Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 102
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 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 –
(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.
Claim(s) 1-6, 8-18 and 20, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dalli (US 20220198254 A1).
Regarding claim 1, Dalli teaches a method of machine learning inferencing, the method comprising (Dalli [24, 61, 100] method for inferencing may be performed by system processor executing instructions stored in medium):
accessing, via a computer, raw data comprising data elements (Dalli [89, 147, 275] input data is received, input data may be sequential sensor (raw) data points (elements));
producing, via the computer, a respective positional encoding vector for each of the data elements, the producing comprising computing coefficients using a discrete functional transform on a sequence of the data elements in the raw data (Dalli [90, 91, 275] discrete functional transform may be applied to input datapoint sequence to determine positional encoding vectors);
producing, via the computer, one or more representational encoding vectors based upon the positional encoding vectors and that represent the raw data(Dalli [6, 121, 130, 289, 303] based on token embeddings (represent the raw data) and positional encoding vectors, encoder may generate encoded output vectors with positional information);
inputting, via the computer, the one or more representational encoding vectors into a neural network; and in response to the inputting, receiving, via the computer, output from the neural network, the output comprising an inference related to the raw data (Dalli [93, 100] neural network uses encoded output vectors to generate inferences, predictions and explanations regarding the data points).
Regarding claim 2, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches setting a local function
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for s = 0, ..., d - 1; and computing an expansion coefficient of
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on a complete discrete basis set {Φ₀, Φd-₁} with a sequence length d; wherein the producing of the positional encoding vector is carried out by arranging the computed expansion coefficients as a vector (Dalli [6, 7, 90, 91] local function for discrete functional transform may return vector of coefficients or weights (expansion coefficients) for sequence).
Regarding claim 3, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein, in the step of producing the positional encoding vector, each corresponding positional encoding vector is represented by e (s) and is based on:
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, are coefficients of the corresponding positional encoding vector (Dalli [6, 7, 90, 91] local function for discrete functional transform may return vector of coefficients or weights (expansion coefficients) for sequence, coefficients are used for positional encoding vector).
Regarding claim 4, Dalli teach(es) the invention as claimed in claim 3 above.
Dalli further teaches wherein each corresponding positional encoding vector has a count of elements equal to a sequence length, d, of the raw data and wherein K is set to (d/2)-1 (Dalli [6, 7, 90, 91, 119, 124, 126, 127, 150-152] transforms are for partitions which may be based on data points , partitions may be for sliding windows and splits based on sequence length being considered).
Regarding claim 5, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein a sequence of the raw data comes from one or more sensors (Dalli [89, 147, 275] input data is received, input data may be sequential sensor (raw) data points (elements)).
Regarding claim 6, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein the inference is a times-series classification (Dalli [275] inference may be classification for time series points or intervals).
Regarding claim 8, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein the inference comprises natural language processing (Dalli [4] inference may be based on natural language processing).
Regarding claim 9, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein the inference comprises speech recognition (Dalli [207] inference may be based on speech recognition).
Regarding claim 10, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein the inference comprises text-to-speech transformation (Dalli [213] inference may be based on text-to-speech recognition).
Regarding claim 11, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein the discrete functional transform is selected from the group consisting of a Fourier transform… and a Hadamard transform (Dalli [90] functional transform may be discrete and may be Fourier or Hadamard).
Regarding claim 12, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein the discrete functional transform is Fourier transform (Dalli [90] functional transform may be discrete and may be Fourier
Claim 13 is directed towards a computer program product storing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale. Dalli further teaches a computer program product, comprising: one or more tangible computer-readable storage media and program instructions stored on at least one of the one or more tangible computer-readable storage media, the program instructions executable by a processor to cause the processor to (Dalli [24, 61, 100] method for inferencing may be performed by system processor executing instructions stored in medium).
Claim 14 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 1, and is rejected under the same rationale. Dalli further teaches a system comprising: a memory; and at least one processor, coupled to said memory, and operative to perform operations (Dalli [24, 61, 100] method for inferencing may be performed by system processor executing instructions stored in medium).
Claim(s) 15, 16, 17, 18 and 20 is/are dependent on claim 14 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 2, 3, 5, 6, and 8 respectively, and is/are rejected under the same rationale.
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 7, 19, are rejected under 35 U.S.C. 103 as being unpatentable over Dalli (US 20220198254 A1), in view of Zellhofer (US 20250002300 A1).
Regarding claim 7, Dalli teach(es) the invention as claimed in claim 1 above.
Dalli further teaches wherein the inference predicts an anomalous event of …a… system …based on sensor data… (Dalli [89, 147, 275] input data is received, input data may be sequential sensor (raw) data points (elements), Dalli [93, 100] neural network uses encoded output vectors to generate inferences, predictions and explanations regarding the data points, Dalli [275, 302] inference may be to predicting anomalies).
Dalli does not specifically teach wherein the inference predicts an anomalous event of an elevator system.
However Zellhofer teaches wherein the inference predicts an anomalous event of an elevator system (Zellhofer Abstract [3, 18-24, 37, 85, 92] model uses fourier transforms on sensor data to predict anomalies in elevators based on installation errors
Zellhofer [111-112] allows to schedule maintenance operations and monitoring).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Zellhofer of wherein the inference predicts an anomalous event of an elevator system, into the invention suggested by Dalli; since both inventions are directed towards inferences predicting an anomalous event of …a… system …based on sensor data…, and incorporating the teaching of Zellhofer into the invention suggested by Dalli would provide the added advantage of allowing scheduling of elevator maintenance operations and monitoring, and the combination would perform with a reasonable expectation of success (Zellhofer Abstract [3, 18-24, 37, 85, 92, 111-112).
Claim(s) 19 is/are dependent on claim 14 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 7, and is/are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cited in IDS dated 5/17/2024.
Phuong et al “Formal Algorithms for Transformers” discloses positional encoding for sequential input, dated 2022 and retrieved from arXiv:2207.09238v1.
Lin et al “Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction”, discloses temporal embeddings, Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4241–4248, dated 2021.
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SANCHITA ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146
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