DETAILED ACTION
This action is in response to the application filed 12/05/2022. Claims 1-20 are pending and have been examined.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites “transmitting a request, via an application programming interface (API) and to a different device that maintains a registry of artificial intelligence usage in connection with locations, resources, or operations, that indicates at least one of a location of the device, a resource that is accessed by the device, or one or more operations being performed by the device” in its first limitation. It’s unclear whether the indications of device location, resource access, or operations are made by the request or the different device. This renders the scope of the claim indefinite. The indication is interpreted as being made by the request, in light of previous claims and paragraph [0023] of the instant specification.
Additionally, it’s not clear which device is being referred to in “a location of the device, a resource that is accessed by the device, or one or more operations being performed by the device” in limitation 1 and “the location of the device, the resource that is accessed by the device, or the one or more operations being performed by the device” in limitation 2, the device of parent claim 9 or the different device of claim 11. Thus, the scope of the claim is further rendered indefinite. “the device” in this context, in light of paragraphs [0022-0223] of the instant specification, is interpreted as referring to the device transmitting the request.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to non-statutory subject matter without significantly more.
Claim 1
Step 1: The claim recites “A system”, and is therefore directed to the statutory category of machine
Step 2A Prong 1: The claim recites the following judicial exception(s)
determine, using at least one machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, whether the decision in connection with the user is erroneous and an amount of a reparation for the user that is to be issued by the entity, wherein the at least one machine learning model is trained to determine whether the decision is erroneous and the amount of the reparation based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This can be performed as a mental process. One can merely decide whether a decision is correct and how many reparations should be sent based on the received information about the use of AI by the entity and similar historical decisions made for the user and / or other users.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: This is mere instruction to execute judicial exceptions using generic computer hardware (MPEP 2106.05(f)).
receive a notification indicating a complaint that a decision in connection with a user is erroneous, the decision being reached by a use of artificial intelligence by an entity: This amounts to mere reception of data and is insignificant extra-solution activity (MPEP 2106.05(g)).
cause, based on the notification, complaint information, indicating the complaint, to be added to a blockchain: This amounts to mere data transmission and is insignificant extra-solution activity (MPEP 2106.05(g)).
determine, using at least one machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, whether the decision in connection with the user is erroneous and an amount of a reparation for the user that is to be issued by the entity, wherein the at least one machine learning model is trained to determine whether the decision is erroneous and the amount of the reparation based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f)).
transmit, in response to the notification, an indication of whether the reparation for the user is to be issued by the entity due to the decision: This constitutes mere data transmission and is insignificant extra-solution activity (MPEP 2106.05(g)).
cause judgment information, indicating whether the decision in connection with the user is erroneous and the amount of the reparation, to be added to a blockchain: This constitutes mere data transmission and is insignificant extra-solution activity (MPEP 2106.05(g)).
cause the at least one machine learning model to be re-trained using the judgment information in the blockchain: This is mere instruction to train a model based on the judgment information in a generic manner (MPEP 2106.05(f))
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: This is mere instruction to execute judicial exceptions using generic computer hardware (MPEP 2106.05(f)).
cause, based on the notification, complaint information, indicating the complaint, to be added to a blockchain: This is an instance of storing information in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
receive a notification indicating a complaint that a decision in connection with a user is erroneous, the decision being reached by a use of artificial intelligence by an entity: This is an instance of retrieving information from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.).
determine, using at least one machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, whether the decision in connection with the user is erroneous and an amount of a reparation for the user that is to be issued by the entity, wherein the at least one machine learning model is trained to determine whether the decision is erroneous and the amount of the reparation based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f))
transmit, in response to the notification, an indication of whether the reparation for the user is to be issued by the entity due to the decision: This is an instance of storing information in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
cause judgment information, indicating whether the decision in connection with the user is erroneous and the amount of the reparation, to be added to a blockchain: This is an instance of storing information in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
cause the at least one machine learning model to be re-trained using the judgment information in the blockchain: This is mere instruction to train a model based on the judgment information in a generic manner (MPEP 2106.05(f))
Claim 2
Step 1: The claim recites a machine, as in claim 1
Step 2A Prong 1: The claim recites the following further judicial exception(s)
wherein the at least one machine learning model is trained to determine the amount of the reparation based on historical reparation data in the blockchain: This can be performed as a mental process. One can merely decide a reparation amount based on similar reparation amounts in historical data.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the at least one machine learning model is trained to determine the amount of the reparation based on historical reparation data in the blockchain: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the at least one machine learning model is trained to determine the amount of the reparation based on historical reparation data in the blockchain: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f)).
Claim 3
Step 1: The claim recites a machine, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
wherein the at least one machine learning model comprises a first machine learning model trained to determine whether the decision is erroneous and a second machine learning model trained to determine the amount of the reparation: Training a generic machine learning model to execute judicial exceptions is still mere instruction to apply when split across two generic models (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
wherein the at least one machine learning model comprises a first machine learning model trained to determine whether the decision is erroneous and a second machine learning model trained to determine the amount of the reparation: Training a generic machine learning model to execute judicial exceptions is still mere instruction to apply when split across two generic models (MPEP 2106.05(f)).
Claim 4
Step 1: The claim recites a machine, as in claim 1
Step 2A Prong 1: The claim recites the following further judicial exception(s)
wherein the first information identifies the entity and a use case associated with the use of artificial intelligence by the entity: Determining whether the decision is erroneous and the amount of reparation can still be performed as a mental process. One can merely make decisions based on similar use cases of AI by the entity in the past.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the additional element(s)
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
Claim 5
Step 1: The claim recites a machine, as in claim 1
Step 2A Prong 1: The claim recites the following further judicial exception(s)
wherein the one or more historical decisions were reached by use of artificial intelligence by the entity: Determining whether the decision is erroneous and the amount of reparation can still be performed as a mental process. One can merely make decisions based on similar use cases of AI by the entity in the past.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the additional element(s)
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
Claim 6
Step 1: The claim recites a machine, as in claim 1
Step 2A Prong 1: The claim recites the following further judicial exception(s)
wherein the decision and the one or more historical decisions relate to a same use case: Determining whether the decision is erroneous and the amount of reparation can still be performed as a mental process. One can merely make decisions based on similar use cases of AI by the entity in the past.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the additional element(s)
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
Claim 7
Step 1: The claim recites a machine, as in claim 1
Step 2A Prong 1: The claim recites the following further judicial exception(s)
identify the one or more historical decisions relating to the one or more other users by performing natural language processing of at least one data source that includes unstructured data indicating the one or more historical decisions: This can be performed as a mental process. One can merely identify historical decisions in the data.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
identify the one or more historical decisions relating to the one or more other users by performing natural language processing of at least one data source that includes unstructured data indicating the one or more historical decisions: This is mere instruction to process data with generic natural language processing (MPEP 2106.05(f)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
identify the one or more historical decisions relating to the one or more other users by performing natural language processing of at least one data source that includes unstructured data indicating the one or more historical decisions: This is mere instruction to process data with generic natural language processing (MPEP 2106.05(f)).
Claim 8
Step 1: The claim recites a machine, as in claim 1
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
complaint information identifies the user, the entity, a use case associated with the use of artificial intelligence by the entity, the decision, and a non-erroneous result associated with the use of artificial intelligence by the entity: Adding complaint information to a blockchain is still an instance of mere data transmission, thus insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
complaint information identifies the user, the entity, a use case associated with the use of artificial intelligence by the entity, the decision, and a non-erroneous result associated with the use of artificial intelligence by the entity: Adding complaint information to a blockchain is still an instance of storing information in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
Claim 9
Step 1: The claim recites “A method”, and is therefore directed to the statutory category of process
Step 2A Prong 1: The claim recites the following judicial exception(s)
determining, by the device, using a machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous, wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This can be performed as a mental process. One can merely decide whether a decision is correct and how many reparations should be sent based on the received information about the use of AI by the entity and similar historical decisions made for the user and / or other users.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
scanning, by a device, a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user: This amounts to mere reception of data and is insignificant extra-solution activity (MPEP 2106.05(g)).
determining, by the device, using a machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous, wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f)).
providing, by the device, a notification indicating that the decision in connection with the user is erroneous: This constitutes mere data transmission and is insignificant extra-solution activity (MPEP 2106.05(g)).
causing, by the device, judgment information, indicating that the decision in connection with the user is erroneous, to be added to the blockchain: This amounts to mere data transmission and is insignificant extra-solution activity (MPEP 2106.05(g)).
causing the machine learning model to be re-trained using the judgment information in the blockchain: This is mere instruction to train a model based on the judgment information in a generic manner (MPEP 2106.05(f))
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
scanning, by a device, a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user: This is an instance of retrieving information from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
determining, by the device, using a machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous, wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f)).
providing, by the device, a notification indicating that the decision in connection with the user is erroneous: This is an instance of storing information in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
causing, by the device, judgment information, indicating that the decision in connection with the user is erroneous, to be added to the blockchain: This is an instance of storing information in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
causing the machine learning model to be re-trained using the judgment information in the blockchain: This is mere instruction to train a model based on the judgment information in a generic manner (MPEP 2106.05(f))
Claims 10, 13-16
Step 1: Claims 10, 13-16 recite a process, as in claim 9.
Step 2A Prong 1: Claims 10, 13-16 recite the same judicial exception(s) as claims 8, 4-7, respectively.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through any additional elements. The limitations of claims 10, 13-16 are disclosed by claims 8, 4-7, respectively, with the exception that claims 10, 13-16 are directed to “identifying, by a device, a use of artificial intelligence by an entity to reach a decision in connection with a user”, a judicial exception, as discussed regarding claim 9. Claims 10, 13-16 are not integrated into a practical application under the same rationales given for claims 8, 4-7, respectively.
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s). The limitations of claims 10, 13-16 are disclosed by claims 8, 4-7, respectively, with the exception that claims 10, 13-16 are directed to “identifying, by a device, a use of artificial intelligence by an entity to reach a decision in connection with a user”, a judicial exception, as discussed regarding claim 9. Claims 10, 13-16 fail to amount to significantly more than the recited judicial exceptions, under the same rationales given for claims 8, 4-7, respectively.
Claim 11
Step 1: The claim recites a method, as in claim 9
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
transmitting a request, via an application programming interface (API), and to a different device that maintains a registry of artificial intelligence usage in connection with locations, that indicates at least one of a location of the device, a resource that is accessed by the device, or one or more operations being performed by the device: This is mere data transmission and constitutes insignificant extra-solution activity (MPEP 2106.05(g)).
receiving a response, via the API, that indicates the use of artificial intelligence by the entity based on the registry including a registration associated with the entity and the at least one of the location of the device, the resource that is accessed by the device, or the one or more operations being performed by the device: This is mere data transmission and constitutes insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
transmitting a request, via an application programming interface (API), and to a different device that maintains a registry of artificial intelligence usage in connection with locations, that indicates at least one of a location of the device, a resource that is accessed by the device, or one or more operations being performed by the device: This is an instance of transmitting data over a network, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. i.).
receiving a response, via the API, that indicates the use of artificial intelligence by the entity based on the registry including a registration associated with the entity and the at least one of the location of the device, the resource that is accessed by the device, or the one or more operations being performed by the device: This is an instance of receiving data over a network, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. i.).
Claim 12
Step 1: The claim recites a process, as in claim 9
Step 2A Prong 1: The claim recites no further judicial exception(s)
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the further additional element(s)
receiving, in response to the notification, an indication of whether a reparation for the user is to be issued by the entity due to the decision: This is mere data reception and constitutes insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: The further additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
receiving, in response to the notification, an indication of whether a reparation for the user is to be issued by the entity due to the decision: This is an instance of retrieving information in memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
Claim 17
Step 1: The claim recites “A non-transitory computer-readable medium”, and is therefore directed to the statutory category of article of manufacture
Step 2A Prong 1: The claim recites the following judicial exception(s)
determine, using a machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This can be performed as a mental process. One can merely decide whether a decision is correct and how many reparations should be sent based on the received information about the use of AI by the entity and similar historical decisions made for the user and / or other users.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through the following additional element(s)
A non-transitory computer-readable medium storing a set of instructions for assessment of artificial intelligence errors using machine learning, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: This is mere execution to execute judicial exceptions with generic computer hardware (MPEP 2106.05(f)).
scan a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user: This amounts to mere reception of data and is insignificant extra-solution activity (MPEP 2106.05(g)).
determine, using a machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f)).
cause the machine learning model to be re-trained using the judgment information in the blockchain: This is mere instruction to train a model based on the judgment information in a generic manner (MPEP 2106.05(f))
Step 2B: The following additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s)
A non-transitory computer-readable medium storing a set of instructions for assessment of artificial intelligence errors using machine learning, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: This is mere execution to execute judicial exceptions with generic computer hardware (MPEP 2106.05(f)).
scan a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user: This is an instance of retrieving information from memory, a limitation known to be well-understood, routine, and conventional (MPEP 2106.05(d) II. iv.)
determine, using a machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users: This is mere instruction to generically train a machine learning model to execute a judicial exception (MPEP 2106.05(f)).
cause the machine learning model to be re-trained using the judgment information in the blockchain: This is mere instruction to train a model based on the judgment information in a generic manner (MPEP 2106.05(f))
Claims 18-20
Step 1: Claims 18-20 recite a process, as in claim 9.
Step 2A Prong 1: Claims 18-20 recite the same judicial exception(s) as claims 11-12, 16, respectively.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application through any additional elements. The limitations of claims 18-20 are disclosed by claims 11-12, 16, respectively, with the exception that claims 18-20 are directed to “A non-transitory computer-readable medium storing a set of instructions for assessment of artificial intelligence errors using machine learning, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to”, generic computer hardware. Claims 18-20 are not integrated into a practical application under the same rationales given for claims 11-12, 16, respectively.
Step 2B: The additional element(s) of the claim, taken alone or in combination, do not amount to significantly more than the recited judicial exception(s). The limitations of claims 18-20 are disclosed by claims 11-12, 16, respectively, with the exception that claims 18-20 are directed to “A non-transitory computer-readable medium storing a set of instructions for assessment of artificial intelligence errors using machine learning, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to”, generic computer hardware. Claims 18-20 are not considered to amount to significantly more than the recited judicial exceptions under the same rationales given for claims 11-12, 16, respectively.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Mueck et al. (ARTIFICIAL INTELLIGENCE REGULATORY MECHANISMS, PCT filed 8/5/2022, US 2024/0273411 A1), hereafter referred to as Mueck, in view of Henryson et al. (MACHINE-LEARNING PREDICTIVE MODELS FOR CLASSIFYING RESPONSES TO AND OUTCOMES OF END-USER COMMUNICATIONS, filed 12/14/2020, US 11,720,903 B1), hereafter referred to as Henryson, and further in view of Patel et al. (System And Method For Auditing, Monitoring, Recording, And Executing Healthcare Transactions, Communications, And Decisions, published 2/20/2020, US 20200058381 A1), hereafter referred to as Patel, and So et al. (PRIORITIZATION AND AUTOMATION OF BILLING DISPUTES INVESTIGATION USING MACHINE LEARNING, published 12/26/2019, US 2019/0392538 A1), hereafter referred to as So.
Regarding claim 1, Mueck discloses [a] system for assessment of artificial intelligence errors using machine learning, the system comprising:
one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: “The storage 3558 may include instructions 3583 in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions 3583 are shown as code blocks included in the memory 3554 and the storage 3558, any of the code blocks may be replaced with hardwired circuits, for example, built into an application specific integrated circuit (ASIC), FPGA memory blocks, and/or the like. In an example, the instructions 3581, 3582, 3583 provided via the memory 3554, the storage 3558, or the processor 3552 may be embodied as a non-transitory, machine-readable medium 3560) including code to direct the processor 3552 to perform electronic operations in the compute node 3550. The processor 3552 may access the non-transitory, machine-readable medium 3560 (also referred to as "computer readable medium 3560)" or "CRM 3560") over the IX 3556” (Mueck, [0316])
receive a notification indicating a complaint that a decision in connection with a user is erroneous, the decision being reached by a use of artificial intelligence by an entity: “the AI system 2310 can identify whether any biases have been observed or detected, whether any erroneous decisions have been made based on the detected biases ( e.g., possibly putting the user at risk), and/or the like. A result of the self-assessment can then be communicated to the requestor indicating whether or not the AI system 2310 is performing appropriately and/or in compliance with the [AIA ]” (Mueck, [0205])
determine, using at least one machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, whether the decision in connection with the user is erroneous and an amount of a reparation for the user that is to be issued by the entity, wherein the at least one machine learning model is trained to determine whether the decision is erroneous and the amount of the reparation based on first information and second information relating to one or more historical decisions in connection with the user or one or more other users:
“the one or more databases (DB) 903 are considered to be under control of the AI system 902 and/or the owner (entity) or operator of the AI system 902” (Mueck, [0087])
“In various implementations, the AI system 2310 is the same or similar as the AI system 902” (Mueck, [0170])
“In various implementations, the outputs 2352 of the AI system 2310 are double-checked and validated before they are used for any objective (e.g., as a decision to signaling an actuator to change the state of a system, as a prediction or inference to evaluate a human or object, as a decision to take an action, and/or the like)“ (Mueck, [0210])
“Here, the output 2352 of the AI system 2310 (e.g., a prediction) is fed into the EAIOSV 2600, the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303 … Additionally or alternatively, the EAIOSV 2600 can be an AI/ML (machine learning) model that is trained using counterfactual examples (second information) to predict whether the obtained prediction is biased w.r.t. historical predictions and/or predictions from alternation models. These training counterfactual examples can be based on known or hypothetical AI decision(s) (first information) that could potentially harm or cause damage to an individual human or object” (Mueck, [0211]). The use of this AI system is inherently associated with the entity that owns / operates the system.
“In case the verification leads to the assessment that the obtained prediction could potential cause harm or damage, then suitable counter measures/actions 2353 (amount of reparation[s]) can be taken, for example, preventing outputs 2352 of the AI system 2310 from being fed to other devices or systems (e.g., actuators, or the like), and an authorized user can be informed about the possibly problematic decision of the AI system 2310 and/or the counter measures/actions 2353 via the access interface 2312. Additionally or alternatively, validated/authorized outputs 2652 of the AI system 2310 can be provided to authorized users over the access interface 2312.” (Mueck, [0235]). The entity, which manages this system, is issuing reparations by having the system automatically perform reparations, such as automatic retraining, making it a better experience for the end-user.
While Mueck fails to disclose the further limitations of the claim, Henryson discloses a system able to transmit, in response to the notification, an indication of whether the reparation for the user is to be issued by the entity due to the decision:
“The service provider (entity) may provide some sort of compensation (e.g., monetary relief) (reparation) in response to these user complaints, such as reimbursing an overdraft fee that may be in error or as a courtesy to the user” (Henryson, column 1, paragraph 2)
“Various embodiments relate to a service provider (entity) computing system” (Henryson, column 1, paragraph 4)
“Various embodiments relate to a method. The method includes receiving a complaint from a user, recording at least a text-based description of the complaint, including an indication of a resolution for the complaint, parsing the text-based description of the complaint to generate a matrix of key terms within the text-based description, executing a 10 machine-learning predictive model using the matrix of key terms to generate, for the complaint, a prediction indicating whether the complaint should have compensation (reparation), and presenting, via a user interface, an indication of the prediction (notification)” (Henryson, column 2, paragraph 2)
Mueck and Henryson relate to machine learning for processing errors and are analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mueck to automatically determine whether to issue reparations in response to a complaint, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
While Henryson fails to disclose the further limitations of the claim, Patel discloses a system able to:
cause, based on the notification, complaint information, indicating the complaint, to be added to a blockchain:
“One example of the dispute resolution through the present invention is described below. Another example will be described in FIG. 10. In one scenario, a Healthcare Provider may have a complaint for a payment from the Payer and as such may file a PDR (notification). The PDR would be centrally logged and all parties associated with the dispute, such as Healthcare Provider, Payer. Health Plan Provider, Member may be able to view that a dispute has been filed. Alternatively, the Control/Verification Manager 809, may provide an alert or a message through the API to all connected parties that a dispute has been filed. The details of the dispute as well as the time/day the dispute has been named would also be visible to all the parties. As the dispute resolution progresses and additional parties or the responding party (payer) provides a response, all progress, times and details of responses, and the timeline of the resolution (complaint information) would also be centrally logged and all relevant parties would be able to view the details. Alternatively, I addition to centrally being logged, all events and communication would also be recorded in the blockchain” (Patel, [0146]).
determine, using at least one machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain: “The Healthcare Provider at 1003 initiates a PDR. As mentioned above, the PDR is centrally logged by the Control manager 410. Additionally, the PDR is also reported to the blockchain by both the Healthcare Provider as well as the Control manager 410. Further, the Control manager 410 may also send an alert to all parties in that are in the network and authorized to receive confidential information in reference to the particular claim or the patient.” (Patel, [0153])
cause judgment information, indicating whether the decision in connection with the user is erroneous and the amount of the reparation, to be added to the blockchain:
“complaints are often made about payer system and their associated medical plan provider denying payment, claiming a payment was processed (decision) and authorized when it was not (erroneous), claiming that a check was mailed when it was not or modifying backend transaction records to say claim was paid on certain date and time but in reality, it was not” (Patel, [0134])
“The system 300, through its Brain 310, functions to record every interaction and transaction between the parties, including, dates and times of the transactions, details of the transactions, information exchanged through the transaction, and the ultimate outcome, e.g. payment, nonpayment, portion of payment etc., time of payment, amount paid (amount of the reparation), parties paid etc.” (Patel, [0067])
“One example of the dispute resolution through the present invention is described below. Another example will be described in FIG. 10. In one scenario, a Healthcare Provider may have a complaint for a payment from the Payer and as such may file a PDR. The PDR would be centrally logged and all parties associated with the dispute, such as Healthcare Provider, Payer. Health Plan Provider, Member may be able to view that a dispute has been filed. Alternatively, the Control/Verification Manager 809, may provide an alert or a message through the API to all connected parties that a dispute has been filed. The details of the dispute as well as the time/day the dispute has been named would also be visible to all the parties. As the dispute resolution progresses and additional parties or the responding party (payer) provides a response, all progress, times and details of responses, and the timeline of the resolution would also be centrally logged and all relevant parties would be able to view the details. Alternatively, I addition to centrally being logged, all events and communication would also be recorded in the blockchain” (Patel, [0146]).
Patel relates to using blockchains in automated complaint management systems and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck and Henryson to store complaint information on a blockchain, as disclosed by Patel. Patel’s method automates adjudication of complaints, claims, and disputes, reducing cumbersome manual audits and evidence gathering. Additionally, decentralized blockchains provide transparency to all involved parties and prevent tampering with records of events. See Patel, [0034], [0147].
While Patel fails to disclose the further limitations of the claim, So discloses a system able to cause the at least one machine learning model to be re-trained using the judgment information in the blockchain:
“At times, the customer may claim that there are discrepancies in the transaction, for instance undelivered items by the seller, incorrect quantity or pricing of the items delivered, damaged goods and so on. In such circumstances, the customer typically pays a part of the bill and initializes a claim on the disputed items. The seller then chooses to either accept or investigate the claim made by the customer. The outcome (judgment information) of the investigation can either be acceptance of the claim and clearing the balance or disputing the customer's claim and requiring further information and/or payment of the balance.” (So, [0002])
“The processor (model) analyses the dispute information and computes a score for determining the validity of the billing dispute. The processor thereupon categorizes the billing dispute based on the computed score, wherein the categories (judgment information) may include segments or brackets, such as valid disputes and invalid disputes.” (So, [0029])
“For determining the label for each dispute, the one or more machine learning techniques may be employed.” (So, [0048])
“The model retraining module 312 is configured to retrain the system 208 periodically, preferably weekly or monthly. The retraining module 312 encapsulates the script necessary for retraining the system 208 by employing or incorporating fresh data and outcomes (judgment information) to the system 208.” (So, [0050])
So relates to using machine learning to automatically resolve and act upon complaint decisions and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the existing combination to periodically retrain the judgment model using observed judgment decisions, as disclosed by So. Doing so would ensure that the system is updated periodically with the latest information, to yield accurate computations and predictions. See So, [0050].
Regarding claim 2, the rejection of claim 1 in view of Mueck, Henryson, Patel, and So is incorporated. Henryson further discloses a system, wherein the at least one machine learning model is trained to determine the amount of the reparation based on historical reparation data in the blockchain:
“obtain a first set of complaint logs (historical reparation data) corresponding to one or more user complaints, each complaint log of the first set of complaint logs including a resolution comments field and an indication whether compensation was provided, the resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to embodiments. identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation” (Henryson, column 1, paragraph 4)
“In other example, the predicted resolution is provided as an indication or an alert, or an interface displayed on the service representative's device is updated to include the predicted resolution information. In some embodiments, the user interface can indicate whether or not compensation is recommended and may even indicate a recommended amount of compensation (amount of reparation). For example, the 10 interface may indicate that ‘Compensation is recommended. Would you like to apply the recommended amount of compensation?’” (Henryson, column 14, paragraph 1)
Henryson relates to machine learning for automatically processing complaints and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Henryson, Patel, and So to automatically determine reparation amounts based on past reparations awarded, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when and how much to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
Regarding claim 3, the rejection of claim 1 in view of Mueck, Henryson, Patel, and So is incorporated. Mueck further discloses a system, wherein the at least one machine learning model comprises a first machine learning model trained to determine whether the decision is erroneous and a second machine learning model trained to determine the amount of the reparation: “Here, the output 2352 of the AI system 2310 (e.g., a prediction) is fed into the EAIOSV 2600, the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303 … Additionally or alternatively, the EAIOSV 2600 can be an AI/ML model (first machine learning model) that is trained using counterfactual examples to predict whether the obtained prediction is biased w.r.t. historical predictions and/or predictions from alternation models. These training counterfactual examples can be based on known or hypothetical AI decision(s) that could potentially harm or cause damage to an individual human or object” (Mueck, [0211]).
While Mueck fails to disclose the further limitations of the claim, Henryson discloses a system, wherein the at least one machine learning model comprises a first machine learning model trained to determine whether the decision is erroneous and a second machine learning model trained to determine the amount of the reparation: “obtain a first set of complaint logs (historical reparation data) corresponding to one or more user complaints, each complaint log of the first set of complaint logs including a resolution comments field and an indication whether compensation was provided, the resolution comments field providing a textual representation of a resolution for a corresponding complaint log, parse the resolution comments field of each complaint log of the first set of complaint logs to embodiments. identify one or more key terms, and execute a machine-learning predictive model using the one or more key terms to generate, for each complaint log, a prediction indicating whether a corresponding complaint should have compensation” (Henryson, column 1, paragraph 4); “In other example, the predicted resolution is provided as an indication or an alert, or an interface displayed on the service representative's device is updated to include the predicted resolution information. In some embodiments, the user interface can indicate whether or not compensation is recommended and may even indicate a recommended amount of compensation (amount of reparation). For example, the 10 interface may indicate that ‘Compensation is recommended. Would you like to apply the recommended amount of compensation?’” (Henryson, column 14, paragraph 1)
Henryson relates to machine learning for automatically processing complaints and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Henryson, Patel, and So to automatically determine reparation amounts based on past reparations awarded, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when and how much to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
Regarding claim 4, the rejection of claim 1 in view of Mueck, Henryson, Patel, and So is incorporated. Mueck further discloses a system, wherein the first information identifies the entity and a use case associated with the use of artificial intelligence by the entity: “Here, the output 2352 of the AI system 2310 (e.g., a prediction) is fed into the EAIOSV 2600, the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303 … Additionally or alternatively, the EAIOSV 2600 can be an AI/ML model that is trained using counterfactual examples (second information) to predict whether the obtained prediction is biased w.r.t. historical predictions and/or predictions from alternation models. These training counterfactual examples can be based on known or hypothetical AI decision(s) (use of artificial intelligence by the entity) that could potentially harm or cause damage to an individual human or object” (Mueck, [0211]). The use of this AI system is inherently associated with the entity that owns / operates the system.
While Mueck and Henryson fail to disclose the further limitations of the claim, Patel further discloses a system, wherein the first information identifies the entity and a use case associated with the use of artificial intelligence by the entity: “The entities electronically execute a healthcare transaction. The centralized control manager records each executed healthcare transaction into the centralized database as well as posts the transaction to the blockchain. Each entity performing the transaction also posts the details of the transaction to the blockchain. Details include the time and date of transaction, the parties (entit[ies]) involved, and other details of medical care provided to the patient relating to the transaction” (Patel, page 1, right column, paragraph 4).
Patel relates to using blockchains in automated complaint management systems and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Henryson, Patel, and So to store the information of each relevant party on a blockchain, as disclosed by Patel. Patel’s method automates adjudication of complaints, claims, and disputes, reducing cumbersome manual audits and evidence gathering. Additionally, decentralized blockchains provide transparency to all involved parties and prevent tampering with records of events. See Patel, [0034], [0147].
Regarding claim 5, the rejection of claim 1 in view of Mueck, Henryson, Patel, and So is incorporated. Mueck further discloses a system, wherein the one or more historical decisions were reached by use of artificial intelligence by the entity: “the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303” (Mueck, [0211]).
Regarding claim 6, the rejection of claim 1 in view of Mueck, Henryson, Patel, and So is incorporated. Mueck further discloses a system, wherein the decision and the one or more historical decisions relate to a same use case: “In various implementations, the interaction function 2403 can provide the following information to authorized users upon request: (i) historic input and/or historic output data received and/or generated by the AI system 2310, which may be available over the entire lifetime of the AI system 2310 or available for a predetermined duration (e.g., hourly, daily, weekly, monthly, annually, and/or the like); and (ii) statistics on the historic input and/or historic output data received and/or generated by the AI system 2310. In some implementations, the statistics can enable the user to identify whether there are any issues with the AI system 2310 (e.g., the AI system 2310 starts developing biases where different groups of individuals are treated differently in the same (same use case) or similar circumstances, for example, users originating from one geographic region are treated differently compared to users originating from another geographic region)” (Mueck, [0182])
Regarding claim 7, the rejection of claim 1 in view of Mueck, Henryson, Patel, and So is incorporated. Mueck further discloses a system, able to identify the one or more historical decisions relating to the one or more other users by performing natural language processing of at least one data source that includes unstructured data indicating the one or more historical decisions:
“the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303” (Mueck, [0211])
“The term "database object" at least in some examples refers to any representation of information that is in the form of an object, attribute-value pair (AVP), key-value pair (KVP), tuple, and the like, and may include variables, data structures, functions, methods, classes, database records, database fields, database entities, associations between data and/or database entities (also referred to as a "relation"), blocks in block chain implementations, and links between blocks in block chain implementations. Furthermore, a database object may include a number of records, and each record may include a set of fields. A database object can be unstructured or have a structure defined by a DBMS (a standard database object) and/or defined by a user (a custom database object)” (Mueck, [0568])
While Mueck fails to disclose the further limitations of the claim, Henryson further discloses a method of identifying the one or more historical decisions relating to the one or more other users by performing natural language processing of unstructured data indicating the one or more historical decisions: “Complaint analyzer 138 may be configured to analyze complaint logs (historical decisions) and generate a predicted resolution. More specifically, complaint analyzer 138 may determine, for a complaint log, whether the complaint should have been resolved by providing the end-user with compensation. To achieve this, complaint analyzer 138 implements natural language processing (NLP) to analyze a ‘resolution’ field of a complaint log” (Henryson, column 7, paragraph 2).
Henryson relates to machine learning for processing complaints and reparations and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Henryson, Patel, and So to automatically parse historical information with NLP, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
Regarding claim 8, the rejection of claim 1 in view of Mueck, Henryson, Patel, and So is incorporated. Mueck further discloses a method, wherein the complaint information identifies the user, the entity, a use case associated with the use of artificial intelligence by the entity, the decision, and a non-erroneous result associated with the use of artificial intelligence by the entity:
“In case the verification leads to the assessment that the obtained prediction could potential cause harm or damage, then suitable counter measures/actions 2353 can be taken, for example, preventing outputs 2352 of the AI system 2310 from being fed to other devices or systems (e.g., actuators, or the like), and an authorized user can be informed about the possibly problematic decision of the AI system 2310 and/or the counter measures/actions 2353 via the access interface 2312. Additionally or alternatively, validated/authorized outputs 2652 of the AI system 2310 can be provided to authorized users over the access interface 2312.” (Mueck, [0212])
“In these implementations, the EAIOSV 2600 is extended to include or provide an interface to communicate or convey the proposed decision or prediction together with accompanying information to better understand the context of proposed decision or prediction, including certain input data 2351, internal states, and/or configuration information of the AI system 2310 so the authorized user( s) can validate or reject the decision.” (Mueck, [0213])
“The data included in the inputs 2351 may depend on the AI/ML domain (use case) or AI/ML tasks (use case) to be performed by the AI system 2310 or otherwise related to the AI system 2310. An AI/ML task describes a desired problem to be solved (or a combination of a dataset with features and a target), an AI/ML domain describes a desired goal to be achieved, and an AI/ML objective describes a metric that an AI/ML model or algorithm is attempting to optimize or solve.” (Mueck, [0173])
“As an example, the EAIOSV 2600 can send a request 2601 to authorized user(s) over the access interface 2312 to request validation/confirmation of the potential predictions/decisions provided by the AI system 2310. Then, the authorized user(s) provide a response 2602 including validation or rejection of the potential predictions/decisions via the AI system access 2301 and the access interface 2312. If the potential predictions/decisions (non-erroneous result[s]) are accepted by the authorized user(s), then the EAIOSV 2600 can provide the predictions/decisions to the intended recipients. In case of rejection or inaction by the authorized user(s), the potential predictions/decisions are held back or discarded, and not forwarded to the related intended recipients.” (Mueck, [0215])
While Mueck fails to disclose the further limitations of the claim, Patel further discloses a method, wherein the complaint information identifies the user, the entity, a use case associated with the use of artificial intelligence by the entity, the decision, and a non-erroneous result associated with the use of artificial intelligence by the entity: “The system is also capable of performing automated dispute (complaint) resolution. Since the data for all transactions are stored both centrally and in the blockchain, there is concrete evidence of all the communications and interactions between the parties. The system uses this data as evidence in analyzing and automatically resolving disputes between parties. For example, a dispute (complaint) between a Payer (the user) and a Healthcare Provider (the entity) on whether appropriate payment was made on a claim is resolved by retrieving data pertaining to the specific claim in dispute, obtaining all communications relating to the specific claim between the Payer and the Healthcare Provider, either obtaining authorized access to bank account for the Healthcare Provider or having them upload bank statements, and reviewing all the data in light of the dispute to then apply decision logic and resolve the dispute (decision)”(Patel, [0038])
Patel relates to using blockchains in automated complaint management systems and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck and Henryson to store complaint information, including user and entity information, on a blockchain, as disclosed by Patel. Patel’s method automates adjudication of complaints, claims, and disputes, reducing cumbersome manual audits and evidence gathering. Additionally, decentralized blockchains provide transparency to all involved parties and prevent tampering with records of events. See Patel, [0034], [0147].
Claims 9-10, 13-15, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Henderson et al. (ARTIFICIAL INTELLIGENCE CONTENT DETECTION SYSTEM, published 2/15/2018, US 20180046712 A1), hereafter referred to as Henderson, in view of Mueck et al. (ARTIFICIAL INTELLIGENCE REGULATORY MECHANISMS, PCT filed 8/5/2022, US 2024/0273411 A1), hereafter referred to as Mueck, and further in view of Patel et al. (System And Method For Auditing, Monitoring, Recording, And Executing Healthcare Transactions, Communications, And Decisions, published 2/20/2020, US 20200058381 A1), hereafter referred to as Patel, and So et al. (PRIORITIZATION AND AUTOMATION OF BILLING DISPUTES INVESTIGATION USING MACHINE LEARNING, published 12/26/2019, US 2019/0392538 A1), hereafter referred to as So.
Regarding claim 9, Mueck discloses [a] method of assessment of artificial intelligence errors using machine learning, comprising:
scanning, by a device, a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user:
“In case the verification leads to the assessment that the obtained prediction could potential cause harm or damage, then suitable counter measures/actions 2353 can be taken, for example, preventing outputs 2352 of the AI system 2310 from being fed to other devices or systems (e.g., actuators, or the like), and an authorized user can be informed about the possibly problematic decision of the AI system 2310 and/or the counter measures/actions 2353 via the access interface 2312. Additionally or alternatively, validated/authorized outputs 2652 of the AI system 2310 can be provided to authorized users over the access interface 2312.” (Mueck, [0212])
“In these implementations, the EAIOSV 2600 is extended to include or provide an interface to communicate or convey the proposed decision or prediction together with accompanying information to better understand the context of proposed decision or prediction, including certain input data 2351, internal states, and/or configuration information of the AI system 2310 so the authorized user( s) can validate or reject the decision.” (Mueck, [0213])
“The data included in the inputs 2351 may depend on the AI/ML domain (use case) or AI/ML tasks (use case) to be performed by the AI system 2310 or otherwise related to the AI system 2310. An AI/ML task describes a desired problem to be solved (or a combination of a dataset with features and a target), an AI/ML domain describes a desired goal to be achieved, and an AI/ML objective describes a metric that an AI/ML model or algorithm is attempting to optimize or solve.” (Mueck, [0173])
determining, by the device, using a machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous, wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users:
“the one or more databases (DB) 903 are considered to be under control of the AI system 902 and/or the owner (entity) or operator of the AI system 902” (Mueck, [0087])
“In various implementations, the AI system 2310 is the same or similar as the AI system 902” (Mueck, [0170])
“In various implementations, the outputs 2352 of the AI system 2310 are double-checked and validated before they are used for any objective (e.g., as a decision to signaling an actuator to change the state of a system, as a prediction or inference to evaluate a human or object, as a decision to take an action, and/or the like)“ (Mueck, [0210])
“Here, the output 2352 of the AI system 2310 (e.g., a prediction) is fed into the EAIOSV 2600, the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303 … Additionally or alternatively, the EAIOSV 2600 can be an AI/ML (machine learning) model that is trained using counterfactual examples (second information) to predict whether the obtained prediction is biased w.r.t. historical predictions and/or predictions from alternation models. These training counterfactual examples can be based on known or hypothetical AI decision(s) (first information) that could potentially harm or cause damage to an individual human or object” (Mueck, [0211]). The use of this AI system is inherently associated with the entity that owns / operates the system.
Mueck relates to machine learning to evaluate AI decisions and is analogous to the claimed invention.
While Mueck fails to disclose the further limitations of the claim, Patel discloses a method, comprising:
scanning, by a device, a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user: “Since the data for all transactions are stored both centrally and in the blockchain, there is concrete evidence of all the communications and interactions between the parties. The system uses this data as evidence in analyzing and automatically resolving disputes between parties. For example, a dispute between a Payer (a user) and a Healthcare Provider (an entity) on whether appropriate payment was made on a claim is resolved by retrieving data pertaining to the specific claim in dispute, obtaining all communications relating to the specific claim between the Payer and the Healthcare Provider, either obtaining authorized access to bank account for the Healthcare Provider or having them upload bank statements, and reviewing all the data in light of the dispute to then apply decision logic and resolve the dispute. In addition, the system also provides steps to perform the steps after resolution, such as transfer the payment based on the resolution to the Payer or engage the mailing processor to automatically send out mailings and checks.” (Patel, [0038])
determining, by the device, using a machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain: “The Healthcare Provider at 1003 initiates a PDR. As mentioned above, the PDR is centrally logged by the Control manager 410. Additionally, the PDR is also reported to the blockchain by both the Healthcare Provider as well as the Control manager 410. Further, the Control manager 410 may also send an alert to all parties in that are in the network and authorized to receive confidential information in reference to the particular claim or the patient.” (Patel, [0153])
causing, by the device, judgment information, indicating that the decision in connection with the user is erroneous, to be added to the blockchain:
“complaints are often made about payer system and their associated medical plan provider denying payment, claiming a payment was processed (decision) and authorized when it was not (erroneous), claiming that a check was mailed when it was not or modifying backend transaction records to say claim was paid on certain date and time but in reality, it was not” (Patel, [0134])
“The system 300, through its Brain 310, functions to record every interaction and transaction between the parties, including, dates and times of the transactions, details of the transactions, information exchanged through the transaction, and the ultimate outcome, e.g. payment, nonpayment, portion of payment etc., time of payment, amount paid (amount of the reparation), parties paid etc.” (Patel, [0067])
“One example of the dispute resolution through the present invention is described below. Another example will be described in FIG. 10. In one scenario, a Healthcare Provider may have a complaint for a payment from the Payer and as such may file a PDR. The PDR would be centrally logged and all parties associated with the dispute, such as Healthcare Provider, Payer. Health Plan Provider, Member may be able to view that a dispute has been filed. Alternatively, the Control/Verification Manager 809, may provide an alert or a message through the API to all connected parties that a dispute has been filed. The details of the dispute as well as the time/day the dispute has been named would also be visible to all the parties. As the dispute resolution progresses and additional parties or the responding party (payer) provides a response, all progress, times and details of responses, and the timeline of the resolution would also be centrally logged and all relevant parties would be able to view the details. Alternatively, I addition to centrally being logged, all events and communication would also be recorded in the blockchain” (Patel, [0146]).
Patel relates to using blockchains in automated complaint management systems and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mueck to store complaint information on a blockchain, as disclosed by Patel. Patel’s method automates adjudication of complaints, claims, and disputes, reducing cumbersome manual audits and evidence gathering. Additionally, decentralized blockchains provide transparency to all involved parties and prevent tampering with records of events. See Patel, [0034], [0147].
While Patel fails to disclose the further limitations of the claim, So discloses a method of causing the machine learning model to be re-trained using the judgment information in the blockchain: “At times, the customer may claim that there are discrepancies in the transaction, for instance undelivered items by the seller, incorrect quantity or pricing of the items delivered, damaged goods and so on. In such circumstances, the customer typically pays a part of the bill and initializes a claim on the disputed items. The seller then chooses to either accept or investigate the claim made by the customer. The outcome (judgment information) of the investigation can either be acceptance of the claim and clearing the balance or disputing the customer's claim and requiring further information and/or payment of the balance.” (So, [0002])
“The processor (model) analyses the dispute information and computes a score for determining the validity of the billing dispute. The processor thereupon categorizes the billing dispute based on the computed score, wherein the categories (judgment information) may include segments or brackets, such as valid disputes and invalid disputes.” (So, [0029])
“For determining the label for each dispute, the one or more machine learning techniques may be employed.” (So, [0048])
“The model retraining module 312 is configured to retrain the system 208 periodically, preferably weekly or monthly. The retraining module 312 encapsulates the script necessary for retraining the system 208 by employing or incorporating fresh data and outcomes (judgment information) to the system 208.” (So, [0050])
So relates to using machine learning to automatically resolve and act upon complaint decisions and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the existing combination to periodically retrain the judgment model using observed judgment decisions, as disclosed by So. Doing so would ensure that the system is updated periodically with the latest information, to yield accurate computations and predictions. See So, [0050].
Regarding claim 10, the rejection of claim 9 in view of Mueck, Patel, and So is incorporated. Patel further discloses a method, wherein the notification is to cause complaint information, indicating a complaint that the decision in connection with the user is erroneous, to be added to a blockchain:
“complaints are often made about payer system and their associated medical plan provider denying payment, claiming a payment was processed (decision) and authorized when it was not (erroneous), claiming that a check was mailed when it was not or modifying backend transaction records to say claim was paid on certain date and time but in reality, it was not” (Patel, [0134])
“One example of the dispute resolution through the present invention is described below. Another example will be described in FIG. 10. In one scenario, a Healthcare Provider may have a complaint for a payment from the Payer and as such may file a PDR (notification). The PDR would be centrally logged and all parties associated with the dispute, such as Healthcare Provider, Payer. Health Plan Provider, Member may be able to view that a dispute has been filed. Alternatively, the Control/Verification Manager 809, may provide an alert or a message through the API to all connected parties that a dispute has been filed. The details of the dispute as well as the time/day the dispute has been named would also be visible to all the parties. As the dispute resolution progresses and additional parties or the responding party (payer) provides a response, all progress, times and details of responses, and the timeline of the resolution would also be centrally logged and all relevant parties would be able to view the details. Alternatively, I addition to centrally being logged, all events and communication would also be recorded in the blockchain” (Patel, [0146]).
Patel relates to using blockchains in automated complaint management systems and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to store complaint information on a blockchain, as disclosed by Patel. Patel’s method automates adjudication of complaints, claims, and disputes, reducing cumbersome manual audits and evidence gathering. Additionally, decentralized blockchains provide transparency to all involved parties and prevent tampering with records of events. See Patel, [0034], [0147].
Regarding claim 13, the rejection of claim 9 in view of Mueck, Patel, and So is incorporated. Mueck further discloses a method, wherein the first information identifies the entity and a use case associated with the use of artificial intelligence by the entity: “Here, the output 2352 of the AI system 2310 (e.g., a prediction) is fed into the EAIOSV 2600, the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303 … Additionally or alternatively, the EAIOSV 2600 can be an AI/ML model that is trained using counterfactual examples (second information) to predict whether the obtained prediction is biased w.r.t. historical predictions and/or predictions from alternation models. These training counterfactual examples can be based on known or hypothetical AI decision(s) (use of artificial intelligence by the entity) that could potentially harm or cause damage to an individual human or object” (Mueck, [0211]). The use of this AI system is inherently associated with the entity that owns / operates the system.
While Mueck fails to disclose the further limitations of the claim, Patel discloses a method, wherein the first information identifies the entity and a use case associated with the use of artificial intelligence by the entity: “The entities electronically execute a healthcare transaction. The centralized control manager records each executed healthcare transaction into the centralized database as well as posts the transaction to the blockchain. Each entity performing the transaction also posts the details of the transaction to the blockchain. Details include the time and date of transaction, the parties (entit[ies]) involved, and other details of medical care provided to the patient relating to the transaction” (Patel, page 1, right column, paragraph 4).
Patel relates to using blockchains in automated complaint management systems and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to store the information of each relevant party on a blockchain, as disclosed by Patel. Patel’s method automates adjudication of complaints, claims, and disputes, reducing cumbersome manual audits and evidence gathering. Additionally, decentralized blockchains provide transparency to all involved parties and prevent tampering with records of events. See Patel, [0034], [0147].
Regarding claim 14, the rejection of claim 9 in view of Mueck, Patel, and So is incorporated. Mueck further discloses a method, wherein the one or more historical decisions were reached by use of artificial intelligence by the entity: “the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303” (Mueck, [0211]).
Mueck, Patel, and So relate to evaluating AI systems and are analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to incorporate historical predictions into the ML system, as disclosed by Mueck. Differences between historic predictions and a current prediction can be used to identify bias in the system. See Mueck, [0182].
Regarding claim 15, the rejection of claim 9 in view of Mueck, Patel, and So is incorporated. Mueck further discloses a method, wherein the decision and the one or more historical decisions relate to a same use case: “In various implementations, the interaction function 2403 can provide the following information to authorized users upon request: (i) historic input and/or historic output data received and/or generated by the AI system 2310, which may be available over the entire lifetime of the AI system 2310 or available for a predetermined duration (e.g., hourly, daily, weekly, monthly, annually, and/or the like); and (ii) statistics on the historic input and/or historic output data received and/or generated by the AI system 2310. In some implementations, the statistics can enable the user to identify whether there are any issues with the AI system 2310 (e.g., the AI system 2310 starts developing biases where different groups of individuals are treated differently in the same (same use case) or similar circumstances, for example, users originating from one geographic region are treated differently compared to users originating from another geographic region)” (Mueck, [0182])
Mueck, Patel, and So relate to evaluating AI systems and are analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to compare the current decision and historical decisions, as disclosed by Mueck. Differences between historic predictions and a current prediction can be used to identify bias in the system. See Mueck, [0182].
Regarding claim 17, Mueck discloses A non-transitory computer-readable medium storing a set of instructions for assessment of artificial intelligence errors using machine learning: “The storage 3558 may include instructions 3583 in the form of software, firmware, or hardware commands to implement the techniques described herein … an example, the instructions 3581, 3582, 3583 provided via the memory 3554, the storage 3558, or the processor 3552 may be embodied as a non-transitory, machine-readable medium 3560) including code to direct the processor 3552 to perform electronic operations in the compute node 3550” (Mueck, [0316])
Said instructions caus[ing] the device to:
scan a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user:
“In case the verification leads to the assessment that the obtained prediction could potential cause harm or damage, then suitable counter measures/actions 2353 can be taken, for example, preventing outputs 2352 of the AI system 2310 from being fed to other devices or systems (e.g., actuators, or the like), and an authorized user can be informed about the possibly problematic decision of the AI system 2310 and/or the counter measures/actions 2353 via the access interface 2312. Additionally or alternatively, validated/authorized outputs 2652 of the AI system 2310 can be provided to authorized users over the access interface 2312.” (Mueck, [0212])
“In these implementations, the EAIOSV 2600 is extended to include or provide an interface to communicate or convey the proposed decision or prediction together with accompanying information to better understand the context of proposed decision or prediction, including certain input data 2351, internal states, and/or configuration information of the AI system 2310 so the authorized user( s) can validate or reject the decision.” (Mueck, [0213])
“The data included in the inputs 2351 may depend on the AI/ML domain (use case) or AI/ML tasks (use case) to be performed by the AI system 2310 or otherwise related to the AI system 2310. An AI/ML task describes a desired problem to be solved (or a combination of a dataset with features and a target), an AI/ML domain describes a desired goal to be achieved, and an AI/ML objective describes a metric that an AI/ML model or algorithm is attempting to optimize or solve.” (Mueck, [0173])
determine, using a machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous, wherein the machine learning model is trained to determine whether the decision is erroneous based on the first information and second information relating to one or more historical decisions in connection with the user or one or more other users:
“the one or more databases (DB) 903 are considered to be under control of the AI system 902 and/or the owner (entity) or operator of the AI system 902” (Mueck, [0087])
“In various implementations, the AI system 2310 is the same or similar as the AI system 902” (Mueck, [0170])
“In various implementations, the outputs 2352 of the AI system 2310 are double-checked and validated before they are used for any objective (e.g., as a decision to signaling an actuator to change the state of a system, as a prediction or inference to evaluate a human or object, as a decision to take an action, and/or the like)“ (Mueck, [0210])
“Here, the output 2352 of the AI system 2310 (e.g., a prediction) is fed into the EAIOSV 2600, the EAIOSV 2600 verifies the plausibility of the prediction or otherwise evaluates the obtained prediction. In some examples, the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303 … Additionally or alternatively, the EAIOSV 2600 can be an AI/ML (machine learning) model that is trained using counterfactual examples (second information) to predict whether the obtained prediction is biased w.r.t. historical predictions and/or predictions from alternation models. These training counterfactual examples can be based on known or hypothetical AI decision(s) (first information) that could potentially harm or cause damage to an individual human or object” (Mueck, [0211]). The use of this AI system is inherently associated with the entity that owns / operates the system.
Mueck relates to machine learning to evaluate AI decisions and is analogous to the claimed invention.
While Mueck fails to disclose the further limitations of the claim, Patel discloses instructions to:
scan a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user: “Since the data for all transactions are stored both centrally and in the blockchain, there is concrete evidence of all the communications and interactions between the parties. The system uses this data as evidence in analyzing and automatically resolving disputes between parties. For example, a dispute between a Payer (a user) and a Healthcare Provider (an entity) on whether appropriate payment was made on a claim is resolved by retrieving data pertaining to the specific claim in dispute, obtaining all communications relating to the specific claim between the Payer and the Healthcare Provider, either obtaining authorized access to bank account for the Healthcare Provider or having them upload bank statements, and reviewing all the data in light of the dispute to then apply decision logic and resolve the dispute. In addition, the system also provides steps to perform the steps after resolution, such as transfer the payment based on the resolution to the Payer or engage the mailing processor to automatically send out mailings and checks.” (Patel, [0038])
determine, using a machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain: “The Healthcare Provider at 1003 initiates a PDR. As mentioned above, the PDR is centrally logged by the Control manager 410. Additionally, the PDR is also reported to the blockchain by both the Healthcare Provider as well as the Control manager 410. Further, the Control manager 410 may also send an alert to all parties in that are in the network and authorized to receive confidential information in reference to the particular claim or the patient.” (Patel, [0153])
cause judgment information, indicating that the decision in connection with the user is erroneous, to be added to a blockchain:
“complaints are often made about payer system and their associated medical plan provider denying payment, claiming a payment was processed (decision) and authorized when it was not (erroneous), claiming that a check was mailed when it was not or modifying backend transaction records to say claim was paid on certain date and time but in reality, it was not” (Patel, [0134])
“The system 300, through its Brain 310, functions to record every interaction and transaction between the parties, including, dates and times of the transactions, details of the transactions, information exchanged through the transaction, and the ultimate outcome, e.g. payment, nonpayment, portion of payment etc., time of payment, amount paid (amount of the reparation), parties paid etc.” (Patel, [0067])
“One example of the dispute resolution through the present invention is described below. Another example will be described in FIG. 10. In one scenario, a Healthcare Provider may have a complaint for a payment from the Payer and as such may file a PDR. The PDR would be centrally logged and all parties associated with the dispute, such as Healthcare Provider, Payer. Health Plan Provider, Member may be able to view that a dispute has been filed. Alternatively, the Control/Verification Manager 809, may provide an alert or a message through the API to all connected parties that a dispute has been filed. The details of the dispute as well as the time/day the dispute has been named would also be visible to all the parties. As the dispute resolution progresses and additional parties or the responding party (payer) provides a response, all progress, times and details of responses, and the timeline of the resolution would also be centrally logged and all relevant parties would be able to view the details. Alternatively, I addition to centrally being logged, all events and communication would also be recorded in the blockchain” (Patel, [0146]).
Patel relates to using blockchains in automated complaint management systems and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Mueck to store complaint information on a blockchain, as disclosed by Patel. Patel’s method automates adjudication of complaints, claims, and disputes, reducing cumbersome manual audits and evidence gathering. Additionally, decentralized blockchains provide transparency to all involved parties and prevent tampering with records of events. See Patel, [0034], [0147].
While Patel fails to disclose the further limitations of the claim, So discloses instructions to cause the machine learning model to be re-trained using the judgment information in the blockchain:
“At times, the customer may claim that there are discrepancies in the transaction, for instance undelivered items by the seller, incorrect quantity or pricing of the items delivered, damaged goods and so on. In such circumstances, the customer typically pays a part of the bill and initializes a claim on the disputed items. The seller then chooses to either accept or investigate the claim made by the customer. The outcome (judgment information) of the investigation can either be acceptance of the claim and clearing the balance or disputing the customer's claim and requiring further information and/or payment of the balance.” (So, [0002])
“The processor (model) analyses the dispute information and computes a score for determining the validity of the billing dispute. The processor thereupon categorizes the billing dispute based on the computed score, wherein the categories (judgment information) may include segments or brackets, such as valid disputes and invalid disputes.” (So, [0029])
“For determining the label for each dispute, the one or more machine learning techniques may be employed.” (So, [0048])
“The model retraining module 312 is configured to retrain the system 208 periodically, preferably weekly or monthly. The retraining module 312 encapsulates the script necessary for retraining the system 208 by employing or incorporating fresh data and outcomes (judgment information) to the system 208.” (So, [0050])
So relates to using machine learning to automatically resolve and act upon complaint decisions and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the existing combination to periodically retrain the judgment model using observed judgment decisions, as disclosed by So. Doing so would ensure that the system is updated periodically with the latest information, to yield accurate computations and predictions. See So, [0050].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Mueck et al. (ARTIFICIAL INTELLIGENCE REGULATORY MECHANISMS, PCT filed 8/5/2022, US 2024/0273411 A1), hereafter referred to as Mueck, and further in view of Patel et al. (System And Method For Auditing, Monitoring, Recording, And Executing Healthcare Transactions, Communications, And Decisions, published 2/20/2020, US 20200058381 A1), hereafter referred to as Patel, So et al. (PRIORITIZATION AND AUTOMATION OF BILLING DISPUTES INVESTIGATION USING MACHINE LEARNING, published 12/26/2019, US 2019/0392538 A1), hereafter referred to as So, and Hive API Reference (Deepfake Detection, published 11/27/2022, retrieved from https://web.archive.org/web/20221127022521/https://docs.thehive.ai/reference/deepfake-detection-1 on 10/9/2025), hereafter referred to as Hive.
Regarding claim 11, the rejection of claim 9 in view of Mueck, Patel, and So is incorporated. Mueck further discloses a method, comprising:
transmitting a request, via an application programming interface (API) and to a different device that maintains a registry of artificial intelligence usage in connection with locations, resources, or operations, that indicates at least one of a location of the device, a resource that is accessed by the device, or one or more operations being performed by the device; and receiving a response, via the API, that indicates the use of artificial intelligence by the entity based on the registry including a registration associated with the entity and the at least one of the location of the device, the resource that is accessed by the device, or the one or more operations being performed by the device:
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(Mueck, Figure 9)
“The AI system architecture 900 includes AI system access 901, which is used by a user to access (request) the AI system 902 (server device) to use AI services … In the example of FIG. 9, the AI system access 901 is depicted as a computing system ( e.g., a desktop compute or workstation), however, the AI system access 901 can be or include any other type of computing device (user device) or system such as any of those discussed herein” (Mueck, [0088]). The user device sends a request to the server device, a different device from itself.
“AI system management entity 913 orchestrates AI system internal processes. For example, when a user requests information on AI system behavior (resource that is accessed by the user device) or similar, the information is recovered from DB 903 (registry), processed and presented to the user, etc” (Mueck, [0093])
“The DB 903 (registry) includes any suitable data storage means, which stores, for example, training data, testing data, US 2024/0273411 A1 validation data, user activity logs. AI system behavior logs (artificial intelligence usage in connection with operations / artificial intelligence by the entity), etc” (Mueck, [0089])
“the one or more databases (DB) 903 (registry) are considered to be under control of the AI system 902 and/or the owner (entity) or operator of the AI system 902” (Mueck, [0087]); “The inputs 2351, the outputs 2352, and/or the internal states of the AI system 2310 are recorded, logged, and/or stored by the ERK 2342 in one or more internal DB(s) or external or remote DB(s) 2303 (registry)” (Mueck, [0196])
While Mueck, Patel, and So fail to disclose the further limitations of the claim, Hive discloses a method of transmitting a request, via an application programming interface (API) and to a different device that maintains a registry of artificial intelligence usage in connection with locations, resources, or operations, that indicates at least one of a location of the device, a resource that is accessed by the device, or one or more operations being performed by the device; and receiving a response, via the API, that indicates the use of artificial intelligence by the entity based on the registry including a registration associated with the entity and the at least one of the location of the device, the resource that is accessed by the device, or the one or more operations being performed by the device: “Our Deepfake Detection API identifies whether or not an image or video query (resource that is accessed by the device) is a deepfake (use of artificial intelligence). Similar to our other detection models. this API product locates faces in an image or frame of video. For each detected face, this model outputs (response) a bounding box for its location, a classification, and accompanying confidence score.” (Hive, page 1, paragraph 1). Each image is inherently associated with its creator – a corresponding entity.
Hive relates to detecting the use of AI and is analogous to the claimed invention. the combination of Mueck, Patel, and So teaches a method of transmitting artificial intelligence use data to a user based on a user request. The claimed invention improves upon this method by making it accessible via an application programming interface. Hive teaches an API for accessing an AI usage detection system, applicable to the combination of Mueck, Patel, and So. A person of ordinary skill in the art would have recognized that making the method of Mueck, Patel, and So accessible via API would lead to the predictable result of remote access through the web, and would improve the known device by making it widely available and accessible (MPEP 2143 I. (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results).
Claims 12, 16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mueck et al. (ARTIFICIAL INTELLIGENCE REGULATORY MECHANISMS, PCT filed 8/5/2022, US 2024/0273411 A1), hereafter referred to as Mueck, and further in view of Patel et al. (System And Method For Auditing, Monitoring, Recording, And Executing Healthcare Transactions, Communications, And Decisions, published 2/20/2020, US 20200058381 A1), hereafter referred to as Patel, So et al. (PRIORITIZATION AND AUTOMATION OF BILLING DISPUTES INVESTIGATION USING MACHINE LEARNING, published 12/26/2019, US 2019/0392538 A1), hereafter referred to as So, and Henryson et al. (MACHINE-LEARNING PREDICTIVE MODELS FOR CLASSIFYING RESPONSES TO AND OUTCOMES OF END-USER COMMUNICATIONS, filed 12/14/2020, US 11,720,903 B1), hereafter referred to as Henryson.
Regarding claim 12, the rejection of claim 9 in view of Mueck, Patel, and So is incorporated. While Mueck, Patel, and So fail to disclose the further limitations of the claim, Henryson discloses a method of receiving, in response to the notification, an indication of whether a reparation for the user is to be issued by the entity due to the decision:
“The service provider (entity) may provide some sort of compensation (e.g., monetary relief) (reparation) in response to these user complaints, such as reimbursing an overdraft fee that may be in error or as a courtesy to the user” (Henryson, column 1, paragraph 2)
“Various embodiments relate to a service provider (entity) computing system” (Henryson, column 1, paragraph 4)
“Various embodiments relate to a method. The method includes receiving a complaint from a user, recording at least a text-based description of the complaint, including an indication of a resolution for the complaint, parsing the text-based description of the complaint to generate a matrix of key terms within the text-based description, executing a 10 machine-learning predictive model using the matrix of key terms to generate, for the complaint, a prediction indicating whether the complaint should have compensation (reparation), and presenting, via a user interface, an indication of the prediction (notification)” (Henryson, column 2, paragraph 2)
Henryson relates to machine learning for processing complaints and reparations and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to automatically determine whether to issue reparations in response to a complaint, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
Regarding claim 16, the rejection of claim 9 in view of Mueck, Patel, and So is incorporated. Mueck further discloses identifying the one or more historical decisions relating to the one or more other users by performing natural language processing of unstructured data indicating the one or more historical decisions:
“the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303” (Mueck, [0211])
“The term "database object" at least in some examples refers to any representation of information that is in the form of an object, attribute-value pair (AVP), key-value pair (KVP), tuple, and the like, and may include variables, data structures, functions, methods, classes, database records, database fields, database entities, associations between data and/or database entities (also referred to as a "relation"), blocks in block chain implementations, and links between blocks in block chain implementations. Furthermore, a database object may include a number of records, and each record may include a set of fields. A database object can be unstructured or have a structure defined by a DBMS (a standard database object) and/or defined by a user (a custom database object)” (Mueck, [0568])
While Mueck, Patel, and So fail to disclose the further limitations of the claim, Henryson discloses a method of identifying the one or more historical decisions relating to the one or more other users by performing natural language processing of unstructured data indicating the one or more historical decisions: “Complaint analyzer 138 may be configured to analyze complaint logs (historical decisions) and generate a predicted resolution. More specifically, complaint analyzer 138 may determine, for a complaint log, whether the complaint should have been resolved by providing the end-user with compensation. To achieve this, complaint analyzer 138 implements natural language processing (NLP) to analyze a ‘resolution’ field of a complaint log” (Henryson, column 7, paragraph 2).
Henryson relates to machine learning for processing complaints and reparations and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to automatically parse historical information with NLP, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
Regarding claim 19, the rejection of claim 17 in view of Mueck, Patel, and So is incorporated. While Mueck, Patel, and So fail to disclose the further limitations of the claim, Henryson discloses instructions to receive an indication of whether a reparation for the user is to be issued by the entity due to the decision:
“The service provider (entity) may provide some sort of compensation (e.g., monetary relief) (reparation) in response to these user complaints, such as reimbursing an overdraft fee that may be in error or as a courtesy to the user” (Henryson, column 1, paragraph 2)
“Various embodiments relate to a service provider (entity) computing system” (Henryson, column 1, paragraph 4)
“Various embodiments relate to a method. The method includes receiving a complaint from a user, recording at least a text-based description of the complaint, including an indication of a resolution for the complaint, parsing the text-based description of the complaint to generate a matrix of key terms within the text-based description, executing a 10 machine-learning predictive model using the matrix of key terms to generate, for the complaint, a prediction indicating whether the complaint should have compensation (reparation), and presenting, via a user interface, an indication of the prediction” (Henryson, column 2, paragraph 2)
Henryson relates to machine learning for processing complaints and reparations and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to automatically determine whether to issue reparations in response to a complaint, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
Regarding claim 20, the rejection of claim 1 in view of Mueck, Patel, and So is incorporated. Mueck further discloses instructions to identify the one or more historical decisions relating to the one or more other users by performing natural language processing of unstructured data indicating the one or more historical decisions:
“the EAIOSV 2600 verifies or otherwise evaluates the obtained prediction based on a comparison of the obtained prediction with one or more historical predictions retrieved from the internal/external DB(s) 2303” (Mueck, [0211])
“The term "database object" at least in some examples refers to any representation of information that is in the form of an object, attribute-value pair (AVP), key-value pair (KVP), tuple, and the like, and may include variables, data structures, functions, methods, classes, database records, database fields, database entities, associations between data and/or database entities (also referred to as a "relation"), blocks in block chain implementations, and links between blocks in block chain implementations. Furthermore, a database object may include a number of records, and each record may include a set of fields. A database object can be unstructured or have a structure defined by a DBMS (a standard database object) and/or defined by a user (a custom database object)” (Mueck, [0568])
While Mueck, Patel, and So fail to disclose the further limitations of the claim, Henryson discloses instructions to identify the one or more historical decisions relating to the one or more other users by performing natural language processing of unstructured data indicating the one or more historical decisions: “Complaint analyzer 138 may be configured to analyze complaint logs (historical decisions) and generate a predicted resolution. More specifically, complaint analyzer 138 may determine, for a complaint log, whether the complaint should have been resolved by providing the end-user with compensation. To achieve this, complaint analyzer 138 implements natural language processing (NLP) to analyze a ‘resolution’ field of a complaint log” (Henryson, column 7, paragraph 2).
Henryson relates to machine learning for processing complaints and reparations and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Mueck, Patel, and So to automatically parse historical information with NLP, as disclosed by Henryson. Conventional complaint systems rely on human judgment to decide when to compensate a user. Humans are prone to error and overlooking all contextual information that should be used to decide the judgment. It’s also difficult to measure and reduce bias in these judgments. Henryson’s system automates this process, and in doing so, makes it easier to maintain equitable decisions and ensures that important contextual data isn’t ignored. See Henryson, column 1, paragraphs 1-3, and column 3, paragraph 2.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Mueck et al. (ARTIFICIAL INTELLIGENCE REGULATORY MECHANISMS, PCT filed 8/5/2022, US 2024/0273411 A1), hereafter referred to as Mueck, in view of Patel et al. (System And Method For Auditing, Monitoring, Recording, And Executing Healthcare Transactions, Communications, And Decisions, published 2/20/2020, US 20200058381 A1), hereafter referred to as Patel, and further in view of So et al. (PRIORITIZATION AND AUTOMATION OF BILLING DISPUTES INVESTIGATION USING MACHINE LEARNING, published 12/26/2019, US 2019/0392538 A1), hereafter referred to as So, and Henderson et al. (ARTIFICIAL INTELLIGENCE CONTENT DETECTION SYSTEM, published 2/15/2018, US 20180046712 A1), hereafter referred to as Henderson.
Regarding claim 18, the rejection of claim 17 in view of Mueck, Patel, and So is incorporated. While Mueck, Patel, and So fail to disclose the further limitations of the claim, Henderson discloses instructions to identify the use of artificial intelligence based on at least one of a location of the device, a resource that is accessed by the device, or one or more operations being performed by the device: “Generally described, the technologies (device[s]) described herein can receive one or more search results (resource that is accessed) from a search engine. Examples of the detection system analyze content from one or more of the search results. The detection system applies an artificial intelligence detection algorithm to detect whether or not the content is partially or wholly provided by an artificial intelligence source. The detection system then provides a visual output or other output to the user indicating the presence of artificial intelligence content in one or more of the search results” (Henderson, [0003]).
Henderson relates to artificial intelligence detection and is analogous to the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the existing combination to determine the use of AI sources based on provided user resources, as disclosed by Henderson. Increasingly so as content producers try to cut costs, a lot of content is generated by AI. With so much AI-generated content, it’s difficult and time-consuming for users to verify the value, truthfulness, and completeness of it. Detecting AI-generated content allows for it to be automatically filtered, reducing user analysis time and potentially computing resources needed to process the myriad of AI content for the user. See Henderson, [0014-0016].
Response to Arguments
The following responses address arguments and remarks made in the instant remarks dated 01/21/2026.
112 Rejections
New claim rejections under 35 U.S.C. 112(b) have been found in light of the instant amendments.
101 Rejections
On page 9 of the instant remarks, the Applicant argues that the amended claims overcome the rejections under 35 U.S.C. 101:
“Claims 1-20 stand rejected under 35 U.S.C. § 101 as allegedly being directed to nonstatutory
subject matter.
For at least the reasons presented in the interview and without acquiescing in the
Examiner's rejection, independent claims 1, 9, and 17, as amended, and the claims that depend
thereon, are patent-eligible under 35 U.S.C. § 101.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw
the rejection of claims 1-20 under 35 U.S.C. § 101.”
Regarding the Applicant’s arguments above, the Examiner respectfully disagrees. In the previous interview, conducted on 01/13/2026, the Examiner argued that the proposed amendments still contained mental processes and were insufficient to overcome rejections under 35 U.S.C. 101. The current amendments, while not identical to those presented in the aforementioned interview, are still found to contain mental processes in the instant amendments.
Thus, no rejections are withdrawn on these grounds. See the 101 rejections section for more detail.
103 Rejections
On page 10 of the instant remarks, the Applicant argues that the cited references fail to disclose the amended limitations of claim 1:
“Claims 1-8 stand rejected under 35 U.S.C. § 103 as allegedly being unpatentable over
MUECK (U.S. Patent Publication No. 2024/0273411), HENRYSON (U.S. Patent No.
11,720,903), and PATEL (U.S. Patent Publication No. 2020/0058381). Applicant respectfully
traverses the rejection.
For at least the reasons presented in the interview and without acquiescing in the
Examiner's rejection, the cited sections of the applied references, whether taken alone or in any
reasonable combination, do not disclose or suggest at least "cause, based on the notification,
complaint information, indicating the complaint, to be added to a blockchain; determine, using at
least one machine learning model and based on obtaining first information relating to the use of
artificial intelligence by the entity from the complaint information in the blockchain, whether the
decision in connection with the user is erroneous and an amount of a reparation for the user that
is to be issued by the entity," and "cause the at least one machine learning model to be re-trained
using the judgment information in the blockchain," as recited in claim 1, as amended (emphasis
added). Therefore, independent claim 1, and the claims that depend thereon, are patentable over
the cited sections of the applied references.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw
the rejection of claims 1-8 under 35 U.S.C. § 103 based on MUECK, HENRYSON, and PATEL.”
Regarding the Applicant’s arguments above, while the Examiner agrees that the combination of Mueck, Henryson, and Patel doesn’t disclose all amended limitations of claim 1, this deficiency is remedied by So.
Regarding “cause, based on the notification, complaint information, indicating the complaint, to be added to a blockchain”, Patel discloses a method of adding complaint information to a blockchain once a dispute is filed (Patel, [0146]).
Regarding “determine, using at least one machine learning model and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, whether the decision in connection with the user is erroneous and an amount of a reparation for the user that is to be issued by the entity”, Mueck discloses a method of using machine learning to determine erroneous AI outputs (Mueck, [0170], [0210-0211]) and issuing reparations that improve the system for the end-user (Mueck, [0235]). While Mueck fails to disclose basing this process on complaint information from a blockchain, this is remedied by Patel, which discloses retrieving complaint information from a blockchain (Patel, [0153]).
Regarding “cause the at least one machine learning model to be re-trained using the judgment information in the blockchain”, So discloses a system that retrains a machine learning model using customer complaint judgment information (So, [0002], [0029], [0048], [0050]). While So doesn’t disclose using a blockchain to store this data, this deficiency is remedied by Patel.
See the 103 rejections section for more detail. No rejections are withdrawn on this basis.
On page 11 of the instant remarks, the Applicant argues that claims 9, 14, and 15 are not disclosed in their entirety by Henderson and Mueck:
“Claims 9, 14, and 15 stand rejected under 35 U.S.C. § 103 as allegedly being
unpatentable over HENDERSON (U.S. Patent Publication No. 2018/0046712) and MUECK.
Applicant respectfully traverses the rejection.
For at least the reasons presented in the interview and without acquiescing in the
Examiner's rejection, the cited sections of the applied references, whether taken alone or in any
reasonable combination, do not disclose or suggest at least,
"scanning, by a device, a blockchain to identify complaint information associated
with a use of artificial intelligence by an entity to reach a decision in connection with a
user;
determining, by the device, using a machine learning model, and based on
obtaining first information relating to the use of artificial intelligence by the entity from
the complaint information in the blockchain, that the decision in connection with the user
is erroneous,
wherein the machine learning model is trained to determine whether the
decision is erroneous based on the first information and second information
relating to one or more historical decisions in connection with the user or one or
more other users;
providing, by the device, a notification indicating that the decision in connection
with the user is erroneous;
causing, by the device, judgment information, indicating that the decision in
connection with the user is erroneous, to be added to the blockchain; and
causing the machine learning model to be re-trained using the judgment
information in the blockchain,"
as recited in claim 9, as amended (emphasis added). Therefore, independent claim 9, and the
claims that depend thereon, are patentable over the cited sections of the applied references.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw
the rejection of claims 9, 14, and 15 under 35 U.S.C. § 103 based on HENDERSON and
MUECK.”
Regarding the Applicant’s arguments above, while the Examiner agrees that the combination of Henderson and Mueck doesn’t disclose all amended limitations of claim 9, the amended limitations are obvious over Mueck in view of Patel and So.
Regarding “scanning, by a device, a blockchain to identify complaint information associated with a use of artificial intelligence by an entity to reach a decision in connection with a user”, Mueck discloses a method of identifying complaint information associated with a use of AI to reach a decision in connection with the user of said AI (Mueck, [0173], [0212-0213]). While Mueck fails to disclose storing the complaint information in a blockchain, this deficiency is remedied by Patel, which discloses retrieving complaint information from a blockchain to resolve disputes (Patel, [0038]).
Regarding “determining, by the device, using a machine learning model, and based on obtaining first information relating to the use of artificial intelligence by the entity from the complaint information in the blockchain, that the decision in connection with the user is erroneous”, this limitation is obvious over Mueck in view of Patel, as discussed in previous responses above.
Regarding “causing, by the device, judgment information, indicating that the decision in connection with the user is erroneous, to be added to the blockchain”, Patel discloses storing judgment information related to resolutions of disputes in a blockchain (Patel, [0134], [0067], [0146]).
Regarding “causing the machine learning model to be re-trained using the judgment
information in the blockchain”, this limitation is disclosed by So, as discussed in previous responses above.
Thus, no rejections are withdrawn on this basis. See the 103 rejections section for more detail.
On page 12 of the instant remarks, the Applicant argues that the relied upon prior art fails to disclose the amended limitations of claims 17-18:
“Claims 17 and 18 stand rejected under 35 U.S.C. § 103 as allegedly being unpatentable
over HENDERSON, MUECK, and PATEL. Applicant respectfully traverses the rejection.
For at least the reasons presented in the interview and without acquiescing in the
Examiner's rejection, the cited sections of the applied references, whether taken alone or in any
reasonable combination, do not disclose or suggest at least,
"scan a blockchain to identify complaint information associated with a use of
artificial intelligence by an entity to reach a decision in connection with a user;
determine, using a machine learning model and based on obtaining first
information relating to the use of artificial intelligence by the entity from the complaint
information in the blockchain, that the decision in connection with the user is erroneous,
wherein the machine learning model is trained to determine whether the
decision is erroneous based on the first information and second information
relating to one or more historical decisions in connection with the user or one or
more other users;
cause judgment information, indicating that the decision in connection with the
user is erroneous, to be added to the blockchain; and
cause the machine learning model to be re-trained using the judgment information
in the blockchain,"
as recited in claim 17, as amended (emphasis added). Therefore, independent claim 17, and the
claims that depend thereon, are patentable over the cited sections of the applied references.
Accordingly, Applicant respectfully requests that the Examiner reconsider and withdraw
the rejection of claims 17 and 18 under 35 U.S.C. § 103 based on HENDERSON, MUECK, and
PATEL.”
Regarding the Applicant’s arguments above, while the Examiner agrees that the combination of Henderson, Mueck, and Patel doesn’t disclose all amended limitations of claim 17, the amended limitations are obvious over Mueck in view of Patel and So. The amended limitations of claim 17 are analogous to amended limitations of claims 1 and 9 discussed above in previous responses, which have been found to be obvious over the recited prior art. The amended limitations of claim 17 are found to be obvious with that same rationale.
Thus, no rejections are withdrawn on this basis. See the 103 rejections section for more detail.
On pages 12-13 of the instant remarks, the Applicant argues that the dependent claims are patentable:
“Rejection of Dependent Claims
Claims 10-13 and 16 depend from independent claim 9, and claims 19 and 20 depend
from independent claim 17. Therefore, claims 10-13, 16, 19, and 20 are patentable for at least
the reasons set forth above with respect to claims 9 and 17, and for their additional distinguishing
features recited therein.”
As discussed in previous responses above, none of the independent claims are found to be patentable over the prior art. Thus, no rejections of dependent claims are withdrawn on this basis.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hingorani et al. (Police Complaint Management System using Blockchain Technology, 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), Thoothukudi, India, 2020, pp. 1214-1219) discloses a method of storing complaint and complaint redress information in blockchains.
Schneider et al. (Deceptive AI Explanations: Creation and Detection, published 2021, arXiv:2001.07641v3) discloses a system for detecting deceptive AI outputs in machine learning models
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. 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 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.
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/AG/Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148