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
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/26/26 has been entered.
In amendments dated 5/26/26, Applicant amended claims 1, 11, 13, and 17, canceled no claims, and added no new claims. Claims 1-20 are presented for examination.
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 clams 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, wherein the one or more data repositories are parsed by executing one or more vector space models, with queries and documents included in the one or more data repositories being represented as vectors in a high-dimensional space; and 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 one or more artificial intelligence models is executing software and is a mental process accomplishable in the human mind or on paper per MPEP 2106.04(d)(I), and merely applying an artificial intelligence model is not significantly more than an abstract idea per 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 having queries and documents by executing a vector space model is recited broadly and is a mental process accomplishable in the human mind or on paper, and determining information is contextually relevant is evaluating and a mental process. Generating a prompt is also recited broadly and is a mental process accomplishable in the human mind or on paper, and applying the one or more computational natural language processing techniques and one or more generative artificial intelligence models is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628. 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 also recites wherein retrieving documents from the one or more data repositories includes performing a similarity search that identifies the documents based on proximity of vector representations of the documents and additional vector representations of the queries, and retrieving data is 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, the 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. Retrieving documents from a repository is retrieving data from a memory and is also a routine and conventional activity 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 executing a data integrity comparison between the one or more test database tables and the database to validate the mapping based on a number of failures to store previously inputted information for the database in the one or more test database tables due to data type or schema mismatches. 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 is recited broadly and performing a testing procedure to determine linkage and execute a data integrity comparison is executing software and also recited broadly are each 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, which is an input step and insignificant extra-solution activity; and providing, by the computing system, the prompt to one or more generative models, which is an 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:
Lohia et al (US 11,455,554) teaches improving trustworthiness of artificial intelligence models in the presence of anomalous data used for training a machine learning model by determining an attribute that decreases confidence for the data and then determining a root cause for the attribute, does not teach receiving additional information indicating classifications for inferences, determining inferences as inaccurate, generating a prompt for an artificial intelligence model, and parsing documents (column 1 lines 21-39, columns 6-7 lines 55-30 figure 3).
Responses to Applicant’s Remarks
Regarding objection to claim 11 for antecedent basis of “the hyperintelligence system,” in view of amendments reciting dependency on claim 10, this objection is withdrawn. 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 10-14 of his Remarks Applicant discusses Enfish LLC v. Microsoft Corporation (822 F.3d 1327) and asserts claims 1 and 13 recite an improvement “in the field of effectively creating artificial intelligence models that can correctly classify incoming data,” in particular in the “parsing” 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, wherein the one or more data repositories are parsed by executing one or more vector space models, with queries and documents included in the one or more data repositories being represented as vectors in a high-dimensional space and retrieving documents from the one or more data repositories includes performing a similarity search that identifies the documents based on proximity of vector representations of the documents and additional vector representations of the queries”). Examiner disagrees as the “parsing” limitation is recited broadly and does not recite details such as how the invention parses the data repositories or how the invention determines information that is contextually relevant to improving the measure of performance. The limitation recites parsing involves executing one or more vector space models, which is mentioned only in specification paragraph 0202 but not described beyond being a model, and executing a model is not an improvement in parsing. The claims in Enfish actually recited an improvement to a component of a computer, namely a self-referential database, but Examiner does not see such an improvement in the present claims.
On pages 13-14 Applicant asserts claim 17 recites an improvement in “minimizing the failures present in databases that store inputted data received from another system or computing device” in the “obtaining … the mapping,” “generating … one or more test database tables,” and ”performing … a testing procedure” limitations (“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 executing a data integrity comparison between the one or more test database tables and the database to validate the mapping based on a number of failures to store previously inputted information for the database in the one or more test database tables due to data type or schema mismatches.”). Examiner disagrees as the mapping is obtained as output from applying one or more generative models, which a conventional activity for a machine learning model per Recentive Analytics. The “generating” limitation is recited broadly and without details of how the invention generates the one or more test database tables having the schema of the database. Likewise the “performing” limitation is recited broadly and does not recite details of how the testing procedure is performed, how a linkage between first and second data fields is determined, or details of the data integrity comparison executed between test database tables and the database to validate the mapping (how validate?) based on a number of failures to store previously imputed information. Examiner does not see the specifics of an improvement in a function of a computer or in a technology recited that minimizes failures present in databases.
On pages 14-15 Applicant notes that mental processes are concepts performed with the human mind, such as observation, evaluation, judgement, and opinion per MPEP 2106.04(a)(2)(III), and Examiner notes that this section also states "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." In claims 1 and 13 Applicant asserts the “parsing” and “generating …. A prompt” limitations are not performable in the human mind. Examiner disagrees and notes the “retrieving documents” portion is not recited in claim 13 and is routine and conventional activity per the rejections above. Querying a repository, larger-scale or not, is searching and involves evaluation to find data being searched for and is a mental process. Generating a prompt is recited broadly and invokes a generic computer as a tool, and a BRI of said generating includes a pen and paper, and the plurality of tokens being generated are generated by applying computational natural language processing techniques, which in specification paragraphs 0138-0139 use transformers and neural networks, and thus are conventional activities of said transformers and neural networks, as would the applying activities of the recited artificial intelligence models in this limitation. Thus Examiner believes this limitation is also a mental process. On page 16 Applicant asserts the “generating … one or more test database tables” and “performing … a testing procedure” limitations 1 claim 17 are not mental processes. Examiner disagrees as the generating step is recited broadly and invokes the computer as a tool, and a BRI includes a pen and paper. Further, performing a testing procedure on a computer system is recited broadly and is executing software to determine a linkage and execute a data integrity comparison (how?) and thus involves evaluating and is a mental process.
On page 18 Applicant discusses the Recentive Analytics case and asserts “these claims [1 and 13] recite a specific architecture for improving Al models by: (1) parsing data repositories using vector space models with documents and queries represented as high-dimensional vectors and similarity searches performed via vector proximity; (2) generating tokens via specific natural language processing techniques that produce embeddings causing generative models to produce improved software code; and (3) verifying that the resulting software code yields measurably fewer inaccurate inferences than the prior software code.” Examiner disagrees and notes the claims do not recite a specific architecture per reasons given above, and (1) the recited similarity searches are not performed via vector proximity but “based on proximity of vector representations,” (2) tokens are not recited as generated but merely included in the prompt being generated and no specific natural language processing techniques are recited plus said techniques do not produce embeddings but only correspond to embeddings, and (3) do not verify anything and do not recite software code yielding fewer inaccurate inferences but instead recite “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,” but the software code is merely generated by (conventionally) applying the artificial intelligence models and the claims still do not recite details showing how the invention classifies inferences as inaccurate.
On page 18-19 Applicant asserts claim 17 now specifically describes how the testing procedure is performed. Examiner disagrees as claim 17 broadly recites executing a data integrity comparison with no details reciting how the invention compares for data integrity, and broadly recites validating the mapping based on a number of failures to store previously inputted information with no detail reciting how the invention validates said mapping. Thus the recited performing a testing procedure is still just executing software and a mental process as shown above. Regarding Applicant’s assertion that Examiner is insufficiently considering support for the claims in Applicant’s specification, when evaluating improvements in the functioning of a computer, or an improvement to any other technology or technical field, MPEP 2106.04(d)(1) states “first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.”
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 8/22/26
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