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 .
Claims 1-20 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “A reliability testing equipment” configured to perform an accelerated aging test and “a performance measuring equipment” configured to measure a plurality of performances of the memory device in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) 1, 13 and 20 recite(s):
obtaining, based on an accelerated aging test performing on a plurality of unused memory devices,… a plurality of performance measurement data…
calculating a plurality of statistical data…
calculating a plurality of conditional probabilities…
training a lifetime calculation model…
output estimated used lifetime data and uncertainty data…
In addition, the claim 13 further recites:
generating a lifetime calculation model…
measuring a plurality of performance data…
obtaining remaining useful lifetime…
In addition, the claim 20 further recites:
measure a plurality of performances…
Step 1: Is the claim to a process, machine, manufacture, or composition of
matter?
Yes.
Claim 1 is a process.
Claim 13 is a process.
Claim 20 is a machine.
Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
Yes: (an) abstract idea(s).
The ‘obtaining’ limitation in #1 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. For example, “obtaining” in the context of the claim encompasses a person reading data from a note.
The “calculating” limitation in #2 and #3 above, as claimed and under BRI, is also a mathematical concept that is defined as one or more of mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “calculating” in the context of the claim encompasses the person using high-level math to calculate the desired values.
The “training”’ limitation in #4 above, as claimed and under BRI, is also a mental process that covers performance of the limitation in the mind. For example, “training” in the context of the claim encompasses the person creating a decision-making rule based on the calculated values based on the obtained data.
The “outputting” limitation in #5, as claimed and under BRI, is also a mathematical concept that is defined as one or more of mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “outputting” in the context of the claim encompasses the person providing a value using a rule-based model.
The “generating” limitation in #6, as claimed and under BRI, is also a mental process that covers performance of the limitation in the mind. For example, “generating” in the context of the claim encompasses the person creating a decision-making rule based on the calculated values based on the obtained data.
The ‘measuring’ limitation in #7 and #10 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. For example, “measuring” in the context of the claim encompasses a person obtaining and recording data on a note.
The “obtaining” limitation in #8, as claimed and under BRI, is also a mathematical concept that is defined as one or more of mathematical relationships, mathematical formulas or equations, and mathematical calculations. For example, “obtaining” in the context of the claim encompasses the person deriving a value using the generated rule/model.
Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No.
The claims 1, 13 and 19 recite at least one or more of the following additional elements:
- at least on processor
- a non-transitory computer readable medium
- a memory device
The claim 12 recites at least one or more of the following additional elements:
- a dynamic random access memory (DRAM) device or a flash memory device
The claim 20 recites at least one or more of the following additional elements:
- a reliability testing equipment
- a performance measuring equipment
These additional elements are recited at a high level of generality (i.e. as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No.
As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components comprising mere instructions to apply the exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Claims 2-4 merely further describe obtaining steps.
Claims 5, 6 merely further describe performance measurement data obtained.
Claims 7, 8 merely further describe the calculating steps recited in claim 1.
Claims 9-11 merely further describes training step recited in claim 1.
Claim 12 merely further describes a memory device.
Claim 14 merely further describes obtaining steps.
Claim 15 merely further describes how the uncertainty data and the estimated used lifetime data is obtained.
Claims 16-17 merely further describes the types of memory devices monitored.
Claim 18 merely further describes target memory devices and obtaining remaining useful lifetime for the target memory devices recited in claim 13.
Claim 19 merely further describes measuring stem recited in claim 13.
For at least the reasoning provided above, claims 1-20 are patent ineligible.
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.
Claim(s) 1, 9-14, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (PG Pub. 2016/0,232,450 A1) [hereafter Chen], and further in view of Kong et al. (PG Pub. 2022/0,215,956 A1) [hereafter Kong].
As per claim 1, Chen teaches:
A modeling method for estimating used lifetime of a memory device, the modeling method being performed by executing program code using at least one processor, the program code being stored in a non-transitory computer readable medium, the modeling method comprising: (Chen, ¶ [0037], a processing unit, a memory unit, ¶ [0009], a storage device lifetime predicting model)
obtaining, based on an accelerated aging test performing on a plurality of unused memory devices, a plurality of performance measurement data, the plurality of performance measurement data being associated with a plurality of performances of the plurality of unused memory devices; (Chen, ¶ [0060], perform a stress test on the storage device to obtain the actual total operational time period, ¶ [0009], a plurality of training data, wherein each of the training data includes operation activity information and a corresponding operation lifetime value, ¶ [0050], ¶ [0064], ¶ [0067-0070], activity information from plurality of storage devices)
calculating a plurality of statistical data based on performing a statistical distribution approximation on the plurality of performance measurement data; (Chen, Fig. 5, ¶ [0064-0066], linear regression testing and creating a prediction curve based on the collected training data)
Chen does not specifically teach:
calculating a plurality of conditional probabilities based on a plurality of sample performance data and the plurality of statistical data, the plurality of sample performance data being associated with the plurality of performances; and
training a lifetime calculation model based on the plurality of conditional probabilities, the lifetime calculation model being configured to output estimated used lifetime data and uncertainty data, the estimated used lifetime data corresponding to the plurality of sample performance data, the uncertainty data representing uncertainty of the estimated used lifetime data
However, Kong in an analogous art teaches:
computing conditional probability from input sequence (Kong, ¶ [0033-0034])
estimating the uncertainty level of the sequence of conditional probabilities and train the sequential learning model (Kong, ¶ [0033-0034])
It would have been obvious to a person of ordinary skill of the art before the effective filing date of the invention to incorporate teachings of Kong into the method of Chen to provide a method of calculating a plurality of conditional probabilities based on a plurality of sample performance data and the plurality of statistical data, the plurality of sample performance data being associated with the plurality of performances; and training a lifetime calculation model based on the plurality of conditional probabilities, the lifetime calculation model being configured to output estimated used lifetime data and uncertainty data, the estimated used lifetime data corresponding to the plurality of sample performance data, the uncertainty data representing uncertainty of the estimated used lifetime data. The modification would be obvious because such method provides not only a sequence of accurate predictions but also the corresponding uncertainty estimations simultaneously to improve the process of predicting parameters (Kong, ¶ [0011]).
As per claim 9, the rejection of claim 1 is incorporated and Kong further teaches:
re-training the lifetime calculation model based on the uncertainty data (Kong, ¶ [0045], training the sequential learning model based on the training dataset comprising uncertainty level until the loss function converges)
As per claim 10, the rejection of claim 9 is incorporated and Kong further teaches:
wherein re-training the lifetime calculation model includes: comparing a data uncertainty value included in the uncertainty data with a data reference value; (Kong, ¶ [0045], training the sequential learning model based on the training dataset comprising uncertainty level until the loss function converges)
based on the data uncertainty value being greater than the data reference value, providing a plurality of additional sample performance data different from the plurality of sample performance data; and training the lifetime calculation model based on the plurality of additional sample performance data (Kong, ¶ [0045], training the sequential learning model based on the training dataset comprising uncertainty level until the loss function converges)
As per claim 11, the rejection of claim 9 is incorporated and Kong further teaches:
wherein re-training the lifetime calculation model includes: comparing a model uncertainty value included in the uncertainty data with a model reference value; (Kong, ¶ [0041], conditional probability may be implicitly modeled, which may capture the uncertainties in the prediction process)
based on the model uncertainty value being greater than the model reference value, correcting the lifetime calculation model; and training the corrected lifetime calculation model based on the plurality of sample performance data (Kong, ¶ [0045], training the sequential learning model based on the training dataset comprising uncertainty level until the loss function converges)
As per claim 12, the rejection of claim 1 is incorporated and Chen further teaches:
wherein each of the plurality of unused memory devices is a dynamic random access memory (DRAM) device or a flash memory device Chen, ¶ [0036], non-volatile memory storage device (SSD))
As per claim 13, Chen teaches:
A method of calculating remaining useful lifetime of a memory device, the method being performed by executing program code using at least one processor, the program code being stored in a non-transitory computer readable medium, the method comprising: (Chen, ¶ [0037], a processing unit, a memory unit, ¶ [0009], a storage device lifetime predicting model, ¶ [0040], program codes)
generating a lifetime calculation model based on a plurality of unused memory devices; (Chen, ¶ [0010], the lifetime estimation training module re-constructs the storage device lifetime predicting model according to the operation activity information and the predicted lifetime value of each of the storage devices)
measuring a plurality of performance data associated with a plurality of performances of at least one target memory device; and (Chen, ¶ [0009], collects the operation activity information corresponding to the storage devices)
obtaining remaining useful lifetime of the at least one target memory device based on the lifetime calculation model and the plurality of performance data associated with the plurality of performances, (Chen, ¶ [0009], lifetime predicting model according to the operation activity information, ¶ [0064], generating a prediction curve to predict the predicted lifetime value of each storage device)
wherein generating the lifetime calculation model includes: obtaining, based on an accelerated aging test performing on a plurality of unused memory devices, a plurality of performance measurement data, the plurality of performance measurement data being associated with a plurality of performances of the plurality of unused memory devices; (Chen, ¶ [0060], perform a stress test on the storage device to obtain the actual total operational time period, ¶ [0009], a plurality of training data, wherein each of the training data includes operation activity information and a corresponding operation lifetime value, ¶ [0050], ¶ [0064], ¶ [0067-0070], activity information from plurality of storage devices)
calculating a plurality of statistical data based on performing a statistical distribution approximation on the plurality of performance measurement data; (Chen, Fig. 5, ¶ [0064-0066], linear regression testing and creating a prediction curve based on the collected training data)
Chen does not specifically teach:
calculating a plurality of first conditional probabilities based on a plurality of sample performance data and the plurality of statistical data, the plurality of sample performance data being associated with the plurality of performances; and
training the lifetime calculation model based on the plurality of first conditional probabilities, the lifetime calculation model outputting first estimated used lifetime data and first uncertainty data, the first estimated used lifetime data corresponding to the plurality of sample performance data, the first uncertainty data representing uncertainty of the first estimated used lifetime data
However, Kong in an analogous art teaches:
computing conditional probability from input sequence (Kong, ¶ [0033-0034])
estimating the uncertainty level of the sequence of conditional probabilities and train the sequential learning model (Kong, ¶ [0033-0034])
It would have been obvious to a person of ordinary skill of the art before the effective filing date of the invention to incorporate teachings of Kong into the method of Chen to provide a method of calculating a plurality of first conditional probabilities based on a plurality of sample performance data and the plurality of statistical data, the plurality of sample performance data being associated with the plurality of performances; and training the lifetime calculation model based on the plurality of first conditional probabilities, the lifetime calculation model outputting first estimated used lifetime data and first uncertainty data, the first estimated used lifetime data corresponding to the plurality of sample performance data, the first uncertainty data representing uncertainty of the first estimated used lifetime data. The modification would be obvious because such method provides not only a sequence of accurate predictions but also the corresponding uncertainty estimations simultaneously to improve the process of predicting parameters (Kong, ¶ [0011]).
As per claim 14, the rejection of claim 13 is incorporated and Chen further teaches:
calculating the remaining useful lifetime of the at least one target memory device based on subtracting used lifetime of the at least one target memory device from initial guaranteed lifetime of the at least one target memory device, the used lifetime corresponding to the second estimated used lifetime data (Chen, ¶ [0060], predicted lifetime value provided by the storage device and subtracting the operated time period to determine how much life is left)
Chen does not teach:
calculating a plurality of second conditional probabilities based on the plurality of performance data and the plurality of statistical data; and
obtaining second estimated used lifetime data based on the plurality of second conditional probabilities and the lifetime calculation model, the second estimated used lifetime data corresponding to the plurality of performance data;
However, Kong in an analogous art teaches:
training until the loss function converges means the model repeats the process until the satisfying result is obtained (Kong, ¶ [0045])
It would have been obvious to a person of ordinary skill of the art before the effective filing date of the invention to incorporate teachings of Kong into the method of Chen to provide a method of calculating a plurality of second conditional probabilities based on the plurality of performance data and the plurality of statistical data; and obtaining second estimated used lifetime data based on the plurality of second conditional probabilities and the lifetime calculation model, the second estimated used lifetime data corresponding to the plurality of performance data. The modification would be obvious because such method provides not only a sequence of accurate predictions but also the corresponding uncertainty estimations simultaneously to improve the process of predicting parameters (Kong, ¶ [0011]).
As per claim 17, the rejection of claim 13 is incorporated and Chen further teaches:
wherein each of the plurality of unused memory devices is provided as a memory package that includes two or more memory chips, wherein the at least one target memory device is provided as a memory circuit that includes two or more memory packages, (Chen, ¶ [0007], SSD)
wherein the method includes: compensating the plurality of performance data, and (Chen, ¶ [0060], perform a stress test on the storage device to obtain the actual total operational time period, ¶ [0009], a plurality of training data, wherein each of the training data includes operation activity information and a corresponding operation lifetime value, ¶ [0050], ¶ [0064])
wherein the remaining useful lifetime of the at least one target memory device is obtained based on the lifetime calculation model and the plurality of compensated performance data (Chen, ¶ [0009], lifetime predicting model according to the operation activity information, ¶ [0064], generating a prediction curve to predict the predicted lifetime value of each storage device)
As per claim 20, Chen teaches:
A system comprising: a reliability testing equipment configured to perform an accelerated aging test on a memory device; (Chen, ¶ [0060], stress test)
a performance measuring equipment configured to measure a plurality of performances of the memory device; (Chen, ¶ [0009], collecting the operation activity information)
at least one processor; and a non-transitory computer readable medium configured to store program codes executed using the at least one processor to generate a lifetime calculation model for obtaining remaining useful lifetime of the memory device, (Chen, ¶ [0037], a processing unit, a memory unit, ¶ [0009], a storage device lifetime predicting model, ¶ [0040], program codes)
wherein the at least one processor is configured, by executing the program codes, to: obtain, based on the reliability testing equipment and the performance measuring equipment, a plurality of performance measurement data, the plurality of performance measurement data being associated with the plurality of performances of a plurality of unused memory devices based on the accelerated aging test performing on the plurality of unused memory devices, the memory device and the plurality of unused memory devices being memory devices of a same type; (Chen, ¶ [0060], perform a stress test on the storage device to obtain the actual total operational time period, ¶ [0009], a plurality of training data, wherein each of the training data includes operation activity information and a corresponding operation lifetime value, ¶ [0050], ¶ [0064], ¶ [0067-0070], activity information from plurality of storage devices)
calculate a plurality of statistical data based on performing a statistical distribution approximation on the plurality of performance measurement data; (Chen, Fig. 5, ¶ [0064-0066], linear regression testing and creating a prediction curve based on the collected training data)
Chen does not specifically teach:
calculate a plurality of conditional probabilities based on a plurality of sample performance data and the plurality of statistical data, the plurality of sample performance data being associated with the plurality of performances; and
train the lifetime calculation model based on the plurality of conditional probabilities, the lifetime calculation model being configured to output estimated used lifetime data and uncertainty data, the estimated used lifetime data corresponding to the plurality of sample performance data, the uncertainty data representing uncertainty of the estimated used lifetime data
However, Kong in an analogous art teaches:
computing conditional probability from input sequence (Kong, ¶ [0033-0034])
estimating the uncertainty level of the sequence of conditional probabilities and train the sequential learning model (Kong, ¶ [0033-0034])
It would have been obvious to a person of ordinary skill of the art before the effective filing date of the invention to incorporate teachings of Kong into the method of Chen to provide a method to calculate a plurality of conditional probabilities based on a plurality of sample performance data and the plurality of statistical data, the plurality of sample performance data being associated with the plurality of performances; and to train the lifetime calculation model based on the plurality of conditional probabilities, the lifetime calculation model being configured to output estimated used lifetime data and uncertainty data, the estimated used lifetime data corresponding to the plurality of sample performance data, the uncertainty data representing uncertainty of the estimated used lifetime data. The modification would be obvious because such method provides not only a sequence of accurate predictions but also the corresponding uncertainty estimations simultaneously to improve the process of predicting parameters (Kong, ¶ [0011]).
Allowable Subject Matter
Claims 2-8, 15, 16, 18, 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and amended to overcome the 35 U.S.C. 101 rejection set forth in this OFFICE ACTION.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PG Pub. 2022/0,100,389 A1 discloses a method for acquiring a model for determining a remaining life of a disk, wherein the model is trained by taking a set of parameters related to a failure of a group of reference disks as an input and taking a reference remaining life of the group of reference disks at the time when the set of parameters are acquired as an output; acquiring a parameter related to a remaining life of a target disk, wherein the parameter indicates usage information of the target disk when it is used; and applying the parameter to the model to determine the remaining life of the target disk.
PG Pub. 2022/0,137,827 A1 discloses methods for life expectancy monitoring for memory devices. A memory device may monitor a parameter that may represent or be associated with a lifetime of the component, a level of wear of the component, or an operating parameter violation of a component of the memory device or the memory device overall.
PG Pub. 2022/0,187,819 A1 discloses methods for predicting failures and remaining useful life (RUL) for equipment.
WO 2021/040,810 A1 discloses a method for predicting lifetime for a target digital device combine a reference performance measure curve, representing a performance measure of the target device, with a noise function that represents a difference between the reference performance measure curve and an actual performance measure curve for the target device.
PG Pub. 2015/0,323,611 A1 discloses a life prediction apparatus for an electrical storage device, which has a life predictor.
See PTO-892 for other references not listed above.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAE M KO whose telephone number is (571)270-3886. The examiner can normally be reached M-F 9 am - 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached at 571-272-0631. 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.
/CHAE M KO/Primary Examiner, Art Unit 2114