NON-FINAL REJECTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/12/2026 has been entered.
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 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-12 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Biswas et al. (2021/350818 A1, cited by Applicants, “Biswas”) in view of Sumika et al. (JP 2021139769 A, cited by the applicants, “Sumika”).
Regarding Claim 1, Biswas teaches a non-invasive method of identifying and labeling defects in a battery cell (Fig.1, [0095] - [0098]), the non-invasive method comprising: transmitting acoustic signals through a battery cell via one or more first transducers ([0095]: “a transmitting transducer Tx 104 or other means for sending excitation sound signals into the battery cell or components (e.g., for transmitting a pulse or pulses of ultrasonic or other acoustic waves, vibrations, resonance measurements, etc., through the battery cell).”); receiving response signals in response to the acoustic signals at one or more second transducers ([0096]: “Rx transducers 104 and/or 106 for controlling the transmission of acoustic signals (e.g., ultrasound signals) and receiving response signals.”); performing a preliminary identification process to identify a potential feature of interest in the battery cell based on analyzing the response signals ([0098]: “These deviations can be used to determine, estimate, or predict the one or more states of the test sample.”); wherein the potential feature of interest may or may not be an actual defect in the battery cell ([0098] discloses “Corresponding metrics of a test sample may be measured against and compared to the reference model's metrics. The measurements or comparisons can reveal deviations of certain characteristics of the test sample from those of the reference model. These deviations can be used to determine, estimate, or predict the one or more states of the test sample.” This indicates that the measurements and comparisons reveal deviations of certain characteristics of the test sample from those of the reference model. Thus, the potential feature of interest may or may not be an actual defect in the battery cell such as one or more states of the test batteries. Therefore, the limitation is implicitly taught by Biswas.); and outputting ([0205]: output, display) a result of the identification and labeling process ([0098]: dynamically updating).
Biswas does not explicitly teach regarding performing a secondary identification process to (1) determine if the potential feature of interest is an actual defect present in the battery cell, and (2) label the actual defect.
Biswas teaches in [0098] “a reference model can be generated using one or more reference battery cells. …. a set of one or more metrics may be used for generating a reference model against which other samples. ….. Corresponding metrics of a test sample may be measured against and compared to the reference model's metrics. The measurements or comparisons can reveal deviations of certain characteristics of the test sample from those of the reference model. These deviations can be used to determine, estimate, or predict the one or more states of the test sample.” Utilizing the teaching of Biswas one of ordinary skill in the art may determine if the potential feature of interest is an actual defect present in the battery cell. Thus, the limitation is implicitly taught by Biswas.
In any event, Sumika teaches a defect detection and classification system and a defect judgment training system [0001] wherein the detection and classification unit 18 obtains a predetermined number of estimation results for one image from the determination model. The detection and classification unit 18 identifies the most frequent label for each pixel from the estimation results obtained over a predetermined number of times, and determines the identified label as the presence or absence of a defect and the type of defect for that pixel. The detection and classification unit 18 causes the display unit 22 to display the detection results including the labels determined for each pixel, and an image obtained by capturing an image of the printed matter. The label may indicate that the same pixel contains multiple defects [0062].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Sumika regarding determining the identified label as the presence or absence of a defect and the type of defect using a defect judgment training system in the system/method of Biswas to “(1) determine if the potential feature of interest is an actual defect present in the sample, and (2) labelling the actual defect” since both arts utilize machine learning to analyze defects and this would enable an improved and accurate detection of the defects [0072].
Regarding Claim 2, the non-invasive method of claim 1 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the secondary identification process is performed using a trained machine learning model ([0098]: “In some examples, learning tools such as machine learning and artificial intelligence can be used in dynamically updating the reference model.”).
Regarding Claim 3, the non-invasive method of claim 2 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the trained machine learning model receives as input, at least one of the response signals and the potential feature of interest and provides at least one labeled defect corresponding to the actual defect, as output ([0098] implicitly teaches the claimed invention as the machine learning model implicitly receives the response signals and dynamically updates as more data being collected from test samples.).
Regarding Claim 4, the non-invasive method of claim 1 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the secondary identification process determines at least one of a corresponding type for the actual defect and a corresponding location of the actual defect in the battery cell [0057].
Regarding Claim 5, the non-invasive method of claim 1 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the actual defect is one or more of a fold, a wrinkle, one or more holes in materials forming the battery cell, cracks or fractures in solid- state ceramic based separators of the battery cell, dry spots within the battery cell, electrode holes, folds, delamination, or layer misalignment, foreign object debris, burrs, metallic particle inclusions, tab defects including tears, folds, and poor quality welds, plating of lithium metal on anode material of the battery cell, evolution of gasses resulting from electrolyte or other chemical decomposition ([0047]; [0057]; [0106]).
Regarding Claim 8, Biswas teaches a system (Fig.1, [0095] - [0098]) comprising: a plurality of transducers (elements 104, 106) configured to at least one of transmit (elements 104) and receive (elements 106) acoustic signals through a battery cell [0095]; and a controller (element 108) communicatively coupled to the plurality of transducers (shown in fig.1) and configured to: control a first subset of the plurality of transducers (elements 104) to transmit acoustic signals through the battery cell ([0095]: “a transmitting transducer Tx 104 or other means for sending excitation sound signals into the battery cell or components (e.g., for transmitting a pulse or pulses of ultrasonic or other acoustic waves, vibrations, resonance measurements, etc., through the battery cell).”); control a second subset of the plurality of transducers (elements 106) to receive response signals in response to the acoustic signals ([0096]: “Rx transducers 104 and/or 106 for controlling the transmission of acoustic signals (e.g., ultrasound signals) and receiving response signals.”); perform a preliminary identification process to identify a potential feature of interest the battery cell based on analyzing the response signals ([0098]: “These deviations can be used to determine, estimate, or predict the one or more states of the test sample.”); wherein the potential feature of interest may or may not be an actual defect in the battery cell ([0098] discloses “Corresponding metrics of a test sample may be measured against and compared to the reference model's metrics. The measurements or comparisons can reveal deviations of certain characteristics of the test sample from those of the reference model. These deviations can be used to determine, estimate, or predict the one or more states of the test sample.” This indicates that the measurements and comparisons reveal deviations of certain characteristics of the test sample from those of the reference model. Thus, the potential feature of interest may or may not be an actual defect in the battery cell such as one or more states of the test batteries. Therefore, the limitation is implicitly taught by Biswas.); and output ([0205]: output, display) a result of the identification and labeling process ([0098]: dynamically updating).
Biswas does not explicitly teach regarding performing a secondary identification process to (1) determine if the potential feature of interest is an actual defect present in the battery cell, and (2) label the actual defect.
Biswas teaches in [0098] “a reference model can be generated using one or more reference battery cells. …. a set of one or more metrics may be used for generating a reference model against which other samples. ….. Corresponding metrics of a test sample may be measured against and compared to the reference model's metrics. The measurements or comparisons can reveal deviations of certain characteristics of the test sample from those of the reference model. These deviations can be used to determine, estimate, or predict the one or more states of the test sample.” Utilizing the teaching of Biswas one of ordinary skill in the art may determine if the potential feature of interest is an actual defect present in the battery cell. Thus, the limitation is implicitly taught by Biswas.
In any event, Sumika teaches a defect detection and classification system and a defect judgment training system [0001] wherein the detection and classification unit 18 obtains a predetermined number of estimation results for one image from the determination model. The detection and classification unit 18 identifies the most frequent label for each pixel from the estimation results obtained over a predetermined number of times, and determines the identified label as the presence or absence of a defect and the type of defect for that pixel. The detection and classification unit 18 causes the display unit 22 to display the detection results including the labels determined for each pixel, and an image obtained by capturing an image of the printed matter. The label may indicate that the same pixel contains multiple defects [0062].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Sumika regarding determining the identified label as the presence or absence of a defect and the type of defect using a defect judgment training system in the system/method of Biswas to “(1) determine if the potential feature of interest is an actual defect present in the sample, and (2) labelling the actual defect” since both arts utilize machine learning to analyze defects and this would enable an improved and accurate detection of the defects [0072].
Regarding Claim 9, the system of claim 8 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the controller is configured to perform the secondary identification process using a trained machine learning model ([0098]: “In some examples, learning tools such as machine learning and artificial intelligence can be used in dynamically updating the reference model.”).
Regarding Claim 10, the system of claim 9 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the trained machine learning model receives as input, at least one of the response signals and the potential feature of interest and provides at least one labeled defect corresponding to the actual defect, as output ([0098] implicitly teaches the claimed invention as the machine learning model implicitly receives the response signals and dynamically updates as more data being collected from test samples.).
Regarding Claim 11, the system of claim 8 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the controller is configured to perform the secondary identification process to determine at least one of a corresponding type for the actual defect and a corresponding location of the actual defect in the battery cell [0057].
Regarding Claim 12, the system of claim 8 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the actual defect is one or more of a fold, a wrinkle, one or more holes in materials forming the battery cell, cracks or fractures in solid-state ceramic based separators of the battery cell, dry spots within the battery cell, electrode holes, folds, delamination, or layer misalignment, foreign object debris, burrs, metallic particle inclusions, tab defects including tears, folds, and poor quality welds, plating of lithium metal on anode material of the battery cell, evolution of gasses resulting from electrolyte or other chemical decomposition ([0047]; [0057]; [0106]).
Regarding Claim 15, Biswas teaches one or more non-transitory computer-readable media ([0205]: memory) comprising computer-readable instructions ([0039]; [0195]; [0207], claim 20), which when executed by a controller of a system for non-invasive inspection of batteries, cause the controller to: control a first subset of a plurality of transducers (elements 104) to transmit acoustic signals through a battery cell ([0095]: “a transmitting transducer Tx 104 or other means for sending excitation sound signals into the battery cell or components (e.g., for transmitting a pulse or pulses of ultrasonic or other acoustic waves, vibrations, resonance measurements, etc., through the battery cell).”); control a second subset of the plurality of transducers (elements 106) to receive response signals in response to the acoustic signals ([0096]: “Rx transducers 104 and/or 106 for controlling the transmission of acoustic signals (e.g., ultrasound signals) and receiving response signals.”); perform a preliminary identification process to identify a potential feature of interest in the battery cell based on analyzing the response signals ([0098]: “These deviations can be used to determine, estimate, or predict the one or more states of the test sample.”); wherein the potential feature of interest may or may not be an actual defect in the battery cell ([0098] discloses “Corresponding metrics of a test sample may be measured against and compared to the reference model's metrics. The measurements or comparisons can reveal deviations of certain characteristics of the test sample from those of the reference model. These deviations can be used to determine, estimate, or predict the one or more states of the test sample.” This indicates that the measurements and comparisons reveal deviations of certain characteristics of the test sample from those of the reference model. Thus, the potential feature of interest may or may not be an actual defect in the battery cell such as one or more states of the test batteries. Therefore, the limitation is implicitly taught by Biswas.); and output ([0205]: output, display) a result of the identification and labeling process ([0098]: dynamically updating).
Biswas does not explicitly teach regarding performing a secondary identification process to (1) determine if the potential feature of interest is an actual defect present in the battery cell, and (2) label the actual defect.
Biswas teaches in [0098] “a reference model can be generated using one or more reference battery cells. …. a set of one or more metrics may be used for generating a reference model against which other samples. ….. Corresponding metrics of a test sample may be measured against and compared to the reference model's metrics. The measurements or comparisons can reveal deviations of certain characteristics of the test sample from those of the reference model. These deviations can be used to determine, estimate, or predict the one or more states of the test sample.” Utilizing the teaching of Biswas one of ordinary skill in the art may determine if the potential feature of interest is an actual defect present in the battery cell. Thus, the limitation is implicitly taught by Biswas.
In any event, Sumika teaches a defect detection and classification system and a defect judgment training system [0001] wherein the detection and classification unit 18 obtains a predetermined number of estimation results for one image from the determination model. The detection and classification unit 18 identifies the most frequent label for each pixel from the estimation results obtained over a predetermined number of times, and determines the identified label as the presence or absence of a defect and the type of defect for that pixel. The detection and classification unit 18 causes the display unit 22 to display the detection results including the labels determined for each pixel, and an image obtained by capturing an image of the printed matter. The label may indicate that the same pixel contains multiple defects [0062].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the teaching of Sumika regarding determining the identified label as the presence or absence of a defect and the type of defect using a defect judgment training system in the system/method of Biswas to “(1) determine if the potential feature of interest is an actual defect present in the sample, and (2) labelling the actual defect” since both arts utilize machine learning to analyze defects and this would enable an improved and accurate detection of the defects [0072].
Regarding Claim 16, the one or more non-transitory computer-readable media of claim 15 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the execution of the computer-readable instructions causes the controller to perform the identification and labeling process using a trained machine learning model ([0098]: “In some examples, learning tools such as machine learning and artificial intelligence can be used in dynamically updating the reference model.”).
Regarding Claim 17, the one or more non-transitory computer-readable media of claim 16 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the trained machine learning model receives as input, at least one of the response signals and the potential feature of interest and provides at least one labeled defect corresponding to the actual defect feature, as output ([0098] implicitly teaches the claimed invention as the machine learning model implicitly receives the response signals and dynamically updates as more data being collected from test samples.).
Regarding Claim 18, the one or more non-transitory computer-readable media of claim 15 is taught by Biswas in view of Sumika.
Biswas further teaches wherein execution of the computer-readable instructions causes the controller to perform the secondary identification process to determine at least one of a corresponding type for the actual defect and a corresponding location of the actual defect in the battery cell [0057].
Regarding Claim 19, the one or more non-transitory computer-readable media of claim 15 is taught by Biswas in view of Sumika.
Biswas further teaches wherein the actual defect is one or more of a fold, a wrinkle, one or more holes in materials forming the battery cell, cracks or fractures in solid-state ceramic based separators of the battery cell, dry spots within the battery cell, electrode holes, folds, delamination, or layer misalignment, foreign object debris, burrs, metallic particle inclusions, tab defects including tears, folds, and poor quality welds, plating of lithium metal on anode material of the battery cell, evolution of gasses resulting from electrolyte or other chemical decomposition ([0047]; [0057]; [0106]).
Claims 6-7, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Biswas in view of Sumika, and further in view of Lee (US 2023/0044388 A1, previously cited).
Regarding Claim 6, the method of claim 1 is taught by Biswas in view of Sumika.
Biswas does not explicitly teach wherein the result includes a segmented visual rendering of the battery cell with the actual defect labeled therein.
However, Lee teaches wherein the output is a segmented visual rendering of the battery cell with the least one defect labeled therein [0115].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Biswas’s system with the teaching of Lee regarding outputting since this is well-known in the art to output visual and/or audible information corresponding to the diagnosis message based on which further action could be taken.
Regarding Claim 7, the method of claim 1 is taught by Biswas in view of Sumika.
Biswas does not explicitly teach regarding the method further comprising: determining a corrective action to be taken with respect to the battery cell based on the result of the secondary identification process wherein the corrective action is with respect to at least one of a process of manufacturing battery cells battery cell or use of the battery cell.
However, Lee teaches regarding determining a corrective action (Fig.7; S780) to be taken with respect to the battery cell based on the result of the identification and the labeling process [0115].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Biswas’s system/method with the teaching of Lee regarding a predetermined protection operation since this is well-known in the art to take the predetermined protection operation as a corrective action based on diagnosis result.
Regarding Claim 13, the system of claim 8 is taught by Biswas in view of Sumika.
Biswas does not explicitly teach wherein the result includes a segmented visual rendering of the battery cell with the least one defect labeled therein.
However, Lee teaches wherein the result includes a segmented visual rendering of the battery cell with the least one defect labeled therein [0115].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Biswas’s system with the teaching of Lee regarding outputting since this is well-known in the art to output visual and/or audible information corresponding to the diagnosis message based on which further action could be taken.
Regarding Claim 14, the system of claim 8 is taught by Biswas in view of Sumika.
Biswas does not explicitly teach regarding the system wherein the controller is configured to determine a corrective action to be taken with respect to the battery cell based on the result of the secondary identification process wherein the corrective action is with respect to at least one of a process of manufacturing battery cells battery cell or use of the battery cell.
However, Lee teaches the controller is configured to determine a corrective action (Fig.7; S780) to be taken with respect to the battery cell based on the result of the identification and the labeling process [0115].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Biswas’s system with the teaching of Lee regarding a predetermined protection operation since this is well-known in the art to take the predetermined protection operation as a corrective action based on diagnosis result.
Regarding Claim 20, the one or more non-transitory computer-readable media of claim 15 is taught by Biswas in view of Sumika.
Biswas does not explicitly teach regarding wherein execution of the computer-readable instructions causes the controller to determine a corrective action to be taken with respect to the battery cell based on the result of the secondary identification process wherein the corrective action is with respect to at least one of a process of manufacturing battery cells battery cell or use of the battery cell.
However, Lee teaches wherein the execution of the computer-readable instructions causes the controller to determine a corrective action to be taken with respect to the battery cell based on the result of the identification and the labeling process [0115].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Biswas’s system with the teaching of Lee regarding a predetermined protection operation since this is well-known in the art to take the predetermined protection operation as a corrective action based on diagnosis result.
Response to Arguments
Applicant's arguments filed on 03/12/2026 with respect to claims 1-20 have been fully considered but they are not persuasive.
(a) With regards to claim rejections of claims 1, 8 and 15 under 35 U.S.C. 103 applicant argues in page 9:
“claim 1 has been amended to recite, inter alia, "performing a preliminary identification process to identify a potential feature of interest in the battery cell based on analyzing the response signals, wherein the potential feature of interest may or may not be an actual defect in the battery cell," and "perform a secondary identification process to (1) determine if the potential feature of interest is an actual defect present in the battery cell, and (2) label the actual defect."
The recitation of two separate identification processes where on identification process is to identify potential features of interest that may or may not be an actual defect in the battery cell, along with a determination of whether a potential feature of interest is in fact an actual defect or not, renders the 103 rejection of claim 1 in view of a hypothetical combination of Biswas, Sumika, and/or Lee, moot. Claims 8 and 15 recite features that are somewhat similar to those recited in claim 1. Therefore, the 103 rejection of claims 8 and 15 as well as claims 2-7, 9-14, and 16-20 that depend from one of claims 1, 8, and 15, is also rendered moot.
For the foregoing reasons, the undersigned representative respectfully requests reconsideration and withdrawal of the rejection of claims 1-20, under 35 U.S.C. § 102.”
The examiner respectfully disagrees. The requirement for a proper response to a rejection may be found in 37 CFR 1.111(b) and MPEP § 707.07. The requirements for obviousness are discussed in MPEP § 2142. The requirements for broadest reasonable interpretation are discussed in MPEP 2111.01 (I) and 2173.01(I). The requirements for analogous art are discussed in MPEP 2141.01(a)(I).
As to claim interpretation, MPEP 2111.01 (I) states “[U]nder a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the time of the invention or as of the effective filing date of the patent application.”
As to analogous art, MPEP 2141.01(a)(I) discloses “[R]ather, a reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention). See Bigio, 381 F.3d at 1325.”
Examiner’s explanation:
The examiner respectfully disagrees. As to the limitation, “wherein the potential feature of interest may or may not be an actual defect in the battery cell,” Biswas discloses in para. [0098] that the corresponding metrics of a test sample may be measured against and compared to the reference model's metrics. The measurements or comparisons can reveal deviations of certain characteristics of the test sample from those of the reference model. These deviations can be used to determine, estimate, or predict the one or more states of the test sample. This indicates that the measurements and comparisons reveal deviations of certain characteristics of the test sample from those of the reference model. Thus, the potential feature of interest may or may not be an actual defect in the battery cell such as one or more states of the test batteries. Therefore, the limitation is implicitly taught by Biswas. Additionally, Sumika art explicitly teaches the identification processes, as explained in the rejection section earlier.
For at least the foregoing reasons, the rejection under 35 U.S.C. §103 is maintained since under a broadest reasonable interpretation, the present arts teach the claimed limitations.
Conclusion
The following prior arts made of record and not relied upon, are considered pertinent to applicant's disclosure:
Dou et al. (US 2020/0358147 A1) teaches systems and techniques for measuring process characteristics including electrolyte distribution in a battery cell. A non-destructive method for analyzing a battery cell includes determining acoustic features at two or more locations of the battery cell, the acoustic features based on one or more of acoustic signals travelling through at least one or more portions of the battery cell during one or more points in time or responses to the acoustic signals obtained during one or more points in time, wherein the one or more points in time correspond to one or more stages of electrolyte distribution in the battery cell. One or more characteristics of the battery cell are determined based on the acoustic features at the two or more locations of the battery cell [Abstract].
Van Tassell et al. (US 2021/0175553 A1) teaches systems and methods for acoustic signal based analysis, include obtaining acoustic response signal data of at least a portion of a battery cell, the acoustic response signal data comprising waveforms generated by transmitting one or more acoustic excitation signals into at least the portion of the battery cell and recording response vibration signals to the one or more acoustic excitation signals. One or more metrics are determined from at least the acoustic response signal data, the one or more metrics being determined based on correlation of the one or more metrics to one or more characteristics of battery cells and a reference model is generated from the one or more metrics. A test battery can be evaluated using the reference model. Actionable insights or recommendations can be generated based on the evaluation. The reference model can also be updated based on the evaluation [Abstract].
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUMAN NATH whose telephone number is (571)270-1443. The examiner can normally be reached on M to F 9:00 am to 5:00 pm.
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/SUMAN K NATH/Primary Examiner, Art Unit 2855