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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Drawings
The applicant’s submitted drawings appear to be acceptable for examination purposes. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the drawings.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant's submission of the Information Disclosure Statements, dated 31 May 2023 and 24 July 2024, are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 forms, initialed and dated by the examiner, are attached to the instant office action.
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.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 27-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitations “means for determining impedance,” “means for generating impedance change information,” and “means for generating an off-body characteristic” (of claim 27) and “means for performing power back-off operations” (of claim 28) invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. While the specification describes several types of processors and different components stored in memory (i.e., software) that may be used to perform these functions (see, e.g., Fig. 9 and associated description in the specification as filed), it does not appear to associate these functions with specific structures capable of performing them. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 28-30 depend upon claim 27, and thus also include the aforementioned limitation(s).
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.
Claim(s) 1-30 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental processes and/or mathematical concepts. This judicial exception is not integrated into a practical application and does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as described below.
Step 1 for all claims:
Under the first part of the analysis, claims 1-16 recite a method and claims 17-30 recite a device. Accordingly, these claims fall within the four statutory categories of invention and the analysis proceeds to Step 2A, prongs 1 and 2, and Step 2B, as described below.
As per claim 1:
Under step 2A, prong 1, the claim recites an abstract idea including the following mental process and/or mathematical concept elements:
generating impedance change information based on a difference between the impedance information and prior impedance information for the wireless transmitter – this is a mathematical calculation to determine the difference between two values (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – a data scientist can determine the change in impedance between two impedance values (a mental process).
generating an off-body characteristic based on processing the impedance change information … wherein the off-body characteristic indicates a probability that the device was off-body when the impedance information was determined – the data scientist determines a probability that the device was off-body based on the impedance change information.
If a claim, under the broadest reasonable interpretation covers a mathematical relationship between variables or numbers, a numerical formula or equation, or a mathematical calculation, it will be considered as falling within the “mathematical concepts” grouping of abstract ideas. If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, it will be considered as falling within the “mental processes” grouping of abstract ideas. Additionally, performing mathematical calculations using a formula that could be practically performed in the human mind may be considered to fall within both the mathematical concepts grouping and the mental process grouping. See MPEP § 2106.04(a)(2).
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A processor-implemented method for proximity detection using machine learning comprising – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
determining impedance information for a wireless transmitter of a device – this is recited at a high level of generality and amounts to insignificant extra-solution activity as data gathering/storage that is limited to a particular type of data (i.e., the impedance info for the wireless transmitter), generally linking the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(g) and (h), and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
using a trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A processor-implemented method for proximity detection using machine learning comprising – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
determining impedance information for a wireless transmitter of a device – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data). The courts have also found limitations directed to obtaining and storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP § 2106.05(d)(II) “receiving or transmitting data over a network,” "electronic record keeping,” and "storing and retrieving information in memory.”
using a trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 2:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
performing power back-off operations for the wireless transmitter based on the off-body characteristic – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
performing power back-off operations for the wireless transmitter based on the off-body characteristic – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 3:
The claim recites the following additional mental process and/or mathematical concept elements:
based on determining that the off-body characteristic satisfies one or more criteria indicating that the device was not off-body when the impedance information was determined – the data scientist determines whether the device was sufficient off-body or not when the impedance information was collected. Alternatively/additionally – this is a mathematical calculation comparing the characteristic to one or more criteria values.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein performing the power back-off operations comprises reducing transmission power of the wireless transmitter – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein performing the power back-off operations comprises reducing transmission power of the wireless transmitter – this amounts to no more than a recitation of the words "apply it" (or an equivalent) including mere instructions to implement an abstract idea or other exception on a computer, and/or at most generally linking the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(f) and (h).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 4:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein generating the impedance change information comprises:
generating a first value representing a magnitude of the difference between the impedance information and the prior impedance information – this is a mathematical calculation, determining a magnitude of the difference between two data points (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the magnitude of the change in impedance.
and generating a second value representing a direction of the difference between the impedance information and the prior impedance information– this is a mathematical calculation, determining a direction of the difference between two data points (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the direction of the change in impedance.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 5:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein: the second value indicates the direction of the difference between a real component of the impedance information and a real component of the prior impedance information – this is a mathematical calculation, determining a direction of the difference between two real components (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the direction of the change in real components of the impedance.
and generating the impedance change information further comprises generating a third value representing a direction of the difference between an imaginary component of the impedance information and an imaginary component of the prior impedance information– this is a mathematical calculation, determining a direction of the difference between two imaginary components (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the direction of the change in imaginary components of the impedance.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 6:
The claim recites the following additional mental process and/or mathematical concept elements:
wherein generating the impedance change information further comprises:
generating a third value representing a magnitude of the difference between the impedance information and an off-body reference point – this is a mathematical calculation, determining a magnitude of the difference between two data points (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the magnitude of the change between the impedance value and the reference point.
and generating a fourth value representing a direction of the difference between the impedance information and the off-body reference point – this is a mathematical calculation, determining a direction of the difference between two data points (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the direction of the change between the impedance value and the reference point.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 7:
The claim recites the following additional mental process and/or mathematical concept elements:
the fourth value indicates the direction of the difference between a real component of the impedance information and a real component of the off-body reference point – this is a mathematical calculation, determining a direction of the difference between two real components (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the direction of the change between the real components.
and generating the impedance change information further comprises generating a fifth value representing a direction of the difference between an imaginary component of the impedance information and an imaginary component of the off-body reference point – this is a mathematical calculation, determining a direction of the difference between two imaginary components (see, e.g., paras. [0046-51] of the specification as filed). Alternatively/additionally – the data scientist determines the direction of the change between the imaginary components.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
The claim does not include any additional elements, under step 2A prong two, or step 2B, except those listed above in prior claim(s). Accordingly, at step 2A, prong two, the claim as a whole does not integrate the judicial exception into a practical application. See MPEP § 2106.04(d). Furthermore, at step 2B, the claim elements both individually and in combination do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 8:
The claim recites the following additional mental process and/or mathematical concept elements:
generating a charging characteristic … wherein the charging characteristic indicates a probability that a charging cable was plugged into the device when the impedance information was determined – the data scientist determines a probability that the charging cable was plugged into the device when the impedance information was determined.
Accordingly, at step 2A, prong one, the claim is directed to an abstract idea.
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
based on processing the impedance change information using the trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
based on processing the impedance change information using the trained machine learning model – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 9:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the trained machine learning model is used to provide proximity detection – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the device does not include a capacitive sensor for proximity detection – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
the trained machine learning model is used to provide proximity detection – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
and the device does not include a capacitive sensor for proximity detection – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 10:
Under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the trained machine learning model was trained based on a set of impedance characterization records, the set comprising: a first subset of impedance characterization records, wherein each respective impedance characterization record in the first subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold similarity to an off-body reference point; and a second subset of impedance characterization records, wherein each respective impedance characterization record in the second subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold dissimilarity to the off-body reference point – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
wherein the trained machine learning model was trained based on a set of impedance characterization records, the set comprising: a first subset of impedance characterization records, wherein each respective impedance characterization record in the first subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold similarity to an off-body reference point; and a second subset of impedance characterization records, wherein each respective impedance characterization record in the second subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold dissimilarity to the off-body reference point – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 11:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A processor-implemented method for training machine learning models to perform proximity detection, comprising – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
updating one or more parameters of the machine learning model based on comparing the off-body characteristic with a ground truth label associated with the impedance information – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A processor-implemented method for training machine learning models to perform proximity detection, comprising – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
updating one or more parameters of the machine learning model based on comparing the off-body characteristic with a ground truth label associated with the impedance information – this amounts to generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 12, see the rejection of claim 4, above.
As per claim 13, see the rejection of claim 5, above.
As per claim 14, see the rejection of claim 6, above.
As per claim 15, see the rejection of claim 7, above.
As per claim 16, see the rejection of claim 10, above.
As per claim 17:
See the rejection of claim 1, above, wherein under step 2A, prong two, the judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A processing system comprising – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
a memory comprising computer-executable instructions – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and one or more processors configured to execute the computer-executable instructions and cause the processing system to perform [the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2A, prong two, these additional elements do not integrate the abstract idea into a practical application for the claim as a whole, because it does not impose any meaningful limits on practicing the abstract idea. See MPEP § 2106.04(d).
Under step 2B, the claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the claim recites the additional elements of:
A processing system comprising – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
a memory comprising computer-executable instructions – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
and one or more processors configured to execute the computer-executable instructions and cause the processing system to perform [the method] – this amounts to mere instructions to apply the exception using a generic computer component, recited at a high level of generality. See MPEP § 2106.05(f).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05.
Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
As per claim 18, see the rejection of claim 2, above.
As per claim 19, see the rejection of claim 3, above.
As per claim 20, see the rejection of claim 4, above.
As per claim 21, see the rejection of claim 5, above.
As per claim 22, see the rejection of claim 6, above.
As per claim 23, see the rejection of claim 7, above.
As per claim 24, see the rejection of claim 8, above.
As per claim 25, see the rejection of claim 9, above.
As per claim 26, see the rejection of claim 10, above.
As per claim 27, see the rejection of claim 1, above.
Examiner’s Note: the elements described in the above rejections are substantially equivalent to the claimed means, as they are performing the same function in substantially the same manner.
As per claim 28, see the rejection of claim 2, above.
As per claim 29, see the rejection of claim 4, above.
As per claim 30, see the rejection of claim 9, above.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 9, 11, 17-19, 25, 27, 28, and 30 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (US 2021/0318423 – cited in an IDS).
As per claim 1, Wang teaches a processor-implemented method for proximity detection using machine learning [the system uses a machine-learned model for detecting the proximity of an object (para. 0050, etc.) implemented via one or more processors and connected memories of a user device (para. 0009, fig. 2, etc.)], comprising:
determining impedance information for a wireless transmitter of a device [the user equipment (UE) calculates one or more voltage standing wave ratios (VSWRs) across multiple antenna elements (wireless transmitters) (para. 0035, etc.), where the VSWRs are impedance information (see, e.g., paras. 0002-3 as well as para. [0020] of the specification of the instant application as filed)];
generating impedance change information based on a difference between the impedance information and prior impedance information for the wireless transmitter [the user equipment (UE) calculates one or more voltage standing wave ratios (VSWRs) across multiple antenna elements (wireless transmitters) (para. 0035, etc.) as well as calculating changes in the VSWRs (paras. 0007-8, 0039-42, etc.), which is an impedance change (see above)]; and
generating an off-body characteristic based on processing the impedance change information using a trained machine learning model, wherein the off-body characteristic indicates a probability that the device was off-body when the impedance information was determined [the machine-learned model generates classification predictions regarding the presence/proximity/location/movement of an object in the near field region of the transmitter(s), based upon the calculated changes in the VSWRs (paras. 0051-55, etc.), where the presence classification can be a proximity of the user’s body or body part, such as the head, an extremity, hand, etc. (paras. 0046, 0052, etc.), where predicting a classification of the presence/not of a user’s body (or body part) within proximity to the device is within the broadest reasonable interpretation of the “off-body characteristic” indicating the probability that the device is off-body (see, e.g., para. [0034] of the specification as filed in the instant application, which describes that “’off body’ generally refers to the device being not in physical contact with a human and not within a defined (small) distance”].
As per claim 2, Wang teaches performing power back-off operations for the wireless transmitter based on the off-body characteristic [the UE includes switching circuitry that may reduce/turn off power (power back-off operations) of one or more of the transmitting antennae in response to the proximity detection prediction (off-body characteristic) (para. 0046, etc.)].
As per claim 3, Wang teaches wherein performing the power back-off operations comprises reducing transmission power of the wireless transmitter based on determining that the off-body characteristic satisfies one or more criteria indicating that the device was not off-body when the impedance information was determined [the UE includes switching circuitry that may reduce/turn off power (power back-off operations) of one or more of the transmitting antennae in response to the proximity detection prediction (off-body characteristic) (para. 0046, etc.); where the prediction/classification is the characteristic satisfying one or more criteria or, alternatively location and movement classifications can also be made and used for the control decisions by the UE (paras. 0053-54, etc.)].
As per claim 9, Wang teaches wherein:
the trained machine learning model is used to provide proximity detection, and
the device does not include a capacitive sensor for proximity detection [the machine-learned model generates classification predictions regarding the presence/proximity/location/movement of an object in the near field region of the transmitter(s), based upon the calculated changes in the VSWRs (paras. 0051-55, etc.); and does not describe any capacitive sensor used for proximity detection].
As per claim 11, see the rejection of claim 1, above, wherein Wang also teaches a processor-implemented method for training machine learning models to perform proximity detection, comprising:
updating one or more parameters of the machine learning model based on comparing the off-body characteristic with a ground truth label associated with the impedance information [various VSWR (impedance) measurements can be collected and categorized with corresponding correct object location/detection and proximity classifications (ground truth labels) to train the machine-learned model (para. 0051, etc.) where the model may be deployed after sufficient training (paras. 0053-54), which includes updating one or more parameters of the machine learning model (as part of training)].
As per claim 17, see the rejection of claim 1, above, wherein Wang also teaches a processing system comprising:
a memory comprising computer-executable instructions; and
one or more processors configured to execute the computer-executable instructions and cause the processing system to perform [the method] [the system uses a machine-learned model for detecting the proximity of an object (para. 0050, etc.) implemented via one or more processors and connected memories of a user device (para. 0009, fig. 2, etc.)].
As per claim 18, see the rejection of claim 2, above.
As per claim 19, see the rejection of claim 3, above.
As per claim 25, see the rejection of claim 9, above.
As per claim 27, see the rejection of claim 1, above.
Examiner’s Note: the elements described in the above rejections are substantially equivalent to the claimed means, as they are performing the same function in substantially the same manner.
As per claim 28, see the rejection of claim 2, above.
As per claim 30, see the rejection of claim 9, above.
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.
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.
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.
The factual inquiries 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.
Claim(s) 4-7, 12-15, 20-23, and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 2021/0318423 – cited in an IDS) in view of Yang (US 2014/0269977).
As per claim 4, Wang the processor-implemented method of claim 1, as described above.
While Wang also teaches generating impedance change information (see above), it has not been relied upon for teaching wherein generating the impedance change information comprises: generating a first value representing a magnitude of the difference between the impedance information and the prior impedance information; and generating a second value representing a direction of the difference between the impedance information and the prior impedance information.
Yang teaches wherein generating the impedance change information comprises:
generating a first value representing a magnitude of the difference between the impedance information and the prior impedance information [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (paras. 0029-30, etc.); where comparing magnitude in the change (difference) in impedance to a set threshold includes generating a value representing that magnitude]; and
generating a second value representing a direction of the difference between the impedance information and the prior impedance information [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (paras. 0029-30, etc.); where determining whether an increase or decrease in the impedance includes generating a value representing the direction of the difference (i.e., increasing vs. decreasing)].
Wang and Yang are analogous art, as they are within the same field of endeavor, namely performing proximity detection of a user’s body parts, etc., to a user device/antenna(s) based upon changes in impedance values.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include calculating direction, magnitude, real parts, and imaginary parts of the changes in impedance to be compared to a desired range/threshold for proximity detection, as taught by Yang, in the calculating the change in impedance to determine/predict the proximity of objects/user in the system taught by Wang.
Because both Wang and Yang teach systems that calculate changes in impedance values at an antenna of a user device to predict/detect proximity of an object or the user, it would have been obvious to one of ordinary skill in the art to include calculating direction, magnitude, real parts, and imaginary parts of the changes in impedance to be compared to a desired range/threshold for proximity detection, as taught by Yang, in the calculating the change in impedance to determine/predict the proximity of objects/user in the system taught by Wang, to achieve the predictable result of providing more fine-grain analysis of the changes in the electrical characteristics in order to better customize the device response.
As per claim 5, Wang/Yang teaches wherein:
the second value indicates the direction of the difference between a real component of the impedance information and a real component of the prior impedance information [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (Yang: paras. 0029-30, etc.); which includes the magnitude and direction of the change in the real part of the impedance], and
generating the impedance change information further comprises generating a third value representing a direction of the difference between an imaginary component of the impedance information and an imaginary component of the prior impedance information [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (Yang: paras. 0029-30, etc.); which includes the magnitude and direction of the change in the imaginary part of the impedance].
As per claim 6, Wang/Yang teaches wherein generating the impedance change information further comprises:
generating a third value representing a magnitude of the difference between the impedance information and an off-body reference point [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (Yang: paras. 0029-30, etc.) and can include comparing the changes to a determined threshold indicating that there are or are not objects in the near-field (Wang: paras. 0039, 0077, etc.); where the threshold set to indicate objects/no objects is the comparison to the off-body reference point]; and
generating a fourth value representing a direction of the difference between the impedance information and the off-body reference point [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (Yang: paras. 0029-30, etc.) and can include comparing the changes to a determined threshold indicating that there are or are not objects in the near-field (Wang: paras. 0039, 0077, etc.); where the threshold set to indicate objects/no objects is the comparison (which would include a direction, i.e., greater or less than) to the off-body reference point].
As per claim 7, Wang/Yang teaches wherein:
the fourth value indicates the direction of the difference between a real component of the impedance information and a real component of the off-body reference point [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (Yang: paras. 0029-30, etc.) and can include comparing the changes to a determined threshold indicating that there are or are not objects in the near-field (Wang: paras. 0039, 0077, etc.); where the threshold set to indicate objects/no objects is the comparison (which would include a direction, i.e., greater or less than) to the off-body reference point, including both real and imaginary components], and
generating the impedance change information further comprises generating a fifth value representing a direction of the difference between an imaginary component of the impedance information and an imaginary component of the off-body reference point [the system may perform proximity detection by calculating changes in electrical characteristics, such as impedance, and determining an increase/decrease (direction) beyond a specified range or threshold in magnitude, phase, a real part, and/or an imaginary part (Yang: paras. 0029-30, etc.) and can include comparing the changes to a determined threshold indicating that there are or are not objects in the near-field (Wang: paras. 0039, 0077, etc.); where the threshold set to indicate objects/no objects is the comparison (which would include a direction, i.e., greater or less than) to the off-body reference point, including both real and imaginary components].
As per claim 12, see the rejection of claim 4, above.
As per claim 13, see the rejection of claim 5, above.
As per claim 14, see the rejection of claim 6, above.
As per claim 15, see the rejection of claim 7, above.
As per claim 20, see the rejection of claim 4, above.
As per claim 21, see the rejection of claim 5, above.
As per claim 22, see the rejection of claim 6, above.
As per claim 23, see the rejection of claim 7, above.
As per claim 29, see the rejection of claim 4, above.
Claim(s) 8 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 2021/0318423 – cited in an IDS) in view of Bit-Babik (US 10,873,349).
As per claim 8, Wang teaches the processor-implemented method of claim 1, as described above.
While Wang teaches generating characteristics based on processing the impedance change information using the trained machine learning model (see above), it has not been relied upon for teaching generating a charging characteristic based on processing the impedance change information using the trained machine learning model, wherein the charging characteristic indicates a probability that a charging cable was plugged into the device when the impedance information was determined.
Bit-Babik teaches generating a charging characteristic based on processing the impedance change information using the trained machine learning model, wherein the charging characteristic indicates a probability that a charging cable was plugged into the device when the impedance information was determined [the system includes proximity detection and optional orientation detection for antennae of an electronic device (col. 11, lines 49-65) as well as detecting a charging state/status of the device (col. 16, line 38 to col. 17, line 3; etc.); to be included in the proximity detection and prediction of the machine learning model of Wang, above].
Wang and Bit-Babik are analogous art, as they are within the same field of endeavor, namely utilizing machine learning models to make predictions to control antennae/electronic device outputs, as well as proximity detection used for the prediction/control.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include the charging state/status detection with the proximity detection in order to control the device antenna(e), as taught by Bit-Babik, in the predictions output by the machine-learned model based on impedance/VSWR changes in the system taught by Wang.
Bit-Babik provides motivation [determining the charging state/status of the device is important for effectively controlling the antennae, as the proximity of the charger device can interfere with the operation of certain antenna elements (col. 16, line 38 to col. 17, line 3; etc.); Examiner’s Note: while this is specific to the placement of the elements in that device, the motivation still applies as it would be useful to be able to control for charging element interference with any antenna, whenever it might occur].
As per claim 24, see the rejection of claim 8, above.
Claim(s) 10, 16, and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 2021/0318423 – cited in an IDS) in view of Patel (US 2022/0309391).
As per claim 10, Wang teaches wherein the trained machine learning model was trained based on a set of impedance characterization records [various measurements can be collected and categorized with corresponding correct object location/detection and proximity classifications to train the machine-learned model (Wang: para. 0051, etc.)],
While Wang teaches training the machine learning model on a training set of impedance characterization records (see above), it has not been relied upon for teaching the set comprising: a first subset of impedance characterization records, wherein each respective impedance characterization record in the first subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold similarity to an off-body reference point; and a second subset of impedance characterization records, wherein each respective impedance characterization record in the second subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold dissimilarity to the off-body reference point.
Patel teaches the set comprising:
a first subset of impedance characterization records, wherein each respective impedance characterization record in the first subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold similarity to an off-body reference point [in response to evaluating the predictive models, additional synthetic training datasets may be generated or selected from a synthetic data area for use in training the set of predictive models, where additional datasets (a first subset) can be selected having a threshold satisfying similarity with the enterprise dataset subject to examining (paras. 0059-61, 0114, etc.); where the datasets subject to examining being compared to is the impedance characterization similar to an off-body reference point in the system of Wang, above]; and
a second subset of impedance characterization records, wherein each respective impedance characterization record in the second subset was selected for training the trained machine learning model based on determining that the respective impedance characterization has at least a threshold dissimilarity to the off-body reference point [when training the predictive models the developer system may also recommend generation of a dissimilar dataset satisfying a dissimilarity threshold with respect to another dataset (paras. 0079-80, 0114, etc.); where the datasets being compared to is the impedance characterization dissimilar from an off-body reference point in the system of Wang, above].
Wang and Patel are analogous art, as they are within the same field of endeavor, namely training predictive machine learning models.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to include selecting subsets of training data for use in further training the predictive model, based upon a similarity and/or dissimilarity threshold comparison, as taught by Patel, in training the machine learning model utilizing impedance characterization data and an off-body reference point to predict object/user proximity/location/etc. in the system taught by Wang.
Patel provides motivation as [Embodiments herein can include processing of a dataset to extract dataset characterizing parameter values thereof and using them for comparing different datasets having threshold satisfying similarities and dissimilarities can be identified and used in purposefully prompted training iterations (para. 0114, etc.), which can facilitate improvement in the performance of the predictive models (para. 0029, etc.)].
As per claim 16, see the rejection of claim 10, above.
As per claim 26, see the rejection of claim 10, above.
Conclusion
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i): claims 1-30 are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Calzolari (US 12,160,258) – a related case that claims a method for wireless communication including using machine learning to determine user interaction states and control device antenna, based on current and historical impedance values.
Casebolt (US 7,113,087 – cited in an IDS) – discloses proximity sensing/detection based on antenna impedance variation, including powering off a device based on determined user proximity.
Gray (US 2022/0091057) – discloses a sensing device that calculates changes in impedance and generates power signals reflecting changes in magnitude, phase, and real/imaginary components.
Cech (US 2009/0319212) – discloses a magnetic crash sensor including determining changes in complex impedance for performing proximity detection.
Ben-Haim (US 2022/0409293) – discloses a system/method for characterizing intracardiac structures based on impedance changes (including proximity detection).
Yankowitz (US 2024/0006932) – discloses detecting impedance change-based proximity detection used to control wireless charging.
Kinomura (US 2023/0048830) – discloses proximity detection and charging status detection of a vehicle.
MacLeod (US 2018/0150607) – discloses synthetic ground truth/training dataset expansion, including similarity reduction and dissimilarity buildup, based on similarity/dissimilarity thresholds.
Sharifi et al. (Impedance Variation and Learning Strategies in Human-Robot Interaction, Jan 2021, pgs. 6462-6475) – discloses various systems/methods that utilize machine learning based on impedance variation for proximity detection, etc. in human-robot interactions.
The examiner requests, in response to this Office action, that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 CFR 1.111(c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE GIROUX whose telephone number is (571)272-9769. The examiner can normally be reached M-F 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GEORGE GIROUX/Primary Examiner, Art Unit 2128
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