DETAILED ACTION
1. This communication is in response to the Amendments and Arguments filed on 12/3/2025. Claims 1-20 are pending and have been examined.
Response to Amendments and Arguments
2. With respect to claim rejections under 35 USC 101 (abstract idea), the applicant’s amendments and arguments are carefully considered, but the 35 USC 101 rejections are maintained because the amended claims are still considered as involving a mental process without integrating into a practical application and further do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In particular, independent claims 1, 10, and 16 and other dependent claims recite “generating, using a language model and map data for an environment, a first tokenized description of the environment .. comprising a machine-readable sequential text string generated based at least on one or more features of semantic, topological, geometric, kinematic, or relational information ..” The limitations as drafted cover a mental process. More specifically, a human can produce a natural language (tokenized/textual) description (and enhanced tokenized/textual description) of an environment such as a geometrical (navigation) route for traveling from a place to a destination based on a map. Note that the “language model” is not specified such as an LLM based on a particular type of neural network trained by a particular type method and database. The applicant is advised to include such amended limitations to overcome 35 USC 101 rejections (Note that the SPEC contains numerous neural-network related descriptions). This suggested amendment is also applicable to overcome the 35 USC 101 rejections over “integrating the judicial exception into a practical application,” “improvement of technical field,” and “solves a problem rooted in computer technology and software” based on general purpose computing devices.
With respect to 35 USC 103 rejections, the applicant’s amendments and arguments are carefully considered. However, the examiner respectfully disagrees with the applicant’s arguments. In particular, the applicant argues that the references do not teach “a first tokenized description of the environment, the first tokenized description comprising a machine-readable sequential text string generated based at least on one or more features of semantic, topological, geometric, kinematic, or relational information extracted from the portion of the environment.”
Note that ROSARIO teaches: [0002] “navigation systems typically output visual and audio directions associated with a mapped route” <read on ‘geometric’> [0020] “natural and descriptive language .. any number of suitable language models <read on ‘semantic’> and other information may be utilized to generate natural language outputs” and [0049] “the identified environmental information <read on ‘environment, kinematic, relational, etc.’> may be utilized during the generation of natural language directions”. BEHR teaches: [0018-0019] “providing route guidance <read on ‘geometric’> and other information .. through the use of tokenized forms. These tokenized forms represent a large amount of textual information <read on ‘semantic’> by one or several alphanumeric characters.”
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.
3. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 10, and 16 recite a method, processor and system, thus relating to a statutory category.
Claims 1, 10, and 16 further recite “generating, using a language model and map data for an environment, a first tokenized description of the environment .. comprising a machine-readable sequential text string generated based at least on one or more features of semantic, topological, geometric, kinematic, or relational information ..” The limitations as drafted cover a mental process. More specifically, a human can produce a natural language (tokenized/textual) description (and enhanced tokenized/textual description) of an environment such as a geometrical (navigation) route for traveling from a place to a destination based on a map.
This judicial exception is not integrated into a practical application because the claims simply recite generating tokenized descriptions for general purpose computing devices. These claims further recite additional elements of “processor” and the inherent “memory,” which amount to general purpose computing devices. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The independent and dependent claims further do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a processor is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitations in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Allowable Subject Matter
4. Claims 2, 3, 11, 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Claims 2, 3, 18 must also overcome 35 USC 101 abstract idea rejections.
Claim Rejections - 35 USC § 103
5. Claims 1, 4, 6-8, 10, 12-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rosario (US 20140236472; hereinafter ROSARIO) in view of Behr, et al. (US 20090191901; hereinafter BEHR).
As per claim 1, ROSARIO (Title: Navigation systems and associated methods) discloses “A computer-implemented method, comprising: generating, using a language model and map data for at least a portion of an environment, a first [ tokenized description ] of the environment, the first tokenized description comprising [ a machine-readable sequential text string ] generated based at least on one or more features of semantic, topological, geometric, kinematic, or relational information extracted from the portion of the environment (ROSARIO, [0002], Navigation systems typically track the location of a vehicle and display the location along with map data in a human-readable format .. navigation systems typically output visual and audio directions associated with a mapped route <read on ‘environment’>; [0020], natural and descriptive language .. any number of suitable language models and other information may be utilized to generate natural language outputs; [0049], the identified environmental information <read on ‘semantic, topological, geometric .. information’> may be utilized during the generation of natural language directions. For example, a driver may be instructed to turn right after an identified parked vehicle); and
generating, using the language model and the first tokenized description, a second tokenized description for at least the portion of the environment, the second tokenized description including additional detail, with respect to the environment, inferred in part using at least one of the one or more features (ROSARIO, [0020], the generated language may be based at least in part upon contextual and/or environmental information <read on ‘semantic, topological, geometric .. information’> associated with a vehicle. In this regard, relatively higher levels of understanding and/or awareness <read on ‘additional detail’ and ‘the second tokenized description’> may be provided to users).”
ROSARIO does not explicitly disclose “tokenized description .. a machine-readable sequential text string ..” However, this limitation is taught by BEHR (Title: Electronic navigation system and method). Note that “tokenized description” is subject to BRI.
In the same field of endeavor, BEHR teaches: [0018-0019] “providing route guidance and other information from a base unit to a remote unit in response to a request from the remote unit .. through the use of tokenized forms. These tokenized forms represent a large amount of textual information by one or several alphanumeric characters.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of BEHR in the system (as taught by ROSARIO) for using tokenized textual description for applications such as navigation route presentation.
As per claim 4 (dependent on claim 1), ROSARIO in view of BEHR further discloses “wherein the additional detail relates to one or more objects identified for inclusion in the environment based at least on the first tokenized description and one or more real-world relationships learned by the language model (BEHR, [0018-0019], providing route guidance and other information .. through the use of tokenized forms; [0048], one or more identified objects (e.g., other vehicles, buildings, signs, trees, etc.) <also read on ‘real-world relationships learned by the second language model’>).”
As per claim 6 (dependent on claim 1), ROSARIO in view of BEHR further discloses “extracting a set of features from the map data; and providing the set of features as input to the language model to generate the first tokenized description (BEHR, [0059], The base unit includes .. a query resolver, a route calculator, a distance and time travel estimator, a surroundings explorer, a map database <read on the associated ‘features’>, an on-line traffic and map updater ..).”
As per claim 7 (dependent on claim 1), ROSARIO in view of BEHR further discloses “providing, as additional input to the language model, at least one of geographic location information or contextual information to be used to determine the additional detail (ROSARIO, [0010], Other contextual information that may be collected in certain embodiments includes, for example, traffic information, crime information, accident report information, etc. Based at least in part upon the obtained contextual information and information associated with a desired route of the vehicle, one or more directions may be generated and output).”
As per claim 8 (dependent on claim 1), ROSARIO in view of BEHR further discloses “wherein the first tokenized description and the second tokenized description are each 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 (BEHR, [0018-0019], providing route guidance and other information .. through the use of tokenized forms. These tokenized forms represent a large amount of textual information by one or several alphanumeric characters; ROSARIO, [0048], one or more identified objects (e.g., other vehicles, buildings, signs, trees, etc.).”
Claims 10, 12, 13, 14 (similar in scope to claims 1, 4, 6, 8, respectively) are rejected under the same rationale as detailed above for claims 1, 4, 6, 8, respectively. For claim 10, OSARIO further teaches: [0020] “any number of suitable language models <read on 1st or 2nd language model> and other information may be utilized to generate natural language outputs.”
As per claim 15 (dependent on claim 10), ROSARIO in view of BEHR further discloses “wherein the processor is comprised in at least one of: a system for rendering graphical output (BEHR, [0040], The responses may include, for example, textual navigational directions and/or maneuver arms showing graphical representations of street intersections and the calculated route through the intersection).”
Claims 16, 20 (similar in scope to claims 1, 15, respectively) are rejected under the same rationale as detailed above for claims 1, 15, respectively. Claim 16 also recites “a representation corresponding to the map at a second level of detail less than the first level of detail (ROSARIO, [0002], Navigation systems .. map data in a human-readable format; [0020], the generated language may be based at least in part upon contextual and/or environmental information associated with a vehicle. In this regard, relatively higher levels of understanding and/or awareness <where the ‘environmental information’ and ‘understanding and awareness’ read on map and the corresponding more or less ‘level of detail’ which is subject to BRI, for example, awareness may refer to noticing a parked car on the roadside> may be provided to users).”
As per claim 17 (dependent on claim 16), ROSARIO in view of BEHR further discloses “extract a set of features from the map at the second level of detail; and provide the set of features as input to the LLM to generate the output (ROSARIO, [0002], a mapped route; [0049], the identified environmental information <read on ‘features from the map’> may be utilized during the generation of natural language directions; [0020], relatively higher levels of understanding and/or awareness <read on ‘at the second level of detail’> may be provided to users).”
As per claim 19 (dependent on claim 16), ROSARIO in view of BEHR further discloses “wherein the representation of the map at the first level of detail and the representation of the map at the second level of detail both correspond to tokenized descriptions of at least a portion of an environment (see Claim 1 rejections).
6. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over ROSARIO in view of BEHR, and further in view of Colgate, et al. (US 20190271559; hereinafter COLGATE).
As per claim 5 (dependent on claim 1), ROSARIO in view of BEHR further discloses “wherein the map data corresponds to [ a standard definition (SD) map ] representation of at least the portion of the environment, and wherein the second tokenized description contains information corresponding to [ a high definition (HD) map ] version of at least the portion of the environment (ROSARIO, [0002], Navigation systems typically track the location of a vehicle and display the location along with map data in a human-readable format; [0020], the generated language may be based at least in part upon contextual and/or environmental information associated with a vehicle. In this regard, relatively higher levels of understanding and/or awareness may be provided to users).”
ROSARIO in view of BEHR does not explicitly disclose “a standard definition (SD) map .. a high definition (HD) ..” However, this limitation is taught by COLGATE (Title: Visualization of high definition map data).
In the same field of endeavor, COLGATE teaches: [0004] “autonomous vehicles need to perform localization to determine their location in the high definition map” which reads on both HD and SD maps in practical use, e.g., HD for autonomous vehicles and SD for other vehicles.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of COLGATE in the system (as taught by ROSARIO and BEHR) for providing different definition/resolution maps for applications with different navigation accuracy requirements.
7. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over ROSARIO in view of BEHR, and further in view of Gu, et al. (ACM Trans. Comput. Healthcare, October 2021; hereinafter GU).
As per claim 9 (dependent on claim 8), ROSARIO in view of BEHR further discloses “wherein the tokenized text string is written in a road topology language (RTL) or a domain specific language (DSL).
ROSARIO in view of BEHR does not explicitly disclose “a road topology language (RTL) or a domain specific language (DSL).” However, this limitation is taught by GU (Title: Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing).
In the same field of endeavor, GU teaches: Fig. 1 “neural language model pretraining .. for domains with abundant text such as biomedicine, domain specific pretraining from scratch can substantially outperform the conventional mixed-domain approach” and [Sec. 1, para 3] “We show that domain-specific pretraining from scratch substantially outperforms continual pretraining of generic language models.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of GU in the system (as taught by ROSARIO and BEHR) for LLM training and application using domain-specific language.
Conclusion
8. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FENG-TZER TZENG whose telephone number is 571-272-4609. The examiner can normally be reached on M-F (8:30-5:00). The fax phone number where this application or proceeding is assigned is 571-273-4609.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras Shah (SPE) can be reached on 571-270-1650.
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/FENG-TZER TZENG/ 3/3/2026
Primary Examiner, Art Unit 2653