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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/13/26 has been entered. Claims 25-27 are new. Claims 1-27 are presented for examination.
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 1-9, 11-16, 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over Braunstein et al. (US 20230196846 A1), in view of Teller et al. (US8810392).
Re-claim 1, Braunstein et al. teach a system comprising:
-a powered device; [0025] a particular battery electric powered machine (BEM); a plurality of BEM's in a fleet of heavy equipment being operated at one or more work sites.
-the powered device comprising one or more wireless sensors operatively connected to the powered device and configured to receive wireless signals;
(see e.g. [0046] The power management device may include or may be connected to sensors.
[0070] The power management device may also include additional components such as information inputs (e.g., sensors, detectors, relays, etc.), one or more processors (e.g., microprocessors), memories (e.g., databases, ROM, RAM, EPROM, etc.), communications devices (e.g., wireless connections), user interfaces (e.g., screens, control panels, etc.), and/or motor control mechanisms.
- at least one programmable processor; and a non-transitory machine-readable medium storing instructions which, when executed by the at least one programmable processor, cause the at least one programmable processor to:
(see e.g. [0021] Such complex measurements and processing may be performed by one or more processors of controllers used in performing various methods according to embodiments of this disclosure. Some embodiments may also employ machine learning or other artificial intelligence techniques to derive more useful indications of SOH for a battery.)
--receive sensor data from the one or more wireless sensors operatively connected to a powered device;
(see e.g. [0046] The power management device may include or may be connected to sensors or other inputs to directly determine some of the information inputs. For example, the power management device may include a pre-set clock (e.g., for the current time and date), one or more optical sensors (e.g., to determine the intensity of sunlight, visibility, distance from nearby machines, etc.), and/or weather sensors (e.g., temperature, wind direction and velocity, air pressure, etc.).
[0034] Any of this information may be acquired by input (e.g., from an external telemetry, keyboard, mouse, voice command, a memory, etc.), sensor (e.g., optical detectors, etc.),
[0050] Furthermore, the power management system may use any of the sensors, gauges and detectors already present in the machine as information inputs. For example, the velocity of the machine may be detected by a speedometer which may pass information on to the power management system)
Braunstein et al. do not explicitly teach the following limitations.
However, Teller et al. teach generate, based on the sensor data, an inventory of detectable items proximate the powered device based on signal strengths of the wireless signals generated from the detectable items and received at the one or more wireless sensors;
(see e.g. col. 5, lines 59-64-- The monitoring device monitors the presence of items in its vicinity, using a short-range wireless communication or sensing technology, such as Near Field Communication (NFC), Bluetooth, RuBee, radio-frequency identification (RFID), or any other method of wireless communication or sensing.
col. 3, lines 3-7-- (a) searching for and receiving any presence signals that are transmitted by the given type of item; (b) based at least in part on a number of presence signals received at the monitoring device from items of the given type, determining a total item count for the given type of item)
--compare the inventory of detectable items to an expected inventory of detectable items that are expected to be proximate the powered device; and
(see e.g. col. 39, lines 36-41 --The monitoring device then compares the current user-context to historical user-context data, as shown by block 1506. Then, based at least in part on the comparison, the monitoring device determines a proximity framework between the monitoring device and one or more items.
--col. 39, lines 48-55 --The comparison of the current user-context to historical user-context may be accomplished using various techniques. For example, the monitoring device may receive currently-available context signals, and then determine what items were usually present at times in the past when substantially the same context signals were received. Based on this comparison, the monitoring device may conclude or at least suggest to the user that the same items should be detected currently.)
--generate an alert at a client device when the inventory of detectable items does not match the expected inventory of detectable items.
(see e.g. col. 3, lines 65-67 – col. 4, lines 1-3 -- monitoring proximity of each of the items relative to the monitoring device, based on a presence signal from each of the items, in order to determine when one of the proximity requirements is not met; and (iv) responsive to determining that one of the proximity requirements is not met, initiating the corresponding notification process.
-- col.5, lines64-67; col. 6, lines 1-3 If the monitoring device is no longer able to detect the presence of the item, thereby implying that the item has been lost, forgotten, stolen, or otherwise missing, the device issues a visual, audible, and/or physical alert to the user. The alert indicates to the user that they may want to search for or retrieve the missing item.)
-- col. 9, lines 31-44 - The alerts module 275 generates alerts associated with missing items. The alerts module 275 determines when the monitoring device 260 should issue an alert, and the type of alert to issue. In determining whether to issue an alert, the alerts module 275 may consider numerous factors, including the nature and type of item that is being monitored, the location of the monitoring device, the date or time, the day of the week, the other items that are being monitored, the other items that had been monitored in the past, the duration of time that the item was sensed, the distance the item is from the monitoring device, the velocity at which the monitoring device is moving relative to the item (either away or toward the item), the preferences of the user, the preferences of other users, trends, or on any other factor.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Braunstein et al., and include the steps cited above, as taught by Teller et al., in order to search for or retrieve the missing item on time (see e.g. col. 6, lines 2-3).
Re-claim 2, Braunstein et al. teach-the system of claim 1, wherein the powered device is a battery-operated outdoor device.
(see e.g. abstract -A control system is programmed for monitoring the health and charge of batteries used to power a battery electric machine (BEM.
[0037] The devices and systems described herein may be used with any appropriate machine, including battery electric-powered machines (BEM), hybrid internal combustion engine/electric battery powered machines, electric machines powered by the electric grid (plug-in), electric machines powered by the sun (solar), and hydrogen fuel cell machines.
[0044] solar powered machines
[0075] In any case, each of the machines may be, for example, an on or off-highway haul truck, or another type of equipment, which may haul a load material. Machines may include motor graders, excavators, dozers, dump trucks, water trucks, or another type of equipment, which may be used to repair or maintain travel route segments.).
Re-claims 3, 4, Braunstein et al. anticipate the system of claim 2, wherein the powered device is a mower. --The system of claim 2, wherein the powered device is a power drill, a handheld mower, or an electric saw.
(see e.g. [0037] The devices and systems described herein may be used with any appropriate machine, including battery electric-powered machines (BEM), hybrid internal combustion engine/electric battery powered machines, electric machines powered by the electric grid (plug-in), electric machines powered by the sun (solar), and hydrogen fuel cell machines).
The Examiner notes that the BEM taught by Braunstein covers multiple powered devices including mowers, power drills, handheld mowers and electric saws.
Re-claim 5, Braunstein et al. teach the system of claim 1, wherein the instructions, when executed, further cause the at least one programmable processor to: generate, at the client device, a last known location of an item from the expected inventory that is not in inventory.
(see e.g. [0098] The method may further include receiving positional data from various sources, including maps, GPS devices, and from other machines operating at a work site.
[0045] For example, the machine location may be provided by a GPS device which may be either a separate device or a portion of the power management device that receives a GPS signal and locates the machine based on the received signal. Geographical and topographical information about the area surrounding the machine may be determined from the location information.)
***The Examiner notes that with the GPS device, and the continuous monitoring of locations, the past or current location of any inventory item can be determined.
Re-claim 6, Braunstein et al. teach the system of claim 1, wherein the instructions, when executed, further cause the at least one programmable processor to: repeatedly generate the inventory over a period of time based on repeated acquisitions of the sensor data; monitor the inventory to determine whether the inventory meets a threshold condition for generating the alert; and generate the alert when the threshold condition is met.
(see e.g. [0083] The sensors can be configured to capture output data at split-second intervals to effectuate “real time” data capture. For example, in one embodiment, the sensors can be configured to generate thousands of data readings per second.
[0031] The power management logic may determine energy requirements imposed upon the one or more machine batteries or other power sources based on information characterizing the machine, operation of the machine, and the environment of the machine, which may include the current location of the machine, …….Any of this information may be acquired by measuring (e.g., from sensors)
[0099] Identification of aberrations or deviations from expected energy usage outside of acceptable thresholds, in some cases after analysis and elimination of outlying data, may also result in an output of a fault alarm, alert, or other report to an operator, possibly indicating a need for maintenance, automatically scheduling maintenance, or directing a manned or autonomous machine to a particular location for maintenance.
[0013] FIGS. 3-5 illustrate additional exemplary processes for segmenting a travel route for a machine, collecting historical data indicative of the health and performance of batteries powering the machine along predetermined travel route segments, comparing present health and performance of batteries powering same or similar machines traversing same or similar travel route segments, and providing fault alarms when comparison reveals results outside of threshold values.
Re-claims 7, 8, Braunstein et al. do not teach the limitations as claimed.
However, Teller et al. teach the system of claim 6, wherein the threshold condition is a time since last detected for an item in the expected inventory exceeding a maximum time since last detected. 8. The system of claim 6, wherein the threshold condition is a distance for an item in the expected inventory having a last known location exceeding a maximum distance from the powered device.
(see e.g. col. 2, lines 45-50--(ii) monitoring a presence signal from the item and detecting when the presence signal is unavailable for a predetermined period of time; and (iii) responsive to detecting that the presence signal is unavailable for a predetermined period of time, initiating the corresponding notification process,
col. 6, lines 37-41 -- If the monitoring device determines that the item is more than a predetermined distance away from the mobile phone, it issues an alert. The alert may be the same as or different than the alert that is generated when an item goes missing.)
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Braunstein et al., and include the steps cited above, as taught by Teller et al. in order to monitoring presence of items (see e.g. abstract).
Re-claim 9, Braunstein et al. teach --the system of claim 1, wherein the instructions, when executed, further cause the at least one programmable processor to: perform the comparing when the powered device crosses a speed threshold that is greater than a top speed of the powered device.
(see e.g. [0078] As a result, a speed of a machine and the radius of curvature of a particular travel route segment traversed by the machine may result in a lateral acceleration of the machine equal to the square of machine speed (V.sup.2) divided by the radius of curvature (R). The lateral acceleration of a machine as it travels along a curved path may exceed a lateral acceleration at which the machine loses traction with the surface and slides along a slide trajectory. Under these types of conditions, when the actual rolling resistance becomes indeterminate, the system may approximate rolling resistance through a comparison of energy usage for two same or similar machines traversing same or similar travel route segments while other energy usage contributing factors such as tire pressure, and gear ratio of the machine drive train, are approximately the same.).
Claims 11, 18 recite similar limitations as claim 1 and are therefore rejected under the same arts and rationale.
Claims 12, 19 recite similar limitations as claim 5 and are therefore rejected under the same arts and rationale.
Claims 13, 20 recite similar limitations as claim 6 and are therefore rejected under the same arts and rationale.
Claims 14, 21 recite similar limitations as claim 7 and are therefore rejected under the same arts and rationale.
Claims 15, 22 recite similar limitations as claim 8 and are therefore rejected under the same arts and rationale.
Claims 16, 23 recite similar limitations as claim 9 and are therefore rejected under the same arts and rationale.
Claims 10, 17, 24 are rejected under 35 U.S.C. 103 as being unpatentable over Braunstein et al. (US 20230196846 A1), in view of Teller et al. (US8810392), in view of SHAH et al. (US 20180270312 A1).
Re-claim 10, Braunstein et al., teach the system of claim 1, wherein the instructions, when executed, further cause the at least one programmable processor to: input inventory sets of powered devices as training data to a machine learning model; train the machine learning model.
(see e.g. [0017] The exemplary embodiments may also include the one or more controllers being configured to measure the actual factors indicative of energy consumption of the machine along the different travel route segments for the machine, including measuring all of the factors that were assumed and/or estimated, and then updating the assumptions and estimations of the energy consumption based on the actual measured factors, in some cases using machine learning, virtual modelling, and other artificial intelligence techniques.
[0085] The machine learning engine may be configured to receive training data comprising historically or empirically derived values for input data representing one or more of physical or operational characteristics of a historical machine that are approximately the same as corresponding physical or operational characteristics of a presently operational machine, --- . The training data may also include a plurality of historically or empirically derived energy usage of the historical machine associated with the historically or empirically derived input data.
[0086] Training of the learning system may include providing the training data as an input to the learning function, with the learning function being configured to use the at least one learning parameter to generate the plurality of projected amounts of energy usage based on the real time input data.
[0073] For example, the user interface may indicate that the power management system is engaged, what the destination (or predicted destination) is, what the optimal speed (or speeds) is, what inputs are missing or estimated, or the like. In some variations, the user interface may display any of the information inputs.)
Braunstein et al., in view of Teller et al., do not teach the following limitations as claimed.
However, SHAH et al. teach train the machine learning model to determine when an item is missing from an input inventory; input the inventory into the machine learning model; and determine, with the machine learning model, that the alert should be generated.
(see e.g. [0030] Further the disclosed system (102) comprises a model training module (212) configured to train the unified model using the collected time series data to enable computation of a plurality of parameters wherein the plurality of parameters are computed by implementing statistical machine learning techniques on the collected time series data. The disclosed system further comprises a model implementation module (214) configured to implement, using the trained unified model, the plurality of parameters on a new data of energy consumption wherein the plurality of parameters are used perform at least one from a group of outlier detection, anomaly detection, missing data imputation and prediction of consumption in energy data.
0038] Next at the step 306 the trained unified model is used to implement the plurality of parameters on a new data of energy consumption by a model implementation module (214) wherein the plurality of parameters are used perform at least one from a group of outlier detection, anomaly detection, missing data imputation and prediction of consumption in energy data.
[0032] The system utilizes this information to generate timely alerts and warnings to take appropriate actions.
0037] At the step 304, the unified model is trained using the collected time series data to enable computation of a plurality of parameters using a model training module (212). According to an aspect the plurality of parameters are computed by implementing statistical machine learning techniques on the collected time series data.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Braunstein et al., in view of Teller et al., and include the steps cited above, as taught by SHAH et al. in order to report anomalous behavior so that appropriate actions can be taken at the right time. (see e.g. [0035]).
Claim 17, 24 recite similar limitations as claim 10 and is therefore rejected under the same arts and rationale.
Claims 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over Braunstein et al. (US 20230196846 A1), in view of Teller et al. (US8810392), in view of Schuman et al. (US 20110050411 A1).
Re-claim 25, Braunstein et al.do not teach the following limitations as claimed.
However, Teller et al. teach-- The system of claim 1, the operations further comprising:
--receiving, at the powered device, a broadcast from a transmitter of the detectable item;
(see e.g. col. 3, lines 3-5--searching for and receiving any presence signals that are transmitted by the given type of item).
--generating, based on the broadcast, a machine-identification for the detectable item,
(see e.g. col. 38, lines 28-29 --The monitoring device may then apply a machine-learning process to the recorded data.
col. 38, lines 3-6 --In particular, the monitoring device may generate and store a data entry that includes: (a) data indicating the one or more context signals determined at the time and (b) data that identifies each nearby item from which a presence signal is received,
--col. 35, lines 13-22-Generally, the timestamp 1304 may be any data that links context information (e.g., context-signals 1306) to item-detection data (e.g., item IDs 1308 and the corresponding indications of presence 1310) that is recorded at the same time. As such, the timestamp 1304 may be the actual time of the snapshot or some other measure of time. Alternatively, the timestamp may be any other type of unique identifier that is assigned to a set of context information and item-detection information that is observed by the monitoring device at substantially the same time.
– col. 18, lines 30-34 --For example, monitoring device 602 may implement a process to periodically or continually observe available context signals and/or items from which presence signals are available, and to store data records recording these observations.
col. 9, lines 49-57 --FIG. 3A depicts a representative interface 300 that is displayed after the monitoring device has detected an item that could be paired with the monitoring device. A text box 310 is displayed by the monitoring device to alert a user that an item was identified that has not previously been paired with the monitoring device. The monitoring device may identify the type of item from an identification code transmitted by the item, and the text box 310 may therefore describe or otherwise identify the detected item.
--the machine-identification comprising a last-known GPS location of the detectable item,
(see e.g. the monitoring device displays additional information about the item and the conditions that existed at the time it was last sensed. --the monitoring device may provide the location coordinates of the monitoring device when the monitoring device last sensed the item.)
--a count of a running number of times that the detectable item has been detected by broadcasts from the detectable item,
(see e.g. col. 35, lines 52-56 --As noted, a monitoring device may use a method that periodically records snapshots of context and item-presence, such as method 1220, alone or in combination with a method that records a snapshot of a new context and item-presence scenario, when a change in the scenario is detected.
-col 34, lines 54-67 -- FIG. 13A is an illustration of snapshots in a historical user-context database, according to an example embodiment. The snapshots 13021-1302n in historical user-context database 1300 may be generated, at least in part, using an example method such as that illustrated by FIG. 12B. Taking snapshot 13021 as a general example of a snapshot, each snapshot may include a timestamp 1304 that indicates a time at which the snapshot was created. Further, each snapshot includes context information, such as an indication of any context signals 1306 that the monitoring device received or determined at the time. --Each snapshot also includes proximity data related to items that were detected at the time. For example, in the illustrated embodiment, each snapshot includes a list of item IDs 1308 and a corresponding indication 1310 of whether or not a presence signal was detected from the associated item at the time.
--a signal strength of the broadcast, and
(see e.g. col. 12, lines 50-55 --For example, it may determine the distance by measuring the attenuation of the signal strength from the item's sensor.
For example, the monitoring device may monitor signal strength to determine when a signal becomes faint or attenuated,
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Braunstein et al., and include the steps cited above, as taught by Teller et al., in order to monitoring presence of items (see e.g. abstract).
Braunstein et al., in view of Teller et al., do not teach the following limitations as claimed.
However, Schuman et al. teach --a summation of signal strengths of the broadcasts; and calculating an overall confidence of the last-known GPS location of the detectable device based on the summation, wherein a larger summation is indicative of a larger confidence due to the robustness of the received broadcasts.
(see e.g. [0058] At block 608, software 300 will determine the location of the tag 182 based on the confidence level or levels previously generated. In each case, the confidence level is determined based on one or more characteristics of the signal received from tag 182 (e.g. the signal strength) and the last known location of the tag 182. The previous location data for each tag is stored in one or more databases or similar computerized data structures of the system 10.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Braunstein et al., in view of Teller et al., and include the steps cited above, as taught by Schuman et al. in order to improve location accuracy. (see e.g. [0007]).
Claim 26, 27 recite similar limitations as claim 25 and is therefore rejected under the same arts and rationale.
Response to Arguments
Applicant’s arguments dated 2/13/26 have been considered but are moot because the argued limitations are taught by the new reference, Teller et al.
The 101 rejection is withdrawn. The combination of additional elements in the amendments such as “generate, based on the sensor data, an inventory of detectable items proximate the powered device based on signal strengths of the wireless signals generated from the detectable items and received at the one or more wireless sensors” uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bailey et al. (US 20210360366 A1) --Proximity-Based Contact Tracing System.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUNA CHAMPAGNE whose telephone number is (571)272-7177. The examiner can normally be reached M-F 8:00-5:00.
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/LUNA CHAMPAGNE/Primary Examiner, Art Unit 3627 May 13, 2026
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