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 .
DETAILED ACTION
Response to Arguments
Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below. The previous reference of Chandler has been withdrawn in light of the claim amendments.
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 1-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20170371861 A1 Barborak; Mike et al. (hereinafter Barborak) in view of US 10795793 B1 Arunachalam; Balaji et al. (hereinafter Arunachalam) and further in view of US 20210132613 A1 Askeland; Jacob Lee et al. (hereinafter Askeland).
Re claim 1, Barborak teaches
1. A method, comprising:
obtaining a plurality of human-authored review entries, associated with a domain, relating to verification or modification of a respective tokenized description, a tokenized description having at least a computer-readable textual format… (in different domains 0056 with tokenized inputs 0256, and text format 0076, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
updating one or more parameters of the language model based in part on: (the system learns 0068-0070 from user review of questions, descriptions)
the plurality of human-authored review entries associated with the domain, the human-authored review entries (the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select…plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
a plaintext description of reasoning for the verification or modification; and (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
a set of rules specific to the domain (semantic representations in different domains that require their own understanding e.g. 0056 are a form of rules)
providing the language model, after updating the one or more parameters, for use in evaluating one or more additional tokenized descriptions associated with the domain (iteratively performed …in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
However, while Barborak teaches a machine learning reasoning system with user/developer options while mapping inputs as well as JAVA and similar coding with testing options for developer, it fails to teach:
the tokenized description having at least a computer-readable textual format expressed in a domain- specific language; (Arunachalam textual format of DSL as in fig. 4, domain specific language mapped to code thereof using a user/developer editor col 5 line 35 to col 6 line 12 with an updateable or learning data store for running simulation scenarios and mapping thereof col 10 lines 13-33)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak to incorporate the above claim limitations as taught by Arunachalam to allow for use of a known technique of using a domain-specific language with simulations to improve similar testing devices using a broader code like Java, in the same manner, wherein a DSL driven testing/simulation system improves communication between domain experts and developers thereby separating models from execution with the option to edit or provide developer/user feedback, further improving model learning or repository updating analogously.
However, while the combination teaches text entries and user authored data, domain specific languages, and rules associated with languages of a domain, it fails to teach a spatial map per se that can be altered by a human analyst, thus failing to teach:
updating a spatial map representation by incorporating at least a portion of the evaluated one or more additional tokenized descriptions into the computer-readable textual format and (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
deploying the updated spatial map representation to at least one simulation system (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak in view of Arunachalam to incorporate the above claim limitations as taught by Askeland to allow for combining prior art elements of DSL usage to known methods of spatial map generation using human-entered parameters to yield predictable results, thereby improving the grey area between high-level human semantic understanding and low-level sensor data, improving the safety, efficiency, and flexibility of navigation systems, for instance utilizing software such as GeoScenario allows developers to easily design, validate, and migrate complex, realistic traffic test cases between different simulation tools using human entered parameters to update spatial maps.
Re claim 2, Barborak teaches
2. The method of claim 1, further comprising:
providing an additional tokenized description, associated with the domain, as input to the language model; and (in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
receiving, as output of the language model, indication of a modification to be made to the tokenized description, along with a plaintext description of reasoning behind the modification. (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 3, Barborak teaches
3. The method of claim 2, wherein the indication of the modification is provided in a tokenized text string. (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 4, while Barborak teaches a machine learning reasoning system with user/developer options while mapping inputs as well as JAVA and similar coding with testing options, it fails to teach:
Re claim 4, while the combination teaches text entries and user authored data, domain specific languages, and rules associated with languages of a domain, it fails to teach a spatial map per se that can be altered by a human analyst, thus failing to teach:
4. The method of claim 3, wherein the tokenized text string is in a road topology language (RTL) or a domain specific language (DSL) (DSL), and wherein the at least one simulation system is configured to simulate an environment for generating one or more scenarios to validate at least one of navigation decisions or trajectory planning of an autonomous navigation system. (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak in view of Arunachalam to incorporate the above claim limitations as taught by Askeland to allow for combining prior art elements of DSL usage to known methods of spatial map generation using human-entered parameters to yield predictable results, thereby improving the grey area between high-level human semantic understanding and low-level sensor data, improving the safety, efficiency, and flexibility of navigation systems, for instance utilizing software such as GeoScenario allows developers to easily design, validate, and migrate complex, realistic traffic test cases between different simulation tools using human entered parameters to update spatial maps.
Re claim 5, Barborak teaches
5. The method of claim 1, further comprising:
providing, as input to the language model, a proposed modification to an additional tokenized description associated with the domain; and (in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
receiving, as output of the language model, verification or rejection of the proposed modification, along with a plaintext description of the reasoning behind the verification or the rejection. (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 6, Barborak teaches
6. The method of claim 1, wherein the domain is a mapping domain, and wherein the human-authorized review entries correspond to review logs generated by a human reviewing a map proposal. (semantics mapped or general language understanding as mapping per se…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 7, Barborak teaches
7. The method of claim 1, wherein the tokenized description corresponds to an object graph for the environment containing a sequence of textual tokens containing semantic, topological, geometric, kinematic, or relational information for one or more objects in the environment. (semantic expressly…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 8, Barborak teaches
8. The method of claim 1, further comprising: providing, as input to the language model, a question relating to the environment; and (system inputs question into model and awaits user response…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
receiving, as output from the language model, an answer to the question along with a plaintext description of reasoning behind the answer. (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 9, Barborak teaches
9. The method of claim 1, wherein the modification relates to at least one of an addition, deletion, or modification of a map annotation. (modification by selecting an answer including custom input as “other” as well as plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Claim 10-17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20170371861 A1 Barborak; Mike et al. (hereinafter Barborak) in view of US 20210132613 A1 Askeland; Jacob Lee et al. (hereinafter Askeland).
Re claim 10, Barborak teaches
10. A processor, comprising:
one or more circuits to: (fig. 19 circuit inherent to have display and processing present)
provide as input to a language model… and (in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
generate, based on at least the language model processing the data, a verification or a modification proposal with respect to the representation data, along with a plaintext description of reasoning behind the verification or modification proposal. (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
However, while the combination teaches text entries and user authored data, domain specific languages, and rules associated with languages of a domain, it fails to teach a spatial map per se that can be altered by a human analyst, thus failing to teach:
…data representing at least a spatial map representation; (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
update the spatial map representation data by incorporating at least a portion of the verification or the modification proposal; and (Askeland human entries are input/preposed 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
deploy the updated spatial map representation data to at least one simulation system. (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak to incorporate the above claim limitations as taught by Askeland to allow for combining prior art elements of DSL usage to known methods of spatial map generation using human-entered parameters to yield predictable results, thereby improving the grey area between high-level human semantic understanding and low-level sensor data, improving the safety, efficiency, and flexibility of navigation systems, for instance utilizing software such as GeoScenario allows developers to easily design, validate, and migrate complex, realistic traffic test cases between different simulation tools using human entered parameters to update spatial maps.
Re claim 11, Barborak teaches
11. The processor of claim 10, wherein the language model is trained using rules for a map domain and a set of human-generated map review entries associated with the map domain, the human generated map review entries including human reasoning information in text format. (semantics mapped or general language understanding as mapping per se…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 12, Barborak teaches
12. The processor of claim 10, wherein the representation data includes one or more initial modification proposals generated for at least a portion of a representation. (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 13, Barborak teaches
13. The processor of claim 10, wherein the modification proposal is presented as a tokenized text string. (in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claim 14, Barborak teaches
14. The processor of claim 10, wherein the one or more circuits are further to receive, to an interface, a question posed with respect to the modification proposal; and (…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
provide, through the interface, an answer to the question as generated using the trained model, the answer including reasoning supporting the answer. (plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Re claims 15 and 20, Barborak teaches
15. The processor of claim 10, wherein the at least one simulation system comprises any one of:
a system for performing simulation operations; (test mode analogous to simulation 0153)
a system for performing simulation operations to test or validate autonomous machine applications; (test mode analogous to simulation 0153)
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for rendering graphical output; (fig. 19 GUI)
a system for performing deep learning operations; (0077 deep understanding)
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; (test mode analogous to simulation 0153)
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; (test mode analogous to simulation 0153)
a system for performing generative operations using a language model (LM); (the system learns 0068-0070 from user review of questions)
a system for synthetic data generation; (fig. 19-21)
a collaborative content creation platform for 3D assets; or a system implemented at least partially using cloud computing resources.
Re claim 16, Barborak teaches
16. A system comprising:
one or more processors to:
provide, using one or more language models…
…the one or more quality decisions including a plaintext description of reasoning behind the one or more quality decisions. (the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076… semantics mapped or general language understanding as mapping per se…plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
However, while the combination teaches text entries and user authored data, domain specific languages, and rules associated with languages of a domain, it fails to teach a spatial map per se that can be altered by a human analyst, thus failing to teach:
…one or more quality decisions with respect to generated spatial map data,… (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
update the generated spatial map data based at least on the one or more quality decisions; and (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
deploy the updated generated spatial map data to at least one simulation system. (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak to incorporate the above claim limitations as taught by Askeland to allow for combining prior art elements of DSL usage to known methods of spatial map generation using human-entered parameters to yield predictable results, thereby improving the grey area between high-level human semantic understanding and low-level sensor data, improving the safety, efficiency, and flexibility of navigation systems, for instance utilizing software such as GeoScenario allows developers to easily design, validate, and migrate complex, realistic traffic test cases between different simulation tools using human entered parameters to update spatial maps.
Re claim 17, Barborak teaches a language model (semantics mapped or general language understanding as mapping per se…plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
However, while the combination teaches text entries and user authored data, domain specific languages, and rules associated with languages of a domain, it fails to teach a spatial map per se that can be altered by a human analyst, thus failing to teach:
17. The system of claim 16, wherein the one or more processors are further to analyze the generated spatial map data using the one or more language models (language model taught by Barborak), wherein the one or more quality decisions relate to at least one of a validation or proposed modification of the generated spatial map data. (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak to incorporate the above claim limitations as taught by Askeland to allow for combining prior art elements of DSL usage to known methods of spatial map generation using human-entered parameters to yield predictable results, thereby improving language modeling language for spatial analysis with a domain-specific language (DSL) to produce a geo map by acting as an intelligent intermediary that translates natural language prompts into executable code (such as Python, SQL, or specialized GIS scripts), such that the language model interprets the spatial intent, selects appropriate data, and generates the mapping code, which is then executed by a computer tool to visualize the results, operating in the grey area between high-level human semantic understanding and low-level sensor data, improving the safety, efficiency, and flexibility of navigation systems, for instance utilizing software such as GeoScenario allows developers to easily design, validate, and migrate complex, realistic traffic test cases between different simulation tools using human entered parameters to update spatial maps.
Re claim 19, Barborak teaches
19. The system of claim 16, wherein the language mode is trained using rules for a map domain and a set of human-generated map review entries associated with the map domain, the human generated map review entries including human reasoning information in text format. (semantics mapped or general language understanding as mapping per se…plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
Claim 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20170371861 A1 Barborak; Mike et al. (hereinafter Barborak) in view of US 20210132613 A1 Askeland; Jacob Lee et al. (hereinafter Askeland) further in view of US 20190095428 A1 Asano; Yu et al. (hereinafter Asano).
Re claim 18, Barborak teaches
… relating to the generated map data, and provide one or more answers, and reasoning supporting the one or more answers, as generated by using the one ore more language models. (semantics mapped or general language understanding as mapping per se…plaintext provided at element 1912 and 1914, human verifies at element 1910…in different domains 0056 with tokenized inputs 0256, the system learns 0068-0070 from user review of questions, descriptions, and explanations/reasoning provided to him/her 0076 with fig. 19-21 having express question-description-reasoning-answer format for a human to select, and fig. 17-26 mapped semantics are altered as the system learns from user feedback to train the system)
However, while the combination teaches text entries and user authored data, domain specific languages, and rules associated with languages of a domain, it fails to teach a spatial map per se that can be altered by a human analyst, thus failing to teach:
Spatial map (Askeland 0011-0012 and 0048 simulations run to generate scenarios on a spatial map with injection of rules/criteria using human entries 0033 and 0070 with fig. 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak to incorporate the above claim limitations as taught by Askeland to allow for combining prior art elements of DSL usage to known methods of spatial map generation using human-entered parameters to yield predictable results, thereby improving the grey area between high-level human semantic understanding and low-level sensor data, improving the safety, efficiency, and flexibility of navigation systems, for instance utilizing software such as GeoScenario allows developers to easily design, validate, and migrate complex, realistic traffic test cases between different simulation tools using human entered parameters to update spatial maps.
However, while Barborak teaches direct question handling, the combination does not allow for traditional input into a learning system as fails to teach:
18. The system of claim 16, wherein the one or more processors further allow a user to pose one or more questions… (Asano posing questions for teaching the system as in 0089 with fig. 11)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Barborak in view Askeland to incorporate the above claim limitations as taught by Asano to allow for a simple substitution of known user-to-system interaction for learning to obtain predictable results of error reduction, wherein the combination is improved to reduce maintenance of the dialogue data and operation costs, as well as using dialogue log data, to identify a failure cause, failure location, and a confirmation thereof via a question sentence from the dialogue log data pertinent to the failure cause, such that the user has the ability to input questions as a form of teaching the system even when not in learning mode by the inherent premise of learning models per se or models.
Conclusion
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/21/2026 has been entered.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20110054899 A1 Phillips; Michael S. et al.
User updating inputs and model learning
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov