DETAILED ACTION
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 .
This office action is in response to the Applicant’s communication filed on 12/15/2023. Claims 1 – 20 are pending in this application.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: “1” in the top line on p. 19 of the specification as filed with respect to FIG 5. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 3, 5 – 8, 13 – 15 and 17 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 20230144796 (DePoy).
Regarding claims 1, 8 and 13, DePoy teaches “A positioning method (FIG 1 – 3 with corresponding description), performed by a positioning device (implemented in computer 116 and antenna 114 in FIG 1, or computer 230 in FIG 2C which may represent computer 116 in FIG 1, as well as the computers 212 and 226 of FIG. 2A and FIG. 2B - see paragraph 0089), the method comprising:
receiving a signal from a mobile device (in FIG 1, paragraph 0043: The antenna 110 transmits the signal 106 within the environment 102. Paragraph 0050: The signal 106 is received by the antenna 114 of the computer 116 (“positioning device”) as received signal 118. Paragraph 0087: In FIG. 2A, the drone 202 (“a mobile device”) transmits signal 206 to the antenna array 210. Paragraph 0088: In FIG. 2B, the drone 216 (“a mobile device”) transmits signal 218 to the antenna array 224. In FIG 2C, paragraph 0091: The process 228 includes providing data corresponding to the received signal #1 232, such as signal 218, and data corresponding to the received signal #2, such as signal 206, to the machine learning network 236.);
determining a positioning information of the mobile device based on the received signal (with respect to FIG 1, paragraph 0051: providing the received signal 118 to the machine learning network 120, obtaining output of the machine learning network 120, such as the prediction 122 which specifically represents direction of arrival (DoA), as shown in FIG 1 (“a positioning information of the mobile device”). With respect to FIG 2, paragraph 0091: the machine learning network 236 can generate output indicating a DoA prediction of signal #1 238 and signal #2 240 (“a positioning information of the mobile device”).);
providing the determined positioning information of the mobile device to a positioning engine (this step is implicit so that “positioning engine” represents operations 124 and 126 in FIG 1. Indeed, the Applicant’s own disclosure in the paragraph bridging pages 12 – 13 states that the positioning engine 30 may be combined with one of the anchors 20a, 20b, 20c, 20d as a common entity. Thus, in DePoy, the “positioning engine” is part of the computer 116 performing the method of FIG 1. With respect to FIG 2, paragraph 0093: The process 228 includes the computer 230 providing the prediction output obtained from the machine learning network 236 to a positioning engine 242.);
obtaining an offset between the determined positioning information and an estimated positioning information determined by the positioning engine (FIG 1 and paragraph 0051: obtaining ground truth data 124, comparing the output of the machine learning network 120 with the ground truth data 124. The comparison between the prediction 122 (“the determined positioning information”) and the ground truth data 124 (“an estimated positioning information determined by the positioning engine”) includes the error term 126 (“obtaining an offset”). Paragraphs 0065 – 0068 with respect to generation of the ground truth data 124. Paragraph 0069 with respect to generation of the error term 126. Paragraph 0067: the error term 126 is an array with one or more elements indicating one or more difference values between the prediction 122 and the ground truth data 124. With respect to FIG 2, paragraph 0093: the positioning engine 242 determines a position of a transmitting device (“an estimated positioning information determined by the positioning engine”). Paragraph 0108: computing an error term by comparing the prediction to a set of ground truths (310). For example, the computer 116 can use a difference between the prediction 122 and the ground truth data 124 to generate the error term 126. Paragraph 0069: the computer 116 computes a loss function, which measures a distance (e.g., a difference) between the prediction 122 and the ground truth data 124.); and
training a positioning algorithm of the positioning device based on the obtained offset (at least paragraphs 0071 – 0073 and FIG 1 showing an additional input to the ML network from the error term 126. Paragraph 0110: The process 300 includes updating the machine-learning network based on the error term (312). For example, model updates can be calculated in the system 100 by the computer 116 to allow predictions of the machine learning network 120 to improve over time. The model updates can iteratively provide improved predictions of DoA upon training. Paragraph 0051: comparing the output of the machine learning network 120 with the ground truth data 124, and updating the machine learning network 120 based on the comparison of the output of the machine learning network 120, such as the prediction 122, and the ground truth data 124. The comparison between the prediction 122 and the ground truth data 124 includes the error term 126.).”
Regarding claims 2 and 14, DePoy teaches “wherein the positioning algorithm comprises a static algorithm and an adaptive machine learning algorithm (this implementation is shown in FIG 5B with additional reference to FIG 6B. Paragraph 0128: FIG. 5B is a diagram showing an example of a system 550 for estimating DoA of electromagnetic energy using a transformer neural network 558. Paragraph 0129: the computer 116 can process the received signal 118 using a transformer network neural network 558. The computer 116 can obtain output of the transformer network and provide the output to the machine learning network 120 which is shown in FIG 5B as regression neural network 560. The prediction 122 can include one or more of the output values 562 indicating a direction signal. If in training mode, the ground truth data 124 can include one or more values for each of the signal directions of the output values 562 in order to generate an error term 126 that includes one or more values corresponding to the signal directions. In other words, in FIG 5B, the transformer neural network 558 corresponds to claimed “a static algorithm”, and the regression neural network 560 (which is also machine learning network 120 in FIG 1) corresponds to claimed “an adaptive machine learning algorithm”), and wherein the training the positioning algorithm of the positioning device based on the obtained offset comprises: training the adaptive machine learning algorithm based on the obtained offset (at least paragraphs 0071 – 0073 and FIG 1 showing an additional input to the ML network from the error term 126. Paragraph 0110: The process 300 includes updating the machine-learning network based on the error term (312). For example, model updates can be calculated in the system 100 by the computer 116 to allow predictions of the machine learning network 120 to improve over time. The model updates can iteratively provide improved predictions of DoA upon training. Paragraph 0051: comparing the output of the machine learning network 120 with the ground truth data 124, and updating the machine learning network 120 based on the comparison of the output of the machine learning network 120, such as the prediction 122, and the ground truth data 124. The comparison between the prediction 122 and the ground truth data 124 includes the error term 126.).”
Regarding claims 3 and 15, DePoy teaches “wherein: the static algorithm comprises a machine learning model with a higher complexity than the adaptive machine learning algorithm, or
wherein the static algorithm (was mapped to the transformer neural network 558 in the rejection of claim 2 above) comprises a super resolution algorithm (particular implementation of the transformer network 558 is shown in FIG 6B. Paragraph 0133: FIG. 6B is a diagram showing an example of a system 650 for a transformer neural network 660 to improve the array 402. Where the number of array elements before a transformer network 660 is less than the number of array elements after a transformer network, the network can be viewed as a form of array interpolation or super-resolution, in which the general capacity of the array 402 is enhanced through the application of the neural network as a generalized non-linear estimation tool. The modified output 662 of the input array of FIG. 6B is output by the transformer neural network 660 as the equivalent of a higher resolution array 664.).”
Regarding claims 5 and 17, DePoy teaches “wherein the training the positioning algorithm comprises:
updating weights of the positioning algorithm based on the offset between the determined positioning information and the estimated positioning information (paragraph 0069: the computer 116 computes a loss function, which measures a distance (e.g., a difference) between the prediction 122 and the ground truth data 124 (“the offset between the determined positioning information and the estimated positioning information”). This loss or difference may also include a maximum of an L1 loss or scaled L1 loss, and an L2 loss or scaled L2 loss, combining multiple distance metrics to exploit the best properties of both L1 and L2 loss convergence in their differing performance regions. A rate of change of the loss function is used to update one or more weights or parameters within the machine-learning network 120. This means that the update is performed “based on the offset” as claimed).”
Regarding claim 6, DePoy teaches “wherein the positioning information of the mobile device comprises at least one of an angle of arrival of the received signal (paragraphs 0011, 0081, 0082, 0085 with respect to “angle of arrival”. With respect to direction of arrival, which is the same as “angle of arrival”, paragraphs 0042, 0046, 0047 and elsewhere in the disclosure) and/or a distance between the mobile device and the positioning device.”
Regarding claim 7, DePoy teaches “wherein the received signal is a wireless communication technology signal (paragraph 0053).”
Claim Rejections - 35 USC § 103
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 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.
Claims 4, 11, 16 and 18 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230144796 (DePoy).
Regarding claims 4, 16 and 18, while DePoy teaches in paragraph 0025 that the optimization process includes stochastic gradient descent (SGD) and in paragraph 0120 teaches that a system 400 for estimating DoA of electromagnetic energy uses a regression neural network 408, DePoy does not explicitly teach “wherein the adaptive machine learning algorithm comprises a stochastic gradient descent linear regression model”.
However, the Examiner takes an official notice that “a stochastic gradient descent linear regression model” as a predictive model that uses the SGD optimization algorithm to find the best-fit line through data was well known in the art at the effective filing date of the application.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize well-known in the art “stochastic gradient descent linear regression model” in the system of DePoy simply as design choice with predictable results since, according to the Supreme Court, “[t]he combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.” KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 416 (2007).
Regarding claim 19, this claim is rejected because of the same reasons as set forth in the rejection of claim 5 because they have similar limitations.
Regarding claim 20, this claim is rejected because of the same reasons as set forth in the rejection of claim 6 because they have similar limitations.
Regarding claim 11, DePoy teaches or fairly suggests “A system (shown in FIG 2) comprising:
a positioning device according to claim 8 (FIG 2C and paragraph 0089: computer 230 performing the process 228); and
a positioning engine (positioning engine 242 in FIG 2C) arranged to determine the estimated positioning information of the mobile device based on the determined positioning information of the mobile device sent by the positioning device and based on further determined positioning information of the mobile device sent to the positioning engine by at least two further positioning devices (paragraph 0093: the positioning engine 242 determines a position of a transmitting device based, in part, on a known distance between antenna array elements. The positioning engine 242 can then triangulate to determine the position 244 of the drone 202 (“determine the estimated positioning information”) based on the separation distance and the DoA predictions 238 and 240 (“based on the determined positioning information of the mobile device sent by the positioning device and based on further determined positioning information of the mobile device sent to the positioning engine by at least person of ordinary skill in the art at the effective filing date of the application to utilize additional one, two or more positioning devices in the environments of FIG 2A and B, each providing the received signal to the machine learning network 236 shown in FIG 2C, since it has been held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960). In this case the results would be totally expected such as improved accuracy in positioning and/or training of the network).”
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230144796 (DePoy) in view of US 20210368470 (SCHAEPPERLE).
Regarding claim 9, DePoy teaches “A positioning method, performed by a system comprising a positioning device and a positioning engine, the positioning method comprising:
receiving, by the positioning device, a signal from a mobile device;
determining, by the positioning device, a positioning information of the mobile device based on the received signal;
providing, by the positioning device, the determined positioning information of the mobile device to the positioning engine (the limitations above are rejected because of the same reasons as explained in the rejection of similar limitations of claim 1 above, the explanation being incorporated herein by reference.)…”
“…determining, by the positioning engine, an estimated positioning information of the mobile device based on the estimated position of the mobile device and a known position of the positioning device (paragraph 0093: the positioning engine 242 determines a position of a transmitting device based, in part, on a known distance between antenna array elements. The positioning engine 242 can then triangulate to determine the position 244 of the drone 202 (“an estimated positioning information of the mobile device”) based on the separation distance (“a known position of the positioning device”) and the DoA predictions 238 and 240 (“based on the estimated position of the mobile device”).);
obtaining, by the positioning device, an offset between the determined positioning information and the estimated positioning information determined by the positioning engine; and
training, by the positioning device, a positioning algorithm of the positioning device based on the obtained offset (the limitations above are rejected because of the same reasons as explained in the rejection of similar limitations of claim 1 above, the explanation being incorporated herein by reference.).”
With respect to “determining, by the positioning engine, an estimated position of the mobile device based on the determined positioning information of the mobile device sent by the positioning device and based on further determined positioning information of the mobile device sent to the positioning engine by at least two further positioning devices”, DePoy teaches only two positioning devices in total in FIG 2A (208 and 210) and 2B (222 and 224), supplying “the determined positioning information of the mobile device sent by the positioning device and” “further determined positioning information of the mobile device sent to the positioning engine by at least
However, DePoy’s paragraphs 0160 and 0161 teach that multiple DoA estimates may be combined in order to obtain location estimates for emitters. Multiple such platforms may combine their DoA and other estimates in order to obtain location estimates from fixed platforms.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize additional one, two or more positioning devices in the environments of FIG 2A and B, each providing the received signal to the machine learning network 236 shown in FIG 2C, since it has been held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced. In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960). In this case the results would be totally expected such as improved accuracy in positioning and/or training of the network.
Additionally or alternatively, SCHAEPPERLE teaches a similar method and a system for positioning (see FIG 2 with corresponding description in paragraphs 0158 – 0165) in which transceivers 110, 120 and 130 (each corresponding to a “positioning device” of instant claim) estimate possible position of the user equipment 310. In the process, transceiver 110 estimates a distance d1 to a point 310, e.g. a possible position of a user equipment, and an angle of arrival φ1 of signals transmitted between the user equipment and the transceiver 110. Transceivers 120 and 130 perform the same and all of the transceivers supply the measured information to the location server 220. The location server 220 combines the distances d1, d2, d3 and the angles of arrival φ1, φ2, φ3 to a position measurement for point (e.g. for the user equipment at point) 310. In other words, the location server 220 determines “an estimated position of the mobile device (310 in FIG 2) based on the determined positioning information of the mobile device sent by the positioning device (based on the distance d1 and the angle of arrival φ1) and based on further determined positioning information of the mobile device sent to the positioning engine by at least two further positioning devices (the distances d2, d3 and the angles of arrival φ2, φ3)”.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by SCHAEPPERLE determination of the estimated position of a mobile device based on individual positional information independently determined by each positioning device by combining these plurality of determined positional information, in the system of DePoy. Doing so would have allowed to increase accuracy of determination by combining multiple uncorrelated measurements.
Regarding claim 10, DePoy in combination with SCHAEPPERLE teaches or fairly suggests “wherein the determining, by the positioning engine, the estimated position of the mobile device comprises:
determining the estimated position of the mobile device based on a sum of the determined positioning information of the mobile device sent by the positioning device and the further determined positioning information of the mobile device sent by the at least two further positioning devices (SCHAEPPERLE, FIG 2 and paragraphs 0158 – 0165: transceiver 110 estimates a distance d1 to a point 310, e.g. a possible position of a user equipment, and an angle of arrival φ1 of signals transmitted between the user equipment and the transceiver 110. The transceiver 110 transmits the distance d1 and the angle of arrival φ1 to the location server 220. This represents “the determined positioning information of the mobile device sent by the positioning device”. Transceiver 120 estimates a distance d2 to the point 310, e.g. the possible position of a user equipment, and an angle of arrival φ2 of signals transmitted between the user equipment and the transceiver 120. The transceiver 120 transmits the distance d2 and the angle of arrival φ2 to the location server 220. Transceiver 130 estimates a distance d3 to the point 310, e.g. the possible position of a user equipment, and an angle of arrival φ3 of signals transmitted between the user equipment and the transceiver 130. The transceiver 130 transmits the distance d3 and the angle of arrival φ3 to the location server 220. This represents “further determined positioning information of the mobile device sent by the at least two further positioning devices”. The location server 220 combines (“based on a sum”) the distances d1, d2, d3 and the angles of arrival φ1, φ2, φ3 to a position measurement for point (e.g. for the user equipment at point) 310. Paragraph 0156: the information acquired by the plurality of access points is centralized and combined in a weighted system (“based on a sum”). The weights reflect the reliability of the position information.).”
Conclusion
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/GENNADIY TSVEY/ Primary Examiner, Art Unit 2648