DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This communication is in response to the Applicant’s submission filed 30 May 2024, where:
Claims 1-20 are pending.
Claims 1-20 are rejected.
Information Disclosure Statement
3. Information disclosure statements were submitted on 11 December 2024, 14 February 2025, 14 October 2025, 13 February 2026, 20 April 2026, 13 July 2026, 27 August 2026, and 02 September 2026. The submissions comply with the provisions of 37 CFR 1.97. Accordingly, the Examiner considered the information disclosure statements.
Drawings
4. The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Fig. 9D, reference 794 is not in the Specification.
Fig. 28, reference 2808 is not in the Specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the Examiner, the Applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 U.S.C. § 112
5. The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
6. Claims 4, 16, and 20 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 4 depends from claim 3. Claim 4, lines 1-2, recites “a first agent module,” while claim 3, lines 1-2, also recites “a first agent module.” Claim 4 is indefinite because it is unclear whether “a first agent module” is intended to draw antecedence from the initial occurrence of the term at claim 3, or whether it is intended as an additional occurrence of the term altogether.
Claim 4 depends from claim 3. Claim 4, line 2, recites “a determination,” while claim 3, line 1, also recites “a determination.” Claim 4 is indefinite because it is unclear whether “a determination” is intended to draw antecedence from the initial occurrence of the term at claim 3, or whether it is intended as an additional occurrence of the term altogether.
Claim 16 depends from claim 15. Claim 16, line 2, recites “a first agent module,” while claim 15, line 2, also recites “a first agent module.” Claim 16 is indefinite because it is unclear whether “a first agent module” is intended to draw antecedence from the initial occurrence of the term at claim 15, or whether it is intended as an additional occurrence of the term altogether.
Claim 16 depends from claim 15. Claim 16, line 2, recites “a determination,” while claim 15, line 2, also recites “a determination.” Claim 16 is indefinite because it is unclear whether “a determination” is intended to draw antecedence from the initial occurrence of the term at claim 15, or whether it is intended as an additional occurrence of the term altogether.
Claim 20 depends from claim 19. Claim 20, lines 2-3, recites “a first agent module,” while claim 19, lines 2-3, also recites “a first agent module.” Claim 20 is indefinite because it is unclear whether “a first agent module” is intended to draw antecedence from the initial occurrence of the term at claim 19, or whether it is intended as an additional occurrence of the term altogether.
Claim 20 depends from claim 19. Claim 20, line 2, recites “a determination,” while claim 19, line 2, also recites “a determination.” Claim 20 is indefinite because it is unclear whether “a determination” is intended to draw antecedence from the initial occurrence of the term at claim 19, or whether it is intended as an additional occurrence of the term altogether.
Claim Rejections - 35 U.S.C. § 101
7. 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.
8. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “in response to the request, generating an agent module to perform the specific task.” The activity of “generating an agent module” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The limitation of “generating an agent module” further recites limitations of “selecting a set of agent building blocks from a plurality of available agent building blocks, wherein each agent building block in the plurality of available agent building blocks has a respective assigned function,” and “connecting the set of agent building blocks to form the agent module.” The activities of “selecting” and “connecting” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include an “agent module,” which is recited at a high level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites limitations of “receiving a request from a user for performance of a specific task,” which is a pre-processing insignificant extra-solution activity of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites, with regard to the limitation of “generating an agent module,” further limitations of “causing the agent module to execute and providing, to the agent module, information from the request;” which is the use of the generic computer component (agent module) to implement the abstract idea, (MPEP § 2106.05(f)), and “providing,” which is a post-processing insignificant extra-solution activity of producing an output, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application.
Also further, with regard to the limitation of “generating an agent module,” recites “in response to providing the agent module information from the request, receiving a response from the agent module corresponding to performance of the specific task,” and “providing the response to the user,” which are post-processing insignificant extra-solution activities of providing status information, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. the additional elements recited in the claim beyond the identified judicial exception include an “agent module,” which is recited at a high level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim also recites limitations of “receiving a request from a user for performance of a specific task,” which is a well-understood, routine, and conventional activity of receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. The claim also recites, with regard to the limitation of “generating an agent module,” further limitations of “causing the agent module to execute and providing, to the agent module, information from the request;” which is the use of the generic computer component (agent module) to implement the abstract idea, (MPEP § 2106.05(f)), and ”providing,” which is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
Also further, with regard to the limitation of “generating an agent module,” recites “in response to providing the agent module information from the request, receiving a response from the agent module corresponding to performance of the specific task,” and “providing the response to the user,” which are well-understood, routine, and conventional activities of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 1 is subject-matter ineligible.
Claim 13 recites a computing system, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “in response to the request, generating an agent module to perform the specific task.” The activity of “generating an agent module” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The limitation of “generating an agent module” further recites limitations of “selecting a set of agent building blocks from a plurality of available agent building blocks, wherein each agent building block in the plurality of available agent building blocks has a respective assigned function,” and “connecting the set of agent building blocks to form the agent module.” The activities of “selecting” and “connecting” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 13 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “control circuitry,” a “memory,” and “one or more sets of instructions stored in the memory and configured for execution by the control circuity,” and an “agent module,” which are recited at a high level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites limitations of “receiving a request from a user for performance of a specific task,” which is a pre-processing insignificant extra-solution activity of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites, with regard to the limitation of “generating an agent module,” further limitations of “causing the agent module to execute and providing, to the agent module, information from the request;” which is the use of the generic computer component (agent module) to implement the abstract idea, (MPEP § 2106.05(f)), and “providing,” which is a post-processing insignificant extra-solution activity of producing an output, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application.
Also further, with regard to the limitation of “generating an agent module,” recites “in response to providing the agent module information from the request, receiving a response from the agent module corresponding to performance of the specific task,” and “providing the response to the user,” which are post-processing insignificant extra-solution activities of providing status information, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application. Therefore, claim 13 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. the additional elements recited in the claim beyond the identified judicial exception include “control circuitry,” a “memory,” “one or more sets of instructions stored in the memory and configured for execution by the control circuity,” and an “agent module,” which is recited at a high level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea.
The claim also recites limitations of “receiving a request from a user for performance of a specific task,” which is a well-understood, routine, and conventional activity of receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. The claim also recites, with regard to the limitation of “generating an agent module,” further limitations of “causing the agent module to execute and providing, to the agent module, information from the request;” which is the use of the generic computer component (agent module) to implement the abstract idea, (MPEP § 2106.05(f)), and ”providing,” which is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
Also further, with regard to the limitation of “generating an agent module,” recites “in response to providing the agent module information from the request, receiving a response from the agent module corresponding to performance of the specific task,” and “providing the response to the user,” which are well-understood, routine, and conventional activities of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 13 is subject-matter ineligible.
Claim 17 recites a non-transitory computer-readable storage medium, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “in response to the request, generating an agent module to perform the specific task.” The activity of “generating an agent module” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)).
The limitation of “generating an agent module” further recites limitations of “selecting a set of agent building blocks from a plurality of available agent building blocks, wherein each agent building block in the plurality of available agent building blocks has a respective assigned function,” and “connecting the set of agent building blocks to form the agent module.” The activities of “selecting” and “connecting” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). Thus, claim 17 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “non-transitory computer-readable storage medium storing one or more sets of instructions configured for execution by a computing device having control circuitry and memory,” and an “agent module,” which are recited at a high level of generality, and accordingly, are generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites limitations of “receiving a request from a user for performance of a specific task,” which is a pre-processing insignificant extra-solution activity of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites, with regard to the limitation of “generating an agent module,” further limitations of “causing the agent module to execute and providing, to the agent module, information from the request;” which is the use of the generic computer component (agent module) to implement the abstract idea, (MPEP § 2106.05(f)), and “providing,” which is a post-processing insignificant extra-solution activity of producing an output, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application.
Also further, with regard to the limitation of “generating an agent module,” recites “in response to providing the agent module information from the request, receiving a response from the agent module corresponding to performance of the specific task,” and “providing the response to the user,” which are post-processing insignificant extra-solution activities of providing status information, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application. Therefore, claim 17 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. the additional elements recited in the claim beyond the identified judicial exception include a “non-transitory computer-readable storage medium storing one or more sets of instructions configured for execution by a computing device having control circuitry and memory,” and an “agent module,” which are recited at a high level of generality, and accordingly, are generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites limitations of “receiving a request from a user for performance of a specific task,” which is a well-understood, routine, and conventional activity of receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim also recites, with regard to the limitation of “generating an agent module,” further limitations of “causing the agent module to execute and providing, to the agent module, information from the request;” which is the use of the generic computer component (agent module) to implement the abstract idea, (MPEP § 2106.05(f)), and ”providing,” which is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
Also further, with regard to the limitation of “generating an agent module,” recites “in response to providing the agent module information from the request, receiving a response from the agent module corresponding to performance of the specific task,” and “providing the response to the user,” which are well-understood, routine, and conventional activities of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Therefore, claim 17 is subject-matter ineligible.
Claim 2 depends from claim 1. Claim 14 depends from claim 13. Claim 18 depends from claim 17. The claims further recite “obtaining information about a plurality of previously generated agent modules,” which under Step 2A Prong Two, is an insignificant extra-solution activity of receiving data, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Also, under Step 2B, is a well-understood, routine, and conventional activity of receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. Also, the claim recites more details or specifics to the abstract idea of “generating the agent module” “wherein the agent module is generated in accordance with a determination that none of the plurality of previously generated agent modules are configured to perform the specific task,” and accordingly, is merely more specifics to the abstract idea. Therefore, claims 2, 14, and 18 a subject-matter ineligible.
Claim 3 depends directly or indirectly from claim 1. Claim 15 depends directly or indirectly from claim 13. Claim 19 depends directly or indirectly from claim 17. The claims further recite more details or specifics of the abstract idea of “generating the agent module,” where “in accordance with a determination that a first agent module of the plurality of previously generated agent modules is configured to perform the specific task, forgoing generating the agent module,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Therefore, claims 3, 15, and 19 are subject-matter ineligible.
Claim 4 depends directly or indirectly from claim 1. Claim 16 depends directly or indirectly from claim 13. Claim 20 depends directly or indirectly from claim 17. The claims further recite more details or specifics of the abstract idea of “generating the agent module,” where “in accordance with a determination that a first agent module of the plurality of previously generated agent modules is configured to perform the specific task, providing the information from the request to the first agent module,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Therefore, claims 4, 16, and 20 are subject-matter ineligible.
Claim 5 depends directly or indirectly from claim 1. The claim recites more details or specifics of the abstract idea of “selecting a set of agent building blocks,” “wherein the plurality of available agent building blocks include one or more of: a set of data building blocks, a set of operator building blocks, and a set of tool building blocks,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Therefore, claim 5 is subject-matter ineligible.
Claim 6 depends directly or indirectly from claim 1. The claim recites more details or specifics of the abstract idea of “selecting a set of agent building blocks,” in which the claim recites “obtaining specification information about the plurality of available agent building blocks, the specification information for each agent building block comprising the respective assigned function, one or more input data types, and one or more output data types,” and accordingly, is merely more specific to the abstract idea. The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Therefore, claim 6 is subject-matter ineligible.
Claim 7 depends directly or indirectly from claim 1. The claim recites more details or specifics to the additional element of the “agent module,” “wherein the agent module is generated and executed automatically without further input from the user,” and accordingly, is merely more specific to the additional element. Therefore, claim 7 is subject-matter ineligible.
Claim 8 depends directly or indirectly from claim 1. The claim further recites “validating the agent module, wherein the agent module is executed in accordance with a determination that the agent module is valid,” and “in accordance with a determination that the agent module is invalid, generating a revised agent module using invalidity data of the agent module.” The activities of “determining” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a)(2)). The additional elements of the claim does not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claims recite no more than the abstract idea. Therefore, claim 8 is subject-matter ineligible.
Claim 9 depends directly or indirectly from claim 1. The claim recites more details or specifics to the additional element of the “agent module,” “wherein the agent module comprises one or more machine-learning models,” and accordingly, is merely more specific to the additional element. Therefore, claim 9 is subject-matter ineligible.
Claim 10 depends directly or indirectly from claim 1. The claim recites more details or specifics to the additional element of an “agent module,” “wherein the agent module comprises a template building block coupled to an output of the agent module and configured to convert information obtained by the agent module to a natural language response,” and accordingly, is merely more specific to the additional element. Therefore, claim 10 is subject-matter ineligible..
Claim 11 depends directly or indirectly from claim 1. The claim recites more details or specifics to the additional element of an “agent module,” “wherein the agent module comprises a template building block coupled to an input of the agent module and configured to convert a received input to a programming language object,” and accordingly, is merely more specific to the additional element. Therefore, claim 11 is subject-matter ineligible.
Claim 12 depends directly or indirectly from claim 1. The claim further recites “receiving a user identifier for the user,” which under Step 2A Prong Two, is an insignificant extra-solution activity of gathering data, (MPEP §2106.05(g)), that does not serve to integrate the abstract idea into a practical application, and under Step 2B, is a well-understood, routine, and conventional activity of receiving data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea. The claim also recites more details or specifics to the abstract idea of “generating the agent module,” “wherein generating the agent module further comprises: identifying one or more datasets accessible to the user based on the user identifier,” and “connecting the set of agent building blocks to the one or more datasets,” an accordingly, is merely more specific to the abstract idea. Therefore, claim 12 is subject-matter ineligible.
Claim Rejections - 35 U.S.C. § 102
9. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
10. Claims 1-7 and 12-20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by US Published Application 20160380904 to Fuller et al. [hereinafter Fuller].
Regarding claims 1, 13, and 17, Fuller teaches [a] method (Fuller, Abstract) of claim 1, [a] computing system comprising: control circuitry; memory; and one or more sets of instructions (Fuller ¶ 0010 teaches “methods and functions described herein may be implemented as one or more software programs executed by a computer processor or controller circuit [(that is, control circuit)]. In accordance with various embodiments, the methods and functions described herein may be implemented as one or more software programs [(that is, one or more sets of instructions)] executed by a computing device [(that is, computing system)] . . . . [A] nonvolatile computer readable storage medium, a data storage device, or memory device, including instructions that, when executed, cause a processor to perform the methods [(that is, memory)]”) of claim 13, and [a] non-transitory computer-readable storage medium storing one or more sets of instructions (see above Fuller ¶¶ 0010, 0032 (computer-readable storage medium) of claim 17, comprising:
receiving a request from a user for performance of a specific task (Fuller, Fig. 1, teaches a system to select instructions based on a task [Examiner annotations in dashed-line text boxes]:
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Fuller ¶ 0004 teaches a “a system may include a processor configured to receive a request to perform a designated task [(that is, receiving a request from a user for performance of a specific task)]”; Fuller ¶ 0013 & Fig. 1 teaches “to select instructions based on a generic directive, in accordance with certain embodiments of the present disclosure. As used herein, the term ‘generic task’ or “generic directive” refers to an instruction, task, or directive that specifies an action to be performed [(that is, a “specific action” is a specific task)]”);
in response to the request, generating an agent module to perform the specific task (Fuller ¶ 0016 teaches “[t]he nodes may be set up at different times and by different teams, and may include different components, and may run different software or software versions”; Fuller ¶ 0022 teaches that “at least some of the nodes on private network 102 may be configured with an agent application 126”; Fuller ¶ 0024 teaches “translation manager 101 may be implemented as a software application that may be executed at one or more of the nodes, or as a circuit or other physical component of one or more of the nodes”; Fuller ¶ 0025 teaches a “translation manager 101 may also store, retrieve, or both retrieve and store information regarding the specific configurations of nodes within the network. . . . Accordingly, the translation manager 101 may allow a user having little or no specific knowledge of the configurations of nodes on a network to perform a variety of tasks using those nodes [(that is, “configurations of nodes” is in response to the request, generating an agent module to perform the specific task)]”;
[Examiner notes that the plain and ordinary meaning of the term “generating an agent module” is selecting and configuring nodes to act on a request, where the broadest reasonable interpretation of the term “generating an agent module” covers the teachings of the translation manager 101 of Fuller, which is not inconsistent with the Applicant’s disclosure. (MPEP § 2111; see Specification ¶ 0057 (“an agent may receive a user query (e.g., requesting information about clinical trials), generate a structure application programming interface (API) call, use the generated API call to query a remote serve to retrieve relevant result”)]), wherein the generating includes:
selecting a set of agent building blocks from a plurality of available agent building blocks (Fuller, Fig. 2, teaches a system configured to select instructions based on a generic directive [Examiner annotations in dashed-line text boxes]:
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Fuller ¶ 0026 teaches “system 200 configured to select instructions based on a generic directive, in accordance with certain embodiments of the present disclosure; Fuller ¶ 0028 teaches “translation manager 202 may include a node configuration database 210, which may include information regarding nodes within a network, such as node identifiers, node configuration information, node operating systems, available node applications or node functions, other information, or any combination thereof [(that is, a plurality of available agent building blocks)]”),
wherein each agent building block in the plurality of available agent building blocks has a respective assigned function (Fuller ¶ 0044 teaches “running a set of applications 232 and an agent application 231. The types, varieties, numbers, and versions of processors, operating systems, and application sets for each node of network 222 may be different, and the specific configuration information for the nodes may be stored in the node configurations database 210 [(that is, “application set” and “agent” are , wherein each agent building block in the plurality of available agent building blocks has a respective assigned function)]”); and
connecting the set of agent building blocks to form the agent module (Fuller, Fig. 3, teaches connecting tables to form an execution instructions table 312 [Examiner annotations in dashed-line text boxes]:
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Fuller ¶ 0046 teaches “a diagram 300 of data elements used by a system configured to select instructions based on a generic directive . . . . Diagram 300 may depict a number of generic tasks or directives 310 received by a translation manager. . . . [T]ables 302, 304, and 306 may be used to identify configurations and settings of nodes associated with a network. In some examples, a table 308 may include instruction sets for performing a variety of tasks at a node based on identified configuration settings for the node; Fuller ¶ 0054 teaches “translation manager 202 may determine a set of instructions configured to direct the specified system to perform the specified task [(that is, a specific task )], as depicted in the execution instructions table 312 [(that is, “tables 302, 304, and 306,” to form “execution instructions table 312” is connecting the set of agent building blocks to form the agent module)]”);
causing the agent module to execute and providing, to the agent module, information from the request (Fuller ¶ 0054 teaches “Tasks table 310 may include a “task name” field identifying the type of generic task or directive to be performed, and a “system ID” field identify a system or node on which to perform the task. A translation manager 202 may determine a set of instructions configured to direct the specified system to perform the specified task, as depicted in the execution instructions table 312. For example, execution instructions table 312 may include a “system configuration ID” field identifying a system configuration (e.g. a specific application loaded on a specific system or node), and an “instruction set” field identifying a set of instructions to execute the task on the corresponding system configuration [(that is, “tasks table” is causing the agent module to execute and providing, to the agent module, information from the request )]”);
in response to providing the agent module information from the request, receiving a response from the agent module corresponding to performance of the specific task (Fuller ¶ 0030 teaches “the translation manager [of the translation manager 200] may receive a task selection, and may retrieve and provide supplementary information on the selected task to a user prior to executing the task [(that is, receiving a response from the agent module corresponding to performance of the specific task)]”); and
providing the response to the user (Fuller ¶ 0035 teaches a “GUI may also provide a user or device with information, such as information regarding the results of a requested task or directive, or a prompt for additional information [(that is, “results” or “prompting” is providing the response to the user)]”).
Regarding claims 2, 14, and 18, Fuller teaches all of the limitations of claims 1, 13, and 17, respectively, as described above in detail.
Fuller teaches -
further comprising
obtaining information about a plurality of previously generated agent modules (Fuller, Fig. 4, teaches instruction selection based on a generic directive [Examiner annotations in dashed-line text boxes]:
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Fuller ¶ 0064 teaches “searching a system list, at 404. . . . [I]f the task received at 402 identifies a target system or node, the method 400 may include verifying that the designated target node is recognized as part of the network or overall system”; Fuller ¶ 0067 teaches “searching the knowledge base for supplementary information for a set of instructions, for a particular task, for one or more system configurations, for other information, at 408”; Fuller ¶ 0069 teaches “determining, at 414, whether any of the applications available at the target node correspond to the applications for which instructions are available for performing the task, as determined at 408 [(that is, with regard to the “system list,” “knowledge base,” “target nodes” is obtaining information about a plurality of previously generated agent modules)]”),
wherein the agent module is generated in accordance with a determination that none of the plurality of previously generated agent modules are configured to perform the specific task (Fuller ¶ 0056 teaches “the translation manager 202 may access a priority list designating an order in which to select applications [(that is, the agent module is generated)] , for example if selected applications are busy or not currently operational [(that is, “busy” or “non-operational” is in accordance wit ha determination that none of the plurality of previously generated agent modules are configured to perform the specific task)]”).
Regarding claims 3, 15, and 19, Fuller teaches all of the limitations of claims 2, 14, and 19, respectively, as described above in detail.
Fuller teaches -
further comprising,
in accordance with a determination that a first agent module of the plurality of previously generated agent modules is configured to perform the specific task (Fuller ¶ 0064 teaches “The method 400 may include determining if a matching system was found, at 406. If the system is not found or if the agent application does not respond, at 406, the method 400 may include, at 418, returning a notification of the inability to perform the task in response to the received task from 402. For example, an error notification may be provided to a user device, or a failure notification may be returned to a requesting node or system [(that is, in accordance with a determination that a first agent module of the plurality of previously generated agent modules is configured to perform the specific task)]”),
forgoing generating the agent module (Fuller ¶ 0036 teaches “the translation module 208 may automatically determine one or more target systems or nodes based on the generic task or directive and may determine the respective configurations of the one or more target systems or nodes. . . . The translation module 208 may further provide the configuration-specific instructions to the target node, to a user or system that submitted the generic task, to an agent application 231, or any combination thereof. In some embodiments, the translation module 208 may store the instructions to a memory, or may otherwise utilize the instructions [(that is, “provide to a user or system that submitted the generic task,” or “store the instructions” is forgoing generating the agent module)]”).
Regarding claims 4, 16, and 20, Fuller teaches all of the limitations of claims 3, 15, and 19, respectively, as described above in detail.
Fuller teaches -
further comprising,
in accordance with a determination that a first agent module of the plurality of previously generated agent modules is configured to perform the specific task (Fuller ¶ 0064 teaches “The method 400 may include determining if a matching system was found, at 406. If the system is not found or if the agent application does not respond, at 406, the method 400 may include, at 418, returning a notification of the inability to perform the task in response to the received task from 402. For example, an error notification may be provided to a user device, or a failure notification may be returned to a requesting node or system [(that is, in accordance with a determination that a first agent module of the plurality of previously generated agent modules is configured to perform the specific task)]”),
providing the information from the request to the first agent module (see above, Fuller, Fig. 3; Fuller ¶ 0054 & Fig. 3 teaches “Tasks table 310 depicts an example selection of generic tasks or directive. Tasks table 310 [(that is, information from the request)] may include a “task name” field identifying the type of generic task or directive to be performed, and a “system ID” field identify a system or node on which to perform the task [(that is, “task table 310” to the “execution instructions table 312” is providing the information from the request to the first agent module)]”).
Regarding claim 5, Fuller teaches all of the limitations of claim 1, as described above in detail.
Fuller teaches -
wherein the plurality of available agent building blocks include one or more of:
a set of data building blocks,
a set of operator building blocks, and
a set of tool building blocks (Fuller ¶ 0011 teaches “Middleware can communicatively couple software components and enterprise applications, and can include web servers, application servers, content management systems, and similar tools that support application development and delivery [(that is, such “tools” are a set of tool building blocks)]”).
Regarding claim 6, Fuller teaches all of the limitations of claim 1, as described above in detail.
Fuller teaches -
further comprising
obtaining specification information about the plurality of available agent building blocks, the specification information for each agent building block comprising the respective assigned function, one or more input data types, and one or more output data types (Fuller ¶ 0028 teaches “translation manager 202 may include a node configuration database 210, which may include information regarding nodes within a network, such as node identifiers, node configuration information, node operating systems, available node applications or node functions [(that is, the respective assigned function)], other information, or any combination thereof [(that is, “node configuration database” is obtaining specification information about the plurality of available agent building blocks)]”; Fuller ¶ 0037 teaches “the input data may be based on information provided by a user, may be generated by the translation manager 202, may be generated by another node coupled to network 222, may originate from another source, or any combination thereof [(that is, “based on information” is one or more input data types)]”; Fuller ¶ 0016 teaches “various nodes may include different types of devices, and may have different combinations of physical architectures, operating systems, computer software, drivers, or other variable parameters [(that is, “different types of devices” inherently is the specification information . . . comprising . . . one or more input data types, and one or more output data types)]”).
Regarding claim 7, Fuller teaches all of the limitations of claim 1, as described above in detail.
Fuller teaches –
wherein the agent module is generated and executed automatically without further input from the user (Fuller ¶ 0036 teaches “The translation module 208 may be configured to receive an indication of a designated generic task or directive, and may determine specific instructions which may be used to implement the task. In some embodiments, the translation module 208 may automatically determine one or more target systems or nodes based on the generic task or directive and may determine the respective configurations of the one or more target systems or nodes. The translation module 208 may automatically select execution instructions for the particular configurations of a target system or node based on the generic task or directive [(that is, wherein the agent module is generated and executed automatically without further input from the user)]”).
Regarding claim 12, Fuller teaches all of the limitations of claim 1, as described above in detail.
Fuller teaches -
further comprising:
receiving a user identifier for the user (Fuller ¶ 0036 teaches the “translation module 208 may further provide the configuration-specific instructions to the target node, to a user or system that submitted the generic task, to an agent application 231”; Fuller ¶ 0063 teaches “the generic task or directive may be data identifying a task or directive to be performed, and in some embodiments may also include an indication of a target computing node or system. . . . For example, the characteristics identified by the indication may include a system ID or other specific node identifier [(that is, receiving a user identifier for the user)]”); and
wherein generating the agent module further comprises:
identifying one or more datasets accessible to the user based on the user identifier (Fuller ¶ 0047 & Fig. 3 teaches “the node configurations database 210 may store data elements as depicted in system identification table 302”; Fuller ¶ 0058 teaches “translation manager 202 may receive input data indicating the task “create website” and a system ID 1. The translation manager 202 may determine what parameters may be used to perform the designated task and the corresponding parameter IDs, such as by consulting parameters table 304 or instructions table 308. For example, the translation manager 202 may determine that the task “create website” can be performed by parameter IDs 1 and 2 (e.g. corresponding to IIS v. 8 or Apache v. 2.3). The translation manager 202 may then determine systems or nodes having the eligible parameters, such as by consulting system configuration table 306. For example, the translation manager 202 may determine that nodes corresponding to both system ID 1 and system ID 2 include an eligible parameter. Because system ID 1 is among the determined systems, the translation manager 202 may retrieve instructions from table 308 corresponding to the determined parameter ID 1 and the designated task “create website” [(that is, “system ID 1” or “system ID 2” is identifying one or more datasets accessible to the user based on the user identifier)]); and
connecting the set of agent building blocks to the one or more datasets (Fuller ¶ 0047 teaches “The translation manager 202 may determine what parameters may be used to perform the designated task and the corresponding parameter IDs, such as by consulting parameters table 304 or instructions table 308 [(that is, connecting the set of agent building blocks to the one or more datasets)]”).
Claim Rejections – 35 USC § 103
11. 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.
12. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
13. This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
14. Claims 8-11 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20160380904 to Fuller et al. [hereinafter Fuller] in view of US Published Application 20220391729 to Duford et al. [hereinafter Duford].
Regarding claim 8, Fuller teaches all of the limitations of claim 1, as described above in detail.
Though Fuller teaches a network computing node determination to perform a task and access a configurations database, Fuller, however, does not explicitly teach -
validating the agent module, wherein the agent module is executed in accordance wit ha determination that the agent module is valid; and
in accordance with a determination that the agent module is invalid, generating a revised agent module using invalidity data of the agent module.
Bit Duford teaches -
further comprising:
validating the agent module,
wherein the agent module is executed (Duford ¶ 0103 teaches “the orchestrator engine is responsible for installation of a process and concurrent execution of one or more process instances [(that is, the agent module is executed)]”) in accordance with a determination that the agent module is valid (Duford ¶ 0103 teaches “section may contain information about the topics to publish and subscribe on and may contain specific POE information. The [processor orchestrator engine (POE)] may fail to start if it cannot honor the deployment descriptor. When processing a deployment descriptor, the POE may execute the following steps. Step 1, validate the process against its schema [(that is, validating the agent module, wherein the agent module is executed in accordance with a determination that the agent module is valid)]. Step 2, subscribe to the required topics. Step 3, update its registered webhooks endpoints”); and
in accordance with a determination that the agent module is invalid (Duford ¶ 0119 teaches “If the process orchestrator fails to look up the process an exception may be logged. Failure may mean that the process cannot be found or is invalid”),
generating a revised agent module using invalidity data of the agent module (Duford ¶ 0131 teaches “[a] task may be executed by an agent and may require specific roles in order to restrict which agent can be assigned to it. A task may define a rendering to be shown when it is executed by a human agent. Human-in-the-loop is a specific example of this where user interface is shown to the human agent for validation and correction of an AI model inference [(that is, “invalidity” prompts generating a revised agent module using invalidity data of the agent module)]”).
Fuller and Duford are from the same or similar field of endeavor. Fuller teaches instruction selection based on a generic task or directive request. Duford teaches machine learning (ML) algorithms can be useful for processing events in an event-driven architecture, and can be configured to make predictions and/or perform other operations.
Thus it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Fuller pertaining to requested task fulfillment by an agent with the agent validation of Duford.
The motivation to do so is because a “modular approach operating transfer learning to enable continuous model improvement [that also] enables operations and monitoring of a plurality of AI models at the same time.” (Duford ¶ 0069).
Regarding claim 9, Fuller teaches all of the limitations of claim 1, as described above in detail.
Though Fuller teaches a network computing node determination to perform a task and access a configurations database, Fuller, however, does not explicitly teach –
wherein the agent module comprises one or more machine-learning models.
But Duford teaches -
wherein the agent module comprises one or more machine-learning models (Duford ¶ 0080 teaches “Events published to the MC 234 may comprise a partial or a full open neural network exchange (ONNX) representation of a model, metadata about what the model is designed to solve, a reference to the latest data used to train the model and a reference to the original model format [(that is, one or more machine-learning models)]”).
Fuller and Duford are from the same or similar field of endeavor. Fuller teaches instruction selection based on a generic task or directive request. Duford teaches machine learning (ML) algorithms can be useful for processing events in an event-driven architecture, and can be configured to make predictions and/or perform other operations.
Thus it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Fuller pertaining to requested task fulfillment by an agent with the AI agents of Duford.
The motivation to do so is because a “modular approach operating transfer learning to enable continuous model improvement [that also] enables operations and monitoring of a plurality of AI models at the same time.” (Duford ¶ 0069).
Regarding claim 10, Fuller teaches all of the limitations of claim 1, as described above in detail.
Though Fuller teaches a network computing node determination to perform a task and access a configurations database, Fuller, however, does not explicitly teach –
wherein the agent module comprises a template building block coupled to an output of the agent module and
configured to convert information obtained by the agent module to a natural language response.
But Duford teaches -
wherein the agent module comprises a template building block coupled to an output of the agent module (Duford ¶ 0151 teaches a “dashboard provides task level KPIs to allow a user to assess performance of the AI solution (e.g., is a ranking useful, is the ranking in the right order, how many times a template is identified, if so is it the right template, etc.) [(that is, the “AI solution” is an output of the agent module)]. As previously mentioned, the dashboard allows monitoring of business level KPIs as well as lower level model performance metric [(that is, the agent module comprises a template building block coupled to an output of the agent module)]”) and
configured to convert information obtained by the agent module to a natural language response (Duford ¶ 0020 teaches “determining that a format of the data output by the first AI agent is not compatible with a format of the data to input to the second AI agent; determining a transform to apply to the data output by the first AI agent to convert the data output by the first AI agent to the format of the data to input to the second AI agent; and applying the transform to the data output by the first AI agent [(that is, a template building block . . . configured to convert information obtained by the agent module)]” ; Duford ¶ 0070 teaches “AI components 210-214 may comprise, for example but without being limitative, . . . a natural language processing (NLP) module . . . . [(that is, "an AI module based on NLP,” which is ”to convert information . . . to a natural language response)]).
Fuller and Duford are from the same or similar field of endeavor. Fuller teaches instruction selection based on a generic task or directive request. Duford teaches machine learning (ML) algorithms can be useful for processing events in an event-driven architecture, and can be configured to make predictions and/or perform other operations.
Thus it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Fuller pertaining to requested task fulfillment by an agent with the conversion of output data by a natural language processing module of Duford.
The motivation to do so is because a “modular approach operating transfer learning to enable continuous model improvement [that also] enables operations and monitoring of a plurality of AI models at the same time.” (Duford ¶ 0069).
Regarding claim 11, Fuller teaches all of the limitations of claim 1, as described above in detail.
Though Fuller teaches a network computing node determination to perform a task and access a configurations database, Fuller, however, does not explicitly teach –
wherein the agent module comprises a template building block coupled to an output of the agent module and
configured to convert a received input to a programming language object.
But Duford teaches -
wherein the agent module comprises a template building block coupled to an input of the agent module (Duford ¶ 0151 teaches a “dashboard provides task level KPIs to allow a user to assess performance of the AI solution (e.g., is a ranking useful, is the ranking in the right order, how many times a template is identified, if so is it the right template, etc.) [(that is, the “AI solution” is an output of the agent module)]. As previously mentioned, the dashboard allows monitoring of business level KPIs as well as lower level model performance metric [(that is, the agent module comprises a template building block coupled to an output of the agent module)]”) and
configured to convert a received input to a programming language object (Duford ¶ 0020 teaches “determining that a format of the data output by the first AI agent is not compatible with a format of the data to input to the second AI agent; determining a transform to apply to the data output by the first AI agent to convert the data output by the first AI agent to the format of the data to input to the second AI agent; and applying the transform to the data output by the first AI agent [(that is, a template building block . . . configured to convert information obtained by the agent module)]”; Duford ¶ 0198 teaches “AI agents in the library of AI agents may be associated with a corresponding container. The container may include various information about the AI agent. The container may provide a unifying representation for all operations in a workflow. In other words, the container may provide a common interface for all nodes in a workflow. The container may allow various models and/or operations to be included in the workflow, regardless of what programming language the models and/or operations were written in. . . . By declaring the inputs and/or outputs of each node in the workflow, the workflow deployment may be type-checked before being put into use [(that is, “regardless of programming language” is a template building block . . . configured to convert a received input to a programming language object)]”).
Fuller and Duford are from the same or similar field of endeavor. Fuller teaches instruction selection based on a generic task or directive request. Duford teaches machine learning (ML) algorithms can be useful for processing events in an event-driven architecture, and can be configured to make predictions and/or perform other operations.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Fuller pertaining to requested task fulfillment by an agent with the input data conversion of to provide interoperability of various workflow models and/or operations regardless of programming language of Duford.
The motivation to do so is because a “modular approach operating transfer learning to enable continuous model improvement [that also] enables operations and monitoring of a plurality of AI models at the same time.” (Duford ¶ 0069).
Conclusion
15. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(US Published Application 20200279635 to Letterie et al.) teaches creating decision model(s) using patient training data; receive patient input data for at least one patient; provide the patient input data as input to the decision model(s); obtain output from the decision model(s); and generate recommendations for patient treatment for presentation via a user interface based on the output of the decision model. The decision model(s) may be created using random decision forests. The output from the decision model(s) may include confidence percentages for potential outcomes. The recommendations may be generated based on the confidence percentages.
(Teemu Leppanen, “Distributed Artificial Intelligence with Multi-Agent Systems for MEC,” IEEE (2019)) teaches Multi-access Edge Computing (MEC) system architecture, as defined by the ETSI standards, is modeled as a multi-agent system. MEC system management services and application execution components are designed as software agents, facilitating distributed artificial intelligence capabilities in their operation and cooperation. Further, the integration of current agent technologies into the standardized MEC system is discussed. Lastly, a case study is presented on how to integrate an existing Internet of Things agent framework and agent-based edge application seamlessly to the MEC system.
(Isoviita et al., "Open Source Infrastructure for Health Care Data Integration and Machine Learning Analyses," Am. Society of Clinical Oncology (2018)) teaches a cloud-based machine learning system (CLOBNET) that is an open-source, lean infrastructure for electronic health record (EHR) data integration and is capable of extract, transform, and load (ETL) processing. CLOBNET enables comprehensive analysis and visualization of structured EHR data.
16. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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