DETAILED ACTION
Notice of Pre-AIA or AIA Status
01. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
02. Applicant’s claim for domestic priority under 35 U.S.C. 119(e) is acknowledged.
Drawings
03. The drawings were received on 11/04/2024. These drawings are accepted.
Claim Objections
04. Claim 20 is objected to because if depends on claim 16, but was likely intended to depend on claim 17. Claim 20 recites a media claim whereas claim 16 recites a system claim, and based on the numbering of the claims, it would appear as though claim 20 was intended to depend on claim 17.
Claim Rejections - 35 USC § 101
05. 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.
06. Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more. The claims are directed to applying a transformation to data, which amounts to an abstract idea, as explained in detail below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Step 1: The claim (claim1) recites a method which recites a series of acts for applying a transformation to determine a representation of data. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
Step 2A, prong one: The claim (claim 1) recites the limitation of “applying…a frequency domain transformation to the set of time-series information to obtain a frequency-domain representation of the set of time-series information comprising a plurality of frequency components”. This claimed limitation is a mathematical calculation in that data is converted from one form/domain into another by way of mathematical calculations. Therefore, this claim limitation describes a purely mathematically calculated “statistic”.
If a claim limitation, under its broadest reasonable interpretation, covers a mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, prong two: The judicial exception is not integrated into a practical application.
The claim recites the additional element of “obtaining…a set of time-series information descriptive of one or more events occurring within a particular period of time”, which represents mere data gathering that is necessary for use of the recited judicial exception and is recited at a high level of generality. This limitation in the claim is thus insignificant extra-solution activity.
The claim recites the additional element of “determining…a dimensionally-reduced frequency-domain representation of the set of time-series information based at least in part on the first subset of frequency components of the plurality of frequency components”. This limitation represents the environment in which the judicial exception is used, in that a determination is performed to represent the obtained data. This element is thus a mere indication of the field of use or technological environment in which the judicial exception is performed. This limitation therefore represents a field of use or mere data gathering that is necessary for use of the recited judicial exception.
Even when viewed in combination, the additional elements in this claim do no more than obtain data, which is performed on generic computing components (e.g. a computing system, processor devices). This does not provide an improvement to the computers and other technology that are recited in the claim. Thus this claim cannon improve computer functionality or other technology.
Step 2B: As discussed previously with respect to Step 2A prong two, the controller in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, but are instead limited to appending well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (abstract idea).
In this instance, the claims includes dimensionally-reduced frequency-domain representation of the data. However, this is a general component of a (frequency domain) transformation, as the data’s representation changes during this process. Therefore this claim limitation is understood to be well-understood, routine, and conventional activity, which can be performed by generic computing components.
The claims also recite that the method includes a computing system comprising one or more processor devices. However, these are merely a generic computer components, and does not impose any meaningful limit on the computer implementation of the abstract idea. Therefore this claim limitation is understood to be well-understood, routine, and conventional activity, which can be performed by generic computing components.
The same analysis is applied to dependent claims 2 – 10, because the limitations recite additional mental processes and/or mathematical calculations and do not integrate into a practical application. Further, they do not include additional elements that amount to significantly more.
Claim 2 includes the additional element of the transformation being a Fast Fourier Transform. However, this is just a specific type of frequency domain transformation, and does not add anything significantly more to the abstract idea.
Claim 3 includes the additional element of selecting a subset of components based on a value. This is generally understood to be data gathering, as different components are first obtained and then some of them are selected. This does not add anything significantly more to the abstract idea.
Claim 4 includes the additional element of performing a pairwise join of number pairs. This is a mathematical calculation, similar to the frequency domain transformation, in that an operation is performed on data. Therefore, this merely adds to the, or includes an additional, mathematical calculation and does not add anything significantly more to the abstract idea.
Claim 5 includes the additional claim limitation of mapping the representation to a location within an embedding space. However, this is merely indicating the technological environment in which the judicial exception is applied to, and does not amount to significantly more than the abstract idea.
Claim 6 includes the additional claim limitations of mapping a vector representation to a query and selecting a representation. Mapping a vector representation is a further recitation of an abstract idea as a comparison of data and is insignificant extra-solution activating. Thus, the claim recites only further abstract ideas or limitations that do not provide significantly more or integrate the abstract idea into a practical application.
Claim 7 recites the additional claim limitation of providing search result information. This is understood to be data gathering. Specifying what type of data is obtained, which in this instance is search result information, does not render the idea of obtaining data any less abstract. This limitation is thus insignificant extra-solution activity.
Claim 8 recites the additional claim limitation of determining an average value based on identified values in an array. This is all part of the above identified abstract idea of a mathematical calculation, as this is merely an additional step of a calculation. Therefore this limitation does not add significantly more to the abstract ideas.
Claim 9 recites the additional claim limitation of performing a stationary test to the data. This is all part of the above identified abstract idea of a mathematical calculation, as this is merely an additional mathematical function to apply to the obtained data. Therefore this limitation does not add significantly more to the abstract ideas.
Claim 10 recites the additional claim limitation of applying a dimensionality reduction process to the information. However, this recites a similar embodiment to that found in claim 2, which is a Fast Fourier Transformation. The same analysis is applied therein.
Claims 11 – 20 recite the same embodiments found in claim 1 – 10 and the same analysis is applied therein. Some of the claims are a combination of other claims; for example, claim 11 recites embodiments that are found in claims 1, 2, 3, and 4 combined, but they are otherwise the same claimed limitations. The same rejections and analysis is applied there in claims 11 – 20.
Claim Rejections - 35 USC § 102
07. 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 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.
08. 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.
09. Claims 1, 8 – 10, 17, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Inagaki (US PGPub 2011/0170777), hereinafter “Inagaki”.
Consider claim 1, Inagaki discloses a method comprising:
obtaining, by a computing system comprising one or more processor devices, a set of time-series information descriptive of one or more events occurring within a particular period of time (paragraphs [0006], [0026], [0069], [0070], time-series data is obtained, which includes data values that are ordered by time, such as by sampling intervals, meaning they are for events that occur at particular and specific time);
applying, by the computing system, a frequency domain transformation to the set of time-series information to obtain a frequency-domain representation of the set of time-series information comprising a plurality of frequency components (paragraphs [0070] – [0073], [0156], [0186], a transformation is applied to the time-series data, which causes a frequency domain representation of the time-series data to be determined, which can be based on a frequency distribution);
determining, by the computing system, a dimensionally-reduced frequency-domain representation of the set of time-series information based at least in part on a first subset of frequency components of the plurality of frequency components (paragraphs [0045], [0059], [0060], [0162], the time-series data has a dimensionality reduction applied to it, which is based on a frequency of the data values present in the time-series).
Consider claim 8, and as applied to claim 1 above, Inagaki discloses a method comprising:
the set of time-series information comprises an array of values (paragraphs [0039], [0041], a vector is used, which is a type of array);
wherein, prior to applying the frequency domain transformation to the set of time-series information, the method comprises:
identifying, by the computing system, a first value of the array of values as being a null value (paragraphs [0103], [0123], the value of the data in the vectors can be assigned to zero);
determining, by the computing system, an average value based on values located prior to the first value within the array of values and and/or values located subsequent to the first value within the array of values (paragraph [0069], an average value can be determined for the values in the vector);
replacing, by the computing system, the first value with the average value (paragraphs [0069], [0095], [0165], the obtained values are replaced based on values that are calculated with the different functions).
Consider claim 9, and as applied to claim 1 above, Inagaki discloses a method comprising:
prior to applying the frequency domain transformation to the set of time-series information, the method comprises: performing, by the computing system, a data stationarity test to determine that the set of time-series information is stationary (paragraphs [0142], [0185], [0216], the values that are transformed may be determined to have not changed as a result of the transformation).
Consider claim 10, and as applied to claim 1 above, Inagaki discloses a method comprising:
applying, by the computing system, one or more dimensionality reduction processes to the set of time-scries information, wherein the one or more dimensionality reduction processes comprises at least one of: a FFT; a Principal Component Analysis (PCA); or an Exponential Moving Average (EMA) (paragraphs [0045], [0071], the dimensionality process can be a Fast Fourier Transformation or a Principal Component Analysis).
Consider claim 17, Inagaki discloses one or more non-transitory computer-readable media that store instructions that, when executed by one or more processors of the computing system, cause the operating system to perform operations, the operations comprising (paragraphs [0234] – [0236], computing hardware is used, including media and processors);
obtaining a set of time-series information descriptive of one or more events occurring within a particular period of time (paragraphs [0006], [0026], [0069], [0070], time-series data is obtained, which includes data values that are ordered by time, such as by sampling intervals, meaning they are for events that occur at particular and specific time);
using a Fast Fourier Transform (FFT) to convert the set of time-series information to a frequency-domain representation of the set of time-series information comprises a plurality of frequency components) (paragraphs [0070] – [0073], [0156], [0186], a Fast Fourier transformation is applied to the time-series data, which causes a frequency domain representation of the time-series data to be determined, which can be based on a frequency distribution);
determining a vector representation of the frequency-domain representation of the set of time-series information (paragraphs [0045], [0059], [0060], [0126], [0162], a vector representation is determined, which is based on the time-series data that is obtained).
Claims 19 – 20 recite the same embodiments as those found in claims 3 – 5, except that either a medium or method is claimed. Since the same claim limitations are otherwise present, the claims have been rejected under the same rational provided above.
Claim Rejections - 35 USC § 103
10. 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 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.
11. 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 of this title, 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.
12. Claims 2 – 7, 11 – 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Inagaki (US PGPub 2011/0170777), hereinafter “Inagaki”, in view of Chhetri et al. (US Patent 11,521,635), hereinafter “Chhetri”.
Consider claim 2, and as applied to claim 1 above, Inagaki discloses a method comprising:
applying, by the computing system, a Fast Fourier Transform (FFT) to the set of time-series information to obtain the frequency-domain representation of the set of time-series information comprising the plurality of frequency components (paragraphs [0070], [0071], [0089], [0156], a Fast Fourier Transform is applied to the time-series data in order to obtain a frequency domain representation of the data, which can be ordered by frequency).
However, Inagaki does not disclose complex number pairs.
In the same field of endeavor, Chhetri discloses a method comprising:
each of the plurality of frequency components comprises a complex number pair of a corresponding plurality of complex number pairs (column 15 line 52 – column 16 line 6, column 16 lines 34 – 51, column 20 lines 4 – 25, time-series data is made up of vectors that comprise complex numbers).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the complex number pairs taught by Chhetri into the Fourier Transformation taught by Inagaki for the purpose of allowing more advanced storage of the data values to be stored so that different types of data comparisons and operations can be applied to the data, so that additional information can be gathered from the time-series data.
Consider claim 3, and as applied to claim 2 above, Inagaki discloses a method comprising:
selecting, by the computing system, the first subset of frequency components based on a frequency value of each of the first subset of frequency components, wherein the frequency value of each of the first subset of frequency components is less than a threshold frequency value (paragraphs [0066] – [0069], [0089], [0121], frequency values are determined for the values, such that they can be placed in groups based on the frequency values).
Consider claim 4, and as applied to claim 3 above, Inagaki discloses a method comprising:
performing, by the computing system, a pairwise join to each of the complex number pairs of the first subset of frequency components to obtain the dimensionally-reduced frequency-domain representation of the set of time-series information, wherein the dimensionally-reduced frequency-domain representation comprises a one-dimensional vector of real numbers (paragraphs [0030], [0125], [0126], [0165], the values obtained undergo the dimensionality reduction process, which can include putting the values into a one dimensional vector of the values, such that values can be combined or reduced into different groupings based on the reduced dimensionality process that is performed on the values).
Consider claim 5, and as applied to claim 1 above, Inagaki discloses the claimed method except that an embedding space is utilized.
In the same field of endeavor, Chhetri discloses a method comprising:
mapping, by the computing system, the dimensionally-reduced frequency-domain
representation to a location within an embedding space (column 17 line 64 – column 18 line 10, column 19 lines 15 – 34, an embedding space is used for the dimensional data that is in the frequency domain representation).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the embedding space taught by Chhetri into the Fourier Transformation taught by Inagaki for the purpose of allowing the space to be able to store more complex data and in a transformed from, so that additional operations could be applied to it to obtain more statistics and accessibility.
Consider claim 6, and as applied to claim 5 above, Chhetri discloses a method comprising:
mapping, by the computing system, a vector representation of a query to the embedding space (column 17 line 64 – column 18 line 10, column 19 lines 15 – 34, an embedding space is used for a query);
and Inagaki discloses a method comprising:
selecting, by the computing system, the dimensionally-reduced frequency-domain representation of the set of time-series information based on a difference between the dimensionally-reduced frequency-domain representation and the vector representation of the query within the embedding space (paragraphs []0124], [0169], a query is used that selects the dimensionally reduced time series data, such that a vector is used for the representation of the query).
Consider claim 7, and as applied to claim 6 above, Inagaki discloses a method comprising:
providing, by the computing system, search result information comprising one or more of: (a) the dimensionally-reduced frequency-domain representation of the set of time-series information; (b) at least a portion of the set of time-series information; or (c) information descriptive of the set of time-series information (paragraphs [0060], [0154], [0160], the data is dimensionally reduced, which is used in order to process a query and to obtain results).
Consider claim 11, Inagaki discloses a computing system comprising:
one or more processors; and one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising (paragraphs [0234], [0238], computing hardware is used for the system, including processors and media);
obtaining a set of time-series information descriptive of one or more events occurring within a particular period of time (paragraphs [0006], [0026], [0069], [0070], time-series data is obtained, which includes data values that are ordered by time, such as by sampling intervals, meaning they are for events that occur at particular and specific time);
applying a Fast Fourier Transform to the set of time-series information to obtain a frequency-domain representation of the set of time-series information comprising a plurality of frequency components… (paragraphs [0070] – [0073], [0156], [0186], a transformation is applied to the time-series data, which causes a frequency domain representation of the time-series data to be determined, which can be based on a frequency distribution);
selecting a first subset of frequency components from the plurality of frequency components based on a frequency value of each of the first subset of frequency components, wherein the frequency value of each of the first subset of frequency components is less than a threshold frequency value (paragraphs [0066] – [0069], [0089], [0121], frequency values are determined for the values, such that they can be placed in groups based on the frequency values, wherein the frequency value is used as a determining means for obtaining and storing the values);
performing a pairwise join to each of the [complex number pairs] to the first subset of frequency components to obtain the dimensionally-reduced frequency-domain representation of the set of time-series information, wherein the dimensionally-reduced frequency-domain representation comprises a one-dimensional vector of real numbers (paragraphs [0030], [0059], [0125], [0165], the values obtained undergo the dimensionality reduction process, which can include putting the values into a one dimensional vector of the values, such that values can be combined or reduced into different groupings based on the reduced dimensionality process that is performed on the values).
However, Inagaki does not specifically disclose complex numbers.
In the same field of endeavor, Chhatri disclose a system comprising:
wherein each of the plurality of frequency components comprises a complex number pair of a corresponding plurality of complex number pairs (column 15 line 52 – column 16 line 6, column 16 lines 34 – 51, column 20 lines 4 – 25, time-series data is made up of vectors that comprise complex numbers that are determined based on their respective frequency).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the complex number pairs taught by Chhetri into the Fourier Transformation taught by Inagaki for the purpose of allowing more advanced storage of the data values to be stored so that different types of data comparisons and operations can be applied to the data, so that additional information can be gathered from the time-series data.
Claims 12 – 16 recite the same limitations as those found in claims 5 – 9, respectively, and have been rejected under the same rational provided above.
Consider claim 18, and as applied to claim 17 above, Inagaki discloses the claimed method except for complex numbers.
In the same field of endeavor, Chhetri discloses a method comprising:
each of the plurality of frequency components comprises a complex number pair of a corresponding plurality of complex number pairs (column 15 line 52 – column 16 line 6, column 16 lines 34 – 51, column 20 lines 4 – 25, time-series data is made up of vectors that comprise complex numbers).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the complex number pairs taught by Chhetri into the Fourier Transformation taught by Inagaki for the purpose of allowing more advanced storage of the data values to be stored so that different types of data comparisons and operations can be applied to the data, so that additional information can be gathered from the time-series data.
Relevant Prior Art Directed to State of Art
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cooper et al. (US PGPub 2024/0419329) discloses a method of using a Fast Fourier Transform to extract frequency-domain features from time-series signals. This allows for making determinations about the obtain data values and being able to perform operations on them.
Conclusion
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15. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Christopher Raab whose telephone number is (571) 270-1090. The Examiner can normally be reached on Monday-Friday from 9:00am to 5:00pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Ajay Bhatia can be reached on (571) 272-3906. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist/customer service whose telephone number is (571) 272-2600.
/CHRISTOPHER J RAAB/Primary Examiner, Art Unit 2156
January 10, 2026