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 .
Response to Amendment
The office action is responsive to the amendment filed on 07/08/2025. As directed by the amendments claims 1-21 are now pending in the application. Claims 1, 3-11, and 13-21 have been amended. No new matter is introduced by the requested claim amendments.
Response to Arguments
Regarding objection to the drawing:
Applicant’s arguments, see pg. 11, 4th paragraph, filed 07/08/2025, with respect to objection to the drawing not containing reference number 502 have been fully considered and are persuasive. The objection of the drawings has been withdrawn.
Regarding objection to the specification:
Applicant’s arguments, see page. 11, 5th paragraph, filed 07/08/2025, with respect to objection to the specification reference number 308 not found in the specification have been fully considered and are persuasive. The objection of the specification has been withdrawn.
Regarding the 35 U.S.C § 112(b) Rejection:
Applicant’s arguments, see page. 12, 2nd paragraph, filed 07/08/2025, with respect to claims 5-6 and 16-17 rejected under 35 U.S.C § 112(b) Rejection have been fully considered and are persuasive. The rejection under 35 U.S.C § 112(b) Rejection of claims 5-6 and 16-17 has been withdrawn.
Regarding the 35 U.S.C § 101 Rejection:
Applicant's further arguments see pg. 13-14 filed 07/08/2025 have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, “With respect to independent claim 1, at page 2 of the outstanding NFOA, the Examiner concludes that the pending claims are directed to a "mental process." The Examiner's conclusion is based on identifying two functional claim limitations that the Examiner alleges could be performed in the human mind. The Examiner provides no analysis of whether or how the remaining limitations of the claim impact whether the claim "as a whole" is direct to patent ineligible subject matter”. Further, Applicant argues under Evaluation 1 “Applicant respectfully submits Applicant respectfully submits that the pending claims, as amended, define non- generic computer components/algorithms performing non-generic computer functions that provide a technical benefit of improving human perception of assets depicted in computer simulations of dynamic physical scenes.” Further in Evaluation 2 applicant argues “Using the previously-described proper application of the BRI rule under Evaluation 1, it is respectfully submitted that, under Evaluation 2, when viewed as a whole, the eligibility of at least the claimed components/algorithms and/or functions reflected by at least technical features (I) - (IV) of the independent claims as amended is self-evident. The claims as properly construed are not directed to well-known generic operations implemented on generic computer hardware performing generic computer functions. Instead, the claims as amended provide a technical benefit of improving human perception of assets depicted in computer simulations of dynamic physical scenes”. Lastly , Evaluation 3 applicant argues “Using the previously-described proper application of the BRI rule under Evaluation 1, it is respectfully submitted that, under Evaluation 3, the claims as a whole are not directed to an abstract idea [...] under Evaluation 4, when viewed as a whole, the claims recite additional elements that amount to significantly more than any judicial exception that might be present in the pending claims. As previously noted herein, the pending claims, as amended, define non-generic computer components/algorithms performing non-generic computer functions that provide a technical benefit of improving human perception of assets depicted in computer simulations of dynamic physical scenes having temporal characteristics”
EXAMINER RESPONSE: Examiner respectfully disagree, in pg. 2-13 of the Non-Final Office Action dated 04/07/2025 the examiner provided analysis for whether claims 1-21 were directed to an abstract idea. The examiner first determined whether the claims recited a “process, machine, manufacture, or composition of matter” as it’s evident in Step 1 in pg. 2 of the Non-Final Office Action dated 04/07/2025, the examiner then proceed to evaluate claims 1-21 under the subject matter eligibility framework ( see MPEP 2106 (III)) which is evident in steps 2A Prong 1, 2A Prong 2 and 2B for all claims in pg. 2-13.
Furthermore, examiner respectfully disagree with applicant argument that “the pending claims, as amended, define non- generic computer components/algorithms performing non-generic computer functions”. For example, amended claim 1 recites the limitation generating, by the computing device, a computer simulation of the dynamic physical scene using the sensor data, wherein the computer simulation comprises assets and non-assets, the assets comprising a first an asset and other assets for which the step of generating a computer simulation is insignificant-pre solution activity and is well-understood, routine and conventional that is supported by McHaney US (US 2002/0095393 A1) in paragraph [0031].
In addition, amended claims 1-21 as presented are not integrated into a practical application under the second prong of the two-prong analysis since the claimed invention does not improves the functioning of a computer or improves another technology or technical field. Rather the claims when view as whole recites additional elements that merely recites the words "apply it" (or an equivalent) as discussed in MPEP § 2106.05(f) and it adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) which the courts have also identified limitations that did not integrate a judicial exception into a practical application (see MPEP 2106.04(d)(1)). The applicant is reminded that any improvement should be mention in the claim language (See MPEP 2106.05 (a)).
Accordingly, claims 1-21 are not patent eligible under 35 U.S.C § 101.
Regarding the 35 U.S.C § 102:
Applicant's further arguments see pg. 29-32 filed 07/08/2025 have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, “It is respectfully submitted that the amendments to independent claims 1, 11, and 21 overcome the anticipation rejections based on Atsmon because Atsmon does not disclose or suggest the combinations defined in independent claims 1, 11, and 21, respectively. In particular, the independent claims as amended require accessing, by a computing device, sensor data generated by heterogeneous sensors in real time, with sufficient volume and variety to represent a dynamic physical scene having temporal characteristics. While Atsmon generally discusses the use of simulation data, it does not disclose or require the real-time acquisition of heterogeneous sensor data with the explicit purpose of representing a dynamic physical scene characterized by temporal changes. Instead, Atsmon is primarily concerned with generating and refining synthetic images for simulation, not with the real-time integration of diverse sensor streams to construct a temporally dynamic scene. The independent claims 1, 11, and 21 further require generating a computer simulation that includes both assets and non-assets, with the assets including a first asset and other assets, and then processing this computer simulation using a machine learning model specifically trained to perform a prediction operation comprising applying the machine learning model to the computer simulation, wherein the prediction operation distinguishes the first asset from other assets and non-assets in the computer simulation, and to output a quantitative number as a proxy for how a human viewer would perceive the first asset in the computer simulation. Atsmon does not teach or suggest a machine learning model trained to predict or classify how a human would perceive an individual asset within a computer simulation, nor does it disclose outputting a quantitative number that serves as a proxy for human perception of a specific asset in a computer simulation. Instead, Atsmon's model scores are directed to evaluating the quality of refined images or the overall realism of a scene, not to predicting human perception of individual assets within a dynamic, temporally-evolving computer simulation. The independent claims 1, 11, and 21 further require that the machine learning model is used to determine that the first asset is a classification identified by the model and to output the quantitative number as a proxy for how the first asset would be perceived by a human viewer of the computer simulation. This focus on asset-level human perception, as opposed to general image refinement or scene-level evaluation, is not found in Atsmon. As such, each of the independent claims recites a combination of features - real time, heterogeneous sensor data acquisition for dynamic scenes with temporal characteristics, asset and non-asset segmentation, and machine learning-based prediction of human perception at the asset level - that are not disclosed or anticipated by Atsmon. Accordingly, all of the anticipation rejections based on Atsmon have been overcome and should be withdrawn”.
EXAMINER RESPONSE: Applicant’s arguments with respect to claims 1-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the 35 U.S.C § 103:
Applicant's further arguments see pg. 33-36 filed 07/08/2025 have been fully considered but they are not persuasive.
APPLICANT ARGUMENT:
Applicant argues, “Reconsideration and withdrawal of this rejection are respectfully requested. All of the obviousness rejections rely on Atsmon as the base reference. Because limitations of the claims, as amended, are completely missing from Atsmon, and because the remaining cited references do not cure the deficiencies in Atsmon, the obviousness rejections based on Atsmon fail for the same reasons the anticipation rejection based on Atsmon fail. Accordingly, it is respectfully submitted that the obviousness rejections have been overcome and should be withdrawn. Further in the interest of advancing prosecution, Applicant has amended dependent claims 5 and 15. The amendments to dependent claim 5 are representative [...] Amended claims 5 and 15 are not disclosed or suggested by the cited references, including Atsmon and O'Malley. While Atsmon discusses generating simulation data and evaluating the quality of refined images, it does not teach or suggest using a quantitative number, generated as a proxy for human perception of an individual asset, to drive a computer-implemented simulation edit process that determines whether to keep or remove a specific asset from the simulation. Similarly, O'Malley addresses filtering or updating object data, but does not disclose or suggest a process in which a machine learning model outputs a quantitative number representing predicted human perception, nor does it use such a number to control the retention or removal of assets in a computer simulation. The specific use of a machine learning-derived quantitative number as a decision metric for automated computer simulation editing, as now recited in amended claims 5 and 15, is neither taught nor rendered obvious by the cited references. Accordingly, it is respectfully submitted that, for these additional reasons, claims 5 and 15 are not anticipated or rendered obvious by the cited references. Further in the interest of advancing prosecution, Applicant has amended dependent claims 10 and 20. The amendments to dependent claim 10 are representative [...] Amended claims 10 and 20 are not disclosed or suggested by the cited references, including Atsmon and O'Malley. While Atsmon discusses generating simulation data and evaluating the quality of refined images, it does not teach or suggest a process in which, based on a quantitative number representing predicted human perception of an asset, the system performs a simulation edit process that includes using an alternative machine learning model or modifying parameters of the model to generate a new computer simulation of the asset. Similarly, O'Malley addresses updating or filtering object data, but it does not disclose or suggest the use of alternative machine learning models or parameter modification in response to a human-perception-based quantitative number.
The specific approach of dynamically selecting or adjusting machine learning models or their parameters as part of an automated simulation editing process, as now recited in amended claims 10 and 20, is neither taught nor rendered obvious by the cited references. Accordingly, it is respectfully submitted that, for these additional reasons, claims 10 and 20 are not anticipated or rendered obvious by the cited references.
In view of the foregoing, Applicants submit that the References fail to teach or suggest each and every element of the claimed invention, either arranged as claimed or arranged so as to perform as the claimed invention performs, and are therefore wholly inadequate in their teaching of the claimed invention as a whole, fail to motivate one skilled in the art to do what the patent Applicants have done, fail to teach a modification to a primary reference being modified that does not render the modified reference unsuitable for its intended purpose, fail to teach a modification to prior art absent the use of hindsight, and discloses a substantially different invention from the claimed invention, and therefore cannot properly be used to establish a prima facie case of obviousness. Accordingly, Applicants respectfully request reconsideration and withdrawal of all rejections under pre-AIA 35 U.S.C. §103(a), which Applicant considers to be traversed”.
EXAMINER RESPONSE: Applicant’s arguments with respect to claims 1-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 10 and 20 objected to because of the following informalities:
Claims 10 and 20 recites the limitation "the machine learning model" and “the computer simulation” in lines 7-8 these should recite "the alternative machine learning model " and “the new computer simulation”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 10 and 20 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 10 recites the limitation “using an alternative machine learning model to generate a new computer simulation of the first asset;”. However, the specification does not contain support for an alternative model, and how this alternative machine learning model is used to generate a new computer simulation. To be specific, paragraphs [0014]-[0019] & [0041]-[0058] teach a machine learning model utilizes simulation data to generate a quantitative number as the proxy for how real the asset would appear to the human viewer. Therefore, the specification does not provides details in how an alternate machine learning model is used to generated a new computer simulation as disclosed in the amended claim 10.
Claim 20 recites similar features to those of claim 10. Therefore, the rejection of claim 10 applies.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-10 are a method type claim. Claims 11-20 are a system claim. Claim 21 is a non-transitory computer readable storage medium. Therefore, claims 1-21 are directed to either a process, machine, manufacture or composition of matter.
Regarding claim 1: 2A Prong 1:
perform a prediction operation ...wherein the prediction operation distinguishes the first asset from other assets and non-assets... ( mental process – of performing a prediction operation that enables to differentiate/distinguish a first asset from other assets and non-assets can be performed by the human mind with the aid of pen and paper (e.g., evaluation & judgment)).
output a quantitative number as a proxy for how a human viewer would perceive the first asset ( mental process – of determine a number can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... that the first asset is a classification... ( mental process – of determine that the first asset is a classification can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
outputting, ...the quantitative number as the proxy for how the first asset would be perceived by the human viewer ( mental process – of determine a number can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
accessing, by a computing device, sensor data generated by heterogeneous sensors in real time, the sensor data having sufficient volume and variety to represent a dynamic physical scene, the dynamic physical scene having temporal characteristics; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
generating, by the computing device, a computer simulation of the dynamic physical scene using the sensor data, wherein the computer simulation having comprises assets and non-assets, the assets comprising a first asset and other assets; ( This step of generating a computer simulation is insignificant-pre solution activity - see MPEP 2106.05(g)).
processing the computer simulation via a machine learning model, the machine learning model being trained to: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...comprising applying the machine learning model to the computer simulation, ...in the computer simulation; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...via the machine learning model, ...identified by the machine learning model; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...from the machine learning model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
accessing, by a computing device, sensor data generated by heterogeneous sensors in real time, the sensor data having sufficient volume and variety to represent a dynamic physical scene, the dynamic physical scene having temporal characteristics; ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
generating, by the computing device, a computer simulation of the dynamic physical scene using the sensor data, wherein the computer simulation having comprises assets and non-assets, the assets comprising a first asset and other assets; ( This step of generating a computer simulation is insignificant-pre solution activity and is well-understood, routine and conventional. This is supported by McHaney US (US 2002/0095393 A1) in paragraph [0031], describing computer simulation as being well known modeling techniques ).
processing the computer simulation via a machine learning model, the machine learning model being trained to: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...comprising applying the machine learning model to the computer simulation, ...in the computer simulation; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...via the machine learning model, ...identified by the machine learning model; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...from the machine learning model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 2: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the machine learning model is trained either on simulated data or from on-road data (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 3: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the asset comprises a car, a person, a bicycle, a motorcycle, a person or an animal (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
Regarding claim 4: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the quantitative number represents how confident the machine learning model is with respect to how the human view would perceive the first asset (The specification of data to be stored is understood to be a field of use limitation. See MEPE 2106.05(h)).
Regarding claim 5: 2A Prong 1:
using the quantitative number to ...determine whether to keep the first asset in the computer simulation or remove the first asset from the computer simulation ( mental process – using the quantitative number to determine decide whether to keep the first asset in the computer simulation or remove the first asset from the computer simulation can be performed by the human mind (e.g., evaluation & judgement)).
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
...perform a computer simulation edit process... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 6: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the computer simulation edit process comprises: when the quantitative number equals at least a threshold value, using the first asset in the computer simulation for managing routes for an autonomous vehicle; and (This is understood to be insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
when the quantitative number does not equal or is below the threshold value, replacing the first asset in the computer simulation with different data for use in managing routes for the autonomous vehicle (This is understood to be insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 7: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein generating the computer simulation of the dynamic physical scene further comprises running the computer simulation multiple times in connection with the first asset with different contexts and then applying the machine learning model to each respective computer simulation of multiple computer simulations in different contexts (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)). Further, this is directed to performing repetitive calculations (e.g., iterations) which is understood to be well understood, routine and conventional activity. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 8: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the different contexts relate to one or more of light source, color, motion, speed, direction, orientation, probable orientation/occlusion, rotation, possible overlapping/occlusion and distance from the first asset to a one of the heterogeneous sensors (The specification of data to be stored is understood to be a field of use limitation. See MPEP 2106.05(h)).
Regarding claim 9: 2A Prong 1: None.
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the quantitative number as the proxy for how to the human viewer would perceive the first asset relates to a confidence level associated with the output from the machine learning model (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 10: 2A Prong 1:
modifying parameters of the machine learning model used to generate the computer simulation of the first asset (mental process – of modifying the parameters of a machine learning model can be performed by the human mind with the help of pen and paper. For example, a human can update/change/modify the parameters/weights of a machine learning model (e.g., evaluation and judgement)).
2A Prong 2 and 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein when the quantitative number reaches a threshold value, then maintaining the first asset in the computer simulation and when the quantitative number does not reach the threshold value, then performing a computer simulation edit process comprising one or more of: (This is understood to be insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP 2106.05(g). Further, this is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
using an alternative machine learning model to generate a new computer simulation of the first asset; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Regarding claim 11: See rejection of claim 1, same rational applies. Claim 11 only recites the additional element of A system comprising: a processor; and a computer-readable storage device storing instructions which, when executed by the processor, cause the processor to perform operations comprising... which are all directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
Regarding claim 12: See rejection of claim 2, same rational applies.
Regarding claim 13: See rejection of claim 3, same rational applies
Regarding claim 14: See rejection of claim 4, same rational applies
Regarding claim 15: See rejection of claim 5, same rational applies.
Regarding claim 16: See rejection of claim 6, same rational applies
Regarding claim 17: See rejection of claim 7, same rational applies
Regarding claim 18: See rejection of claim 8, same rational applies.
Regarding claim 19: See rejection of claim 9, same rational applies
Regarding claim 20: See rejection of claim 10, same rational applies
Regarding claim 21: 2A Prong 1:
perform a prediction operation comprising ...wherein the prediction operation distinguishes the first asset from other assets and non-assets... ( mental process – of performing a prediction operation that enables to differentiate/distinguish a first asset from other assets and non-assets can be performed by the human mind with the aid of pen and paper (e.g., evaluation & judgment)).
output a quantitative number as a proxy for how a human viewer would perceive the first asset; ( mental process – of determine a number can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
determining... that the first asset is a classification... ( mental process – of determine that the first asset is a classification can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
outputting, ...the quantitative number as the proxy for how the first asset would be perceived by the human viewer ( mental process – of determine a number can be performed by the human mind with the aid of pen and paper (e.g., evaluation)).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory computer-readable storage medium storing instructions which, when executed by a computing device cause the computing device to perform operations comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing sensor data generated by heterogeneous sensors in real time, the sensor data having sufficient volume and variety to represent a dynamic physical scene, the dynamic physical scene having temporal characteristics; (This is understood to be insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
generating a computer simulation of the dynamic physical scene using the sensor data, wherein the computer simulation comprises assets and non-assets, the assets comprising a first an asset and other assets; ( This step of generating a computer simulation is insignificant-pre solution activity - see MPEP 2106.05(g)).
processing the computer simulation via the machine learning model, the machine learning model being trained to: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...applying the machine learning model to the computer simulation, ...in the computer simulation; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...via the machine learning model, ...identified by the machine learning model; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...from the machine learning model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer-readable storage medium storing instructions which, when executed by a computing device cause the computing device to perform operations comprising: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
accessing sensor data generated by heterogeneous sensors in real time, the sensor data having sufficient volume and variety to represent a dynamic physical scene, the dynamic physical scene having temporal characteristics; ( This is directed to well understood, routine of storing and retrieving information in memory. See MPEP 2106.05 (d)(II)).
generating a computer simulation of the dynamic physical scene using the sensor data, wherein the computer simulation comprises assets and non-assets, the assets comprising a first an asset and other assets; ( This step of generating a computer simulation is insignificant-pre solution activity and is well-understood, routine and conventional. This is supported by McHaney US (US 2002/0095393 A1) in paragraph [0031], describing computer simulation as being well known modeling techniques ).
processing the computer simulation via the machine learning model, the machine learning model being trained to: (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...applying the machine learning model to the computer simulation, ...in the computer simulation; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...via the machine learning model, ...identified by the machine learning model; and (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
...from the machine learning model,... (This is directed to using computers or other machinery merely as a tool to perform an existing process. See MPEP 2106.05(f)).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
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 1-4, 7-9, 11-14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Atsmon et al. US 2021/0312244 A1 (hereinafter Atsmon) in view of Wyrwas et al. US 2021/0286924 A1 (hereinafter Wyrwas).
Regarding claim 1:
Atsmon teaches A method comprising: ( Atsmon, Abstract).
accessing, by a computing device, sensor data generated by heterogeneous sensors in real time, the sensor data having sufficient volume and variety to represent a dynamic physical scene, the dynamic physical scene having temporal characteristics; ( Atsmon [0068] teaches accesses real input data, collected from a driving environment... the real input data comprises real data collected by one or more sensors. Some examples of a sensor are a camera, an electromagnetic radiation sensor, a radar, a Light Detection and Ranging (LIDAR) sensor, a microphone, a thermometer, an accelerometer and a video camera for which one skilled in the relevant art will recognize such sensor are capable of representing a dynamic physical scene with temporal characteristic).
generating, by the computing device, a computer simulation of the dynamic physical scene using the sensor data, wherein the computer simulation comprises assets and non-assets, the assets comprising a first asset and other assets; ( Atsmon [0002] teaches generating simulation sensory data, and [0005] teaches how the sensory simulated data comprises object such as vehicle and pedestrians ([0004]). Further, [0006] teaches the simulation sensory data can comprise a plurality of images such as video sequence of images and photo realistic synthetic images that looks as though it were photographed by a camera).
processing the computer simulation via a machine learning model, the machine learning model being trained to: ( Atsmon [0071] teaches a processing unit analyzes the transformed input data (computer simulation) using at least one machine learning model).
output a quantitative number as a proxy for how a human viewer would perceive the first asset; (Atsmon [0091] teaches an image refining model ( machine learning model) that is trained to generate a refined image in response to input comprising a synthetic image and teaches computing model score for the image refining model, where the model score is indicative of a quality of realism of the respective refined image generated by the respective image refined model in response to input comprising the synthetic image. The model score can be views as the “quantitative number as a proxy for how real the asset would appear to a human viewer”).
determining, via the machine learning model, that the first asset is a classification identified by the machine learning model; and ( Atsmon [0071] teaches the moving agent classes are identifies using the machine learning model. Therefore, the asset is indeed a classification identified by the machine learning model).
outputting, from the machine learning model, the quantitative number as the proxy for how the first asset would be perceived by the human viewer ( Atsmon ([0024] left column lines 10-11 and right column line 1) teaches the model score is computed by “computing a plurality of realism scores, each indicative of a quality of realism of one of the plurality of refined output images” in which the “realism scores” can be viewed as part of the model’s output).
Atsmon does not teach the computer simulation being generated by a computing device, and the computer simulation comprises assets and non-assets, the assets comprising a first asset and other assets; and perform a prediction operation comprising applying the machine learning model to the computer simulation, wherein the prediction operation distinguishes the first asset from other assets and non-assets in the computer simulation; and
Nevertheless, Wyrwas teaches the following:
generating, by the computing device, a computer simulation ...wherein the computer simulation comprises assets and non-assets, the assets comprising a first asset and other assets; ( Wyrwas [0045] teaches a simulation data generator that operates on the computer system to generate simulation data. Therefore, this suggest the simulation data is generated by a computing device. In addition, Wyrwas [0053] teaches on or more actors includes for example, other vehicles, static environmental objects, and pedestrians and FIG.12(B) and [0098] teaches a simulation that comprises assets (actor vehicle – element 704 & actors 1252,1270) and non-asset (deleted actor – element 1270)).
perform a prediction operation comprising applying the machine learning model to the computer simulation, wherein the prediction operation distinguishes the first asset from other assets and non-assets in the computer simulation; and ( Wyrwas [0006] teaches “providing the simulation scenario as a training input to the machine learning model to generate a predicted output of the machine learning model” and [0063] teaches a neural network model (machine leaning model) can be trained to make predictions in order to distinguish assets in the environment. In addition, Wyrwas [0088] teaches the machine learning model ability to make predictions over a range of actor types, this further suggest the ability of the machine learning model to distinguish a first assets from other assets and non-assets).
Wyrwas is also in the same field of endeavor as Atsmon (simulation data). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the functionality of processing the simulation data via machine learning model to generate predictive outputs, as being disclosed and taught by Wyrwas, in the system taught by Atsmon to yield the predictable results of improve the accuracy of the machine learning model ( Wyrwas [0083]).
Regarding claim 2:
Atsmon and Wyrwas teach The method of claim 1. Atsmon specifically teaches wherein the machine learning model is trained either on simulated data or from on-road data ( Atsmon [0010] and [0011] teaches a simulation generation model trained by analyzing real input data collected from a driving environment. Further, [0022] teaches collecting data from on-road such that the from a group of sensors that can include “a camera, an electromagnetic radiation sensor, a radar, a Light Detection and Ranging (LIDAR) sensor, a microphone, a thermometer, an accelerometer, and a video camera”).
Regarding claim 3:
Atsmon and Wyrwas teach The method of claim 1. Atsmon specifically teaches wherein the asset comprises a car, a person, a bicycle, a motorcycle, a person or an animal ( Atsmon [0070] teaches examples of a moving agent (objects) are “a car, a truck, a motorized vehicle, a train, a boat, an air-born vehicle, a waterborne vessel, a motorized scooter, a scooter, a bicycle and a pedestrian”). Regarding claim 4:
Atsmon and Wyrwas teach The method of claim 1. Atsmon specifically teaches wherein the quantitative number represents how confident the machine learning model is with respect to how the human viewer would perceive the first asset ( Atsmon, [0091] teaches computing model score for the image refining model, where the model score is indicative of a quality of realism of the respective refined image generated by the respective image refined model in response to input comprising the synthetic image. The model score can be views as the “quantitative number as a proxy for how real the asset would appear to a human viewer”).
Regarding claim 7:
Atsmon and Wyrwas teach The method of claim 1. Atsmon specifically teaches wherein generating the computer simulation of the dynamic physical scene further comprises running the computer simulation multiple times in connection with the first asset with different contexts and then applying the machine learning model to each respective computer simulation of multiple computer simulations in different contexts ( Atsmon [0008] teaches performing plurality of training iteration. Further, [0087] and Fig. 5 teaches receiving a new environment class of the plurality of environment classes (element 510) to generate the simulated driving environment according to the new environment class and generates a plurality of simulated agents, each associated with one of another plurality of moving agent classes and one of another plurality of movement pattern classes. In order to, “enhance realism of generated simulation data...for the purpose of training an autonomous driving agent” ([0088]). Therefore, the environment class can be viewed as containing ““different context” since “each of the environment classes describes a driving environment, and optionally comprises one or more environment values of a plurality of environment attributes describing the driving environment” ([0045]).
Regarding claim 8:
Atsmon and Wyrwas teach The method of claim 7. Atsmon specifically teaches wherein the different contexts relate to one or more of light source, color, motion, speed, direction, orientation, probable orientation/occlusion, rotation, possible overlapping/occlusion and distance from the first asset to one of the heterogenous sensors ( Atsmon [0045] teaches real traffic data is analyzed to identify a plurality of environment class, in which each of the environment class describes a driving environment and environment values of a plurality of environment attribute describing the driving environment. Some examples of the driving attribute are light source ( laylight amount, amount of clouds, type of clouds artificial light) and geography ( direction of a curve). Further, [0068] teaches a Light Detection and Ranging (LIDAR) sensor for which one ordinary in the skill of the art will recognize that a LIDAR sensor can be used to measure a distance from the asset to a sensor).
Regarding claim 9:
Atsmon and Wyrwas teach The method of claim 1. Atsmon specifically teaches wherein the quantitative number as the proxy for how the human viewer would perceive the first assets relates to a confidence level associated with the output from the machine learning model ( Atsmon, [0091] teaches the image refining model uses a Generative Adversarial Network and teaches computing model score for the image refining model, where the model score is “indicative of a quality of realism of the respective refined image generated by the respective image refined model in response to input comprising the synthetic image”. The model score can be views as the “quantitative number as a proxy for how real the asset would appear to a human viewer”).
Regarding claim 11: See rejection of claim 1, same rational applies. Claim 11 only recites the additional element of A system comprising: a processor; and a computer-readable storage device storing instructions which, when executed by the processor, cause the processor to perform operations comprising... for which Atsmon Fig. 6 teaches a system with a processor (element 601) and [0057] teaches “a computer readable storage medium (