DETAILED ACTION
Claims 1-20 are pending. Claims 1-20 are rejected.
The instant application has PRO 63/736,930 filed on 12/20/2024.
The instant application has PRO 63/735,617 filed on 01/06/2025.
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 Objections
Claims 2-3 are objected to because of the following informalities: claim 2 states “The method of claim 41”, this is clearly a typo that should be “The method of claim 1”. The Examiner will treat the claim as depending on claim 1. Appropriate correction is required.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (All Claims)
According to the first part of the analysis, in the instant case, claims 1-7 are directed to a method, claims 8-14 are directed to a system comprising at least one computer hardware processor and at least one non-transitory computer-readable storage medium, and claims 15-20 are directed to at least one non-transitory computer-readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2A, Prong 1 (Claims 1, 8, and 15)
Regarding claim 1, the following limitations are abstract ideas:
identifying whether a user query is associated with a stored object by: is a step that can be practically performed in the human mind and is a mental process which encompasses observation, evaluation and/or judgement;
determining whether the user query matches one or more semantic tokens associated with the stored object in a short-term database, is a step that can be practically performed in the human mind and is a mental process which encompasses observation, evaluation and/or judgement;
determining whether the query matches one or more semantic tokens associated with the stored object in a long-term database; is a step that can be practically performed in the human mind and is a mental process which encompasses observation, evaluation and/or judgement;
upon determining that the user query matches one or more semantic tokens associated with the stored object in the long-term database, determining whether the stored object is associated with an event log; is a step that can be practically performed in the human mind and is a mental process which encompasses observation, evaluation and/or judgement;
determining the one or more tasks for completing the user query based on the observed properties for the stored object, the last known observed properties for the stored object, or the previously known observed properties for the stored object; is a step that can be practically performed in the human mind and is a mental process which encompasses observation, evaluation and/or judgement;
generating an execution order of the one or more tasks. is a step that can be practically performed in the human mind and is a mental process which encompasses observation, evaluation and/or judgement;
The above analysis for claim 1 applies to each independent claim as they contain similar limitations.
Step 2A, Prong 2 (Claims 1, 8, and 15)
Regarding claim 1, the following limitations are additional elements:
A method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises: is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
if a match is determined with the one or more semantic tokens associated with the stored object in the short-term database, receiving observed properties for the stored object; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
if a match is determined with the one or more semantic tokens associated with the stored object in the long-term database, received last known observed properties for the stored object; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
if the stored object is associated with the event log, receiving previously known observed properties for the stored object that are different from the last known observed properties for the stored object and receiving a last known time of the previously known observed properties; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
Regarding claim 8, the following limitations are additional elements:
A system, comprising: at least one computer hardware processor; is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
at least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by the at least one computer hardware processor perform a method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises: is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Regarding claim 15, the following limitations are additional elements:
At least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by at least one computer hardware processor perform a method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises: is a high-level recitation of a generic computer component and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The above analysis for claim 1 applies to each independent claim as they contain similar limitations.
Step 2B (Claims 1, 8, and 15)
Regarding claim 1, the following limitations are additional elements:
A method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises: when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
if a match is determined with the one or more semantic tokens associated with the stored object in the short-term database, receiving observed properties for the stored object; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory as identified in MPEP 2106.05(d)II “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”
if a match is determined with the one or more semantic tokens associated with the stored object in the long-term database, received last known observed properties for the stored object; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory as identified in MPEP 2106.05(d)II “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”
if the stored object is associated with the event log, receiving previously known observed properties for the stored object that are different from the last known observed properties for the stored object and receiving a last known time of the previously known observed properties; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory as identified in MPEP 2106.05(d)II “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”
Regarding claim 8, the following limitations are additional elements:
A system, comprising: at least one computer hardware processor; ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
at least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by the at least one computer hardware processor perform a method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises: ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
Regarding claim 15, the following limitations are additional elements:
At least one non-transitory computer-readable storage medium storing processor executable instructions that when executed by at least one computer hardware processor perform a method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises: ((i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
The above analysis for claim 1 applies to each independent claim as they contain similar limitations.
The dependent claims are directed to the same abstract ideas as their parent claims. The dependent claims add further steps of identifying certain information, using a spatiotemporal database, clarifying that the user query is audio, generating a semantic token, using machine learning, or executing the task. The limitations directing to identifying certain information or generating are further mental processes similar to the above identified mental processes. The other limitations are further additional elements similar to the above identified additional elements. Therefore, the claims are still rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4-9, 11-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sen et al., Patent Application Publication No. 2020/0073983 (hereinafter Sen) in view of Li et al., Patent Application Publication No. 2022/0245503 (hereinafter Li) and Perks et al., Patent Application Publication No. 2024/0364582 (hereinafter Perks).
Regarding claim 1, Sen teaches:
identifying whether a user query is associated with a stored object by (Sen Paragraph [0018], a natural language query 160 (which is received or otherwise generated by the user interface 132) is input to the NLQ parser module 141):
determining whether the user query matches one or more semantic tokens associated with the stored object in a short-term database (Sen Paragraph [0038], The nested query detection module 143 performs semantic pattern matching to map tokens of the parse tree representation into ontology elements (block 204) via operation of the pattern matching module 144, Paragraph [0005], converting the natural language query into a semantic representation which comprises tokens that correspond to elements of a database schema of the database (Li teaches a database with short-term memory in paragraph 40)), and
determining whether the query matches one or more semantic tokens associated with the stored object in a long-term database (Sen Paragraph [0038], The nested query detection module 143 performs semantic pattern matching to map tokens of the parse tree representation into ontology elements (block 204) via operation of the pattern matching module 144, Paragraph [0005], converting the natural language query into a semantic representation which comprises tokens that correspond to elements of a database schema of the database (Li teaches a database with long-term memory in paragraph 40));
upon determining that the user query matches one or more semantic tokens associated with the stored object in the long-term database (Sen Paragraph [0038], The nested query detection module 143 performs semantic pattern matching to map tokens of the parse tree representation into ontology elements (block 204) via operation of the pattern matching module 144, Paragraph [0005], converting the natural language query into a semantic representation which comprises tokens that correspond to elements of a database schema of the database (Li teaches a database with long-term memory in paragraph 40)),
if a match is determined with the one or more semantic tokens associated with the stored object in the short-term database (Sen Paragraph [0038], The nested query detection module 143 performs semantic pattern matching to map tokens of the parse tree representation into ontology elements (block 204) via operation of the pattern matching module 144, Paragraph [0005], converting the natural language query into a semantic representation which comprises tokens that correspond to elements of a database schema of the database (Li teaches a database with short-term memory in paragraph 40)), receiving observed properties for the stored object (Sen Paragraph [0021], The semantic parsing module 145 implements methods that are configured to determine, for each token in the set of tokes, which property of the ontology element the given token corresponds);
Sen does not expressly disclose:
A method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises:
determining whether the stored object is associated with an event log;
if the stored object is associated with the event log,
However, Li teaches:
A method of generating one or more tasks for execution by a robotic system in response to receiving a user query, wherein generating the one or more tasks comprises (Li Paragraph [0006], the robotic task can be a comprehensive robotic task that itself includes discrete tasks/portions, Paragraph [0020], a query can be provided to remote human(s) and teleoperation input(s), received in response to the query (Sen teaches a user query)):
determining whether the stored object is associated with an event log (Li Paragraph [0039], state data from the episode logs 158 and one or more automatic techniques to determine whether a robotic task, and/or portion(s) thereof, was successful);
if the stored object is associated with the event log (Li Paragraph [0039], state data from the episode logs 158 and one or more automatic techniques to determine whether a robotic task, and/or portion(s) thereof, was successful),
The claimed invention and Li are from the analogous art of robotic systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Sen in view of Li to have combined Sen in view of Li. Li teaches the benefits of using a reinforced learning policy model that can be used to at least selectively replace a portion of the engineering policy (Paragraph 5).
Sen in view of Li does not expressly disclose:
if a match is determined with the one or more semantic tokens associated with the stored object in the long-term database, received last known observed properties for the stored object;
receiving previously known observed properties for the stored object that are different from the last known observed properties for the stored object and receiving a last known time of the previously known observed properties;
determining the one or more tasks for completing the user query based on the observed properties for the stored object, the last known observed properties for the stored object, or the previously known observed properties for the stored object; and
generating an execution order of the one or more tasks.
However, Perks teaches:
if a match is determined with the one or more semantic tokens associated with the stored object in the long-term database (Perks Paragraph [0067], the operating system 145ab of witness sled 145 may support APIs that support queries from a vSAN application, where such queries may request the last known state of stored data that is recorded by the witness sled (Sen teaches the tokens)), received last known observed properties for the stored object (Perks Paragraph [0067], the operating system 145ab of witness sled 145 may support APIs that support queries from a vSAN application, where such queries may request the last known state of stored data that is recorded by the witness sled. In response to such queries, the witness sled 145 consults the state information stored in SSD 145h in order to determine which of the storage drives stores the correct version of the data);
receiving previously known observed properties for the stored object that are different from the last known observed properties for the stored object and receiving a last known time of the previously known observed properties (Perks Paragraph [0067], the operating system 145ab of witness sled 145 may support APIs that support queries from a vSAN application, where such queries may request the last known state of stored data that is recorded by the witness sled. In response to such queries, the witness sled 145 consults the state information stored in SSD 145h in order to determine which of the storage drives stores the correct version of the data, Paragraph [0066], the reported transactions may include transaction metadata that identifies the time of the transaction and the storage drive of the vSAN in which the transaction occurred);
determining the one or more tasks for completing the user query based on the observed properties for the stored object, the last known observed properties for the stored object (Perks Paragraph [0067], the operating system 145ab of witness sled 145 may support APIs that support queries from a vSAN application, where such queries may request the last known state of stored data that is recorded by the witness sled. In response to such queries, the witness sled 145 consults the state information stored in SSD 145h in order to determine which of the storage drives stores the correct version of the data), or the previously known observed properties for the stored object; and
generating an execution order of the one or more tasks (Perks Paragraph [0092], The order in which each operation of a given method is performed may be changed, and various operations may be added, reordered, combined, omitted, modified, etc).
The claimed invention and Perks are from the analogous art of systems using queries. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Sen in view of Li and Perks to have combined Sen in view of Li and Perks. Perks teaches the benefits of using virtualized data stored applications that provide virtualized storage of data using a poll of storage drives, where the stored data is replicated in multiple different storage resources, thus providing fault tolerance and also supporting improved access to the data (Paragraph 4).
Regarding claim 2, Sen in view of Li and Perks further teaches:
The method of claim 1, further comprising identifying whether the user query is associated with a current environment around the robotic system or a previous environment around the robotic system (Li Paragraph [0105], determining, based on the current state data, whether to utilize the reinforcement learning policy model or the engineered policy in controlling the robot to perform the portion of the robotic task).
Regarding claim 4, Sen in view of Li and Perks further teaches:
The method of claim 1, wherein the user query is received as an audio input (Sen Paragraph [0018], the natural language processing system 140 may include a speech recognition system to convert voice queries into textual queries).
Regarding claim 5, Sen in view of Li and Perks further teaches:
The method of claim 4, wherein the audio input is processed to generate one or more semantic tokens associated with the task (Sen Paragraph [0018], the natural language processing system 140 may include a speech recognition system to convert voice queries into textual queries, Paragraph [0021], the semantic parsing module 145 parses the tokens of the given query to determine dependencies between tokens of the given query (Li teaches the task while Sen teaches the query and tokens)).
Regarding claim 6, Sen in view of Li and Perks further teaches:
The method of claim 5, wherein generating the one or more semantic tokens comprises processing the audio input with a trained machine learning model (Li Paragraph [0002], at a given iteration multiple candidate actions can be processed along with a state and using the machine learning model to generate a corresponding value for each (Li teaches the machine learning while Sen teaches the audio input and tokens)).
Regarding claim 7, Sen in view of Li and Perks further teaches:
The method of claim 1, further comprising executing the one or more tasks associated with the user query (Li Paragraph [0001], at state xt in time t, the agent chooses and executes action ut according to its policy (Sen teaches the claimed user query)).
Claims 8, 9, 11-16, and 18-20 are rejected in the same manner as claims 1, 2, and 4-7 but are merely directed to a different statutory category (method, system, or non-transitory computer-readable storage medium). Sen further teaches a computer readable storage medium and processor (Paragraph 102).
Claim(s) 3, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sen in view of Li, Perks, and Vemulapalli et al., Patent Application Publication No. 2024/0346057 (hereinafter Vemulapalli).
Regarding claim 3, Sen in view of Li and Perks teaches parent claim 2.
Sen in view of Li and Perks does not expressly disclose:
further comprising, upon determining the user query is association with the current environment around the robotic system, identifying whether the user query is associated with a stored object by determining whether the query matches one or more semantic tokens associated with a short-term spatiotemporal database or a long-term spatiotemporal database.
However, Vemulapalli teaches:
further comprising, upon determining the user query is association with the current environment around the robotic system (Vemulapalli Paragraph [0102], the query may request all vehicles that travelled within a county within a time interval, grouped by zip code or grouped by car manufacturers, or filtered by specific attribute such as an attribute describing the drivers, assuming the information is stored in the document database. Although these examples are based on vehicles, the techniques apply to any type of entity that may be represented using the document database, for example, robots, devices, sensors, people, and so on that have spatial and/or temporal attributes that can be collected), identifying whether the user query is associated with a stored object by determining whether the query matches one or more semantic tokens associated with a short-term spatiotemporal database or a long-term spatiotemporal database (Vemulapalli Paragraph [0101], the system stores spatiotemporal data in the document database, creates indexes based on the spatiotemporal data, and processes queries based on the spatiotemporal data (Sen teaches the tokens and Vemulapalli teaches the spatiotemporal database)).
The claimed invention and Vemulapalli are from the analogous art of database systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Sen in view of Li, Perks, and Vemulapalli to have combined Sen in view of Li, Perks, and Vemulapalli. Vemulapalli teaches improvements in storage efficiency as well as improvements in computational efficiency in processing queries with spatiotemporal constraints (Paragraph 120).
Claims 10 and 17 are rejected in the same manner as claim 3 but are merely directed to a different statutory category (method, system, or non-transitory computer-readable storage medium).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Garg et al., Patent Application Publication No. 2024/0386733 (hereinafter Garg). Garg teaches receiving a query from a user of a robotic system where the query may be a task (Paragraph 30). This shows that the claimed invention and Garg are analogous art as both are directed to performing tasks in a robotic system.
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/DUSTIN D EYERS/ Examiner, Art Unit 2164
/AMY NG/ Supervisory Patent Examiner, Art Unit 2164
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