DETAILED ACTION
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.
2. Claims 1-4, 7-12 and 28 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without being integrated into a practical application and do not include additional elements that amount to significantly more than the judicial exception.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline, Federal Register Vol. 84, No., Jan 2019)), determination of the subject matter eligibility under the 35 USC 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong one), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then proceeding to the second part of Step 2A (Prong two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination, provide “inventive concept” that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 USC 101.
Looking at the claims, the claims satisfy the first part of the test 1A, namely the claims are directed to two of the four statutory classes, apparatus and method. In Step 2A Prong one, we next identify any judicial exceptions in the claims. In Claim 1 (as a representative example), we recognize that the limitations “obtaining dynamic state information for the vehicle, the dynamic state information including position information for the vehicle at a first time in a time sequence, estimating a position of the vehicle at the second time in the time sequence on the dynamic state information and the communication network information,” are abstract ideas as they involve mental process, including observation, evaluation, judgment and opinion, under the BRI. Similar rejections are made for other independent and dependent claims. With the identification of abstract ideas, we proceed to Step 2A, Prong two, where with additional elements and taken as a whole, we evaluate whether the identified abstract idea is being integrated into a practical application.
In Step 2A, Prong two, the claims additionally recite “transmitting, via a transceiver of the node, a wireless data signals to the vehicle, receiving from the vehicle, via the transceiver, communication network information for the vehicle, the communication network information including a result of a measurement carried out by the vehicle on a wireless data signal at a second time in the time sequence that is after the first time,” “vehicle being operable to connect to a communication network, the communication network node,” “the dynamic state information including sensor information generated by a sensor in the vehicle”, “position information generated by a satellite positioning system,” “a communication network node, comprising a processing circuitry,” “memory coupled to the processing circuitry,” but said limitations, recited at high level of generality, are merely directed to insignificant data collection activity in a generic environment involving vehicle, general purpose computer and network. Furthermore, the limitation “using a trained Machine Learning model” provide nothing more than mere instructions to implement an abstract idea on a generic computer. The claims do not improve the functioning of any devices or processing circuity and do not improve other technology due to lack of sufficient detail in the claims, particularly the recited machine learning model to estimate the position of the vehicle. In short, the claims do not provide sufficient evidence to show that they are more than a drafting effort to monopolize the abstract idea. As such, the abstract idea is not integrated into a practical application. Consequently, with the identified abstract idea not being integrated into a practical application, we proceed to Step 2B and evaluate whether the additional elements provide “inventive concept” that would amount to significantly more than the abstract idea.
In Step 2B, the claims additionally recite “transmitting, via a transceiver of the node, a wireless data signals to the vehicle, receiving from the vehicle, via the transceiver, communication network information for the vehicle, the communication network information including a result of a measurement carried out by the vehicle on a wireless data signal at a second time in the time sequence that is after the first time,” “vehicle being operable to connect to a communication network, the communication network node,” “the dynamic state information including sensor information generated by a sensor in the vehicle”, “position information generated by a satellite positioning system,” “a communication network node,” comprising a processing circuitry,” “memory coupled to the processing circuitry,” but said limitations, recited at high level of generality, are merely directed to insignificant data collection activity in a generic environment involving vehicle, general purpose computer and network that are well-understood, routine and conventional. Furthermore, the limitation “using a trained Machine Learning model” provide nothing more than mere instructions to implement an abstract idea on a generic computer. As such, the claims do not provide additional elements that would amount to significantly more than the abstract idea.
In Summary, the claims recite abstract idea without being integrated into a practical application, and do not provide additional elements that would amount to significantly more than the abstract idea. As such, taken as a whole, the claims are ineligible under the 35 USC 101.
The claims 5, 13-18 and 30 are withdrawn based on the Applicant’s argument, under the practical application.
Claim Rejections - 35 USC § 102
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 1-4, 7-8, 10-11 and 28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Teslenko et al., US-PGPUB 2019/0199419 (Teslenko)
Regarding Claims 1 and 28. Teslenko discloses performed by a node of a communication network for estimating a position of a vehicle (Abstract; Fig. 1), comprising:
obtaining dynamic state information for the vehicle, the dynamic state information including position information for the vehicle at a first time in a time sequence (Paragraphs [0006]-[0008], positioning measurements);
transmitting, via a transceiver of the node, a wireless data signal to the vehicle (Fig. 2);
receiving, from the vehicle via the transceiver, communication network information for the vehicle, the communication network information including a result of a measurement carried out by the vehicle on the wireless data signal at a second time in the time sequence that is after the first time (Paragraph [0006], positioning measurements, which includes first and second time in a time sequence) and estimating a position of the vehicle at the second time in the time sequence based on the dynamic state information and the communication network information (Paragraphs [0009], [0012]-[0013], and the rest Paragraphs [0014]-[0018]; [0028]-[0029]; [0042])
Regarding Claim 2. Teslenko discloses obtaining dynamic state information for the vehicle omprises receiving the dynamic state information from the vehicle, the dynamic state information comprising position information at the first time in the time sequence generated by a satellite positioning system (Paragraph [0034], GPS).
Regarding Claims 3 and 18. Teslenko discloses the dynamic state information further includes sensor information generated by a sensor on the vehicle at the first time in the time sequence (Paragraph [0034]; [0059])
Regarding Claim 4. Teslenko discloses obtaining dynamic state information for the vehicle comprises retrieving an estimated position of the vehicle at the first time in the time sequence, the estimated position of the vehicle at the first time in the time sequence being generated during a previous iteration of the computer implemented method (Paragraph [0035])
Regarding Claim 7. Teslenko discloses estimating the position of the vehicle at the second time in the time sequence, comprises generating an estimated position of the vehicle using a combined positioning mode, the combined positioning model being configured to accept the dynamic state information and the communication network information as inputs (Fig. 2)
Regarding Claim 8. Teslenko discloses estimating the position of the vehicle at the second time in the time sequence, comprises: using a dynamic positioning model to generate a first estimated position of the vehicle, the dynamic positioning model being configured to accept dynamic state information as inputs to the model, using an observation positioning model to generate a second estimated position of the vehicle, the observation positioning model being configured to accept communication network information as inputs to the model, and combining the first estimated position and the second estimated position to generate an output estimated position of the vehicle (Fig. 2, based on route information and measurements from the vehicle)
Regarding Claim 10. Teslenko discloses estimating the position of the vehicle at the second time in the time sequence, comprises: using a dynamic state transition model to generate potential estimated positions of the vehicle, using an observation model to refine the generated potential estimated positions, and generating an output estimated position of the vehicle from the refined potential estimated positions (Fig. 2, adjusting based on beamforming to refine the position)
Regarding Claim 11. Teslenko discloses sending the estimated position of the vehicle at the second time in the time sequence to the vehicle (Fig. 2)
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Claim Rejections - 35 USC § 102
Claims 1-10, 17-18, 28 and 30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Durand et al., US Pat No. 11,113567 (hereinafter Durand)
Regarding Claims 1 and 28. Durand discloses node of a communication network estimating a position of a vehicle (Figs. 1-5; Abstract), comprising: obtaining dynamic state information for the vehicle, the dynamic state information including position information for the vehicle at a first time in a time sequence (Fig. 9, sensor data while in transit; Col. 3, lines 43-51, in real-time; Col. 5, lines 14-23, periodically; Col. 10, lines 65-67; Col. 11, lines 1-7, time correlated), transmitting, via a transceiver of the node, a wireless data signal to the vehicle (Col. 7, lines 7-10, wireless; Fig. 3-5, bidirectional), receiving from the vehicle communication network information for the vehicle, the communication network information including a result of a measurement carried out by the vehicle on a signal exchanged with the communication network (Fig. 5, 6, receive position data) at a second time in the time sequence that is after the first time (Fig. 9 and Col. 6, lines 11-27; Col. 10, lines 65-67; Col. 11, lines 1-7, time correlated; Fig. 6, training process; Fig. 7-9; Col. 6, lines 1- 27, second training data at later times; Col. 12, lines 6-11, position and sensor data, second sensor data, third sensor data, etc subsequent in time) and estimating a position of the vehicle at the second time in the time sequence using a based on the obtained dynamic state information and the communication network information (Fig. 6, Fig. 9; Col. 4, lines 43-55)
Regarding Claims 17 and 30. Durand discloses node of a communication network estimating a position of a vehicle, the vehicle being operable to connect to the communication network (Figs. 1-5; Abstract), comprising:
obtaining dynamic state information for the vehicle, the dynamic state information including position information for the vehicle at a plurality of times forming in a time sequence over a training period generated by a satellite positioning system (Fig. 2, GNSS, Col. 4, lines 25-55, to produce training data, correlate position from GNSS timestamp; Fig. 9, sensor data while in transit; Col. 3, lines 43-51, in real-time; Col. 5, lines 14-23, periodically; Col. 6, lines 1-10, first training data; Col. 10, lines 65-67; Col. 11, lines 1-7, time correlated; Col. 12, lines 6-11, position and sensor data, second sensor data, third sensor data, etc subsequent in time), obtaining communication network information for the vehicle, the communication network information including a result of a measurement carried out by the vehicle on a signal exchanged with the communication network at the plurality of times over the training period (Fig. 6, training process; Fig. 7-9; Col. 6, lines 1- 27, second training data at later times), and using the dynamic state information and the communication network information to train the ML model for estimating a position of the vehicle based on subsequent dynamic state information associated with a first time and subsequent communication network information associated with a second time that is after than the first time (Figs. 6, 9; Col. 4, lines 43-55; Col. 6, lines 28-43, using the first and second training data at different times to detect moving vehicles)
Regarding Claim 2. Durand discloses obtaining dynamic state information for the vehicle comprises receiving the dynamic state information from the vehicle, the dynamic state information comprising position information at the first time in the time sequence generated by a satellite positioning system (Fig. 2, GNSS, Col. 4, lines 25-55)
Regarding Claims 3 and 18. Durand discloses the dynamic state information further includes sensor information generated by a sensor on the vehicle at the first time in the time sequence (Fig. 2, GNSS, Col. 4, lines 25-55, to produce training data, correlate position from GNSS timestamp; Fig. 9, sensor data while in transit; Col. 3, lines 43-51, in real-time; Col. 5, lines 14-23, periodically; Col. 6, lines 1-10, first training data; Col. 10, lines 65-67; Col. 11, lines 1-7, time correlated)
Regarding Claim 4. Durand discloses obtaining dynamic state information for the vehicle comprises retrieving an estimated position of the vehicle at the first time in the time sequence, the estimated position of the vehicle at the first time in the time sequence being generated during a previous iteration of the computer implemented method (Fig. 2, GNSS, Col. 4, lines 25-55, to produce training data, correlate position from GNSS timestamp; Fig. 9, sensor data while in transit; Col. 3, lines 43-51, in real-time; Col. 5, lines 14-23, periodically; Col. 6, lines 1-10, first training data; Col. 10, lines 65-67; Col. 11, lines 1-7, time correlated; Col. 4, lines 43-55; Col. 6, lines 28-43, using the first and second training data at different times to detect moving vehicles in real time)
Regarding Claim 5. Durand discloses estimating the position of the vehicle at the second time in the time sequence comprises: assembling an input feature vector from the dynamic state information based on a time difference between the first time in the time sequence and the second time in the time sequence and the communication network information, and inputting the input feature vector to the trained ML model, the ML model having been trained using training data assembled from additional dynamic state information and additional communication network information received from the vehicle during a training period, the additional dynamic state information including position information generated by a satellite positioning system (Col. 11, lines 31-66, difference in data points)
Regarding Claim 7. Durand discloses estimating the position of the vehicle at the second time in the time sequence, comprises generating an estimated position of the vehicle using a combined positioning mode, the combined positioning model being configured to accept the dynamic state information and the communication network information as inputs (Figs. 6, 9; Col. 4, lines 43-55; Col. 6, lines 28-43, using the first and second training data at different times to detect moving vehicles)
Regarding Claim 8. Durand discloses estimating the position of the vehicle at the second time in the time sequence, comprises: using a dynamic positioning model to generate a first estimated position of the vehicle, the dynamic positioning model being configured to accept dynamic state information as inputs to the model, using an observation positioning model to generate a second estimated position of the vehicle, the observation positioning model being configured to accept communication network information as inputs to the model, and combining the first estimated position and the second estimated position to generate an output estimated position of the vehicle (Figs. 6, 9; Col. 4, lines 43-55; Col. 6, lines 28-43, using the first and second training data at different times to detect moving vehicles)
Regarding Claim 9. Durand discloses estimating the position of the vehicle at the second time in the time sequence, comprises using a filtering algorithm to reduce error in an estimated position of the vehicle (Col. 13, lines 33-37 continuing to refine or tune the machine learning system, including what is shown in Fig. 7)
Regarding Claim 10. Durand discloses estimating the position of the vehicle at the second time in the time sequence, comprises: using a dynamic state transition model to generate potential estimated positions of the vehicle (Paragraph [0029, prior to using various algorithms), using an observation model to refine the generated potential estimated positions, and generating an output estimated position of the vehicle from the refined potential estimated positions (Col. 13, lines 25-37, further training data to continue to refine or tune the machine learning)
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Durand et al., US Pat No. 11,113567 in view of Cho et al., US-PGPUB 2021/0125494 (hereinafter Cho)
Regarding Claim 11. Durand is silent to sending the estimated position of the vehicle at the second time in the time sequence to the vehicle.
Cho discloses sending the estimated position of the vehicle at the second time in the time sequence to the vehicle (Figs. 7-8; Paragraph [0069], where the trajectory includes the position of the vehicle; Paragraph [0001])
At the time of the invention filed, it would have been obvious to a person of ordinary skill in the art to use the teaching of Cho in Durand and send the estimated position of the vehicle at the second time in the time sequence to the vehicle, so as to control the vehicle for its optimal safe operation.
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 13. The prior arts do not teach or suggest a combination, including receiving, from the vehicle, dynamic state information for the vehicle at the second time in the time sequence, the dynamic state information including position information generated by a satellite positioning system, calculating a similarity score between the position information generated by the satellite positioning system for the vehicle at the second time and the estimated position of the vehicle at the second time from a machine learning ("ML") based on the dynamic state information for the vehicle at the second time in the time sequence, and responsive to the calculated similarity score being below a second threshold value, performing at least one of: initiating retraining of the ML model; and reporting an anomaly.
The claims 14-16 are allowable due to their dependencies to said dependent claim 13.
Claim 32 would be allowable if rewritten to overcome the rejection under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Regarding Claim 32. Prior arts do not teach or suggest a combination, including
calculating a similarity score between position information generated for the vehicle at
the second time and the estimated position of the vehicle at the second time from a machine learning ("ML"); and responsive to the calculated similarity score being below a threshold value, performing at least one of: initiating retraining of the ML model; and
reporting an anomaly.
Response to Arguments
Applicant’s arguments with respect to claims have been considered but are moot in view of new grounds of rejection.
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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/HYUN D PARK/Primary Examiner, Art Unit 2857