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 .
Response to Amendment
02. This action is in response to Applicant’s amendment filed on 06/29/2026. Claims 1 – 4, 6 – 11, and 13 – 19 are pending in the present application.
Applicants’ arguments and amendments are considered but are not persuasive.
This action is made final.
Response to Arguments
03. Applicant argues that the claims, as amended, recite more than the abstract idea of performing a mathematical calculation. In view of the claim limitation of mapping the dimensionally-reduced frequency-domain representation to a location within an embedding space, this provides something significantly more than the abstract idea of performing a mathematical calculation, which in this case is frequency domain transformation.
Applicant argues that the prior art references, Inagaki and Chhetri, do not disclose the newly amended claim limitation in claim 1, which recites “mapping, by the computer system, the dimensionally-reduced frequency-domain representation to a location within an embedding space”. Examiner respectfully disagrees. The main argument presented by Applicant is that Chhetri is limited to feeding full, non-reduced frequency domain data, and not reduced frequency-domain data. However, Examiner asserts that the combination of Inagaki and Chhetri disclose this claim limitations.
As stated above, Applicant argues that Chhetri only discloses mapping the non-reduced data, as opposed to the claim language which recites that the reduced data is mapped. Examiner generally agrees with this assertion and interpretation of the prior art. However, Chhetri does still teach that the data is mapped to an embedding space. Therefore, the claimed concept of mapping data to an embedding space is taught by Chhetri. The only difference is that, and as explained above, is that Chhetri teaches mapping non-reduced data. Inagaki discloses that a transformation is applied to the time-series data, and that the dimensionality is reduced based on a frequency of data values present in the time-series. Based on the teachings of each prior art reference, the combined teachings would allow for the data to be reduced (Inagaki) and to have data stored in an embedding space (Chhetri). In other words, neither reference is relied upon to determine all of the claimed limitations in claim 1 (and specifically the “mapping” limitations), but since they individually teach that data can be reduced and that data can be stored in an embedding space, it is an obvious combination between them to allow for the reduced data to be stored in an embedding space.
Applicant also argues that Inagaki does not teach the “determining” step in claim 1. The main argument presented by Applicant here is that Inagaki does not teach that the dimensional reduction is based on a “first subset of frequency components” from the “frequency-domain representation of the set of time-series information”. However, the Applicant’s claims merely recite “frequency components”, without providing any particular definition as to what they are. The frequency components could therefore be any type of component that has an associated frequency, and in particular could be word frequency in a document. Furthermore, Examiner does not agree that Inagaki is limited to only having the frequency of words be a frequency component. For example, the frequency component could be a “frequency distribution” (spectral decomposition) (paragraphs [0081], [0156]). Regardless, the Applicant’s claims do not recite a detailed embodiment that makes it clear that the frequency components could not be keyword frequencies in a document. Examiner suggests expanding upon the definition of the frequency components to encompass a more specific definition as a way to further define the claims and help differentiate over the current prior art.
Claim Rejections - 35 USC § 103
04. 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.
05. 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.
06. Claims 1 – 4, 6 – 11, and 13 – 19 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 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).
However, Inagaki does not disclose 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 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).
And 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).
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 6, and as applied to claim 1 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 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 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 or that an embedding space is utilized.
In the same field of endeavor, Chhetri discloses a method 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);
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 complex number pairs and embedding space 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 13 – 16 recite the same limitations as those found in claims 6 – 9, respectively, and have been rejected under the same rational provided above.
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, 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).
However, Inagaki does not disclose 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 18, and as applied to claim 17 above, 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).
Claim 19 recites the same embodiments as those found in claims 3 and 4, 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.
Conclusion
07. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
08. 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.
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, 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.
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) or 703-305-3028.
/CHRISTOPHER J RAAB/Primary Examiner, Art Unit 2156
September 14, 2026
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