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 .
Response to Amendment
Applicant’s amendment filed March 13th 2026 has been entered and made of record. Claims 1-2 and 20 are amended. New Claims 25-26 are added. Claims 10, 15-19, 21 and 23 are cancelled. Claims 1-9, 11-14, 20, 22 and 24-26 are pending.
Applicant’s remarks in view of the newly presented amendments have been considered but are not found to be persuasive for at least the following reasons:
Applicant has amended the independent claims 1 and 20 to include limitations drawn to determining a distance of a person approaching the vehicle. Examiner cites previously cited prior art reference USPN 2020/0193005 to Babala et al. to teach the added features. The rejection is accordingly made FINAL as necessitated by the amendment.
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 1-2, 4-5, 7-9, 11, 13-14, 20, 22 and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of USPNs 2022/0410841 to Anderson et al. and 2020/0193005 to Babala et al.
With regard to claim 1, Anderson discloses a system comprising:
at least one camera mounted on a first vehicle (Fig. 1, camera 106); and
at least one processing device (Fig. 3, AV computer) configured to:
update a machine-learning model trained using data from the at least one camera and data from sensors of a second vehicle (paragraph [0013], Each time an image is gathered and successfully authenticated, the images can be used to perform additional training/updating of the rider’s authentication model. The system operates with multiple different automated vehicles (AVs).);
receive first(paragraph [0012], The rider’s biometric facial image is acquired);
determine, using the first data as input to the machine-learning model, that a first person approaching the first side of the first vehicle is an authorized user (paragraphs [0012]-[0013] and [0019]-[0023], A rider’s facial image is acquired and authenticated each time they enter a different automated vehicle. The new facial image is used to update and further train the face recognition model. The facial recognition model is transferred or dispatched to a different vehicle each time the specific user requests a ride. Then Anderson discloses in paragraph [0023], the user is imaged to recognize the user’s face image. The positive authentication match of the user at pickup location is then used to trigger the applications that were predicted with the previously sent dispatch information such as temperature settings, music selections, etc.); and
in response to determining that the first person is the authorized user, perform at least one action for the first vehicle (paragraph [0012], When the rider is authenticated the AV computing system can unlock and/or open the door),
wherein the at least one action comprises performing an over-the-air update of software used by a controller of the first vehicle (paragraphs [0024]-[0025], Anderson teaches that updated recognition models are triggered by rider authentications and sent back to the management system. See also paragraphs [0012] and [0021]. The update model is received at the vehicle over the air when the vehicles is dispatched to pick up the user. The updated model is explicitly sent from the management system over the air to the vehicle. The new recognition model will be used the next time the rider is authenticated and is therefore interpreted as “performing an over-the air update of software used by a controller of the first vehicle.” The software or recognition model is updated in response to a rider authentication and the update occurs “over-the-air” in the system and used in the controller of the vehicle, either the same vehicle or a new vehicle, in subsequent recognitions. The operation of Anderson is recursive and the software is continually updated over-the air at both the management system (206) and the vehicle (204) via the updates (216) and dispatch (212). See also paragraphs [0012] and [0021]. The updated model is received at the vehicle over the air when the vehicles is dispatched to pick up the user).
Anderson does not explicitly disclose the newly added limitations of determining the distance of the first person approaching a first side of the vehicle.
Babala discloses determine, based on the first data, that a first person is approaching a first side of the first vehicle (Figs 1 and 2, and paragraphs [0029]-[0032], Cameras monitor both sides of the vehicle for an approaching person in order to determine if the person is allowed access to the vehicle and unlock the door(s) accordingly);
determine, based on the first data and that the first person is approaching the first side of the first vehicle, that a distance between the first person and the first side of the first vehicle satisfies a threshold distance (Fig. 2, d1 and d2 represent predetermined distances of a person approaching the vehicle. See also paragraphs [0005] and [0019]-[0028]. Babala discloses that approaching persons are identified according to being within predetermined distances of the vehicle and that the person is identified through facial recognition to determine if the person should be allowed access to the vehicle and accordingly unlocking/locking the door or doors).
Therefore it would have bee obvious to one of ordinary skill in the art before time of filing to use the person detection within predetermined distances as taught by Babala in order to detect authorized persons in the authorized persons detection of Anderson in order to identify nearby persons to allow access to the vehicle.
With regard to claim 2, Anderson discloses the system of claim 1, wherein the at least one action further comprises launching software for use by the first person (paragraphs [0021] and [0023], When the rider is authenticated and matched the AV system may initiate software or trigger changes according the rider’s preferences such as music, route preferences, and adjusting the temperature in the vehicle).
With regard to claim 4, Anderson discloses the system of claim 1, wherein the at least one processing device is further configured to:
determine a context of the first vehicle (paragraphs [0019] and [0021], The context of the vehicle is the GPS location);
wherein the at least one action further comprises selecting software based on the context, and launching the software (paragraph [0021], The AV launches direction software based on the user’s preferred route in the context of the GPS location).
With regard to claim 5, Anderson discloses the system of claim 1, further comprising at least one sensor configured to detect the first person, wherein the first data is image data, and the at least one camera is configured to start collecting the image data in response to the detection by the at least one sensor (paragraph [0016], retrieval of camera images can be triggered based on the vehicle reaching a location or by detecting a pedestrian approach to the vehicle).
With regard to claim 7, Anderson discloses the system of claim 5, wherein the at least one processing device is further configured to:
transmit the image data to a server that serves multiple vehicles including the first vehicle, wherein the server is configured to perform an identification process using the image data to provide an identification of a person; and receive, from the server, the identification (Fig. 2, and paragraph [0024], Management system 206 receives new rider authentication data such as successful authentications so that the recognition model can be further refined. The model is accordingly updated each time identification is performed).
With regard to claim 8, Anderson discloses the system of claim 7, wherein the server is further configured to collect data from the second vehicle, and the identification is based on the collected data (Fig. 2, and paragraph [0024], Management system 206 receives new rider authentication data such as successful authentications so that the recognition model can be further refined. The model is accordingly updated each time identification is performed).
With regard to claim 9, Anderson discloses the system of claim 5, wherein the at least one sensor is at least one of a LiDAR sensor, radar sensor, infrared sensor, or microphone (paragraph [0031], Anderson discloses using infrared cameras, dot projectors, etc. to generate 3D point clouds of proximate objects such as faces of approaching pedestrians).
With regard to claim 11, Anderson discloses the system of claim 1, wherein the at least one action further comprises executing software associated with the first person (paragraphs [0021] and [0023], When the rider is authenticated and matched the AV system may initiate software or trigger changes according the rider’s preferences such as music, route preferences, and adjusting the temperature in the vehicle).
With regard to claim 13, Anderson discloses the system of claim 1, wherein the processing device is further configured to:
determine a movement of the first person (paragraphs [0016], [0022], [0031], [0035], retrieval of camera images can be triggered based on detecting a pedestrian approach to the vehicle);
wherein determining that the first person is an authorized user is based on the movement of the first person (paragraphs [0016], [0022], [0031], [0035], retrieval of camera images can be triggered based on detecting a pedestrian approach to the vehicle. The authentication of the pedestrian is based on their detected movement or approach toward the vehicle).
With regard to claim 14, Anderson discloses the system of claim 1, wherein the at least one action further comprises adjusting at least one setting of the first vehicle (paragraphs [0021] and [0023], When the rider is authenticated and matched the AV system may initiate software or trigger changes or adjustments to settings of the vehicle according the rider’s preferences such as music, route preferences, and adjusting the temperature in the vehicle) .
With regard to claim 20, Anderson discloses a method comprising:
receiving an update to a machine-learning model of a first vehicle, the machine-learning model trained using data from a second vehicle (paragraph [0013], Each time an image is gathered and successfully authenticated, the images can be used to perform additional training/updating of the rider’s authentication model. The system operates with multiple different automated vehicles (AVs).);
receiving first data from at least one camera of the first vehicle (paragraph [0012], The rider’s biometric facial image is acquired);
determining, using the first data as input to the machine-learning model, that the first person approaching the first side of the first vehicle is an authorized user (paragraphs [0012]-[0013] and [0019]-[0023], A rider’s facial image is acquired and authenticated each time they enter a different automated vehicle. The new facial image is used to update and further train the face recognition model. The facial recognition model is transferred or dispatched to a different vehicle each time the specific user requests a ride. Then Anderson discloses in paragraph [0023], the user is imaged to recognize the user’s face image. The positive authentication match of the user at pickup location is then used to trigger the applications that were predicted with the previously sent dispatch information such as temperature settings, music selections, etc.); and
in response to determining that the first person is the authorized user, performing at least one action for the first vehicle (paragraph [0012], When the rider is authenticated the AV computing system can unlock and/or open the door), wherein the at least one action comprises performing an over-the-air updated of software used by a controller of the first vehicle (paragraphs [0024]-[0025], Anderson teaches that updated recognition models are triggered by rider authentications and sent back to the management system. See also paragraphs [0012] and [0021]. The update model is received at the vehicle over the air when the vehicles is dispatched to pick up the user. The updated model is explicitly sent from the management system over the air to the vehicle. The new recognition model will be used the next time the rider is authenticated and is therefore interpreted as “performing an over-the air update of software used by a controller of the first vehicle.” The software or recognition model is updated in response to a rider authentication and the update occurs “over-the-air” in the system and used in the controller of the vehicle, either the same vehicle or a new vehicle, in subsequent recognitions. The operation of Anderson is recursive and the software is continually updated over-the air at both the management system (206) and the vehicle (204) via the updates (216) and dispatch (212). See also paragraphs [0012] and [0021]. The updated model is received at the vehicle over the air when the vehicles is dispatched to pick up the user).
Anderson does not explicitly disclose the newly added limitations of determining the distance of the first person approaching a first side of the vehicle.
Babala discloses determining, based on the first data, that a first person is approaching a first side of the first vehicle (Figs 1 and 2, and paragraphs [0029]-[0032], Cameras monitor both sides of the vehicle for an approaching person in order to determine if the person is allowed access to the vehicle and unlock the door(s) accordingly);
determining, based on the first data and that the first person is approaching the first side of the first vehicle, that a distance between the first person and the first side of the first vehicle satisfies a threshold distance (Fig. 2, d1 and d2 represent predetermined distances of a person approaching the vehicle. See also paragraphs [0005] and [0019]-[0028]. Babala discloses that approaching persons are identified according to being within predetermined distances of the vehicle and that the person is identified through facial recognition to determine if the person should be allowed access to the vehicle and accordingly unlocking/locking the door or doors).
Therefore it would have been obvious to one of ordinary skill in the art before time of filing to use the person detection within predetermined distances as taught by Babala in order to detect authorized persons in the authorized persons detection of Anderson in order to identify nearby persons to allow access to the vehicle.
With regard to claim 22, Anderson discloses the system of claim 1, wherein the at least one action comprises booting up a computing device of the first vehicle (paragraphs [0021] and [0023], When the rider is authenticated and matched the AV system may initiate software or trigger changes according the rider’s preferences such as music, route preferences, and adjusting the temperature in the vehicle. Initiating the software according to the user’s preference is considered booting up a computing device).
With regard to claim 24, the discussion of claim 22 applies.
With regard to claim 25, Anderson and Babala disclose the system of claim 1, wherein the at least one action further comprises unlocking one or more doors of the first vehicle (Anderson: paragraph [0012]), adjusting one or more seats of the first vehicle (Babala: paragraph [0018]), adjusting one or more mirrors of the first vehicle, setting a temperature of the first vehicle (Anderson: paragraph [0023]), configuring a radio station of the first vehicle (Anderson: paragraph [0023], music settings) , setting a vehicle mode of operation of the first vehicle (Anderson: [0019], GPS coordinate data), or any combination thereof.
With regard to claim 26, both Anderson and Babala disclose the system of claim 1, wherein the first person that is approaching the first side of the first vehicle is a passenger of the first vehicle (Babala: paragraph [0013], Anderson: paragraph [0012]).
Claims 3, 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of USPNs 2022/0410841 to Anderson et al., 2020/0193005 to Babala et al., and 2019/0143936 to Abel Rayan et al.
With regard to claim 3, Anderson discloses the system of claim 1, but does not explicitly disclose wherein at least one processing device is further configured to: determine a behavior of the first person; wherein determining that the first person is the authorized user comprises providing an output from the machine-learning model using the behavior of the first person as input.
Abel Rayan discloses a similar system of imaging a potential passenger for allowing access to a vehicle based on specific identified people, and further teaches that the users rules and preferences can be learned and updated over time for each specific user. Examples are recognitions of gestures as the user approaches the vehicle, the user’s behavior within the vehicle or the context for approaching the vehicle such as with another person, etc. (paragraphs [0027]-[0030]).
Therefore, it would have been obvious to one of ordinary skill in the art at before the time of filing to enable the system to use a learning model for recognizing behavior as taught by Abel Rayan in order to customize the system based on learned user settings in combination with the user specific settings recognition of Anderson.
With regard to claim 6, Anderson discloses the system of claim 5, but does not specifically disclose wherein the image data is used to determine a behavior of the first person. The discussion of claim 3 applies. Abel Rayan discloses determining a behavior of the person being granted access to the vehicle through the use of images (paragraphs [0027]-[0030]). Therefore, it would have been obvious to one of ordinary skill in the art before the time of filing to enable the system to use a learning model for recognizing behavior as taught by Abel Rayan in order to customize the system based on learned user settings in combination with the user specific settings recognition of Anderson.
With regard to claim 12, Anderson discloses the system of claim 1, wherein determining that the first person is the authorized user comprises analyzing, using an artificial neural network (paragraphs [0025]-[0027], Anderson disclose using and updating many different machine learning models such as recurrent and convolution neural networks).
However, Anderson does not explicitly disclose determining a behavior of the first person when approaching the first vehicle. Abel Rayan discloses determining a behavior of the person being granted access to the vehicle through the use of images (paragraphs [0027]-[0030]). The discussion of claim 3 applies. Therefore, it would have been obvious to one of ordinary skill in the art before the time of filing to enable the system to use a learning model for recognizing behavior as taught by Abel Rayan in order to customize the system based on learned user settings in combination with the user specific settings recognition of Anderson.
FINAL REJECTION
Applicant’s amendment necessitated the new grounds of rejection presented in the 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 37CFR 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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESLEY J TUCKER whose telephone number is (571)272-7427. The examiner can normally be reached 9AM-5PM Monday-Friday.
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/WESLEY J TUCKER/Primary Examiner, Art Unit 2661