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 7/31/26, Applicant amended claims 1, 3, 6, 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 claims 1, 13, and 17 each recites automatically providing the prompt to an artificial intelligence process; automatically generating, by the artificial intelligence process, an analysis of the prompt, wherein the analysis includes automatically determining a threshold value based on user relevant data corresponding to a dataset associated with the user, wherein the user relevant data includes tracking data identifying one or more data associated with the user including contextually relevant information associated with one or more external factors used to generate a predictive data set; automatically generating, in real-time, the predictive data set based on the analysis of the prompt, the one or more external factors, and the subset of data associated with the user, wherein generating the predictive data set further includes analyzing the tracking data utilizing the contextually relevant information, wherein the one or more external factors are determined based on a plurality of information received from one or more sensors and the dataset associated with the user, wherein the plurality of information received from the one or more sensors is recorded in a first format and is converted from the first format into a second format that is determined based on a property of a machine learning model used to generate the predictive data set, the plurality of information in the second format being provided as an input to the machine learning model used to generate the predictive data set, wherein performing the analysis further includes identifying an output including an identified type of the predictive data set based on the prompt, an identified metric related to the prompt, an identified data attribute related to the prompt, a determined granularity of data for a response to the prompt, and a determined timeframe of analysis for a response to the prompt; and in response to generating the predictive data set, triggering a first automated action based on the predictive data set exceeding the threshold value corresponding to the subset of data associated with the user, wherein the automated action includes automatically reconfiguring a computing system that generates the predictive data set by adjusting a resource capacity of the computing system, and wherein the resource capacity is a server capacity or a database capacity. Automatically providing a prompt to an artificial intelligence process is recited broadly and a mental process accomplishable in the human mind or on paper. Automatically generating an analysis of a prompt by an artificial intelligence process is recited broadly and a mental process accomplishable in the human mind or on paper. Examiner notes that applying an artificial intelligence process to generate an analysis and applying a machine learning model to generate a predictive data set are each not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Applying filters to data and generating the predictive data set are each recited generally and are mental processes accomplishable in the human mind or on paper. Automatically generating a predictive data set based on the analysis of the prompt, the one or more external factors, and the subset of data associated with the user, and analyzing the tracking data utilizing the contextually relevant information are each recited broadly and are mental processes accomplishable in the human mind or on paper. Converting a plurality of information from a first format to a second format and providing the plurality of information in the second format as input to a machine learning model are each recited broadly and are mental processes accomplishable in the human mind or on paper. Identifying an output including a type of the predictive data set is evaluating and a mental process. Triggering an action based on a data set exceeding a threshold is evaluating and a mental process. Reconfiguring a computing system is editing software and adjusting a resource capacity of the computing system is recited broadly and a mental process accomplishable in the human mind or on paper. Each claim recites additional elements of receiving a prompt from a user, which is an input step and insignificant extra-solution activity; and automatically retrieving only a subset of data associated with the user corresponding to the tracking data by applying one or more filters to the tracking data associated with the user based on the analysis of the prompt, wherein the subset of data associated with the user reduces a total amount of data utilized to generate the predictive data set, and retrieving data is also insignificant extra-solution activity. Claim 13 recites a data storage device and a processor and claim 17 recites non-transitory machine-readable medium storing instructions, which are each generic components of a computer system. Examiner notes specification paragraph 0003 describes frustration by a user when looking up information and not knowing how to acquire the most relevant information and paragraphs 0030-0031 describe users being overwhelmed with information and manually looking up sources and not being able to forecast trends based on data. Paragraphs 0032-0033 describes how the invention addresses these challenges by tracking data, analyzing data to predict trends, and providing contextually relevant data with trends to a user. Examiner found support in specification paragraph 0077 for adjusting a resource capacity but this paragraph did not describe how adjusting a resource capacity addresses one of the drawbacks identified in paragraphs 0003 or 0030-0031 as described here. The claim steps still do not recite details of improvements 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, receiving a prompt from a user is recited broadly and amounts to receiving data across a network per specification 0048 and figure 1 140, and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Retrieving a subset of data is retrieving data from memory which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. The data storage device and processor and non-transitory machine-readable medium storing instructions are each still generic components of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claim 2 recites detecting a threshold change for the threshold value in the user relevant data, and detecting a change is evaluating and a mental process; determining a category associated with the threshold change, and determining a category is evaluating and a mental process; determining a key driver based on the threshold change and the category, and determining a key driver is evaluating and a mental process; and in response to determining the key driver, triggering a second automated action based on the key driver, and triggering a second automated action is evaluating and a mental process accomplishable in the human mind or on paper. Claim 3 recites wherein the the artificial intelligence process uses the prompt as an input to provide an output corresponding to the dataset associated with the user, and applying an artificial intelligence process with a prompt is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claims 4, 14, and 18 each recites wherein the identified type of the predictive data set based on the prompt is one of: a time series forecast, and trend analysis, or a key driver analysis. , and requesting data is a mental process accomplishable in the human mind or with pen and paper.
Claims 5 and 15 each recites storing the generated predictive data set, and storing data is routine and conventional per list of such activities in MPEP 2106.05(d) part II; and determining additional predictive data using the stored generated predictive data set, and determining data is a mental process accomplishable in the human mind or with pen and paper. Claim 6 recites wherein the tracking data is stored in a semantic architecture, and storing data is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II, wherein the semantic architecture receives the tracking data in a first tracking-data format or a second tracking-data format, the first tracking-data format being different from the second tracking-data format, and receiving data is recited broadly and amounts to receiving data across a network, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II, and wherein the semantic architecture translates the tracking data from the first tracking-data format or the second tracking-data format to a third tracking-data format, wherein the third tracking-data format is useable to automatically generate the predictive data set, and translating data is recited broadly and is a mental process accomplishable in the human mind or on paper.
Claims 7 and 19 each recites storing the prompt, the analysis of the prompt, and the generated predictive data set in a database with information about past user prompts, and storing data is routine and conventional per list of such activities in MPEP 2106.05(d) part II, wherein generating the identified type of predictive data is further based on the information about past user prompts, and generating data is recited broadly and a mental process accomplishable in the human mind or with pen and paper. Claim 8 recites identifying a subset of data of the user relevant data associated with the prompt, and identifying a subset of data is evaluating and a mental process; and prioritizing the subset of data based on one or more of a user level of access, a user level of interaction, or a frequency of user requests, and prioritizing data is evaluating it and a mental process accomplishable in the human mind or with pen and paper. Claim 9 recites receiving historical user relevant data is recited broadly and amounts to receiving data across a network per specification 0048 and figure 1 140, and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; receiving at least one of contextually-relevant factors or external factors is recited broadly and amounts to receiving data across a network per specification 0048 and figure 1 140, and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; and extracting at least one of a trend, a seasonality, or a residual data from the historical user relevant data, and extracting data is a mental process accomplishable in the human mind or with pen and paper, wherein generating the predictive data set is further based on the contextually-relevant factors, the trend, the seasonality, or the residual data, and generating data is recited broadly and a mental process accomplishable in the human mind or with pen and paper.
Claim 10 recites triggering an automated action based on the generated predictive data set not exceeding the threshold value, and triggering an automated action is an evaluation which is a mental process accomplishable in the human mind or with pen and paper. Claim 11 recites wherein the automated action is performing an additional analysis on the predictive data set, and performing an analysis is recited broadly and a mental process accomplishable in the human mind or with pen and paper. Claims 12, 16, and 20 each recites providing, to the artificial intelligence process, metadata describing the identified metric related to the prompt and metadata describing the identified data attribute related to the prompt, and providing data is a mental process accomplishable in the human mind or with pen and paper.
Relevant Prior Art
During his search for prior art, Examiner found the following references to be relevant to Applicant's claimed invention. Each reference is listed on the Notice of References form included in this office action:
Goyal et al (US 20210406762) teaches generating data to interpret an output of an artificial intelligence model, generating a subset of the data by filtering from constraints on training data used in the model, does not teach external factors, tracking contextually relevant information to user data, sensors, generating a predictive dataset, generating output with the predictive dataset, a timeframe of analysis, and adjusting a resource capacity (paragraphs 0004-0005, 0039-0049 figure 3); and
Dinh et al (US 20210133594) teaches obtaining data from transaction flows across data sources and forecasting anomalies in said data by applying artificial intelligence techniques to the data, does not teach a prompt for the artificial intelligence techniques, analysis of said prompt with tracking contextually relevant information to user data, sensors, converting format of sensed data, timeframe of analysis, and adjusting a resource capacity (paragraphs 0003, 0037-0-043 figure 3).
Responses to Applicant’s Remarks
With respect to 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 page 13 of his Remarks Applicant begins discussing Ex Parte Desjardins and points out a new example of an improvement in computer functionality added in MPEP § 2106.05(a), subsection I, from this decision, example (xiv): “Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams.” On page 14 Applicant states claim 1 adds concrete technical elements that integrate the exception into a practical application and cites the amendment “wherein the plurality of information received from the one or more sensors is recorded in a first format and is converted from the first format into a second format that is determined based on a property of a machine learning model used to generate the predictive data set, the plurality of information in the second format being provided as an input to the machine learning model used to generate the predictive data set,” and cites support for the amendment in specification paragraph 0068. Examiner disagrees that this integrates the claims into a practical application and notes the inherency of data being recorded in a format, native or not. Converting data from that [first] format into a second format is recited broadly and without details showing how the invention performs the conversion, and a BRI of converting data includes use of a physical aid such as pen and paper, thus Examiner believes it is a mental process. Determining the second format based on a property of a machine learning model is also recited broadly with now details showing what property or how the format is determined, and a BRI of such determining also includes use of pen and paper, thus Examiner believes it is a mental process. Providing the converted data as input to the machine learning model is also recited broadly, and providing data as input to a machine learning model is executing software to input the data into the machine learning model, thus Examiner believes it is a mental process. Examiner did not see any improvements to computer functionality recited in the amendment nor did Examiner see an improvement to a machine learning model described in paragraph 0068.
On pages 14-15 Applicant discusses the second amendment (“wherein the automated action includes automatically reconfiguring a computing system that generates the predictive data set by adjusting a resource capacity of the computing system, and wherein the recourse capacity is a server capacity or a database capacity”) and asserts it cannot be performed in the human mind. Examiner disagrees as reconfiguring a computer system is broad by itself and means adjusting or modifying said computer system, in this case “by adjusting a resource capacity or a database capacity,” which uses a computer as a tool and amounts to editing software code, and a BRI includes use of a physical aid such as pen and paper. Thus Examiner believes the amendment recites a mental process. On pages 15-16 Applicant discusses Example 47 claim 3 and Example 48 claim 2, which each reflected an improvement in network security or speech-separation technology by adding concrete steps showing said specific improvements from their respective specifications, but the present claims do not recite such specific steps showing an improvement to a computer or a technology. On page 17 Applicant asserts “The Examiner has also alleged that the claims were "recited broadly" and did not recite "how" the resource capacity is adjusted. (Office Action, pages 11-12.) Claim 1 as amended addresses this concern by specifying that the automated action includes ‘automatically reconfiguring a computing system that generates the predictive data set by adjusting a resource capacity of the computing system,’ where ‘the resource capacity is a server capacity or a database capacity.’” Examiner disagrees as reconfiguring the computing system is recited as the effect of adjusting a resource capacity but does not recite how or why the resource capacity is adjusted. Examiner does not see how this provides an improvement to or addresses the problems describes in paragraphs 0003 and 0030-0031, namely “the user may not know how to acquire the most relevant information for determining future actions based on relevant data” or “users may be provided current or past data but may not be able to forecast future trends based on the current or past data.”
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 10/9/26
