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
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve spatial maps using user input data supported by the specification and reflected by the claims e.g. in spec: 0065-0067. In other words, the claims enable the invention to improve accuracy and efficiency based on user input and model updating.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Response to Arguments
Applicant's arguments filed 05/01/2026have been fully considered but they are not persuasive. On pages 10-12 of the arguments Applicant argues that the prior art of Barborak and Askeland fails to teach:
formulating at least a portion of the evaluated one or more additional tokenized descriptions into the computer-readable textual format;
updating a spatial map representation using at least the portion formulated into the computer-readable textual format; and deploying the updated spatial map representation to at least one simulation system.
Examiner does not concur. The specification of the present invention supports that DSL is used to produce a spatial map representation for the user such as a for a vehicle or road as in 0036, 0080, and 0098.
Under BRI, Askeland provides this in 0011-0012 where “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…”
It is clear that as amended under BRI, Askeland shows that DSL is a tokenized description (supported by present invention spec), and it is further expressed or transformed into a “machine-readable format read by a computer device” i.e. a computer-readable textual format as claimed.
Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”
Further amendment is strongly recommended.
Additionally, Examiner does not concur. On pages 12-13 of the arguments Applicant argues that the prior art of Barborak fails to teach:
providing the language model after updating the one or more parameters, to generate an evaluation of one or more additional tokenized descriptions associated with the domain;
Examiner does not concur. Note, this limitation is introduced BEFORE the definition of DSL, and thus is given its ordinary meaning under BRI such that a language models utilizes tokenization and updating as is well known and taught. Other than a semantic past-tense change to the claim language, the system of Barborak learns as previously explained in the office action 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.
More precisely, but not limited to, in 0256 “The queries may include the words or phrases, tokenized versions of the words/phrases, or other representations of words/phrases.” And 0076 “The computing system 102 identifies these actions as possibly belonging to a frame that might involve a restaurant where Ben is the waiter and Ava is the customer. Accordingly, the learning and understanding computing system 102 formulates a response that assumes the implication of a restaurant. In FIG. 1, an exemplary response is sent to the device 118(D) and presented in the UI 122 as “The story mentions that Ben brought a menu and food to Ava. I believe Ben and Ava are in a restaurant”. The computing system can further use this interaction not only to teach the student 110(1) but also evaluate the system's own understanding by adding a second statement, such as “Would you agree?”. If the system's responsive explanation is accurate to the student, the student can confirm that understanding, such as by answering “yes” as shown in UI 122.”
These are well known language model, and learning uses with express user feedback to update the system i.e. human authored or evaluated per se with plain text interaction.
Significant amendment is strongly suggested
Additionally, Examiner does not concur. On pages 15-16 of the arguments Applicant argues that the prior art of Barborak fails to teach the language model of Barborak:
Examiner does not concur. Note, this limitation is introduced BEFORE the definition of DSL, and thus is given its ordinary meaning under BRI such that a language models utilizes tokenization and updating as is well known and taught. Other than a semantic past-tense change to the claim language, the system of Barborak learns as previously explained in the office action 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.
Additionally, Examiner does not concur. On pages 15-16 of the arguments Applicant argues that the prior art of Barborak and Askeland does not teach plaintext descriptions
A DSL allows a user to operate in plain language and code such as Gherkin, SQL, etc. where a user can see plain text equivalents.
More precisely, but not limited to, in 0256 “The queries may include the words or phrases, tokenized versions of the words/phrases, or other representations of words/phrases.” And 0076 “The computing system 102 identifies these actions as possibly belonging to a frame that might involve a restaurant where Ben is the waiter and Ava is the customer. Accordingly, the learning and understanding computing system 102 formulates a response that assumes the implication of a restaurant. In FIG. 1, an exemplary response is sent to the device 118(D) and presented in the UI 122 as “The story mentions that Ben brought a menu and food to Ava. I believe Ben and Ava are in a restaurant”. The computing system can further use this interaction not only to teach the student 110(1) but also evaluate the system's own understanding by adding a second statement, such as “Would you agree?”. If the system's responsive explanation is accurate to the student, the student can confirm that understanding, such as by answering “yes” as shown in UI 122.”
These are well known language model, and learning uses with express user feedback to update the system i.e. human authored or evaluated per se with plain text interaction.
Further, in 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… explicitly 0011-0012, “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…” Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”
Significant amendment is strongly suggested
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, to generate an evaluation of one or more additional tokenized descriptions associated with the domain; (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… in 0256 “The queries may include the words or phrases, tokenized versions of the words/phrases, or other representations of words/phrases.” And 0076 “The computing system 102 identifies these actions as possibly belonging to a frame that might involve a restaurant where Ben is the waiter and Ava is the customer. Accordingly, the learning and understanding computing system 102 formulates a response that assumes the implication of a restaurant. In FIG. 1, an exemplary response is sent to the device 118(D) and presented in the UI 122 as “The story mentions that Ben brought a menu and food to Ava. I believe Ben and Ava are in a restaurant”. The computing system can further use this interaction not only to teach the student 110(1) but also evaluate the system's own understanding by adding a second statement, such as “Would you agree?”. If the system's responsive explanation is accurate to the student, the student can confirm that understanding, such as by answering “yes” as shown in UI 122.”)
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:
formulating at least a portion of the evaluated one or more additional tokenized descriptions into the computer-readable textual format; (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… explicitly 0011-0012, “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…” Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”)
updating a spatial map representation using at least the portion formulated into the computer-readable textual format; (in 0011-0012 where “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…” Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”)
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, from 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… 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… in 0256 “The queries may include the words or phrases, tokenized versions of the words/phrases, or other representations of words/phrases.” And 0076 “The computing system 102 identifies these actions as possibly belonging to a frame that might involve a restaurant where Ben is the waiter and Ava is the customer. Accordingly, the learning and understanding computing system 102 formulates a response that assumes the implication of a restaurant. In FIG. 1, an exemplary response is sent to the device 118(D) and presented in the UI 122 as “The story mentions that Ben brought a menu and food to Ava. I believe Ben and Ava are in a restaurant”. The computing system can further use this interaction not only to teach the student 110(1) but also evaluate the system's own understanding by adding a second statement, such as “Would you agree?”. If the system's responsive explanation is accurate to the student, the student can confirm that understanding, such as by answering “yes” as shown in UI 122.”)
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)
formulate at least a portion of the verification or the modification proposal into the representation data; (USING the evaluation of Barborak… introducing Askeland… note a DSL allows a user to operate in plain language and code such as Gherkin, SQL, etc. where a user can see plain text equivalents. 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… 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… explicitly 0011-0012, “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…” Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”)
update the spatial map representation using the[[a]] portion formulated into the representation data
(Askeland, note a DSL allows a user to operate in plain language and code such as Gherkin, SQL, etc. where a user can see plain text equivalents. 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… 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… explicitly 0011-0012, “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…” Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”)
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, from one or more language models… (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… 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… in 0256 “The queries may include the words or phrases, tokenized versions of the words/phrases, or other representations of words/phrases.” And 0076 “The computing system 102 identifies these actions as possibly belonging to a frame that might involve a restaurant where Ben is the waiter and Ava is the customer. Accordingly, the learning and understanding computing system 102 formulates a response that assumes the implication of a restaurant. In FIG. 1, an exemplary response is sent to the device 118(D) and presented in the UI 122 as “The story mentions that Ben brought a menu and food to Ava. I believe Ben and Ava are in a restaurant”. The computing system can further use this interaction not only to teach the student 110(1) but also evaluate the system's own understanding by adding a second statement, such as “Would you agree?”. If the system's responsive explanation is accurate to the student, the student can confirm that understanding, such as by answering “yes” as shown in UI 122.”)
…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… 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… 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… in 0256 “The queries may include the words or phrases, tokenized versions of the words/phrases, or other representations of words/phrases.” And 0076 “The computing system 102 identifies these actions as possibly belonging to a frame that might involve a restaurant where Ben is the waiter and Ava is the customer. Accordingly, the learning and understanding computing system 102 formulates a response that assumes the implication of a restaurant. In FIG. 1, an exemplary response is sent to the device 118(D) and presented in the UI 122 as “The story mentions that Ben brought a menu and food to Ava. I believe Ben and Ava are in a restaurant”. The computing system can further use this interaction not only to teach the student 110(1) but also evaluate the system's own understanding by adding a second statement, such as “Would you agree?”. If the system's responsive explanation is accurate to the student, the student can confirm that understanding, such as by answering “yes” as shown in UI 122.”)
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… Askeland, note a DSL allows a user to operate in plain language and code such as Gherkin, SQL, etc. where a user can see plain text equivalents. 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… 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… explicitly 0011-0012, “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…” Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”)
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… Askeland, note a DSL allows a user to operate in plain language and code such as Gherkin, SQL, etc. where a user can see plain text equivalents. 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… 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… explicitly 0011-0012, “The domain specific language can be executable by a computer system to instantiate scenarios in an environment… Such scenarios can be enumerated in a scenario description language (SDL) that can be expressed in a Scenario Definition Format (SDF), which can be an intermediate, machine-readable format read by a computer device…” Further in 0011-0012, scenarios and simulations are tied to DSL to SDF concepts, where “The environment can be a real environment and/or a simulated environment. In at least one example, a real environment can correspond to one or more objects that interact with a map. A simulated environment can be constructed through the use of primitives which interact with a map. Primitives can include, but are not limited to, entities (which can be either static or dynamic), entity behaviors, actions, conditions, noises, faults, etc. In at least one example, a scenario can include one or more primitives that interact with a map. In some examples, entities can be associated with a coordinate system (e.g., a two-dimensional or three-dimensional coordinate system), such as an inertial coordinate system, a track based coordinate system, a map based coordinate system, or the like, in which a position and/or velocity of an entity can be described. ”)
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
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
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
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Michael.Colucci@uspto.gov
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