Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claims 1-9 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: The claims recite a device and non-transitory machine-readable medium, each of which are one of the four categories of eligible subject matter.
Claims 1 and 16
Step 2A Prong 1: The claims recite the following limitations:
comparing the first clock reference and the second clock reference to determine a first phase offset (Mental Process);…to predict future phase offsets (Mental Process).
Under the broadest reasonable interpretation of the claim language, comparing clock references and predicting future phase offsets are mental processes because a human mind can practically perform the processes with the aid of a pencil and paper. Accordingly, the claims recite an abstract idea.
Step 2A Prong 2: The judicial exceptions are not integrated into a practical
application. The claims recite the following additional elements:
a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising receiving a first clock reference from a global network satellite system (GNSS); receiving a second clock reference from a packet network;… and providing the first phase offset to a predictive artificial intelligence (AI) engine to train the predictive AI engine.
The processors, memory, and instructions configuring the processors are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). Training a predictive AI engine is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). Receiving clock references are mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are directed towards an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to
amount to significantly more than the judicial exception. The processors, memory, and instructions configuring the processors are generic computing components recited at a high level as a means to apply the judicial exception, as discussed in MPEP 2106.05(f). Training a predictive AI engine is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). Receiving clock references are mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). The claims are not patent eligible.
Dependent Claims:
Claims 6-9: These claims recite further abstract ideas (mental processes) and thus are ineligible.
Claims 2-9 and 17-20: These claims recite further mere data gathering and generally linking the abstract ideas to the technological environment of machine learning and as explained above these do not provide a practical application or inventive concept and thus are ineligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6 and 8-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fan et al (Pub. No.: US 20230016860 A1), hereafter Fan.
Regarding claim 1, Fan teaches receiving a first clock reference from a global network satellite system (GNSS) (“Step 1: The master clock periodically sends a sync packet, and records a precise sending timestamp T1 when the sync packet leaves the master clock. In addition, the sync packet is sent periodically with either sending time information carried or not…a primary reference time clock (PRTC) serves as a source end of a packet to provide time reference information.”, P0053, P0065. “the network device traces clock time information of the global navigation satellite system (global navigation satellite system, GNSS) or another frequency reference source to obtain the time information, so as to obtain the reference time information”, P0092); receiving a second clock reference from a packet network (“Step 2: The master clock encapsulates the precise sending timestamp T1 into a follow_up packet and sends the packet to the slave clock. Step 3: The slave clock records a precise arrival timestamp T2 when the sync packet arrives at the slave clock”, P0054-P0055); comparing the first clock reference and the second clock reference to determine a first phase offset (“In the synchronization method, a delay prediction value is predicted for a packet that arrives at a network device in a future time, and the delay prediction value is used to compensate for PDV noise introduced by an intermediate network link, so that a compensated timestamp difference more directly reflects frequency offset and phase offset information of a crystal oscillator relative to a frequency reference source”, P0060); and providing the first phase offset to a predictive artificial intelligence (AI) engine to train the predictive AI engine to predict future phase offsets (Under the broadest reasonable interpretation of the claim language, a phase offset in the context of clock synchronization may include a difference in timestamps. A predictive model is trained using a timestamp difference sequence to predict timestamp differences, P0015, P0083).
Regarding claim 10, Fan teaches a global network satellite system (GNSS) receiver to receive a GNSS clock (“Step 1: The master clock periodically sends a sync packet, and records a precise sending timestamp T1 when the sync packet leaves the master clock. In addition, the sync packet is sent periodically with either sending time information carried or not…a primary reference time clock (PRTC) serves as a source end of a packet to provide time reference information.”, P0053, P0065. “the network device traces clock time information of the global navigation satellite system (global navigation satellite system, GNSS) or another frequency reference source to obtain the time information, so as to obtain the reference time information”, P0092); a packet interface to receive a plurality of precision time protocol (PTP) clock references from a packet network (time information may be transferred in the form of PTP packets being sent between a slave and master clock, P0052); and one or more processors configured to implement a predictive AI engine that is configured to select a first PTP clock reference of the plurality of PTP clock references when a lock to the GNSS clock is lost (A predictive model is trained using a timestamp difference sequence to predict timestamp differences when clocks are not synchronized, P0015, P0083).
Regarding claim 16, Fan teaches receiving phase offset values from a plurality of boundary clock devices in a packet network, wherein the phase offset values represent differences between global network satellite system (GNSS) derived clocks, and precision time protocol (PTP) derived clocks (“Step 1: The master clock periodically sends a sync packet, and records a precise sending timestamp T1 when the sync packet leaves the master clock. In addition, the sync packet is sent periodically with either sending time information carried or not…a primary reference time clock (PRTC) serves as a source end of a packet to provide time reference information.”, P0053, P0065. “the network device traces clock time information of the global navigation satellite system (global navigation satellite system, GNSS) or another frequency reference source to obtain the time information, so as to obtain the reference time information”, P0092. Time information may be transferred in the form of PTP packets being sent between a slave and master clock, P0052); receiving network parameters from the plurality of boundary clock devices (“If the delay-related network parameter data on the NCE can be obtained, the delay-related network parameter data is sent to the learning module for learning to obtain the predictive model”, P0113); and training a predictive artificial intelligence (AI) engine to predict future phase offsets for the plurality of boundary clock devices based on the network parameters (A predictive model is trained using a timestamp difference sequence and network parameter data to predict timestamp differences, P0015, P0083).
Regarding claim 2, Fan teaches the limitations of claim 1 as outlined above. Fan further teaches wherein the predictive AI engine resides within the device (machine learning may be done offline using a network device connecting multiple clock devices, P0092, P0096).
Regarding claim 3, Fan teaches the limitations of claim 1 as outlined above. Fan further teaches wherein the predictive AI engine resides outside the device (the learning model for machine learning is separated from the network device used for synchronizing time, P0113, figure 5).
Regarding claim 5, Fan teaches the limitations of claim 4 as outlined above. Fan further teaches wherein the network parameters comprise a packet delay variation (“As a result, a time when a packet arrives at a client through the intermediate network changes, and packet delay variation (PDV) is generated…If network parameter data on a network control engine (network control engine, NCE) cannot be obtained, the delay after packet selection (noise reduction preprocessing) is sent to a learning module, that is, an initial machine learning model for learning. If the delay-related network parameter data on the NCE can be obtained, the delay-related network parameter data is sent to the learning module for learning to obtain the predictive model”, P0004, P0113).
Regarding claims 6 and 12, Fan teaches the limitations of claims 1 and 10 as outlined above. Fan further teaches wherein the operations further comprise: receiving a first predictive phase offset from the predictive AI engine (clock time information of the GNSS is passed through a phase detector to generate a phase adjustment value, P0113); determining a loss of the first clock reference (GNSS loss is determined based on the delay prediction phase corresponding to clock time information of the GNSS, P0113-P0114); and applying the first predictive phase offset to the second clock reference (“Then, the compensated timestamp difference is sent to the loop filter for filtering, and a filtering result is input to the DDS for direct digital frequency synthesis, to generate a frequency and/or phase adjustment value. Time and/or clock synchronization is performed based on the frequency and/or phase adjustment value to achieve clock and/or time synchronization between the master clock device and the slave clock device”, P0114).
Regarding claims 4 and 13, Fan teaches the limitations of claims 1 and 12 as outlined above. Fan further teaches wherein the operations further comprise providing network parameters to the predictive AI engine (“If the delay-related network parameter data on the NCE can be obtained, the delay-related network parameter data is sent to the learning module for learning to obtain the predictive model”, P0113).
Regarding claim 8, Fan teaches the limitations of claim 6 as outlined above. Fan further teaches wherein the determining the loss of the first clock reference comprises determining that at least one circuit for receiving the first clock reference has been powered down (when determining GNSS loss is present, the GNSS tracing loop has been determined to be disconnected. The loop filter is a linear circuit, P0108, P0114).
Regarding claim 9, Fan teaches the limitations of claim 1 as outlined above. Fan further teaches wherein the operations further comprise: receiving a third clock reference from the packet network (“Step 4: The slave clock sends a delay_req packet and records a precise sending timestamp T3. Step 5: The master clock records a precise arrival timestamp T4 when the delay_resp packet arrives at the master clock”, P0056-P0057); comparing the first clock reference and the third clock reference to determine a second phase offset (“In the synchronization method, a delay prediction value is predicted for a packet that arrives at a network device in a future time, and the delay prediction value is used to compensate for PDV noise introduced by an intermediate network link, so that a compensated timestamp difference more directly reflects frequency offset and phase offset information of a crystal oscillator relative to a frequency reference source”, P0060); and providing the second phase offset to the predictive artificial intelligence (AI) engine to train the predictive AI engine to predict future phase offsets (A predictive model is trained using a timestamp difference sequence to predict timestamp differences, P0015, P0083).
Regarding claim 11, Fan teaches the limitations of claim 10 as outlined above. Fan further teaches wherein the predictive AI engine is further configured to provide a phase offset to be applied to the first PTP clock reference (A predictive model is trained using a timestamp difference sequence to predict timestamp differences, P0015, P0083).
Regarding claim 14, Fan teaches the limitations of claim 13 as outlined above. Fan further teaches wherein each of the plurality of PTP clock references are associated with a respective one of a plurality of timing trails, and the plurality of network parameters comprises network parameters associated with each of the plurality of timing trails (network parameters include timestamp difference sequences associated with clock source devices, P0015, P0113, P0118).
Regarding claim 15, Fan teaches the limitations of claim 12 as outlined above. Fan further teaches wherein the predictive AI engine is further configured to determine whether each of the plurality of PTP clock references represents a viable backup clock reference, and to select one of the plurality of PTP clock references only if at least one of the PTP clock references is determined to be viable (PTP packets are transmitted between a slave clock and master clock, P0051-P0052. Packet selection algorithm decides which packets are viable and should be selected, and selected packets are sent to the learning module for learning to obtain the predictive model, P0112-P0113).
Regarding claim 17, Fan teaches the limitations of claim 16 as outlined above. Fan further teaches wherein the receiving the phase offset values comprises receiving a plurality of phase offset values from each of the plurality of boundary clock devices, wherein each of the plurality of phase offset values corresponds to a different one of a plurality of PTP clock sources (PTP packets are transmitted between a slave clock and master clock, P0051-P0052. “In the synchronization method, a delay prediction value is predicted for a packet that arrives at a network device in a future time, and the delay prediction value is used to compensate for PDV noise introduced by an intermediate network link, so that a compensated timestamp difference more directly reflects frequency offset and phase offset information of a crystal oscillator relative to a frequency reference source”, P0060).
Regarding claim 18, Fan teaches the limitations of claim 16 as outlined above. Fan further teaches wherein the operations further comprise training the predictive AI engine to predict which of a plurality of PTP clock sources should be used by one of the plurality of boundary clock devices if one of the GNSS derived clocks is lost (A predictive model is trained using a timestamp difference sequence to predict timestamp differences when clocks are not synchronized, P0015, P0083).
Regarding claim 19, Fan teaches the limitations of claim 16 as outlined above. Fan further teaches wherein the operations further comprise training the predictive AI engine to determine which of the plurality of boundary clock devices should apply a phase offset to a PTP derived clock if one of the GNSS derived clocks is lost (A predictive model is trained using a timestamp difference sequence to predict timestamp differences when clocks are not synchronized, P0015, P0083. The predictive model determines a delay prediction value sequence including determining a compensated timestamp difference that reflects phase offset information, P0105, P0109, P0114).
Regarding claim 20, Fan teaches the limitations of claim 16 as outlined above. Fan further teaches providing the future phase offsets to the plurality of boundary clock devices (master clock device and slave clock device depend on phase adjustment values included in the compensated timestamp difference, P0109, P0114).
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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fan in view of Shimon et al (Pub. No.: US 20250060487 A1), hereafter Shimon.
Regarding claim 7, Fan teaches the limitations of claim 6 as outlined above. Fan does not appear to explicitly teach “wherein the determining the loss of the first clock reference comprises determining that the first clock reference has been spoofed”.
Shimon teaches wherein the determining the loss of the first clock reference comprises determining that the first clock reference has been spoofed (When determining an error in a clock value of a GNSS, an alert may be indicating GNSS readings are being spoofed, P0169-P0172).
Accordingly, it would have been obvious to a person having ordinary skill in the
art before the effective filing date of the claimed invention, having the teachings of
Fan and Shimon before them, to include Shimon’s specific teaching of alerts indicating GNSS readings are being spoofed in Fan’s method of synchroniation. One would have been motivated to make such a combination of alerts indicating GNSS readings are being spoofed (see Shimon P0169-P0172) and determining GNSS loss (see Fan P0114) for improved positioning accuracy by correcting errors affecting GNSS signals (see Shimon P0141).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 11962402 B2 (Hoptroff) teaches a machine learning algorithm that incorporates clock synchronization.
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/I.M./ Examiner, Art Unit 2141
/MATTHEW ELL/ Supervisory Patent Examiner, Art Unit 2141
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