DETAILED ACTION
This office action is in response to the amendment filed on 1/29/2026. In the amendment, claims 1 and 13 have been amended, and claims 17-20 are withdrawn. Overall, claims 1-20 are pending in this application.
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 Objections
Claims 2 and 14 are objected to because of the following informalities:
In lines 1-2 in claims 2 and 14 respectively recites “a vehicle” however the recitation should be amended to “the vehicle” in order to improve the form of the claims.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In Reference to Claims 1-16
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
For example:
In Claim 1, line 7, “determining a ground truth of the wait state” however, the resulting claim does not clearly set forth the metes and bounds of the patent protection desired since it is not clear as to what is required by the recitations. For the purposes of treating the claim under prior art, the language is interpreted as any confirmation as to the data.
In Claim 13, line 11, “determine a ground truth of the wait state” however, the resulting claim does not clearly set forth the metes and bounds of the patent protection desired since it is not clear as to what is required by the recitations. For the purposes of treating the claim under prior art, the language is interpreted as any confirmation as to the data.
The following errors explicitly found in Claims 1 and 13 are given way of examples only and not inclusive of all errors. Applicant should carefully review and amend all the claims to ensure all errors are corrected.
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.
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.
Claims 1-3 and 7-14 (as best understood) are rejected under 35 U.S.C. 103 as being unpatentable over Pub No. US 2022/0097636 A1 to Beach et. al. (Beach) in view of Pub No. US 2024/0020642 A1 to Akamine et. al. (Akamine).
In Reference to Claim 1
Beach teaches (except for the bolded and italic recitations below):
A method comprising:
processing a sensor observation to determine a wait event, wherein the wait event indicates that at least one person is in a wait state inside a stationary vehicle (see at least Beach Figs. 1-4 and paragraphs [0020], [0024], “The vehicle 110 includes a car seat monitor 114. The car seat monitor 114 can be a built-in component of the car seat 115, or can be a separate physical device connected to the car seat 115. The car seat monitor 114 includes one or more sensors 116, a processor 120, and an alert generator 124. The sensors 116 can include, for example, pressure sensors, thermometers, motion sensors, microphones, and carbon monoxide sensors. A pressure sensor can determine if the child 105 is in the car seat 115 based on the weight of the child 105. A motion sensor can be used to distinguish between the child 105 and a stationary object that may be placed in the car seat 115. A microphone can detect sounds made by the child 105. A thermometer can measure the temperature of the vehicle 110 over time to determine life-threatening conditions, since long periods of modest heat can be as dangerous as extremely high temperatures. A carbon monoxide sensor can measure and track the carbon monoxide levels within the vehicle to determine life-threatening conditions, e.g., if the child 105 was left inside the vehicle 110 in the garage 111 with the engine on” and “The car seat monitor 114 receives, from the onboard computer 108 of the vehicle 110, vehicle data 126. The vehicle data 126 can include, for example, the vehicle 110's ignition status, indicating that the vehicle 110's ignition is off. The vehicle data 126 can also include the vehicle 110's speed, indicating that the vehicle is stationary. The vehicle data 126 can include a determination of whether or not the driver's seat is occupied based on pressure sensors installed in the driver's seat. The vehicle data 126 can also include the vehicle's location. For example, the vehicle 110's location can come from a global positioning system (GPS) receiver. In some examples, the vehicle 110's location can be determined by the monitoring system 104 of the property 102. For example, the monitoring system 104 may include a geofence for determining when the vehicle 110 enters and exits the garage 111. Based on crossing the geofence, the onboard computer 108 can provide vehicle data 126 to the car seat monitor 114 indicating that the vehicle 110 is inside the garage 111”);
processing the sensor observation to determine one or more contextual features associated with a location of the wait event, a time of the wait event, the at least one person, or a combination thereof (see at least Beach Figs. 1-4 and paragraphs [0025], “In stage (B) of FIG. 1, the processor 120 receives the sensor data 118 and the vehicle data 126. The processor 120 analyzes the sensor data 118 to determine if a child is inside the vehicle 110. For example, the processor 120 can analyze the images from the scanner 122, e.g., using video analytics. The processor 120 can determine if there is a form or shape of a child within the images. The processor 120 also analyzes the data from the motion sensor to determine if there is a moving object inside the vehicle 110. The processor analyzes the audio data from the microphone using audio analytics, e.g., to detect cries of distress. The processor analyzes the pressure data from the pressure sensor to determine if a weighted object is in the car seat 115. The processor 120 determines, based on a combination of the sensor data 118 from the motion sensor, microphone, pressure sensor, and scanner images, if there is a child inside the vehicle 110”);
determining a ground truth of the wait state (see at least Beach Figs. 1-4 and paragraphs [0026] and [0027], “The combination of data from multiple sources improves the processor 120's accuracy in identifying the presence of the child 105. For example, if the child 105 is asleep, and therefore not moving or making audible sounds, the microphone and motion sensor will not trigger detection of the child 105. The processor 120 can still determine the presence of the child 105 based on analyzing a combination data from the pressure sensor and scanner images. Likewise, if the child 105 is inside the vehicle 110, but not in the car seat 115, pressure sensors and motion sensors that are built in to the car seat 115 will not trigger detection of the child 105. The processor 120 can still determine the presence of the child 105 based on analyzing data from a PIR motion sensor, a microphone, and scanner images” and “In some examples, a rule may state that a combination of sensor data 118 from at least two sensors 116 is required to determine that the child 105 is inside the vehicle 110. The processor 120 can then determine that the child 105 is inside the vehicle 110 based on, e.g., a combination of video analytics of the images from the scanner 122 and audio analytics from the audio data from the microphone. In some examples, the processor 120 may calculate a probability that the child 105 is inside the vehicle 110, based on a combination of sensor data 118. The rules can include a threshold probability, e.g., 50 percent. If the probability that the child 105 is inside the vehicle 110 exceeds the threshold probability, the processor 120 can determine that the child 105 is inside the vehicle 110”);
vectorizing the one or more contextual features and the ground truth into a training vector;
using the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and
providing the trained machine learning model as an output (see at least Beach Figs. 1-4 and paragraphs [0029], [0031], “In some examples, the processor 120 can used a machine learning approach to determine if the child 105 is inside the vehicle 110. The processor 120 can include one or more neural networks, linear or logistic regression models, decision trees, support vector machines, Bayesian techniques, nearest-neighbor or clustering techniques, or other machine learning approaches. The machine learning approach of the processor 120 may include supervised and/or unsupervised learning” and “Through machine learning, the processor 120 can learn the typical sensor data 118 collected when the child 105 is inside the vehicle 110. For example, the processor 120 can learn the child's typical weight detected by the pressure sensor, the audio frequencies of the child's voice detected by the microphone, the amount of motion typically detected by the motion sensor, and the characteristics of the images from the scanner 122 that include the child 105 inside the vehicle 110. Likewise, the processor 120 can learn the typical sensor data 118 that is collected when the child 105 is not inside the vehicle 110. The processor 120 can then determine when the child 105 is inside the vehicle 110 based on the alignment of the sensor data 118 with the typical sensor data 118 values”) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
Beach teaches a model for creating dwell time prediction model however is silent (bolded and italic recitations above) as to vectorizing the one or more contextual features and the ground truth into a training vector; using the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and providing the trained machine learning model as an output. However, it is known in the art before the effective filing date of the claimed invention to vectorizing the one or more contextual features and the ground truth into a training vector; using the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and providing the trained machine learning model as an output. For example, Akamine teaches to vectorizing the one or more variables into a training vector; using the training vector to train a machine learning model to determine predicted data based on one or more input vectors; and providing the trained machine learning model as an output. Akamine further teaches that performing such steps provide improve accuracy and efficient prediction of values (see at least Akamine Figs. 1-3, 9, 11, 13 and paragraphs 23-27, 104, 108, 110, 113, 115, 118 and 131). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Beach to perform the steps of vectorizing the one or more contextual features and the ground truth into a training vector; using the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and providing the trained machine learning model as an output as taught by Akamine in order to improve accuracy and efficient prediction of values.
In Reference to Claim 2
The method of claim 1 (see rejection to claim 1 above), wherein the wait state includes the at least one person waiting inside a vehicle (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
In Reference to Claim 3
The method of claim 1 (see rejection to claim 1 above), wherein the sensor observation comprises sensor data captured by one or more sensors (116, 122, sensors that measures vehicle data (126)) of a device, a vehicle, an infrastructure element, or a combination thereof associated with or within a field of view of the at least one person (see at least Beach Figs. 1-4 and paragraphs 22-24, 36, 84).
In Reference to Claim 7
The method of claim 1 (see rejection to claim 1 above), wherein the predicted waiting data includes a predicted likelihood of at least one subsequent person waiting at a predicted location, a predicted time, or a combination thereof (see at least Beach Figs. 1-4 and paragraphs [0029], [0031], “In some examples, the processor 120 can used a machine learning approach to determine if the child 105 is inside the vehicle 110. The processor 120 can include one or more neural networks, linear or logistic regression models, decision trees, support vector machines, Bayesian techniques, nearest-neighbor or clustering techniques, or other machine learning approaches. The machine learning approach of the processor 120 may include supervised and/or unsupervised learning” and “Through machine learning, the processor 120 can learn the typical sensor data 118 collected when the child 105 is inside the vehicle 110. For example, the processor 120 can learn the child's typical weight detected by the pressure sensor, the audio frequencies of the child's voice detected by the microphone, the amount of motion typically detected by the motion sensor, and the characteristics of the images from the scanner 122 that include the child 105 inside the vehicle 110. Likewise, the processor 120 can learn the typical sensor data 118 that is collected when the child 105 is not inside the vehicle 110. The processor 120 can then determine when the child 105 is inside the vehicle 110 based on the alignment of the sensor data 118 with the typical sensor data 118 values”) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
In Reference to Claim 8
The method of claim 1 (see rejection to claim 1 above), wherein the predicted waiting data includes a predicted reason for the at least one person being in the wait state (see at least Beach Figs. 1-4 and paragraphs [0026] “The combination of data from multiple sources improves the processor 120's accuracy in identifying the presence of the child 105. For example, if the child 105 is asleep, and therefore not moving or making audible sounds, the microphone and motion sensor will not trigger detection of the child 105. The processor 120 can still determine the presence of the child 105 based on analyzing a combination data from the pressure sensor and scanner images. Likewise, if the child 105 is inside the vehicle 110, but not in the car seat 115, pressure sensors and motion sensors that are built in to the car seat 115 will not trigger detection of the child 105. The processor 120 can still determine the presence of the child 105 based on analyzing data from a PIR motion sensor, a microphone, and scanner images”)
In Reference to Claim 9
The method of claim 8 (see rejection to claim 8 above), wherein the predicted reason includes at least one of: the at least one person waiting to pick another person; the at least one person waiting for a point of interest to open; or the at least one person sleeping in a vehicle (see at least Beach Figs. 1-4 and paragraphs [0056] “In some cases, a user 106 with a garage 111 or a parking spot at a safe temperature may choose to leave a sleeping child 105 inside the vehicle 110 with the windows down and check on the child 105 periodically. The car seat monitor 114 can include an option to assist the user 106 using a combination of sensors at the property 102. The car seat monitor 114 can include a reminder feature can be set to remind the user 106 to check on the child periodically, e.g., every 5 minutes. Additionally, carbon monoxide detectors and thermometers can be activated at a stricter threshold compared to normal settings. The microphones and motion sensors can be set to notify the user 106 at the detection of any sound or motion within the vehicle, to alert the user 106 to the child 105 waking”).
In Reference to Claim 10
The method of claim 1 (see rejection to claim 1 above), further comprising: using the trained machine learning model to determine the predicted waiting data; and initiating at least one of: generating navigation routing data, mapping data, or a combination thereof based on the predicted waiting data; recommending at least one other person for meeting up, carpooling, or a combination thereof based on the predicted waiting data; recommending at least one activity, at least one good, at least one service, marketing information, vehicle infotainment option, or a combination thereof based on the predicted waiting data; delivering at least one good, at least one service, or a combination thereof to a location of a wait event based on the predicted waiting data; presenting a warning message based on the predicted waiting data; or predicting an impact on parking, traffic, or a combination thereof based on the predicted waiting data (see at least Beach Figs. 1-4 and paragraphs [0056] “In some cases, a user 106 with a garage 111 or a parking spot at a safe temperature may choose to leave a sleeping child 105 inside the vehicle 110 with the windows down and check on the child 105 periodically. The car seat monitor 114 can include an option to assist the user 106 using a combination of sensors at the property 102. The car seat monitor 114 can include a reminder feature can be set to remind the user 106 to check on the child periodically, e.g., every 5 minutes. Additionally, carbon monoxide detectors and thermometers can be activated at a stricter threshold compared to normal settings. The microphones and motion sensors can be set to notify the user 106 at the detection of any sound or motion within the vehicle, to alert the user 106 to the child 105 waking”) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
In Reference to Claim 11
The method of claim 1 (see rejection to claim 1 above), further comprising: using the trained machine learning model to determine the predicted waiting data; and using the predicted waiting data as an input to an autonomous vehicle control system for at least one of: risk calculation; routing; adapting a safety distance at a specific location, a specific time, or a combination thereof; selecting a travel lane; and sharing a ride (see at least Beach Figs. 1-4 and paragraphs [0032] “Once the processor 120 determines that the child 105 is inside the vehicle 110, the processor 120 analyzes the sensor data 118 and the vehicle data 126 to determine if the child 105 is in a dangerous situation within the vehicle 110. The processor 120 can determine if the child 105 is in a dangerous situation based on sensor data 118, e.g., from the thermometer and carbon monoxide detector, and based on vehicle data 126, e.g., indicating the location, speed, and presence a driver. For example, the vehicle data 126 may indicate that the vehicle 110 is inside the garage 111 with the ignition off, and the driver's seat is unoccupied. The sensor data 118 may indicate that the interior temperature is 90° F., and that no carbon monoxide is detected”) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
In Reference to Claim 12
The method of claim 1 (see rejection to claim 1 above), wherein the wait state indicates that the at least one person is remaining within a predetermined proximity of the location of the wait event until an occurrence of an anticipated event (see at least Beach Figs. 1-4 and paragraphs [0033] “The processor can used a rules-based system to determine that the child 105 is in a dangerous situation due to being left alone inside the vehicle 110 in the hot garage 111. For example, a rule may state that a dangerous situation exists for the child 105 after 20 minutes alone in a cool garage, and after 10 minutes alone minutes in a hot garage. Another rule may state that a dangerous situation exists after 10 minutes alone in a public location at low temperatures, and after 5 minutes alone in a public location at higher temperatures. In some examples, the rules can be customized according to the particular geographical location, season of the year, age of the child, or other factors”) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
In Reference to Claim 13
Beach teaches (except for the bolded and italic recitations below):
An apparatus for machine-learning of a vehicular wait event comprising:
at least one processor (108, 120); and
at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor (108, 120) (see at least Beach Figs. 1-4 and paragraphs [0164] “Each computer program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language may be a compiled or interpreted language. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, a processor will receive instructions and data from a read-only memory and/or a random access memory. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and Compact Disc Read-Only Memory (CD-ROM). Any of the foregoing may be supplemented by, or incorporated in, specially designed ASICs (application-specific integrated circuits)”), cause the apparatus to perform at least the following,
process a sensor observation to determine a wait event, wherein the wait event indicates that at least one person is in a wait state inside a stationary vehicle (see at least Beach Figs. 1-4 and paragraphs [0020], [0024], “The vehicle 110 includes a car seat monitor 114. The car seat monitor 114 can be a built-in component of the car seat 115, or can be a separate physical device connected to the car seat 115. The car seat monitor 114 includes one or more sensors 116, a processor 120, and an alert generator 124. The sensors 116 can include, for example, pressure sensors, thermometers, motion sensors, microphones, and carbon monoxide sensors. A pressure sensor can determine if the child 105 is in the car seat 115 based on the weight of the child 105. A motion sensor can be used to distinguish between the child 105 and a stationary object that may be placed in the car seat 115. A microphone can detect sounds made by the child 105. A thermometer can measure the temperature of the vehicle 110 over time to determine life-threatening conditions, since long periods of modest heat can be as dangerous as extremely high temperatures. A carbon monoxide sensor can measure and track the carbon monoxide levels within the vehicle to determine life-threatening conditions, e.g., if the child 105 was left inside the vehicle 110 in the garage 111 with the engine on” and “The car seat monitor 114 receives, from the onboard computer 108 of the vehicle 110, vehicle data 126. The vehicle data 126 can include, for example, the vehicle 110's ignition status, indicating that the vehicle 110's ignition is off. The vehicle data 126 can also include the vehicle 110's speed, indicating that the vehicle is stationary. The vehicle data 126 can include a determination of whether or not the driver's seat is occupied based on pressure sensors installed in the driver's seat. The vehicle data 126 can also include the vehicle's location. For example, the vehicle 110's location can come from a global positioning system (GPS) receiver. In some examples, the vehicle 110's location can be determined by the monitoring system 104 of the property 102. For example, the monitoring system 104 may include a geofence for determining when the vehicle 110 enters and exits the garage 111. Based on crossing the geofence, the onboard computer 108 can provide vehicle data 126 to the car seat monitor 114 indicating that the vehicle 110 is inside the garage 111”);
process the sensor observation to determine one or more contextual features associated with a location of the wait event, a time of the wait event, the at least one person, or a combination thereof (see at least Beach Figs. 1-4 and paragraphs [0025], “In stage (B) of FIG. 1, the processor 120 receives the sensor data 118 and the vehicle data 126. The processor 120 analyzes the sensor data 118 to determine if a child is inside the vehicle 110. For example, the processor 120 can analyze the images from the scanner 122, e.g., using video analytics. The processor 120 can determine if there is a form or shape of a child within the images. The processor 120 also analyzes the data from the motion sensor to determine if there is a moving object inside the vehicle 110. The processor analyzes the audio data from the microphone using audio analytics, e.g., to detect cries of distress. The processor analyzes the pressure data from the pressure sensor to determine if a weighted object is in the car seat 115. The processor 120 determines, based on a combination of the sensor data 118 from the motion sensor, microphone, pressure sensor, and scanner images, if there is a child inside the vehicle 110”);
determine a ground truth of the wait state (see at least Beach Figs. 1-4 and paragraphs [0026] and [0027], “The combination of data from multiple sources improves the processor 120's accuracy in identifying the presence of the child 105. For example, if the child 105 is asleep, and therefore not moving or making audible sounds, the microphone and motion sensor will not trigger detection of the child 105. The processor 120 can still determine the presence of the child 105 based on analyzing a combination data from the pressure sensor and scanner images. Likewise, if the child 105 is inside the vehicle 110, but not in the car seat 115, pressure sensors and motion sensors that are built in to the car seat 115 will not trigger detection of the child 105. The processor 120 can still determine the presence of the child 105 based on analyzing data from a PIR motion sensor, a microphone, and scanner images” and “In some examples, a rule may state that a combination of sensor data 118 from at least two sensors 116 is required to determine that the child 105 is inside the vehicle 110. The processor 120 can then determine that the child 105 is inside the vehicle 110 based on, e.g., a combination of video analytics of the images from the scanner 122 and audio analytics from the audio data from the microphone. In some examples, the processor 120 may calculate a probability that the child 105 is inside the vehicle 110, based on a combination of sensor data 118. The rules can include a threshold probability, e.g., 50 percent. If the probability that the child 105 is inside the vehicle 110 exceeds the threshold probability, the processor 120 can determine that the child 105 is inside the vehicle 110”);
vectorize the one or more contextual features and the ground truth into a training vector;
use the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and
provide the trained machine learning model as an output (see at least Beach Figs. 1-4 and paragraphs [0029], [0031], “In some examples, the processor 120 can used a machine learning approach to determine if the child 105 is inside the vehicle 110. The processor 120 can include one or more neural networks, linear or logistic regression models, decision trees, support vector machines, Bayesian techniques, nearest-neighbor or clustering techniques, or other machine learning approaches. The machine learning approach of the processor 120 may include supervised and/or unsupervised learning” and “Through machine learning, the processor 120 can learn the typical sensor data 118 collected when the child 105 is inside the vehicle 110. For example, the processor 120 can learn the child's typical weight detected by the pressure sensor, the audio frequencies of the child's voice detected by the microphone, the amount of motion typically detected by the motion sensor, and the characteristics of the images from the scanner 122 that include the child 105 inside the vehicle 110. Likewise, the processor 120 can learn the typical sensor data 118 that is collected when the child 105 is not inside the vehicle 110. The processor 120 can then determine when the child 105 is inside the vehicle 110 based on the alignment of the sensor data 118 with the typical sensor data 118 values”) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
Beach teaches a model for creating dwell time prediction model however is silent (bolded and italic recitations above) as to vectorize the one or more contextual features and the ground truth into a training vector; use the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and provide the trained machine learning model as an output. However, it is known in the art before the effective filing date of the claimed invention to vectorize the one or more contextual features and the ground truth into a training vector; use the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and provide the trained machine learning model as an output. For example, Akamine teaches to vectorize the one or more variables into a training vector; use the training vector to train a machine learning model to determine predicted data based on one or more input vectors; and provide the trained machine learning model as an output. Akamine further teaches that performing such steps provide improve accuracy and efficient prediction of values (see at least Akamine Figs. 1-3, 9, 11, 13 and paragraphs 23-27, 104, 108, 110, 113, 115, 118 and 131). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Beach to perform the steps of vectorize the one or more contextual features and the ground truth into a training vector; use the training vector to train a machine learning model to determine predicted waiting data based on one or more input vectors; and provide the trained machine learning model as an output as taught by Akamine in order to improve accuracy and efficient prediction of values.
In Reference to Claim 14
The apparatus of claim 13 (see rejection to claim 13 above), wherein the wait state includes the at least one person waiting inside a vehicle (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
Claims 4-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Beach in view of Akamine and further in view of Pub No. US 2020/0167575 A1 to Nayak et. al. (Nayak).
In Reference to Claim 4
Beach in view of Akamine teaches (except for the bolded and italic recitations below):
The method of claim 1 (see rejection to claim 1 above), further comprising: map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location of the wait event (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
Beach teaches to determine the locations of the vehicle by GPS however Beach in view of Akamine is silent (bolded and italic recitations above) as to map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location of the wait event. However, it is known in the art before the effective filing date of the claimed invention to map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location. For example, Nayak teaches to map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location. Nayak further implicitly teaches that performing such step provides accurate determination of the locations (see at least Nayak Figs. 1-3 and paragraphs 52, 63). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kamiya in view of Akamine to perform the function of map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location as taught by Nayak in order to provide accurate determination of the locations
In Reference to Claim 5
The method of claim 4 (see rejection to claim 4 above), wherein the one or more contextual features further includes one or more attributes of the map link determined from a geographic database (e.g. garage, parking lot…) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39, 47, 62 and 77-88).
In Reference to Claim 6
The method of claim 5 (see rejection to claim 5 above), wherein the one or more attributes include a functional class, a speed limit, a presence of a road sign, a bi-directionality, a number of lanes, a speed category, a distance to a point of interest, a stopping or parking sign, a designated stopping or parking area, or a combination thereof associated with the map link (e.g. garage, parking lot…) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39, 47, 62 and 77-88).
In Reference to Claim 15
Beach in view of Akamine teaches (except for the bolded and italic recitations below):
The apparatus of claim 13 (see rejection to claim 13 above), wherein the apparatus is further caused to: map-match location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location of the wait event (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39 and 77-88).
Beach teaches to determine the locations of the vehicle by GPS however Beach in view of Akamine is silent (bolded and italic recitations above) as to map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location of the wait event. However, it is known in the art before the effective filing date of the claimed invention to map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location. For example, Nayak teaches to map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location. Nayak further implicitly teaches that performing such step provides accurate determination of the locations (see at least Nayak Figs. 1-3 and paragraphs 52, 63). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kamiya in view of Akamine to perform the function of map-matching location data of the sensor observation to a map link, an offset on the map link, or a combination thereof to determine the location as taught by Nayak in order to provide accurate determination of the locations
In Reference to Claim 16
The apparatus of claim 15 (see rejection to claim 15 above), wherein the one or more contextual features further includes one or more attributes of the map link determined from a geographic database (e.g. garage, parking lot…) (see at least Beach Figs. 1-4 and paragraphs 20, 24, 26-27, 29-39, 47, 62 and 77-88).
Response to Arguments
Applicant's arguments filed 1/29/2026 have been fully considered but they are not persuasive.
In response to Applicant's argument that Akamine is nonanalogous art, it has been held that the determination that a reference is from a nonanalogous art is twofold. First, we decide if the reference is within the field of the inventor's endeavor. If it is not, we proceed to determine whether the reference is reasonably pertinent to the particular problem with which the inventor was involved. In re Wood, 202 USPQ 171, 174. In this case, the examiner respectfully disagree with the applicant since Akamine is reasonably pertinent to the problem faced by the inventor as the claimed invention since by performing the steps “vectorizing the one or more contextual features and the ground truth into a training vector; using the training vector to train a machine learning model to determine data based on one or more input vectors; and providing the trained machine learning model as an output” in machine learning models to improve the accuracy of the prediction values. Therefore in combination of Beach in view of Akamine teaches the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub No. US 2020/0182638 A1 to Suzuki et. al (Suzuki) teaches automated driving buses or by a driver.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON DONGPA LEE whose telephone number is (571)270-3525. The examiner can normally be reached Monday - Friday, 8:00 am - 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aniss Chad can be reached at (571) 270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRANDON D LEE/Primary Examiner, Art Unit 3662 May 19, 2026
Prosecution Insights