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 .
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.
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Claims 1,6,15,18,20-22,39 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,2,6,9,11,12,15 of copending Application No. 18/472941 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the additional claim limitations in the ‘941 reference application are not necessary to realize the functionality of the claims in the instant invention. See mapping below.
18/365966
18/472941
1.A computer-implemented method, comprising: generating, based at least on a language model processing data associated with at least a portion of a static or dynamic environment, a tokenized description representing one or more environmental features of at least a portion of the environment, the one or more environmental features determined based on at least one of semantic, topological, geometric, kinematic and relational information of features data; and performing one or more operations based at least on the tokenized description.
2. The computer-implemented method of claim 1, wherein the data includes a set of observations including at least one of semantic information, location information, contextual information, geometric information, motion information, state information, or previously generated map data corresponding to at least a portion of the environment.
The computer-implemented method of claim 1, wherein at least one of: the tokenized description includes at least one feature in addition to one or more features represented in the set of observations; or the tokenized description is generated using at least some semantic, topological, geometric, kinematic and relational information not represented in the set of observations.
21. The processor of claim 20, wherein the textual data is expressed using a domain specific language (DSL).
22. The processor of claim 21, wherein the DSL includes a road topology language (RTL).
18. The method of claim 15, wherein the sensor data is captured using at least one of a camera, an infrared sensor, a distance sensor, a LIDAR system, an ultrasonic sensor, or a RADAR system.
15. A method, comprising: generating, based on sensor data corresponding to at least a part of an environment, a set of semantic features representative of at least the part of the environment; and generating, using a large language model (LLM) and based at least on the set of semantic 4 features, a structured text string including a set of tokens representing objects and semantic relationships between the objects.
20. A processor, comprising: one or more circuits to generate data corresponding to a map based on one or more language models to process textual data containing at least one of semantic information, location information, or geometric information corresponding to one or more features of the map.
39. The processor of claim 20, 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.
1. A method, comprising: generating, based at least on a set of observations captured using one or more sensors, a tokenized representation of the set of observations for at least a portion of an environment; generating, based at least on a language model processing the tokenized representation of the set of observations, a tokenized description of at least the portion of the environment, the tokenized description determined based in part on at least one of semantic, topological, geometric, kinematic, or relational information of features in the tokenized representation of the set of observations; and generating a map for at least the portion of the environment using the tokenized description.
2. The method of claim 1, wherein the tokenized representation of the set of observations is generated using the language model, a second language model, or a data encoder.
3. The method of claim 1, wherein the map is generated using the tokenized description corresponding to the set of observations using an automated end-to-end process.
4. The method of claim 1, wherein the map generated using the tokenized description includes one or more maps, or sets of map data, in one or more of a set of map formats.
5. The method of claim 1, wherein the tokenized description is a tokenized text string representative of at least the portion of the environment, the tokenized text string including a sequence of tokens associated with objects in the environment.
6. The method of claim 5, wherein the tokenized text string is written in a road topology language (RTL) or a domain specific language (DSL).
7. The method of claim 1, wherein set of observations further includes at least one of lighting data, weather data, human annotations, prior map data, or other data relevant to use cases considered.
8. The method of claim 1, wherein at least a subset of observations is captured using one or more sensors on a machine positioned in, or moving through, the portion of the environment.
9. The method of claim 1, wherein the sensors include at least one of camera sensors, radar sensors, LiDAR sensors, ultrasonic sensors, or depth sensors.
10. The method of claim 1, wherein the tokenized description of at least the portion of the environment generated by the language model includes at least one additional feature, corrected feature, or enhanced feature with respect to features contained in the tokenized representation of the set of observations.
11. A processor, comprising: one or more circuits to: generate, based at least on a large language model (LLM) processing sensor data obtained using one or more sensors, a tokenized description of at least the portion of an environment, the tokenized description determined based in part on at least one of semantic, topological, geometric, kinematic, or relational information of features represented in the sensor data; and generate, based at least on the tokenized description, a map for at least the portion of the environment.
12. The processor of claim 11, wherein the tokenized representation of at least the portion of the environment is generated using a tokenized representation of the sensor data.
13. The processor of claim 11, wherein the tokenized description is a tokenized text string representative of at least the portion of the environment, the tokenized text string including a sequence of tokens associated with one or more objects or one or more features in the environment.
14. The processor of claim 11, wherein the tokenized description of at least the portion of the environment includes at least one additional feature, corrected feature, or enhanced feature with respect to features contained in the tokenized representation.
15. 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 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. A system, comprising: one or more processors to generate a map of an environment based at least on a tokenized description of at least a portion of the environment, the tokenized description generated based at least on a language model processing a set of observations of the environment determined using one or more sensors.
17. The system of claim 16, wherein the language model is to process a tokenized representation of the set of observations.
18. The system of claim 16, wherein the tokenized description is a tokenized text string representative of at least the portion of the environment, the tokenized text string including a sequence of tokens associated with one or more objects or one or more features in the environment.
19. The system of claim 16, wherein the tokenized description of at least the portion of the environment includes at least one additional feature, corrected feature, or enhanced feature with respect to features contained in the tokenized representation of the set of observations.
20. The system of claim 16, wherein the simulation 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.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-14,20-25, 27,28,31,35-37,39-42 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hong (20220180056) .
As per claim 1, Hong (20220180056) teaches a computer-implemented method, comprising:
generating, based at least on a language model processing data associated with at least a portion of a static or dynamic environment (as, natural language model used during query processing – para 0038),
a tokenized description representing one or more environmental features of at least a portion of the environment, the one or more environmental features (as, translating the natural language query to a service execution, via the machine learning model that generates semantic labels based on the context vector – para 0064,0065; furthermore, in para 0066, -- showing intent/slot values, based on the semantic interpretation, which has a direct link to the environment – see table 5, e.g. “spatial relation”, directions – start/waypoint/path, locations – point on map, etc.) determined based on at least one of semantic, topological, geometric, kinematic and relational information of features data (as, using tokenized representations – para 0063, and establishing a semantic label/relationship using the tokens – see para 0063 and see table 5 reflecting back on para 0065, 0066);
and performing one or more operations based at least on the tokenized description (and, preforming a response to the query – para 0074, or outputting an executable command – para 0063.
As per claim 2, Hong (20220180056) teaches the computer-implemented method of claim 1, wherein the data includes
a set of observations including at least one of semantic information, location information, contextual information, geometric information, motion information, state information, or previously generated map data corresponding to at least a portion of the environment (as, generating semantic information – para 0034, corresponding to geographic/map information – para 0040).
As per claim 3, Hong (20220180056) teaches the computer-implemented method of claim 1, wherein the data corresponds to sensor data, a set of feature embeddings determined from the sensor data, an existing map, an internal state recording of a vehicle or a robot, an activity log of human control or interaction (as, responding to queries for location search for a map – para 0040, which is measure relative to the location of the person – para 0038 – when the query is for “get directions”).
As per claim 4, Hong (20220180056) teaches the computer-implemented method of claim 1, wherein the tokenized description is represented using a text string, the text string being generated in a structured description language that represents features in at least a portion of the environment as a set of textual tokens (as, the processing of the natural language can be in the format of text – English syntax and structure – para 0038, with tokenized representation of the words – para 0052; see also para 0036 – semantics labels between text).
As per claim 5, Hong (20220180056) teaches the computer-implemented method of claim 1, wherein at least one of: the tokenized description includes at least one feature in addition to one or more features represented in the set of observations; or the tokenized description is generated using at least some semantic, topological, geometric, kinematic and relational information not represented in the set of observations (as, using tokens for the query and deriving semantic information – para 0052; and using this information, with additional/secondary information – restaurants/museums/auto dealerships/repair, and the like – para 0098; in other words, the first feature/function, is providing directions/mapping from the current location to a desired location – para 0038, and the second/feature function is finding listings for a certain category of businesses/buildings/landmarks, etc – para 0098; see also, processing of visual objects, in hand with, the queries – para 0085-0086).
As per claim 6, Hong (20220180056) teaches the computer-implemented method of claim 1, wherein the tokenized description includes a sequence of tokens corresponding to objects in the environment, and their kinematic, spatial and semantic information (as, mapped above in claims 1,2, using tokens to represent the query – para 0052, tied to semantic information/relationships between the words of the query – para 0052, and used in a situation for maps/directions – para0038).
Claims 7-11 {12-14} are system {processor} claims performing steps commonly found method claims 1-6 above and as such, certain claim elements in claims 7-11 {12-14} are similar in scope and content to claims 1-6 above; therefore, claims 7-11 {12-14} are rejected under similar rationale as presented against claims 1-6 above. Furthermore, Hong (20220180056) teaches a processor/memory performing the steps (para 0047). Further to claim 7, Hong (20220180056) teaches both structured – see above, as well as unstructured queries -- see para 0041, voice commands, which do not necessarily have a full natural language structured query, the unstructured textual description derived from one or more observations of the environment ( as using the current mapping/routing/navigation location, via API, -- para 0067, see table 4/5 for the restricted types). Further to claim 11, Hong (20220180056) teaches the use of the system, for graphical output of a map/directions – para 0040.
Claims 20-25,27,31 are processor claims that perform steps commonly found in method claims 1-6 above and as such, claims 20-25, 27,31 are rejected under similar rationale as presented against claims 1-6 above; therefore, claims 20-25, 27,31 are rejected under similar rationale as presented against claims 1-6 above. Further, to claim 21, Hong (20220180056) teaches domain specific databases – see para 0085, geographic databases, with map features, attributes, categories, etc, represented in the data. Further to claim 22, Hong (20220180056) teaches a dedicated map database with geographic features, including road marking, lanes, etc – para 0086 (by definition, this is map topology). Further to claim 24, Hong (20220180056) teaches geographic translation data/text – Fig. 13, subblock 1309, and para 0096.
As per claim 28, Hong (20220180056) teaches updating of the map database – para 0104.
As per claim 35,36 Hong (20220180056) teaches the processor of claim 20, wherein the location information includes one or more coordinates of the one or more features, and the one or more coordinates are represented in the textual data using grid-based tokenization (as, sensed data is mapped to geographic coordinates/location – para 0081; including text content – para 0078).
As per claim 37, Hong (20220180056) teaches the processor of claim 20, wherein the data corresponding to the map is provided to a navigation system to determine one or more actions or recommendations for a vehicle in an environment corresponding to the map (as, suggesting directions for a vehicle – para 0085).
As per claim 39, Hong (20220180056) teaches the processor of claim 20, 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 (examiner notes, the listing above, is in the alternate format; Hong (20220180056) teaches the system to be implemented in autonomous driving applications – para 0079).
Claims 40-42 are processor claims whose steps are common to steps found in claims 1-14, 20-25, 27,28,31,35,37,39 above and as such, claims 40-42 are similar in scope and content to claims 1-14, 20-25, 27,28,31,35,37,39 above; therefore, claims 40-42 are rejected under similar rationale as presented against 1-14, 20-25, 27,28,31,35,37,39 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.
Claim(s) 15-19, 26, 29, 30, 32-34, 38 are rejected under 35 U.S.C. 103 as being unpatentable over Hong (20220180056) in view of Li et al (20240161520).
As per claims 15-19, 26, 29, 30, 32-34, 38, Hong (20220180056) teaches various claim limitations, as detailed above against claims 1-14, 20-25, 27, 28, 31, 35-37, 39-42; furthermore, as detailed above, Hong (20220180056) teaches implementation of the techniques for a mapping/direction application, as detailed above, operating with map databases, and image information, which includes text information; Li et al (20240161520) furthers this link by teaching vision-language models that take image and query text information (Fig. 1, subblocks 110,120, 105,106) to a generative learning tying generated text to the image information (see Fig. 1, subblock 102, and Fig. 2, Fig. 3 – subblock 233). Therefore, it would have been obvious to one of ordinary skill in the art of analyzing and generating image/text information, to modify the text/image relationships of Hong (20220180056) with visual-language models, as taught by Li et al (20240161520) because it would advantageously improve the efficiency of older image/text relationship processing, by adding the capability of taking multimodal inputs as well as providing instantaneous captioning/commenting to the image (see Li et al (20240161520) para 0005, 0022-0025).
As per claim 15, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches a method, comprising:
generating, based on sensor data corresponding to at least a part of an environment, a set of semantic features representative of at least the part of the environment (See Hong (20220180056), generating semantic information – para 0034, corresponding to geographic/map information – para 0040); wherein the set of semantic features comprises at least one of spatial, topological, geometric, minematic, or relational properties of the environment ( and generating, using a large language model (LLM) (See Li et al (20240161520), para 0027-0029) and based at least on the set of semantic features, a structured text string including a set of tokens representing objects and semantic relationships between the objects ((see Hong (20220180056), the processing of the natural language can be in the format of text – English syntax and structure – para 0038, with tokenized representation of the words – para 0052; see also para 0036 – semantics labels between text).
As per claim 16, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the method of claim 15, wherein the tokens represent nodes, edges and their attributes in a graph (see Li et al (20240161520), teaching neural networks with nodes/edges – para 0064, nodes and edges, weighted according to attributes).
As per claim 17, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the method of claim 16, wherein each feature of the set of semantic features define at least one of the nodes, the edges, or the attributes represented by the tokens.
As per claim 18, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the method of claim 15, wherein the sensor data is captured using at least one of a camera, an infrared sensor, a distance sensor, a LIDAR system, an ultrasonic sensor, or a RADAR system (See Hong (20220180056), para 0085, wherein the geographic database is generated using LIDAR).
As per claim 19, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the method of claim 15, further comprising: determining one or more embeddings in a latent space corresponding to the set of semantic features (see Li et al (20240161520), combining image information and language information into vectors – para 0027; this combination is well known in the art as latent vector(s,ing)); and generating the structured text string based on at least one or more embeddings (see Hong (20220180056), the processing of the natural language can be in the format of text – English syntax and structure – para 0038, with tokenized representation of the words – para 0052; see also para 0036 – semantics labels between text).
As per claim 26, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the processor of claim 25 (see mapping of Hong (20220180056) to claim 25 above), wherein the one or more relationships are represented using a knowledge graph (see Li et al (20240161520), figs 10A, 10B, wherein the dialog flow is derived from a knowledge graph).
As per claim 29, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the processor of claim 20, wherein the textual data further corresponds to perception information derived from sensor data generated using one or more sensors of one or more machines (see Li et al (20240161520), teaching extracting perceptron/intent information from one object – the images, and then extracting language context from that image object – para 0036).
As per claim 30, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the processor of claim 20, wherein the one or more language models include a large language model (LLM) (see Li et al (20240161520), LLM’s – para 0024).
As per claims 32, 38 the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the processor of claim 20, wherein the data corresponding to the map includes simulated map information, and the simulated map information is used to generate one or more simulated scenes in one or more virtual environments (see Hong (20220180056) teaches the system to be implemented in autonomous driving applications – para 0079, and virtual machines – para 0058; furthermore, Hong (20220180056) teaches both structured, as well as unstructured queries -- see para 0041, voice commands, which do not necessarily have a full naturel language structured query ).
As per claim 33, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the processor of claim 20, wherein the textual data is represented using one or more documents (see Li et al (20240161520), para 0005, 0051 – teaching the use of ground-truth text, in the learning aspect of the model; examiner notes it is old and notoriously well known in the art of language models, that ground-truth information/databases are derived from well known/accepted texts/documents).
As per claim 34, the combination of Hong (20220180056) in view of Li et al (20240161520) teaches the processor of claim 33, wherein the one or more documents include one or more vector embeddings (see Hong (20220180056), para 0063, wherein the embeddings include word/text vectors).
Response to Arguments
Applicant's arguments filed 11/28/2025 have been fully considered but they are not persuasive. As to applicants arguments, found on pp12-13 of the response, against the Hong reference, examiner disagrees and argues, that the semantic processing of the natural language input, determines an intent of the user, and chooses relatable terms (as mapped above, and found in para 0063-0066 of Hong, and the tables in between (such as mapping items including location/turns/etc. Applicants argue that the parameters in Hong are converted into machine executable commands – yes, to find elements corresponding to the request, including mapping functions based on the current environment/location of the user – again, see Tables 4/5, including getting directions/routes (which would be specific turns), point on map, location, etc., etc. To propose that the semantic interpretation of the user input to be a generic ‘executable function’ ignores, in totality, the disclosure of Hong in the aforementioned paragraphs/tables.
Furthermore, examiner notes, as an example, Shalev-Shwartz et al (20210162995) teaches, semantic analysis of the human driving policy – “follow the car in front of you” (para 0395) and generating map/speed functions accordingly (end of para 0395), and translating the semantics into longitudinal/latitude/the taking of gps/sensor/accelerometer/map data (para 0005, para 0102), to generate practical mapping solutions – para 0396; and further into the end of para 0396 – “the semantic language may be an important enabler for defining HD-maps that can be constructed using low-bandwidth sensing data”.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, the following references were found to be pertinent to applicants disclosure/claim elements:
Shalev-Shwartz et al (20210162995) teaches, semantic analysis of the human driving policy – “follow the car in front of you” (para 0395) and generating map/speed functions accordingly (end of para 0395),
Sharifi (20240210194) teaches navigation directions via search queries – abstract, para 0011, 0028, and using semantic analysis (para 0035-0036) using LLM’s (para 0038).
Dintenfass (20180158157) teaches generating maps using tokenized descriptions (fig. 7, para 0161)
Bougeurra et al (20240354491) teaches using road topology language and domain specific languages, aka digest engines – para 0026)
Barborak et al (20170371861) teaches domain specific languages (java) – para 0265
Asano et al (20190095428) teaches query input as learning/training ( Figure 11)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 02/19/2026