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
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07 July 2023, 31 March 2025, 15 December 2025 and 19 March 2026 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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, 2, 27-31, 33-37, 39, 45, 48, 50, 52, 53, 55 and 56 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 27 (and dependent claims 28-31, 33-37, 39, 45, 48, 50, 52, 53, 55 and 56) recite “Method for force inference of a sensor arrangement for measuring forces, the sensor arrangement comprising at least an elastically deformable wall, the elastically deformable wall comprising an outside measurement surface and an inside reflective surface, wherein the inside reflective surface partially delimits an interior space, a light source arrangement comprising a plurality of light sources being arranged to emit light towards the interior space, and an image sensor being mounted in the interior space; the method for force inference comprising the following steps: reading out image data from the image sensor, and calculating a force map on the outside measurement surface based on the image data using a feed-forward neural network, the force map comprising a plurality of force vectors.”
Claims 1, 2, 27-31, 33-37, 39, 45, 48, 50, 52, 53, 55 and 56, in view of the claim limitations, recite the abstract idea of “reading out image data from the image sensor, and calculating a force map on the outside measurement surface based on the image data using a feed-forward neural network, the force map comprising a plurality of force vectors.”
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited “reading out image data from the image sensor, and calculating a force map on the outside measurement surface based on the image data using a feed-forward neural network, the force map comprising a plurality of force vectors.”; therefore, the claims recite mental processes. Accordingly, the claims recite a mental process, and thus, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of“[a] computer- implemented method” and “the method is carried out by one or more physical processors configured by machine-readable instructions” as recited in claims 55 and 56, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 28-31, 33-37, 39, 45, 48, 50, 52, 53 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0320] and [0323] (describing that the disclosure is not limited to the disclosed implementations, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 28-31, 33-37, 39, 45, 48, 50, 52, 53, 55 and 56 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea. Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 2, 27-31, 33-37, 39, 45, 48, 50, 52, 53, 55 and 56 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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)(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.
Claim(s) 1, 2, 27-31, 33-37, 39, 45, 48, 50, 52, 53, 55 and 56 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Zhang et al (US 12,214,487).
Zhang et al disclose the following claimed features:
Regarding claim 1, method for force inference of a sensor arrangement for measuring forces (Figures 1 and 2), the sensor arrangement comprising at least an elastically deformable wall, the elastically deformable wall comprising an outside measurement surface and an inside reflective surface, wherein the inside reflective surface partially delimits an interior space (column 5, lines 40-50), a light source arrangement comprising a plurality of light sources being arranged to emit light towards the interior space, and an image sensor being mounted in the interior space (column 4, line 65 to column 5, line 7); the method for force inference comprising the following steps: reading out image data from the image sensor (Abstract; column 5, lines 51-57), and calculating a force map on the outside measurement surface based on the image data using a feed-forward neural network, the force map comprising a plurality of force vectors (Abstract; column 7, lines 38-48).
Regarding claim 2, wherein the feed-forward neural network was trained with the following steps performed before the force inference: performing a plurality of force tests on the sensor arrangement, each force test comprising application of a force by one indenter on a position on the outside measurement surface of the sensor arrangement, simultaneously measuring a force applied by the indenter and simultaneously reading out image data from the image sensor, for each force test, performing a corresponding simulation with a model of the sensor arrangement, each simulation comprising application of a simulated force on a simulated measurement surface of the model, thereby calculating a simulated force map on the simulated measurement surface, the simulated force map comprising a plurality of simulated force vectors, the simulated force corresponding to the measured force and being applied on a position on the simulated measurement surface corresponding to the position on the outside measurement surface, and training the feed-forward neural network with the image data and the corresponding calculated simulated force maps (Figure 34; column 20, line 20 to column 22, line 37).
Regarding claim 27, method for training a feed-forward neural network, wherein the feed-forward neural network calculates a force map on a measurement surface of a sensor arrangement based on image data of an image sensor, the force map comprising a plurality of force vectors, wherein the feed-forward neural network is trained with the following steps: performing a plurality of force tests on the sensor arrangement, each force test comprising application of a force by one indenter on a position on the measurement surface of the sensor arrangement, simultaneously measuring a force applied by the indenter and simultaneously reading out image data from the image sensor, for each force test, performing a corresponding simulation with a model of the sensor arrangement, each simulation comprising application of a simulated force on a simulated measurement surface of the model, thereby calculating a simulated force map on the simulated measurement surface, the simulated force map comprising a plurality of simulated force vectors, the simulated force corresponding to the measured force and being applied on a position on the simulated measurement surface corresponding to the position on the measurement surface, and training the feed-forward neural network with the image data and the corresponding calculated simulated force maps (Figure 34; column 20, line 20 to column 22, line 37).
Regarding claim 28, wherein force tests for training the feed-forward neural network are performed with a plurality of indenters each having a respective indenter shape (column 16, lines 25-37).
Regarding claim 29, wherein the indenter shapes are selected out of a group comprising at least tip, round, triangular cross section, square cross section, hemi-sphere, cube, and cylinder (column 16, lines 25-37).
Regarding claim 30, wherein the simulations are performed with simulated forces applied by simulated indenters with respective simulated indenter shapes corresponding to real indenter shapes used in the corresponding force test (column 16, lines 25-37).
Regarding claim 31, wherein the feed-forward neural network is trained using a plurality of different indenter shapes; and/or wherein the feed-forward neural network is trained using a plurality of indenters with different sizes (column 16, lines 25-37).
Regarding claim 33, wherein the feed-forward neural network is trained with the indenters, at least for a part of the force tests for training the feed-forward neural network, being applied with respective shear forces (column 16, line 62 to column 17, line 30).
Regarding claim 34, wherein the measured forces each comprise a normal force component, a first shear force component and a second shear force component (column 16, line 62 to column 17, line 30).
Regarding claim 35, wherein, of the measured forces, the first shear force component corresponds to a first shear force and the second shear force component corresponds to a second shear force, and wherein the first shear force is perpendicular to the second shear force (Figures 26 and 28).
Regarding claim 36, wherein each of the measured forces comprises three components in a reference coordinate system (Figures 26 and 28).
Regarding claim 37, wherein the feed-forward neural network is trained using a plurality of forces having different shear force components; and/or wherein the feed-forward neural network is trained using a plurality of forces having different normal force components (Figures 28 and 29; column 17, lines 51-67).
Regarding claim 39, wherein the forces are measured using a force sensor in the indenter or positioned adjacent to the indenter (Figure 26).
Regarding claim 45, wherein each force vector comprises a normal force component, a first shear force component, and a second shear force component (Figure 32; column 18, lines 19-37).
Regarding claim 48, wherein the feed-forward neural network is trained with an additional image of an inside reflective surface of the sensor arrangement without external impact as part of the image data; and/or wherein the feed-forward neural network is trained with an image of a skeleton of a wall of the sensor arrangement as part of the image data (Figure 34; column 20, line 20 to column 22, line 37).
Regarding claim 50, wherein the feed-forward neural network is trained with a greyscale gradient image for position encoding as part of the image data; and/or wherein the feed-forward neural network is trained with one or more of a greyscale gradient image, an image of a skeleton, and/or a reference light pattern (Figure 34; column 20, line 20 to column 22, line 37).
Regarding claim 52, wherein the sensor arrangement is a sensor arrangement for sensing forces, the sensor arrangement comprising: an elastically deformable wall, the elastically deformable wall comprising an outside measurement surface and an inside reflective surface, wherein the inside reflective surface partially delimits an interior space (column 5, lines 40-50), a light source arrangement comprising a plurality of light sources and arranged to emit light towards the interior space, and an image sensor being mounted in the interior space (column 4, line 65 to column 5, line 7).
Regarding claim 53, wherein the sensor arrangement is a sensor arrangement for sensing forces, the sensor arrangement comprising: a base portion, a top portion comprising an elastically deformable wall, the top portion being mounted on the base portion such that the top portion and the base portion define an interior space, the elastically deformable wall comprising an outside measurement surface and an inside reflective surface, wherein the inside reflective surface partially delimits the interior space (column 5, lines 40-50), a light source arrangement comprising a plurality of light sources being mounted on the base portion and arranged to emit light towards the interior space, and an image sensor being mounted on the base portion in the interior space (column 4, line 65 to column 5, line 7).
Regarding claim 55, Force inference module for force inference of a sensor arrangement for sensing forces (Figures 1 and 2).
Regarding claim 56, Sensor arrangement for sensing forces, the sensor arrangement comprising: a base portion, a top portion comprising an elastically deformable wall, the top portion being mounted on the base portion such that the top portion and the base portion define an interior space, the elastically deformable wall comprising an outside measurement surface and an inside reflective surface, wherein the inside reflective surface partially delimits the interior space (column 5, lines 40-50), a light source arrangement comprising a plurality of light sources being mounted on the base portion and arranged to emit light towards the interior space, an image sensor being mounted on the base portion in the interior space (column 4, line 65 to column 5, line 7), and a force inference module (Figures 1 and 2).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sun et al (US 12,578,242) disclose a sensor arrangement for sensing forces, including a measurement surface and optical detection of reflected light. Lu et al (US 11,461,519) disclose a method that includes: receiving load-displacement data from one or more instrumented indentation tests on the material, determining, by at least one computer processor, the indentation parameters for the material based, at least in part, on the received load-displacement data, providing as input to a trained neural network, the indentation parameters for the material, determining, based on an output of the trained neural network, the one or more mechanical properties of the material, and displaying an indication of the determined one or more mechanical properties of the material to a user of the computer system.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AN H DO whose telephone number is (571)272-2143. The examiner can normally be reached on M-F 7:00am-4:00pm.
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/AN H DO/Primary Examiner, Art Unit 2853
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