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 Objections
Claims 12, 19 are objected to because of the following informalities:
In Claim 12, lines 1-2, “the computer-implemented data” was probably meant to be: the computer-implemented data structure.
In Claim 19, line 2, the word “in” should probably be deleted.
Appropriate correction is required.
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 18, 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 18, line 2, recites the limitation “the method” that lacks antecedent basis.
Claim 20, line 1, recites the limitation “the computer program” that lacks antecedent basis.
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 11-12 are rejected under 35 U.S.C. 101 because the “computer-implemented data structure” can be purely software per se and therefore directed to non-statutory subject matter.
Claims 13-15, 19 are rejected under 35 U.S.C. 101 because the “computing apparatus” of Claims 13 and 15 can be purely software per se and therefore directed to non-statutory subject matter. A computing apparatus as recited in the claims without specifically disclosing any hardware in the claim or in the specification is considered under its broadest reasonable interpretation to be a software element. Claims 14 and 19 suffers the same deficiency of reciting no hardware.
Claim 20 is rejected under 35 U.S.C. 101 because the "data carrier signal" is considered as transitory and therefore would contain elements embodied outside the four statutory categories.
Claims 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong One:
Independent Claim 13 recites (the same analysis applies to the similar limitations of independent Claim 11):
a description for a technical component;
a designation for a feature of the component;
these limitations, under their broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person is capable of providing a description for a technical component and designating a feature for the component using observation, evaluation and judgement.
Step 2A, Prong Two:
Claim 13 recites the additional elements of (the same analysis applies to the similar limitations of independent Claim 11):
An inspection apparatus for use during transfer of a machine learning model,
This limitation is considered as using a computer or its equivalent as a tool to perform an abstract idea - see MPEP 2106.05(f).
comprising: a detection sensor;
This limitation is considered as linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
and a computing apparatus including a memory;
This limitation is considered as merely using a computer as a tool recited at a high level of generality to perform an abstract idea - see MPEP 2106.05(f).
wherein the computing apparatus is configured to create, apply or train a model for machine learning via a data set which is based on a computer- implemented data structure for transferring the machine learning model, the computer-implemented data structure comprising:
This limitation is considered as using a machine learning model as a tool to perform an abstract idea which also includes its training - see MPEP 2106.05(f).
a classification model for the feature of the component;
This limitation is considered as using a machine learning model (classification model) as a tool to perform an abstract idea - see MPEP 2106.05(f).
and at least one sensor parameter, which describes acquisition of the component via at least one sensor.
This limitation is considered as adding insignificant extra-solution activity (acquiring data) to the judicial exception - see MPEP 2106.05(g).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are considered as using a computer or its equivalent as a tool to perform an abstract idea - see MPEP 2106.05(f), linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h), using a machine learning model as a tool to perform an abstract idea which also includes its training - see MPEP 2106.05(f), and appending well-understood, routine, conventional activities previously known to the industry (acquiring data), specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d). The claims are therefore not patent eligible.
Step 2A, Prong One:
Dependent Claim 14 recites (the same analysis applies to the similar limitations of independent Claims 16 and 19):
wherein the first inspection apparatus is configured to derive a data set, which is based on the data structure as recited in claim 13, from a model of the first inspection apparatus,
this limitation, under its broadest reasonable interpretation, covers concepts that can be performed in the human mind and therefore would fall under the “Mental Processes” groupings of abstract ideas. That is a person is capable of deriving data sets from a data structure using observation and judgement.
Step 2A, Prong Two:
Claim 14 recites the additional elements of (the same analysis applies to the similar limitations of independent Claims 16 and 19):
A system for transferring a machine learning model, comprising: a first and at least one second inspection apparatus connected to each other;
This limitation is considered as adding insignificant extra-solution activity (transferring data between two systems) to the judicial exception - see MPEP 2106.05(g).
and is configured to transfer the data set to the at least one second inspection apparatus,
This limitation is considered as adding insignificant extra-solution activity (transferring data between two systems) to the judicial exception - see MPEP 2106.05(g).
said at least one second inspection apparatus being configured to create, to apply or to train another model of the at least one second inspection apparatus with the data set received from the first inspection apparatus.
This limitation is considered as using a computer or its equivalent as a tool to perform an abstract idea - see MPEP 2106.05(f).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are considered as using a computer or its equivalent as a tool to perform an abstract idea - see MPEP 2106.05(f), and appending well-understood, routine, conventional activities previously known to the industry (transferring data), specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d). The claims are therefore not patent eligible.
Dependent Claim 12 is considered as appending well-understood, routine, conventional activities previously known to the industry (transferring data), specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
Dependent Claim 15 is considered as appending well-understood, routine, conventional activities previously known to the industry (receiving data), specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d), and using a computer or its equivalent as a tool to perform an abstract idea - see MPEP 2106.05(f).
Dependent Claim 17 is considered as using a machine learning model as a tool to perform an abstract idea which also includes its training - see MPEP 2106.05(f).
Dependent Claim 18 is considered as using a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Dependent Claim 20 is considered as appending well-understood, routine, conventional activities previously known to the industry (transmitting data), specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 11-20 are rejected under 35 U.S.C. 102(a)(1) and 102 (a)(2) as being anticipated by Trenholm, US 2016/0034809 A1.
Regarding Claim 13, Trenholm teaches:
An inspection apparatus for use during transfer of a machine learning model (paragraph 112: “application provisioning at a station 238 of a specialized facility 244 and training a classification model for use at the station in a supervised training mode 402. At blocks 1102 to 1106 a connection is established to an engine by a client; metadata and other configuration information is sent using schema services from the station 238 and from the client, which may not be located on the same network as the station. An application, session and associated workflows are generated on the basis of the uploaded information and may be sent to a computing device communicatively linked to the station for execution. At block 1108 peripherals connected to the station may be configured for use with the application and workflows. At block 1110 sensors connected to peripherals at the station 238 collect training data. At block 1116 the training data is sent to the engine. At block 1118 the engine uses the training data to train a computational module, which may comprise a neural network. The training data may be labeled and used as reference data to train the computational module, such as a classification model, in a supervised learning method. At blocks 1120 to 1122 a trained model of the computational module is sent back to the station 238 for use in a normal mode”; And paragraph 134: “a training mode 2302 of a model for use at a station of a specialized location at the site level 234 comprises: receiving sensor data at block 2304 from connected peripherals and any associated sensors; applying a customized data filtering technique at block 2306; uploading filtered data to the cloud at block 2308 and downloading a trained model from the cloud at block 2310. A cloud engine 2322 of servers 112 may provide training of a model by, acquiring data from the specialized location at block 2324, applying customized data processing techniques to the data at block 2326, and training the model at block 2328, before sending it back to the station”; And paragraph 114: “Training data may in some instances be collected from local inspection stations”),
comprising: a detection sensor (paragraph 8: “applying a deep learning neural network to data obtained from one or more sensors on a client system”);
and a computing apparatus including a memory (paragraph 56);
wherein the computing apparatus is configured to create, apply or train a model for machine learning via a data set (paragraph 112: “sensors connected to peripherals at the station 238 collect training data. At block 1116 the training data is sent to the engine. At block 1118 the engine uses the training data to train a computational module, which may comprise a neural network”)
which is based on a computer-implemented data structure for transferring the machine learning model (paragraph 112: “application provisioning at a station 238 of a specialized facility 244 and training a classification model for use at the station in a supervised training mode 402. At blocks 1102 to 1106 a connection is established to an engine by a client; metadata and other configuration information is sent using schema services from the station 238 and from the client, which may not be located on the same network as the station. An application, session and associated workflows are generated on the basis of the uploaded information and may be sent to a computing device communicatively linked to the station for execution. At block 1108 peripherals connected to the station may be configured for use with the application and workflows. At block 1110 sensors connected to peripherals at the station 238 collect training data. At block 1116 the training data is sent to the engine. At block 1118 the engine uses the training data to train a computational module, which may comprise a neural network. The training data may be labeled and used as reference data to train the computational module, such as a classification model, in a supervised learning method. At blocks 1120 to 1122 a trained model of the computational module is sent back to the station 238 for use in a normal mode”; And paragraph 134: “a training mode 2302 of a model for use at a station of a specialized location at the site level 234 comprises: receiving sensor data at block 2304 from connected peripherals and any associated sensors; applying a customized data filtering technique at block 2306; uploading filtered data to the cloud at block 2308 and downloading a trained model from the cloud at block 2310. A cloud engine 2322 of servers 112 may provide training of a model by, acquiring data from the specialized location at block 2324, applying customized data processing techniques to the data at block 2326, and training the model at block 2328, before sending it back to the station”),
the computer-implemented data structure comprising: a description for a technical component (paragraph 112: “the raw data may be pre-processed at the site and data may be labeled”, And paragraph 132: “Updating a part can be accomplished by retraining the classifier model. If a new part is identical to a previous part, a part number translator can be used for a one-to-one replacement. Specifically, a part label 2004 may be changed through a translator module which may map the new part label to the old part label and visa-versa. If the replacement part has visible changes, in addition to updating the part label, the classifier model should be tuned”. The labeling of the data representing the description);
a designation for a feature of the component (paragraph 72: “features may be computed locally in the factory and transferred to the cloud engine in order to train a classifier model. Pre-processing may include feature extraction at the site level (optionally, once data is normalized), carried out with feature extraction techniques by a classification model, such as by a scene parser”; And paragraph 112: “the raw data may be pre-processed at the site and data may be labeled and feature extraction techniques may be performed locally before sending the features to the engine”);
a classification model for the feature of the component (paragraph 115: “a method for training a classification model. At block 1202 raw data is collected from peripherals and any connected sensors. At blocks 1204 and 1205, raw data may be processed for feature extraction locally or sent for feature extraction to a cloud engine. At block 1206, interface module 240 may anonymize, encrypt, compress data and/or apply jurisdictional identifiers before transferring data to the engine. At block 1208 the data is transferred to the engine. At block 1210 the data is used as training data for training a model”);
and at least one sensor parameter, which describes acquisition of the component via at least one sensor (paragraph 72: “configuration includes sensor configuration settings”; And, paragraph 85: “the example application may be able to, through workflow and session services, control direction and brightness of the lights, control the motion of the conveyor belt 510, control precise positioning of a steering wheel 520 being imaged through the robotic arm 130, control positioning for cameras 120, and obtain images from the cameras 120, and obtain other appropriate sensor information from other components”; see also paragraph 130).
The limitations of Claim 11 are contained in Claim 13 and is rejected under the same rationale as stated above for that claim.
Regarding Claim 12, Trenholm further teaches:
The computer-implemented data structure as claimed in claim 11, wherein the computer-implemented data is utilized to transfer the machine learning model during a visual quality inspection (Fig. 2; paragraph 72: “The interface module 240 comprises sensor interfaces and system interfaces for transfer of data to the engine 202. Components of the interface module may be provided at each inspection station or for a particular facility”; And paragraph 149: “Various implementations for monitoring manufacturing quality are contemplated, as described with reference to FIG. 14(a). Surface Inspection implementations of two and three-dimensional objects are contemplated, optionally over time, such as for automotive glass inspection. For example, exterior/interior surface inspection of a vehicle for variable and random defects in paint could be carried out. Coating and painting inspection implementations are further contemplated. With regards to medical coatings, inspection may be required at the two microns level. Implementations of optical interference patterns to detect surface defects are contemplated, such as to provide inspection of contact lenses to detect surface defects, such as holes. Braid inspection implementations are contemplated, such as for inspecting braided hoses in lengths of up to 1 mile which may require inspection for missing braid strands or braid strand sub components at up to 1 ft/sec. In addition a braid may need to be inspected for variance to the angle of the braid relative to a hose. Microscopic inspection of defects to identify a type of contamination for root cause analysis is contemplated, such as for manufacturing forensics. Motor stator wire winding pattern inspection implementations are contemplated to detect random bunching or contamination that could lead to premature failure. Image processing for some mobile sensor networks, such as for unmanned aerial vehicles (UAV) is contemplated”);
and wherein the sensor parameter comprises a camera parameter (paragraph 85: “the example application may be able to, through workflow and session services, control direction and brightness of the lights, control the motion of the conveyor belt 510, control precise positioning of a steering wheel 520 being imaged through the robotic arm 130, control positioning for cameras 120, and obtain images from the cameras 120, and obtain other appropriate sensor information from other components”; see also paragraph 131).
Regarding Claim 14, Trenholm further teaches:
A system for transferring a machine learning model, comprising: a first and at least one second inspection apparatus connected to each other (paragraph 112: “application provisioning at a station 238 of a specialized facility 244 and training a classification model for use at the station in a supervised training mode 402. At blocks 1102 to 1106 a connection is established to an engine by a client; metadata and other configuration information is sent using schema services from the station 238 and from the client, which may not be located on the same network as the station. An application, session and associated workflows are generated on the basis of the uploaded information and may be sent to a computing device communicatively linked to the station for execution. At block 1108 peripherals connected to the station may be configured for use with the application and workflows. At block 1110 sensors connected to peripherals at the station 238 collect training data. At block 1116 the training data is sent to the engine. At block 1118 the engine uses the training data to train a computational module, which may comprise a neural network. The training data may be labeled and used as reference data to train the computational module, such as a classification model, in a supervised learning method. At blocks 1120 to 1122 a trained model of the computational module is sent back to the station 238 for use in a normal mode”);
wherein the first inspection apparatus is configured to derive a data set, which is based on the data structure as recited in claim 13, from a model of the first inspection apparatus, and is configured to transfer the data set to the at least one second inspection apparatus, said at least one second inspection apparatus being configured to create, to apply or to train another model of the at least one second inspection apparatus with the data set received from the first inspection apparatus (paragraph 134: “a training mode 2302 of a model for use at a station of a specialized location at the site level 234 comprises: receiving sensor data at block 2304 from connected peripherals and any associated sensors; applying a customized data filtering technique at block 2306; uploading filtered data to the cloud at block 2308 and downloading a trained model from the cloud at block 2310. A cloud engine 2322 of servers 112 may provide training of a model by, acquiring data from the specialized location at block 2324, applying customized data processing techniques to the data at block 2326, and training the model at block 2328, before sending it back to the station”; And paragraph 114: “Training data may in some instances be collected from local inspection stations”).
The limitations of Claim 16 are contained in Claim 14 and is rejected under the same rationale as stated above for that claim.
Claim 19 is similar to Claim 16 whose limitations are contained in Claim 14 and is rejected under the same rationale as stated above for that claim.
Regarding Claim 15, Trenholm further teaches:
The system as claimed in claim 14, wherein the first and at least one second inspection apparatus are interconnected via a server including a computing apparatus having a memory, the computing apparatus being configured to receive the data set from the first inspection apparatus and to create, apply or train an overall model from the received data set (paragraph 134: “a training mode 2302 of a model for use at a station of a specialized location at the site level 234 comprises: receiving sensor data at block 2304 from connected peripherals and any associated sensors; applying a customized data filtering technique at block 2306; uploading filtered data to the cloud at block 2308 and downloading a trained model from the cloud at block 2310. A cloud engine 2322 of servers 112 may provide training of a model by, acquiring data from the specialized location at block 2324, applying customized data processing techniques to the data at block 2326, and training the model at block 2328, before sending it back to the station”; And paragraph 114: “Training data may in some instances be collected from local inspection stations”).
Regarding Claim 17, Trenholm further teaches:
The method as claimed in claim 16, wherein at least one weight function for the first machine learning model is applied during training of the second model (paragraph 88: “neural networks 320 can operate in at least two modes. In a first mode, a training mode 402, the neural network can be trained (i.e. learn) based on known scenes containing known components to perform a breakdown. The training typically involves modifications to the weights and biases of the neural network, based on training algorithms”; And, paragraph 90: “During operation of a neural network, each of the nodes in the hidden layer applies an activation/transfer function and a weight to any input arriving at that node (from the input layer or from another layer of the hidden layer), and the node may provide an output to other nodes”; And, paragraph 134: “a training mode 2302 of a model for use at a station of a specialized location at the site level 234 comprises: receiving sensor data at block 2304 from connected peripherals and any associated sensors; applying a customized data filtering technique at block 2306; uploading filtered data to the cloud at block 2308 and downloading a trained model from the cloud at block 2310. A cloud engine 2322 of servers 112 may provide training of a model by, acquiring data from the specialized location at block 2324, applying customized data processing techniques to the data at block 2326, and training the model at block 2328, before sending it back to the station”. That is the weight function of the model that is trained is applied to the second model as sent back to the station).
Regarding Claim 18, Trenholm further teaches:
A computer program, comprising commands which, when executed by a computer, cause said computer to implement the method as claimed in claim 15 (paragraph 53).
Regarding Claim 20, Trenholm further teaches:
A data carrier signal, which transmits the computer program as claimed in claim 17 (paragraphs 55, 59. Transmission of data that includes programs is being done by the carrier signal).
Examiner's Note:
The Examiner cites particular pages, sections, columns, line numbers, and/or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in its entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner and the additional related prior arts made of record that are considered pertinent to applicant's disclosure to further show the general state of the art. The Examiner's interpretations in parenthesis are provided with the cited references to assist the applicants to better understand how the examiner interprets the prior art to read on the claims. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 for the relevant prior art where for example Wang, US 2020/0019938 A1, teaches artificial-intelligence-based automated surface inspection for anomaly detection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVE MISIR whose telephone number is (571)272-5243. The examiner can normally be reached M-R 8-5 pm, F some hours.
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, Abdullah Al Kawsar can be reached at 5712703169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVE MISIR/Primary Examiner, Art Unit 2127
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