DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 11 is objected to because of the following informalities: minor typo.
Please remove the apparently typos from the claim 11.
Appropriate correction is required.
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 an abstract idea without significantly
more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the
claim does fall within one of the statutory categories, the second step in the analysis is
to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A
analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined
whether or not the claims recite a judicial exception (e.g., mathematical concepts,
mental processes, certain methods of organizing human activity). If it is determined in
Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the
second prong (Step 2A, Prong 2), where it is determined whether or not the claims
integrate the judicial exception into a practical application. If it is determined at step 2A,
Prong 2 that the claims do not integrate the judicial exception into a practical
application, the analysis proceeds to determining whether the claim is a patent-eligible
application of the exception (Step 2B). If an abstract idea is present in the claim, any
element or combination of elements in the claim must be sufficient to ensure that the
claim integrates the judicial exception into a practical application, or else amounts to
significantly more than the abstract idea itself. Applicant is advised to consult the 2019
PEG for more details of the analysis.
Step 1
According to the first part of the analysis, in the instant case, claims 1-7, 8-14, 15-20 are directed to a method, device and medium of identifying a property of an object. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Step 2A,
Step 2A, Prong 1
Following the determination of whether or not the claims fall within one of the four
categories (Step 1), it must be determined if the claims recite a judicial exception (e.g.
mathematical concepts, mental processes, certain methods of organizing human
activity) (Step 2A, Prong 1). In this case, the claims are determined to recite a judicial
exception as explained below.
Regarding Claims 1, 8 and 15 these claims recite
obtaining sensor data from at least one sensor; identifying, using the sensor data, a property of interest of an object; training, using one or more neural networks, a model to predict a next uncertainty about a state of the object based on an action; and based on identifying the next uncertainty about the state of the object, controlling a movement of a robotic element to perform the action.
The claims recite a mental process. As set forth in MPEP 2106.04(a)(2)(III)(C), “Claims can recite a mental process even if they are claimed as being performed on a computer”. These are recited at a high level such that they are disclosed as a human user performing these functions, simply using a computer as a tool-see spec, [0063]-[0073], etc., Fig. 9. Thus, the claim recites abstract ideas.
Step 2A, Prong 2
Following the determination that the claims recite a judicial exception, it must be
determined if the claims recite additional elements that integrate the exception into a
practical application of the exception (Step 2A, Prong 2). In this case, after considering
all claim elements individually and as an ordered combination, it is determined that the
claims do not include additional elements that integrate the exception into a practical
application of the exception as explained below.
In Prong Two, a claim is evaluated as a whole to determine whether the recited judicial exception is integrated into a practical application of that exception. A claim is not “directed to” a judicial exception, and thus is patent eligible, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d). The claims recite an abstract idea and further the claims as a whole does not integrate the recited judicial exception into a practical application of the exception. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. MPEP 2106.04(d).
Regarding Claims 1, 8, 15 these claims
This limitation recites using one or more neural networks as a tool to perform an
abstract idea, which is not indicative of integration into a practical application. MPEP 2106.05(f).)
This limitation is understood to be generic computer equipment and mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.0S(f))
Step 2B
Based on the determination in Step 2A of the analysis that the claims are
directed to a judicial exception, it must be determined if the claims contain any element
or combination of elements sufficient to ensure that the claim amounts to significantly
more than the judicial exception (Step 2B). In this case, after considering all claim
elements individually and as an ordered combination, it is determined that the claims do
not include additional elements that are sufficient to amount to significantly more than
the judicial exception for the same reasons given above in the Step 2A, Prong 2
analysis. Furthermore, each additional element identified above as being insignificant
extra-solution activity is also well-known, routine, conventional as described below.
Claims 1, 8 and 15: The claims do not include additional elements, alone or in combination, 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 amount to no more than generic computing components and field of use/technological environment which do not amount to significantly more than the abstract idea. The underlying concept merely receives information, analyzes it, and store the results of the analysis – this concept is not meaningfully different than concepts found by the courts to be abstract (see Electric Power Group, collecting information, analyzing it, and displaying certain results of the collection and analysis; see Cybersource, obtaining and comparing intangible data; see Digitech, organizing information through mathematical correlations; see Grams, diagnosing an abnormal condition by performing clinical tests and thinking about the results; see Cyberfone, using categories to organize store and transmit information; see Smartgene, comparing new and stored information and using rules to identify options).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as a combination do not amount to significantly more than the abstract idea. For example, claim 1 recites training, using one or more neural networks, a model to predict a next uncertainty about a state of the object based on an action; and based on identifying the next uncertainty about the state of the object, controlling a movement of a robotic element to perform the action. These elements are recited at a high level of generality and are well-understood, routine, and conventional activities in the computer art. Generic computers performing generic computer functions, without an inventive concept, do not amount to significantly more than the abstract idea. Looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims do not amount to significantly more than the abstract idea itself.
Step 2A/2B Prong 2 Dependent Claims
Regarding to claim 2, 9, 16
Claim 2, 9, 16 merely recite other additional elements that define training process which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 3, 10, 17
Claim 3, 10, 17 merely recite other additional elements that define minimizing the training loss which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 4, 11, 18
Claim 4, 11, 18 merely recite other additional elements that define manipulating the object and read the data from the sensor which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 5-6, 12-13, 19-20
Claim 5-6, 12-13, 19-20 merely recite other additional elements that define identifying the property of the object which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
Regarding to claim 7, 14
Claim 7, 14 merely recite other additional elements that define the model which performing generic functions that when looking at the elements as a combination does not add anything more than the elements analyzed individually. Therefore, these claims also do not amount to significantly more than the abstract idea itself. These claims are not patent eligible.
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.
Claims 1, 4-8, 11-15, 18-20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Goldberg et al. (Goldberg) US 2020/0198130.
In regard to claim 1, Goldberg disclose A method for identifying a property of an object, ([0016][0021]-[0022][0026]-[0029][0033]-[0034] identify properties of an target object, see Fig. 2, for example)
Goldberg disclose the method comprising:
obtaining sensor data from at least one sensor; (Fig. 2,[0026][0038]-[0041] obtaining sensor image from sensor 220)
identifying, using the sensor data, a property of interest of an object; (Fig. 2, [0029]-[0033] [0038]-[0052] identify, using the sensor image, properties for the target the object, for example, note: “the object” or “anther object” )
training, using one or more neural networks, a model to predict a next uncertainty about a state of the object based on an action; (Fig. 2, [0013][0014][0020] [0028] [0038]-[0052] [0053]-[0056] training, using, NN, a model to model uncertainty to compute (estimate) robust grasp configuration for the object, the uncertainty in variables related to initial state, contact, motion, combination, etc.) and
based on identifying the next uncertainty about the state of the object, controlling a movement of a robotic element to perform the action. (Fig. 2, [0013][0014][0020] [0028] [0038]-[0044] [0053]-[0056][0062]-[0065] based on the modelled uncertainty to compute (estimate) robust grasp configuration for the object, to control a movement of a robotic grasping mechanism to grasp, etc. note: please further define an object and properties, etc. and action, state, etc. to help move forward the prosecution)
In regard to claim 4, Goldberg disclose The method of claim 1,
Goldberg disclose wherein the action comprises pressing the object with the robotic element and obtaining readings from the at least one sensor. (Fig. 2, [0010]- [0012] [0038]-0052] grasp the object with 240 and take sensor image then return data positions and forces, torques, etc.)
In regard to claim 5, Goldberg disclose The method of claim 1,
Goldberg disclose wherein the identifying the property of interest of the object comprises pressing the object with the robotic element at multiple points of the object and obtaining readings from the at least one sensor. (Fig. 2, [0010]- [0012] [0038]-[0053] grasp the object with 240 at target points of the object and then return data from the sensor about positions and forces, torques, etc.)
In regard to claim 6, Goldberg disclose The method of claim 1,
Goldberg disclose wherein the identifying the property of interest comprises lifting the object with the robotic element. (Fig. 2, [0012] [0053] [0068] lifting the object with 240)
In regard to claim 7, Goldberg disclose The method of claim 1,
Goldberg disclose wherein the model comprises a dynamics model and an observation model. ([0016]-[0023] [0052] object models and grasp quality CNN model, etc., note: please further define)
In regard to claims 8, 11-14, claims 8, 11-14 are system claims corresponding to the method claims 1, 4-7 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1, 4-7.
In regard to claims 15, 18-20, claims 15, 18-20 are medium claims corresponding to the method claims 1, 4-6 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 1, 4-6.
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.
Claims 2-3, 9-10, 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Goldberg et al. (Goldberg) US 2020/0198130 in view of Chen US 2019/0377949
In regard to claim 2, Goldberg disclose The method of claim 1,
But Goldberg fail to explicitly disclose “wherein the training comprises repeatedly performing the training until a convergence is identified based on a reduced training error.”
Chen disclose wherein the training comprises repeatedly performing the training until a convergence is identified based on a reduced training error. ([0041]-[0045] [0068]-[0070] training are iterated until converged based on a reduced error)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen‘s ML training into Goldberg’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Chen‘s ML training with training iteration would help to provide more training error control into Goldberg’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more training error control with iteration would help to improve accuracy of prediction precision and training efficiency.
In regard to claim 3, Goldberg disclose The method of claim 1,
But Goldberg fail to explicitly disclose “wherein the training comprises minimizing a training loss by approximating a belief state.”
Chen disclose wherein the training comprises minimizing a training loss by approximating a belief state. ([0041]-[0046] [0068]-[0070] minimizing the loss by a target loss function, such as true boundary box, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Chen‘s ML training into Goldberg’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Chen‘s ML training with training iteration would help to provide more training error control into Goldberg’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing more training error control with iteration would help to improve accuracy of prediction precision and training efficiency.
In regard to claims 9-10, claims 9-10 are system claims corresponding to the method claims 2-3 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 2-3.
In regard to claims 16-17, claims 16-17 are medium claims corresponding to the method claims 2-3 above and, therefore, are rejected for the same reasons set forth in the rejections of claims 2-3.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 20200269429 A1 2020-08-27 Chavez et al.
ROBOTIC HANDLING OF SOFT PRODUCTS IN NON-RIGID PACKAGING
Chavez et al. disclose to perform robotic handling of soft products in non-rigid packaging. In various embodiments, sensor data associated with a workspace is received. An action to be performed in the workspace using one or more robotic elements is determined, the action including moving an end effector of one of the robotic elements relatively quickly to a location in proximity to an item to be grasped; actuating a grasping mechanism of the end effector to grasp the item using an amount of force and structures associated with minimized risk of damage to one or both of the item and its packaging; and using sensor data generated subsequent to the item being grasped to ensure the item has been grasped securely. Control communications are sent to the robotic element via the communication interface to cause robotic element to perform the action… see abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143