DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-13 have been presented for examination based on the application filed on 11/17/2022.
Claims 1-13 are rejected under 35 U.S.C. 101.
Claims 6-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph.
Claim 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.
Claims 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph/Enablement.
This action is made Non-Final.
Examiner Note
Applicants are encouraged to request an interview before responding to this action. Although in this CIP, the form of the disclosure is improved to address how the shell is modelled (3D matrix) is added, the disclosure remains deficient in showing how the shell (3D matrix) is updated to detect collision with the use of neural network. Specifically the disclosure is lacking details of (1) the training of neural network and (2) how the output of neural network is used to detect collision (how and why matrix is updated). An explanation and mapping of the above in the interview would further the prosecution.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Claims 1, 5- & 6:
Step 1: the claims 1, 5, & 6 are drawn to a method, A non-transitory computer-readable storage medium and system respectively, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The limitations are bolded for abstract idea/judicial expception identification.
Claim 1
Mapping Under Step 2A Prong 1
1. (Original) A calculation method for a real-time physical engine enhancement based on a neural network, comprising the following steps: a multi-layer and multi-surface pre-collision shell constructing step: dynamically constructing a multi-layer and multi-surface pre-collision shell according to key concave and convex vertices of an object to be
subjected to collision detection;
a relation matrix acquisition step: obtaining an initial collision detection correspondence matrix according to the multi-layer and multi-surface pre-collision shell; and
a screening and determining step: setting a collision detection condition, inputting a relevant parameter of the collision detection condition into the neural network for parameter screening, and determining whether a collision condition satisfies a safety condition after screening;
wherein when the collision condition satisfies the safety condition, a collision detection correspondence matrix is not updated; and
when the collision condition does not satisfy the safety condition, a current collision detection correspondence matrix is updated, and
the multi-layer and multi-surface pre-collision shell constructing step is triggered according to the updated collision detection correspondence matrix to reconstruct the multi-layer and multi-surface pre-collision shell.
Abstract Idea/Mathematical Concept/Mental Process: The a multi-layer and multi-surface pre-collision shell constructing step recites mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)) as matrix as recited in the specification1, & mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)) as operation on the matrix to determine the vertices of matrix (based on Euclidian distance). The operations on vertices (adding Euclidian distance to determine circumference) may be considered as mental step that can be performed with pen & paper or computer as a tool.
Abstract Idea/Mathematical Concept: The implied collision detection is further mathematical operation.
See Step 2A Prong 2 & step 2B.
Abstract Idea/Mathematical Concept/Mental Process: The Input to neural network (based on provided inputs of (as collision detection condition, inputting a relevant parameter of the collision detection condition) is considered as mathematical relationships (as in MPEP 2106.04(a)(2)(I)(A)) or mathematical calculations (as in MPEP 2106.04(a)(2)(I)(C)). Generic recitation of neural network (without any details) is considered mathematical calculations. Specification is silent on how the neural network is trained or how it determines the collision condition satisfies a safety condition after screening.
Abstract Idea/Mathematical Concept/Mental Process: The wherein condition is mental step because the opinion/judgement (to not update the correspondence matrix) is based on observation (collision condition satisfies the safety condition) that recites mental process (as in MPEP 2106.04(a)(2)(III)(A)). When collision condition does not satisfy the safety condition the current collision detection correspondence matrix is updated is also a mental step and a mathematical concept (update to a mathematical construct).
Abstract Idea/Mathematical Concept/Mental Process: This is also a mental step to update the multi-layer and multi-surface pre-collision shell where shell and represents mathematical concept (update to matrix) as identified above.
Under its broadest reasonable interpretation, these covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. This is not a computer implemented step. Also the mathematical concepts disclosed may also be performed in the mind or with the aid of pencil and paper.
Step 2A, Prong 2: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). As per (1) the additional elements are identified as bolded parts of the limitations in column 1 of the table below, and as per (2) the evaluation is shown in the mapping section of the table.
In accordance with this step, the judicial exception is not integrated into a practical application.
Claim 1
Mapping Under Step 2A Prong 2
1. (Original) A calculation method for a real-time physical engine enhancement based on a neural network, comprising the following steps:
a multi-layer and multi-surface pre-collision shell constructing step: dynamically constructing a multi-layer and multi-surface pre-collision shell according to key concave and convex vertices of an object to be subjected to collision detection;
a relation matrix acquisition step: obtaining an initial collision detection correspondence matrix according to the multi-layer and multi-surface pre-collision shell; and
a screening and determining step: setting a collision detection condition, inputting a relevant parameter of the collision detection condition
into the neural network for parameter screening, and determining whether a collision condition satisfies a safety condition after screening;
wherein when the collision condition satisfies the safety condition, a collision detection correspondence matrix is not updated; and
when the collision condition does not satisfy the safety condition, a current collision detection correspondence matrix is updated, and
the multi-layer and multi-surface pre-collision shell constructing step is triggered according to the updated collision detection correspondence matrix to reconstruct the multi-layer and multi-surface pre-collision shell.
Under MPEP 2106.05(f), use of neural network is to perform real-time physical enhancement is an idea of solution and at best field of use (although it is not clear what technological field the abstract concept of neural network is currently being applied to). Further see MPEP 2106.05(h).
See Step 2A Prong 1.
Under MPEP 2106.05(g) determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. In this case : obtaining an initial collision detection correspondence matrix according to the multi-layer and multi-surface pre-collision shell is mere data gathering at best.
Under MPEP 2106.05(g), setting a collision detection condition, inputting a relevant parameter of the collision detection condition is considered as gathering information to be input to mathematical construct (neural network).
Under MPEP 2106.05(f)(1) the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". This is alternate rejection as the it unclear how the solution (determining whether a collision condition satisfies a safety condition) is achieved by use of the neural network. There are no training of neural network steps involved/claimed for this particular application so that determination can be made whether a collision condition satisfies a safety condition. Specification [0063]2 & [0067] shows use of neural network to compute V.sub.m1.
Generic statement related training3, and implementation, without details how the network is trained is not sufficient to show steps how the neural network to calculate a correspondence matrix of a distance change of each marked vertex according to a set safety distance4. Hence application of neural network remains an idea of solution.
See Step 2A Prong 1.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the claim does not integrate abstract idea (use of neural network) into practical application (collision detection) because the steps to train and use of neural network are not claimed/and not disclosed in the specification (See footnotes in mapping to specification). Figs.2 & [0063]-[0079] (Embodiment 1) and Fig.3 & [0080]-[0096] (Embodiment 2) do not disclose how the neural network adds significantly more to the application of collision detection. Specifically the specification falls short as stated in ¶[0093]:
[0093] An appropriate neural network is established by the collision mode in which the vertices and the vertex faces have the specified mode of motion and velocity. The appropriate neural network is configured to screen the collision vertex model data set so as to accelerate the collision vertex screening of similar objects.
The key phrase here is “appropriate neural network” makes the point, as no details of what is an appropriate neural network is disclosed. Hence the claim fails to disclose how the neural network adds significantly more to the application of collision detection in real time. This (collision detection) is merely a field of use as per MPEP 2106.05(h). Therefore claim 1 is considered as patent ineligible.
Claim 5 is A non-transitory computer-readable storage medium claim which recites similar system limitations as claim 1 is also considered patent ineligible for the same reasons above.
Claim 6 recites similar system limitations as claim 1 is also considered patent ineligible for the same reasons above. The various modules recited in claim 6 mapped as additional elements appear to software and or generic hardware5.
Claims 2-4 recite generally contribute to the abstract idea of using the neural network (based on mathematical concept of matrix computation and mental step to evaluate whether the safety condition is violated performing further mathematical calculations). See Step 2A Prong 1. Even if this considered under Step 2A Prong 2 this type of limitation merely confines the use of the abstract idea to a particular technological environment (collision simulation) without details of how the neural network is trained to “…to calculate and obtain a correspondence matrix of a distance change of each marked vertex according to a set safety distance” ; and thus fails to add significantly more to the claims. MPEP 2106.05(g) & (h).
Claim 7 is rejected with same rationale as claim 2 above.
Claims 8-9 are rejected with same rationale as claim 3 above.
Claim 10 is rejected with same rationale as claim 4 above.
Claims 11-13 are rejected with same rationale as claims 2-4 above.
Claims 6-10 may additionally be rejected as software per se (in view of specification [0099]) as the claim & disclosure do not disclose the structure for the system claim.
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Claim Interpretation
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “ a multi-layer and multi-surface pre-collision shell constructing module”, “a relation matrix acquisition module “, “a screening and determining module” in claim 6-7 specifically and 8-10 by inheritance.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 6-10 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.
Claims 6-10 recites the limitation “ a multi-layer and multi-surface pre-collision shell constructing module”, “a relation matrix acquisition module “, “a screening and determining module”. The specification does not disclose the structure for the said modules or recite generic modules. See specification [0099]:
[0099] For those skilled in the art, the system and its devices, modules and units provided by the present invention is achieved by means of a pure computer-readable program code, and also, the steps of the method of the present invention may be logically programmed to enable the system and its devices, modules and units provided by the present invention to achieve the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers and the likes. Therefore, the system and its devices, modules and units provided by the present invention may be regarded as a hardware component, and the devices, modules and units included in the system to realize various functions may also be regarded as the structures in the hardware component. The devices, modules and units used to realize various functions may also be regarded as both the software modules of the implementation method and the structures in the hardware component.
It is unclear what structural implementation for the system claims 6 (and dependent claims 7-10) should be interpreted for each of the claimed modules. The claim(s) is/are therefore considered as indefinite for failing to particularly point out and distinctly claim the subject matter what constitutes a structure for the module. Generic recitation of pure computer-readable program code or code run on hardware component does not define the structure for the system claim.
Claim Rejections - 35 USC § 112(a) Written Description Requirement
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 6-10 recites the limitation “ a multi-layer and multi-surface pre-collision shell constructing module”, “a relation matrix acquisition module “, “a screening and determining module”. The specification does not disclose the structure for the said modules or recite generic modules. See specification [0099]:
[0099] For those skilled in the art, the system and its devices, modules and units provided by the present invention is achieved by means of a pure computer-readable program code, and also, the steps of the method of the present invention may be logically programmed to enable the system and its devices, modules and units provided by the present invention to achieve the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers and the likes. Therefore, the system and its devices, modules and units provided by the present invention may be regarded as a hardware component, and the devices, modules and units included in the system to realize various functions may also be regarded as the structures in the hardware component. The devices, modules and units used to realize various functions may also be regarded as both the software modules of the implementation method and the structures in the hardware component.
For lack of disclosure what structure various modules disclose, the specification is deficient.
Claim 1, 5 & 6 are additionally rejected as they do not address following limitation:
Claims
Mapping in Specification in US PGPUB No. 20210406432 A1
Examiner Comment
a screening and determining step: setting a collision detection condition, inputting a relevant parameter of the collision detection condition into the neural network for parameter screening, and determining whether a collision condition satisfies a safety condition after screening;
¶[0055], [0067]
While the relevant parameter are enumerated (¶[0079]) there is no disclosure how the neural network is configured to screen and determine collision for this Embodiment I. A generic statement under another embodiment is presented that neural network to do this is known (¶[0067]), however no relation is shown how it is integrated with current application. See Specification [0055]-[0096] (in Embodiment I & II). Even in Embodiment II there is no disclosure how this step is done.
Claim 2-4, 7-10 and 11-13 dependent on claims 1, 6 and 5 respectively do not cure the above deficiencies and are rejected likewise.
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Claim Rejections - 35 USC § 112(a) Enablement Requirement
Claims 1-13 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Specifically the disclosure is deficient because the it lacks disclosure of how the neural network is trained to determine the correspondence matrix6 as there are no details of how it is trained to screen[ing] and determine[ing] a collision (detection). Specification merely states an appropriate neural network is established7 with conventional tools8.
It is noted from MPEP that while applying In re Wands test that While the analysis and conclusion of a lack of enablement are based on the factors discussed in MPEP § 2164.01(a) and the evidence as a whole, it is not necessary to discuss each factor in the written enablement rejection. The language should focus on those factors, reasons, and evidence that lead the examiner to conclude that the specification fails to teach how to make and use the claimed invention without undue experimentation, or that the scope of any enablement provided to one skilled in the art is not commensurate with the scope of protection sought by the claims.
Applying In re Wands, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988) factors:
(A) The breadth of the claims - The focus of the examination inquiry is whether everything within the scope of the claim is enabled (MPEP 2164.08). Specifically, the limitation "... a screening and determining step:
setting a collision detection condition, inputting a relevant parameter of the collision detection condition into the
neural network for parameter screening, and determining whether a collision condition satisfies a safety condition after screening;..." is so broad that it does not disclose any mechanism how the neural network is trained to determine whether collision has happened. As stated earlier, specification fails to show any training mechanism for detection of collision or how the correspondence matrix is updated in real time using the neural network. See specification [0063], [0067] & [0074]. The shell is described as 3D matrix, but how is the neural network trained to detect/screen if a collision has happened so that the matrix values are updated, is not disclosed. Simply stating use of an appropriate neural network is established shows the claim is very broad and not supported for collision detection application specifically.
(B) & (C) The nature of the invention & The state of the prior art - The nature of the invention becomes the backdrop to determine the state of the art and the level of skill possessed by one skilled in the art. The state of the prior art is what one skilled in the art would have known, at the time the application was filed, about the subject matter to which the claimed invention pertains. The relative skill of those in the art refers to the skill of those in the art in relation to the subject matter to which the claimed invention pertains at the time the application was filed (MPEP 2164.05(a)). In this case the Miyamoto, Kazuyoshi et al. (US PGPUB US 20040073385 A1) shows a multi-layer and multi-surface pre-collision shell constructing step of a golf ball 10’
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and then with collision where the vertices of the ball 10’ are deformed
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Golf ball is selected as the multiple layers are concave/convex following the contour of the ball before and after collision. What this reference does not teach is use of neural network to compute the collision/deformation.
Similarly, Deng; Boyang et al. (US 20210319209 A1) shows concave/convex object ([0031]9) being modeled as single layer using the neural network (Fig.1 & [0027]10), for purpose of the collision simulation ([0067]11). What this reference does not teach is use of neural network to compute the collision/deformation with a multi-layer and multi-surface pre-collision shell.
Derek (US PGPUB 20190043214) is used to show how the two matrices can be used to show collision (See Relevant Prior Art Section) with possibility of collision. However the matrix represent an environment and not specific individual objects discretized at object level. Also the prior art does not show use of neural network for collision detection.
Further Prior art David (US PGPUB No. 20100114633) shows multi-layer collision however this also does not use neural network for detection. The backdrop shows the current state of the art.
Further, “The state of the prior art is also related to the need for working examples in the specification.” (MPEP 2164.05(a)) which are not present in this case. The two disclosed embodiments (Embodiment 1 –[0055]-[0079]; Embodiment 2 –[0080]-[0098]) fail to show any training mechanism for detection of collision or how the correspondence matrix is updated in real time using the neural network.
(D) The level of one of ordinary skill - MPEP 2164.05(b) states “The relative skill of those in the art refers to the skill of those in the art in relation to the subject matter to which the claimed invention pertains at the time the application was filed. While specification states the general framework and neural network of Tensorflow or Caffe can be used ([0067]) training the neural network for collision detection such that correspondence matrix and breach of safety condition based on neural network output ([0063] V.sub.m1) is not shown. The various prior arts cited above, do not show neural network application in multi-layer/multi-surface shell implementation being assessed using neural network for collision detection. These prior arts range from 2010-2021.
(E) The level of predictability in the art - The “predictability or lack thereof” in the art refers to the ability of one skilled in the art to extrapolate the disclosed or known results to the claimed invention. If one skilled in the art can readily anticipate the effect of a change within the subject matter to which the claimed invention pertains, then there is predictability in the art. On the other hand, if one skilled in the art cannot readily anticipate the effect of a change within the subject matter to which that claimed invention pertains, then there is lack of predictability in the art. Accordingly, what is known in the art provides evidence as to the question of predictability. In particular, the court in In re Marzocchi, 439 F.2d 220, 223-24, 169 USPQ 367, 369-70 (CCPA 1971) (MPEP 2164.03). Here applicant’s disclosure explicitly cites in specification that implementation using neural networks is well known (Specification ¶[0067]). Even though generic neural network may be well known but the specific implementation that integrates neural network so that neural network screens and determines the collision based on the relation matrix is not well known or generally taught in the art. The training details are not disclosed (see [0067]). One of the ordinary skill in the art would not be able to plug and play this component. Also examiner believes at least the step of relation matrix and using it with neural network may be the inventive step of the current invention which lack proper description and implementation. The level of ordinary skill is demonstrated by Derek (US PGPUB 20190043214) and David (US PGPUB No. 20100114633) as explained above. None show integration with collision detection.
(G) The existence of working examples - MPEP 2164.02 states “When considering the factors relating to a determination of non-enablement, if all the other factors point toward enablement, then the absence of working examples will not by itself render the invention non-enabled.” The two disclosed embodiments (Embodiment 1 –[0055]-[0079]; Embodiment 2 –[0080]-[0098]) fail to show any training mechanism for detection of collision or how the correspondence matrix is updated in real time using the neural network.
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure - MPEP 2164.06(a) related to ELECTRICAL AND MECHANICAL DEVICES OR PROCESSES - gives guidance that drawings by block diagrams with functional labels, was held to be nonenabling in In re Gunn, 537 F.2d 1123, 1129, 190 USPQ 402, 406 (CCPA 1976).-The disclosure fails to show how the training for the neural network is achieved such that the limitation:
a screening and determining step: setting a collision detection condition, inputting a relevant parameter of the collision detection condition into the neural network for parameter screening, and determining whether a collision condition satisfies a safety condition after screening;
is taught. There is no training claimed and none disclosed (see [0067]) to meet the limitation:
[0067] The neural network is used for the assigning a value for V.sub.m1. Regarding to the value range of V.sub.m1, the minimum value for V.sub.m1 is the minimum adjacent distance between global vertices in the three-dimensional object, and the maximum value for V.sub.m1 is N times the maximum adjacent distance, wherein N depends on the computing performance and Convergence Mode presets. Neural network computations are used to speed up the decision to classify local surfaces and then provide the corresponding best-fit value for V.sub.m1. [ON WHAT BASIS?] The source of the training set is a local angle surface [THERE IS NO LOCAL ANGLE as parameter in training or use of the Neural Network] of a random shell model. Without considering the performance and time, the training of the neural network includes iterating calculations to convergence with the minimum adjacent distance, so that a V.sub.m1 value where the performance and convergence tend to be stable can be obtained. The neural network is continued to be trained with new random surfaces and iterate till a corresponding best-fit V.sub.m1 is generated. [HOW?]The neural network, after numerous times of training, is used to identify a trend between a type of a surface and a best-fit V.sub.m1[HOW?], thereby the determination of V.sub.m1 can be achieved by the neural network performing classification of the type of the surface. For example, the general framework and neural network of Tensorflow or Caffe can be used, and the automatic association of surface classification and V.sub.m1 corresponding convergence numbers can be determined by a neural network. The neural network can be implemented with different centralized network models on mobile and non-mobile terminals. Since the training data uses randomly generated surface segments, and the output is the stepping V.sub.m1 values corresponding to the vertex positions on different surface segments, the training result data is stored in a database and used in the assignment of V.sub.m1 when the matrix of the shell layer of the specific object modeling is updated.
Even if considered, how is assigning V.sub.m1 leads to determination of collision detection condition into the neural network for parameter screening, and determining whether a collision condition satisfies a safety condition after screening?,
Claims 5 & 6 disclose similar limitations as claim 1 and are rejected likewise. In re Wands test performed above also apply in this case.
Claim 2-4, 7-10 and 11-13 dependent on claims 1, 6 and 5 respectively do not cure the above deficiencies and are rejected likewise.
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Communication
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AKASH SAXENA
Primary Examiner
Art Unit 2188
/AKASH SAXENA/Primary Examiner, Art Unit 2188 Saturday, January 10, 2026
1 See Specification [0055]-[0061] & Fig.1 shows the procedure to represent shell with multiple layers as 3D matrix with preset Euclidian distance between the layers also represented in 3D.
2 Specification [0063] "... a new value of V.sub.m1 can be determined based on, but is not limited to, “average value of step size” and “non-average value of surface type”. Here, a neural network is used to calculate and assign new V.sub.m1 values...."
3 Specification [0067] "... The source of the training set is a local angle surface of a random shell model... For example, the general framework and neural network of Tensorflow or Caffe can be used, and the automatic association of surface classification and V.sub.m1 corresponding convergence numbers can be determined by a neural network. The neural network can be implemented with different centralized network models on mobile and non-mobile terminals.."
4 Specification [0074] "... 0074] The neural network is used to calculate and obtain a correspondence matrix of a distance change of each marked vertex according to a set safety distance, positions and displacements of each marked vertex at the moment T-1 and the moment T-2 before the collision occurs, and it is determined whether the distance change satisfies the warning distance...." – this is use of neural network not training.
5 See Specification [0099] "... [0099] For those skilled in the art, the system and its devices, modules and units provided by the present invention is achieved by means of a pure computer-readable program code... Therefore, the system and its devices, modules and units provided by the present invention may be regarded as a hardware component, and the devices, modules and units included in the system to realize various functions may also be regarded as the structures in the hardware component. The devices, modules and units used to realize various functions may also be regarded as both the software modules of the implementation method and the structures in the hardware component. "
6 Specification [0074] "... The neural network is used to calculate and obtain a correspondence matrix of a distance change of each marked vertex according to a set safety distance, positions and displacements of each marked vertex at the moment T-1 and the moment T-2 before the collision occurs, and it is determined whether the distance change satisfies the warning distance...." – this is use of neural network and not training.
7 Specification [0093] "... An appropriate neural network is established by the collision mode in which the vertices and the vertex faces have the specified mode of motion and velocity. The appropriate neural network is configured to screen the collision vertex model data set so as to accelerate the collision vertex screening of similar objects...."
8 Specification [0067] "... For example, the general framework and neural network of Tensorflow or Caffe can be used, and the automatic association of surface classification and V.sub.m1 corresponding convergence numbers can be determined by a neural network...."
9 Deng; Boyang et al. (US 20210319209 A1) [0031] "... [0031] The network 200 takes an input that depicts an object. The object can be a 2D object or a 3D object of any arbitrary shape that is either convex or concave. For example, the 2D binary image 202 illustrates the geometry of a 2D object 201 with a shape like the letter “X”. The “X” shaped object 201 can be represented in black color on a white background in a 2D input image. The “X” shaped object 201 has a non-convex shape. In real-life, many objects are non-convex, such as the shapes of an animal, a person, a desk, a truck, etc. The example object 201 in FIG. 2 is a simplified example used to illustrate decomposing non-convex objects into convex elements by the network 200...."
10 Deng; Boyang et al. (US 20210319209 A1) [0027] "... [0027] Using the neural network 104, a shape of the airplane 101 can be represented by a small number of convex elements, allowing a low-dimensional representation to be automatically inferred from the input image, without any human supervision...."
11 Deng; Boyang et al. (US 20210319209 A1) [0067] "... [0067] In some implementations, the system can use the convex representation of the object in real-time computer graphics applications (306) where an explicit representation of a surface is required. Examples of real-time computer graphics applications can include 3D reconstruction, part-based shape retrieval, collision simulation, etc...."