DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Using the subject matter eligibility test from page 74621 of the Federal Register Notice titled “2014 Interim Guidance on Patent Subject Matter Eligibility,” a two-step process is performed. Under step 1, the claims are analyzed to determine if the claim is directed to a process, machine, article of manufacture, or composition of matter. In this case, claims 1-10 are directed to a system, which is a machine or an article of manufacture; claims 11-19 are directed to a method, which is a process; claim 20 is directed to computer-readable media, which is a machine or an article of manufacture. Step 2A (part 1 of the Mayo test), using the guidance from pages 50-57 of the Federal Register Vol. 84 No. 4 from Monday, January 7, 2019, requires applying a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception, determining if the claim is directed to a law of nature, a natural phenomenon, or an abstract idea. In this case, claim 1 recites obtaining text segments, and composing a prompt, which are mental processes. In Prong Two, examiners evaluate whether the judicial exception is integrated into a practical application that imposes a meaningful limit on the judicial exception. In this case, additional limitations of receiving and presenting data and prompting a model using a prompt are mere extrasolution activity, and do not integrate the abstract idea into a practical application.
Step 2B (part 2 of the Mayo test) requires analyzing the claims to determine if they recite additional elements that amount to significantly more than the judicial exception. In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea itself.
Regarding claims 1, 11, and 20, obtaining text segments and composing a prompt are mental processes, which is an abstract idea. For example, a human could determine similarity of text segments to a query, and compose a prompt using a template by filling in placeholders. Additional limitations of receiving and presenting data and prompting a model using a prompt are mere extrasolution activity, while use of processor, memory, computer readable media, and a generative AI model are generic computing components, and do not integrate the abstract idea into a practical application or constitute significantly more.
Regarding claims 2 and 12, converting a query into an embedding is a mental process or a mathematical calculation, which are abstract ideas without integration into a practical application and without significantly more.
Regarding claims 3 and 13, measuring similarities is a mental process or a mathematical calculation, which are abstract ideas without integration into a practical application and without significantly more.
Regarding claims 4 and 14, ranking and identifying top matches are mental processes, which is an abstract idea without integration into a practical application and without significantly more.
Regarding claims 5 and 15, the limitations are further clarifications of the above abstract ideas.
Regarding claims 6 and 16, dividing documents into text section is a mental process, which is an abstract idea without integration into a practical application and without significantly more.
Regarding claims 7 and 17, converting text into embeddings and performing indexing are mental processes or mathematical calculations, which are abstract ideas without integration into a practical application and without significantly more.
Regarding claims 8-9 and 18-19, inserting information into a prompt is a mental process, which is an abstract idea. Additional limitations of fetching documents is mere extrasolution activity, and does not integrate the abstract idea into a practical application or constitute significantly more.
The limitations of the claims, taken alone, do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Applicable case law cited in the Federal Register includes, but is not limited to: Alice Corp., 134 S. Ct. at 2355-56, Digitech Image Tech., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014), Benson, 409 U.S. at 63.
See "Preliminary Examination Instructions in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.," dated June 25, 2014, and the Federal Register notice titled "2014 Interim Guidance on Patent Subject Matter Eligibility" (79 FR 74618).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 11-14, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen et al. (US 2026/0095487 A1), hereinafter referred to as Chen.
Regarding claim 1, Chen teaches:
A computing system comprising:
memory (Fig. 5 element 504, para [0069], where memory is used);
one or more hardware processors coupled to the memory (Fig. 5 element 502, para [0069], where a processor is used); and
one or more computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations (Fig. 5 element 562, para [0069], where storage device is used) comprising:
receiving, from a user interface, a query entered by a user, wherein the query inquires usage of a set of dashboards associated with a process (Fig. 1 element 102, para [0021], where an input query is received, and Fig. 4A, para [0065], where the query is about the policies displayed on the interface or dashboard);
obtaining, in runtime, one or more text segments that are semantically related to the query (Fig. 1 elements 105, 107, para [0024], where the query is embedded into a vector and compared with a database of predefined query embedding vectors to find similar queries and corresponding instructions);
composing, in runtime, a prompt using a prompt template, wherein the prompt template includes at least one placeholder for receiving the one or more text segments (Fig. 2, elements 204, 206, para [0040], where a template prompt is populated with information including the instructions);
prompting, in runtime, a generative artificial intelligence (AI) model using the prompt to generate a guide instructing usage of at least one dashboard in response to the query (para [0025-26], [0040], where the prompt is sent to the generative machine learning model and an output is generated, and Fig. 4B, para [0066], where recommendations on the policies from the dashboard are displayed); and
presenting the guide generated by the generative AI model on the user interface (Fig. 4B element 404, para [0066], where the query response is outputted in an interface).
Regarding claim 2, Chen teaches:
The computing system of claim 1, wherein the operation of obtaining one or more text segments semantically related to the query comprises converting the query into an input vector embedding (Fig. 1 elements 105, 107, para [0024], where the query is embedded into a vector and compared with a database of predefined query embedding vectors to find similar queries and corresponding instructions).
Regarding claim 3, Chen teaches:
The computing system of claim 2, wherein the operation of obtaining one or more text segments semantically related to the query further comprises measuring similarities between the input vector embedding and a plurality of vector embeddings stored in a vector database (Fig. 1 elements 105, 107, para [0024], where the query is embedded into a vector and compared with a database of predefined query embedding vectors to find similar queries and corresponding instructions such as by using Euclidian or cosine distance).
Regarding claim 4, Chen teaches:
The computing system of claim 3, wherein the operation of obtaining one or more text segments semantically related to the query further comprises ranking the similarities and identifying top N vector embeddings that are associated with highest similarities, wherein N is a predefined positive integer (Fig. 1 elements 105, 107, para [0024], where the similarity is determined using a k-nearest neighbor search algorithm, the k-nearest neighbors interpreted as the N embeddings with the highest similarity).
Regarding claim 11, Chen teaches:
A computer-implemented method comprising:
receiving, from a user interface, a query entered by a user, wherein the query inquires usage of a set of dashboards associated with a process (Fig. 1 element 102, para [0021], where an input query is received, and Fig. 4A, para [0065], where the query is about the policies displayed on the interface or dashboard);
obtaining, in runtime, one or more text segments that are semantically related to the query (Fig. 1 elements 105, 107, para [0024], where the query is embedded into a vector and compared with a database of predefined query embedding vectors to find similar queries and corresponding instructions);
composing, in runtime, a prompt using a prompt template, wherein the prompt template includes at least one placeholder for receiving the one or more text segments (Fig. 2, elements 204, 206, para [0040], where a template prompt is populated with information including the instructions);
prompting, in runtime, a generative artificial intelligence (AI) model using the prompt to generate a guide instructing usage of at least one dashboard in response to the query (para [0025-26], [0040], where the prompt is sent to the generative machine learning model and an output is generated, and Fig. 4B, para [0066], where recommendations on the policies from the dashboard are displayed); and
presenting the guide generated by the generative AI model on the user interface (Fig. 4B element 404, para [0066], where the query response is outputted in an interface).
Regarding claim 12, Chen teaches:
The computer-implemented method of claim 11, wherein obtaining one or more text segments semantically related to the query comprises converting the query into an input vector embedding (Fig. 1 elements 105, 107, para [0024], where the query is embedded into a vector and compared with a database of predefined query embedding vectors to find similar queries and corresponding instructions).
Regarding claim 13, Chen teaches:
The computer-implemented method of claim 12, wherein obtaining one or more text segments semantically related to the query further comprises measuring similarities between the input vector embedding and a plurality of vector embeddings stored in a vector database (Fig. 1 elements 105, 107, para [0024], where the query is embedded into a vector and compared with a database of predefined query embedding vectors to find similar queries and corresponding instructions such as by using Euclidian or cosine distance).
Regarding claim 14, Chen teaches:
The computer-implemented method of claim 13, wherein obtaining one or more text segments semantically related to the query further comprises ranking the similarities and identifying top N vector embeddings that are associated with highest similarities, wherein N is a predefined positive integer (Fig. 1 elements 105, 107, para [0024], where the similarity is determined using a k-nearest neighbor search algorithm, the k-nearest neighbors interpreted as the N embeddings with the highest similarity).
Regarding claim 20, Chen teaches:
One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method, the method (Fig. 5 element 562, para [0069], where storage device is used) comprising:
receiving, from a user interface, a query entered by a user, wherein the query inquires usage of a set of dashboards associated with a process (Fig. 1 element 102, para [0021], where an input query is received, and Fig. 4A, para [0065], where the query is about the policies displayed on the interface or dashboard);
obtaining, in runtime, one or more text segments that are semantically related to the query (Fig. 1 elements 105, 107, para [0024], where the query is embedded into a vector and compared with a database of predefined query embedding vectors to find similar queries and corresponding instructions);
composing, in runtime, a prompt using a prompt template, wherein the prompt template includes at least one placeholder for receiving the one or more text segments (Fig. 2, elements 204, 206, para [0040], where a template prompt is populated with information including the instructions);
prompting, in runtime, a generative artificial intelligence (AI) model using the prompt to generate a guide instructing usage of at least one dashboard in response to the query (para [0025-26], [0040], where the prompt is sent to the generative machine learning model and an output is generated, and Fig. 4B, para [0066], where recommendations on the policies from the dashboard are displayed); and
presenting the guide generated by the generative AI model on the user interface (Fig. 4B element 404, para [0066], where the query response is outputted in an interface).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 5-7 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, in view of Dhar et al. (US 2026/0050961 A1), hereinafter referred to as Dhar.
Regarding claim 5, Chen teaches:
The computing system of claim 3,
Chen does not teach:
wherein the operations further comprise creating the vector database based on a set of dashboard documents containing descriptions of the set of dashboards.
Dhar teaches:
wherein the operations further comprise creating the vector database based on a set of dashboard documents containing descriptions of the set of dashboards (para [0021-22], where documents are related to the use of AI services, and the documents are parsed into segments that are converted to embeddings and stored in a database).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen by creating the database of Chen (Chen Fig. 1 element 105) using the documents of Dhar (Dhar para [0022]), in order to provide systems and techniques that automate selection of AI services for various user tasks (Dhar para [0022]).
Regarding claim 6, Chen in view of Dhar teaches:
The computing system of claim 5, wherein the operation of creating the vector database comprises dividing the set of dashboard documents into a plurality of text segments (Dhar para [0022], where the documents are parsed into segments that are converted to embeddings and stored in a database).
Regarding claim 7, Chen in view of Dhar teaches:
The computing system of claim 6, wherein the operation of creating the vector database further comprises converting the plurality of text segments into respective vector embeddings, and indexing the plurality of text segments and respective vector embeddings in the vector database (Dhar para [0022], where the segments are converted to embeddings, and where the embeddings are indexed to the corresponding text segments in the database).
Regarding claim 15, Chen teaches:
The computer-implemented method of claim 13,
Chen does not teach:
further comprising creating the vector database based on a set of dashboard documents containing descriptions of the set of dashboards.
Dhar teaches:
further comprising creating the vector database based on a set of dashboard documents containing descriptions of the set of dashboards (para [0021-22], where documents are related to the use of AI services, and the documents are parsed into segments that are converted to embeddings and stored in a database).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen by creating the database of Chen (Chen Fig. 1 element 105) using the documents of Dhar (Dhar para [0022]), in order to provide systems and techniques that automate selection of AI services for various user tasks (Dhar para [0022]).
Regarding claim 16, Chen in view of Dhar teaches:
The computer-implemented method of claim 15, wherein creating the vector database comprises dividing the set of dashboard documents into a plurality of text segments (Dhar para [0022], where the documents are parsed into segments that are converted to embeddings and stored in a database).
Regarding claim 17, Chen in view of Dhar teaches:
The computer-implemented method of claim 16, wherein creating the vector database further comprises converting the plurality of text segments into respective vector embeddings, and indexing the plurality of text segments and respective vector embeddings in the vector database (Dhar para [0022], where the segments are converted to embeddings, and where the embeddings are indexed to the corresponding text segments in the database).
Claim(s) 8-9 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, in view of Agrawal et al. (US 2026/0038020 A1), hereinafter referred to as Agrawal.
Regarding claim 8, Chen teaches:
The computing system of claim 1,
Chen does not teach:
wherein the operations further comprise fetching one or more process documents containing domain knowledge of the process using an AI agent, and inserting at least some of the domain knowledge into the prompt, wherein the AI agent is configured to iteratively invoke predefined functions based on the query entered by the user.
Agrawal teaches:
wherein the operations further comprise fetching one or more process documents containing domain knowledge of the process using an AI agent, and inserting at least some of the domain knowledge into the prompt, wherein the AI agent is configured to iteratively invoke predefined functions based on the query entered by the user (Fig. 4, para [0037], [0134], where input data is domain specific and includes entity connection data, profile data, and content data, all of which are interpreted as process documents, and the input data is retrieved for use by the model by querying the three databases, interpreted as the iterative functions, and where the domain specific information is included into the prompt).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen by using the prompt generation process of Agrawal (Agrawal para [0052]) for the prompt of Chen (Chen para [0040]), in order to generate recommendations in order of relevance to the user (Agrawal para [0056]).
Regarding claim 9, Chen teaches:
The computing system of claim 1,
Chen does not teach:
wherein the operations further comprise retrieving, in runtime, a profile of the user, and inserting the profile of the user into the prompt.
Agrawal teaches:
wherein the operations further comprise retrieving, in runtime, a profile of the user, and inserting the profile of the user into the prompt (para [0052], where profile data is obtained and inserted into a prompt).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen by using the prompt generation process of Agrawal (Agrawal para [0052]) for the prompt of Chen (Chen para [0040]), in order to generate recommendations in order of relevance to the user (Agrawal para [0056]).
Regarding claim 18, Chen teaches:
The computer-implemented method of claim 11,
Chen does not teach:
further comprising fetching one or more process documents containing domain knowledge of the process using an AI agent, and inserting at least some of the domain knowledge into the prompt, wherein the AI agent is configured to iteratively invoke predefined functions based on the query entered by the user.
Agrawal teaches:
further comprising fetching one or more process documents containing domain knowledge of the process using an AI agent, and inserting at least some of the domain knowledge into the prompt, wherein the AI agent is configured to iteratively invoke predefined functions based on the query entered by the user (Fig. 4, para [0037], [0134], where input data is domain specific and includes entity connection data, profile data, and content data, all of which are interpreted as process documents, and the input data is retrieved for use by the model by querying the three databases, interpreted as the iterative functions, and where the domain specific information is included into the prompt).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen by using the prompt generation process of Agrawal (Agrawal para [0052]) for the prompt of Chen (Chen para [0040]), in order to generate recommendations in order of relevance to the user (Agrawal para [0056]).
Regarding claim 19, Chen teaches:
The computer-implemented method of claim 11,
Chen does not teach:
further comprising retrieving, in runtime, a profile of the user, and inserting the profile of the user into the prompt.
Agrawal teaches:
further comprising retrieving, in runtime, a profile of the user, and inserting the profile of the user into the prompt (para [0052], where profile data is obtained and inserted into a prompt).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen by using the prompt generation process of Agrawal (Agrawal para [0052]) for the prompt of Chen (Chen para [0040]), in order to generate recommendations in order of relevance to the user (Agrawal para [0056]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, in view of Bregman et al. (US 12,554,625 B2), hereinafter referred to as Bregman.
Regarding claim 10, Chen teaches:
The computing system of claim 1,
Chen does not teach:
wherein the process is a continuous integration and continuous delivery (CI/CD) process for microservices, wherein the set of dashboards are associated with a plurality of sequential stages of the CI/CD process.
Bregman teaches:
wherein the process is a continuous integration and continuous delivery (CI/CD) process for microservices, wherein the set of dashboards are associated with a plurality of sequential stages of the CI/CD process (Fig. 2, col. 6 line 62 - col. 7 line 4, where an interface corresponds to CI/CD pipelines or jobs, interpreted as microservices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Chen by using the CI/CD process of Bregman (Bregman col. 6 line 62 - col. 7 line 4) for the process of Chen (Chen para [0065]), in order to produce executable code from source code by using working copies into a shared code base (Bregman col. 1 lines 13-28).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 12,106,860 B1 col. 21 lines 23-39, and 64 – col. 22 line 10 teaches including the audiences and domain identifiers to generate a prompt, as well as using profile information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRYAN S BLANKENAGEL whose telephone number is (571)270-0685. The examiner can normally be reached 8:00am-5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRYAN S BLANKENAGEL/Primary Examiner, Art Unit 2658
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