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 .
Detailed Action
In amendments dated 11/7/25, Applicant amended claims 1-6, 9-11, 13, 15-18, and 20, canceled no claims, and added no new claims. Claims 1-20 are presented for examination.
Objections
Claim 11 is objected to because of the following informality: the claim depends on claim 9 and recites “the hyperintelligence system” which lacks antecedent basis. Examiner notes antecedent basis is now in claim 10.
Rejections under 35 U.S.C. 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 mental processes without significantly more. Independent claims 1 and 13 each recites executing, by the computing apparatus, one or more first artificial intelligence models to determine one or more inferences with respect to the inputted data; analyzing, by the computing apparatus, a measure of performance of the one or more first artificial intelligence models by determining a number of the one or more inferences that are classified as inaccurate; parsing, by the computing apparatus, one or more data repositories to determine information that is contextually relevant to improving the measure of performance of the one or more first artificial intelligence models; generating, by the computing apparatus and based on the information that is contextually relevant to improving the measure of performance of the one or more first artificial intelligence models, a prompt that includes: first software code of the one or more first artificial intelligence models; the inputted data; the one or more inferences generated based on the inputted data; the classifications of the one or more inferences; and a plurality of tokens that correspond to input to one or more generative artificial intelligence models, the plurality of tokens being generated using one or more computational natural language processing techniques and corresponding to embeddings that cause the one or more generative artificial intelligence models to generate additional software code for one or more additional artificial intelligence models having one or more additional measures of performance, the one or more additional measures of performance indicating that an additional number of inferences determined by the one or more additional artificial intelligence models that are classified as inaccurate is less than the number of the one or more inferences determined by the one or more first artificial intelligence models that are classified as inaccurate. Executing an artificial intelligence model is recited broadly and merely applying the artificial intelligence model, and is a mental process accomplishable in the human mind or on paper per MPEP 2106.04(d)(I) and Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628. Analyzing a measure of performance by determining a number is recited broadly and a mental process accomplishable in the human mind or on paper, parsing one or more data repositories is also recited broadly and is a mental process accomplishable in the human mind or on paper, and generating a prompt is also recited broadly and is a mental process accomplishable in the human mind or on paper. Each claim recites additional elements of receiving, by a computing apparatus including hardware processing resources and memory, inputted data from a computing device or system; receiving, by the computing apparatus, additional information from one or more computing devices or systems indicating classifications for the one or more inferences, the classifications indicating that individual inferences of the one or more inferences are accurate or inaccurate, which are both input steps and insignificant extra-solution activity; and providing, by the computing apparatus, the prompt to the one or more generative artificial intelligence models; and receiving, by the computing apparatus, second software code generated by the one or more generative artificial intelligence models, the second software code corresponding to one or more second artificial intelligence models that determine inferences based on inputted information, which are both output steps and also insignificant extra-solution activity. Claim 13 recites one or more hardware processors and memory storing computer-readable instructions, which are both generic components of a computer. Examiner notes specification paragraphs 0003 and 0132 describes data error correction as an application of performing artificial intelligence and describes current applications as being reactive when errors are detected, allowing errors to remain and introduce risk and inaccuracies in a system. These paragraphs then discuss a need for a data intelligence system that addresses this problem. The limitations in claims 1 and 13 still recite general actions and do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Taking the claims as a whole, input steps and output steps are recited broadly ands amount to sending and receiving data across a network per specification paragraphs 0124, 0137, 0226, 0274 and figures 1-3, which are routine and conventional activities per the list of such activities in MPEP 2106.05(d) part II. The one or more hardware processors and memory storing computer-readable instructions are both still generic components of a computer. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Independent claim 17 recites determining, by the computing system, first data types corresponding to first data fields of the inputted data; obtaining, by the computing system, a schema of a database corresponding to the computing device or system, wherein the inputted data originated in the database; generating, by the computing system, a prompt that includes: the inputted data; the first data types; the schema of the database; and a request to produce a mapping between second data types of second data fields of the database and the first data types of the first data fields; obtaining, by the computing system, the mapping from the one or more generative models, the mapping indicating first data types of individual first data fields that correspond to second data types of individual second data fields; generating, by the computing system, one or more test database tables in the database having the schema of the database; and performing, by the computing system, a testing procedure to determine linkage between the first data fields of the inputted data and the second data fields of the database by determining whether previous inputted data is stored in the one or more test database tables. Determining data type is evaluating and a mental process. Obtaining a schema of a database, generating a prompt, and obtaining a mapping are each recited broadly and are mental processes accomplishable in the human mind or on paper. Obtaining the mapping as output from one or more generative models is merely applying the models and is not significantly more than the broadly-recited obtaining. Generating one or more test database tables and performing a testing procedure are each recited broadly and are mental processes accomplishable in the human mind or on paper. Claim 17 recites additional elements of receiving, by a computing system including one or more hardware processors and memory, inputted data from a computing device or system, and input step and insignificant extra-solution activity; and providing, by the computing system, the prompt to one or more generative models, and output step and also insignificant extra-solution activity. Examiner notes the specification’s descriptions of a problem in the art in paragraphs 0003 and 0132 above, and also notes claim 17 does not address this problem plus the claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, both the input step and output step are recited broadly and amount to sending or receiving data across a network per specification paragraphs 0124, 0137, 0226, 0274 and figures 1-3, which are routine and conventional activities per the list of such activities in MPEP 2106.05(d) part II. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claim 2 recites receiving, by the computing apparatus and from the one or more generative artificial intelligence models, one or more second measures of performance of the one or more second artificial intelligence models, which is recited broadly and amounts to receiving data across a network per specification paragraphs 0124, 0137, 0729, 0778 and figures 1-3 and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; determining, by the computing apparatus, that differences between the one or more second measures of performance and the first measure of performance are less than a threshold amount of difference, and determining differences is evaluating and a mental process; modifying, by the computing apparatus, the prompt to generate an additional prompt, and modifying data is recited broadly and a mental process accomplishable in the human mind or on paper; and providing, by the computing apparatus, the additional prompt to the one or more generative artificial intelligence models, which is recited broadly and amounts to receiving data across a network per specification paragraphs 0124, 0137, 0729, 0778 and figures 1-3 and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 3 recites wherein the prompt is modified by at least one of (i) modifying at least one of words or phrases of the prompt, (ii) modifying information in the prompt that is provided to the one or more generative artificial intelligence models, and (iii) providing one or more instructional tokens in the prompt, and modifying data is recited broadly and a mental process accomplishable in the human mind or on paper.
Claim 4 recites wherein at least one of the prompt or the additional prompt include commands related to one or more features of the one or more generative artificial intelligence models that include at least one of a temperature of the one or more generative artificial intelligence models, top-p of the one or more generative artificial intelligence models, or constraints on tokens provided to the one or more generative artificial intelligence models, and generating a prompt is recited broadly and a mental process accomplishable in the human mind or on paper. Claim 5 recites performing, by the one or more generative artificial intelligence models, at least one of one or more testing operations or one or more validation operations with respect to the one or more second artificial intelligence models to determine the one or more additional measures of performance; wherein at least one of the one or more testing operations or the one or more validation operations are performed using the one or more inferences and the classifications of the one or more inferences included in the prompt, and performing testing operations or validation operations is recited broadly and a mental process accomplishable in the human mind or on paper.
Claim 6 recites wherein the one or more first artificial intelligence models are executed by a peer-to-peer network implemented by the computing apparatus; and the method comprises: performing a security protocol in response to information being exchanged between a first computing device or system of the peer-to-peer network and a second computing device or system of the peer-to-peer network, the security protocol comprising: generating, by the first computing device and using a cryptographic hash function, a message digest of the information, and generating a message digest using a hash function is recited broadly and is a mental process per Personalweb Technologies LLC v. Google 8 F.4th 1310, 2021 U.S.P.Q.2d 853 (Fed. Cir. 2021); generating, by the first computing device, a digital signature for the message digest using a private key related to the first computing device, and generating a digital signature is recited broadly and is a mental process per Personalweb Technologies LLC v. Google 8 F.4th 1310, 2021 U.S.P.Q.2d 853 (Fed. Cir. 2021); and sending, by the first computing device, the information, the digital signature, and a public key related to the first computing device to the second computing device, and sending information is recited broadly and amounts to sending data across a network which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 7 recites obtaining, by the second computing device, the information and the digital signature from the first computing device and obtaining information is recited broadly and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; decrypting, by the second computing device, the digital signature using the public key related to the first computing device to produce a decrypted message digest, and decrypting data is recited broadly and is a mental process per Personalweb Technologies LLC v. Google 8 F.4th 1310, 2021 U.S.P.Q.2d 853 (Fed. Cir. 2021); generating, by the second computing device, a calculated message digest of the information, ad generating a digest is a mental process per Personalweb Technologies LLC v. Google 8 F.4th 1310, 2021 U.S.P.Q.2d 853 (Fed. Cir. 2021); analyzing, by the second computing device, the decrypted message digest with respect to the calculated message digest to determine modification of the information, and analyzing a message is recited broadly and a mental process accomplishable in the human mind or on paper; and determining, by the second computing device, an authenticity of the information in response to determining that the information is not modified, and determining authenticity is evaluating and a mental process.
Claim 8 recites wherein inter process communication techniques are implemented between computing devices of the peer-to-peer network, and inter process communication techniques is sending and receiving data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 9 recites wherein the information being exchanged between a first computing device or system of the peer-to-peer network and a second computing device or system of the peer-to-peer network includes at least one first artificial intelligence model of the one or more first artificial intelligence models or at least one second artificial intelligence model of the one or more second artificial intelligence models, and exchanging information is recited broadly and amounts to sending and receiving data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 10 recites receiving, by the computing apparatus, a request to integrate the hyperintelligence system with an additional system, and receiving a request is recited broadly and amounts to receiving data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; generating, by the computing apparatus, one or more queries to one or more data stores to identify access information for the additional system, and generating queries is recited broadly and is a mental process accomplishable in the human mind or on paper; and implementing, by the computing apparatus, the access information for the additional system within the hyperintelligence system to access at least one of data or functionality of the additional system, and implementing information is recited broadly and is a mental process accomplishable in the human mind or on paper.
Claim 11 recites wherein the hyperintelligence system includes a generative service that enables communications between the hyperintelligence system and the one or more generative models, and communication between systems and models over a network is routine and conventional per the list of such activities in MPEP 2106.05(d) part II. Claim 12 recites wherein intermediate results of the one or more first artificial intelligence models and the one or more second artificial intelligence models are provided to one or more additional computational algorithms that generate a final result, and providing data is recited broadly and amounts to sending data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional per the list of such activities in MPEP 2106.05(d) part II. Claim 14 recites in response to the prompt, one or more retrieval augmented generation algorithms are executed to analyze information stored in a database to determine portions of the information to provide to the one or more generative models to produce the one or more second artificial intelligence models; and the information stored in the database includes the inputted data, the one or more inferences, and the classifications of the one or more inferences, and executing algorithms is recited broadly and a mental process accomplishable in the human mind or on paper.
Claim 15 recites sending an additional prompt to the one or more generative artificial intelligence models, the additional prompt including instructions to (i) identify one or more items of the inputted data stored by one or more databases in communication with the computing system and (ii) perform one or more functions with respect to the one or more items of the inputted data, and sending data is recited broadly and amounts to sending data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional per the list of such activities in MPEP 2106.05(d) part II; and receiving, from the one or more generative artificial intelligence models, results of performing the one or more functions with respect to the one or more items of the inputted data, and receiving results is recited broadly and amounts to sending data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional per the list of such activities in MPEP 2106.05(d) part II. Claim 16 recites obtaining input from one or more sources, the input including at least one of brain-computer interface signals, gestures, tactile feedback, text, images, video, computer readable instructions, network data, or binary data, and obtaining input is recited broadly and amounts to sending data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional per the list of such activities in MPEP 2106.05(d) part II; and generating one or more prompts from the input to provide to the one or more generative artificial intelligence models, and generating a prompt is recited broadly and a mental process accomplishable in the human mind or on paper.
Claim 18 recites generating, by the computing system, an additional prompt with an additional request to (i) create test data fields, test input data, and the one or more test database tables having the schema of the database and (ii) perform a validation of the mapping, and generating a prompt is recited broadly and a mental process accomplishable in the human mind or on paper; providing, by the computing system, the additional prompt to the one or more generative models, and providing a prompt is recited broadly and amounts to sending data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional per the list of such activities in MPEP 2106.05(d) part II; and receiving, by the computing system and from the one or more generative models, a result of the validation of the mapping, and receiving a result is recited broadly and amounts to receiving data across a network per specification paragraphs 0124, 0137, 0226, and 0274 and figures 1-3, which is routine and conventional per the list of such activities in MPEP 2106.05(d) part II. Claim 19 recites in response to the validation of the mapping, initializing, by the computing system, one or more functions to build one or more artificial intelligence models, the one or more functions being specified by a corresponding to a template the one or more artificial intelligence models and a type of the one or more artificial intelligence models, and initializing functions is recited broadly and a mental process accomplishable in the human mid or on paper. Claim 20 recites wherein receiving the inputted data and determining the first data types of the first data fields are performed asynchronously, and performing operations is a mental process accomplishable in the human mid or on paper.
Relevant Prior Art
During his search for prior art, Examiner found the following reference to be relevant to Applicant's claimed invention. Said reference is listed on the Notice of References form included in this office action:
Kwak et al (US 20250132046) teaches classifying inputted data and using a trained model to infer classes of said inputted data as well as determine a confidence that the inputted data will be classified into those classes, does not teach classification of inferences/predictions, parsing the data, generating a prompt including the inputted data, generating a test database, and performing a testing procedure using the test database (paragraphs 0010, 0018).
Responses to Applicant’s Remarks
Regarding objections for numbering of claims per 37 C.F.R. 1.121(c)(1), Examiner acknowledges Applicant’s amendments in renumbering the claims and withdraws these objections. Regarding rejections of claims 1-16 under 35 U.S.C. 112(b) for indefiniteness of measures being greater than other undefined measures, in view of this language being removed, Examiner withdraws these rejections. Regarding rejection of claim 20 under 35 U.S.C. 112(b) for reciting inputted data can be performed, in view of amendments reciting receiving inputted data and correcting a grammatical issue, Examiner withdraws this rejection. Regarding rejections of claims 1-20 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicant’s arguments have been considered but are not persuasive. On pages 11-13 of his Remarks Applicant asserts the claims are directed to a technical improvement “in the field of effectively creating artificial intelligence models that can correctly classify incoming data.” Examiner notes the claims do not classify incoming data but instead they classify inferences of inputted data. Applicant discusses inventive details in specification paragraphs 0132, 0142-0185, and 0103-0107 but Examiner notes these details are not claimed. For example, the claims do not produce revised versions of generative models, and providing information to a model is not an improvement to a technology. On page 13 Applicant asserts the “features added to amended claim 17 are directed to minimizing the failures present in databases that store inputted data received from another system or computing device.” Examiner disagrees as the amended limitations in claim 17 recite generating test database tables and performing as testing procedure to determine linkage between data fields of inputted data and data fields of the database, which together do not minimize any failures in databases.
On pages 13-14 Applicant asserts the limitation “parsing, by the computing apparatus, one or more data repositories to determine information that is contextually relevant to improving the measure of performance of the one or more first artificial intelligence models” is not a mental process. Examiner disagrees as it is recited broadly without inventive details and it uses a computer as a tool to perform the parsing. Parsing data is merely dividing the data into parts, and parsing to determine information that is contextually relevant to something involves evaluating the data which is a mental process. On pages 15-16 Applicant discusses the “generating a prompt” limitation, in particular the inclusion o the prompt of plurality of tokens, and discusses specification paragraphs 0192-0196 describing details of the tokens such as text normalization of inputted data and encoding mapped tokens. While some of said details may describe an improvement in a technology, they are not claimed. What is claimed amounts to generating a prompt, and while the prompt includes the software code, inputted data, one or more inferences, classifications of the inferences, and a plurality of tokens, generating a prompt is recited broadly and uses a computer as a tool, and a prompt is still data. MPEP 2106.04(a)(2)(III), "the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation." Thus Examiner believes this limitation recites a mental process accomplishable in the human mind or on paper. Applicant asserts the amended limitations “generating, by the computing system, one or more test database tables in the database having the schema of the database;” and “performing, by the computing system, a testing procedure to determine linkage between the first data fields of the inputted data and the second data fields of the database by determining whether previous inputted data is stored in the one or more test database tables” are not mental processes. Examiner disagrees as both limitations are also recited broadly without details showing how test tables are generated, how the testing procedure is performed, what the testing procedure is, or how linkage is determined, and both limitations use a computer as a tool. Thus Examiner again cites MPEP 2106.04(a)(2)(III) and believes generating data and performing a procedure are both mental processes accomplishable in the human mind or on paper.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 2/22/26