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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/10/2026 has been entered.
Response to Arguments
The 35 U.S.C. 112(b) rejection set forth in the prior Office action is withdrawn.
Applicant argues at page 10 that Teo’s forecasted SOH is not a “composite score” as claimed. The examiner respectfully disagrees for the reasons set forth at pages 2-7 of the Advisory action mailed 6/24/2026, the substance of which is duplicated in the claim rejections set forth below.
Applicant argues at page 10 that neither Gallegos or Teo, whether taken alone or in combination, disclose or suggest pack-level aggregation of composite scores, as required in Applicant's independent claims. The examiner respectfully disagrees. Claim 1 recites “considering an aggregate of the composite scores”. Claim 26 uses similar language. Gallegos discloses in paragraph 51, for example:
The energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keeps track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600. Thus, each pack may be addressable and may be queried as to the health and status at any time. If there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation.
The examiner maintains that at least the highlighted language above establishes that Gallegos considers the aggregate (i.e., all) of the individual status information when determining whether a modified use pattern is necessary, and that Gallegos as modified considers the aggregate of the composite scores for making the same determination. The same reasoning is applied to claim 25 and 35, which each recite the language “determining … based at least in part on an aggregate of the composite scores”, which is tantamount to making the determination based on all of the composite scores.
Applicant argues at page 11 in response to the Advisory action mailed 6/24/2026 that even assuming arguendo that Teo's SOH can be considered a "composite score," (which Applicant does not concede), nowhere does Teo disclose or suggest "a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the battery element," as recited in the amended independent claims. The examiner respectfully disagrees. Teo discloses in paragraph 29, for example, that “aspects of the behavioral forecast system 108 provide a model framework and a class of machine learning algorithms that may use time-series data of fundamental battery measurements such as current, voltage, temperature, or the like, to forecast battery behavior”. Teo also discloses in connection with its dependent claims 4-6, for example, that current data, voltage data and temperature data may form the basis for the forecasted SOH. In this regard, the examiner notes that the forecasted SOH is derived from and distinct from battery parameters such as current, voltage, temperature.
In response to applicant’s arguments at pages 11-12 pertaining to “considering an aggregate of composite scores”, the examiner maintains (see above) that Gallegos considers the aggregate (i.e., all) of the individual status information when determining whether a modified use pattern is necessary, and that Gallegos as modified considers the aggregate of the composite scores for making the same determination. The same reasoning is applied to claim 25 and 35, which each recite the language “determining … based at least in part on an aggregate of the composite scores”, which is tantamount to making the determination based on all of the composite scores. The examiner emphasizes that Gallegos teaches considering cell parameters in the aggregate (see, e.g., paragraph 28, energy storage master unit keeps track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation), and Gallegos as modified such that the tracked cell parameters also include composite scores in the form of forecasted SOH considers the composite scores for the cells in the aggregate. Whether or not Teo considers the forecasted SOH in the aggregate is not necessary to support the rejections as presented. The examiner notes that the remarks in the Advisory action pertaining to Teo’s teachings in paragraph 7 were merely in response to applicant’s characterization of these teachings in the Response after final mailed 6/5/2026. The examiner nonetheless maintains the position presented in the Office action pertaining to Teo’s teachings in paragraph 7.
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 1-8, 10-14, 17-22 and 24-35 are rejected under 35 U.S.C. 103 as being unpatentable over Gallegos in view of US 2022/0284747 to Teo et al. (Teo).
Regarding claim 1, Gallegos discloses a method of mitigating battery defects in battery packs, the method comprising:
(a) monitoring a plurality of battery parameters associated with one or more battery elements of a battery pack comprising a plurality of battery elements (Gallegos, e.g., Fig. 3 and paragraph 37, referring to FIG. 3, examples of arrangements and interconnections within packs and strings are shown; the power connections in a string may consist of two packs in series and those series packs may be paralleled with two other packs; each pack may consist of eight Local Module Units connected in series; each Local Module Unit may balance ten battery cells also connected in series; the examiner notes in Fig. 3 that the two top packs are in series and the bottom two packs are in series; also see Fig. 6 and paragraphs 51-76 which discloses one example of the architecture of a battery pack of Fig. 3, with the pack including a number of battery modules 600; each battery module 600 may have a Local Module Unit 601 which feeds data to a Pack Master 610; the Pack Master 610 may then send aggregated data back to an Energy Storage Master which may interface with a Vehicle Master Controller; the energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; the examiner notes that the term “battery element” is broad in scope and includes (1) a battery module of a battery pack having a plurality of battery modules, or (2) a battery cell of a battery pack having a plurality of battery cells; Gallegos’ monitoring meets either interpretation of “battery element” because Gallegos monitors one or more battery parameters associated with one or more battery modules of a battery pack comprising a plurality of battery modules, and because Gallegos monitors one or more battery parameters associated with one or more battery cells of a battery pack comprising a plurality of battery cells);
(b) determining individual status information, each corresponding to one of the one or more battery elements of the battery pack based on the plurality of battery parametersthere is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation); and
(c) modifying a use pattern of the battery pack based at least in part by considering an aggregate of the individual status information associated with the different battery elements of the battery pack, wherein the modified use pattern reduces a probability of a future defect in the battery pack (see Gallegos as discussed above, e.g., Fig. 6 and paragraph 51, energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; thus, each pack may be addressable and may be queried as to the health and status at any time; if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation; also see, e.g., paragraph 55, if the current is in excess of 350 Amps, either charging or discharging, and this condition has existed continuously for five seconds, a request may be made to open the contactor for the string exceeding this limit; if the temperature is in excess of 65 degrees Celsius, a request may be made to open a string contactor and notify the operator of a fault; also see, e.g., paragraph 61, if temperature in excess of +58 C, the operator shall be notified of a temperature warning, and the charge and discharge shall be derated; also see paragraph 37, each Local Module Unit may balance ten battery cells also connected in series; at least the underlined teachings of Gallegos above constitutes considering an aggregate of the individual status information for the individual battery cells and modifying a use pattern of the battery pack based on this consideration to reduce a probability of a future defect in the battery pack, e.g., by removing it from service).
Gallegos is not relied upon as explicitly disclosing wherein the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the corresponding battery element, and that considering the aggregate of the composite scores forms the basis for modifying the use pattern. Regarding the meaning of the term “composite score”, applicant’s disclosure provides at paragraph 90:
[0090] Battery health index may be a metric that is normalized (e.g., to a value between 0 and 100 as shown in FIG. 7D). The battery health index may be a composite score that is determined based on multiple battery parameter values, such as CPV, diffusion time, partial relaxation time, full relaxation time, DC impedance, EIS quantities (e.g., real impedance and/or complex impedance), temperature, capacity, or any combination thereof. In some implementations, the battery health index may be determined using a mathematical function or a model (e.g., a regression model, or the like), that takes, as inputs, one or more battery parameters (e.g., one, two, three, five, etc. battery parameters) and generates the battery health index as an output. In some embodiments, the battery health index may be determined by employing a function that takes, as inputs, one or more battery parameters and determines the battery health index as an output. The function may be a weighted sum, an exponential function, a polynomial function, utilize the output of a machine learning model, or the like. In one example, a battery health index may be determined as a combination of CPV and diffusion information. In another example, a battery health index may be determined based on high frequency impedance information, which may include real and/or imaginary impedances. In yet another example, in some implementations, a battery health index may be determined based on DC impedance. In some implementations, one or more battery parameter values used to determine a battery health index may be proxy values for a given battery parameter. A proxy value may be a value of a surrogate parameter that is varied to keep a different battery parameter within a given range (or above or below a given threshold) in a closed-loop system during charging and/or discharging. By way of example, current may be modified (e.g., during charging of a battery) to control a DC impedance or an EIS characteristics of the battery, e.g., to force the DC impedance and/or the EIS characteristics to remain within a given range or above or below a predetermined threshold. Continuing with this example, the current values used to control the DC impedance and/or the EIS characteristics may be considered a proxy value, and may in turn by used to determine the battery health index.
Teo relates to, among other things, an approach to forecasting battery health as a dynamic time-series problem as opposed to a static prediction problem (Teo, e.g., paragraph 5). Teo discloses determining individual status information, each corresponding to one of the one or more battery elements of the battery pack based on the plurality of battery parameters, wherein the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the corresponding battery element. For example, paragraphs 28, 29, 33, 49 and 52 of Teo disclose:
0028] As shown in FIG. 1, the behavioral forecast system 108 may receive battery data from the sensor module 102 including, for example, current, voltage, temperature, capacity, load, state-of-health (SOH) data, remaining useful life (RUL) data or the like. The sensor module 102 may be in communication with the battery 110 and the sensors 104, 106. According to one aspect, the sensors 104, 106 (and/or sensor module 102) may be included in the battery 110 or may be auxiliary sensors in communication with the battery 110 and sensor module 102. According to one aspect, the behavioral forecast system 108 may function to process data from the battery 110 to process and forecast a degradation or trajectory to failure of the battery. Moreover, while depicted as a standalone component, in one or more embodiments, the behavioral forecast system 108 may be integrated with the locomotion module 126, the sensor module 102, or another module of the vehicle 128. The noted functions and methods will become more apparent with a further discussion of the figures.
[0029] As described herein, aspects of the behavioral forecast system 108 provide a model framework and a class of machine learning algorithms that may use time-series data of fundamental battery measurements such as current, voltage, temperature, or the like, to forecast battery behavior. The behavioral forecast system 108 may include a convolutional neural network or a Gaussian-process convolutional neural network (CNN/GP-CNN 112, as described herein. The behavioral forecast system 108 may, using machine learning, forecast battery state-of-health (SOH) based on data measured over a finite window without need for costly, explicit diagnostic cycles. Additionally, the behavioral forecast system 108 may forecast a trajectory to failure or end of life under current and/or modified user behavior, for example, in second life applications.
0033] According to one aspect in which the behavioral forecast system 108 is implemented in the car 128, once the Gaussian Processor (or another regressor) is trained, the model may be embedded in a BMS 100 or other onboard computing device. In the car 128, the BMS 100 and behavioral forecast system 108 may forecast, and continuously refine, the future trajectory of the vehicle as the sensor module 108 continually collects new data. Further, since training data will capture similar present and past states as the car 128, but different future use-cases, the model may offer the driver suggestions on how different use-patterns will improve or maximize certain performance metrics of one or more of the cells of a battery pack or module, for example, max power vs max capacity, per charge).
[0049] An outcome may be a static prediction or dynamic (or continuous or rolling prediction). A static prediction may take some time window of data and predict the time taken for a cell to reach a threshold life, or SOH at a future time. Dynamic/continuous predictions take this a considerable step further by using historical data (allowing the use of any arbitrary window of time) and forecasting the trajectory the cell takes to reach end-of-life. As such, and shown in block 206, input vectors may be selected in the form of raw time-series data and/or engineered features within the window of time are used as a training dataset, along with their time-stamps relative to a reference age of the cell. The input vectors may be raw time-series data, such as vector X1 depicted in the Voltage (V) vs. Time (t.sub.1 to t.sub.2) plot of FIG. 3(b). Alternatively or additionally, the input vectors may include feature engineering along time domain (i.e., interpolation along time domain to ensure a uniformly space intervals, or interpolation along voltage axis to ensure important electrochemical signatures are captured, without requiring complete cycles or the full range of charge/discharge. The outcomes may be one or more future times at which a predicted SOH is desired.
[0052] The convolutional layers may also be used to capture how both the inter-cycle, and intra-cycle convolutions may affect future health of a battery, which may change fundamentally depending on the sequence of its loading/usage history. The number, stride and kernel size of the convolution layers may be tuned to capture both short and long-term aging effects within the time window of measurement. As shown in block 212, the CNN/GP-CNN may be tuned after its initial build training the model on additional data. Once the model is fit, for a given window of measurement in test dataset and current SOH, the time taken to reach any future SOH can be estimated. That is, the model trained on data from arbitrarily long windows and at different stages of a battery life, inference of a SOH may be faster and more efficient.
The highlighted portions of at least paragraphs 28-29 clearly establish that Teo’s forecasted SOH constitutes, according to applicant’s own disclosure, “a composite score that is determined based on multiple battery parameter values”, that “may be determined using a mathematical function or a model (e.g., a regression model, or the like), that takes, as inputs, one or more battery parameters (e.g., one, two, three, five, etc. battery parameters) and generates the battery health index as an output”, that “may be determined by employing a function that takes, as inputs, one or more battery parameters and determines the battery health index as an output” and that “may be a weighted sum, an exponential function, a polynomial function, utilize the output of a machine learning model, or the like”. Moreover, the examiner notes that Teo’s forecasted SOH is distinct from any of the plurality of battery parameters (e.g., see Teo, paragraph 29, time-series data of fundamental battery measurements such as current, voltage, temperature, or the like, to forecast battery behavior) used to derive it. Further, Teo discloses in connection with the behavioral forecast system 108 that the model may offer the driver suggestions on how different use-patterns will improve or maximize certain performance metrics of one or more of the cells of a battery pack or module, for example, max power vs max capacity, per charge (Teo, e.g., paragraph 33). Teo therefore at least suggests modifying a use pattern of the battery pack based at least in part by considering an aggregate of the individual status information.
It 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 to modify Gallegos such that the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the corresponding battery element, and that considering the aggregate of the composite scores forms the basis for modifying the use pattern. In this way, the list of parameters considered by Gallegos when determining if there is ever a problem with an individual battery cell that necessitates applying a modified use pattern (e.g., voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600) can be expanded to include forecasted cell SOH as taught by Teo. Such an approach provides the advantage of modifying the use pattern not only when a cell SOH is currently problematic, but also when a cell SOH is forecasted to be problematic.
Regarding claim 2, Gallegos in view of Teo discloses wherein the composite score for a given battery element of the plurality of battery elements is based on at least one of: an individual health score for the given battery element, a temperature associated with the given battery element, a state of charge (SOC) for the given battery element, a location within the battery pack, or any combination thereof (see Gallegos in view of Teo as applied to claim 1, e.g., Teo, paragraph 29, machine learning algorithms that may use time-series data of fundamental battery measurements such as current, voltage, temperature, or the like, to forecast battery behavior; also see Teo, e.g., claim 1 and claims 4-6 pertaining to the use of features corresponding to current data, voltage data and temperature data for forecasting SOH).
Regarding claim 3, Gallegos in view of Teo discloses wherein a battery element comprises a plurality of battery cells (see Gallegos in view of Teo as applied to claim 1, noting that under the interpretation of “battery element” as a battery module, each of the battery modules 600 of Fig. 6 of Gallegos may contain multiple cells).
Regarding claim 4, Gallegos in view of Teo discloses wherein the plurality of battery cells associated with the battery element are operatively coupled in parallel (the examiner notes that this language has a scope that includes (1) the plurality of cells being coupled in with each other, or (2) the plurality of cells being coupled in parallel with other cells; Gallegos discloses that power connections in a string may consist of two packs in series and those series packs may be paralleled with two other packs; each pack may consist of eight Local Module Units connected in series, with each Local Module Unit having ten battery cells also connected in series; see, e.g., Fig. 3 and paragraph 37; accordingly, Gallegos discloses that each module (e.g., each module 600 of Fig. 6) may have ten series-connected cells, with this cells being coupled in parallel will the cells of another pack such as shown in Fig. 3).
Regarding claim 5, Gallegos in view of Teo discloses wherein the battery pack comprises a plurality of battery elements operatively coupled in series (see Gallegos in view of Teo as applied to claim 1, noting that each of the battery modules 600 of Fig. 6 of Gallegos are coupled in series; also see Gallegos, paragraph 37, each pack may consist of eight Local Module Units connected in series, and each Local Module Unit may balance ten battery cells also connected in series).
Regarding claim 6, Gallegos in view of Teo discloses wherein a battery element comprises a single battery cell (see Gallegos in view of Teo as applied to claim 1, noting that under the interpretation of “battery element” as a battery cell, each battery element constitutes a single battery cell).
Regarding claim 7, Gallegos in view of Teo discloses looping through (a)-(c) multiple times (see Gallegos in view of Teo as applied to claim 1, Gallegos, e.g., Fig. 10 and paragraph 89; also see Fig. 7B and paragraphs 79-80).
Regarding claim 8, Gallegos in view of Teo discloses wherein the looping occurs at a rate of once per second or greater (see Gallegos in view of Teo as applied to claim 1, Gallegos, e.g., Fig. 10 and paragraph 89; also see Gallegos, Fig. 7B and paragraphs 79-80; note in Fig. 10 for example that readings by the Pack Master Unit may occur every 250 ms; note in Fig. 7B for example, that Energy Storage Master internal main loop may run on a 100 ms, 250 ms, and 1000 ms period for sending CAN bus messages, and the messages therefore may be sent at the following times each second: 100 ms, 200 ms, 250 ms, 300 ms, 400 ms, 500 ms, 600 ms, 750 ms, 800 ms, 900 ms and 1000 ms).
Regarding claim 10, Gallegos in view of Teo discloses wherein modifying the use pattern of the battery pack is based at least in part on a variance of the individual status information across the battery elements (see Gallegos in view of Teo as applied to claim 1, e.g., Gallegos, paragraph 37, each Local Module Unit may balance ten battery cells also connected in series; the examiner notes that cell balancing is necessarily based on voltage variance across the battery cells).
Regarding claim 11, Gallegos in view of Teo discloses wherein the individual status information of each of the plurality of battery elements are utilized to determine a poorest performing battery element of the battery pack, and wherein the modified use pattern is determined based at least in part on performance of the poorest performing battery element (see Gallegos in view of Teo as discussed above, e.g., Gallegos, Fig. 6 and paragraph 51, energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; thus, each pack may be addressable and may be queried as to the health and status at any time; if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation; also see, e.g., also see paragraph 37, each Local Module Unit may balance ten battery cells also connected in series; accordingly, the poorest performing cell may be a cell with a problem that prompts its removal from service; similarly, a cell with a voltage that is high relative to remaining cells within a module is the poorest performing cell for which balancing is provided).
Regarding claim 12, Gallegos in view of Teo discloses ranking the individual status information associated with the one or more battery elements, wherein considering the aggregate of the individual status information of the plurality of battery elements is based at least in part on the ranking (Gallegos in view of Teo, e.g., Gallegos, paragraph 78, Energy Storage Master 700 may have several capabilities, including collecting a database for display to the Vehicle Master Controller for high/low/average voltage, SOC, SOH, and high/low/average temperatures for the Traction Packs; it also keeps track of which cell has temperature or voltage extremes).
Regarding claim 13, Gallegos in view of Teo discloses determining an overall health score associated with the battery pack by aggregating the individual status information associated with each of the one or more battery elements, wherein considering the aggregate of the individual status information used to modify the use pattern is based on the overall health score (see Gallegos in view of Teo as applied to claim 1, Gallegos, e.g., paragraph 78, Energy Storage Master 700 may have several capabilities, including collecting a database for display to the Vehicle Master Controller for high/low/average voltage, SOC, SOH, and high/low/average temperatures for the Traction Packs; it also keeps track of which cell has temperature or voltage extremes; also see, e.g., paragraph 50, Vehicle Master Controller may interface with the Energy Storage Master which may receive aggregated data from each of the battery packs through Pack Master Boards on each battery pack; each pack may have its own BMS and therefore may operate as a complete unit independently from other packs, but may also integrate with a master controller to provide greater overall functionality, such as functionality that may be achieved through aggregation and consolidation of information to the Vehicle Master Controller).
Regarding claim 14, Gallegos in view of Teo discloses wherein aggregating the individual status information associated with each of the one or more battery elements comprises providing the individual status information associated with each of the one or more battery elements to a function or a model that generates the overall health score (see Gallegos in view of Teo as applied to claim 13, e.g., paragraph 78, Energy Storage Master 700 may have several capabilities, including collecting a database for display to the Vehicle Master Controller for high/low/average voltage, SOC, SOH, and high/low/average temperatures for the Traction Packs; the examiner notes that at least average SOH corresponds to function or a model that generates the overall health score based on aggregating SOH values, which in modified Gallegos will include average predicted SOH).
Regarding claim 17, Gallegos in view of Teo discloses wherein the modified use pattern comprises modifying a charging process and/or a discharging process of the battery pack (see Gallegos in view of Teo as applied to claim 1, e.g., Gallegos, Fig. 6 and paragraph 51, if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation; the examiner notes that removal of battery pack from service at least stops a discharging process; also see, e.g., paragraph 55, if the current is in excess of 350 Amps, either charging or discharging, and this condition has existed continuously for five seconds, a request may be made to open the contactor for the string exceeding this limit; if the temperature is in excess of 65 degrees Celsius, a request may be made to open a string contactor and notify the operator of a fault; also see, e.g., paragraph 61, if temperature in excess of +58 C, the operator shall be notified of a temperature warning, and the charge and discharge shall be derated).
Regarding claim 18, Gallegos in view of Teo discloses wherein modifying the charging process comprises modifying a charging rate (see Gallegos in view of Teo as applied to claim 17, e.g., Gallegos, paragraph 61, if temperature in excess of +58 C, the operator shall be notified of a temperature warning, and the charge and discharge shall be derated).
Regarding claim 19, Gallegos in view of Teo discloses wherein modifying the discharging process comprises at least one of: modifying a depth of discharge; modifying an output current; modifying an output power; modifying an output energy; modifying a discharge duration; modifying a cutoff voltage; modifying limits for one or more discharge parameters; modifying heat transfers or flux for a cell or the battery pack; modifying temperature rises for a cell or the battery pack; or modifying temperature gradients for the battery pack (see Gallegos in view of Teo as applied to claim 17, e.g., Gallegos, paragraph 61, if temperature in excess of +58 C, the operator shall be notified of a temperature warning, and the charge and discharge shall be derated; the examiner notes that derating charge and discharge includes at least modifying an output current, modifying an output power, modifying an output energy, modifying limits for one or more discharge parameters, modifying temperature rises for a cell or the battery pack).
Regarding claim 20, Gallegos in view of Teo discloses providing an alert that a particular battery element of the battery pack is defective (see Gallegos in view of Teo as applied to claim 1, e.g., Gallegos, e.g., paragraph 87, Pack Master Unit 800 may also monitor all Cells located inside Battery Module units and alert the Energy Storage Master if certain operation limits are exceeded; also see paragraphs 56, 61 and 63, warning messages and system responses may include: (1) for temperature in excess of +58 C, the operator shall be notified of a temperature warning, and the charge and discharge shall be derated, and (2) Lose Pack Contactor/Battery Cell/Battery Error).
Regarding claim 21, Gallegos in view of Teo discloses providing instructions to a temperature control system associated with the battery pack to maintain or modify a battery pack temperature within a given temperature range (see Gallegos in view of Teo as applied to claim 1, e.g., Gallegos, paragraph 133; also see paragraph 52). Gallegos in view of Teo is not relied upon as explicitly disclosing providing instructions to the temperature control system that cause it to maintain or modify temperature. The examiner nonetheless takes Official notice of the fact that the use of closed-loop temperature control using temperature feedback (e.g., from a temperature sensor) in conjunction with a setpoint to maintain within a given temperature range was well-known and conventional before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. It 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 to modify Gallegos in view of Teo to include providing instructions to the temperature control system in the form of, e.g., temperature feedback and setpoint information. In this way, as is well-known in the art, closed-loop temperature control may be implemented by Gallegos’ cooling system to maintain the packs at temperatures within their limits).
Regarding claim 22, Gallegos in view of Teo discloses wherein the wherein the individual status information for each of the one or more battery elements are determined based on at least one of: a current temperature, or a current state of charge (SOC) of the battery pack (see Gallegos in view of Teo as applied to claim 1, e.g., Gallego, Fig. 6 and paragraphs 51-76 which discloses one example of the architecture of a battery pack of Fig. 3, with the pack including a number of battery modules 600; each battery module 600 may have a Local Module Unit 601 which feeds data to a Pack Master 610; the Pack Master 610 may then send aggregated data back to an Energy Storage Master which may interface with a Vehicle Master Controller; the energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600).
Regarding claim 24, Gallegos in view of Teo discloses wherein the plurality of battery parameters comprise at least one of: an open circuit voltage of the battery element, or a current associated with the battery element (see Gallegos in view of Teo as applied to claim 1, Gallegos, e.g., paragraph 51, energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of current for all cells within each of the battery modules 600; also see paragraph 89, Pack Master Unit may determine State of Charge using open circuit voltage if the current is less than a certain threshold; also see Teo, e.g., paragraph 29, machine learning algorithms that may use time-series data of fundamental battery measurements such as current, voltage, temperature, or the like, to forecast battery behavior).
Regarding claim 25, Gallegos discloses a method of diagnosing battery defects in battery packs, the method comprising:
(a) monitoring a plurality of battery parameters associated with one or more battery elements of a battery pack comprising a plurality of battery elements (Gallegos, e.g., Fig. 3 and paragraph 37, referring to FIG. 3, examples of arrangements and interconnections within packs and strings are shown; the power connections in a string may consist of two packs in series and those series packs may be paralleled with two other packs; each pack may consist of eight Local Module Units connected in series; each Local Module Unit may balance ten battery cells also connected in series; the examiner notes in Fig. 3 that the two top packs are in series and the bottom two packs are in series; also see Fig. 6 and paragraphs 51-76 which discloses one example of the architecture of a battery pack of Fig. 3, with the pack including a number of battery modules 600; each battery module 600 may have a Local Module Unit 601 which feeds data to a Pack Master 610; the Pack Master 610 may then send aggregated data back to an Energy Storage Master which may interface with a Vehicle Master Controller; the energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; in Fig. 6, the battery modules 600 constitute one or more battery elements of a battery pack, with each module 600 comprising a plurality of battery elements in the form of battery cells);
(b) determining individual status information, each corresponding to one of the one or more battery elements of the battery pack based on the plurality of battery parameterskeep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; thus, each pack may be addressable and may be queried as to the health and status at any time; if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation); and
(c) determining a presence of a defect in the battery pack based at least in part on an aggregate of the status information associated with the different battery elements of the battery pack (Gallegos, e.g., paragraph 87, Pack Master Unit 800 may also monitor all Cells located inside Battery Module units and alert the Energy Storage Master if certain operation limits are exceeded; also see paragraphs 56, 61 and 63, warning messages and system responses may include: (1) for temperature in excess of +58 C, the operator shall be notified of a temperature warning, and the charge and discharge shall be derated, and (2) Lose Pack Contactor/Battery Cell/Battery Error; also see paragraph 78, the Energy Storage Master 700 may have several capabilities. Its main function is to interpret Vehicle Master Controller commands to and from the Pack Masters (via connections 701 and 702); it also collects a database for display to the Vehicle Master Controller for High/Low/Average Voltage, SOC, SOH, and High/Low/Average temperatures for the Traction Packs; also see Gallegos, e.g., paragraph 51, energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; thus, each pack may be addressable and may be queried as to the health and status at any time; if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation).
Gallegos is not relied upon as explicitly disclosing wherein the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the corresponding battery element, and that considering the aggregate of the composite scores forms the basis for determining the likelihood of a presence of a defect in the battery pack.
For the same reasons discussed above in connection with claim 1,Teo discloses determining individual status information, each corresponding to one of the one or more battery elements of the battery pack based on the plurality of battery parameters, wherein the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the corresponding battery element.
It 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 to modify Gallegos such that the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the corresponding battery element, and that considering the aggregate of the composite scores forms the basis for determining a presence of a defect in the battery pack. In this way, the list of parameters considered by Gallegos when determining if there is ever a defect/problem with an individual battery cell that necessitates a modified use pattern is necessary (e.g., voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600) can be expanded to include forecasted cell SOH as taught by Teo. Such an approach provides the advantage of determining the presence of a defect not only when a cell SOH is currently problematic, but also when a cell SOH is forecasted to be problematic.
Gallegos in view of Teo is not relied upon as explicitly disclosing determining the likelihood of a presence of a defect. One of ordinary skill in the art would nonetheless understand that a particular cell having parameter values that consistently differ from those of other cells over time suggests the likelihood of a defect in the particular cell. For example, Gallegos tracks High/Low/Average Voltage, SOC, SOH, and High/Low/Average temperatures for the Traction Packs (Gallegos, e.g., paragraph 78). For a cell/battery having extremes (e.g., with respect to SOH) consistently over a period of time, one of ordinary skill in the art would conclude that a cell defect is more probable than if the cell exhibited infrequent extremes over the time period. Such reasoning falls well within the inferences and creative steps that a person of ordinary skill in the art would employ in light of the teachings of Gallegos and the scientific and engineering principles applicable to the pertinent art. For this reason, the recitation of determining the likelihood of a presence of a defect does not patentably define over Gallegos in view of Teo.
Claim 26 recites an apparatus for mitigating battery defects in battery packs, the apparatus comprising:
monitoring circuitry, coupled to a battery pack; and
control circuitry, coupled to the monitoring circuitry, configured to:
(a) monitor a plurality of battery parameters associated with one or more battery elements of a battery pack comprising a plurality of battery elements;
(b) determine individual status information, each corresponding to one of the one or more battery elements of the battery pack based on the plurality of battery parameters, wherein the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the corresponding battery element; and
(c) modify a use pattern of the battery pack based at least in part by considering an aggregate of the composite scores associated with the different battery elements of the battery pack, wherein the modified use pattern reduces a probability of a future defect in the battery pack,
and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 1, recognizing that both Gallegos and Teo necessarily employs monitoring circuitry coupled to the battery pack for monitoring the battery parameters (e.g., Gallegos, Fig. 6, circuitry LMU for acquiring cell parameters such a current, voltage, temperature; Teo, e.g., Fig. 1 and paragraph 23, sensor module 102 may be used to obtain battery measurements via different sensors, such as a first sensor 104, a second sensor 106; first sensor 104 may be a voltage, current or other electrical sensor; second sensor 106 may include a temperature sensor, thermometer, or the like) as well as circuitry determining individual status information and modifying the use pattern (e.g., Gallegos’ circuitry for automatically removing a string from service when a problem occurs to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation; also see Gallegos, e.g., paragraph 55, circuitry for determining if the current is in excess of 350 Amps, either charging or discharging, and this condition has existed continuously for five seconds and requesting to open the contactor for the string exceeding this limit; circuitry for determining if the temperature is in excess of 65 degrees Celsius and requesting to open a string contactor and notify the operator of a fault; also see Gallegos, e.g., paragraph 61, circuitry for determining if temperature is in excess of +58 C and derating the charge and discharge; also see Gallegos, paragraph 37, Local Module Unit circuitry for determining when to balance battery cells; Teo, e.g., paragraph 28, behavioral forecast system 108 may receive battery data from the sensor module 102 including, for example, current, voltage, temperature, capacity, load, state-of-health (SOH) data, remaining useful life (RUL) data or the like; behavioral forecast system 108 may function to process data from the battery 110 to process and forecast a degradation or trajectory to failure of the battery), and further recognizing in the combination of Gallegos in view of Teo that Gallegos’ approach to a battery cell problem (Gallegos paragraph 51, if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation) is applicable to problems that are either presently detected or are predicted/forecasted to occur (e.g., a forecasted trajectory to failure or end of life under current and/or modified user behavior) as disclosed by Teo, e.g., paragraph 29.
Claim 27 recites wherein the individual status information for a given battery element of the plurality of battery elements comprises at least one of: an individual health score for the given battery element, a temperature associated with the given battery element, a state of charge (SOC) for the given battery element, a location within the battery pack, or any combination thereof and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 2.
Claim 28 recites wherein the control circuitry is configured to loop through (a)-(c) multiple times and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 7.
Claim 29 recites wherein modifying the use pattern of the battery pack is based at least in part on a variance of the individual status information across the battery elements and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 10.
Claim 30 recites wherein the individual status information of each of the plurality of battery elements are utilized to determine a poorest performing battery element of the battery pack, and wherein the modified use pattern is determined based at least in part on performance of the poorest performing battery element and is r rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 11.
Claim 31 recites wherein the control circuitry is further configured to rank the individual status information associated with the one or more battery elements, wherein considering the aggregate of the individual status information of the plurality of battery elements is based at least in part on the ranking and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 12.
Claim 32 recites wherein the modified use pattern comprises modifying a charging process and/or a discharging process of the battery pack and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 17.
Claim 33 recites wherein modifying the charging process comprises modifying a charging rate and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 18.
Claim 34 recites wherein modifying the discharging process comprises at least one of: modifying a depth of discharge; modifying an output current; modifying an output power; modifying an output energy; modifying a discharge duration; modifying a cutoff voltage; modifying limits for one or more discharge parameters; modifying heat transfers or flux for a cell or the battery pack; modifying temperature rises for a cell or the battery pack; or modifying temperature gradients for the battery pack and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 19.
Claim 35 recites an apparatus for diagnosing battery defects in battery packs, the apparatus comprising:
monitoring circuitry coupled to a battery pack; and
control circuitry, coupled to the monitoring circuitry, configured to:
(a) monitor a plurality of battery parameters associated with one or more battery elements of a battery pack comprising a plurality of battery elements;
(b) determine individual status information, each corresponding to one of the one or more battery elements of the battery pack based on the one or more battery parameters, wherein the individual status information is a composite score derived from and distinct from any of the plurality of battery parameters and is predictive of a future state of the battery element; and
(c) determine the likelihood of a presence of a defect in the battery pack based at least in part on an aggregate of the composite scores for the battery elements of the battery pack,
and is rejected under 35 U.S.C. 103 as unpatentable over Gallegos in view of Teo for reasons analogous to those discussed above in connection with claim 25, recognizing that both Gallegos and Teo necessarily employs monitoring circuitry coupled to the battery pack for monitoring the battery parameters (e.g., Gallegos, Fig. 6, circuitry LMU for acquiring cell parameters such a current, voltage, temperature; Teo, e.g., Fig. 1 and paragraph 23, sensor module 102 may be used to obtain battery measurements via different sensors, such as a first sensor 104, a second sensor 106; first sensor 104 may be a voltage, current or other electrical sensor; second sensor 106 may include a temperature sensor, thermometer, or the like) as well as circuitry determining individual status information and determining presence of defects (e.g., Gallegos’ circuitry for automatically removing a string from service when a problem occurs to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation; also see Gallegos, e.g., paragraph 55, circuitry for determining if the current is in excess of 350 Amps, either charging or discharging, and this condition has existed continuously for five seconds and requesting to open the contactor for the string exceeding this limit; circuitry for determining if the temperature is in excess of 65 degrees Celsius and requesting to open a string contactor and notify the operator of a fault; also see Gallegos, e.g., paragraph 61, circuitry for determining if temperature is in excess of +58 C and derating the charge and discharge; also see Gallegos, paragraph 37, Local Module Unit circuitry for determining when to balance battery cells; Teo, e.g., paragraph 28, behavioral forecast system 108 may receive battery data from the sensor module 102 including, for example, current, voltage, temperature, capacity, load, state-of-health (SOH) data, remaining useful life (RUL) data or the like; behavioral forecast system 108 may function to process data from the battery 110 to process and forecast a degradation or trajectory to failure of the battery), and further recognizing in the combination of Gallegos in view of Teo that Gallegos’ approach to a battery cell problem (Gallegos paragraph 51, if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation) is applicable to problems that are either presently detected or are predicted/forecasted to occur (e.g., a forecasted trajectory to failure or end of life under current and/or modified user behavior) as disclosed by Teo, e.g., paragraph 29.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Gallegos in view of Teo, and further in view of US 2016/0315363 to Esteghlal (Esteghlal).
Regarding claim 9, Gallegos in view of Teo is not relied upon as explicitly disclosing wherein the individual status information for different battery elements of the battery pack vary based at least in part on differences in temperature gradients within the battery pack experienced by the one or more battery elements. Esteghlal discloses that the temperature difference (temperature gradient), which is admissible for the operation of the battery cells, in a battery cell and/or within a battery module or a battery typically lies between 5 Kelvin and 10 Kelvin (Esteghlal, e.g., paragraph 6). In the case of larger temperature gradients, different regions of a battery cell or different battery cells of a battery module or a battery can experience different stresses or even be (partially) overloaded and/or damaged (Esteghlal, e.g., paragraph 6). In addition, a danger of condensation forming in the battery exists due to temperature gradients and/or temperature changes (Esteghlal, e.g., paragraph 6). The damage can lead to an accelerated ageing of the battery cells or to a thermal runaway of the battery cells, which presents a danger for humans and the environment (Esteghlal, e.g., paragraph 6). Esteghlal therefore discloses that temperature gradients can affect battery parameters such as temperature (thermal runaway), humidity (condensation), and state of health (ageing), with at least state of health (SOH) being a parameter monitored by both Gallegos and Teo and reflected in the individual status information determined by Gallegos in view of Teo. The recitation that the individual status information for different battery elements of the battery pack vary based at least in part on differences in temperature gradients within the battery pack experienced by the one or more battery elements therefore does not patentably distinguish over Gallegos in view of Teo when further considered in light of Esteghlal.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Gallegos in view of Teo, and further in view of US 2022/0291287 to Yoon et al. (Yoon).
Regarding claim 15, Gallegos in view of Teo is not relied upon as explicitly disclosing determining a rate of change of at least one parameter of the one or more battery parameters for at least one battery element of the plurality of battery elements. Yoon discloses determining a rate of change of at least one parameter of one or more battery parameters for at least one battery element of a plurality of battery elements (Yoon, e.g., paragraphs 67-69, diagnosis unit 212 receives SOH information of each of the plurality of battery modules from the storage unit 210 and calculates the change rate of the SOH of each of the plurality of battery modules; if the SOH change rate of a specific battery module is greater than the first value and less than the second value compared to the SOH change rate of other battery modules, the specific battery module is diagnosed as including a degenerate battery cell). It 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 to modify Gallegos in view of Teo to include determining a rate of change of at least one parameter of the one or more battery parameters for at least one battery element of the plurality of battery elements. In this way, in the manner disclosed by Yoon, a battery module can be diagnosed as including a degenerate battery cell.
Regarding claim 16, Gallegos in view of Teo and Yoon discloses wherein the modified use pattern is determined based at least in part on a determination that a rate of change of a given battery parameter differs by more than a threshold amount for a first battery element relative to a rate of change of the given battery parameter for two or more other battery elements of the battery pack (see Gallegos in view of Teo and Yoon as applied to claim 15, Yoon, paragraphs 67-69, if the SOH change rate of a specific battery module is greater than the first value and less than the second value compared to the SOH change rate of other battery modules, the specific battery module is diagnosed as including a degenerate battery cell; also see Gallegos as applied to claim 1, e.g., Fig. 6 and paragraph 51, energy storage master unit may communicate with all Pack Master units 610, a bus controller, and a curbside charger(s), and may keep track of voltage, current 604, temperature, humidity, state of charge (SOC) and state of health (SOH) for all cells within each of the battery modules 600; thus, each pack may be addressable and may be queried as to the health and status at any time; if there is ever a problem with an individual battery cell, an entire string may be automatically removed from service to allow the vehicle to continue operating in a reduced capacity mode until a vehicle returns from operation).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Gallegos in view of Teo, and further in view of US 2022/0344734 to Tanovic et al. (Tanovic).
Regarding claim 23, Gallegos in view of Teo is not relied upon as explicitly disclosing wherein at least one of the one or more battery parameters is indicative of ion diffusion, and wherein the method further comprises determining the current temperature based at least on the one or more battery parameters indicative of ion diffusion. Gallegos may use temperature sensors attached at the module level for measuring temperature (see Gallegos, e.g., paragraph 120). Tanovic discloses that battery cell temperature as measured using a surface thermocouple may differ significantly from the actual temperature at the inside of the battery due to delay in heat conductivity through the body of the battery, from the inside to the surface, as well as to battery self-heating, for example (Tanovic, e.g., paragraph 20). Tanovic discloses that the internal temperature of a Li-ion rechargeable battery, such as an EV battery, may be estimated using data obtained using EIS measurement technology (Tanovic). It 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 to modify Gallegos in view of Teo such that at least one of the one or more battery parameters is indicative of ion diffusion (e.g., EIS parameter measurements, which the examiner notes are indicative of ion diffusion), and wherein the method further comprises determining the current temperature based at least on the one or more battery parameters indicative of ion diffusion. In this way, in the manner disclosed by Tanovic, the actual temperature at the inside of the battery can be determined without the delay and other shortcomings associated with surface-mounted temperature sensors.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL R MILLER whose telephone number is (571)270-1964. The examiner can normally be reached 9AM-5PM EST M-F.
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, Lee Rodak, can be reached at 571-270-5628. 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.
/DANIEL R MILLER/Primary Examiner, Art Unit 2858
Prosecution Insights