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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Joint Inventors
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.
Response to Amendment
The amendments filed on 11/24/2025 have been entered. Claims 1-4, 6-13, 15, and 21-27 remain pending in the application.
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.
Claims 21, 24-25, and 27 are rejected under 35 U.S.C. 103 as being obvious over Halder et al., US 20200181879 A1, herein referred to as Halder, and in view of Arun et al., WO 2023240170 A1, herein referred to as Arun.
Regarding claim 21, Halder discloses one or more processors (Fig. 11, Paragraph 0016; system includes one or more processors), one or more memory in data communication with the one or more processors, the one or more memory including computer readable data stored thereon, the computer readable data including instructions that, when executed by the one or more processors, performs a method for operating a mobile robot with respect to a reference location (Fig. 11, Paragraph 0016; system includes memory that store instructions for executing programming), generating first state data using first sensor data obtained from a first set of sensors, the first set of sensors relating to a first sensor modality (Paragraphs 0094, 0110; a first set of observation data may be obtained by multiple sensors; such sensors can include lidar, GPS, etc. which may be considered to be related through a single modality), generating second state data using second sensor data obtained from a second set of sensors, the second set of sensors providing remote sensing and the second state data being generated from remote features of the second sensor data (Paragraphs 0043, 0125; a second set of new observation data may be obtained by multiple sensors, these sensors could be remote sensors which operate through a remote basis, remote sensor data may be considered a remote feature), generating a first distribution of the first state data (Figs. 5, 6A, Paragraphs 0091-0093, 0096-0098; a distribution may be generated regarding the first set of observation data, this distribution can be a prior distribution or a first posterior distribution), generating a second distribution of the second state data (Figs. 5, 6A, 6B, Paragraphs 0091, 0096-0098; a distribution may be generated based on a second, new set of observation data, this second distribution can be obtained through the new observations 565 or could be the prior distribution 525 during an update of the trained model), computing a posterior distribution by performing a probabilistic fusion of the first probability distribution and the second probability distribution (Figs. 5, 6A, 6B, Paragraphs 0096-0098, 0101-0104; the second observation data distribution may be used to narrow down a prior or previous posterior distribution that was obtained using a first set of observation data, the newly observed data is used to calculate an updated posterior distribution which can be considered a fusing of first and second probability distributions; a fusion involving probability distributions can be considered as a probabilistic fusion), generating optimal state data along with associated uncertainty data using the posterior distribution, the optimal state data including a position estimate of the mobile robot (Fig. 5, Paragraphs 0055, 0059, 0116; a predicted output may be generated utilizing the updated posterior distribution, the predicted output is based on the updated posterior distribution and includes an uncertainty (likelihood region), predicted output may considered as optimal state data as it is a predicted path which has associated position estimates of the vehicle through the sensor inputs), and controlling the mobile robot using at least the optimal state data (Paragraph 0116; vehicle may be controlled based on the predicted output (optimal state data)), but fails to disclose generating second state data using second sensor data obtained from a second set of sensors, the second set of sensors providing wireless sensing and the second state data being generated from wireless features of the second sensor data.
However, Arun, in an analogous field of endeavor, teaches generating second state data using second sensor data obtained from a second set of sensors, the second set of sensors providing wireless sensing and the second state data being generated from wireless features of the second sensor data (Paragraphs 0014, 0086; RSSI and CSI may be used for localization of a robot through RF sensors; RSSI may provide position and proximity data regarding the robot; RSSI and CSI are wireless features of the RF sensors).
Therefore, from the teaching of Arun, it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified, with a reasonable expectation for success, the sensing system of Halder to include generating second state data using second sensor data obtained from a second set of sensors, the second set of sensors providing wireless sensing and the second state data being generated from wireless features of the second sensor data, as taught/suggested by Arun. The motivation to do so would be to utilize an effective sensing technology for determining locations of objects. By utilizing wireless sensing, the system will be able to determine distances and locations even when obstruction occurs. This allows for an increase of accuracy in the system by utilizing an additional sensing scheme.
Regarding claim 24, Halder in view of Arun renders obvious all the limitations of claim 21. Halder further discloses the position estimate includes a distance of the mobile robot with respect to the reference location (Paragraph 0124; control of the vehicle may be based on an output of the trained model; output may be a predicted path which can be compared to an actual path; this can be considered as determining distancing from the vehicle to at least one reference location), but fails to disclose the wireless features include at least received signal strength indicator (RSSI) data. However, Arun teaches the wireless features include at least received signal strength indicator (RSSI) data (Paragraphs 0014, 0086; RSSI and CSI may be used for localization of a robot through RF sensors; RSSI may provide position and proximity data regarding the robot; RSSI and CSI are wireless features of the RF sensors). Therefore, from the teaching of Arun, it would have been obvious to one of ordinary skill in the art before the effective filing date to have further modified the sensing system of Halder and Arun to include the wireless features include at least received signal strength indicator (RSSI) data, as taught/suggested by Arun. The motivation to do so would be to utilize a well-known metric for wireless communication quality to assess the wireless sensing quality.
Regarding claim 25, Halder in view of Arun renders obvious the limitations of claim 21. Halder further discloses the position estimate includes a two-dimensional (2D) position of the mobile robot with respect to the reference location (Paragraphs 0056, 0124; output of trained model has associated localization inputs which can include GPS coordinates of the vehicle which are two-dimensional), but fails to disclose the wireless features include at least channel state information (CSI) data. However, Arun teaches the wireless features include at least channel state information (CSI) data (Paragraphs 0014, 0086; RSSI and CSI may be used for localization of a robot through RF sensors; RSSI may provide position and proximity data regarding the robot; RSSI and CSI are wireless features of the RF sensors). Therefore, from the teaching of Arun, it would have been obvious to one of ordinary skill in the art before the effective filing date to have further modified the sensing system of Halder and Arun to include the wireless features include at least channel state information (CSI) data, as taught/suggested by Arun. The motivation to do so would be to utilize another well-known metric for wireless communication quality to assess the wireless sensing quality.
Regarding claim 27, Halder in view of Arun renders obvious all the limitations of claim 21. Halder further discloses the probabilistic fusion being performed via Bayesian filters (Figs. 5, 6A, 6B, Paragraphs 0096-0098, 0101-0104; updated posterior distributions may be utilized for estimating functions for control system; a Bayesian model may be used which can include a Gaussian mean function; the mean function within the Bayesian model can be considered a filter as it effectively filters data between prior and posterior distributions by utilizing a mean of the data; the Bayesian model can be applied to new posterior distributions as well).
Claim 22 is rejected under 35 U.S.C. 103 as being obvious over Halder, in view of Arun, and further in view of Brazeau, US 20180113475 A1, herein referred to as Brazeau.
Regarding claim 22, Halder in view of Arun renders obvious all the limitations of claim 21. Halder further discloses the first set of sensors are configured to perform visual odometry (Paragraph 0056; localization system may utilize sensor data for visual odometry), but fails to disclose the first set of sensors are configured to perform visual odometry and fiducial tag sensing. However, Brazeau, in an analogous field of endeavor, teaches the first set of sensors are configured to perform visual odometry and fiducial tag sensing (Paragraph 0062; robot may utilize a position sensor to detect fiducial marks within a camera field of view, and can be used for localization of the robot). Therefore, from the teaching of Brazeau, it would have been obvious to one of ordinary skill in the art before the effective filing date to have further modified, with a reasonable expectation for success, the sensing system of Halder and Arun to include the first set of sensors are configured to perform visual odometry and fiducial tag sensing, as taught/suggested by Brazeau. The motivation to do so would be to utilize a well-known sensing technology for localization which can lead to improved accuracy of the system.
Allowable Subject Matter
Claims 1-4, 6-13, and 15 are allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding independent claim 1, the examiner has completed a thorough search and has not found a piece of prior art, either alone or in combination with other prior art, that discloses, teaches, suggests, or renders obvious the claim limitations. The closest piece of prior art, US 20200181879 A1 by Halder, discloses a portion of the claim limitations, but fails to disclose generating, via a machine learning model, position data as output upon receiving the wireless features as input, wherein, the machine learning model is a regression model, and the wireless features include fine time measurement (FTM) data. These features are novel in that they allow a specific machine learning model to be trained based on wireless features for determining position data of a robot. This can allow for the robot to determine its positioning based on wireless sensing technology, which may be advantageous in areas where standard sensors have limited or no functionality (GPS in dense urban canyons, etc.). Additionally, utilizing fine time measurement (FTM) as the feature input for the regression ML allows for specific ML position outputs that may be more accurate compared to other wireless feature inputs.
Regarding independent claim 7, the examiner has completed a thorough search and has not found a piece of prior art, either alone or in combination with other prior art, that discloses, teaches, suggests, or renders obvious the claim limitations. The closest piece of prior art, US 20200181879 A1 by Halder, discloses a portion of the claim limitations, but fails to disclose generating, via a machine learning model, two-dimensional (2D) position data as output upon receiving the wireless features as input, wherein, the machine learning model is a classifier, and the wireless features include channel state information (CSI) data. These features are novel in that they allow a specific machine learning model to be trained based on wireless features for determining position data of a robot. This can allow for the robot to determine its positioning based on wireless sensing technology, which may be advantageous in areas where standard sensors have limited or no functionality (GPS in dense urban canyons, etc.). Additionally, utilizing channel state information (CSI) as the feature input for the classifier ML allows for specific ML position outputs that may be more accurate compared to other wireless feature inputs.
Regarding independent claim 10, the examiner has completed a thorough search and has not found a piece of prior art, either alone or in combination with other prior art, that discloses, teaches, suggests, or renders obvious the claim limitations. The closest piece of prior art, US 20200181879 A1 by Halder, discloses a portion of the claim limitations, but fails to disclose generating, via a machine learning model, two-dimensional (2D) position data as output upon receiving the wireless features as input, wherein, the machine learning model is a classifier, and the wireless features include channel state information (CSI) data. These features are novel in that they allow a specific machine learning model to be trained based on wireless features for determining position data of a robot. This can allow for the robot to determine its positioning based on wireless sensing technology, which may be advantageous in areas where standard sensors have limited or no functionality (GPS in dense urban canyons, etc.). Additionally, utilizing channel state information (CSI) as the feature input for the classifier ML allows for specific ML position outputs that may be more accurate compared to other wireless feature inputs.
Claims 23 and 26 are 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 23, the examiner has completed a thorough search and has not found a piece of prior art, either alone or in combination with other prior art, that discloses, teaches, suggests, or renders obvious the claim limitations. The closest piece of prior art, US 20200181879 A1 by Halder, discloses the position estimate includes the position data, the position data including a distance of the mobile robot with respect to the reference location, but fails to disclose generating, via a machine learning model, position data as output upon receiving the wireless features as input, wherein, the machine learning model is a regression model, and the wireless features include fine time measurement (FTM) data. These features are novel in that they allow a specific machine learning model to be trained based on wireless features for determining position data of a robot. This can allow for the robot to determine its positioning based on wireless sensing technology, which may be advantageous in areas where standard sensors have limited or no functionality (GPS in dense urban canyons, etc.). Additionally, utilizing fine time measurement (FTM) as the feature input for the regression ML allows for specific ML position outputs that may be more accurate compared to other wireless feature inputs.
Regarding claim 26, the examiner has completed a thorough search and has not found a piece of prior art, either alone or in combination with other prior art, that discloses, teaches, suggests, or renders obvious the claim limitations. The closest piece of prior art, US 20200181879 A1 by Halder, discloses the position estimate includes the 2D position data, but fails to disclose generating, via a machine learning model, two-dimensional (2D) position data as output upon receiving the wireless features as input, wherein, the machine learning model is a classifier, and the wireless features include channel state information (CSI) data. These features are novel in that they allow a specific machine learning model to be trained based on wireless features for determining position data of a robot. This can allow for the robot to determine its positioning based on wireless sensing technology, which may be advantageous in areas where standard sensors have limited or no functionality (GPS in dense urban canyons, etc.). Additionally, utilizing channel state information (CSI) as the feature input for the classifier ML allows for specific ML position outputs that may be more accurate compared to other wireless feature inputs.
Response to Arguments
Applicant's arguments filed 11/24/2025 have been fully considered but they are not persuasive.
Regarding independent claim 21, Applicant is arguing that Halder fails to explicitly disclose computing a posterior distribution by performing a probabilistic fusion of the first and second probability distributions. Specifically, Applicant is stating that Halder’s updating of a prior distribution to a posterior distribution is not a “probabilistic fusion” of two probability distributions. However, utilizing one probability distribution to narrow down (update/filter) another distribution can be considered a probabilistic fusion as the data from both probability distributions are utilized to generate a new posterior distribution. Additionally, any model or filter (see claim 27) that affects posterior distributions (new or updated) is effectively doing the same fusion process as previous data is being filtered utilizing new data (mean in the case of the Gaussian function, see claim 27 rationale).
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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/C.A.B./Examiner, Art Unit 3658
/JASON HOLLOWAY/Primary Examiner, Art Unit 3658