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 Objections
Claims 2 and 7 are objected to because of the following informalities: In claim 2, line 4 should end with “and”. In claim 7, line 4 should end with “and”. Appropriate correction is required.
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.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-11 recite a method, claim 12 recites an access control device comprising a processor (a system), and claim 13 recites a non-transitory computer-readable recording medium (a product). A method, a system, and a product each falls within one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Filtering information available for the at least one
Step 2A Prong 2: At least one artificial intelligence (AI) model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Acquiring output data of the at least one AI model based on the filtered data, wherein the filtered data includes at least one of learning information for the at least one AI model or requested information for the at least one AI model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The limitations “learning information” and “requested information” indicate the filtered data is used for learning or inference.
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: At least one artificial intelligence (AI) model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Acquiring output data of the at least one AI model based on the filtered data, wherein the filtered data includes at least one of learning information for the at least one AI model or requested information for the at least one AI model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The limitations “learning information” and “requested information” indicate the filtered data is used for learning or inference.
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 2 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The learning information corresponds to learning allowable information associated with access right meta information corresponding to at least one of the access right or the access control policy among the available information amounts to a mere field of use and technological environment under MPEP 2106.05(h).
A learning process for the at least one AI model is performed based on the learning information amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated.
Step 2A Prong 2 and Step 2B: The learning allowable information is applied to fine-tuning learning for the at least one AI model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 2.
Step 2A Prong 1: The abstract ideas of claim 2 are incorporated.
Step 2A Prong 2 and Step 2B: The access right meta information includes information on at least one of a creator, a modifier, an owner, a manager, a management group, a right identifier, a right level or a security level amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The requested information is input to the at least one AI model learned based on the learning information included in the filtered data or at least one AI model learned based on general learning information not included in the filtered data amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
The output data is acquired through an inference process based on the requested information amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f). The claim is not patent eligible.
Claim 6 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The requested information includes at least one of user information, direct input information or additional input information amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 7 incorporates the rejection of claim 6.
Step 2A Prong 1: The abstract ideas of claim 6 are incorporated.
Step 2A Prong 2 and Step 2B: The additional input information is based on extension allowable information associated with access right meta information corresponding to the access control policy among the available information amounts to a mere field of use and technological environment under MPEP 2106.05(h).
The access control policy is based on at least one of the user information or the direct input information amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 8 incorporates the rejection of claim 7.
Step 2A Prong 1: The abstract ideas of claim 7 are incorporated.
Step 2A Prong 2 and Step 2B: The additional input information includes at least one of knowledge information related to the direct input information or a keyword generated based on the direct input information amounts to a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 9 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The at least one AI model includes at least one of at least one generic AI model or at least one right-specialized AI model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 10 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated.
Step 2A Prong 2 and Step 2B: Each of a plurality of specialized AI models corresponds to a different access right or access control policy amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) and a mere field of use and technological environment under MPEP 2106.05(h). The claim is not patent eligible.
Claim 11 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated.
Step 2A Prong 2 and Step 2B: The filtered data is input to one right-specialized AI model or a plurality of right-specialized AI models amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f). The claim is not patent eligible.
Claim 12
Step 2A Prong 1: Filter information available for the at least one
Step 2A Prong 2: An access control device for at least one artificial intelligence (AI) model, the device comprising: a transceiver, a memory, a user interface, and a processor amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). An AI model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Store filtered data in the memory amounts to insignificant post-solution activities under MPEP 2106.05(g).
Acquire output data of the at least one AI model based on the filtered data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Store output data in the memory amounts to insignificant post-solution activities under MPEP 2106.05(g).
The limitation “the filtered data includes at least one of learning information for the at least one AI model or requested information for the at least one AI model” indicates the filtered data is used for learning or inference, which amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: An access control device for at least one artificial intelligence (AI) model, the device comprising: a transceiver, a memory, a user interface, and a processor amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). An AI model amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Store filtered data in the memory is a well-understood, routine, conventional activity recognized by the courts under MPEP 2106.05(d)(II).
Acquire output data of the at least one AI model based on the filtered data amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Store output data in the memory is a well-understood, routine, conventional activity recognized by the courts under MPEP 2106.05(d)(II).
The limitation “the filtered data includes at least one of learning information for the at least one AI model or requested information for the at least one AI model” indicates the filtered data is used for learning or inference, which amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 13 recites a product which implements the same features as the method of claim 1 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and Step 2B, a non-transitory computer-readable recording medium storing a computer program for executing a method at a computer amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ramakrishnan et al. (US 20240177049 A1).
Regarding claim 1, Ramakrishnan teaches: An access control method for at least one artificial intelligence (AI) model, the method comprising: filtering information available for the at least one AI model based on at least one of an access right or an access control policy and acquiring filtered data for the at least one AI model; and ([0016], [0019], lines 1-8 and [0042], lines 1-10 teaches fine-tuning a pre-trained model using a restricted tuning data set. The data set may have user data access restrictions, which may only allow access to the data set for tuning purposes by machine learning system, and block access to entities without access privileges.)
acquiring output data of the at least one AI model based on the filtered data, ([0014], lines 7-14 and [0016] indicates that during training and fine-tuning, a model provides an output by processing input data. The input data may include the restricted tuning data set.)
wherein the filtered data includes at least one of learning information for the at least one AI model or requested information for the at least one AI model. ([0019], lines 1-8 and [0042], lines 1-10, where a restricted tuning data set corresponds to the limitation “learning information for the at least one AI model” as claimed.)
Regarding claim 2, Ramakrishnan teaches: The method of claim 1, wherein: the learning information corresponds to learning allowable information associated with access right meta information corresponding to at least one of the access right or the access control policy among the available information, ([0019], lines 1-8 and [0042], lines 1-10 teaches allowing permitted users to access the tuning data set while preventing other users from accessing it. The limitation “access right meta information” includes location and access credentials to obtain the data set.)
a learning process for the at least one AI model is performed based on the learning information. ([0016] where a learning process includes fine-tuning.)
Regarding claim 3, Ramakrishnan teaches: The method of claim 2, wherein: the learning allowable information is applied to fine-tuning learning for the at least one AI model. ([0016])
Regarding claim 4, Ramakrishnan teaches: The method of claim 2, wherein: the access right meta information includes information on at least one of a creator, a modifier, an owner, a manager, a management group, a right identifier, a right level or a security level. ([0019], lines 1-8 teaches the limitation “access right meta information includes information on… a right level”.)
Regarding claim 5, Ramakrishnan teaches: The method of claim 1, wherein: the requested information is input to the at least one AI model learned based on the learning information included in the filtered data or at least one AI model learned based on general learning information not included in the filtered data, ([0014], lines 7-17 and [0016], lines 1-14 teaches performing inference on a confidentially tuned model. The feature of inputting requested information to the AI model corresponds to inputting new, unseen data to the confidentially tuned model.)
the output data is acquired through an inference process based on the requested information. ([0014], lines 13-17)
Regarding claim 6, Ramakrishnan teaches: The method of claim 1, wherein: the requested information includes at least one of user information, direct input information or additional input information. ([0014], lines 7-17 and [0016], lines 1-14 teaches performing inference on a confidentially tuned model. The limitation “direct input information” corresponds to inputting new, unseen data to the confidentially tuned model.)
Regarding claim 7, Ramakrishnan teaches: The method of claim 6, wherein: the additional input information is based on extension allowable information associated with access right meta information corresponding to the access control policy among the available information, the access control policy is based on at least one of the user information or the direct input information. ([0039]-[0041] teaches a model user account may be associated with the confidentially tuned model, and only that model user account may send inference requests to the confidentially tuned model. The limitation “additional input information” includes an access control list that associates the model user account with the confidentially tuned model.)
Regarding claim 8, Ramakrishnan teaches: The method of claim 7, wherein: the additional input information includes at least one of knowledge information related to the direct input information or a keyword generated based on the direct input information. ([0014], lines 13-17 and [0039]-[0041] teaches a model user account may be associated with the confidentially tuned model, and only that user account may send inference requests to the confidentially tuned model. The limitation “knowledge information related to the direct input information” includes knowledge that a model user account is associated with the confidentially tuned model, and only that account may send inference requests to the model.)
Regarding claim 9, Ramakrishnan teaches: The method of claim 1, wherein: the at least one AI model includes at least one of at least one generic AI model or at least one right-specialized AI model. ([0016], [0038]-[0039], where “at least one generic AI model” is a pre-trained model, and “at least one right-specialized AI model” is a confidentially tuned model. A user account must have a right to use the tuned model.)
Regarding claim 10, Ramakrishnan teaches: The method of claim 9, wherein: each of a plurality of specialized AI models corresponds to a different access right or access control policy. ([0038]-[0039] indicates that different model user accounts may tune the same pre-trained model to generate a plurality of confidentially tuned models. Each model user account has a different access right.)
Regarding claim 11, Ramakrishnan teaches: The method of claim 9, wherein: the filtered data is input to one right-specialized AI model or a plurality of right-specialized AI models. ([0014], final 5 lines and [0019], lines 1-8 teaches periodically retraining a model by using new training data, and the model may include a confidentially tuned model. This claim amounts to retraining a confidentially tuned model by using new training inputs from a restricted tuning data set.)
Regarding claim 12, Ramakrishnan teaches: An access control device for at least one artificial intelligence (AI) model, the device comprising: ([0059], lines 1-5)
a transceiver; ([0065] where a transceiver is a network interface)
a memory; ([0063], lines 1-2)
a user interface; and ([0060] on page 8, col. 1, lines 3-5. A user interface is a display including a computer monitor.)
a processor, wherein the processor is configured to: ([0061], lines 1-6)
filter information available for the at least one AI model based on at least one of an access right or an access control policy, acquire filtered data for the at least one AI model and store it in the memory; and ([0016], [0019], lines 1-8 and [0042], lines 1-10 teaches fine-tuning a pre-trained model using a restricted tuning data set. The data set may have user data access restrictions, which may only allow access to the data set for tuning purposes by machine learning system, and block access to entities without access privileges. [0067], lines 1-5 and 7-9 teaches storing data in memory, which would include restricted tuning data set.)
acquire output data of the at least one AI model based on the filtered data and store it in the memory, ([0014], lines 7-14, [0016], and [0067], lines 1-5 and 7-9 indicates that during training and fine-tuning, a model provides an output by processing input data to be stored in memory. The input data may include the restricted tuning data set.)
wherein the filtered data includes at least one of learning information for the at least one AI model or requested information for the at least one AI model. ([0019], lines 1-8 and [0042], lines 1-10, where a restricted tuning data set corresponds to the limitation “learning information for the at least one AI model” as claimed.)
Claim 13 is recites a product which implements the same features as the method of claim 1 as is therefore rejected for at least the same reasons.
Ramakrishnan teaches: A non-transitory computer-readable recording medium storing a computer program for executing a method at a computer ([0058], lines 3-8)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Asher H. Jablon whose telephone number is (571)270-7648. The examiner can normally be reached Monday - Friday, 9:00 am - 6:00 pm.
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/A.H.J./Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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