DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to the submission filed February 3, 2025. Claims 1-20 are pending.
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. Claim 1 is directed to a method of determining a natural language output regarding a digital network using a large language model. Claim 1 recites limitations for:
formulating a desired output dependent upon information associated with the digital network; can be achieved by a person creating (mentally or with pen and paper) a desired output of information.
providing, to the large language model, the information associated with the digital network and a first prompt requesting the large language model to generate a query dependent upon the information and the desired output; can be achieved by the person, using algorithms of rules and principles of natural language processing to determine a query or request based on network information and the desired output.
receiving, from the large language model, the query dependent upon the information and the desired output; can be achieved by the person writing the query on paper.
determining, dependent upon a graph database, a response to the query with the graph database being representative of at least a portion of the digital network; can be achieved by the person accessing a database and determining the correct query response.
providing, to the large language model, the response and a second prompt requesting the large language model to generate the natural language output dependent upon the response; can be achieved by the person, using algorithms of rules and principles of natural language processing to determine the natural language output based on the response.
receiving, from the large language model, the natural language output dependent upon the response and associated with the digital network; can be achieved by the person writing the output on paper.
The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the large language model, which is recited as a generic model that can be implemented via mathematical algorithms and using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic large language model amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea. The claims are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the generic large language model and modules to perform the various steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 2-20 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing or manipulating functions and commands for modifying and replacing words, analyzing words and information, performing mathematical calculations using natural language rules and principles, performing recognition processing, and organizing and manipulating database entries.
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.
Claims 1-4 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ferenczi (US Patent Application Publication No. 2025/0111142) in view of DiFonzo et al (US Patent Application Publication No. 2022/0414228), hereinafter DiFonzo.
Regarding claim 1, Ferenczi teaches a method of determining a natural language output regarding a digital network using a large language model comprising:
formulating a desired output dependent upon information associated with the digital network [para 0011 – information wanted];
providing, to the large language model, the information associated with the digital network and a first prompt requesting the large language model to generate a query dependent upon the information and the desired output [para 0014-0016; 0029-0031 0034-0038; 0042; 0051-0055 -- upon receiving the response request 106 from the receiver entity 103, including the multiple prompts 115 and corresponding keys 121, the sender entity 109 can submit the prompts 115 as inputs to a LLM 118 with a request];
receiving, from the large language model, the query dependent upon the information and the desired output [para 0014-0026; 0029-0031; 0051-0055 -- the output of the LLM 118 can include a plurality of reformatted prompt];
determining, dependent upon a database, a response to the query with the database being representative of at least a portion of the digital network [0014-0016; 0029-0031 0034-0038; 0042; 0051-0055 -- the LLM 118 can be further trained to generate responses to the prompts 115 using the data obtained from the privacy database 112 as inputs to the LLM 118 along with response formatting rules 221 that define how to generate the responses to each of the prompts 115 based at least in part on the received data from the privacy database 112, user data rules 224, and/or other type of input that can be used to format];
providing, to the large language model, the response and a second prompt requesting the large language model to generate the natural language output dependent upon the response [0014-0016; 0029-0031 0034-0038; 0042; 0051-0055 -- the LLM 118 can be further trained to generate responses to the prompts 115 using the data obtained from the privacy database 112 as inputs to the LLM 118 along with response formatting rules 221 that define how to generate the responses to each of the prompts 115 based at least in part on the received data from the privacy database 112, user data rules 224, and/or other type of input that can be used to format]; and
receiving, from the large language model, the natural language output dependent upon the response and associated with the digital network [0014-0016; 0029-0031 0034-0038; 0042; 0051-0055 -- the LLM 118 can be further trained to generate responses to the prompts 115 using the data obtained from the privacy database 112 as inputs to the LLM 118 along with response formatting rules 221 that define how to generate the responses to each of the prompts 115 based at least in part on the received data from the privacy database 112, user data rules 224, and/or other type of input that can be used to format].
Ferenczi fails to specifically teach the database is a graph database. DiFonzo teaches methods and systems for natural language processing of graph database queries of digital networks [Fig 4B; 5; para 0007-0008; 0030-0031; 0041; 0043] and specifically teaches the system is advantageous in facilitating ease-of-use and enable efficient access to powerful graph database analytical tools [para 0006]. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the graph database security techniques suggested by DiFonzo, in the privacy -preserving system of Ferenczi, for the purpose of improving the privacy and security measures of the system to ensure user information remains confidential and secure, thereby improving the system and the user’s experience.
Regarding claim 2, the combination of Ferenczi and DiFonzo teaches the desired output is at least one of the following: an explanation as to why one device on the digital network failed to connect to another device on the digital network; an analysis as to how the digital network responds to an outage of at least one specified device; and an answer to an inquiry asking how many devices and the names of those devices that are connected to a first device on the digital network [DiFonzo para 0048-0053; 0070-0076 – graph-model based cybersecurity analysis system].
Regarding claim 3, the combination of Ferenczi and DiFonzo teaches wherein the natural language output is indicative of an outcome of an event affecting the digital network as represented by the graph database [DiFonzo para 0048-0053; 0070-0076 – graph-model based cybersecurity analysis system].
Regarding claim 4, the combination of Ferenczi and DiFonzo teaches the query includes at least a portion of the information associated with the digital network [DiFonzo para 0048-0053; 0070-0076 – graph-model based cybersecurity analysis system].
Regarding claims 5 and 9-11, the combination of Ferenczi and DiFonzo teaches before providing the information to the large language model, identifying multiple words in the information associated with the digital network that are to be encrypted [Ferenczi encrypted responses; 0014-0017; 0026-27; 0029-0031 0034-0038; 0042; 0051-0055]; replacing each word of the multiple words that are to be encrypted with a corresponding key to form encrypted information [Ferenczi encrypted responses; 0014-0017; 0026-27; 0029-0031 0034-0038; 0042; 0051-0055]; and providing the encrypted information, in place of the unencrypted information, to the large language model along with the first prompt [Ferenczi para 0037-0038].
Regarding claim 6, the combination of Ferenczi and DiFonzo teaches the step of identifying multiple words that are to be encrypted is performed by a computer processor using name recognition artificial intelligence software [DiFonzo para 0057-0058].
Regarding claims 7-8, the combination of Ferenczi and DiFonzo teaches each key that replaces each corresponding word to be encrypted maintains a similar format to the corresponding word so that the encrypted information maintains a similar context to the unencrypted information [DiFonzo 0059-0060 – semantic similarity checking].
Regarding claim 14, the combination of Ferenczi and DiFonzo teaches generating, by the large language model, the natural language output dependent upon the response [0014-0016; 0029-0031 0034-0038; 0042; 0051-0055 -- the LLM 118 can be further trained to generate responses to the prompts 115 using the data obtained from the privacy database 112 as inputs to the LLM 118 along with response formatting rules 221 that define how to generate the responses to each of the prompts 115 based at least in part on the received data from the privacy database 112, user data rules 224, and/or other type of input that can be used to format].
Regarding claim 15, the combination of Ferenczi and DiFonzo teaches the graph database is stored at a location distant from the large language model [Ferenczi para 0040; DiFonzo para 0048-0053; 0070-0076 – graph-model based cybersecurity analysis system].
Regarding claim 16, the combination of Ferenczi and DiFonzo teaches the graph database is stored at a location that is at least partially under the control of a user such that the graph database is not provided to the large language model [Ferenczi para 0040; DiFonzo para 0048-0053; 0070-0076 – graph-model based cybersecurity analysis system].
Regarding claim 17, the combination of Ferenczi and DiFonzo teaches the step of determining the response to the query is performed by a graph database management system with access to the graph database [DiFonzo para 0048-0053; 0070-0076 – graph-model based cybersecurity analysis system].
Regarding claim 18, the combination of Ferenczi and DiFonzo teaches the graph database management system is a Neo4j system [DiFonzo para 0047; 0052]
Regarding claim 19, the combination of Ferenczi and DiFonzo teaches the query is a Cypher query and the graph database management system is configured to receive the Cypher query and generate a response to the Cypher query dependent upon the graph database [DiFonzo para 0114].
Regarding claim 20, the combination of Ferenczi and DiFonzo teaches the step of determining the response to the query is performed automatically by the graph database management system in response to the reception of the query [DiFonzo para 0048-0053; 0070-0076; 0114 – graph-model based cybersecurity analysis system].
Allowable Subject Matter
Claims 12-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the rejections under 35 USC 101 can be overcome.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659
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