DETAILED ACTION
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101-analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the examiner has determined
that the following limitations recite a process that, under broadest reasonable interpretation, recites abstract idea but for the recitation of generic computer components:
Regarding claim 1,
determining, based on a first trained language model and a user request, one or more first functions; and
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves selecting and identifying operation (functions) based on a machine learning model and analyzing inputs. See (MPEP 2106.04)).
executing the one or more first functions based on the data to generate first analysis data.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves analyzing instruction to generate evaluation or analysis data. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Regarding claim 2, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
performing one or more operations to generate, based on a second trained language model and the user request, one or more second functions;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating input information (user request) and a trained machine learning model to selecting or generating operational steps/actions. See (MPEP 2106.04)).
executing the one or more second functions based on the data to generate second analysis data.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves analyzing instruction to generate evaluation or analysis data. See (MPEP 2106.04)).
Claim 12 recites similar subject matter as claim 2, so it is rejected under the same rationale.
Regarding claim 3, dependent upon claim 2, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein performing the one or more operations to generate the one or more second functions comprises: requesting the second trained language model to generate program code for the one or more second functions.
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 13 recites similar subject matter as claim 3, so it is rejected under the same rationale.
Regarding claim 4, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein information about the one or more first functions is included in a context that is input into the first trained language model.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 14 recites similar subject matter as claim 4, so it is rejected under the same rationale.
Regarding claim 5, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
generating at least one of a summary or a report based on the first analysis data; and
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves organizing and summarizing analyzed information. See (MPEP 2106.04)).
displaying the at least one of the summary or the report to a user.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves presenting or organizing information for human visual receipt. See (MPEP 2106.04)).
Claim 15 recites similar subject matter as claim 5, so it is rejected under the same rationale.
Regarding claim 6, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the data includes simulation data,
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
the method further comprises performing a simulation to generate the simulation data.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves modeling or evaluating hypothetical scenarios to generate predicted data. See (MPEP 2106.04)).
Claim 16 recites similar subject matter as claim 6, so it is rejected under the same rationale.
Regarding claim 7, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein executing the one or more first function comprises configuring, via a second trained language model, execution of the one or more first functions.
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 17 recites similar subject matter as claim 7, so it is rejected under the same rationale.
Regarding claim 8, dependent upon claim 1, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
determining one or more goals associated with the user request; and
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves identifying and interpreting intent or objectives from an input request. See (MPEP 2106.04)).
determining the one or more first functions based on the one or more goals.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating identified goals and selecting/matching specific action of function to satisfy these goals. See (MPEP 2106.04)).
Claim 18 recites similar subject matter as claim 8, so it is rejected under the same rationale.
Regarding claim 9, dependent upon claim 8, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein determining the one or more goals comprises performing one or more zero-shot classification operations.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 10, dependent upon claim 8, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the one or more goals include at least one of data exploration, cluster analysis, outlier detection, pattern recognition, statistical testing, predictive modeling, or generating one or more visualizations.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 11,
The rest of the limitations recite similar subject matter as claim 1, so are rejected for the same rationale.
One or more non-transitory computer-readable storage media including instructions that, when executed by at least one processor, cause the at least one processor to perform steps for analyzing data
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 19, dependent upon claim 11, and fail to resolve the deficiencies identified above by integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the first analysis data includes at least one of one or more images, one or more videos, or multidimensional time-varying data.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 20,
The rest of the limitations recite similar subject matter as claim 1, so are rejected for the same rationale.
A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1 – 2, 4 – 5, 7, 11 – 12, 14 – 15, 17 and 19 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lehrmann, et al., Pre-Grant Publication No. US20170300781A1.
Regarding claim 1, Lehrmann teaches: A computer-implemented method for analyzing data, the method comprising: determining, [ ], and a user request, one or more first functions; and
(Lehrmann, “[0020] In some implementations, image component 108 may be configured to obtain images responsive to one or more requests to analyze images [determining, [ ], one or more first functions]. In some implementations, a given request may be associated with one or more of a user, an entity that is external to system 100 [and a user request,] an entity that is internal to system 100, and/or other sources of requests. By way of non-limiting example, a user request may be associated with user input provided via a user interface (see, e.g., FIG. 3) presented on a computing platform 118.”)
… based on a first trained language mode l…
(Lehrmann, “[0007] By way of non-limiting example, a set of models may comprise one or more of a first model, a second model, a third model, and/or other models. The first model [… based on a first trained language mode l…] may be configured to generate outputs from analysis of a first image parameter and/or other image parameters. The generation of outputs by the first model may be independent from any outputs of the second model, third model, and/or other models. The second model may be configured to generate outputs from analysis of a second image parameter and/or other image parameters.
executing the one or more first functions based on the data to generate first analysis data.
(Lehrmann, “[0007] By way of non-limiting example, a set of models may comprise one or more of a first model, a second model, a third model, and/or other models. The first model may be configured to generate outputs from analysis of a first image parameter and/or other image parameters [executing the one or more first functions based on the data to generate first analysis data]. The generation of outputs by the first model may be independent from any outputs of the second model, third model, and/or other models. The second model may be configured to generate outputs from analysis of a second image parameter and/or other image parameters. In some implementations, the execution of the second model may be conditioned on outputs of the first model and/or other models. The third model may be configured to generate outputs from analysis of a third image parameter and/or other image parameters.”)
Regarding claim 2, Lehrmann teach the method of claim 1.
Lehrmann further teaches: further comprising: performing one or more operations to generate, based on a second trained language model and the user request, one or more second functions; and
(Lehrmann, “[0020] In some implementations, image component 108 may be configured to obtain images responsive to one or more requests to analyze images [one or more second functions]. In some implementations, a given request may be associated with one or more of a user, an entity that is external to system 100 [performing one or more operations to generate, [ ] and the user request,] an entity that is internal to system 100, and/or other sources of requests. By way of non-limiting example, a user request may be associated with user input provided via a user interface (see, e.g., FIG. 3) presented on a computing platform 118.”)
… based on a second trained language model …
(Lehrmann, “[0007] By way of non-limiting example, a set of models may comprise one or more of a first model, a second model, a third model, and/or other models. The first model may be configured to generate outputs from analysis of a first image parameter and/or other image parameters. The generation of outputs by the first model may be independent from any outputs of the second model, third model, and/or other models. The second model [… based on a second trained language model …] may be configured to generate outputs from analysis of a second image parameter and/or other image parameters.
executing the one or more second functions based on the data to generate second analysis data.
(Lehrmann, “[0007] By way of non-limiting example, a set of models may comprise one or more of a first model, a second model, a third model, and/or other models. The first model may be configured to generate outputs from analysis of a first image parameter and/or other image parameters. The generation of outputs by the first model may be independent from any outputs of the second model, third model, and/or other models. The second model may be configured to generate outputs from analysis of a second image parameter and/or other image parameters [executing the one or more second functions based on the data to generate second analysis data]. In some implementations, the execution of the second model may be conditioned on outputs of the first model and/or other models.”)
Claim 12 recites limitations analogous to claim 2, so it is rejected under the same rationale.
Regarding claim 4, Lehrmann teach the method of claim 1.
Lehrmann further teaches: wherein information about the one or more first functions is included in a context that is input into the first trained language model.
(Lehrmann, “[0024] The first model 202 may represent a hierarchically first occurring model. In some implementations, input into the first model 202 may comprise image information 200 and/or other information [wherein information about the one or more first functions is included in a context that is input into the first trained language model]. The first model 202 may be associated with analysis of a first image parameter 204 and/or other image parameters. Execution of the first model 202 may generate first output 205 from the analysis of the first image parameter 204 and/or other image parameters. By way of non-limiting example, first output 205 may comprise one or more parameter values of the first image parameter 204, and/or other outputs.”)
Claim 14 recites limitations analogous to claim 4, so it is rejected under the same rationale.
Regarding claim 5, Lehrmann teach the method of claim 1.
Lehrmann further teaches: further comprising: generating at least one of a summary or a report based on the first analysis data; and displaying the at least one of the summary or the report to a user.
(Lehrmann, “[0069] In some implementations, first display element 308 may comprise a results display area and/or other display elements. The first display element 308 may be configured to present output of analysis of one or more image parameters as determined by user selection via third input element 306 [generating at least one of a summary or a report based on the first analysis data; and displaying the at least one of the summary or the report to a user].”)
Claim 15 recites limitations analogous to claim 5 so is rejected under the same rationale.
Regarding claim 7, Lehrmann teach the method of claim 1.
Lehrmann further teaches: wherein executing the one or more first function comprises configuring, via a second trained language model, execution of the one or more first functions.
(Lehrmann, “[0025] The second model 206 may represent a hierarchically second occurring model. In some implementations, input into the second model 206 may comprise the first output 205 derived from the execution of the first model 202. The second model 206 may be associated with analysis of a second image parameter 208 and/or other image parameters. Execution of the second model 206 may generate second output 209 from the analysis of the second image parameter 208 and/or other image parameters [wherein executing the one or more first function comprises configuring, via a second trained language model, execution of the one or more first functions]. By way of non-limiting example, second output 209 may comprise one or more parameter values of the second image parameter 208, and/or other outputs.”)
Claim 17 recites limitations analogous to claim 7 so is rejected under the same rationale.
Regarding claim 11, Lehrmann teaches: One or more non-transitory computer-readable storage media including instructions that, when executed by at least one processor, cause the at least one processor to perform steps for analyzing data, the steps comprising
(Lehrmann, “[0019] In some implementations, image component 108 may be configured to obtain images from one or more storage locations. An image storage location may comprise one or more of electronic storage 115 [One or more non-transitory computer-readable storage media including instructions that] that may be locally assessable to server 102 and/or processor(s) 104 [when executed by at least one processor, cause the at least one processor to perform steps for analyzing data], a computing platform 118, an external resource 120, and/or other storage locations.”)
The rest of the limitations are analogous to claim 1, so are rejected under similar rationale.
Regarding claim 19, Lehrmann teach the method of claim 11.
Lehrmann further teaches: wherein the first analysis data includes at least one of one or more images, one or more videos, or multidimensional time-varying data.
(Lehrmann, “[0006] The analysis component may be configured to analyze an image by executing individual models in the hierarchical set of models [wherein the first analysis data includes at least one of one or more images, one or more videos, or multidimensional time-varying data]. Executing the individual models in the set of models may generate outputs from analysis of different image parameters of the image.”)
Regarding claim 20, Lehrmann teaches: A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
(Lehrmann, “[0019] In some implementations, image component 108 may be configured to obtain images from one or more storage locations. An image storage location may comprise one or more of electronic storage 115 [A system, comprising: one or more memories storing instructions] that may be locally assessable to server 102 and/or processor(s) 104 [one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to], a computing platform 118, an external resource 120, and/or other storage locations.”)
The rest of the limitations are analogous to claim 1, so are rejected under similar rationale.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Lehrmann et al.,
in view of Tomkins et al., Pub. No.: US20210294828A1.
Regarding claim 3, Lehrmann teaches the method of claim 2.
Lehrmann does not teach:
wherein performing the one or more operations to generate the one or more second functions comprises requesting the second trained language model to generate program code for the one or more second functions.
Tomkins teaches:
wherein performing the one or more operations to generate the one or more second functions comprises requesting the second trained language model to generate program code for the one or more second functions
(Tomkins, “[0169] For example, some embodiments may provide a UI that permits a user to add workflow blocks to a set of workflow blocks, remove workflow blocks from the set of workflow blocks, reconfigure workflow blocks of the set of workflow blocks, or otherwise update the set of workflow blocks. In response to a change to the set of workflow blocks, some embodiments may update a compiled version of a program code implementing a set of operations [requesting the second trained language model to generate program code for the one or more second functions] represented by the set of the workflow blocks.”)
Tomkins and Lehrmann are related to the same field of endeavor (i.e.: Natural language analysis). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Tomkins with teachings of Lehrmann to automatically update natural language instructions on the topic based on the detected changes in the concepts and storing the updated natural language instructions in memory. (Tomkins, Abstract)
Claim 13 recites limitations analogous to claim 3, so it is rejected under the same rationale.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lehrmann et al.,
in view of Hoetzer et al., Pub. No.: US20180032505A1.
Regarding claim 6, Lehrmann teaches the method of claim 1.
Lehrmann does not teach:
wherein the data includes simulation data, and the method further comprises performing a simulation to generate the simulation data.
Hoetzer teaches:
wherein the data includes simulation data, and the method further comprises performing a simulation to generate the simulation data.
(Hoetzer, “[0079] The data controller 1022 is a module that controls the access to the data (a different controller may be used to access other memory based databases) and transfers the required data in memory to and from the memory-based database 1002. The read action is related to data (e.g., input and output parameters) used in the learning phase and also to simulation/prediction input data in productive usage [wherein the data includes simulation data, and the method further comprises performing a simulation to generate the simulation data]. The write operation is related to output parameters in productive usage that are then stored in separate tables. The tables may be generated by the table generator 1016 on the fly and reflect the input and output parameters of the network(s).”)
Hoetzer and Lehrmann are related to the same field of endeavor (i.e.: Natural language analysis). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Hoetzer with teachings of Lehrmann to add a multimodal input interface to enable faster, more intuitive visual querying. (Hoetzer, Abstract)
Claim 16 recites limitations analogous to claim 6, so it is rejected under the same rationale.
Claim(s) 8 – 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lehrmann et al.,
in view of Cheng et al., Pub. No.: US20190065492A1.
Regarding claim 8, Lehrmann teaches the method of claim 1.
Lehrmann does not teach:
wherein determining the one or more first functions comprises: determining one or more goals associated with the user request; and determining the one or more first functions based on the one or more goals.
Cheng teaches:
wherein determining the one or more first functions comprises: determining one or more goals associated with the user request; and
(Cheng, “[0013] In some embodiments the computing device includes a search request module [determining one or more goals associated with the user request] and a user interface device for enabling an initiation of a content detection action. In some embodiments the computing device includes at least one of an optical character recognition system to generate text transcripts of text in the multimodal content and an automatic speech recognition system to generate text transcripts of speech in the multimodal content…”)
determining the one or more first functions based on the one or more goals.
(Cheng, “[0064] In various embodiments of zero-shot event detection in accordance with the present principles, one goal is to recognize complex events in videos without the need for training examples [determining the one or more first functions based on the one or more goals] and based on the multimedia content of the video, including still-image concepts like objects and scenes, action concepts, OCR, and ASR…”)
Cheng and Lehrmann are related to the same field of endeavor (i.e.: Natural language analysis). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Cheng with teachings of Lehrmann to instantly recognize novel objects and execute open ended search queries without requiring pre trained visual classifiers or manual model retraining. (Cheng, Abstract)
Claim 18 recites limitations analogous to claim 8, so it is rejected under the same rationale.
Regarding claim 9, Lehrmann and Cheng teaches the method of claim 8.
Cheng further teaches: wherein determining the one or more goals comprises performing one or more zero-shot classification operations.
(Cheng, “[0064] In various embodiments of zero-shot event [wherein determining the one or more goals comprises performing one or more zero-shot classification operations] detection in accordance with the present principles, one goal is to recognize complex events in videos without the need for training examples and based on the multimedia content of the video, including still-image concepts like objects and scenes, action concepts, OCR, and ASR…”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Cheng with teachings of Lehrmann for the same reasons disclosed for claim 8.
Regarding claim 10, Lehrmann and Cheng teaches the method of claim 8.
Cheng further teaches: wherein the one or more goals include at least one of data exploration, cluster analysis, outlier detection, pattern recognition, statistical testing, predictive modeling, or generating one or more visualizations.
(Cheng, “[0064] In various embodiments of zero-shot event detection in accordance with the present principles, one goal is to recognize complex events in videos without the need for training examples [wherein the one or more goals include at least one of data exploration, cluster analysis, outlier detection, pattern recognition, statistical testing, predictive modeling, or generating one or more visualizations] and based on the multimedia content of the video, including still-image concepts like objects and scenes, action concepts, OCR, and ASR…”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Cheng with teachings of Lehrmann for the same reasons disclosed for claim 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim et al., Pub. No.: US20210165800A1.
Kim teaches generating a user question vector for a user question; selecting a similar question based on a similar analysis result to the user question vector; and determining an answer to the similar question as an answer to the user question.
Nassar et al., Pub. No.: US20220374428A1.
Nassar teaches a query engine for designing, analyzing, optimizing, verifying, and/or validating simulations and simulated data in autonomous machine applications.
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to MATIYAS T MARU whose telephone number is (571)270-0902 or via email: matiyas.maru@uspto.gov. The examiner can normally be reached Monday 8:00am - Friday 4:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a
USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to
use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor,
Michelle Bechtold can be reached on (571)431-0762. The fax phone number for the organization were this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from
Patent Center. Unpublished application information in Patent Center is available to registered users.
To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit
https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and
https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional
questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like
assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA)
or 571-272-1000.
/M.T.M./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
Prosecution Insights