Detailed Action
This action is in response to the RCE filed on 03/31/2026 for the amended claims filed 03/31/2026 for application 17/960,221, in which:
Claim 1 is the independent claim.
Claim 1 is currently amended.
Claims 1-6 are currently pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/31/2026 has been entered.
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 .
Response to Arguments
Applicant's arguments filed 03/31/2026 have been fully considered but they are not persuasive.
Regarding the 35 USC § 101 Rejections:
Applicant's arguments regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses (Page 5) the rejections and asserts that each of the claims, as pending when the Final Action, is directed to patent-eligible subject matter, and would be deemed as such when properly analyzed under the 2019 Revised Patent Subject Matter Eligibility Guidance ("2019 Guidance") and the October 2019 Update ("October Update").
Examiner respectfully disagrees. The 35 U.S.C. § 101 rejections for the amended claims are directed to an abstract idea (Step 2A Prong 1) and do not integrate the abstract idea into a practical application (Step 2A Prong 2). The pending Claims are directed to a judicial exception due to reciting limitations which fall within the “mental processes” group of abstract ideas; where the judicial exception is unable to be directed to significantly more than the judicial exception due to the pending Claims not including additional elements that contribute to an “inventive concept”. Due to the additional elements falling under MPEP 2106.05, the judicial exception is not integrated into a practical application and the specific details are discussed below within the Examiner’s Responses and 35 USC § 101 Rejections. The claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered an abstract idea. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims. The rejections follow the steps of the analysis laid out within the MPEP which was followed for the previous and current examination (see MPEP 2106). The rejection also follows the steps of the analysis as laid out in the MPEP which was followed for the previous and current examination (see MPEP 2106). More specific details are discussed below within the responses and 35 USC § 101 Rejections.
Applicant asserts (Page 5), that the rejection of claims 1-6 under 35 U.S.C. § 101, as alleged in the Final Action, is rendered moot in view of the amendments made to the claims-particularly to independent claim 1. In particular, Applicant submits that the additional limitations and features incorporated into claim 1 render the claims patent-eligible-namely, outside the scope of any judicial exception, and/or even if falling within such judicial exception, directed to practical application(s). Thus, Applicant requests that the rejections of claims 1-6 under 35 U.S.C. § 101 be withdrawn.
Examiner respectfully disagrees. The claims do not integrate the judicial exception into a practical application nor amount to significantly more. The claim is not patent eligible. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims.
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology … the claim must include the components or steps of the invention that provide the improvement described in the specification
…
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.
Applicant fails to show how any alleged technical improvement would be provided by anything more than the judicial exception on its own. Additionally, applicant fails to show how the claim includes components or steps that would provide the alleged improvement described in the specification. By MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished". Moreover, the examiner maintains that the Claim does not impose any meaningful limits on the judicial exceptions. As noted in the rejection, the Claim does not include additional elements that are sufficient to amount to an integration of the identified abstract idea into a practical application, thus the claim is directed to an abstract idea. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1 and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 101 Rejections.
Regarding the 35 USC § 102 Rejections:
Applicant's arguments regarding the 35 U.S.C. 102 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses (Pages 5-7), the 102 rejections and notes that each and every element needs to be taught by the prior art reference(s). Applicant further asserts that independent claim 1 is not anticipated by Chien. However, to further prosecution claim 1 is now amended to further clarify the subject matter; thus, the claim 1 rejection is now rendered moot. Applicant then further notes that Chien does not teach the new amended claim and reiterates the independent Claim; thus, the rejection should be withdrawn.
Examiner respectfully disagrees. Applicant merely notes that Chien does not teach explicitly the independent claims by reiterating the limitations. Chien discloses all limitations, including creating a trained neural network … (initialization of the CNN model), continuing iterations … (continuing the training of the initial model), receiving/processing the input data … (receiving camera capture images to numeralize the data received), and using the trained neural networks … (using ml models to classify the data and identifying the ideal next move for the robotic arm), but does not disclose the application of the specific data and assessing the data for affirmations/correction/refinement. However, these newly amended limitations are explicitly taught by the new prior art reference; thus, the arguments pertaining to the application of the specific data and assessing the data for affirmations/correction/refinement are moot. More specific details are discussed below within the responses and 35 USC § 103 Rejections. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. However, the amended independent claim rejection has been updated with a new reference to explicitly teach elements of the newly added limitations.
Applicant traverses (Pages 7-8), the rejections of claims 2-3 and 5-6 as they are not anticipated by Chien. However, based on at least the foregoing, Applicant believes that the rejection of independent claim 1 under 35 U.S.C. § 102(a)(1) based on Chien has been overcome. Therefore, at least because these claims depend from independent claim 1, Applicant submit that claims 2-3 and 5-6 are consequently not anticipated by Chien at least for the reasons stated above with regard to the rejection of claim 1 under 35 U.S.C. § 102(a)(1). Accordingly, Applicant requests that the rejections of claims 2-3 and 5-6 under 35 U.S.C. § 102(a)(1) based on Chien be withdrawn.
Examiner respectfully disagrees. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, similar independent claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the responses and 35 USC § 103 Rejections.
Regarding the 35 USC § 103 Rejections:
Applicant's arguments regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant traverses (Pages 8-9) the 103 rejections and notes that determination of obviousness is a legal conclusion based on underlying findings fact. Applicant also notes Sanofi-Synthelabo v. Apotex and Graham v. John Deere Co. to support their assertions and also notes that the burden is on the Examiner to establish a prima facie case of obviousness via MPEP 2142. Further, if the Examiner does not produce a prima facie case, "the applicant is under no obligation to submit secondary evidence to show nonobviousness". Applicant further reminds the examiner of the determination of obviousness via KSR, other case law, and MPEP. Applicant respectfully traverses the claim rejections under § 103 for the follow reasons.
Examiner respectfully disagrees. The office action establishes a proper and well-supported prima facie case as the claims are explained to be not patentable over the prior art(s) under the guidance of the MPEP; where each limitation is explicitly disclosed via combination of the references. More explained below within the response to arguments and within the office action. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant traverses (Pages 9-10) the 103 rejection for Claim 4 as pending when the Final Action was issued, are patentable over the cited references and all combination thereof. However, based on at least the foregoing, Applicant believes that the rejection of independent claim 1 under 35 U.S.C. § 102(a)(1) based on Chien has been overcome. Therefore, because claim 4 depends from independent claim 1, Applicant submits that claim 4 is consequently patentable over the cited references and all combination thereof at least for the reasons stated above with regard to the rejections of claim 1 under 35 U.S.C. § 102(a)(1 ). Accordingly, Applicant requests that the rejections of claim 4 under 35 U.S.C. § 103 be withdrawn. Applicant also reserves the right to argue additional reasons beyond those set forth herein, should such a need arise.
Examiner respectfully disagrees. As noted above, Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Further, they do not show how the amendments avoid such references or objections. The office action establishes a proper and well-supported prima facie case as the claims are explained to be not patentable over the prior art(s) under the guidance of the MPEP; where each limitation is explicitly disclosed via combination of the references. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 103 Rejections.
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 1-6 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 method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 1 further recites the process comprising of:
assessing identification of the non-labeled input data … , wherein the assessing comprises affirming or correcting the identification (a human being can mentally apply evaluation to assess identification of specific data to affirm or correct the identification)
… determining initial information relating to an initial environment and/or condition associated with the received input data (a human being can mentally apply evaluation to determine initial information relating to a specific environment and/or condition associated with the received input data)
… classifying the received input data as valid or invalid … (a human being can mentally apply evaluation to classify received input data as valid or invalid)
… identifying, based on at least the initial information, an optimal algorithm to be used for further processing of the element (a human being can mentally apply evaluation to identify an optimal algorithm for further processing based on initial information)
Claim 1 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
A method of controlling selection of algorithms used by computer-controlled processing systems, comprising: (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
creating a trained neural network, wherein the creating comprises: (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
providing a neural network in an initial untrained state (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
applying into the neural network input data comprising non-labeled input data (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
… via the neural network … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
continuing training iterations until an acceptable level of performance is obtained (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
receiving input data related to an element designated for processing under control of a computer-controlled processing system algorithm (which is insignificant extra-solution activity of data gathering, by MPEP 2106.05(g))
… processing the received input data, wherein the processing comprises … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
using the trained neural network … where if invalid preventing any further processing of the element, otherwise … (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional elements a and f are only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Additional elements b, e and h-i are merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Additional elements c-d and g fall within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 2:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 2 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 2 further recites the method comprising of … identifying an algorithm best suited for the ascertained working condition data for further processing of the element (a human being can mentally apply evaluation to identify an algorithm best suited for the working condition data). Claim 2 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
wherein the computer-controlled processing system utilizes a plurality of different algorithms, each algorithm associated with a defined working condition, the method including the additional steps of: (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
if the received input data is valid, using the trained neural network to ascertain working condition data from the received input data (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Additional element b is merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 3 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 3 further recites the method comprising of … identifying an algorithm associated with the classified product type for use in further processing (a human being can mentally apply evaluation to identify an algorithm with the classified product type). Claim 3 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
wherein the computer-controlled processing system utilizes a plurality of different algorithms, each algorithm for performing a specific task on a specific product type, the method including the steps of: (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
if the received input data is valid, using the trained neural network to classify the received input data with respect to the specific product type; and (to perform a mental process and the performance of an abstract idea on a computer is no more than instructions to “apply it” on a computer, by MPEP 2106.05(f))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Additional element b is merely applying the abstract idea on a computer (MPEP 2106.05(f)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 4:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 4 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 4 further recites the process comprising of:
if the trained neutral network evaluation finds the received input data to be a valid presentation of the product, performing additional NN-based evaluation to classify the received data with respect to specific product type (a human being can mentally apply evaluation to perform a NN-based evaluation to classify the received data in a specific manner)
performing additional NN-based evaluation to determine if there is more than one algorithm associated with a classified process associated with the specific product type … (a human being can mentally apply evaluation to perform an NN-based evaluation to determine if there is more than one algorithm associated with a specific process with a specific product type)
if the NN-based evaluation determines the existence of multiple algorithms for the classified process, performing additional NN-based evaluation to ascertain an optimum algorithm to be used for further processing of the element (a human being can mentally apply evaluation to perform a NN-based evaluation to confirm an optimum algorithm)
Claim 4 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the additional elements recited consists of:
wherein the computer-controlled processing system includes a plurality of different classifications of processes and at least one algorithm associated with each classification, where at least one classification further comprises individual algorithms for use with different initial states of a product, the method including the steps of: (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h))
… and if not, continuing with presented the element to a classified algorithm associated with the classified process (which is insignificant extra-solution activity of data display or output, by MPEP 2106.05(g))
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements recited, alone or in combination, do not provide significantly more than the abstract idea itself. Additional element a is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Additional element b falls within MPEP 2106.05(d) as well-understood, routine and conventional activities of receiving or transmitting data over a network (MPEP 2106.05(d)(II): buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)). Thus, the claim is subject-matter ineligible.
Regarding Claim 5:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 5 recites the method of Claim 1. Claim 1 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 5 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 1. Claim 5 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the new sole additional element recited consists of wherein the computer-controlled processing systems includes at least one computer-controlled vision system (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 1:
Dependent Claim 6 recites the method of Claim 5. Claim 5 is a method, thus a process, one of the four statutory categories of patentable subject matter.
Subject Matter Eligibility Analysis Step 2A Prong 1:
However, Claim 6 does not recite any additional abstract ideas and only inherits the abstract ideas from Claim 5. Claim 6 thus recites an abstract idea (that falls into the “mental processes” group of abstract ideas).
Subject Matter Eligibility Analysis Step 2A Prong 2:
This judicial exception is not integrated into a practical application because the new sole additional element recited consists of wherein the received input data includes image data (which is restricting the abstract idea to a Particular Technological Environment, by MPEP 2106.05(h)).
Subject Matter Eligibility Analysis Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the new sole additional element recited, alone or in combination, does not provide significantly more than the abstract idea itself. The additional element is only restricting the abstract idea to a Particular Technological Environment (MPEP 2106.05(h)) which cannot provide significantly more. Thus, the claim is subject-matter ineligible.
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.
Claim 1-3, and 5-6 is rejected under 35 U.S.C. 103 as being unpatentable over Chien et al., “Application of image recognition in workpiece classification”, in view of Appu et al., US-20180299841-A1.
Regarding Claim 1:
Chien teaches:
A method of controlling selection of algorithms used by computer-controlled processing systems, comprising:
(Chien, Page 8, Figure 18. Figure 18 shows a flowchart of the entire process (interpreted as a method by the examiner) for controlling the robotic arm (computer-controlled processing system) based on image recognition).
creating a trained neural network, wherein the creating comprises: providing a neural network in an initial untrained state;
(Chien, Page 5, Column 1, Paragraph 4, “Training model. After inputting the dataset into the system, the system initializes the Python programming language, Tensorflow deep learning library, OpenCV library for image processing of chess characters, and CNN model training. Then, all the characteristics of the data set are evaluated and analyzed, system is compared and tested according to the test samples, and recognition rate after the test is corrected until the recognition rate reaches a stable level”; Page 5, Figure 6. The Training model section, within Chien, describes being initialized and providing an image set for processing chess characters for CNN model training; thus, this is interpreted by the examiner as a neural network in an initial untrained state as the neural network model would be in an initial untrained state prior to the model training to reach a stable level of recognition).
…
continuing training iterations until an acceptable level of performance is obtained;
(Chien, Page 5, Column 1, Paragraph 4, “Training model. After inputting the dataset into the system, the system initializes the Python programming language, Tensorflow deep learning library, OpenCV library for image processing of chess characters, and CNN model training. Then, all the characteristics of the data set are evaluated and analyzed, system is compared and tested according to the test samples, and recognition rate after the test is corrected until the recognition rate reaches a stable level”; Page 5, Figure 6. The model is continues to train until the recognition rate reaches a stable level; thus, interpreted by the examiner as continuing training iterations until an acceptable level of performance is obtained).
receiving input data related to an element designated for processing under control of a computer-controlled processing system algorithm;
(Chien, Page 8, Figure 18: ‘Camera capture image’. Figure 18: ‘Camera capture image’ is the system receiving input data (image data) to process for image recognition and determining how the robotic arm will handle the data received; thus, receiving input data related to an element designated for processing under control of a computer-controlled processing system algorithm).
processing the received input data, wherein the processing comprises determining initial information relating to an initial environment and/or condition associated with the received input data;
(Chien, Page 8, Figure 18: ‘Image numeralization’. Figure 18: ‘Image numeralization’ is the system processing the received input data (image data) for image recognition and determining how the robotic arm will handle the data received; thus, the image numeralization is when the system identifies the initial information relating to the image in terms of the chess board/environment to recognize the chess image).
using the trained neural network, classifying the received input data as valid or invalid, where if invalid preventing any further processing of the element, otherwise,
(Chien, Page 8, Figure 18: ‘Chess image recognized’; Page 5, Figure 6. Figure 18: ‘Chess image recognized’ shows using the trained neural network to classify the received input data to verify the validity of recognizing the element (which is a chess board within this experiment) in the image (valid = recognition rate higher than 95%; invalid = recognition rate lower than 95% ); thus, the trained neural network (VGGNet shown in Figure 6 that utilizes a CNN) is used to classify the received input data as either valid or invalid and if invalid… not processing as the flow chart depicts going back to image capturing as the current input image data does not pass the validity threshold (which is interpreted by the examiner as preventing any further processing of the element)).
using the trained neural network, based on at least the initial information, identifying an optimal algorithm to be used for further processing of the element.
(Chien, Page 8, Figure 18: ‘Chess image recognized -> Send a signal to Arduino Uno’; Page 8, Column 2, Paragraph 1, “When the recognition rate is higher than 95%, the computer transfers the recognition result to the Arduino Uno board through a serial port. Then, it controls the robotic arm move to a specific location … To ensure that the robotic arm moves to the specified position stably, each of the designated positioning programs is equipped with a delay to ensure that the arm moves to the correct position”. Figure 18: ‘Chess image recognized -> Send a signal to Arduino Uno’ shows using the trained neural network to classify the image data as valid or invalid and then passing a signal the Arduino Uno Board to process the signal and control the robotic arm stably and placing the arm in the correct position based on the signal received; thus, the Arduino Uno Board is identifying an optimal algorithm to be used for further processing of the element based on the received signal from the VGGNet model (Figure 6) for the robotic arms specific (target) location (where the DH model and kinematics are used to optimize the algorithm of moving the robot arm to the specific location without errors)).
Nevertheless, Chien does not explicitly disclose:
applying into the neural network input data comprising non-labeled input data;
assessing identification of the non-labeled input data via the neural network, wherein the assessing comprises affirming or correcting the identification; and
However, Appu teaches:
applying into the neural network input data comprising non-labeled input data;
(Appu, Fig 13; Page 17, [0177], “… The untrained neural network 1306 can learn groupings within the unlabeled input and can determine how individual inputs are related to the overall dataset”. Figure 13 shows applying into the untrained neural network (1306) a training data set (1302) of input data comprising unlabeled input data; thus, interpreted by the examiner as non-labeled input data).
assessing identification of the non-labeled input data via the neural network, wherein the assessing comprises affirming or correcting the identification; and
(Appu, Page 14, [0146], “ … the output produced by the network in response to the input representing an instance in a training data set is compared to the "correct" labeled output for that instance, an error signal representing the difference between the output and the labeled output is calculated, and the weights associated with the connections are adjusted to minimize that error …”. The unlabeled input data produces an output which is reviewed via assessing the predicted label (interpreted as the identification) to the correct label to update weights to minimize error; thus, the assessing comprises affirming or correcting the identification via updating weights assesses the differences between correct and incorrect identification).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize Chien’s methodology of creating a trained neural network for controlling the selection of algorithms via image recognition by utilizing a validity threshold with Appu’s creating of a trained neural network with applying specific data and assessing of the data to determine correct identification of specific data. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of the claimed invention, as this leads to optimizing algorithms and increasing accuracy with potential to parallel process (Appu, Page 14, [0147], “The accuracy of a machine learning algorithm can be affected significantly by the quality of the data set used to train the algorithm. The training process can be computationally intensive and may require a significant amount of time on a conventional general-purpose processor. Accordingly, parallel processing hardware is used to train many types of machine learning algorithms. This is particularly useful for optimizing the training of neural networks, as the computations performed in adjusting the coefficients in neural networks lend themselves naturally to parallel implementations. Specifically, many machine learning algorithms and software applications have been adapted to make use of the parallel processing hardware within general-purpose graphics processing devices”).
Regarding Claim 2:
Chien/Appu teach the method of Claim 1 and Chien further teaches:
wherein the computer-controlled processing system utilizes a plurality of different algorithms, each algorithm associated with a defined working condition, the method including the additional steps of:
if the received input data is valid, using the trained neural network to ascertain working condition data from the received input data; and
(Chien, Page 8, Figure 18: ‘Chess image recognized’; Page 3, Figure 2 & 3; Page 4, Figure 4. Figure 18: ‘Chess image recognized’ shows the capture image being considered valid when the recognition rate is higher than 95%. The trained neural network (VGGNet) utilizes a CNN with a convolutional layer + pooling layer (shown in Figures 2 and 3, respectively) to extract object features from the image and process through the FC layer (fully connected layer shown in Figure 4). The examiner interprets working condition data as information on worker activities; where the robot is interpreted as the worker and the images with different chess board placements are the different environments/conditions where actions/activities can take place; thus, the parsing of image elements for image recognition is ascertaining working condition data from the received input data (image data)).
identifying an algorithm best suited for the ascertained working condition data for further processing of the element.
(Chien, Page 8, Figure 18: ‘Chess image recognized -> Send a signal to Arduino Uno’; Page 8, Column 2, Paragraph 1, “When the recognition rate is higher than 95%, the computer transfers the recognition result to the Arduino Uno board through a serial port. Then, it controls the robotic arm move to a specific location … To ensure that the robotic arm moves to the specified position stably, each of the designated positioning programs is equipped with a delay to ensure that the arm moves to the correct position”. Figure 18: ‘Chess image recognized -> Send a signal to Arduino Uno’ shows using the trained neural network to classify the image data as valid or invalid and then passing a signal the Arduino Uno Board to process the signal and control the robotic arm stably and placing the arm in the correct position based on the signal received; thus, the Arduino Uno Board is identifying an algorithm best suited for the ascertained working condition data for further processing of the element as the input data contains features of the captured image from the VGGNet convolutional layer of the chess board’s working condition data).
Regarding Claim 3:
Chien/Appu teach the method of Claim 1 and Chien further teaches:
wherein the computer-controlled processing system utilizes a plurality of different algorithms, each algorithm for performing a specific task on a specific product type, the method including the steps of:
if the received input data is valid, using the trained neural network to classify the received input data with respect to the specific product type; and
(Chien, Page 5, Column 1, Paragraph 3, “First, we place each chess on a different background and at a different position to capture images … we classify the captured images to place the chess with the same font into the same code and sort in the folder. Finally, we have 14 varieties of chess pawns and approximately 14,000 images … OpenCV library for image processing of chess characters, and CNN model training … recognition rate after the test is corrected until the recognition rate reaches a stable level”. The image recognition system captures images of the chessboard which contains a chess pattern of chess pieces (received input data). The image recognition system classifies the captured objects (received input data) and classifies if valid (for example: labeling a chess character as knight within a specific layout; thus, with respect to the specific product type (where the chess piece/pawn/character is the specific product type)).
identifying an algorithm associated with the classified product type for use in further processing.
(Chien, Page 8, Figure 18: ‘Chess image recognized -> Send a signal to Arduino Uno’; Page 8, Column 2, Paragraph 1, “When the recognition rate is higher than 95%, the computer transfers the recognition result to the Arduino Uno board through a serial port. Then, it controls the robotic arm move to a specific location … To ensure that the robotic arm moves to the specified position stably, each of the designated positioning programs is equipped with a delay to ensure that the arm moves to the correct position”. The Arduino Uno Board receives a signal from VGGNet and identifies an algorithm with the classified product type for further processing as the input data contains features of the captured image from the VGGNet convolutional layer which is classified with a chess piece label where the board is able to process the action based off the signal).
Regarding Claim 5:
Chien/Appu teach the method of Claim 1 and Chien further teaches:
wherein the computer-controlled processing systems includes at least one computer-controlled vision system.
(Chien, Page 8, Figure 17).
Regarding Claim 6:
Chien/Appu teach the method of Claim 5 and Chien further teaches:
wherein the received input data includes image data.
(Chien, Page 8, Figure 17; Page 8, Figure 18: ‘Camera capture image’; Page 7, Column 2, Paragraph 5, “Image recognition. When the CNN model is established, the captured image can be input into the CNN model, and the chess image is recognizable”).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Chien et al., “Application of image recognition in workpiece classification”, in view of Appu et al., US-20180299841-A1, in view of Maharaj et al., “Chess AI: Competing Paradigms for Machine Intelligence”.
Regarding Claim 4:
Chien/Appu teach the method of Claim 1. Chien/Appu fails to explicitly disclose different initial states of a product, performing additional NN-based evaluations, and multiple algorithms.
However, Maharaj teaches:
wherein the computer-controlled processing system includes a plurality of different classifications of processes and at least one algorithm associated with each classification, where at least one classification further comprises individual algorithms for use with different initial states of a product, the method including the steps of:
(Maharaj, Page 3, Paragraph 4, “The optimal sequential decision problem is solved by calculating the values of the Q-matrix, denoted by Q(s, a) for state s and action a … The Q-value matrix describes the value of performing action a (a chess move) in our current state s (the chess board position) and then acting optimally henceforth. The current optimal value and policy function are as follows
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”; Pages 6-7, Tables 1-3. The Q-value matrix is used to describe different initial states to predict the most optimal value based in consideration of the policy and can be seen ).
if the trained neutral network evaluation finds the received input data to be a valid presentation of the product, performing additional NN-based evaluation to classify the received data with respect to a specific product type;
(Maharaj, Page 4, Paragraph 10, “… a heuristic evaluation function is applied to determine whether the ending position favours White or Black …The efficiently updatable neural network (NNUE) evaluation function was originally invented by [25] for Shogi, a Japanese chess variant. Stockfish implemented it for their chess engine in version 12 [26]. … Its architecture comprises a shallow, four-layer neural network specifically optimized for speed on CPU machines”. Maharaj utilizes the Stockfish 14 engine which includes an NNUE (efficiently updatable neural network) to perform additional NN-based evaluations to determine optimal positions/target locations with respect to a specific product type (specific chess pieces within the chess board being evaluated for optimal sequencing)).
performing additional NN-based evaluation to determine if there is more than one algorithm associated with a classified process associated with the specific product type, and if not, continuing with presented the element to a classified algorithm associated with the classified process; and
(Maharaj, Page 4, Paragraph 5, “Stockfish uses the alpha-beta pruning search algorithm … avoiding variations that will never be reached in optimal play because either player will redirect the game. Since it is often computationally infeasible to search until the end of the game, the search is terminated early when it reaches a certain depth. … Stockfish incrementally increases the depth of its search tree in a process known as iterative deepening [20] …”. Stockfish uses a search algorithm to find if there are more than one algorithm associated with a classified process (specific chess piece (interpreted by the examiner as specific product type) placements within a chess board environment) associated with the specific product types available for moving. Thus, if nothing (based on the specific product types) is more optimal than what has been searched so far (due to policy thresholds/constraints) then the engine presents the target location to the device for moving the specific piece (which is interpreted by the examiner as continuing with processing (please review the 112b rejection for further details of the current interpretation by the examiner) the element to a classified algorithm)).
if the NN-based evaluation determines the existence of multiple algorithms for the classified process, performing additional NN-based evaluation to ascertain an optimum algorithm to be used for further processing of the element.
(Maharaj, Page 4, Paragraph 5, “Stockfish uses the alpha-beta pruning search algorithm … avoiding variations that will never be reached in optimal play because either player will redirect the game. Since it is often computationally infeasible to search until the end of the game, the search is terminated early when it reaches a certain depth. … Stockfish incrementally increases the depth of its search tree in a process known as iterative deepening [20] …”. Stockfish uses a search algorithm to find variations and optimal play for the device. The search is always done to ascertain an optimum algorithm (within the policy threshold/constraints) and if a more optimal algorithm is found… the device utilizes the most optimal play for further processing of the element).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize Chien/Appu’s methodology of creating a trained neural network for controlling the selection of algorithms via image recognition by utilizing a validity threshold with Maharaj’s explicit states/actions using Stockfish to perform further neural network based evaluations to determine optimal algorithms for further processing. One having ordinary skill in the art would have been motivated to implement this change before the effective filing date of the claimed invention, as this leads to optimizing algorithms based on calculations, states, actions, sequences, safety, and further performs evaluations instead of hardcoding location maps (Maharaj, Page 10, Paragraph 1, “Stockfish and LCZero represent two competing paradigms in the race to build the best chess engine. The magic of the Stockfish engine is programmed into its search, the magic of LCZero into its evaluation. When tasked with solving Plaskett’s Puzzle, Stockfish’s approach proved superior … After annotating 40 games between Stockfish and LCZero, FIDE Chess Master Bill Jordan concluded that “Stockfish represents calculation” … safest approach to engine-building may still involve cold, hard calculation, even in seemingly unpromising variations … For end users, our work implies that Stockfish may currently be the better tool for studying deep puzzles”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM RAHMAN whose telephone number is (703)756-1646. The examiner can normally be reached M-F 8am-5pm.
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/I.R./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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