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
Claims 21-40 are pending. Claims 21, 33 and 40 are independent. Claims 1-20 were canceled by a preliminary amendment and Claims 21-40 added.
This Application was published as U.S. 20250124240.
Apparent priority to provisional filed 5 October 2021.
This Application is a continuation of US 17/533358 issued as US 12,217,011. A Terminal Disclaimer over the term of the parent is required.
Independent Claims
Independent Claims include parallel language.
21. An apparatus, comprising:
a memory storing instructions;
a communications interface; and
at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
based on an application of an artificial intelligence process to feature values of an input dataset associated with a device, generate elements of output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, the artificial intelligence process being trained based on a plurality of training datasets associated with a first prior temporal interval, and being validated based on a plurality of validation datasets associated with a second prior temporal interval;
provision, to an explainability process, a corresponding one of the feature values and a Shapley feature value associated with the corresponding feature value, and based on an application of the explainability process to the corresponding feature value and to the Shapley feature value, generate a first element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, the explainability process being trained based on the plurality of validation datasets; and
transmit a portion of the output data and the first element of textual content to a computing system via the communications interface, the computing system being configured to generate or modify interaction data associated with the device based on the portion of the output data, and to provision notification data comprising the first element of textual content to the device.
33. A computer-implemented method, comprising:
based on an application of an artificial intelligence process to feature values of an input dataset associated with a device, generating, using at least one processor, elements of output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, the artificial intelligence process being trained based on a plurality of training datasets associated with a first prior temporal interval, and being validated based on a plurality of validation datasets associated with a second prior temporal interval;
using the at least one processor, provisioning, to an explainability process, a corresponding one of the feature values and a Shapley feature value associated with the corresponding feature value, and based on an application of the explainability process to the corresponding feature value and to the Shapley feature value, generating, using the at least one processor, a first element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, the explainability process being trained based on the plurality of validation datasets; and
transmitting a portion of the output data and the first element of textual content to a computing system using the at least one processor, the computing system being configured to generate or modify interaction data associated with the device based on the portion of the output data, and to provision notification data comprising the first element of textual content to the device.
40. A tangible, non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform a method, comprising:
based on an application of an artificial intelligence process to feature values of an input dataset associated with a device, generating elements of output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, the artificial intelligence process being trained based on a plurality of training datasets associated with a first prior temporal interval, and being validated based on a plurality of validation datasets associated with a second prior temporal interval;
provisioning, to an explainability process, a corresponding one of the feature values and a Shapley feature value associated with the corresponding feature value, and based on an application of the explainability process to the corresponding feature value and to the Shapley feature value, generating a first element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, the explainability process being trained based on the plurality of validation datasets; and
transmitting a portion of the output data and the first element of textual content to a computing system, the computing system being configured to generate or modify interaction data associated with the device based on the portion of the output data, and to provision notification data comprising the first element of textual content to the device.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12,217,011 as shown below. Although the claims at issue are not identical, they are not patentably distinct from each other because of the following mapping:
Instant Claims
Reference
21. An apparatus, comprising:
a memory storing instructions;
a communications interface; and
at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
based on an application of an artificial intelligence process to feature values of an input dataset associated with a device, generate elements of output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, the artificial intelligence process being trained based on a plurality of training datasets associated with a first prior temporal interval, and being validated based on a plurality of validation datasets associated with a second prior temporal interval;
provision, to an explainability process, a corresponding one of the feature values and a Shapley feature value associated with the corresponding feature value, and …
… based on an application of the explainability process to the corresponding feature value and to the Shapley feature value, generate a first element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, the explainability process being trained based on the plurality of validation datasets; and
transmit a portion of the output data and the first element of textual content to a computing system via the communications interface, the computing system being configured to generate or modify interaction data associated with the device based on the portion of the output data, and to provision notification data comprising the first element of textual content to the device.
1. An apparatus, comprising:
a memory storing instructions;
a communications interface; and
at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to:
train adaptively an artificial intelligence process based on a plurality of training datasets associated with a first temporal interval, and validate the trained artificial intelligence process based on a plurality of validation datasets associated with a second temporal interval, wherein the second temporal interval is subsequent to the first temporal interval;
receive an input dataset based on elements of first interaction data associated with a third temporal interval and a temporal identifier characterizing a fourth temporal interval, the fourth temporal interval being subsequent to the third temporal interval, and the input dataset comprising feature values of a plurality of input features corresponding to the first interaction data;
based on an application of the trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during the fourth temporal interval;
train an explainability process based on the plurality of validation datasets;
provision pairs of the feature values and Shapley feature values to the trained explainability process, where each pair comprises a corresponding one of the feature values and a Shapley feature value associated with the corresponding feature value;
based on an application of the trained explainability process to the pairs of the feature values and Shapley feature values, generate a first element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, the first element of textual content being associated with the corresponding feature value; and
transmit a portion of the output data and the first element of textual content to a computing system via the communications interface, wherein the computing system is configured to generate or modify second interaction data based on the portion of the output data, and to provision notification data comprising the first element of textual content to a device associated with the first interaction data.
Allowable Subject Matter
Subject to overcoming the Obviousness Double Patenting rejection above, the pending Claims are considered allowable in view of the prior art.
The following is an examiner’s statement of reasons for allowance: In view of each of the particular limitations of the independent Claims when considered in the order established by the Claim language and in the context of the language of the independent Claims when each Claim is considered as a whole, the independent Claims of this Application were not found in the prior art that was viewed.
In particular, refer to the prosecution of the parent application. The instant Claims are cleaned up versions of the allowed claim of the parent as provided above in the ODP rejection.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Close Art of Record
In addition to the art applied to the Claims during the prosecution of the parent application, note the following.
Cmielowski (US 20220067578):
21. An apparatus, comprising:
a memory storing instructions; [Cmielowski, Figure 2, Memory 206.]
a communications interface; and [Cmielowski, Figure 2, Processor Unit 204
at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to: [Cmielowski, Figure 2, Persistent Storage 208. Computer Program Product 258.]
based on an application of an artificial intelligence process to feature values of an input dataset [Cmielowski, Figure 2, Significant Input Feature Manager 218. ML Model 228 including Input Features 230.]
associated with a device, [Cmielowski pertains to predicting fraud and nis not about a device operation.]
generate elements of output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, [Cmielowski, Figure 2, Prediction 234 and Probability of Prediction 232 and Output 244 associated with Input Feature 242. “[0001] The disclosure relates generally to machine learning and more specifically to identifying significance of input features of a machine learning model based on correlation coefficient values corresponding to input features and their respective outputs (i.e., predictions and probabilities of those predictions) from the machine learning model.” “[0035] Outputs 232 represent an output from machine learning model 228 corresponding to each respective input feature of input features 230 inputted into machine learning model 228. In this example, outputs 232 include prediction 234 and probability of prediction 236. Prediction 234 refers to an output of machine learning model 228 after machine learning model 228 has been trained on a historical dataset and is applied to new data when forecasting the likelihood of a particular outcome, such as whether a customer is likely to churn, possibility of fraudulent activity, and the like. For example, if machine learning model 228 outputs a prediction that a customer is likely to churn, then the user can target that customer with specific communications and outreach that may prevent the loss of that customer. Probability of prediction 236 indicates the likelihood of prediction 234 occurring or being true. The higher probability of prediction 236 is, the more likely it is that prediction 234 will occur or be true. Outputs 232 also include explainability, which provides an explanation, description, or justification for each prediction 234 in human understandable terms allowing the user to comprehend why machine learning model 228 made prediction 234.”]
the artificial intelligence process being trained based on a plurality of training datasets associated with a first prior temporal interval, and being validated based on a plurality of validation datasets associated with a second prior temporal interval; [Cmielowski, “[0002] Machine learning is the study of computer algorithms that improve automatically through experience over time. Machine learning may be considered as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data (i.e., training data) in order to make predictions without being explicitly programmed to do so.”]
provision, to an explainability process, a corresponding one of the feature values and a Shapley feature value associated with the corresponding feature value, and [Cmielowski, Figure 2, “Correlation Coefficient Value 240 indicates the correlation/explainability. The input is on the “input feature 242” and the use of SHAP or SHAPLEY feature is not taught. “[0037] Significant input feature manager 218 compares correlation coefficient value threshold 246 with correlation coefficient value 240 of each input feature and its corresponding output from machine learning model 228 within correlation coefficient matrix 238. Correlation coefficient value threshold 246 is a predefined minimum threshold level for correlation coefficient value 240. In other words, significant input feature manager 218 only selects those input features with a corresponding correlation coefficient value greater than or equal to correlation coefficient value threshold 246 as significant input features 248. Significant input features 248 have the greatest impact on the output of machine learning model 228. By identifying significant input features 248, significant input feature manager 218 can increase the prediction accuracy of machine learning model 228.” Figure 3, 312, 314.]
based on an application of the explainability process to the corresponding feature value and to the Shapley feature value, generate a first element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, the explainability process being trained based on the plurality of validation datasets; and [Cmielowski, Figure 3, 316. “[0175] The computer identifies a set of significant input features per batch, each significant input feature in the set having a corresponding correlation coefficient value greater than or equal to a predefined correlation coefficient threshold level (step 312). The computer ranks the set of significant input features having correlation coefficient values greater than or equal to the predefined correlation coefficient threshold level per batch according to a corresponding correlation coefficient value of a respective significant input feature of a batch (step 314). The computer also performs a set of action steps automatically regarding the set of significant input features per batch (step 316). Thereafter, the process terminates.”]
transmit a portion of the output data and the first element of textual content to a computing system via the communications interface, [Cmielowski, Figure 2, Communications Unit 210. Singificant Input Features 248 ranked 250 according to Correlation Coefficient Value Threshold 246.]
the computing system being configured to generate or modify interaction data associated with the device based on the portion of the output data, and to provision notification data comprising the first element of textual content to the device. [Cmielowski informs of possibility of fraud.]
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Sarabi (US 20230403225):
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Zamft (US 20220301658):
Input is removed and Shap is substituted. But not input+SHAP as input.
[0084] Now that a mapping is created between each node in the first hidden layer and its corresponding genes in the input space, the input layer of the deep neural network mode can be removed and XAI such as SHAP or integrated gradients can be used in downstream processing to obtain feature importance scores for the neural network. Since the first hidden layer of the deep neural network now becomes the input layer, a set of feature importance scores is obtained for every single node in the first hidden layer. The set of feature importance scores obtained for every single node in the first hidden layer may be joined with the mapping of nodes in the first hidden layer to the original genes to get a set of feature importance scores for these clusters or sets of genes.
Balayan (US 20220114595):
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Odibat (US 20210248457)
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Nourian (11645581)
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Rho (U.S. 12124925): (same assignee)
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Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARIBA SIRJANI whose telephone number is (571)270-1499. The examiner can normally be reached on 9 to 5, M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Desir can be reached on 571-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Fariba Sirjani/
Primary Examiner, Art Unit 2659
Prosecution Insights