DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on January 30, 2026, for the application with serial number 18/302,820.
Claims 1, 2, 11, and 12 are amended.
Claims 3, 4, 13, and 14 are canceled.
Claims 1, 2, 5, 6, 8, 9, 11, 12, 15, 16, 18, and 19 are pending.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the present claims recite a technical improvement in data processing. See Remarks p. 10. In response, the Examiner submits that the claims merely recite disparate steps for calculating a storage latency window, deduplicating data, and updating records. No apparent improvement in data processing is recited in the claims. Moreover, recitations in the claims merely amount to observations, such as the determination of a storage latency that acknowledges a time lag between a data generation event and actual storage. The redundancy and deduplication do not provide a technological improvement.
35 USC §103 Rejections
Amendments to the independent claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Herbel reference, cited in rejection of the independent claims, below. The Applicant’s arguments are moot in light of the newly cited reference.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
Claim Rejections - 35 USC § 101
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1, 2, 5, 6, 8, 9, 11, 12, 15, 16, 18, and 19 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1, 2, 5, 6, 8, 9, 11, 12, 15, 16, 18, and 19 are all directed to one of the four statutory categories of invention, the claims are directed to updating and deduplicating data (as evidenced by exemplary independent claim 1; wherein the data detection module judges whether the detection data has deduplicated data according to the detection data and the recorded data); abstract ideas. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “generate a changed data records;” “store[ ] the changed data records;” “execute[ ] a detection operation;” “calculate[ ] an adjusted time window of the detection operation by an original time window and an offset time;” “capture[ ] detection data . . . from [a] database;” “judge[ ] whether the detection data has deduplicated data according to the detection data and the recorded data;” “initiate[ ] the deduplicated data;” “obtain[ ] corresponding data initiation information;” “update[ ] the recorded data;” “record[ ] successfully deduplicated data;” “calculates a detection start time;” and “establish[ ] the time window of the detection operation according to detection start time and detection end time.” The steps are all steps for managing personal behavior related to the abstract ideas of updating and deduplicating data that, when considered alone and in combination, are part of the abstract ideas of updating and deduplicating data. The dependent claims further recite steps for updating and deduplicating data that are part of the abstract ideas of updating and deduplicating data. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes maintaining an accurate time series history of data records and removing duplicate data records.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a storage device, processor, and an external business process system in independent claim 1; and a processor and external business process system in independent claim 11). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims require no more than a generic computer (a storage device, processor, and an external business process system in independent claim 1; and a processor and external business process system in independent claim 11) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190012575 A1 to Xiao et al. (hereinafter ‘XIAO’) in view of US 20200004645 A1 to Reed et al. (hereinafter ‘REED’), US 20170082731 A1 to Herbel et al. (hereinafter ‘HERBEL’), US 20220164311 A1 to Das et al. (hereinafter ‘DAS’), and US 20210248130 A1 to Yoshida et al. (hereinafter ‘YOSHIDA’).
Claim 1 (Currently Amended)
XIAO discloses a data processing system, coupled to an external process system and a database (see ¶[0029]-[0031] and Fig. 1; storage system 102 and computing device 104 communicate using network 106, which may comprise a local area network, a wireless network, or the Internet, or any combination thereof, among other examples. Access storage system via network. See also ¶[0105]; a data deduplication engine may be employed by a client that communicates with a server. The server may provide data to and from a client computing device through a network), comprising: a storage device (see ¶[0024]; a storage apparatus for storing one or more programs), used to store a timing module (see ¶[0011 and [0078] and Fig. 6; a timing diagram for updating a deep learning model. Periodically inspect a training data set) and
a data detection module (see abstract; the new training data set being detected by the client in a preset path); and
a processor, coupled to the storage device and used to execute the timing module and the data detection module (see ¶[0024]-[0025]; one or more processors),
XIAO does not specifically disclose, but REED discloses, wherein when the external process system is configured to generate a changed data records with a generation time (see ¶[0035] and [0045]-[0046]; new versions of data sets may be stored at specific time intervals. Provide actual versions of the data set so that a user can ascertain a time window the data was corrupted), the external process system stores the changed data records with the generation time into the database at a storage time, wherein the storage time is later than the generation time (see ¶[0035] and [0045]; new versions of data sets may be stored at specific time intervals. Versions may contain only data that has changed from preceding versions. Determine a timestamp associated with the earliest versions);
XIAO further discloses wherein the timing module outputs a detection command to the data detection module at a preset time, so that the data detection module executes a detection operation (see ¶[0027]; periodically inspect whether a new training data set exists in a preset path),
XIAO does not specifically disclose, but HERBEL discloses, wherein the data detection module calculates an adjusted time window of the detection operation by an original time window and an offset time, and captures detection data comprising the changed data from the database in the adjusted time window of the detection operation, wherein the offset time is configured based on a measured responsiveness of the database representing a time deviation between the generation time and the storage time, wherein the original time window does not cover the generation time but covers the storage time, and the adjusted time window covers the generation time and the storage time (see ¶[0055]; one or more communication delays may be known to occur between generation of the signal at T.sub.o and recording the current time of detection, T′.sub.o. That is to say, the time at which the timing validation system records detection of the signal generation lags the actual time of signal generation. Assume the total time of all such communication delays is known and given by ΔC.sub.1. The analysis component 122 may employ the measured T′.sub.o and known ΔC.sub.1 to determine T.sub.o according to the relationship T.sub.o=T′.sub.o−ΔC.sub.1),
XIAO does not specifically disclose, but REED discloses, wherein a previous time window of a previous detection operation covers the generation time but does not cover the storage time, and the changed data is generated during the previous time window and was not successfully initiated (see again ¶[0046]; take the data set at the time before the corruption, and then apply forward recovery steps to recover the data set).
XIAO does not specifically disclose, but DAS discloses, wherein the data detection module judges whether the detection data has deduplicated data according to the detection data and recorded data (see ¶[0040]; updated deduplication hash table periodically or according to a schedule. Identify changed blocks based at least in in part on a snapshot that was taken when the hash table was last updated), and initiates the deduplicated data to an external process system (see ¶[0029]-[0031] and Fig. 1; storage system 102 and computing device 104 communicate using network 106, which may comprise a local area network, a wireless network, or the Internet, or any combination thereof, among other examples. Access storage system via network. See also ¶[0105]; a data deduplication engine may be employed by a client that communicates with a server. The server may provide data to and from a client computing device through a network), wherein the deduplicated data is the changed data (see again ¶[0040]; updated deduplication hash table periodically or according to a schedule. Identify changed blocks based at least in in part on a snapshot that was taken when the hash table was last updated),
wherein the data detection module obtains corresponding data initiation information from the external process system, and updates the recorded data according to the data initiation information (see again ¶[0105]; a data deduplication engine may be employed by a client that communicates with a server. The server may provide data to and from a client computing device through a network. See also ¶[0017] and [0028]; update file metadata to reference a shared block),
wherein the data detection module records successfully initiated data in the deduplicated data according to the data initiation information to update the recorded data (see ¶[0040]; update deduplication hash table in response to events or a schedule. Update a weak reference or remove a weak reference).
XIAO discloses updating a deep learning model based on periodic updates that relies on data sets (see abstract). REED discloses data corruption source and timeline analysis to analyze data corruption in data sets. It would have been obvious to include the timeline analysis as taught by REED in the system executing the method of XIAO with the motivation to recover corrupt data and prevent corruption from happening (see REED ¶[0004]).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. It would have been obvious to deduplicate data as taught by DAS in the system executing the method of XIOA with the motivation to reduce redundant data in a computer network executing a deep learning model.
The combination of XIAO and DAS does not explicitly disclose, but YOSHIDA discloses, wherein a part of the adjusted time window of the detection operation is overlapped with the previous time window of the previous detection operation (see ¶[0025] and [0039]; compile time-series data with different resolutions for an overlapping period. For example, central historian 130 may compile native resolution data received from embedded historian 110, medium resolution data received from local historian 120, and low resolution data received from local historian 120 for overlapping time periods. Eliminate redundant data in overlapping periods).
XIAO does not specifically disclose, but HERBEL discloses, wherein the data detection module reads first time information representing a completion time of the previous detection operation, calculates a detection start time of the detection operation according to the first time information and offset time information, and uses a current time as a detection end time of the detection operation (see ¶[0055]; one or more communication delays may be known to occur between generation of the signal at T.sub.o and recording the current time of detection, T′.sub.o. That is to say, the time at which the timing validation system records detection of the signal generation lags the actual time of signal generation. Assume the total time of all such communication delays is known and given by ΔC.sub.1. The analysis component 122 may employ the measured T′.sub.o and known ΔC.sub.1 to determine T.sub.o according to the relationship T.sub.o=T′.sub.o−ΔC.sub.1. In certain embodiments, ΔC.sub.1 is a constant value and may be obtained by the analysis component 122 from the data store 114 or a user input. In embodiments where the communication delay between T.sub.o and T′.sub.o is negligible, ΔC.sub.1 may be assumed to be zero and T′.sub.o may be taken to be equal to T.sub.o. For the purposes of the discussion below, it will be assumed that T′.sub.o=T.sub.o. However, if this assumption is not appropriate for a given circumstance, T.sub.o may be replaced with T′.sub.o−Δc.sub.1),
wherein the data detection module establishes the time window of the detection operation according to detection start time and the detection end time (see again ¶[0055]; one or more communication delays may be known to occur between generation of the signal at T.sub.o and recording the current time of detection, T′.sub.o. That is to say, the time at which the timing validation system records detection of the signal generation lags the actual time of signal generation. Assume the total time of all such communication delays is known and given by ΔC.sub.1. The analysis component 122 may employ the measured T′.sub.o and known ΔC.sub.1 to determine T.sub.o according to the relationship T.sub.o=T′.sub.o−ΔC.sub.1. In certain embodiments, ΔC.sub.1 is a constant value and may be obtained by the analysis component 122 from the data store 114 or a user input. In embodiments where the communication delay between T.sub.o and T′.sub.o is negligible, ΔC.sub.1 may be assumed to be zero and T′.sub.o may be taken to be equal to T.sub.o. For the purposes of the discussion below, it will be assumed that T′.sub.o=T.sub.o. However, if this assumption is not appropriate for a given circumstance, T.sub.o may be replaced with T′.sub.o−Δc.sub.1).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. It would have been obvious to deduplicate data as taught by DAS in the system executing the method of XIOA with the motivation to reduce redundant data in a computer network executing a deep learning model.
XIAO discloses updating a deep learning model based on periodic updates. HERBEL discloses timing validation for data fusion that includes accounting for communication delays between time of signal generation and time of signal recording. It would have been obvious for one of ordinary skill in the art at the time of invention to include the accounting for delays in generation and recording as taught by HERBEL in the system executing the method of XIAO with the motivation to provide accurate time data to be used in a deep learning model.
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. YOSHIDA discloses data collection in overlapping time periods that results in redundancies that are eliminated. It would have been obvious for one of ordinary skill in the art at the time of invention to collect data in overlapping periods as taught by YOSHIDA in the system executing the method of XIAO and DAS with the motivation to publish data in a lower resolution in a central historian than a local historian in a network (see YOSHIDA ¶[0025]).
Claim 11 (Currently Amended)
XIAO discloses a method for automatically capturing data, comprising: executing, through a processor (see ¶[0024]-[0025]; one or more processors), a timing module to output a detection command to a data detection module at a preset time, so that the data detection module executes a detection operation (see ¶[0011 and [0078] and Fig. 6; a timing diagram for updating a deep learning model. Periodically inspect a training data set).
XIAO does not specifically disclose, but REED discloses, generating, through the processor (see ¶[0020]; a processor), a changed data record with a generation time (see ¶[0035] and [0045]-[0046]; new versions of data sets may be stored at specific time intervals. Provide actual versions of the data set so that a user can ascertain a time window the data was corrupted);
storing, through the processor, the changed data record with the generation time into a database at a storage time, wherein the storage time is later than the generation time (see ¶[0035] and [0045]; new versions of data sets may be stored at specific time intervals. Versions may contain only data that has changed from preceding versions. Determine a timestamp associated with the earliest versions);
XIAO does not specifically disclose, but HERBEL discloses, executing, through the processor, the data detection module to calculate an adjusted time window of the detection operation by an original time window and an offset time, and capturing, through the data detection module, detection data comprising the changed data from the database in the adjusted time window of the detection operation, wherein the offset time is configured based on a measured responsiveness of the database representing a time deviation between the generation time and the storage time, wherein the original time window does not cover the generation time but covers the storage time, and the adjusted time window covers the generation time and the storage time (see ¶[0055]; one or more communication delays may be known to occur between generation of the signal at T.sub.o and recording the current time of detection, T′.sub.o. That is to say, the time at which the timing validation system records detection of the signal generation lags the actual time of signal generation. Assume the total time of all such communication delays is known and given by ΔC.sub.1. The analysis component 122 may employ the measured T′.sub.o and known ΔC.sub.1 to determine T.sub.o according to the relationship T.sub.o=T′.sub.o−ΔC.sub.1),
XIAO does not specifically disclose, but REED discloses, wherein a previous time window of a previous detection operation covers the generation time but does not cover the storage time, and the changed data is generated during the previous time window and was not successfully initiated (see again ¶[0046]; take the data set at the time before the corruption, and then apply forward recovery steps to recover the data set).
XIAO does not specifically disclose, but DAS discloses, judging, through the data detection module, whether the detection data has deduplicated data according to the detection data and recorded data (see ¶[0040]; updated deduplication hash table periodically or according to a schedule. Identify changed blocks based at least in in part on a snapshot that was taken when the hash table was last updated), and initiating the deduplicated data to an external process system (see ¶[0029]-[0031] and Fig. 1; storage system 102 and computing device 104 communicate using network 106, which may comprise a local area network, a wireless network, or the Internet, or any combination thereof, among other examples. Access storage system via network. See also ¶[0105]; a data deduplication engine may be employed by a client that communicates with a server. The server may provide data to and from a client computing device through a network), wherein the deduplicated data is the changed data (see again ¶[0040]; updated deduplication hash table periodically or according to a schedule. Identify changed blocks based at least in in part on a snapshot that was taken when the hash table was last updated),
obtaining, through the data detection module, corresponding data initiation information from the external process system, and updating the recorded data according to the data initiation information (see again ¶[0105]; a data deduplication engine may be employed by a client that communicates with a server. The server may provide data to and from a client computing device through a network. See also ¶[0017] and [0028]; update file metadata to reference a shared block),
comprising: recording, through the data detection module, successfully initiated data in the deduplicated data according to the data initiation information to update the recorded data (see ¶[0040]; update deduplication hash table in response to events or a schedule. Update a weak reference or remove a weak reference).
XIAO discloses updating a deep learning model based on periodic updates that relies on data sets (see abstract). REED discloses data corruption source and timeline analysis to analyze data corruption in data sets. It would have been obvious to include the timeline analysis as taught by REED in the system executing the method of XIAO with the motivation to recover corrupt data and prevent corruption from happening (see REED ¶[0004]).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. It would have been obvious to deduplicate data as taught by DAS in the system executing the method of XIOA with the motivation to reduce redundant data in a computer network executing a deep learning model.
The combination of XIAO and DAS does not explicitly disclose, but YOSHIDA discloses, wherein a part of the adjusted time window of the detection operation is overlapped with the previous time window of the previous detection operation (see ¶[0025] and [0039]; compile time-series data with different resolutions for an overlapping period. For example, central historian 130 may compile native resolution data received from embedded historian 110, medium resolution data received from local historian 120, and low resolution data received from local historian 120 for overlapping time periods. Eliminate redundant data in overlapping periods).
XIAO does not specifically disclose, but HERBEL discloses, wherein the data detection module reads first time information representing a completion time of the previous detection operation, calculates a detection start time of the detection operation according to the first time information and offset time information, and uses a current time as a detection end time of the detection operation see ¶[0055]; one or more communication delays may be known to occur between generation of the signal at T.sub.o and recording the current time of detection, T′.sub.o. That is to say, the time at which the timing validation system records detection of the signal generation lags the actual time of signal generation. Assume the total time of all such communication delays is known and given by ΔC.sub.1. The analysis component 122 may employ the measured T′.sub.o and known ΔC.sub.1 to determine T.sub.o according to the relationship T.sub.o=T′.sub.o−ΔC.sub.1. In certain embodiments, ΔC.sub.1 is a constant value and may be obtained by the analysis component 122 from the data store 114 or a user input. In embodiments where the communication delay between T.sub.o and T′.sub.o is negligible, ΔC.sub.1 may be assumed to be zero and T′.sub.o may be taken to be equal to T.sub.o. For the purposes of the discussion below, it will be assumed that T′.sub.o=T.sub.o. However, if this assumption is not appropriate for a given circumstance, T.sub.o may be replaced with T′.sub.o−Δc.sub.1),
wherein the data detection module establishes the time window of the detection operation according to detection start time and the detection end time (see again ¶[0055]; one or more communication delays may be known to occur between generation of the signal at T.sub.o and recording the current time of detection, T′.sub.o. That is to say, the time at which the timing validation system records detection of the signal generation lags the actual time of signal generation. Assume the total time of all such communication delays is known and given by ΔC.sub.1. The analysis component 122 may employ the measured T′.sub.o and known ΔC.sub.1 to determine T.sub.o according to the relationship T.sub.o=T′.sub.o−ΔC.sub.1. In certain embodiments, ΔC.sub.1 is a constant value and may be obtained by the analysis component 122 from the data store 114 or a user input. In embodiments where the communication delay between T.sub.o and T′.sub.o is negligible, ΔC.sub.1 may be assumed to be zero and T′.sub.o may be taken to be equal to T.sub.o. For the purposes of the discussion below, it will be assumed that T′.sub.o=T.sub.o. However, if this assumption is not appropriate for a given circumstance, T.sub.o may be replaced with T′.sub.o−Δc.sub.1).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. It would have been obvious to deduplicate data as taught by DAS in the system executing the method of XIOA with the motivation to reduce redundant data in a computer network executing a deep learning model.
XIAO discloses updating a deep learning model based on periodic updates. HERBEL discloses timing validation for data fusion that includes accounting for communication delays between time of signal generation and time of signal recording. It would have been obvious for one of ordinary skill in the art at the time of invention to include the accounting for delays in generation and recording as taught by HERBEL in the system executing the method of XIAO with the motivation to provide accurate time data to be used in a deep learning model.
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. YOSHIDA discloses data collection in overlapping time periods that results in redundancies that are eliminated. It would have been obvious for one of ordinary skill in the art at the time of invention to collect data in overlapping periods as taught by YOSHIDA in the system executing the method of XIAO and DAS with the motivation to publish data in a lower resolution in a central historian than a local historian in a network (see YOSHIDA ¶[0025]).
Claim(s) 2, 8, 12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190012575 A1 to XIAO et al. in view of US 20200004645 A1 to REED et al., US 20170082731 A1 to HERBEL et al., US 20220164311 A1 to DAS et al. and US 20210248130 A1 to YOSHIDA et al. as applied to claim 1 above, and further in view of US 20140337215 A1 to Howe (hereinafter ‘HOWE’).
Claim 2 (Currently Amended)
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA discloses the data processing system according to claim 1.
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA does not explicitly disclose, but HOWE discloses, wherein the data detection module calculates the adjusted time window of the detection operation according to the first time information and a current time (see ¶[0083]; the table may be updated for the time period spanning from the current to the most recent update).
XIAO discloses updating a deep learning model based on periodic updates. HOWE discloses data updates in time periods based on determining that a period of time has spanned from a last time to a current time. It would have been obvious to include the determining of a spanning of time as taught by HOWE in the system executing the method of XIAO with the motivation to perform periodic data updates.
Claim 8 (Previously Presented)
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA discloses the data processing system according to claim 1.
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA does not specifically disclose, but HOWE discloses, wherein the detection data is the changed data (see abstract; updated personal data).
XIAO discloses updating a deep learning model based on periodic updates. HOWE discloses data updates in time periods based on determining that a period of time has spanned from a last time to a current time, where the updates are to cardholder data. It would have been obvious to include the updating of cardholder data as taught by HOWE in the system executing the method of XIAO with the motivation to perform periodic data updates and use cardholder data in a deep learning model.
Claim 12 (Currently Amended)
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA discloses the method according to claim 11.
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA does not explicitly disclose, but HOWE discloses, wherein the step of calculating the adjusted time window of the detection operation comprises: calculating the adjusted time window of the detection operation according to the first time information and the current time (see ¶[0083]; the table may be updated for the time period spanning from the current to the most recent update).
XIAO discloses updating a deep learning model based on periodic updates. HOWE discloses data updates in time periods based on determining that a period of time has spanned from a last time to a current time. It would have been obvious to include the determining of a spanning of time as taught by HOWE in the system executing the method of XIAO with the motivation to perform periodic data updates.
Claim 18 (Previously Presented)
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA discloses the method according to claim 11.
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA does not specifically disclose, but HOWE discloses, wherein the detection data is the changed data (see abstract; updated personal data).
XIAO discloses updating a deep learning model based on periodic updates. HOWE discloses data updates in time periods based on determining that a period of time has spanned from a last time to a current time, where the updates are to cardholder data. It would have been obvious to include the updating of cardholder data as taught by HOWE in the system executing the method of XIAO with the motivation to perform periodic data updates and use cardholder data in a deep learning model.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190012575 A1 to XIAO et al. in view of US 20200004645 A1 to REED et al., US 20170082731 A1 to HERBEL et al., US 20220164311 A1 to DAS et al. and US 20210248130 A1 to YOSHIDA et al. as applied to claim 1 above, and further in view of US 20220083250 A1 to Gupta et al. (hereinafter ‘GUPTA’).
Claim 9 (Previously Presented)
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA discloses the data processing system according to claim 1.
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA does not specifically disclose, but GUPTA discloses, wherein the data detection module judges whether a data amount of the deduplicated data is greater than a preset data amount threshold to decide whether to initiate the deduplicated data in batches (see abstract and ¶[0063] and [0067]-[0069]; data chunks are deduplicated, and a batch of data chunks is generated. When the generated batch size is greater than the maximum size for an element object, the generated batch is split into two or more batches).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. GUPTA discloses efficiently storing data in a cloud storage, where data is duplicated and split into batches of a limited size. It would have been obvious to split data into batches as taught by GUPTA in the system executing the method of XIAO with the motivation to keep batches at less than a threshold size for data element objects.
Claim 19 (Previously Presented)
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA discloses the method according to claim 11.
The combination of XIAO, REED, HERBEL, DAS, and YOSHIDA does not specifically disclose, but GUPTA discloses, wherein the step of initiating the deduplicated data comprises: judging, through the data detection module, whether a data amount of the deduplicated data is greater than a preset data amount threshold to decide whether to initiate the deduplicated data in batches (see abstract and ¶[0063] and [0067]-[0069]; data chunks are deduplicated, and a batch of data chunks is generated. When the generated batch size is greater than the maximum size for an element object, the generated batch is split into two or more batches).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. GUPTA discloses efficiently storing data in a cloud storage, where data is duplicated and split into batches of a limited size. It would have been obvious to split data into batches as taught by GUPTA in the system executing the method of XIAO with the motivation to keep batches at less than a threshold size for data element objects.
Claim(s) 5, 6, 15, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20190012575 A1 to XIAO et al. in view of US 20200004645 A1 to REED et al., US 20170082731 A1 to HERBEL et al., US 20220164311 A1 to DAS et al., US 20210248130 A1 to Yoshida et al., and US 20140337215 A1 to HOWE as applied to claims 1 and 2 above, and further in view of US 20110238792 A1 to Phillips (hereinafter ‘PHILLIPS’).
Claim 5 (Previously Presented)
The combination of XIAO, REED, HERBEL, DAS, YOSHIDA, and HOWE discloses the data processing system according to claim 2.
The combination of XIAO, REED, HERBEL, DAS, YOSHIDA, and HOWE does not explicitly disclose, but PHILLIPS discloses, wherein when the data detection module judges that all the deduplicated data is successfully initiated according to the data initiation information, the data detection module updates the first time information according to information of the current time (see ¶[0277]; reset a timer in response to execution of a command).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. PHILLIPS discloses a method that is periodically triggered, where the timer is reset on successful execution. It would have been obvious to reset a timer on successful execution as taught by PHILLIPS in the system executing the method of XIAO and DAS with the motivation to execute periodic updates and processes in a deep learning model.
Claim 6 (Previously Presented)
The combination of XIAO, HERBERL, REED, DAS, YOSDHIA, HOWE, and PHILLIPS discloses the data processing system according to claim 5.
XIAO does not specifically disclose, but PHILLIPS discloses, wherein when the data detection module judges that not all the deduplicated data is successfully initiated according to the data initiation information, the data detection module does not update the first time information (see ¶[0277]; reset a timer in response to execution of a command. If the configuration manager does not detect an inconsistency, the configuration manager may, in some embodiments, restart a timer.).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. PHILLIPS discloses a method that is periodically triggered, where the timer is reset on successful execution. It would have been obvious to reset a timer on successful execution as taught by PHILLIPS in the system executing the method of XIAO and DAS with the motivation to execute periodic updates and processes in a deep learning model.
Claim 15 (Original)
The combination of XIAO, REED, HERBEL, DAS, YOSHIDA, and HOWE discloses the method according to claim 12.
The combination of XIAO, REED, HERBEL, DAS, YOSHIDA, and HOWE does not explicitly disclose, but PHILLIPS discloses, further comprising: updating, through the data detection module, the first time information according to information of the current time when the data detection module judges that all the deduplicated data is successfully initiated according to the data initiation information (see ¶[0277]; reset a timer in response to execution of a command).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. PHILLIPS discloses a method that is periodically triggered, where the timer is reset on successful execution. It would have been obvious to reset a timer on successful execution as taught by PHILLIPS in the system executing the method of XIAO and DAS with the motivation to execute periodic updates and processes in a deep learning model.
Claim 16 (Original)
The combination of XIAO, REED, HERBEL, DAS, YOSHIDA, HOWE, and PHILLIPS discloses the method according to claim 15.
XIAO does not specifically disclose, but PHILLIPS discloses, further comprising: not updating, through the data detection module, the first time information when the data detection module judges that not all the deduplicated data is successfully initiated according to the data initiation information (see ¶[0277]; reset a timer in response to execution of a command. If the configuration manager does not detect an inconsistency, the configuration manager may, in some embodiments, restart a timer.).
XIAO discloses updating a deep learning model based on periodic updates. DAS discloses weak references of allocated logical clusters that are used for data deduplication when block hashes are unchanged based on periodic checks. PHILLIPS discloses a method that is periodically triggered, where the timer is reset on successful execution. It would have been obvious to reset a timer on successful execution as taught by PHILLIPS in the system executing the method of XIAO and DAS with the motivation to execute periodic updates and processes in a deep learning model.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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