Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
A computer-implemented system, comprising: a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components comprise: a data generation component that generates a set of structured test data to test likelihood of an artificial intelligence (AI) model generating biased outputs, based on analysis of payload logging data; and an alerting component that alerts a user of likelihood that the AI model will generate the biased outputs.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, the “test likelihood of an artificial intelligence (AI) model generating biased outputs test..” step encompasses a user looking at the data and observation, evaluation or judgement about whether the data is biased or unbiased. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A computer-implemented system, comprising: a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components comprise: a data generation component that generates a set of structured test data to test likelihood of an artificial intelligence (AI) model generating biased outputs, based on analysis of payload logging data; and an alerting component that alerts a user of likelihood that the AI model will generate the biased outputs.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “generates a set of structured test data..” the examiner submits that these limitation is recited at a high level of generality (i.e., as a general means of generating/alerting information based on the biased data, and amounts to mere post solution output, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “generating and alerting” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
In addition, the additional elements in claim 1, “first receiver”, “processor”, “memory” are recited at a high level of generality, i.e., as a generic computer performing a generic computer function of determining the type of data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2-11 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. More specifically, the limitations of transmitting.., alert.., store.. are additional elements that do not integrate the abstract idea into a practical application. Furthermore, the determining steps in the dependent claims constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind.
Independent claims 12 and 19 are also rejected using the same reasons and rationale used to reject claim 1.
Therefore, dependent claims 2-11, and 13-18, and 20 are not patent eligible under the same rationale as provided for in the rejection of [independent claim]. Therefore, claim(s) 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Farrar (2022/0156646) in view ZAPPELLA (US 20240112011 A1).
Regarding claim 1, Farrar discloses a computer-implemented system (FIG. 1, system 100), comprising:
a memory configured to store computer executable components (¶0010, “memory hardware stores instructions that when executed on the data processing hardware”); and
a processor configured to execute the computer executable components stored in the memory (¶0010, “data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed on the data processing hardware cause the data processing hardware to perform operations”), wherein the computer executable components (¶0053, the bias scoring model 400”) comprise:
a data generation component that generates a set of structured test data to test likelihood of an artificial intelligence (AI) model generating biased outputs, based on analysis of payload logging data (¶0053, “the bias scoring model 400 determines whether the bias score 416 for the training data set 302 satisfies a score threshold 422. Here, the score threshold 422 indicates a degree of confidence that a data set is unbiased or negligibly biased for purposes of the prediction at the machine learning model 300. For example, the score threshold 422 is an acceptable bias score value.”).
Farrar does not explicitly disclose but, ZAPPELLA teaches an alerting component that alerts a user of likelihood that the AI model will generate the biased outputs (¶0083, “ the model monitor 128 can be sent (streamed) to the monitoring and observability service 116 which can process the statistics using user-configured rules and thresholds to alert the user when there is a significant bias or feature attribution drift.”).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with alerting user about the biased data taught in ZAPPELLA with a reasonable expectation of success because it would have targeted method for providing a user-friendly, flexible, and customizable continual learning process.
Regarding claim 2, Farrar discloses wherein the alerting component generates an alert to the user in response to at least a first set of records approaching a defined threshold (¶0053, “the score threshold 422 indicates a degree of confidence that a data set is unbiased or negligibly biased for purposes of the prediction at the machine learning model 300. For example, the score threshold 422 is an acceptable bias score value.”).
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Farrar (2022/0156646) in view ZAPPELLA (US 20240112011 A1) as applied to claim 1, and further in view of Sankaranarayanan (US 20230131834 A1).
Regarding claim 3, Farrar does not explicitly disclose but, Sankaranarayanan teaches further comprising: an analysis component that analyzes the payload logging data, using a first auto-encoder, to determine a first set of records in the payload logging data, for which the AI model generates the biased outputs, wherein the set of structured test data comprises a percentage of at least the biased outputs (¶0069, ¶0068).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the auto-encoder taught in Sankaranarayanan with a reasonable expectation of success because it would have targeted method for reducing the susceptibility to bias models.
Regarding claim 4, Sankaranarayanan teaches further comprising: an artificial intelligence (AI) component that trains the first auto-encoder to determine the first set of records for which the AI model generates the biased outputs (¶0069-¶0071).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the auto-encoder taught in Sankaranarayanan with a reasonable expectation of success because it would have targeted method for reducing the susceptibility to bias models.
Regarding claim 5, Sankaranarayanan teaches wherein the AI component further trains a second auto-encoder to determine a second set of records in the payload logging data, for which the AI model generates unbiased outputs (¶0047).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the auto-encoder taught in Sankaranarayanan with a reasonable expectation of success because it would have targeted method for reducing the susceptibility to bias models.
Regarding claim 6, Sankaranarayanan teaches wherein the analysis component further analyzes the payload logging data, by using a second auto-encoder, to determine a second set of records for which the AI model generates unbiased outputs (¶0047).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the auto-encoder taught in Sankaranarayanan with a reasonable expectation of success because it would have targeted method for reducing the susceptibility to bias models.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Farrar (2022/0156646) in view ZAPPELLA (US 20240112011 A1), Sankaranarayanan (US 20230131834 A1) as applied to claim 3, and further in view of WU (CN 112163668 A).
Regarding claim 7, Farrar does not explicitly disclose but, WU teaches a monitoring component that monitors respective outputs of the first auto-encoder and a second auto-encoder to enable computation of a disparate impact ratio for the AI model based on a sliding window analysis (page 07, lines 8-18).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the sliding window analysis taught in WU with a reasonable expectation of success because it would have targeted a more enhanced resource allocation and thus reduce the data transmission amount.
Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Farrar (2022/0156646) in view ZAPPELLA (US 20240112011 A1), Sankaranarayanan (US 20230131834 A1) as applied to claim 1, and further in view of Donovan (US 20210299576 A1).
Regarding claim 8, Farrar does not explicitly disclose but, Donovan teaches wherein a computation component computes an estimated fairness score for the AI model based on an alert that a disparate impact ratio is approaching a defined threshold (¶0076).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the impact ratio taught in Donovan with a reasonable expectation of success because it would have targeted a more accurate and unbiased data and models.
Regarding claim 9, Donovan teaches wherein the estimated fairness score is computed by computing a quantity of biased outputs generated by the AI model and a quantity of unbiased outputs generated by the AI model, based on perturbation of individual records of a set of structured training data (¶0076).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the impact ratio taught in Donovan with a reasonable expectation of success because it would have targeted a more accurate and unbiased data and models.
Regarding claim 10, Donovan teaches wherein the estimated fairness score is computed by computing a first percentage of unbiased outputs generated by the AI model and a second percentage of unbiased outputs generated by the AI model, based on an unperturbed set of structured training data (¶0076).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the impact ratio taught in Donovan with a reasonable expectation of success because it would have targeted a more accurate and unbiased data and models.
Regarding claim 11, Donovan teaches wherein the first percentage of unbiased outputs and the second percentage of unbiased outputs respectively represent a minority group and a majority group of payload logging data for which the AI model generates unbiased outputs (¶0076).
Accordingly, It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning model disclosed in Farrar with the impact ratio taught in Donovan with a reasonable expectation of success because it would have targeted a more accurate and unbiased data and models.
Regarding claims 12-20, claims 12-20 are rejected using the same art and rationale used to reject claims 1-11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. DeLuca (20210334096) discloses Techniques are provided for determining bias in an artificial intelligence/machine learning system. A plurality of users contributing to content of the source code base are identified. A plurality of user contributions are generated by determining each user contribution to the source code base by analyzing attributes of the content. The plurality of user contributions are mapped to respective profiles of the users. A determination is made as to whether categortties of contribution defined for the source code base are met, based upon the mapping of the plurality of user contributions to respective profiles (abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REDHWAN K MAWARI whose telephone number is (571)270-1535. The examiner can normally be reached mon-Fri 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vivek Koppikar can be reached at 571-272-5109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REDHWAN K MAWARI/Primary Examiner, Art Unit 3667