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 .
Status of Claims
In response to communications filed on 21 August 2024, claims 1-15 are presently pending in the application, of which, claims 1 and 11 are presented in independent form.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings, filed 21 August 2024, have been reviewed and accepted by the Examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
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.
Regarding claims 1-20, under Step 2A claims 1-10 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites:
obtaining a quantized weight by quantizing a weight of a neural network;
obtaining a quantization error that is a difference between the weight and the quantized weight;
obtaining input data with respect to the neural network; obtaining a first convolution result by performing convolution on the quantized weight and the input data;
obtaining a second convolution result by performing convolution on the quantization error and the input data;
obtaining a scaled second convolution result by scaling the second convolution result based on bit shifting; and
obtaining output data by using the first convolution result and the scaled second convolution result.
These limitations recite mental processes, such as concepts performed in the human mind (see: 2019 PEG, p. 52). This is because the each of the limitations above recite a series of steps that may be mentally performed by which an evaluation is made for an abstract data. For example, the limitations of ‘obtaining a quantized weight by quantizing a weight of a neural network ;obtaining a quantization error that is a difference between the weight and the quantized weight; obtaining input data with respect to the neural network; obtaining a first convolution result by performing convolution on the quantized weight and the input data; obtaining a second convolution result by performing convolution on the quantization error and the input data; obtaining a scaled second convolution result by scaling the second convolution result based on bit shifting; and obtaining output data by using the first convolution result and the scaled second convolution result,’ illustrate a judgement being performed to find matching results and does not perform any technical operation. This represents a judgement or decision which are concepts performed in the human mind and falls under certain methods of mental processes. Accordingly, under step 2A (prong 1) the claim recites an abstract idea because the claim recites limitations that fall within the “Certain methods of mental processes” grouping of abstract ideas (see again: 2019 PEG, p. 52).
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including hardware processing circuitry, such as edge device.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application (see again: 2019 Revised Patent Subject Matter Eligibility Guidance).
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. That is, the limitations of ‘obtaining output data by using the first convolution result and the scaled second convolution result,’ are additional elements that are insignificant extra solution activities that that do not amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(lI)), including at least:
• receiving or transmitting data over a network, and/or
• storing and retrieving information in memory
• performing repetitive calculations
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Dependent claim 2 also does not integrate the abstract idea into a practical application. Notably, claim 2 recites ‘ wherein the obtaining the quantized weight comprises converting the weight from floating-point data into quantized fixed-point data of n-bits,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 2 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 2 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 2 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claim 2 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 3 also does not integrate the abstract idea into a practical application. Notably, claim 3 recites ‘wherein the obtaining the quantization error comprises quantizing the difference,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 3 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 3 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 3 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claim 3 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 4 also does not integrate the abstract idea into a practical application. Notably, claim 4 recites ‘wherein the obtaining the scaled second convolution result comprises determining a bit shift value based on a first scale factor with respect to the weight and a second scale factor with respect to the quantization error,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 4 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 4 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 4 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 4 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 5 also does not integrate the abstract idea into a practical application. Notably, claim 5 recites ‘wherein, the obtaining the scaled second convolution result comprises determining, based on a magnitude of the quantization error being equal to the first scale factor, the bit shift value to be n-bits, where n denotes a quantization bit value,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 5 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 5 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 5 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 5 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 6 also does not integrate the abstract idea into a practical application. Notably, claim 6 recites ‘wherein, the obtaining the scaled second convolution result comprises determining, based on a relationship between the first scale factor and the second scale factor being expressed as a square number of 2, the bit shift value to be n+k bits, where n denotes a quantization bit value and k denotes a value of the square number of 2,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 6 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 6 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 6 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 6 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 7 also does not integrate the abstract idea into a practical application. Notably, claim 7 recites ‘wherein, the obtaining the scaled second convolution result comprises determining, based on the relationship between the first scale factor and the second scale factor not being expressed as the square number of 2, the bit shift value based on k, wherein k is determined through a log operation and a rounding operation,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 7 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 7 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 7 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 7 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 8 also does not integrate the abstract idea into a practical application. Notably, claim 8 recites ‘wherein the obtaining the scaled second convolution result comprises determining a range of the first scale factor based on a maximum value and a minimum value of the weight,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 8 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 8 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 8 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 8 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 9 also does not integrate the abstract idea into a practical application. Notably, claim 9 recites ‘wherein the obtaining the scaled second convolution result comprises determining a range of the second scale factor based on a maximum value and a minimum value of the quantization error,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 9 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 9 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 9 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 9 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 10 also does not integrate the abstract idea into a practical application. Notably, claim 10 recites ‘wherein the first scale factor is greater than the second scale factor’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 10 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 10 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 10 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 10 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Claims 11-15 appear to include similar subject matter as in claims 1-10 as discussed above. More specifically, independent claim 11 additionally recites ‘a data processing apparatus…’ which is recited at a high level of generality and are recited as performing mere generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system in addition to merely indicating a field of use or technological environment in which the judicial exception do not amount to significantly more than the exception itself. All the comments made with respect to the rejection of claims 1-10 equally apply and therefore stand rejected.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being unpatentable by Deisher, Michael (U.S.2019/0042935 and known hereinafter as Deisher).
As per claim 1, Deisher teaches a data processing method for neural network quantization, comprising:
obtaining a quantized weight by quantizing a weight of a neural network (e.g. Deisher, see paragraph [0051], which discloses dynamic quantization of neural network);
obtaining a quantization error that is a difference between the weight and the quantized weight (e.g. Deisher, see paragraph [0010-0011], which discloses the neural networks are often trained using floating point arithmetic, then quantized into integer neural networks, where the accuracy of integer neural networks depend on minimizing the quantization error of the neural network parameters as well as the inputs to the network, where a quantization error describes the error present when quantizing a floating point set of values to an integer set of values.);
obtaining input data with respect to the neural network (e.g. Deisher, see paragraph [0028], which discloses obtaining input data, where the current context is concatenated with previous contexts and used as an input to the neural network.);
obtaining a first convolution result by performing convolution on the quantized weight and the input data (e.g. Deisher, see paragraph [0018], which discloses obtaining weight matrices and input data.);
obtaining a second convolution result by performing convolution on the quantization error and the input data (e.g. Deisher, see paragraph [0010], which discloses neural network are often trained using floating point arithmetic, then quantized into integer neural networks, where the accuracy of integer neural network includes minimizing quantization error.);
obtaining a scaled second convolution result by scaling the second convolution result based on bit shifting (e.g. Deisher, see paragraph [0030], which discloses data may be scaled according to the particular design of the processor to execute the quantized neural network and weights are slightly reduced (shifted) to provide additional headroom when the product of the input and weights are accumulated.); and
obtaining output data by using the first convolution result and the scaled second convolution result (e.g. Deisher, see paragraph [0019-0020], which discloses an output scale factor based on the compared weight matrices and convolution filters.).
As per claim 11, Deisher teaches a data processing apparatus for neural network quantization, comprising:
a neural processor (e.g. Deisher, see paragraph [0030], which discloses a processor coupled to memory); and
memory storing instructions that, when executed by the neural processor (e.g. Deisher, see paragraph [0030], which discloses a processor coupled to memory) cause the data processing apparatus to:
obtaining a quantized weight by quantizing a weight of a neural network (e.g. Deisher, see paragraph [0051], which discloses dynamic quantization of neural network);
obtaining a quantization error that is a difference between the weight and the quantized weight (e.g. Deisher, see paragraph [0010-0011], which discloses the neural networks are often trained using floating point arithmetic, then quantized into integer neural networks, where the accuracy of integer neural networks depend on minimizing the quantization error of the neural network parameters as well as the inputs to the network, where a quantization error describes the error present when quantizing a floating point set of values to an integer set of values.);
obtaining input data with respect to the neural network (e.g. Deisher, see paragraph [0028], which discloses obtaining input data, where the current context is concatenated with previous contexts and used as an input to the neural network.);
obtaining a first convolution result by performing convolution on the quantized weight and the input data (e.g. Deisher, see paragraph [0018], which discloses obtaining weight matrices and input data.);
obtaining a second convolution result by performing convolution on the quantization error and the input data (e.g. Deisher, see paragraph [0010], which discloses neural network are often trained using floating point arithmetic, then quantized into integer neural networks, where the accuracy of integer neural network includes minimizing quantization error.);
obtaining a scaled second convolution result by scaling the second convolution result based on bit shifting (e.g. Deisher, see paragraph [0030], which discloses data may be scaled according to the particular design of the processor to execute the quantized neural network and weights are slightly reduced (shifted) to provide additional headroom when the product of the input and weights are accumulated.); and
obtaining output data by using the first convolution result and the scaled second convolution result (e.g. Deisher, see paragraph [0019-0020], which discloses an output scale factor based on the compared weight matrices and convolution filters.).
As per claim 2, Deisher teaches the data processing method of claim 1, wherein the obtaining the quantized weight comprises converting the weight from floating-point data into quantized fixed-point data of n-bits (e.g. Deisher, see paragraph [0010-0011], which discloses the neural networks are often trained using floating point arithmetic, then quantized into integer neural networks, where the accuracy of integer neural networks depend on minimizing the quantization error of the neural network parameters as well as the inputs to the network.).
As per claim 3, Deisher teaches the data processing method of claim 1, wherein the obtaining the quantization error comprises quantizing the difference (e.g. Deisher, see paragraph [0010-0011], which discloses the neural networks are often trained using floating point arithmetic, then quantized into integer neural networks, where the accuracy of integer neural networks depend on minimizing the quantization error of the neural network parameters as well as the inputs to the network, where a quantization error describes the error present when quantizing a floating point set of values to an integer set of values.).
As per claims 4 and 12, Deisher teaches the data processing method of claim 1 and the data processing apparatus of claim 11, respectively, wherein the obtaining the scaled second convolution result comprises determining a bit shift value based on a first scale factor with respect to the weight and a second scale factor with respect to the quantization error (e.g. Deisher, see paragraph [0018-0019], which discloses weight matrices and convolution filters tend to be large and storing the original floating-point weights, where the scale factor can be viewed as flowing through the graph.).
As per claims 5 and 13, Deisher teaches the data processing method of claim 4 and the data processing apparatus of claim 12, respectively, wherein, the obtaining the scaled second convolution result comprises determining, based on a magnitude of the quantization error being equal to the first scale factor, the bit shift value to be n-bits, where n denotes a quantization bit value (e.g. Deisher, see paragraph [0077-0087], which discloses obtaining an initial scale factor of an input and parameters of a floating point neural network, deriving a scale factor for each arc of a graph of the integer neural network by traversing the graph, and updating a scale factor.).
As per claims 6 and 14, Deisher teaches the data processing method of claim 4 and the data processing apparatus of claim 12, respectively, wherein, the obtaining the scaled second convolution result comprises determining, based on a relationship between the first scale factor and the second scale factor being expressed as a square number of 2, the bit shift value to be n+k bits, where n denotes a quantization bit value and k denotes a value of the square number of 2 (e.g. Deisher, see paragraph [0077-0087], which discloses obtaining an initial scale factor of an input and parameters of a floating point neural network, deriving a scale factor for each arc of a graph of the integer neural network by traversing the graph, and updating a scale factor.).
As per claims 7 and 15, Deisher teaches the data processing method of claim 6 and the data processing apparatus of claim 14, respectively, wherein, the obtaining the scaled second convolution result comprises determining, based on the relationship between the first scale factor and the second scale factor not being expressed as the square number of 2, the bit shift value based on k, wherein k is determined through a log operation and a rounding operation (e.g. Deisher, see paragraph [0077-0087], which discloses obtaining an initial scale factor of an input and parameters of a floating point neural network, deriving a scale factor for each arc of a graph of the integer neural network by traversing the graph, and updating a scale factor.).
As per claim 8, Deisher teaches the data processing method of claim 4, wherein the obtaining the scaled second convolution result comprises determining a range of the first scale factor based on a maximum value and a minimum value of the weight (e.g. Deisher, see paragraphs [0020-0021], which discloses the desired scale factor may be determined by scanning the current parameter values for a minimum value, maximum value, offset and the like and where an initial desired scale factor is calculated, were the parameters include inputs, weights, and a bias.).
As per claim 9, Deisher teaches the data processing method of claim 4, wherein the obtaining the scaled second convolution result comprises determining a range of the second scale factor based on a maximum value and a minimum value of the quantization error (e.g. Deisher, see paragraph [0021], which discloses the desired scale factor may be determined by scanning the current parameter values for a minimum value, maximum value, offset and the like.).
As per claim 10, Deisher teaches the data processing method of claim 4, wherein the first scale factor is greater than the second scale factor (e.g. Deisher, see paragraph [0021], which discloses the desired scale factor may be determined by scanning the current parameter values for a minimum value, maximum value, offset and the like.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 September 8, 2026
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