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 .
DETAILED ACTION
Claims 1-20 are present in this application. Claims 1-20 are pending in this office
action.
This office action is NON-FINAL.
Drawings
The Drawings filed on 03/18/24 are acceptable for examination purposes.
Specification
The Specification filed on 03/18/24 is acceptable for examination purposes.
Examiner’s Note - 35 U.S.C. § 112
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.
Use of the word “means” (or “step for”) in a claim with functional language
creates a rebuttable presumption that the claim element is to be treated in accordance
with 35 U.S.C. § 112(f). The presumption that 35 U.S.C. § 112(f) is invoked is rebutted
on when the function is recited with sufficient structure, material, or acts within the claim
itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable
presumption that the claim element is not to be treated in accordance with 35 U.S.C. §
112(f). The presumption that 35 U.S.C. § 112(f) is not invoked is rebutted when the
claim element recites function but fails to recite sufficiently definite structure, material or
acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are
presumed to invoke 35 U.S.C. § 112(f) except as otherwise indicated in an Office
action. Similarly, claim elements that do not use the word “means” (or “step for”) are
presumed not to invoke 35 U.S.C. § 112(f) except as otherwise indicated in an Office
action.
Claims 1-20 limitations “configured to be trained “, “configured to, after,” “ configured to generate”, “configured to send,” and “configured to feed,” have been interpreted under 35 U.S.C. § 112(f) because they use generic placeholders
“configured to”, coupled with functional language “trained,” “after,” “generate,” “send,” “feed” without reciting sufficient structure to achieve the functions. Furthermore,
the generic placeholders are not preceded by structural modifiers.
Since the claim limitations invokes 35 U.S.C. § 112(f), the specification was
reviewed to find a description of the corresponding structure to achieve the claimed
functions. Examiner found that the specification does not explicitly show a specific
corresponding structure.
If Applicants wishes to provide further explanation or dispute the examiner’s
interpretation of the corresponding structure, Applicants must identify the corresponding
structure with reference to the specification by page and line number, and to the
drawing, if any, by reference characters in response to this Office action.
If Applicants does not intend to have the claim limitations treated under 35 U.S.C.
§ 112(f) Applicants may amend the claims so that they will clearly not invoke 35 U.S.C.
§ 112(f) or present a sufficient showing that the claims recites sufficient structure,
material, or acts for performing the claimed functions to preclude application of 35
U.S.C. § 112(f).
For more information, see MPEP § 2173 et seq. and Supplementary Examination
Guidelines for Determining Compliance With 35 U.S.C. § 112 and for Treatment of
Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 as directed to non-statutory subject matter of software, per se. The claim(s) lack(s) the necessary physical articles or objects to constitute a machine or manufacture within the meaning of 35 U.S.C. 101. In this case, applicant has claimed “A machine-learning based stabilized beam combining apparatus, comprising: an optical phase controller; " in the preamble to these claims without reciting any hardware element in the bodies of these claims; this implies that Applicant is claiming a device of software, per se, lacking the hardware necessary to realize any of the underlying functionality. Therefore, claim 22 is directed to non-statutory subject matter as computer programs, per se. Examiner suggests adding a recitation of a processor or memory.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 06/19/24 has been considered by the Examiner and made of record in the application file.
Claim Rejections 35 U.S.C. §103
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any
correction of the statutory basis 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.
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
Claims 1-2, 5-6, 8-13, 15-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kaditz et al. (US 2020/0265328 A1) in view of Kewitsch et al. (US 2006/0239312 A1).
Regarding claim 1, Kaditz teaches machine-learning based stabilized beam combining apparatus, comprising:
an optical phase controller, (See Kaditz paragraph [0010], an optical detector);
an (optical beam combining system, (See Kaditz paragraph [0010], an electromagnetic beam in an optical band of wavelengths);
a neural network configured to be trained, (See Kaditz paragraph [0092], neural network 500 may be trained), with multi-state dither information
from the optical beam combining system, (See Kaditz paragraph [0010], an electromagnetic beam in an optical band of wavelengths);
wherein the neural network is configured to, after being trained with labelled
data as said multi-state dither, (See Kaditz paragraph [0093], The convolutional neural network may include eight learned layers with weights, including five convolutional layers and three fully connected layers with a final 1000-way softmax or normalized exponential function that produces a distribution over the 1000 class labels for different possible model parameters), map (i) a target, (See Kaditz paragraph [0098], a targeted type of tissue) and (ii) interference diffractive
patterns measured from the optical beam combining system, (See Kaditz paragraph [0066], a neutron beam, an electron beam, an electromagnetic beam in an optical band of wavelengths, an electromagnetic beam in an infrared band of wavelengths, a sound wave in an ultrasound band of wavelengths, a proton beam, an electric field associated with an impedance measurement device), to error in the interference diffractive patterns measured from the optical beam combining
system, and compare the error to a reference, (See Kaditz paragraph [0088], a machine-learning model 400. In this machine-learning model, a weighted (using weights 408) linear or nonlinear combination 416 of measurements 410, one or more corresponding excitations 412 and one or more errors 414 between the one or more measurements 410 and one or more predicted responses determined using a forward model);
wherein, based on said comparison, the neural network is configured to
generate phase control variables as feedback on error correction for the optical
phase, (See Kaditz paragraph [0061], The difference between the first and the second model parameters from these two ‘inverse solvers’ may be used as the error in the neural-network-based approach. This approach may allow the neural network to learn because the numerical approach may be able to give real-time feedback to the neural network and to back propagate/update the weights in the neural network).
Kaditz does not explicitly disclose controller to compensate for drift and noise in the optical beam combining system, and adjust system output to near target, and wherein the neural network is configured to send the generated phase control variables to the optical phase controller, whereby the optical phase controller can use the generated phase control variables, to stabilize the optical beam combining system against drift and noise.
However, Kewitsch teaches controller to compensate for drift and noise in the optical beam combining system, (See Kewitsch paragraph [0088], The purpose of locking a high power local emitter to a low power, low noise reference laser is to transfer the low phase noise characteristics onto the high power emitter. The output beam 15 then exhibits the superior optical power characteristics of laser), and adjust system output to near target, (See Kewitsch paragraph [0096], Coherent combining with a single diffraction limited composite output beam requires, in addition to phase locking, that the phases of each emitter circuit be adjusted to produce a composite beam with constant phase front…determine phase set points which accomplish the target phase front); and wherein the neural network is configured to send the generated phase control variables to the optical phase controller, (See Kewitsch paragraph [0058], The phase of each emitter 14 or 14' is controlled by phase control unit 51 to produce an optical phased array source in which the phase of each beam segment), whereby the optical phase controller can use the generated phase control variables, (See Kewitsch paragraph [0058], The phase of each emitter 14 or 14' is controlled by phase control unit 51 to produce an optical phased array source in which the phase of each beam segment), to stabilize the optical beam combining system against drift and noise, (See Kewitsch paragraph [0039], emitting 100 to 200 mW of optical power at a single frequency with a phase noise spectrum characterized by a <10 MHz width).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify controller to compensate for drift and noise in the optical beam combining system, and adjust system output to near target, and wherein the neural network is configured to send the generated phase control variables to the optical phase controller, whereby the optical phase controller can use the generated phase control variables, to stabilize the optical beam combining system against drift and noise of Kewitsch in order to improving systems and methods of selecting hyper-parameters for deep convolutional networks.
Regarding claim 2, Kaditz taught the apparatus of claim 1, as described above.
Pillai further teaches wherein said apparatus allows training on a system not yet controlled, (See Kaditz paragraph [0061], allow the neural network to learn because the numerical approach may be able to give real-time feedback to the neural network and to back propagate/update the weights in the neural network), and for continuous learning as the stabilizer operates, (See Kaditz paragraph [0100], This fitting operation may be repeated at all the boundaries in the model-parameter-solution space. Moreover, the largest continuous surface within the boundary).
Claim 12 recites the same limitations as claim 2 above. Therefore, claim 12 is rejected based on the same reasoning.
Regarding claim 5, Kaditz taught the apparatus of claim 1, as described above.
Kaditz further teaches wherein the feedback is configured to feed the neural network, (See Kaditz paragraph [0061], give real-time feedback to the neural network and to back propagate/update the weights in the neural network), after training, a current measurement, which need not be contained in a training dataset, together with a desired pattern in the observation space, from which the neural network predicts the action needed to move apparatus output between these two states in a deterministic way, (See Kaditz paragraph [0053], the second predictive model may revise a sampling frequency, a characterization technique, etc. to determine additional information that allows the determination of the model parameters using the first predictive model to converge (i.e., to have an accuracy less than the predefined value). Stated differently, the next perturbation or disturbance may be chosen to minimize the error or the difference across the hyper-dimensional space).
Claim 19 recites the same limitations as claim 5 above. Therefore, claim 19 is rejected based on the same reasoning.
Regarding claim 6, Kaditz taught the apparatus of claim 1, as described above.
Kaditz tfurther teaches wherein said apparatus is capable of continuous learning while operating, (See Kaditz paragraph [0100], the largest continuous surface within the boundary defined by the cubic splines may be determined and the model-parameter-solution calculation may be repeated to determine a new continuous surface that is within the previous continuous surface).
Claim 13 recites the same limitations as claim 6 above. Therefore, claim 13 is rejected based on the same reasoning.
Regarding claim 8, Kaditz taught the apparatus of claim 1, as described above.
Kaditz tfurther teaches wherein said apparatus does not require being stabilized during the multi-state dither information training process, (See Kaditz paragraph [0061], allow the neural network to learn because the numerical approach may be able to give real-time feedback to the neural network and to back propagate/update the weights in the neural network. This hybrid approach would still not require or need a priori training).
Regarding claim 9, Kaditz taught the apparatus of claim 1, as described above.
Kaditz does not explicitly disclose wherein said apparatus, operating in an application subject to periodical non-uniqueness mapping, requires only a fraction of the training dataset near the operating point, instead of mapping the entire parameter space, toward obtaining rapid training speed on large scale systems.
However, Kewitsch teaches wherein said apparatus, operating in an application subject to periodical non-uniqueness mapping, requires only a fraction of the training dataset near the operating point, instead of mapping the entire parameter space, toward obtaining rapid training speed on large scale systems, (See Kewitsch paragraph [0098], The determination of phase offsets can potentially be performed in parallel by associating the optical phase of each emitter with a unique dither frequency in step 119-j. This has the benefit that frequency acquisition can be performed more quickly).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein said apparatus, operating in an application subject to periodical non-uniqueness mapping, requires only a fraction of the training dataset near the operating point, instead of mapping the entire parameter space, toward obtaining rapid training speed on large scale systems of Kewitsch in order to improving systems and methods of selecting hyper-parameters for deep convolutional networks.
Claim 15 recites the same limitations as claim 9 above. Therefore, claim 15 is rejected based on the same reasoning.
Regarding claim 10, Kaditz taught the apparatus of claim 9, as described above.
Kaditz does not explicitly disclose wherein said periodical non-uniqueness mapping comprises interferometric control on coherent beam combining.
However, Kewitsch teaches wherein said periodical non-uniqueness mapping comprises interferometric control on coherent beam combining, (See Kewitsch paragraph [0033], Electronic frequency and phase-locking is achieved by high-speed integrated electronics that provide both a large electrical bandwidth as well as the control and functionality necessary for stable coherent beam combination).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein said periodical non-uniqueness mapping comprises interferometric control on coherent beam combining of Kewitsch in order to improving systems and methods of selecting hyper-parameters for deep convolutional networks.
Claim 16 recites the same limitations as claim 10 above. Therefore, claim 16 is rejected based on the same reasoning.
Regarding claim 11, Kaditz an apparatus for stabilizing drift in a system, comprising:
a neural network that is trained, (See Kaditz paragraph [0092], neural network 500 may be trained), with output signals from the system, (The output of the mixer 57 is a baseband signal), wherein the trained neural network maps a target, (See Kaditz paragraph [0098], a targeted type of tissue), and output signals from the system, (The output of the mixer 57 is a baseband signal), to system error, compares the system error to a reference, (See Kaditz paragraph [0088], a machine-learning model 400. In this machine-learning model, a weighted (using weights 408) linear or nonlinear combination 416 of measurements 410, one or more corresponding excitations 412 and one or more errors 414 between the one or more measurements 410 and one or more predicted responses determined using a forward model).
Kaditz does not explicitly disclose generates control variables for a controller coupled to the system to adjust system output to near target whereby the system is stabilized against drift.
However, Kewitsch teaches generates control variables for a controller coupled to the system, (See Kewitsch paragraph [0088], The purpose of locking a high power local emitter to a low power, low noise reference laser is to transfer the low phase noise characteristics onto the high power emitter. The output beam 15 then exhibits the superior optical power characteristics of laser), to adjust system output to near target whereby the system is stabilized against drift, (See Kewitsch paragraph [0096], Coherent combining with a single diffraction limited composite output beam requires, in addition to phase locking, that the phases of each emitter circuit be adjusted to produce a composite beam with constant phase front…determine phase set points which accomplish the target phase front).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify controller to generates control variables for a controller coupled to the system to adjust system output to near target whereby the system is stabilized against drift of Kewitsch in order to improving systems and methods of selecting hyper-parameters for deep convolutional networks.
Regarding claim 17, Kaditz taught the apparatus of claim 9, as described above. Kaditz further teaches further comprising:
an optical beam combining system, (See Kaditz paragraph [0010], an electromagnetic beam in an optical band of wavelengths);
wherein the neural network is configured to be trained with multi-state dither, (See Kaditz paragraph [0093], The convolutional neural network may include eight learned layers with weights, including five convolutional layers and three fully connected layers with a final 1000-way softmax or normalized exponential function that produces a distribution over the 1000 class labels for different possible model parameters), map (i) a target, (See Kaditz paragraph [0098], a targeted type of tissue), information from the optical beam combining system, (See Kaditz paragraph [0010], an electromagnetic beam in an optical band of wavelengths);
wherein the neural network is configured to, after being trained with labelled data as said multi-state dither, (See Kaditz paragraph [0093], The convolutional neural network may include eight learned layers with weights, including five convolutional layers and three fully connected layers with a final 1000-way softmax or normalized exponential function that produces a distribution over the 1000 class labels for different possible model parameters), map (i) a target, (See Kaditz paragraph [0098], a targeted type of tissue), and (ii) interference diffractive patterns measured from the optical beam combining system, (See Kaditz paragraph [0066], a neutron beam, an electron beam, an electromagnetic beam in an optical band of wavelengths, an electromagnetic beam in an infrared band of wavelengths, a sound wave in an ultrasound band of wavelengths, a proton beam, an electric field associated with an impedance measurement device),to error in the interference diffractive patterns measured from the optical beam combining system, and compare the error to a reference, (See Kaditz paragraph [0088], a machine-learning model 400. In this machine-learning model, a weighted (using weights 408) linear or nonlinear combination 416 of measurements 410, one or more corresponding excitations 412 and one or more errors 414 between the one or more measurements 410 and one or more predicted responses determined using a forward model);
wherein, based on said comparison, the neural network is configured to generate phase control variables as feedback on error correction, (See Kaditz paragraph [0061], The difference between the first and the second model parameters from these two ‘inverse solvers’ may be used as the error in the neural-network-based approach. This approach may allow the neural network to learn because the numerical approach may be able to give real-time feedback to the neural network and to back propagate/update the weights in the neural network).
Kaditz does not explicitly disclose for the controller to compensate for drift and noise in the optical beam combining system and adjust system output to near target; and wherein the neural network is configured to send the generated phase control variables to the controller, whereby the optical phase controller can use the generated phase control variables to stabilize the optical beam combining system against drift and noise.
However, Kewitsch teaches for the controller to compensate for drift and noise in the optical beam combining system, (See Kewitsch paragraph [0088], The purpose of locking a high power local emitter to a low power, low noise reference laser is to transfer the low phase noise characteristics onto the high power emitter. The output beam 15 then exhibits the superior optical power characteristics of laser), and adjust system output to near target, (See Kewitsch paragraph [0096], Coherent combining with a single diffraction limited composite output beam requires, in addition to phase locking, that the phases of each emitter circuit be adjusted to produce a composite beam with constant phase front…determine phase set points which accomplish the target phase front); and wherein the neural network is configured to send the generated phase control variables to the controller, (See Kewitsch paragraph [0058], The phase of each emitter 14 or 14' is controlled by phase control unit 51 to produce an optical phased array source in which the phase of each beam segment), can use the generated phase control variables, (See Kewitsch paragraph [0058], The phase of each emitter 14 or 14' is controlled by phase control unit 51 to produce an optical phased array source in which the phase of each beam segment), to stabilize the optical beam combining system against drift and noise, (See Kewitsch paragraph [0039], emitting 100 to 200 mW of optical power at a single frequency with a phase noise spectrum characterized by a <10 MHz width).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify for the controller to compensate for drift and noise in the optical beam combining system and adjust system output to near target; and wherein the neural network is configured to send the generated phase control variables to the controller, whereby the optical phase controller can use the generated phase control variables to stabilize the optical beam combining system against drift and noise of Kewitsch in order to improving systems and methods of selecting hyper-parameters for deep convolutional networks.
Regarding claim 20, Kaditz teaches a machine-learning based apparatus for stabilizing drift in a system, the apparatus comprising:
a neural network configured to be trained, (See Kaditz paragraph [0092], neural network 500 may be trained), with measured output signals from the system, (The output of the mixer 57 is a baseband signal), the measured output signals including system drift; (The output of the mixer 57 is a baseband signal),
wherein the neural network is configured to, (See Kaditz paragraph [0092], neural network 500 may be trained), after being trained, map the output signals and a target to system error and compare the system error to a reference, (See Kaditz paragraph [0088], a machine-learning model 400. In this machine-learning model, a weighted (using weights 408) linear or nonlinear combination 416 of measurements 410, one or more corresponding excitations 412 and one or more errors 414 between the one or more measurements 410 and one or more predicted responses determined using a forward model);
Kaditz does not explicitly disclose wherein, based on said comparison, the neural network is configured to generate control variables for a controller to compensate for the system drift and adjust output signals from the system to near target; and wherein the neural network is configured to send the generated control variables to the controller, whereby the controller can use the generated control variables to stabilize the system against drift.
However, Kewitsch teaches wherein, based on said comparison, the neural network is configured to generate control variables for a controller to compensate for the system drift (See Kewitsch paragraph [0088], The purpose of locking a high power local emitter to a low power, low noise reference laser is to transfer the low phase noise characteristics onto the high power emitter. The output beam 15 then exhibits the superior optical power characteristics of laser), and adjust output signals from the system to near target, (See Kewitsch paragraph [0096], Coherent combining with a single diffraction limited composite output beam requires, in addition to phase locking, that the phases of each emitter circuit be adjusted to produce a composite beam with constant phase front…determine phase set points which accomplish the target phase front); and wherein the neural network is configured to send the generated control variables to the controller, (See Kewitsch paragraph [0058], The phase of each emitter 14 or 14' is controlled by phase control unit 51 to produce an optical phased array source in which the phase of each beam segment), whereby the controller can use the generated control variables, (See Kewitsch paragraph [0058], The phase of each emitter 14 or 14' is controlled by phase control unit 51 to produce an optical phased array source in which the phase of each beam segment), to stabilize the system against drift, (See Kewitsch paragraph [0039], emitting 100 to 200 mW of optical power at a single frequency with a phase noise spectrum characterized by a <10 MHz width).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein, based on said comparison, the neural network is configured to generate control variables for a controller to compensate for the system drift and adjust output signals from the system to near target; and wherein the neural network is configured to send the generated control variables to the controller, whereby the controller can use the generated control variables to stabilize the system against drift of Kewitsch in order to improving systems and methods of selecting hyper-parameters for deep convolutional networks.
Claims 3-4, 7, 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kaditz et al. (US 2020/0265328 A1) in view of Kewitsch et al. (US 2006/0239312 A1) and further in view of Commons (US Patent No. 9, 875, 440 B1).
Regarding claim 3, Kaditz taught the apparatus of claim 1, as described above.
Kaditz together with Kewitsch does not explicitly disclose wherein said multi-state dither information is obtained differentially with a known action being input, the results of which are registered before and after, thus providing a multi-state in observation space, from which a trained neural network, is capable of building the map between the differential observation space and controller action space, as opposed to conventional learning requiring observation of absolute value and action.
However, Commons teaches wherein said multi-state dither information is obtained differentially with a known action being input, the results of which are registered before and after, thus providing a multi-state in observation space, from which a trained neural network, (See Commons Col. 3 lines 49-52, Learning algorithms search through the solution space to find a function that has the smallest possible cost. For applications where the solution is dependent on some data, the cost must necessarily be a function of the observations), is capable of building the map between the differential observation space and controller action space, (See Commons Col. 8 lines 36-41, The self-organizing map (SOM) invented by Teuvo Kohonen performs a form of unsupervised learning. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these), as opposed to conventional learning requiring observation of absolute value and action, (See Commons Col. 20 lines 51-57, The weighting may be derived empirically, or adaptively, or as a part of the basic training of a network…if the absolute value of all weights applied to an input or set of related inputs are (in the aggregate) small relative to other inputs).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein said multi-state dither information is obtained differentially with a known action being input, the results of which are registered before and after, thus providing a multi-state in observation space, from which a trained neural network, is capable of building the map between the differential observation space and controller action space, as opposed to conventional learning requiring observation of absolute value and action of Commons in order to find patterns in data. In more practical terms neural networks are non-linear statistical data modeling or decision making tools.
Claim 18 recites the same limitations as claim 3 above. Therefore, claim 18 is rejected based on the same reasoning.
Regarding claim 4, Kaditz taught the apparatus of claim 1, as described above.
Kaditz together with Kewitsch does not explicitly disclose wherein training with said multi-state dither information enables identification on a free-drifting many-in-many-out system, without a knowledge of a mathematical model.
However, Commons teaches wherein training with said multi-state dither information enables identification on a free-drifting many-in-many-out system, without a knowledge of a mathematical model, (See Commons Col. 38 lines 35-42, The neural networks could also be trained to complete different tasks of varying complexity, from basic verbal logical and mathematical issues, to assisting a person with basic motor tasks, such as brushing teeth. At higher levels, the robot is trained to, and capable of, solving calculus problems and piloting an automobile. The networks may be individually trained, and functional capabilities provided ab initio).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein training with said multi-state dither information enables identification on a free-drifting many-in-many-out system, without a knowledge of a mathematical model of Commons in order to find patterns in data. In more practical terms neural networks are non-linear statistical data modeling or decision making tools.
Regarding claim 7, Kaditz taught the apparatus of claim 1, as described above.
Kaditz together with Kewitsch does not explicitly disclose wherein said apparatus automatically updates its training as conditions change whereby there is no need to re-train.
However, Commons teaches wherein said apparatus automatically updates its training as conditions change whereby there is no need to re-train, (See Commons Col. 48 lines 6-11, The automatic driver is designed, in part, to solve these problems by automatically processing more information than is typically available to a human driver and by automatically implementing all of the driving rules and modifying the driving behavior in response to a perceived threat of an accident).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made to modify wherein said apparatus automatically updates its training as conditions change whereby there is no need to re-train of Commons in order to find patterns in data. In more practical terms neural networks are non-linear statistical data modeling or decision making tools.
Claim 14 recites the same limitations as claim 7 above. Therefore, claim 14 is rejected based on the same reasoning.
Conclusions/Points of Contacts
The prior art made of record and not relied upon is considered pertinent to
applicant’s disclosure. See form PTO-892.
Unrath et al. (US Patent No. 8, 847, 113 B2), provides a graph database query
construction and execution method including receiving a first database query including
one or more selection sets each defining at least one database field to be queried from
a graph database, where the first database query is coded in a generic query language,
where the at least one database field is represented in the graph database as a
property of a vertex; generating, for each of the one or more selection set.
TALATHI et al. (US 2016/0224903 A1) an apparatus for selecting hyper-parameters for training a deep convolutional network is presented. The apparatus includes a memory and at least one processor coupled to the memory. The one or more processors are configured to select a number of network architectures as part of a database. Each of the network architectures includes one or more local logistic regression layers and is trained to generate a corresponding validation error that is stored in the database.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MULUEMEBET GURMU whose telephone number is (571)270-7095. The examiner can normally be reached M-F 9am - 5pm.
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, Tony Mahmoudi can be reached at 5712724078. 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.
/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163
Prosecution Insights