CTNF 18/476,076 CTNF 94574 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. DETAILED ACTION This action is a responsive to the application filed on 09/27/2023. Claims 1-10 are pending. Claims 1-10 are rejected. Claim Objections 07-29-01 AIA Claim s 1 and 9-10 are objected to because of the following informalities: Claims 1 and 9-10 recite a typo, “training sequence in which a prespecified event takes at least once at one or more respective event time points” and an optional way to amend for correcting this includes “training sequence in which a prespecified event takes occurs at least once at one or more respective event time points”, or “training sequence in which a prespecified event takes place at least once at one or more respective event time points” . Appropriate correction is required. 07-30-03-h AIA Claim Interpretation 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. 07-30-06 This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “ a training device configured to ” perform the steps of claim 9 Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Further, the limitations of “module[s]” for the above corresponding operations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, however, applicant’s pages 7 and 11 recite sufficient structure stating a “a corresponding training device (e.g., which can be the vehicle control device 102”; and “The vehicle control device 102 comprises data-processing components, e.g. a processor (e.g. a CPU (central unit)) 103 and a memory 104 for storing control software, according to which the vehicle control device 102 operates, and data processed by the processor 103.” Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim 7 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. 07-34-05 Claim 7 recites the limitation “ the weighting factor ” with insufficient antecedent basis for this limitation in the claim. 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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 9-10 are respectively drawn to a system, method, and non-transitory computer readable storage medium, hence each falls under one of four categories of statutory subject matter (Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significantly more. Claims 1 and 9-10 recite the following, or analogous, limitations “ determining a plurality of training sequences of training-input data elements, wherein for each training sequence of the training sequences, each of the training- input data elements contains…data for a time point from a time period assigned to the training sequence in which a prespecified event takes at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains…data and one of the one or more respective event time points; ”. These limitations, as claimed, under its broadest reasonable interpretation, can be evaluated in a human mind, except for the recitation of generic computer components (using artificial intelligence/machine learning, a computer including one or more microprocessors, and a non-transitory computer readable storage medium) (Step 2A). Other than reciting “ training a machine learning model ”, “ training device ”, “ training the machine learning model depending on the determined temporal distances ”, “ sensor ”, “ non-transitory computer-readable medium ”, and “ a processor ” to perform the exceptions, nothing in the claims preclude the steps from practically being performed in the human mind. For example, a human expert can: mentally/with the aid of pen and paper determining a plurality of training sequences of training-input data elements, wherein for each training sequence of the training sequences, each of the training- input data elements contains…data for a time point from a time period assigned to the training sequence in which a prespecified event takes at least once at one or more respective event time points (e.g. by thinking of/writing out data samples within a time window of a specific occurrence for tuning a calculations coefficients), mentally/with the aid of pen and paper determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains…data and one of the one or more respective event time points (e.g. by thinking of/writing out a determination of space between each data sample within the specific time window). Thus, the claims recite a mental process (Step 2A, Prong 1). Claims 1 and 9-10 include additional elements, “ training a machine learning model ”, “ training device ”, “ training the machine learning model depending on the determined temporal distances ”, “ sensor ”, “ non-transitory computer-readable medium ”, and “ a processor ”, however the recitations of these elements are at a high level of generality, and adding the words “apply it” (or an equivalent) with the judicial exception (i.e., “training a machine learning model”, “training the machine learning model depending on the determined temporal distances”, “by a computing system”, and “the user-generated text entered by a user through an electronic user interface”), or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., “training device”, “sensor”, “non-transitory computer-readable medium”, and “a processor”) ( see MPEP 2106.05(f)). Hence, each of the additional limitations or in combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional elements in the claim do not amount to significantly more than an abstract idea. Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “ training a machine learning model ”, “ training device ”, “ training the machine learning model depending on the determined temporal distances ”, “ sensor ”, “ non-transitory computer-readable medium ”, and “ a processor ” to perform the steps of the independent claims amounts to no more than mere adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as this cannot provide an inventive concept. (STEP 2B). As such, claims 1 and 9-10 are not patent eligible. Dependent claims 2-8 are also ineligible for the same reasons given with respect to claims 1 and 9-10. The dependent claims describe additional mental processes: mentally/with the aid of pen and paper determining, for each training-input data element, a target output of the…model; supplying the training-input data elements to the…model, and determining a loss which, for each training-input data element, includes a deviation between an output of the…model for the training-input data element and the target output determined for the training-input data element, wherein, for each training-input data element, the deviation in the loss is weighted with a weighting factor which depends on the temporal distance determined for the training-input data element (claim 2) (e.g. by mentally/writing out an error between predicted and expected values, including a bias variable based on the determined samples spaces, from the calculation based on the input data samples) mentally/with the aid of pen and paper wherein the lower the value of the temporal distance determined for the training-input data element, the greater the weighting factor is (claim 3) (e.g. by mentally/writing out an inverse computation between the bias variable and the determined sample spaces) mentally/with the aid of pen and paper wherein the weighting factor depends on whether the time point for which the training- input data element contains…data lies before or after the one of the one or more respective event time points (claim 4) (e.g. by mentally/writing out the bias variable is based on data samples occurring prior to or after the specific occurrence) mentally/with the aid of pen and paper wherein training-input data elements are selected from the training-input data elements, wherein for each training-input data element, a probability of the training-input data element being selected is dependent on the temporal distance determined for the training-input data element (claim 5) (e.g. by mentally/writing out selecting data samples from the window based on a likelihood computed from the determined sample spaces) mentally/with the aid of pen and paper wherein the lower the value of the temporal distance determined for the training-input data element, the greater the probability is (claim 6) (e.g. by mentally/writing out an inverse computation between the bias variable and the determined sample spaces) mentally/with the aid of pen and paper wherein the weighting factor depends on whether the time point for which the training- input data element contains…data lies before or after the one of the one or more respective event time points (claim 7) (e.g. by mentally/writing out the bias variable is based on data samples occurring prior to or after the specific occurrence) mentally/with the aid of pen and paper wherein for a training sequence to which a time period is assigned in which the predetermined event occurs several times, the temporal distance of a training-input data element of the training sequence between the time point for which the training-input data element contains…data and the event time point closest to the time point for which the training-input data element contains…data is determined (claim 8) (e.g. by mentally/writing out the specific occurrence frequency is greater than one within a determined time window and computing the sample spaces around the occurrence) Again, the dependent claims continued to cover the performance of the limitation in the mind as inherited from the independent claims (Step 2A, Prong 1). The dependent claims 2 recitation of “ machine learning ” and “ training the neural network to reduce the loss ”, claims 4 and 7-8 recitation of “ sensor ”, claims 5 recitation of “ and the machine learning model is trained using the selected training-input data elements ” is again recited at a high level and adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2; see MPEP 2106.05(h)). The additional element in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than mere instructions to apply the exception using generic computer components and adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. (STEP 2B). As such, dependent claims 2-8 do not amount to significantly more than an abstract idea nor provide any inventive concept, therefore are not patent eligible. 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 set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kaifosh et al (US Pub 20200046265) hereinafter Kaifosh, in view of Hashemi et al (“Weighted Machine Learning for Spatial-Temporal Data”, 2020) hereinafter Hashemi . Regarding claims 1 and 9-10, Kaifosh teaches a method for training a machine learning model; a training device configured to train a machine learning model, the training device configured to; non-transitory computer-readable medium on which are stored commands for training a machine learning model, the commands, when executed by a processor (paragraphs 0085-0091 and 0128teach one or more memories communicatively coupled to one or more processors to execute program instructions for performing the embodiments of the disclosure) , causing the processor to perform the following steps: determining a plurality of training sequences of training-input data elements, wherein for each training sequence of the training sequences, each of the training-input data elements contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes at least once at one or more respective event time points (paragraphs 0085, 0103, and 0105 teach an iterative training process, wherein “[a]fter the plurality of spike events have been detected, process 500 proceeds to act 512, where the detected spike events ( prespecified ) are clustered, based on their spatiotemporal characteristics, to identify spike events likely arising from the same biological source. Clustering of spike events may occur in any suitable way. In one simplistic example of clustering, a window (e.g., a 10 ms window) around each of the peaks of the spike events ( prespecified ) may be used to define the temporal bounds of the event ( respective event time points ). Each spike event may then be defined as a vector of values, where each vector includes N×M samples, where N corresponds to the number of samples in the window for the event, and M corresponds to the number of neuromuscular sensors ( training-input data elements contains sensor data for a time point from a time period )…some embodiments employ neural networks to detect spike event data, and the labeled data output from act 512 in process 500 may be used to train the neural network”) ; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points (paragraphs 0085, 0102-0103, and 0105 teach detecting spike events in sensor channel data, where “clustering, a window (e.g., a 10 ms window) around each of the peaks of the spike events may be used to define the temporal bounds of the event ( temporal distance ). Each spike event may then be defined as a vector of values, where each vector includes N×M samples, where N corresponds to the number of samples in the window for the event, and M corresponds to the number of neuromuscular sensors”) ; and training the machine learning model depending on the determined temporal distances (paragraphs 0085, 0102-0103, and 0105 teach an iterative neural network training process using labeled spike event data) . Kaifosh at least implies training the machine learning model depending on the determined temporal distances (see mappings above); however, Hashemi teaches training the machine learning model depending on the determined temporal distances (section 3A teaches computing spatial-temporal weighting of training samples based on “temporal distance” accounting for “temporally closer observations are more likely to have similar responses than temporally farther observations” and thus higher weights; further, section 4, pages 3074-3075 teach “if all training samples participate in taking the weighted average, there will be no need to sort the weights of training samples but if one decides to exclude the training samples with very small weights, the weight vector needs to be sorted. The sorting function, which is invoked only if a subset of training samples needs to be deployed, has a greater time complexity than the function that takes the weighted average of training samples’ responses”). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement Hashemi’s teachings of determining training data sample spatial-temporal weights for training machine learning models into Kaifosh‘s teaching of machine learning training on clustered spike window data in order to increase model prediction accuracy from the most temporally relevant training data (Hashemi, section 3A, section 4, pages 3074-3075, and section 5). Regarding claim 2, the combination of Kaifosh and Hashemi teach all the claim limitations of claim 1 above; and further teach wherein the training includes: determining, for each training-input data element, a target output of the machine learning model (Hashemi, section 1 teaches “This study is only concerned with supervised machine learning”, and section 4 and Table 3 teach determining RMSE of the training data output vs expected result) ; supplying the training-input data elements to the machine learning model, and determining a loss which, for each training-input data element, includes a deviation between an output of the machine learning model for the training-input data element and the target output determined for the training-input data element, wherein, for each training-input data element, the deviation in the loss is weighted with a weighting factor which depends on the temporal distance determined for the training-input data element (Hashemi, section 1 teaches “This study is only concerned with supervised machine learning”, and section 4 and Table 3 teach finding highly weighted training samples from temporal distance for training the machine learning model and determining RMSE ( loss ) of the output vs expected result) ; and training the neural network to reduce the loss (Kaifosh, paragraph 0085 teaches “training may employ a cross-entropy loss function and/or any other suitable loss function” for iterative optimization) . Kaifosh and Hashemi are combinable for the same rationale as set forth above with respect to claims 1 and 9-10. Regarding claim 3, the combination of Kurutach and Rajaretnam teach all the claim limitations of claim 2 above; and further teach wherein the lower the value of the temporal distance determined for the training-input data element, the greater the weighting factor is (Hashemi, section 3A teaches computing spatial-temporal weighting of training samples based on “temporal distance” accounting for “temporally closer observations are more likely to have similar responses than temporally farther observations” and thus higher weights, since “spatial-temporal weight is proportional to the inverse of the overall semivariance at specific spatial and temporal distances”) . Kaifosh and Hashemi are combinable for the same rationale as set forth above with respect to claims 1 and 9-10. Regarding claim 4, the combination of Kurutach and Rajaretnam teach all the claim limitations of claim 2 above; and further teach wherein the weighting factor depends on whether the time point for which the training-input data element contains sensor data lies before or after the one of the one or more respective event time points (Hashemi, section 3A teaches computing spatial-temporal weighting of training samples based on “temporal distance” accounting for “temporally closer observations are more likely to have similar responses than temporally farther observations” and thus higher weights measured in “a straight line” for the temporal lags from the observed data point; further, section 4, pages 3074-3075 teach “if all training samples participate in taking the weighted average, there will be no need to sort the weights of training samples but if one decides to exclude the training samples with very small weights, the weight vector needs to be sorted. The sorting function, which is invoked only if a subset of training samples needs to be deployed, has a greater time complexity than the function that takes the weighted average of training samples’ responses”) . Kaifosh and Hashemi are combinable for the same rationale as set forth above with respect to claims 1 and 9-10. Regarding claim 5, the combination of Kurutach and Rajaretnam teach all the claim limitations of claim 1 above; and further teach wherein training-input data elements are selected from the training-input data elements, wherein for each training-input data element, a probability of the training-input data element being selected is dependent on the temporal distance determined for the training-input data element, and the machine learning model is trained using the selected training-input data elements (Kaifosh, paragraphs 0085, 0102-0103, and 0105 teach an iterative neural network training process using labeled spike event data) . However, Kaifosh does not explicitly teach wherein for each training-input data element, a probability of the training-input data element being selected is dependent on the temporal distance determined for the training-input data element . Hashemi teaches wherein for each training-input data element, a probability of the training-input data element being selected is dependent on the temporal distance determined for the training-input data element (section 3A teaches computing spatial-temporal weighting of training samples based on “temporal distance” accounting for “temporally closer observations are more likely to have similar responses than temporally farther observations” and thus higher weights; further, section 4, pages 3074-3075 teach “if all training samples participate in taking the weighted average, there will be no need to sort the weights of training samples but if one decides to exclude the training samples with very small weights, the weight vector needs to be sorted. The sorting function, which is invoked only if a subset of training samples needs to be deployed, has a greater time complexity than the function that takes the weighted average of training samples’ responses”). Kaifosh and Hashemi are combinable for the same rationale as set forth above with respect to claims 1 and 9-10. Regarding claim 6, the combination of Kurutach and Rajaretnam teach all the claim limitations of claim 5 above; and further teach wherein the lower the value of the temporal distance determined for the training-input data element, the greater the probability is (Hashemi, section 3A teaches computing spatial-temporal weighting of training samples based on “temporal distance” accounting for “temporally closer observations are more likely to have similar responses than temporally farther observations” and thus higher weights, since “spatial-temporal weight is proportional to the inverse of the overall semivariance at specific spatial and temporal distances”. Further, section 4, pages 3074-3075 teach “if all training samples participate in taking the weighted average, there will be no need to sort the weights of training samples but if one decides to exclude the training samples with very small weights, the weight vector needs to be sorted. The sorting function, which is invoked only if a subset of training samples needs to be deployed, has a greater time complexity than the function that takes the weighted average of training samples’ responses”) . Kaifosh and Hashemi are combinable for the same rationale as set forth above with respect to claims 1 and 9-10. Regarding claim 7, the combination of Kurutach and Rajaretnam teach all the claim limitations of claim 5 above; and further teach wherein the weighting factor depends on whether the time point for which the training-input data element contains sensor data lies before or after the one of the one or more respective event time points (Hashemi, section 3A teaches computing spatial-temporal weighting of training samples based on “temporal distance” accounting for “temporally closer observations are more likely to have similar responses than temporally farther observations” and thus higher weights measured in “a straight line” for the temporal lags from the observed data point; further, section 4, pages 3074-3075 teach “if all training samples participate in taking the weighted average, there will be no need to sort the weights of training samples but if one decides to exclude the training samples with very small weights, the weight vector needs to be sorted. The sorting function, which is invoked only if a subset of training samples needs to be deployed, has a greater time complexity than the function that takes the weighted average of training samples’ responses”) . Kaifosh and Hashemi are combinable for the same rationale as set forth above with respect to claims 1 and 9-10. Regarding claim 8, the combination of Kurutach and Rajaretnam teach all the claim limitations of claim 1 above; and further teach wherein for a training sequence to which a time period is assigned in which the predetermined event occurs several times, the temporal distance of a training-input data element of the training sequence between the time point for which the training-input data element contains sensor data and the event time point closest to the time point for which the training-input data element contains sensor data is determined (Hashemi, section 3A teaches computing spatial-temporal weighting of training samples based on “temporal distance” accounting for “temporally closer observations are more likely to have similar responses than temporally farther observations” including on the same day and thus higher weights measured in “a straight line” for the temporal lags from the observed data point; further, section 4, pages 3074-3075 teach “if all training samples participate in taking the weighted average, there will be no need to sort the weights of training samples but if one decides to exclude the training samples with very small weights, the weight vector needs to be sorted. The sorting function, which is invoked only if a subset of training samples needs to be deployed, has a greater time complexity than the function that takes the weighted average of training samples’ responses”) . Kaifosh and Hashemi are combinable for the same rationale as set forth above with respect to claims 1 and 9-10. Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Rose et al (US Pub 20220365239) teach time series sensor data and determining the distances between events . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CLINT MULLINAX whose telephone number is 571-272-3241. The examiner can normally be reached on Mon - Fri 8:00-4:30 PT. 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, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /C.M./Examiner, Art Unit 2123 /ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123 Application/Control Number: 18/476,076 Page 2 Art Unit: 2123 Application/Control Number: 18/476,076 Page 3 Art Unit: 2123 Application/Control Number: 18/476,076 Page 4 Art Unit: 2123 Application/Control Number: 18/476,076 Page 5 Art Unit: 2123 Application/Control Number: 18/476,076 Page 6 Art Unit: 2123 Application/Control Number: 18/476,076 Page 7 Art Unit: 2123 Application/Control Number: 18/476,076 Page 8 Art Unit: 2123 Application/Control Number: 18/476,076 Page 9 Art Unit: 2123 Application/Control Number: 18/476,076 Page 10 Art Unit: 2123 Application/Control Number: 18/476,076 Page 11 Art Unit: 2123 Application/Control Number: 18/476,076 Page 13 Art Unit: 2123 Application/Control Number: 18/476,076 Page 14 Art Unit: 2123 Application/Control Number: 18/476,076 Page 15 Art Unit: 2123 Application/Control Number: 18/476,076 Page 16 Art Unit: 2123 Application/Control Number: 18/476,076 Page 17 Art Unit: 2123 Application/Control Number: 18/476,076 Page 18 Art Unit: 2123 Application/Control Number: 18/476,076 Page 19 Art Unit: 2123 Application/Control Number: 18/476,076 Page 20 Art Unit: 2123 Application/Control Number: 18/476,076 Page 21 Art Unit: 2123
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