify terms or point types from lists to modify the assigned point types and data points (devices in the building) from classifier) In regard to claim 10, Park disclose The method of claim 1, Park disclose further comprising pre-processing, by the one or more processors, unstructured data to generate the plurality of unstructured object names. ([0028]-[0047] [0050]-[0067] generate symbol list with object names from the data) In regard to claim 11, Park disclose The method of claim 1, Park disclose wherein the machine-learning model is trained to generate one or more sequential keywords based on input data. ([0028]-[0047] [0050]-[0067] [0076] the classifier is trained to generate keywords from the data) In regard to claim 12, Park disclose The method of claim 11, Park disclose wherein the machine-learning model is trained to generate at least one classification of the input data. ([0028]-[0047] [0050]-[0067] [0076] the classifier is trained to generate classes from the data) In regard to claims 13-16, claims 13-16 are system claims corresponding to the method claims 1-4 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1-4 . Claim Rejections - 35 USC § 103 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. 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. 07-21-aia AIA Claim s 5, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Park US 2020/0125043 in view of Park et al. (Park2) US 20200104530 In regard to claim 5, Park disclose The method of claim 2, But Park fail to explicitly disclose “further comprising generating, by the one or more processors, one or more alerts based on data associated with the plurality of building devices, the one or more alerts identifying at least one of the respective plurality of structured object names.” Park2 disclose further comprising generating, by the one or more processors, one or more alerts based on data associated with the plurality of building devices, the one or more alerts identifying at least one of the respective plurality of structured object names. ([0134]-[0137][0144]-[0157] providing an alert message to a user from the data inputs from the building devices and the alert identify the name of the devices to repair) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Park2‘s building devices management system into Park’s invention as they are related to the same field endeavor of building devices management system. The motivation to combine these arts, as proposed above, at least because Park2‘s method of device fault notification would help to provide fault identification method into Park’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that provide fault identification by providing notification would help to identify the faulty devices in the building and therefore improve user experience using the devices. In regard to claim 17, claim 17 is a system claim corresponding to the method claim 5 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 5 . 07-21-aia AIA Claim s 6-9, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Park US 2020/0125043 in view of Dekel et al. (Dekel) US 2024/0419382 In regard to claim 6, Park disclose The method of claim 1, But Park fail to explicitly disclose “wherein the machine-learning model comprises a transformer including a multi-head attention layer.” Dekel disclose wherein the machine-learning model comprises a transformer including a multi-head attention layer. ([0119] [0197]-[0209] a transformer and a multi-head attention layer) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Dekel‘s method of ML model training into Park’s invention as they are related to the same field endeavor of training a ML model. The motivation to combine these arts, as proposed above, at least because Dekel‘s method of ML model training using a transformer would help to provide ML model training method into Park’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing a transformer would help to improve the accuracy of ML model training and therefore improve user experience using the devices. In regard to claim 7, Park disclose The method of claim 1, But Park fail to explicitly disclose “wherein the machine-learning model comprises a tokenizer and a feed-forward layer.” Dekel disclose wherein the machine-learning model comprises a tokenizer and a feed-forward layer ([0197]-[0209] a tokenizer and a feed-forward layer) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Dekel‘s method of ML model training into Park’s invention as they are related to the same field endeavor of training a ML model. The motivation to combine these arts, as proposed above, at least because Dekel‘s method of ML model training using a tokenizer would help to provide ML model training method into Park’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing a tokenizer would help to improve the accuracy of ML model training and therefore improve user experience using the devices. In regard to claim 8, Park disclose The method of claim 1, But Park fail to explicitly disclose “wherein determining the plurality of embeddings comprises performing, by the one or more processors, a lookup in an embedding table corresponding to the machine-learning model.” Dekel disclose wherein determining the plurality of embeddings comprises performing, by the one or more processors, a lookup in an embedding table corresponding to the machine-learning model. ([0130] matching using hash table corresponding to the subsets of key points (feature vectors) related to the model) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Dekel‘s method of ML model training into Park’s invention as they are related to the same field endeavor of training a ML model. The motivation to combine these arts, as proposed above, at least because Dekel‘s method of ML model training using a hash table lookup would help to provide ML model training method into Park’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that using a hash table lookup to help training the model would help to improve the accuracy of ML model training and therefore improve user experience using the devices. In regard to claim 9, Park disclose The method of claim 1, But Park fail to explicitly disclose “wherein training the machine-learning model comprises calculating, by the one or more processors, a focal loss value.” Dekel disclose wherein training the machine-learning model comprises calculating, by the one or more processors, a focal loss value. ([0106]-[0113][0265] calculating a focal loss) It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Dekel‘s method of ML model training into Park’s invention as they are related to the same field endeavor of training a ML model. The motivation to combine these arts, as proposed above, at least because Dekel‘s method of ML model training with a focal loss calculation would help to provide ML model training method into Park’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that calculating a focal loss to help training the model would help to improve the accuracy of ML model training and therefore improve user experience using the devices. In regard to claims 18-20, claims 18-20 are system claims corresponding to the method claims 6-8 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 6-8 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. PATENT PUB. # PUB. DATE INVENTOR(S) TITLE US 20210350795 A1 2021-11-11 Kenter et al. Speech Synthesis Prosody Using A BERT Model Kenter et al. disclose A method for generating a prosodic representation includes receiving a text utterance having one or more words. Each word has at least one syllable having at least one phoneme. The method also includes generating, using a Bidirectional Encoder Representations from Transformers (BERT) model, a sequence of wordpiece embeddings and selecting an utterance embedding for the text utterance, the utterance embedding representing an intended prosody. Each wordpiece embedding is associated with one of the one or more words of the text utterance. For each syllable, using the selected utterance embedding and a prosody model that incorporates the BERT model, the method also includes generating a corresponding prosodic syllable embedding for the syllable based on the wordpiece embedding associated with the word that includes the syllable and predicting a duration of the syllable by encoding linguistic features of each phoneme of the syllable with the corresponding prosodic syllable embedding for the syllable… see abstract. Any inquiry concerning this communication or earlier communications from the examiner should be directed to XUYANG XIA whose telephone number is (571)270-3045. The examiner can normally be reached Monday-Friday 8am-4pm. 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, Jennifer Welch can be reached at 571-272-7212. 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. XUYANG XIA Primary Examiner Art Unit 2143 /XUYANG XIA/Primary Examiner, Art Unit 2143 Application/Control Number: 18/436,799 Page 2 Art Unit: 2143 Application/Control Number: 18/436,799 Page 3 Art Unit: 2143 Application/Control Number: 18/436,799 Page 4 Art Unit: 2143 Application/Control Number: 18/436,799 Page 5 Art Unit: 2143 Application/Control Number: 18/436,799 Page 6 Art Unit: 2143 Application/Control Number: 18/436,799 Page 7 Art Unit: 2143 Application/Control Number: 18/436,799 Page 8 Art Unit: 2143 Application/Control Number: 18/436,799 Page 9 Art Unit: 2143 Application/Control Number: 18/436,799 Page 10 Art Unit: 2143 Application/Control Number: 18/436,799 Page 11 Art Unit: 2143 Application/Control Number: 18/436,799 Page 12 Art Unit: 2143 Application/Control Number: 18/436,799 Page 13 Art Unit: 2143 Application/Control Number: 18/436,799 Page 14 Art Unit: 2143 Application/Control Number: 18/436,799 Page 15 Art Unit: 2143 Application/Control Number: 18/436,799 Page 16 Art Unit: 2143 Application/Control Number: 18/436,799 Page 17 Art Unit: 2143 Application/Control Number: 18/436,799 Page 18 Art Unit: 2143 Application/Control Number: 18/436,799 Page 19 Art Unit: 2143
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