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 1,2,5-13,15-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/409018 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the extra claim steps in the ‘018 (such as road level route plans, etc.) are not necessary to realize the functionality of the claims in the instant invention.
18/502747
18/409018
1. A method, comprising: generating, based at least on a language model processing data associated with a set of observations corresponding to at least a portion of an environment, a tokenized description of at least the portion of the environment; the tokenized description representing one or more environmental features including at least one of spatial, topological, geometric, kinematic, or relational properties of the portion of the environment;
generating, based in part on the tokenized description, a feature vector representative of at least the portion of the environment; performing, using the generated feature vector, a similarity search of a set of one or more previously-determined feature vectors to determine one or more similar feature vectors; and associating one or more labels, applied to the one or more similar feature vectors, with at least the portion of the environment.
2. The method of claim 1, wherein the one or more previously-determined feature vectors correspond to a set of points in a latent space, and wherein the one or more similar feature vectors are determined for the generated feature vector based in part upon a proximity in the latent space.
5. The method of claim 1, further comprising updating one or more maps based at least on the associating.
6. The method of claim 1, wherein the one or more previously-determined feature vectors are determined from one or more sub-graphs selected from an existing graph including a plurality of operational design domain (ODD) labels, attributes, or tags.
7. The method of claim 1, wherein the tokenized description includes a tokenized sequence representative of at least the portion of the environment, in which tokens are associated with objects or features, and wherein the feature vector is generated based in part on the tokenized sequence.
8. 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 determined based on at least one of semantic, topological, geometric, kinematic, or relational information of features in the set of observations.
10. A processor, comprising: one or more circuits to: generate, based at least on a language model processing data associated with a set of observations corresponding to a location, a tokenized description of one or more features corresponding to the location; the tokenized description representing one or more environmental features including at least one of spatial, topological, geometric, kinematic, or relational properties of the portion of the environment;
perform, using the tokenized description, a similarity search of a set of one or more previously-determined tokenized descriptions to determine one or more similar tokenized descriptions; and associate information, corresponding to the one or more similar tokenized descriptions, with the location.
11. The processor of claim 10, wherein the information includes at least one of a type of location, rules for the location, or observed behavior for the location.
12. The processor of claim 10, wherein the one or more circuits are further to: use the information to automatically determine one or more operations to perform at the location.
13. The processor of claim 10, wherein the one or more previously-determined tokenized descriptions correspond to a set of points in a latent space, and wherein the one or more similar tokenized descriptions are determined for the generated tokenized descriptions based in part upon a proximity in the latent space.
14. The method of claim 13, wherein the points in the latent space are members of a cluster determined based in part upon the proximity of the points in the latent space, and wherein at least some of the information is associated with the cluster.
15. The processor of claim 14, 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 determine one or more labels to associate with one or more features associated with a first physical location based at least on generating, based at least on a language model processing data associated with a set of observations corresponding to at least a portion of the first physical location, a tokenized description of at least a portion of the first physical location, and performing a similarity search with respect to one or more previously-generated tokenized descriptions for one or more second physical locations; wherein the tokenized description representing one or more environmental features including at least one of spatial, topological, geometric, kinematic, or relational properties of the portion of the environment;
17. The system of claim 16, wherein the one or more processors are further to: use the one or more labels to determine one or more operations to perform at the physical location.
18. The system of claim 16, wherein the one or more previously-determined tokenized descriptions correspond to a set of points in a latent space, and wherein the one or more similar tokenized descriptions are determined for the generated tokenized descriptions based in part upon a proximity in the latent space.
19. The system of claim 16, wherein the tokenized description includes a sequence of tokens, corresponding to the one or more features of the physical location, and a set of token descriptors indicating spatial and semantic information for the one or more features.
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.
1. A method, comprising: generating, based at least on a language model processing data associated with a set of observations corresponding to at least a portion of an environment, a feature vector corresponding to a tokenized description of at least the portion of the environment; performing, using the feature vector, a similarity search of a set of one or more previously-determined feature vectors to determine one or more similar feature vectors; updating the tokenized description of at least the portion of the environment based in part upon one or more additional observations obtained for at least the portion of the environment until a single similar feature vector is identified though the similarity search; and identifying a geographic location, associated with the single similar feature vector, as a current location in the environment.
2. The method of claim 1, wherein the one or more previously-determined feature vectors correspond to a set of points in a latent space, and wherein the one or more similar feature vectors are determined for the feature vector based at least on a proximity in the latent space.
3. The method of claim 1, further comprising: capturing sensor data, at an initial location of a vehicle in the environment, to be used to generate at least a subset of observations; and capturing additional sensor data over one or more subsequent positions of the vehicle to generate the additional observations.
4. The method of claim 1, further comprising: providing the geographic location, the tokenized description, and a road-level route plan as input to a second language model; and receiving, as output of the second language model, a tokenized representation of routing data, including a further level of detail, to be used to follow the road-level route plan.
5. The method of claim 4, further comprising: causing the second language model to identify a sequence of goals corresponding to the road-level route plan; determining a set of path options for satisfying the sequence of goals; and selecting, from the set of path options, an optimal path option to use to generate the tokenized representation of the routing data.
6. The method of claim 4, further comprising: providing the tokenized representation of the routing data as input to a control system for operating an object according to the routing data in the tokenized representation.
7. The method of claim 1, wherein the tokenized description includes a tokenized sequence representative of at least the portion of the environment, in which tokens are associated with objects or features, and wherein the feature vector is generated based in part on the tokenized sequence.
8. 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 determined based on at least one of semantic, topological, geometric, kinematic, or relational information of features identified from the set of observations.
10. A processor, comprising: one or more circuits to: generate, based at least on a language model processing data associated with a set of observations corresponding to a current location, a tokenized description of one or more features corresponding to the current location; perform, using the tokenized description, a similarity search of a set of one or more previously-determined tokenized descriptions to determine one or more similar tokenized descriptions; update the tokenized description of the one or more features, corresponding to the current environment, based in part upon one or more additional observations obtained for the current location until a similar previously-determined tokenized description is identified though the similarity search; and identify a geographic location, associated with the similar previously-determined tokenized description, as the current location.
11. The processor of claim 10, wherein the one or more circuits are further to: capture sensor data, at an initial location of a vehicle, to be used to generate at least a subset of the set of observations; and capture additional sensor data over one or more subsequent positions of the vehicle to generate the additional observations.
12. The processor of claim 10, wherein the one or more circuits are further to: provide the geographic location, the tokenized description, and a road-level route plan as input to a second language model; and receive, as output of the second language model, a tokenized representation of routing data to be used to follow the road-level route plan.
13. The processor of claim 12, wherein the one or more circuits are further to: cause the second language model to identify a sequence of goals corresponding to the 2 road-level route plan; determine a set of path options for satisfying the sequence of goals; and select, from the set of path options, an optimal path option to use to generate the tokenized representation of the routing data.
14. The processor of claim 12, wherein the one or more circuits are further to: provide the tokenized representation of the routing data as input to a control system for operating an object according to the routing data in the tokenized representation.
15. The processor of claim 14, 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 determine a location of a vehicle based in part on a tokenized description of a set of observations obtained for the location, the tokenized description to be used in a similarity search of a set of previously-generated tokenized descriptions to identify a geographic location associated with a most similar result of the similarity search.
17. The system of claim 16, wherein the one or more processors are further to: use additional observations obtained for the location to narrow down a set of similarity search results and identify the most similar result.
18. The system of claim 16, wherein the one or more processors are further to: provide the geographic location, the tokenized description, and an initial route plan as input to a language model; and receive, as output of the language model, a tokenized representation of routing data to be used to follow the initial route plan.
19. The system of claim 18, wherein the one or more processors are further to: cause the language model to identify a sequence of goals corresponding to the initial route plan; determine a set of path options for satisfying the sequence of goals; and select, from the set of path options, an optimal path option to use to generate the tokenized representation of the routing data.
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)(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) 1,5-13,15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hong (20220180056).
As per claim 1, Hong (20220180056) teaches a method, comprising:
generating, based at least on a language model processing data associated with a set of observations corresponding to at least a portion of an environment as, natural language model used during query processing – para 0038),
a tokenized description of at least the portion of the environment; generating, based in part on the tokenized description, a feature vector representative of at least the portion of the environment (as, using tokenized representations – para 0063, and establishing a semantic label/relationship using the tokens – see para 0063), the tokenized description representing one or more environmental features including at least one of spatial, topological, geometric, kinematic, or relational properties of the portion of the environment (as, the semantic relationships above, are applied to geographic/environmental features of maps – para 0099 – 0101; see figure 1, wherein the query phrases, subblock 123, are integrated to a mapping platform with a geographic database – fig. 1, subblock 111, 113);
performing, using the generated feature vector, a similarity search of a set of one or more previously-determined feature vectors to determine one or more similar feature vectors (as, performing a closest-match search for the proper label – para 0064, first 4 lines; then proceeding, using a loss function to measure the accuracy of a closest match, to the ground truth model – last 2 full sentences of para 0064);
and associating one or more labels, applied to the one or more similar feature vectors, with at least the portion of the environment (as, after determining the most accurate label, as noted above, tying the label to the tokenized vectors – para 0063).
As per claim 5, Hong (20220180056) teaches the method of claim 1, further comprising updating one or more maps based at least on the associating (as updating maps - para 0104, that are used for the first feature/function, which is providing directions/mapping from the current location to a desire location – para 0038, 0098).
As per claim 6, Hong (20220180056) teaches the method of claim 1, wherein the one or more previously-determined feature vectors are determined from one or more sub-graphs selected from an existing graph including a plurality of operational design domain (ODD) labels, attributes, or tags ( (as, sensed data is mapped to geographic coordinates/location – para 0081; including text content – para 0078).
As per claim 7, Hong (20220180056) teaches the method of claim 1, wherein the tokenized description includes a tokenized sequence representative of at least the portion of the environment, in which tokens are associated with objects or features (as, generating semantic information – para 0034, corresponding to geographic/map information – para 0040), and wherein the feature vector is generated based in part on the tokenized sequence (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 8, Hong (20220180056) teaches the method of claim 1, wherein the tokenized description is written in a road topology language (RTL) or other domain specific language (DSL) – (as, teaching a dedicated map database with geographic features, including road marking, lanes, etc. – para 0086 (by definition, this is map topology).
As per claim 9, Hong (20220180056) teaches 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 (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).
Claims 10-13,15 are processor claims that perform steps that are commonly found in method claims 1,2,6-9 and as such, claims 10-13,15 are similar in scope and content to claims 1,2,6-9 above; therefore, claims 10-13,15 are rejected under similar rationale as presented against claims 1,2,6-9 above. Hong (20220180056) teaches processor/memory executing the steps (para 0004). Furthermore, examiner notes that the numerical mapping, between the processor claims and the method claims, is not direct. The following mappings are a supplement, to the direct numerical mappings. As per claim 10, see mappings of claim 6 for location based parameters. As per claim 15, 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 16-20 are system claims that perform the steps of processor claims 10-13, 15 above, and mapped to claims 1,5-9 and as such, claims 16-20 are similar in scope and content to these claims and therefore, claims 16-20 are rejected under similar rationale as presented against these claims, as noted above. Hong (20220180056) teaches processor/memory executing the steps (para 0004).
Claim Rejections - 35 USC § 103
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 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) 2-4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hong (20220180056) in view of Bellegarda (20190355346).
As per claims 2-4, Hong (20220180056) teaches various claim limitations, as detailed above against claim 1 above; 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; Bellegarda (20190355346) furthers this link by teaching latent semantic mapping of training data – para 0246, toward the application of natural language queries, tied to location (para 0230, Fig 7b, subblock 730, and toward navigation services – fig. 1, subblock 120). 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 latent semantic mapping, as taught by Bellegarda (20190355346) because it would advantageously improve the efficiency of older image/text relationship processing, by adding the capability of analysis subsections of the global matrix factorization, and thereby emphasizing more rare words/data, and improving accuracy, that may be missed by a bigger, global processing (see Bellegarda (20190355346), para 0246).
As per claim 2, the combination of Hong (20220180056) in view of Bellegarda (20190355346) teaches the method of claim 1, wherein the one or more previously-determined feature vectors correspond to a set of points in a latent space, and wherein the one or more similar feature vectors are determined for the generated feature vector based in part upon a proximity in the latent space (see Bellegarda (20190355346), performing latent semantic analysis – para 0246, 0260, 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 distance/masking measures, to measure the differential/proximity – para 0043, 0047) .
As per claims 3,4,14, the combination of Hong (20220180056) in view of Bellegarda (20190355346) teaches the method of claim 2, wherein the points in the latent space are members of a cluster determined based in part upon the proximity of the points in the latent space, and wherein the one or more labels are associated with the cluster, unsupervised learning (as, the learning network, on the nodes, are clustered/subclustered, according to actionable nodes and property nodes – para 0224; the learning is based on input word sequences in training data – para 0244, shown in unsupervised format).
Response to Arguments
Applicants amendments, to address the 35 USC 112 rejections, as well as the Title objection, have overcome these issues. The 35 USC 112 rejection has been removed, as well as, the objection to the title. Applicant's arguments filed 11/28/2025 have been fully considered but they are not persuasive. Applicants arguments against the Hong reference, commence on pp 10 of the response; as to applicants arguments against Hong “does not disclose deceiving ‘a set of observations corresponding to at least a portion of the environment’…nowhere does Hong disclose using ‘observations….portion of an environment”, examiner disagrees and notes that the observations operated by Hong, are the user’s natural language input queries, that are interpreted and matched to mapping information and the environment that applies to the user’s query (see Figure 1, execution calls derived from query phrases, interacting with a mapping platform, and returing the query results to the user). As to applicants arguments on pp11 of Hong, towards “not generated, updated, or encoded by Hong’s language model…”, examiner argues that the parameters from the user’s speech is processed/translated/generated, to interface with the mapping platform. Applicants arguments, starting on the bottom of pp 11 to pp 13, are similar to the aspect presented on the previous pages of the response; examiner applies the same arguments/rationale as presented above. Furthermore, as to the 103 rejection, examiner notes the use of the Bellagarda reference further expanding upon the teaching of latent semantic tie-ins to training data and navigation services.
Lastly, the following references were found, on an updated search, toward features that were argued by applicant:
Mair et al (20220092289) teaches the use of language models to navigate a mapping of an environment (see para 0038)
Nakagiri et al (9519643) teaches natural language models intertwined with mapping functions (see abstract).
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:
Mair et al (20220092289) teaches the use of language models to navigate a mapping of an environment (see para 0038)
Nakagiri et al (9519643) teaches natural language models intertwined with mapping functions (see abstract).
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 12/13/2025
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