DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 01/16/2026, 03/02/2026, and 04/15/2026 is/are being considered by the examiner.
Response to Amendment
The Amendment filed on 01/16/2026 has been entered. Claims 1-20 remain pending in this application.
Response to Arguments
Applicant’s arguments filed 01/16/2026 have been fully considered but are not persuasive.
With respect to the Double Patenting rejection, on page 7, that rejection shall be held until a terminal disclaimer pursuant to 37 C.F.R. 1.321(c) is filed.
With respect to the 35 U.S.C. 101 rejection, on pages 8-16, the Applicant asserts that the claims are not directed to an abstract idea. The Applicant asserts that the rejection is improper when viewed in light of the October 2019 Update on Subject Matter Eligibility. The Applicant asserts that claim 1 is improperly categorized as an abstract idea and provides an improvement for computer-based sensor data processing and map generation systems. They also argue that claim 1 is integrated into a practical application. They state that embodiments of the present application are directed to using one or more trained language models to generate tokenized, text-based representations of physical environments based on observations and local map data, where those representations are used to produce machine-readable environmental models that directly support autonomous operation and simulation. The Applicant asserts that claim 1 improves a technical field related to computer-implemented sensor data processing, automated environment representation, and machine-based navigation and control. They also state that amended claim 1 is directed to a specific, computer-implemented solution to a problem rooted in computer technology and software, namely, the inefficiency, rigidity, and fragmentation of conventional sensor-data interpretating and environment mapping pipelines.
The Examiner respectfully disagrees. The original claims, and the claims as amended, are merely utilizing computing devices, in this case “one or more sensors of a machine” and “a trained language model”, as tools to perform a method which is directed to an abstract idea. The claim, under its broadest reasonable interpretation, recites a method of processing and analyzing data that is received from sensors and then input into a language model. This is an abstract idea in the form of certain methods of organizing human activity (i.e. mental processes such as observation, evaluation, judgement, and opinion). The steps of receiving and processing the data could be performed by a human using pen and paper or by purely mental reasoning, save for the recitation of generic computing components. Further, the claims do not integrate the judicial exception into a practical application. The recitation of “one or more sensors of a machine” and “a trained language model” is a generic instruction to perform the abstract idea on a computer or using a computing device and does not impose a meaningful limit on the judicial exception. The “one or more sensors of a machine” and “a trained language model” are recited at a high-level of generality and are used merely as tools to perform the abstract idea faster or more efficiently. There is no reasonable improvement to the functioning of the sensors of the machine, the trained language model, the computational systems used as a whole, nor to any other technology or technical field. The claims do not include any additional elements that amount to significantly more than the judicial exception. The claims, as written and amended, do not include more than mere instructions to perform the abstract method using generic computer components. Hence, the Applicant’s arguments are not persuasive.
With respect to the U.S.C. 102 rejection, on pages 16-20, of claims 1-9, 11-15, and 17-19 under Dintenfass (US Patent Application Publication No. 2018/0158157), the Applicant asserts that Dintenfass fails to disclose the amended limitations of the claims, specifically “generating, based at least on a trained language model processing the local map data and at least a subset of the set of observations, a tokenized description of at least a portion of the environment” and “performing one or more planning, navigation, or control operations corresponding to the machine based at least on the tokenized description”. With respect to the U.S.C. 103 rejection, on pages 20-21, of claim 10 under Dintenfass, in view of Bouguerra et al. (US Patent Application Publication No. 2024/0354491), hereinafter referred to as Bouguerra, and claims 16 and 20 under Dintenfass, in view of Li et al. (US Patent Application Publication No. 2024/0395261), hereinafter referred to as Li, the Applicant asserts that because Dintenfass fails to disclose the limitations of the amended independent claims, therefore the dependent claims are allowable. The Applicant also asserts that neither Bouguerra nor Li cure these deficiencies.
In response to the argument that Dintenfass, Bouguerra, and Li fail to teach the amended limitations of “generating, based at least on a trained language model processing the local map data and at least a subset of the set of observations, a tokenized description of at least a portion of the environment” and “performing one or more planning, navigation, or control operations corresponding to the machine based at least on the tokenized description”, Dintenfass para [0042] reads “For example, the augmented reality user device 200 uses geographic location information provided by a GPS sensor with a map database to determine the location of the user 106,” and Dintenfass para [0035] reads “The augmented reality user device uses object recognition and optical character recognition of images to quickly retrieve information for generating tokens. The augmented reality user device allows information for generating tokens to be retrieved based on an image of an object which significantly reduces the amount of time required to make a data request compared to existing systems that rely on the user to manually enter all of the information for the request. Using object recognition and optical character recognition to identify and retrieve information also allows the augmented reality user device to be less dependent on user input, which reduces the likelihood of user input errors and improves reliability of the system.” This shows that the augmented reality user device takes in information from the user’s environment and uses that to generate tokens. Machine learning models and language learning models are commonly used within augmented reality devices, and can be shown further in Dintenfass Fig. 2 reference characters 220-232. Further, Dintenfass Fig. 7 reference characters 710-714 show sending the property token to a remote server, receiving virtual assessment data, and generating a map based on neighborhood information. This thereby covers the amended limitations of performing one or more planning, navigation, or control operations based on the tokenized description. Generating a map based on the information received is both a planning and navigational operation. Hence, the Applicant’s arguments are not persuasive.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 6, 10, 12-13, 16-18, and 20 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 9, 11, 16-17, and 20 of copending Application No. 18/417,105 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because simply removing inherent and/or unnecessary limitations/steps would be within the level of one of ordinary skill in the art. In re Karlson, 136 USPQ 184 (CCPA 1963). Also note Ex parte Rainu, 168 USPQ 375 (Bd. App. 1969). Omission of a reference element or step whose function is not needed would be obvious to one of ordinary skill in the art.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Application No.: 18/590,609
Application No.: 18/417,105
1. A method, comprising: obtaining sensor data using one or more sensors of a machine, the sensor data corresponding to a set of observations corresponding to a physical environment; identifying local map data that is aligned with the set of observations; [[ and ]] generating, [[ using ]] based at least on a trained language model processing the local map data and at least a subset of the set of observations, a tokenized description of at least a portion of the environment [[ , ]]; and performing one or more planning, navigation, or control operations corresponding to [[ a ]] the machine based at least on the tokenized description.
1. A method, comprising: obtaining a set of observations corresponding to a region of a physical environment; identifying local map data corresponding to the region; generating, based at least on a trained language model processing data representative of the local map data and at least a subset of the set of observations, a tokenized description indicating one or more differences between the local map data and the set of observations; and determining, based at least on the tokenized description, whether one or more updates are to be performed with respect to the local map data based on the one or more differences.
2. The method of claim 1, further comprising: capturing, using one or more sensors of the machine, sensor data corresponding to a physical environment of the machine; extracting domain-relevant features from the sensor data using a perception module; and generating the set of observations corresponding to the physical environment.
2. The method of claim 1, wherein the set of observations includes at least one of sensor data, captured using one or more sensors in the region, or perception data generated using at least the sensor data.
3. The method of claim 2, wherein the set of observations and the local map data are determined relative to a current location of the machine in the physical environment, and wherein the one or more operations correspond to at least one of planning, control, or navigation.
4. The method of claim 1, further comprising: determining an approximate reference location in the physical environment; selecting map data including the approximate reference location; and correlating the map data with the set of observations to identify the local map data that is aligned with the set of observations. 4. The method of claim 1, further comprising: determining an approximate reference location in the physical environment; selecting map data including the approximate reference location; and correlating the map data with the set of observations to identify the local map data that is aligned with the set of observations.
5. The method of claim 4, further comprising: using a trained model to correlate the map data with the set of observations.
6. The method of claim 1, wherein the trained language model fuses the local map data and the portion of the set of observations to generate a single, consistent representation of the portion of the environment, and wherein the tokenized description is generated based in part on the single, consistent representation.
3. The method of claim 1, wherein the tokenized description is compared with additional tokenized descriptions received that correspond to the region in order to determine, with at least a minimum level of confidence, whether to perform the one or more updates with respect to the local map data.
7. The method of claim 1, wherein the one or more operations are specific to a domain, and wherein the tokenized description is generated in a domain-specific language corresponding to the domain.
8. The method of claim 1, wherein the tokenized description includes tokens for static objects and dynamic objects in the physical environment.
9. The method of claim 1, wherein the language model generates the tokenized description using incomplete local map data or an incomplete set of observations for the portion of the environment.
10. The method of claim 1, wherein the tokenized description is written in a road topology language (RTL) or other domain specific language (DSL).
9. The method of claim 1, wherein the tokenized description is written in a road topology language (RTL) or other domain specific language (DSL).
11. The method of claim 1, wherein the tokenized description is determined based on at least one of semantic, topological, geometric, kinematic, or relational information of features in the set of observations.
12. A processor, comprising: one or more circuits to: generate, based at least on sensor data obtained from one or more sensors of a machine, a set of observations corresponding to a physical environment; identify local map data that is aligned with the set of observations; and generate, based at least on a trained language model processing data including the local map data and at least a subset of the set of observations, a tokenized description of at least a portion of the environment.
11. A processor, including one or more logical units to: generate a set of observations corresponding to a region of a physical environment; identify local map data corresponding to the region; and generate, based at least on a large language model (LLM) processing data corresponding to the local map data and at least a subset of the set of observations, a tokenized description indicating one or more differences identified between the local map data and the set of observations, wherein the tokenized description is used to determine whether to perform one or more updates to the local map data.
13. The processor of claim 12, wherein the trained language model is to fuse the local map data and the portion of the set of observations to generate a single, consistent representation of the portion of the environment, and wherein the tokenized description is generated based in part on the single, consistent representation.
11. A processor, including one or more logical units to: generate a set of observations corresponding to a region of a physical environment; identify local map data corresponding to the region; and generate, based at least on a large language model (LLM) processing data corresponding to the local map data and at least a subset of the set of observations, a tokenized description indicating one or more differences identified between the local map data and the set of observations, wherein the tokenized description is used to determine whether to perform one or more updates to the local map data.
14. The processor of claim 12, wherein the tokenized description is to be used to perform an operation specific to a domain, and wherein the tokenized description is generated in a domain-specific language corresponding to the domain.
15. The processor of claim 12, wherein the trained language model is to generate the tokenized description using incomplete local map data or an incomplete set of observations for the portion of the environment.
16. The processor of claim 12, wherein the processor is comprised in at least one of: a system for performing simulation operations; a system for performing simulation operations to test or validate autonomous machine applications; a system for performing digital twin operations; a system for performing light transport simulation; a system for rendering graphical output; a system for performing deep learning operations; a system for performing generative AI operations using a large language model (LLM); a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system incorporating one or more Virtual Machines (VMs); a system implemented at least partially in a data center; a system for performing hardware testing using simulation; a system for performing generative operations using a language model (LM); a system for synthetic data generation; a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources.
16. The processor of claim 11, wherein the processor is comprised in at least one of: a system for performing simulation operations; a system for performing simulation operations to test or validate autonomous machine applications; a system for performing digital twin operations; a system for performing light transport simulation; a system for rendering graphical output; a system for performing deep learning operations; a system for performing generative Al operations using a large language model (LLM);a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system incorporating one or more Virtual Machines (VMs);a system implemented at least partially in a data center; a system for performing hardware testing using simulation; a system for performing generative operations using a language model (LM);a system for synthetic data generation; a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources.
17. A system comprising: one or more processors to use a trained language model to generate a tokenized description of at least a portion of a physical environment based at least on a set of observations corresponding to the physical environment and local map data aligned with the set of observations, the set of observation being generated by processing sensor data obtained from one or more sensors and associated with the portion of the physical environment.
17. A system comprising: one or more processors to determine one or more updates to map data based at least on one or more differences between the map data and a set of observations for the region, the one or more differences being identified based at least on a language model processing the map data and data corresponding to the set of observations.
18. The system of claim 17, wherein the trained language model fuses the local map data and at least a portion of the set of observations to generate a single, consistent representation of the portion of the environment, and wherein the tokenized description is generated based at least in part on the single, consistent representation.
17. A system comprising: one or more processors to determine one or more updates to map data based at least on one or more differences between the map data and a set of observations for the region, the one or more differences being identified based at least on a language model processing the map data and data corresponding to the set of observations.
19. The system of claim 17, wherein the tokenized description includes a sequence of tokens, the sequence of tokens indicating spatial and semantic information for one or more static objects or dynamic objects identified in the physical location.
20. The system of claim 17, wherein the system comprises at least one of: a system for performing simulation operations; a system for performing simulation operations to test or validate autonomous machine applications; a system for performing digital twin operations; a system for performing light transport simulation; a system for rendering graphical output; a system for performing deep learning operations; a system for performing generative AI operations using a large language model (LLM); a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system incorporating one or more Virtual Machines (VMs); a system implemented at least partially in a data center; a system for performing hardware testing using simulation; a system for performing generative operations using a language model (LM); a system for synthetic data generation; a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources.
20. The system of claim 17, wherein the system comprises at least one of: a system for performing simulation operations; a system for performing simulation operations to test or validate autonomous machine applications; a system for performing digital twin operations; a system for performing light transport simulation; a system for rendering graphical output; a system for performing deep learning operations; a system for performing generative AI operations using a large language model (LLM);a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system incorporating one or more Virtual Machines (VMs);a system implemented at least partially in a data center; a system for performing hardware testing using simulation; a system for performing generative operations using a language model (LM);a system for synthetic data generation; a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources.
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.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 12, and 17 recite “obtaining sensor data using one or more sensors of a machine, the sensor data corresponding to a set of observations”, “identifying local map data”, “generating, [[ using ]] based at least on a trained language model processing the local map data and at least a subset of the set of observations, a tokenized description”, and “performing one or more planning, navigation, or control operations”. These limitations, as drafted, are a process that, under a broadest reasonable interpretation, covers the abstract idea of “mental processes” because they cover concepts performed in the human mind, including observation, evaluation, judgement, and opinion. See MPEP 2106.04(a)(2). That is, other than reciting “[[ using ]] based at least on a trained language model”, nothing in the claimed elements preclude the steps from practically being performed by a person looking at their environment, looking at a map of their environment, and writing out a description of what they see based on those two items, and then performing an arbitrary action based upon what they write down.
This judicial exception is not integrated into a practical application because the additional elements “[[ using ]] based at least on a trained language model” are recited at such a high level of generality. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims as a whole are directed to an abstract idea (Step 2A, prong two).
Claims 1, 12, and 17 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical applications, the additional elements of “[[ using ]] based at least on a trained language model” amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (Step 2B).
Dependent claims 2-11, 13-16, and 18-20 are directed to describing the obtained observations, the map, and the operations. These limitations are also related to the abstract idea of “mental processes.” That is, nothing in the claimed elements preclude the steps from practically being performed by a person looking at their environment, looking at a map of their environment, and writing out a description of what they see based on those two items, and then performing an arbitrary action based upon what they write down.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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) 1-9, 11-15, and 17-19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Dintenfass (US Patent Application Publication No. 2018/0158157).
Regarding claim 1, Dintenfass discloses a method, comprising: obtaining sensor data using one or more sensors of a machine, the sensor data corresponding to a set of observations corresponding to a physical environment (Dintenfass para [0066] and "In another embodiment, the virtual assessment engine 230 is configured to use object recognition and/or optical character recognition to identify the location of the user 106. For example, the virtual assessment engine 230 is configured to identify the location of the user 106 based on the identification of buildings, structures, landmarks, signs, and/or any other types of objects around the user 106," Dintenfass para [0081]);
identifying local map data that is aligned with the set of observations (Dintenfass para [0081]);
and generating, [[ using ]] based at least on a trained language model processing the local map data and at least a subset of the set of observations, a tokenized description of at least a portion of the environment (Dintenfass para [0035] and “ For example, the augmented reality user device 200 uses geographic location information provided by a GPS sensor with a map database to determine the location of the user 106,” Dintenfass para [0042]) [[ , ]];
and performing one or more planning, navigation, or control operations corresponding to [[ a ]] the machine based at least on the tokenized description (Dintenfass Fig. 7 reference characters 710-714).
Regarding claim 2, Dintenfass discloses all of the limitations of claim 1. Dintenfass further discloses capturing, using one or more sensors of the machine, sensor data corresponding to a physical environment of the machine (Dintenfass para [0042]);
extracting domain-relevant features from the sensor data using a perception module ("The GPS sensor 216 is configured to capture and to provide geographical location information," Dintenfass para [0071]);
and generating the set of observations corresponding to the physical environment ("In another embodiment, the virtual assessment engine 230 is configured to use object recognition and/or optical character recognition to identify the location of the user 106. For example, the virtual assessment engine 230 is configured to identify the location of the user 106 based on the identification of buildings, structures, landmarks, signs, and/or any other types of objects around the user 106," Dintenfass para [0081]).
Regarding claim 3, Dintenfass discloses all of the limitations of claim 2. Dintenfass further discloses wherein the set of observations and the local map data are determined relative to a current location of the machine in the physical environment (Dintenfass para [0042]), and wherein the one or more operations correspond to at least one of planning, control, or navigation ("FIGS. 9-11 provide examples of how the augmented reality system 100 may operate when a user 106 wants to aggregate information for a renovation project for a real estate property 150. The following is another non-limiting example of how the augmented reality system 100 may operate when a user 106 is planning a renovation project for a real estate property 150," Dintenfass para [0174]).
Regarding claim 4, Dintenfass discloses all of the limitations of claim 1. Dintenfass further discloses determining an approximate reference location in the physical environment ("For example, the augmented reality user device 200 is configured to identify the location of the user 106 based on the identification of buildings, structures, landmarks, signs, and/or any other types of objects around the user 106," Dintenfass para [0042]);
selecting map data including the approximate reference location ("For example, the virtual assessment engine 230 uses geographic location information provided by the GPS sensor 216 with a map database to determine the location of the user 106," Dintenfass para [0081]);
and correlating the map data with the set of observations to identify the local map data that is aligned with the set of observations ("In another embodiment, the virtual assessment engine 230 is configured to use object recognition and/or optical character recognition to identify the location of the user 106," Dintenfass para [0081]).
Regarding claim 5, Dintenfass discloses all of the limitations of claim 4. Dintenfass further discloses using a trained model to correlate the map data with the set of observations (Dintenfass paras [0174] and [0175]).
Regarding claim 6, Dintenfass discloses all of the limitations of claim 1. Dintenfass further discloses wherein the trained language model fuses the local map data and the portion of the set of observations to generate a single, consistent representation of the portion of the environment, and wherein the tokenized description is generated based in part on the single, consistent representation (Dintenfass paras [0077] and [0078]).
Regarding claim 7, Dintenfass discloses all of the limitations of claim 1. Dintenfass further discloses wherein the one or more operations are specific to a domain, and wherein the tokenized description is generated in a domain-specific language corresponding to the domain (Dintenfass para [0174]).
Regarding claim 8, Dintenfass discloses all of the limitations of claim 1. Dintenfass further discloses wherein the tokenized description includes tokens for static objects and dynamic objects in the physical environment (Dintenfass para [0174]).
Regarding claim 9, Dintenfass discloses all of the limitations of claim 1. Dintenfass further discloses wherein the language model generates the tokenized description using incomplete local map data or an incomplete set of observations for the portion of the environment (Dintenfass para [0174]).
Regarding claim 11, Dintenfass discloses all of the limitations of claim 1. Dintenfass further discloses wherein the tokenized description is determined based on at least one of semantic, topological, geometric, kinematic, or relational information of features in the set of observations (Dintenfass paras [0071] and [0147]).
As to claims 12 and 17, method claim 1 and system claims 12 and 17 are related as system and method of using same, with each claimed element’s function corresponding to the method step. Accordingly, claims 12 and 17 are similarly rejected under the same rationale as applied above with respect to the method claim.
As to claims 13 and 18, method claim 6 and system claims 13 and 18 are related as system and method of using same, with each claimed element’s function corresponding to the method step. Accordingly, claims 13 and 18 are similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 14, method claim 7 and system claim 14 are related as system and method of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 15, method claim 9 and system claim 15 are related as system and method of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 19, method claim 8 and system claim 19 are related as system and method of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to the method claim.
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.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dintenfass, in view of Bouguerra et al. (US Patent Application Publication No. 2024/0354491) hereinafter referred to as Bouguerra.
Regarding claim 10, Dintenfass discloses all of the limitations of claim 1. However, Dintenfass fails to disclose wherein the tokenized description is written in a road topology language (RTL) or other domain specific language (DSL). Bouguerra teaches a method for electronic inbox digest.
Bouguerra teaches wherein the tokenized description is written in a road topology language (RTL) or other domain specific language (DSL) (Bouguerra paras [0026] and [0027]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Dintenfass’ method of generating tokens describing an environment by including Bouguerra’s method of utilizing domain specific language for the tokenization because the structured summary message LLM can be trained to determine in simple bullet points, information from received message(s) (Bouguerra para [0033]). This would have been obvious to include, as providing the tokenization in a domain specific language pertaining to environments and topology would facilitate the model’s understanding of the physical environments and maps.
Claim(s) 16 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dintenfass, in view of Li et al. (US Patent Application Publication No. 2024/0395261) hereinafter referred to as Li.
Regarding claim 16 and 20, Dintenfass discloses all of the limitations of claim 12 and 17, respectively. However, Dintenfass fails to disclose wherein the processor is comprised in at least one of: a system for performing simulation operations; a system for performing simulation operations to test or validate autonomous machine applications; a system for performing digital twin operations; a system for performing light transport simulation; a system for rendering graphical output; a system for performing deep learning operations; a system for performing generative AI operations using a large language model (LLM); a system implemented using an edge device; a system for generating or presenting virtual reality (VR) content; a system for generating or presenting augmented reality (AR) content; a system for generating or presenting mixed reality (MR) content; a system incorporating one or more Virtual Machines (VMs); a system implemented at least partially in a data center; a system for performing hardware testing using simulation; a system for performing generative operations using a language model (LM); a system for synthetic data generation; a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources. Li teaches a method for a virtual assistant.
Li teaches a system for rendering graphical output; a system for performing deep learning operations (Li para [0033]); a system for performing generative AI operations using a large language model (LLM) (Li para [0024]); a system for generating or presenting augmented reality (AR) content (Dintenfass Fig. 1 reference character 100).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Dintenfass’ method of generating tokens describing an environment by including Li’s method of utilizing systems for graphical output, deep learning, and AI using LLM. This would beneficially allow for natural language processing support so that the learning personality of the virtual assistance is reflected in the responses returned by the NLP (Li, Abstract).
Conclusion
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/ADAM MICHAEL WEAVER/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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