DETAILED ACTION
1. This action is responsive to an amendment filed on 12/23/2025.
2. Claims 1, 2 and 4-21 are pending. Claims 1, 11 and 17 are independent. Claims 1, 8, 11, 16 and 17 are currently amended. Claim 3 is canceled. Claim 21 is new. Amendments to the claims have been entered.
Response to Arguments
3. Applicant's arguments have been fully considered; however, they are not persuasive based on new ground(s) of rejection. Notice that objection of claim 16 has been removed due to amendment.
Claim Rejections - 35 USC § 103
4. 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 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.
5. 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.
6. Claims 1, 2, 4-14, 16, 17 and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Nadler (US PG Pub. 2020/0410129) in view of Pai (US PG Pub. 2020/0234158).
As regarding claim 1, Nadler discloses A method comprising:
determining that a data quality value associated with an input to an artificial intelligence (AI) model satisfies a first threshold value, wherein the AI model is characterized by a plurality of features [para. 33-34; a high score may indicate that the removed feature affects the model];
identifying a particular feature of the plurality of features that is associated with the data quality value [para. 33-34; identifying features with high score];
determining a contribution level that indicates a relative contribution of the particular feature to an output of the AI model [para. 44; determining contribution level for dominant features, e.g. height and weight];
determining a risk score for the particular feature based on the contribution level [para. 34 and 64; determining relevance score];
Nadler does not explicitly disclose determining a risk impact for the particular feature, wherein the risk impact is based on a number of AI models that use the particular feature, and wherein the number of AI models includes the Al model; however, Pai discloses it [para. 17].
It would have been obvious to one of ordinary skill in the art at the time the effective filing of the invention to modify Nadler’s system to further comprise the missing claim features, as discloses by Pai, in order to make the system more flexible, efficient and accurate [Pai para.4].
Nadler further discloses outputting an alert in response to the risk score satisfying a second threshold value [para. 65; issuing an alert], wherein outputting the alert comprises outputting the alert to an external platform for display via a user interface [para. 101-102], wherein the alert identifies one or more AI models affected by the particular feature and the associated risk impact [para. 35 and 101-102].
As regarding claim 2, Nadler further discloses The method of claim 1, comprising: determining an importance rank of the particular feature, wherein the importance rank is based on a percentage that the particular feature contributes to an output of the AI model relative to other features of the plurality of features [para. 31 and 34].
As regarding claim 4, Nadler further discloses The method of claim 3, wherein the user interface comprises a plurality of user interface widgets configured to display the determined risk impact, the determined importance rank, the determined risk score, or a combination thereof [para. 92; risk assessment].
As regarding claim 5, Nadler further discloses The method of claim 1, wherein the alert comprises an incident, and wherein the risk score is greater than the second threshold value, a third threshold value, and a fourth threshold value [para. 34].
As regarding claim 6, Nadler further discloses The method of claim 1, wherein the alert comprises a defect, and wherein the risk score is greater than the second threshold value and a third threshold value, but less than a fourth threshold value [para. 34].
As regarding claim 7, Nadler further discloses The method of claim 1, wherein the alert comprises a request, and wherein the risk score is greater than the second threshold value, but less than a third threshold value and a fourth threshold value [para. 34].
As regarding claim 8, Nadler further discloses The method of claim 1, wherein outputting the alert comprises generating and transmitting a notification to one or more respective profiles associated with the AI models that use the particular feature [para. 46-47, 56, 65 and 92].
As regarding claim 9, Nadler further discloses The method of claim 1, wherein determining that the data quality value for the input to the AI model is below the first threshold value is performed automatically by monitoring the data quality value [para. 33-34].
As regarding claim 10, Nadler further discloses The method of claim 1, wherein determining that the data quality value for the input to the AI model is below the first threshold value is based on an input received from the user interface [para. 33-34].
As regarding claim 11, Nadler discloses A system, comprising:
processing circuitry [para. 108]; and
memory, accessible by the processing circuitry, the memory storing instructions that, when executed by the processing circuitry, cause the processing circuitry to perform operations [para. 103, 104 and 108] comprising:
determining that a data quality value associated with an input to an artificial intelligence (AI) model satisfies a first threshold value, wherein the AI model is characterized by a plurality of features [para. 33-34; a high score may indicate that the removed feature affects the model];
identifying a particular feature of the plurality of features that is associated with the data quality value [para. 33-34; identifying features with high score];
determining a contribution level that indicates a relative contribution of the particular feature to an output of the AI model [para. 44; determining contribution level for dominant features, e.g. height and weight];
determining an importance rank of the particular feature, wherein the importance rank is based on a percentage that the particular feature contributes to an output of the AI model relative to other features of the plurality of features [para. 31 and 34];
Nadler does not explicitly disclose determining a risk impact for the particular feature, wherein the risk impact is a number of AI models, including the AI model, that use the particular feature; however, Pai discloses it [para. 17].
It would have been obvious to one of ordinary skill in the art at the time the effective filing of the invention to modify Nadler’s system to further comprise the missing claim features, as discloses by Pai, in order to make the system more flexible, efficient and accurate [Pai para.4].
Nadler further discloses determining a risk score for the particular feature based on the risk impact and the contribution level [para. 34 and 64; determining relevance score]; and
outputting an alert in response to the risk score satisfying a second threshold value [para. 65; issuing an alert], wherein outputting the alert comprises outputting the alert to an external platform for display via a user interface [para. 101-102], wherein the alert identifies one or more models affected by the particular feature and the associated risk impact [para. 35 and 101-102].
As regarding claim 12, Nadler further discloses The system of claim 11, wherein the alert comprises an incident, and wherein the risk score is greater than the second threshold value, a third threshold value, and a fourth threshold value [para. 34].
As regarding claim 13, Nadler further discloses The system of claim 11, wherein the determining that the data quality value for the input to the AI model is below the first threshold value is based on an input received from the user interface [para. 34].
As regarding claim 14, Nadler further discloses The system of claim 11, wherein the contribution level is a weighted value based on a predictive power of the particular feature [para. 44; determining contribution level for dominant features, e.g. height and weight].
As regarding claim 16, Nadler further discloses The system of claim 11, wherein the processing circuitry performs operations comprising: assessing the AI model based on one or more privacy guidelines and/or safety guidelines [para. 58]; outputting a safety level of the AI model based on the assessment; determining that the safety level is below a third threshold; and outputting, in response to the safety level of the AI model being below the third threshold, an additional alert indicating that the safety level of the AI model is below the third threshold [para. 64-65].
As regarding claim 17, Nadler discloses A non-transitory computer-readable storage medium, comprising processor-executable routines that, when executed by a processor, cause the processor to perform operations comprising:
determining that a data quality value associated with an input to an artificial intelligence (AI) model satisfies a first threshold value, wherein the AI model is characterized by a plurality of features [para. 33-34; a high score may indicate that the removed feature affects the model];
identifying a particular feature of the plurality of features that is associated with the data quality value [para. 33-34; identifying features with high score];
determining a contribution level that indicates a relative contribution of the particular feature to an output of the AI model [para. 44; determining contribution level for dominant features, e.g. height and weight];
Nadler does not explicitly disclose determining a risk impact for the particular feature, wherein the risk impact is based on a number of AI models, including the AI model, that use the particular feature; however, Pai discloses it [para. 17].
It would have been obvious to one of ordinary skill in the art at the time the effective filing of the invention to modify Nadler’s system to further comprise the missing claim features, as discloses by Pai, in order to make the system more flexible, efficient and accurate [Pai para.4].
Nadler further discloses determining a risk score for the particular feature based on the risk impact and the contribution level [para. 34 and 64; determining relevance score]; and
outputting an alert in response to the risk score satisfying a second threshold value [para. 65; issuing an alert], wherein outputting the alert comprises outputting the alert to an external platform for display via a user interface [para. 101-102], wherein the alert identifies one or more models affected by the particular feature [para. 35 and 101-102].
As regarding claim 19, Nadler further discloses The non-transitory computer-readable storage medium of claim 17, wherein the processor performs operations comprising: determining an importance rank of the particular feature, wherein the importance rank is based on a percentage that the particular feature contributes to an output of the AI model relative to other features of the plurality of features [para. 31 and 34].
As regarding claim 20, Nadler further discloses The non-transitory computer-readable storage medium of claim 17, wherein the risk score is indicative of a risk associated with continued implementation of a particular AI model of a plurality of AI models [para. 33-34; a high score may indicate that the removed feature affects the model].
As regarding claim 21, Nadler further discloses The method of claim 1, wherein the risk impact is assigned a value based on a percentile of the plurality AI models using the particular feature [para. 31, 53 and 55].
7. Claims 15 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Nadler (US PG Pub. 2020/0410129) in view of Pai (US PG Pub. 2020/0234158) and further in view of Franceschini (US PG Pub. 2019/0287027).
As regarding claims 15 and 18, Nadler and Pai do not disclose receiving a submission for a new AI model, wherein the submission is based on an additional input received from the user interface or an additional user interface; generating a demand for the new AI model, wherein the demand is based on the submission for the new AI model; receiving an approval of the demand for the new AI model; and generating, in response to receiving the approval of the demand for the new AI model, the new AI model. However, Franceschini discloses it [para. 20-23 and 41-43].
It would have been obvious to one of ordinary skill in the art at the time the effective filing of the invention to modify Nadler and Pai’s system to further comprise the missing claim features, as discloses by Franceschini, in order to allow the users to generate new IA models that suit their needs [Franceschini para. 3].
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THONG P TRUONG whose telephone number is (571)270-7905. The examiner can normally be reached on M-F 8:30AM - 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Pwu can be reached on 57127267986798. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THONG TRUONG/
Examiner, Art Unit 2433
/JEFFREY C PWU/Supervisory Patent Examiner, Art Unit 2433
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