DETAILED ACTION
This action is in response to the application filed 05/02/2024. Claims 1-10 are pending and have been examined.
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 .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“A data preprocessing module” in claim 1 claiming “a data preprocessing module, matching the real-time machine status data with the product specification measurement data to form an input dataset”
“A model building module” in claim 1 claiming “a model building module, building a hybrid model framework through the plurality of algorithms”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 1-10 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.
Regarding claim 1, claim limitations “a data preprocessing module, matching the real-time machine status data with the product specification measurement data to form an input dataset” and “a model building module, building a hybrid model framework through the plurality of algorithms” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In this instance, the corresponding structures refer to computer implemented means-plus function. 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. A data preprocessing module, matching the real-time machine status data with the product specification measurement data to form an input dataset and a model building module, building a hybrid model framework through the plurality of algorithms are specialized computer functions that would require an algorithm to be disclosed, in addition to the physical structure that would perform the algorithm. While Figures 1-3 show a system containing the modules and the architecture of the modules, there is no physical structure shown in Figures 1-3 that would match data or building a model framework. 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.
Regarding claims 2-10, claims 2-10 are rejected for at least the same reasons as claim 1 since claims 2-10 depend on claim 1.
Regarding claim 2, claim 2 recites “the measuring machine comprises a transient detector.” It is unclear as to what this transient detector is or what it is used for due to the lack of description in the specification and drawings. For the purposes of examination, Examiner has interpreted this transient detector to be the measuring machine that received input.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 1, “a data preprocessing module, matching the real-time machine status data with the product specification measurement data to form an input dataset” and “a model building module, building a hybrid model framework through the plurality of algorithms” as described above, do not provide adequate structure to perform the claimed function (see 112(b) rejection above). Therefore, the specification does not appear to provide sufficient detail such that one of ordinary skill can reasonably conclude that the inventor had possession of the claimed invention.
Regarding claims 2-10, claims 2-10 are rejected for at least the same reasons as claim 1 since claims 2-10 depend on claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 1:
Claim 1 recites a system and is thus an apparatus, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
Matching the real-time machine status data with the product specification measurement data to form an input dataset (This limitation is a mental process as it encompasses a human mentally matching data with other data and is thus an evaluation.)
building a hybrid model framework through the plurality of algorithms, (This limitation is a mental process as it encompasses a human mentally building a framework using algorithm and is thus an evaluation.)
setting a minimax rule and an outbound rule, and the input dataset being encoded (This limitation is a mental process as it encompasses a human mentally setting rules and encoding data and is thus an evaluation.)
Therefore, claim 1 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
an input device, connected to a production machine and a measuring machine, (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
and receiving real-time machine status data of the production machine and product specification measurement data of the measuring machine; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
a storage device, connected to the input device, (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
and the storage device storing the real-time machine status data, the product specification measurement data, and a plurality of algorithms; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
a processing device, connected to the storage device, the processing device executing a plurality of control commands to access the storage device to establish a quality prediction model, (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
and the quality prediction model comprising following modules: a data preprocessing module, (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
a model building module, (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
the hybrid model framework comprising a rule encoder and a bidirectional
long short term memory network, the rule encoder (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
then inputted into the bidirectional long short term memory network to generate output data; (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
an output device, connected to the processing device and the storage device, (This element does not integrate the abstract idea into a practical application because it recites generic computing components on which to perform the abstract idea (see MPEP 2106.05(f)).)
and outputting the output data to define an accuracy rate of the hybrid quality prediction model. (This element does not integrate the abstract idea into a practical application because it recites insignificant extra-solution activity of data gathering (see MPEP 2106.05(g)).)
Therefore, claim 1 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
an input device, connected to a production machine and a measuring machine uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and receiving real-time machine status data of the production machine and product specification measurement data of the measuring machine is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
a storage device, connected to the input device uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and the storage device storing the real-time machine status data, the product specification measurement data, and a plurality of algorithms is the well understood, routine, and conventional activity of “storing and retrieving information in memory” (see MPEP 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
a processing device, connected to the storage device, the processing device executing a plurality of control commands to access the storage device to establish a quality prediction model, uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and the quality prediction model comprising following modules: a data preprocessing module uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a model building module uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the hybrid model framework comprising a rule encoder and a bidirectional long short term memory network, the rule encoder uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
then inputted into the bidirectional long short term memory network to generate output data is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
an output device, connected to the processing device and the storage device uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
and outputting the output data to define an accuracy rate of the hybrid quality prediction model is the well understood, routine, and conventional activity of “transmitting or receiving data over a network” (see MPEP 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network)).
Therefore, claim 1 is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 2 recites the same abstract ideas as claim 1. Therefore, claim 2 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
wherein the production machine comprises a stamping machine, and the measuring machine comprises a transient detector. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 2 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the production machine comprises a stamping machine, and the measuring machine comprises a transient detector specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 2 is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 3 recites the same abstract ideas as claim 2. Therefore, claim 3 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
wherein the real-time machine status data comprises dataset numbering, machine rotation speed, machine status, and number of stamping per second. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 3 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the real-time machine status data comprises dataset numbering, machine rotation speed, machine status, and number of stamping per second specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 3 is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 4 recites the same abstract ideas as claim 2. Therefore, claim 4 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
wherein the product specification measurement data comprises dataset numbering, inspection time, and specification measurement value. (This element does not integrate the abstract idea into a practical application because it recites a technological environment in which to apply a judicial exception (see MPEP 2106.05(h)).)
Therefore, claim 4 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the product specification measurement data comprises dataset numbering, inspection time, and specification measurement value specifies a particular technological environment to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(h)).
Therefore, claim 4 is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
sets default time difference between inspection time of the measuring machine and production time of the production machine and selects the real-time machine status data with a smallest difference from the default time difference to conduct matching for forming the input dataset. (This limitation is a mental process as it encompasses a human mentally setting a time difference and selecting data with the smallest difference and is thus an evaluation.)
Therefore, claim 5 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
wherein from the real time machine status data and the product specification measurement data with same dataset numbering, the data preprocessing module (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 5 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein from the real time machine status data and the product specification measurement data with same dataset numbering, the data preprocessing module uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 5 is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
continuously extracts multiple periods of the real-time machine status data from a time point with the smallest difference from the default time difference by a bootstrap aggregating method to generate a subset of the input dataset. (This limitation is a mental process as it encompasses a human mentally extracting data and is thus an evaluation.)
Therefore, claim 6 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 6 further recites additional elements of
wherein the data preprocessing module (This element does not integrate the abstract idea into a practical application because it amounts to mere “apply it on a computer” (see MPEP 2106.05(f)).)
Therefore, claim 6 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
The additional elements of claim 6 do not provide significantly more than the abstract idea itself, taken alone and in combination because
wherein the data preprocessing module uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 6 is subject-matter ineligible.
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
wherein the minimax rule determines whether a maximum value of predicted specification is greater than a minimum value of the predicted specification every time, and if not, a first loss value is generated through a minimax value loss function. (This limitation is a mental process as it encompasses a human mentally determining whether a maximum is greater than a minimum and is thus an evaluation.)
Therefore, claim 7 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 7 does not further recite any additional elements. Therefore, claim 7 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 7 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 7 is subject-matter ineligible.
Regarding Claim 8:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
wherein the outbound rule determines whether an actual value and a predicted value of each specification fall within a minimax standard, and if not, a second loss value is generated through an outbound rule loss function. (This limitation is a mental process as it encompasses a human mentally determining whether values fall within a standard and is thus an evaluation.)
Therefore, claim 8 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 8 does not further recite any additional elements. Therefore, claim 8 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 8 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 8 is subject-matter ineligible.
Regarding Claim 9:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 9 recites
wherein the first loss value comprises a first weight, the second loss value comprises a second weight, the first weight ranges from 0 to 0.4, and the second weight ranges from 0 to 0.6. (This limitation is a mental process as it further describes the mental process of generating a loss through a minimax function from claim 8.)
Therefore, claim 9 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 9 does not further recite any additional elements. Therefore, claim 9 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 9 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 9 is subject-matter ineligible.
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1:
Claim 10 recites
wherein the first weight is 0.1 and the second weight is 0.1. (This limitation is a mental process as it further describes the mental process of generating losses of claim 9.)
Therefore, claim 10 recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
Claim 10 does not further recite any additional elements. Therefore, claim 10 is not integrated into a practical application.
Subject Matter Eligibility Analysis Step 2B:
Since there are no additional elements, claim 10 does not provide significantly more than the abstract idea itself, taken alone and in combination. Therefore, claim 10 is subject-matter ineligible.
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 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Qi et al. (“Using Stacked Auto-Encoder and Bi-Directional LSTM for Batch Process Quality Prediction”) (hereafter referred to as Qi).
Regarding claim 1, Qi teaches
A real-time quality prediction system, comprising: an input device, connected to a production machine and a measuring machine, and receiving real-time machine status data of the production machine and product specification measurement data of the measuring machine (Qi, page 4, 1st column, 1.4. The combined model SAE-LSTM, “Step 1: Generate training data using an improved simulation model, and obtain input variables through correlation analysis. The initial conditions, process variables and quality of each batch were normalized by averaging treatment; Step 2: Input the data obtained by Step 1 into SAE according to time direction, carry out error iteration through back propagation, and then encode to obtain timing characteristics;” where “To match the needs of customers and market for light, multi-variety and high value-added products, batch process has been widely used in fine chemicals, biomedical and other fields” (Qi, page 1, 1st column, 1st paragraph) and “Motivated by these difficulties, a hybrid prediction model is proposed, which combines the advantages of stacked auto-encoder (SAE) and bi-directional long short-term memory (BLSTM) for the final quality prediction of a batch process” (Qi, page 1, Abstract) and where “In order to ensure the timeliness of a prediction model and reduce data redundancy, correlation analysis was carried out between the measured variable and penicillin concentration” (Qi, page 5, 1st column, last paragraph). Examiner notes that the input device, production machine and measuring machine is the SAE-LSTM model. Examiner further notes that the real-time machine status data is the measured variable and the product specification measurement is the penicillin concentration.);
a storage device, connected to the input device, and the storage device storing the real-time machine status data, the product specification measurement data, and a plurality of algorithms (Qi, page 3, 2nd column, 1.3., BLSTM Neural Network, “Compared to the original RNN hidden layer with only unit state h sensitive for short-term memory, LSTM has one more unit state c for saving the long-term status. If expand in the LSTM network structure with time, we find an additional information conveyor to update the saved information, as shown in Figure 4. At each instance, the input of LSTM consists of three parts (current time network input, previous time Block output and time unit state) , and two parts of outputs (current time Block output value, current time unit state). The core idea of LSTM is newly defined unit states that can adaptively remember important information and forget redundant information” where “The structure of the batch process quality prediction model based on SAE-BLSTM is shown in Figure 6, and the detailed in each step is described as follow: Step 1: Generate training data using an improved simulation model, and obtain input variables through correlation analysis. The initial conditions, process variables and quality of each batch were normalized by averaging treatment; Step 2: Input the data obtained by Step 1 into SAE according to time direction, carry out error iteration through back propagation, and then encode to obtain timing characteristics;” (Qi, page 4, 1st column, 1.4. The combined model SAE-LSTM). Examiner notes that the storage device is the SAE-LSTM. Examiner further notes that the SAE-LSTM inputs and thus stores real-time machine status data and product specification measurement data. Examiner further notes that steps of the SAE-LSTM are algorithms.);
a processing device, connected to the storage device, the processing device executing a plurality of control commands to access the storage device to establish a quality prediction model, and the quality prediction model comprising following modules (Qi, page 7, 2nd column, Conclusion, “In this study, we proposed a new batch quality prediction method based on deep learning, which combines the data feature extraction ability of the SAE network and the bidirectional long-term learning ability of BLSTM. It is a combination of two models with different characteristics in the final quality prediction of batch process.” Examiner notes that the SAE-LSTM is the processing device and quality prediction model.):
a data preprocessing module, matching the real-time machine status data with the product specification measurement data to form an input dataset (Qi, page 5, 1st column, last paragraph, “In order to ensure the timeliness of a prediction model and reduce data redundancy, correlation analysis was carried out between the measured variable and penicillin concentration” where “The structure of the batch process quality prediction model based on SAE-BLSTM is shown in Figure 6, and the detailed in each step is described as follow: Step 1: Generate training data using an improved simulation model, and obtain input variables through correlation analysis. The initial conditions, process variables and quality of each batch were normalized by averaging treatment; Step 2: Input the data obtained by Step 1 into SAE according to time direction, carry out error iteration through back propagation, and then encode to obtain timing characteristics;” (Qi, page 4, 1st column, 1.4. The combined model SAE-LSTM). Examiner notes that the SAE-LSTM is the data preprocessing module, and the matching is the correlation analysis. );
and a model building module, building a hybrid model framework through the plurality of algorithms, the hybrid model framework comprising a rule encoder and a bidirectional long short term memory network, the rule encoder setting a minimax rule and an outbound rule, and the input dataset being encoded and then inputted into the bidirectional long short term memory network to generate output data (Qi, page 4, 1st column, 1.4. The combined model SAE-LSTM, “The structure of the batch process quality prediction model based on SAE-BLSTM is shown in Figure 6, and the detailed in each step is described as follow: Step 1: Generate training data using an improved simulation model, and obtain input variables through correlation analysis. The initial conditions, process variables and quality of each batch were normalized by averaging treatment; Step 2: Input the data obtained by Step 1 into SAE according to time direction, carry out error iteration through back propagation, and then encode to obtain timing characteristics;” where “Considering the large difference in values between the sampling variables, before entering the model, the normalized formula (19) was used to reduce the influence of such data dimension differences, and the numerical value was limited to [0,1].
PNG
media_image1.png
68
518
media_image1.png
Greyscale
Herein, xi,j is the i_th data of the j_th characteristic variable in original data input; xjmax and xjmin are maximum and minimum values of the j_th characteristic variable respectively, and xi,jnorm is the normalized result.” and Qi, page 4, Fig. 6,
PNG
media_image2.png
608
1022
media_image2.png
Greyscale
Examiner notes that the model building module is the SAE-BLSTM, the algorithms are the steps, the rule encoder is the autoencoder, and both the minimax rule and outbound rule is equation 18.);
and an output device, connected to the processing device and the storage device, and outputting the output data to define an accuracy rate of the hybrid quality prediction model (Qi, page 6, 1st column, third to last paragraph, “Ratio is used to reflect accuracy, where Ratio is the compliance rate, hit means the absolute value of prediction error is less than 0.02” and Qi, page 4, Fig. 6,
PNG
media_image2.png
608
1022
media_image2.png
Greyscale
Examiner notes that the output device, processing device and storage device is the SAE-BLSTM.).
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi in view of Dev et al. (US 2024/0210935 A1) (hereafter referred to as Dev).
Regarding claim 2, Qi teaches the real-time quality prediction system of claim 1. Qi further teaches
and the measuring machine comprises a transient detector (Qi, page 4, Fig. 6, see below
PNG
media_image2.png
608
1022
media_image2.png
Greyscale
Examiner notes that the transient detector is the SAE-BLSTM.).
Qi does not teach, but Dev does teach
wherein the production machine comprises a stamping machine (Dev, page 10, paragraph 0019, “In examples, the trained models output predictive information associated with the industrial equipment, such as planned production time, performance, and quality” where “the operational site also includes at least one sensor hub 108. The sensor hub 108 captures sensor data associated with industrial equipment 104 and transits the sensor data using the network 100. The industrial equipment 104 includes, for example, pumps heavy duty industrial tools, compressors, automated assembly equipment, and the like. The industrial equipment can further include machines such as turning machines (e.g., lathes and boring mills), shapers and planers, drilling machines, milling machines, grinding machines, power saws, cutting machines, stamping machines, and presses”),
Qi and Dev are analogous to the claimed invention because they both use machine learning for quality prediction. It would have been obvious to one of ordinary skill in the art prior to the effective filing date to have modified Qi to predict quality in a stamping machine. Doing so is advantageous because “some of the advantages of these techniques include a robust predictive OEE metric based on historical data and precise, high resolution (with respect to time) usage data. Further, large amounts of sensor data associated with the industrial equipment are distilled down to an informative visual representation of the data that enables visual recognition of the operating conditions of the industrial equipment. Additionally, visualization of sensor signatures across extended periods on a spectrogram can be deployed on edge devices and presented on a user interface, such as to operators on the factory floor at an operational site” (Dev, page 9, paragraph 0007).
Regarding claim 3, Qi teaches the real-time quality prediction system of claim 2. Qi further teaches
wherein the real-time machine status data comprises dataset numbering, …, machine status, … (Qi, page 6, 1st column, 1st paragraph, “The three indicators are defined as follows.
PNG
media_image3.png
96
516
media_image3.png
Greyscale
RMSE is used to measure the deviation between predicted values and true values. Herein, N is the number of batches of the test set, yi is actual production value of the i,h batch, and
y
i
^
is the predicted value given by the corresponding model.” Examiner notes that the index of i is the dataset numbering and the real-time machine status data and machine status is the actual production value.)
Qi does not teach, but Dev does teach
wherein the real-time machine status data comprises … machine rotation speed, … and number of stamping per second (Dev, page 10, paragraph 0023, “other suitable sensors may be, for example pressure sensors, humidity sensors, current sensors, voltage sensors, particle count sensors, flow-rate sensors, level measurement sensors, speed sensors, distance sensors, proximity detection sensors, up-down sensors, etc. In some embodiments, multiple sensor hubs are mounted (e.g., physically coupled) in various locations on the industrial equipment 104 to measure and record sensor data (e.g., vibrations in real time).” Examiner notes that the rotation speed is the speed sensors and number of stamping per second is the up-down sensors and flow-rate sensors.).
Qi and Dev are analogous to the claimed invention because they both use machine learning for quality prediction. It would have been obvious to one of ordinary skill in the art prior to the effective filing date to have modified Qi to predict quality in a stamping machine. Doing so is advantageous because “some of the advantages of these techniques include a robust predictive OEE metric based on historical data and precise, high resolution (with respect to time) usage data. Further, large amounts of sensor data associated with the industrial equipment are distilled down to an informative visual representation of the data that enables visual recognition of the operating conditions of the industrial equipment. Additionally, visualization of sensor signatures across extended periods on a spectrogram can be deployed on edge devices and presented on a user interface, such as to operators on the factory floor at an operational site” (Dev, page 9, paragraph 0007).
Regarding claim 4, Qi teaches the real-time quality prediction system of claim 2. Qi further teaches
wherein the product specification measurement data comprises dataset numbering, … and specification measurement value (Qi, page 6, 1st column, 1st paragraph, “The three indicators are defined as follows.
PNG
media_image3.png
96
516
media_image3.png
Greyscale
RMSE is used to measure the deviation between predicted values and true values. Herein, N is the number of batches of the test set, yi is actual production value of the i,h batch, and
y
i
^
is the predicted value given by the corresponding model.” Examiner notes that the index of i is the dataset numbering and the product specification measurement data is the predicted production value and specification measurement value.)
Qi does not teach, but Dev does teach
wherein the product specification measurement data… inspection time (Dev, page 10, paragraph 0023, “other suitable sensors may be, for example pressure sensors, humidity sensors, current sensors, voltage sensors, particle count sensors, flow-rate sensors, level measurement sensors, speed sensors, distance sensors, proximity detection sensors, up-down sensors, etc. In some embodiments, multiple sensor hubs are mounted (e.g., physically coupled) in various locations on the industrial equipment 104 to measure and record sensor data (e.g., vibrations in real time).” Examiner notes that the inspection time is the real time.).
Qi and Dev are analogous to the claimed invention because they both use machine learning for quality prediction. It would have been obvious to one of ordinary skill in the art prior to the effective filing date to have modified Qi to predict quality in a stamping machine. Doing so is advantageous because “some of the advantages of these techniques include a robust predictive OEE metric based on historical data and precise, high resolution (with respect to time) usage data. Further, large amounts of sensor data associated with the industrial equipment are distilled down to an informative visual representation of the data that enables visual recognition of the operating conditions of the industrial equipment. Additionally, visualization of sensor signatures across extended periods on a spectrogram can be deployed on edge devices and presented on a user interface, such as to operators on the factory floor at an operational site” (Dev, page 9, paragraph 0007).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi in view of Dev in further view of Zhu et al. (“AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life”) (hereafter referred to as Zhu).
Regarding claim 5, Qi in view of Dev teaches the real-time quality prediction system according to claim 2. Qi in view of Dev does not teach, but Zhu does teach
wherein from the real-time machine status data and the product specification measurement data with same dataset numbering, the data preprocessing module sets default time difference between inspection time of the measuring machine and production time of the production machine and selects the real-time machine status data with a smallest difference from the default time difference to conduct matching for forming the input dataset (Zhu, page 6, Algorithm 1
PNG
media_image4.png
334
668
media_image4.png
Greyscale
Examiner notes that the index of i is the dataset numbering where the sampled batch is the real-time machine status data and the reconstructed time series is the product specification measurement data. Examiner additionally notes that the default time difference is the i-th time of the sample and the i-th time of the reconstruction. Since the difference is non-existent, the production machine selects the real-time machine status data with a smallest difference to conduct matching. Examiner further notes that reconstructing the time series is matching to form the input dataset.).
Qi, Dev, and Zhu are considered analogous to the claimed invention because they predict quality using Bi-LSTMs in machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Qi in view of Dev to conduct matching by selected time differences. Doing so is advantageous because “the three modules are trained jointly and the loss of the model is optimized simultaneously, which improves the extraction of degenerate information from the time series and the prediction of RUL” (Zhu, page 11, Conclusion).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi in view of Dev in further view of Zhu and Min et al. (US 2022/0150404 A1) (hereafter referred to as Min).
Regarding claim 6, Qi in view of Dev and Zhu teaches the real-time quality prediction system of claim 5. Qi in view of Dev and Zhu further teaches
wherein the data preprocessing module continuously extracts multiple periods of the real-time machine status data from a time point with the smallest difference from the default time difference … to generate a subset of the input dataset (Zhu, page 6, Algorithm 1
PNG
media_image4.png
334
668
media_image4.png
Greyscale
Examiner notes that the index of i is the dataset numbering where the sampled batch is the real-time machine status data and the reconstructed time series is the product specification measurement data. Examiner additionally notes that the default time difference is the i-th time of the sample and the i-th time of the reconstruction. Since the difference is non-existent, the production machine selects the real-time machine status data with a smallest difference to conduct matching. Examiner further notes that reconstructing the time series is matching to form the input dataset. Additionally, Examiner notes that the for loop is the continuous extraction.).
Qi in view of Dev and Zhu do not teach, but Min does teach
extracts multiple periods of the … status data … by a bootstrap aggregating method to generate a subset of the input dataset (Min, page 16, paragraph 0087, “A random forest (RF) may be an ensemble model that utilizes bootstrap aggregating or bagging to divide training and test data into multiple subsets. Bagging may be a basic technique of RF, and may have an effect of reducing variance of predicted values. Therefore, thousands of input variables may be handled with higher accuracy.”)
Qi, Dev, Zhu, and Min are considered analogous to the claimed invention because they predict quality using machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Qi in view of Dev and Zhu to use bootstrap aggregation. Doing so is advantageous because “Bagging may be a basic technique of RF, and may have an effect of reducing variance of predicted values. Therefore, thousands of input variables may be handled with higher accuracy” (Min, page 16, paragraph 0087).
Claim(s) 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Qi in view of Zhu.
Regarding claim 7, Qi teaches the real-time quality prediction system according to claim 1. Qi does not teach, but Zhu does teach
wherein the minimax rule determines whether a maximum value of predicted specification is greater than a minimum value of the predicted specification every time, and if not, a first loss value is generated through a minimax value loss function (Zhu, page 5, 2nd-3rd paragraphs, “Generative Adversarial Network (GAN) is a training procedure proposed by Goodfellow [19]. A generator G and a discriminator D construct a mini-max adversarial game, in which G samples from random noise space and generates fake sample x' to trick the discriminator D. The discriminator needs to learn whether the data generated by G is real or fake [18]. The optimization process of GAN is that G and D reach a nash equilibrium. In our model, the encoder can be regarded as the generator of GAN, which maps the input x to the latent code vector z, hoping to match a certain prior distribution p(z). Following the original VAE [17], we let p(z) be the standard normal distribution p( z) ~ N ( 0, 1). At the same time, a discriminator is added to discriminate whether z is derived from q(z) (fake samples), which is computed by the encoder, or from the prior distribution p( z) ( real samples). The discriminator adds a regularization constraint to the encoder, encouraging q(z) to math p(z). The loss of adversarial training procedure can be expressed as:
PNG
media_image5.png
40
628
media_image5.png
Greyscale
” Examiner notes that equation 9 is the minimax rule. Examiner further notes that equation 9 determines the maximum value of the discriminator or the predicted specification and if it does or does not, it produces Ladversarial, the first loss value.).
Qi and Zhu are considered analogous to the claimed invention because they predict quality using Bi-LSTMs in machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Qi to use the minimax rule in Zhu. Doing so is advantageous because “the three modules are trained jointly and the loss of the model is optimized simultaneously, which improves the extraction of degenerate information from the time series and the prediction of RUL” (Zhu, page 11, Conclusion).
Regarding claim 8, Qi teaches the real-time quality prediction system according to claim 7. Qi does not teach, but Zhu does teach
wherein the outbound rule determines whether an actual value and a predicted value of each specification fall within a minimax standard, and if not, a second loss value is generated through an outbound rule loss function (Zhu, page 5, 2nd-3rd paragraphs, “Generative Adversarial Network (GAN) is a training procedure proposed by Goodfellow [19]. A generator G and a discriminator D construct a mini-max adversarial game, in which G samples from random noise space and generates fake sample x' to trick the discriminator D. The discriminator needs to learn whether the data generated by G is real or fake [18]. The optimization process of GAN is that G and D reach a nash equilibrium. In our model, the encoder can be regarded as the generator of GAN, which maps the input x to the latent code vector z, hoping to match a certain prior distribution p(z). Following the original VAE [17], we let p(z) be the standard normal distribution p( z) ~ N ( 0, 1). At the same time, a discriminator is added to discriminate whether z is derived from q(z) (fake samples), which is computed by the encoder, or from the prior distribution p( z) ( real samples). The discriminator adds a regularization constraint to the encoder, encouraging q(z) to math p(z). The loss of adversarial training procedure can be expressed as:
PNG
media_image5.png
40
628
media_image5.png
Greyscale
” Examiner notes that equation 9 is the outbound rule. Examiner further notes that equation 9 determines whether the actual value (G) and predicted value (D) fall within the minimax standard. If it does or does not, it produces Ladversarial, the second loss value.).
Qi and Zhu are considered analogous to the claimed invention because they predict quality using Bi-LSTMs in machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Qi to use the outbound rule in Zhu. Doing so is advantageous because “the three modules are trained jointly and the loss of the model is optimized simultaneously, which improves the extraction of degenerate information from the time series and the prediction of RUL” (Zhu, page 11, Conclusion).
Regarding claim 9, Qi teaches the real-time quality prediction system according to claim 8. Qi does not teach, but Zhu does teach
wherein the first loss value comprises a first weight, the second loss value comprises a second weight, the first weight ranges from 0 to 0.4, and the second weight ranges from 0 to 0.6 (Zhu, page 5, 2nd-3rd paragraphs, “Generative Adversarial Network (GAN) is a training procedure proposed by Goodfellow [19]. A generator G and a discriminator D construct a mini-max adversarial game, in which G samples from random noise space and generates fake sample x' to trick the discriminator D. The discriminator needs to learn whether the data generated by G is real or fake [18]. The optimization process of GAN is that G and D reach a nash equilibrium. In our model, the encoder can be regarded as the generator of GAN, which maps the input x to the latent code vector z, hoping to match a certain prior distribution p(z). Following the original VAE [17], we let p(z) be the standard normal distribution p( z) ~ N ( 0, 1). At the same time, a discriminator is added to discriminate whether z is derived from q(z) (fake samples), which is computed by the encoder, or from the prior distribution p( z) ( real samples). The discriminator adds a regularization constraint to the encoder, encouraging q(z) to math p(z). The loss of adversarial training procedure can be expressed as:
PNG
media_image5.png
40
628
media_image5.png
Greyscale
” Examiner notes that Ladversarial, is the first and second loss value. Examiner further notes that p(z) is the first and second weight that ranges from 0-1 which includes 0-0.4 and 0-0.6).
Qi and Zhu are considered analogous to the claimed invention because they predict quality using Bi-LSTMs in machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Qi to use the minimax and outbound rule in Zhu. Doing so is advantageous because “the three modules are trained jointly and the loss of the model is optimized simultaneously, which improves the extraction of degenerate information from the time series and the prediction of RUL” (Zhu, page 11, Conclusion).
Regarding claim 10, Qi teaches the real-time quality prediction system according to claim 9. Qi does not teach, but Zhu does teach
wherein the first weight is 0.1 and the second weight is 0.1 (Zhu, page 5, 2nd-3rd paragraphs, “Generative Adversarial Network (GAN) is a training procedure proposed by Goodfellow [19]. A generator G and a discriminator D construct a mini-max adversarial game, in which G samples from random noise space and generates fake sample x' to trick the discriminator D. The discriminator needs to learn whether the data generated by G is real or fake [18]. The optimization process of GAN is that G and D reach a nash equilibrium. In our model, the encoder can be regarded as the generator of GAN, which maps the input x to the latent code vector z, hoping to match a certain prior distribution p(z). Following the original VAE [17], we let p(z) be the standard normal distribution p( z) ~ N ( 0, 1). At the same time, a discriminator is added to discriminate whether z is derived from q(z) (fake samples), which is computed by the encoder, or from the prior distribution p( z) ( real samples). The discriminator adds a regularization constraint to the encoder, encouraging q(z) to math p(z). The loss of adversarial training procedure can be expressed as:
PNG
media_image5.png
40
628
media_image5.png
Greyscale
” Examiner notes that Ladversarial, is the first and second loss value. Examiner further notes that p(z) is the first and second weight that ranges from 0-1 which includes 0.1).
Qi and Zhu are considered analogous to the claimed invention because they predict quality using Bi-LSTMs in machine learning. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Qi to use the minimax and outbound rule in Zhu. Doing so is advantageous because “the three modules are trained jointly and the loss of the model is optimized simultaneously, which improves the extraction of degenerate information from the time series and the prediction of RUL” (Zhu, page 11, Conclusion).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Singh et al. (“HDR image-based deep learning approach for automatic detection of split defects on sheet metal stamping parts”) discloses identifying defects in a stamping machine.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAITLYN R LAU whose telephone number is (571)272-1429. The examiner can normally be reached Monday - Thursday: 8:00 am - 6:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached at (571) 431-0762. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.R.L./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
