DETAILED ACTION
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 .
Response to Arguments
Regarding the previous 35 U.S.C. 101 rejection, Applicant's arguments have been fully considered but they are not persuasive. Applicants argue that the limitation identify, by the natural language processing engine…, map the intent by the natural language processing engine...using an intent mapper, and successively prompt…and identify…from the internal node mapped using the intent mapper… require action that cannot practically be performed in the human mind. Applicants argue that there is an improvement to the functioning of another technology by the system may reduce the quantity of prompts and inputs received in response to the prompts, thereby improving the computing system. Applicants argue that the need to navigate the entirety depth of a decision tree based on the rules may also be avoided and reducing the number of edges of a fault-determination decision tree that need to be traversed. Applicants argue the improved computer capabilities and have a specific application in fault determination, and a particular solution identifying an internal node in a decision tree. Applicants argue that the claims to the system and method are applied in a meaningful way beyond general linking and that the additional features play a significant role in realizing the inventive concept in a way that results in an improvement in providing a recommendation as to a fault determination and the consequent benefits to a computer system. Applicants argue that there is no analysis or determination of a factual determination in light of Berkheimer. Examiner respectfully disagrees. MPEP 2106.05(a) recites, in computer-related technologies, the examiner should determine whether the claim purports to improve computer capabilities or, instead invokes computers merely as a tool. Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). The claim recites additional elements that merely use generic computer components as a tool to perform the abstract idea, and mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The additional elements are limitations drawn to generic computer implementation of the recited judicial exception. The instant claimed invention, when implemented, does not improve the functionality of the computer nor does it improve a technology/technical field because Applicant’s arguments are directed to the abstract idea itself, namely mapping an intent to an internal node and providing a fault determination. An improvement in the abstract idea itself is not an improvement in technology (see at least MPEP 2106.05(a)). Further, the argued improvement is not claimed. In response to the Berkheimer argument, please see page 6 of the previous non-final office action 09/24/2025 regarding that a factual determination required to support a conclusion and relying on what the courts have recognized as elements that are well-understood, routine, conventional activity. Accordingly, the previous 35 U.S.C. 101 rejection is maintained.
Regarding the previous 35 U.S.C. 103 rejection, a new ground of rejection is made necessitated by amendment in view of Brandmeier. Applicants argue that Applicant respectfully submits Ringhiser fails to teach or suggest a leaf node corresponding to fault-determination rule as recited in amended claim 1, as Ringhiser fails to mention or even hint at a fault-determination, and the trigger in Ringhiser is for the handoff of control of a conversation, not for providing a recommendation as to a determination of fault based on a fault-determination rule corresponding to the leaf node. Ringhiser discloses a leaf node (see at least [0035]: observations until a leaf node is reached; leaf nodes ) but does not explicitly disclose a fault-determination rule and a determination of fault based on the fault-determination rule. However, Brandmaier teaches a fault-determination rule (see at least column 17 lines 4-46: fault determination module 438 may analyze the event data and the supplemental driving data to identify the at-fault participant and to determine the fault probability values (step 806). The fault determination module 438 may apply a fault determination ruleset 456 (step 808) when analyzing the event data and supplemental driving data. The fault determination ruleset may be configured, for example to apply the rules of the road to the vehicle telematics received from the vehicles…fault determination ruleset may be implemented, for example, as a decision tree or decision table, using conditional statements or switch statements, and combinations thereof, column 17 lines 46-55: Based on the fault determination ruleset 456, the fault determination module 438 may generate one or more fault probability values that respectively indicate the likelihood each participant is at fault for the collision (step 810). The fault determination module 438 may then attribute fault to one of the participants based on the fault probability values (step 812)). It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Brandmaier with a reasonable expectation of success in order to provide an improved approach to processing insurance claims in response to a vehicle accident. The combination would yield predictable results.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-9, 11-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A claim that recites an abstract idea, a law of nature, or a natural phenomenon is directed to a judicial exception. Abstract ideas include the following groupings of subject matter, when recited as such in a claim limitation: (a) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; (b) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See 2019 PEG.
Even when a judicial element is recited in the claim, an additional claim element(s) that integrates the judicial exception into a practical application of that exception renders the claim eligible under §101. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The following examples are indicative that an additional element or combination of elements may integrate the judicial exception into a practical application:
the additional element(s) reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
the additional element(s) that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
the additional element(s) implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
the additional element(s) effects a transformation or reduction of a particular article to a different state or thing; and
the additional element(s) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Examples in which the judicial exception has not been integrated into a practical application include:
the additional element(s) merely recites the words ‘‘apply it’’ (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
the additional element(s) adds insignificant extra-solution activity to the judicial exception; and
the additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
See 2019 PEG.
101 Analysis – Step 1
Claims 1, 13, 20 are directed to a system, method, and non-transitory computer-readable storage medium. Therefore, the claims are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim(s) 1 includes limitations that recite an abstract idea (bolded below) and will be used as a representative claims for the remainder of the 101 rejection.
Claim 1 recites:
a processor;
a natural language processing engine;
a memory storing instructions that, when executed by the computer system, cause the computer system to:
receive, by the processor, unstructured text;
identify, by the natural language processing engine and based on the unstructured text, an intent;
map the intent identified by the natural language processing engine to an internal node of a decision tree;
successively prompt, via a computing device, and
receive, by the processor and from the computing device, input responsive to the prompting and corresponding to details of the unstructured text and
identify, based on the input received from the computer device responsive to the prompting, a path through the decision tree starting from the internal node mapped by using the intent mapper and ending at a leaf node corresponding to a fault-determination rule; and
provide, by the processor, a recommendation as to a determination of fault based on the fault-determination rule.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, under broadest reasonable interpretation, the limitation(s) in the context of this claim encompasses a person, such as via an algorithm on paper, gathering and interpreting text such as drivers’ statements, identifying whether the drivers’ statement or sequence of events for the collision knowingly violated a law, and determining the vehicle/driver that violated the law is at fault for the collision based on the drivers’ statements and laws. A person could observe or receive information, driver statement, and sequence of events about a collision; identify whether the driver statement or sequence of events for the collision knowingly violated a law such as a vehicle reversing at an intersection into another vehicle, a vehicle running a red light, or not following right of way regulation; and determine that the vehicle violating the law is at fault for the collision.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitation” while the bolded portions continue to represent the abstract idea):
Claim 1 recites:
a processor;
a natural language processing engine;
a memory storing instructions that, when executed by the computer system, cause the computer system to:
receive, by the processor, unstructured text;
identify, by the natural language processing engine and based on the unstructured text, an intent;
map the intent identified by the natural language processing engine to an internal node of a decision tree;
successively prompt, via a computing device, and
receive, by the processor and from the computing device, input responsive to the prompting and corresponding to details of the unstructured text and
identify, based on the input received from the computer device responsive to the prompting, a path through the decision tree starting from the internal node mapped by using the intent mapper and ending at a leaf node corresponding to a fault-determination rule; and
provide, by the processor, a recommendation as to a determination of fault based on the fault-determination rule.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations, the examiner submits that these limitations are generic computer computers and/or insignificant extra-solution activities that merely use a computer to perform the process. The additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. The additional limitation steps are recited at a high level of generality (i.e. as a general means of gathering data via successive prompting, transmitting signals, outputting), and amounts to mere data gathering and storing and transmitting do not add a meaningful limitation to the process (MPEP 2106.05(g) v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754), which are forms of insignificant extra-solution activities. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than drafting effort designed to monopolize the exception (MPEP 2106.05). The additional limitations merely describe how to generally apply the otherwise mental judgements in a generic or general purpose vehicle environment. The additional limitations are recited at a high level of generality and merely automates the steps. Accordingly additional limitation(s) do/does not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations do not provide any indication that the additional elements are anything other than a conventional computer. Also, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 indicate that mere collection or receipt of data over a network, receiving or transmitting data over a network, and storing and retrieving information in memory are a well-understood, routine, and conventional functions when claimed in a merely generic manner. Further, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. MPEP 2106.05(d)(II) recites at least the following cases that the courts have recognized the following computer function as well-understood, routine, and conventional functions when they are claimed in a merely generic manner: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93. Furthermore, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function.
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Dependent claims 2-9, 11-12, 14-19 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application.
MPEP 2106.04(d) and MPEP 2106.05(f) recites that the courts have identified limitations that did not integrate a judicial exception into a practical application: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea. MPEP 2106.05 recites that the courts have identified limitations found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include: i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer.
The additional elements are recited at a high level of generality and merely automates the steps. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The additional limitations are recited at a high level of generality and amounts to mere data gathering such as collecting input data via a computing device, which is a form of insignificant extra-solution activity; the additional limitations are well-understood, routine, and conventional activity because the specification does not provide any indication that the additional elements are anything other than a conventional computer components. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Moreover, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. MPEP 2106.05(d)(II) recites at least the following cases that the courts have recognized the following computer function as well-understood, routine, and conventional functions when they are claimed in a merely generic manner: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93. Furthermore, the Federal Circuit in Trading Techs. Int’l v. IBGLLC, 921 F.3d1084,1093(Fed. Cir.2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Moreover, mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Claim(s) 2, 14 recite the limitation(s) wherein the instructions, when executed by the computer system, further cause the computer system to: initiate processing of an insurance claim related to a motor vehicle collision wherein responsibility for the motor vehicle collision is to be apportioned between one or more insurers based on the recommendation. The limitation(s), but for the additional elements of the computer system identified in claim 1, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Claims 3, 15 recite the limitation(s) wherein the prompting includes presenting prompts based on intermediate nodes along the path between the internal node and the leaf node of the decision tree. The limitation(s), but for the additional elements of the computer system of claim 1, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Claim(s) 4, 16 recite the limitation(s) wherein the rule corresponds to a regulatory jurisdiction and wherein providing the recommendation includes mapping the rule to a corresponding rule of a second regulatory jurisdiction and providing the recommendation based on the corresponding rule. The limitation(s), but for the additional elements of the computer system of claim 1, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Claim(s) 5-7, 17-18 recite the limitation(s) wherein the instructions, when executed by the computer system, further cause the computer system to: identify the second regulatory jurisdiction from amongst a plurality of secondary regulatory jurisdictions; wherein the second regulatory jurisdiction is identified based on a current location of the computing device used to provide the input received responsive to the prompting; wherein the unstructured text describes circumstances of a motor vehicle collision and is received from the computing device used to provide the input received responsive to the prompting. The limitation(s), but for the additional elements identified in claim 1, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Claim(s) 8 recites the limitation wherein the instructions, when executed by the computer system, further cause the computer system to: calculate a confidence level in association with the recommendation. The limitation(s), but for the additional elements of a computing system of claim 1, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Claim(s) 9 recite the limitation wherein the instructions, when executed by the computer system, further cause the computer system to: add the recommendation and the associated confidence level to a data set for training a natural-language processing neural network for use in evaluating unstructured text. The limitation(s), but for the additional elements of a computing system of claim 1 and a neural network, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Claim(s) 11 recite the limitation wherein at least one of the unstructured text and the input received responsive to the prompting is received via chat bot. The limitation(s), but for the additional elements identified in claim 1 and a chat bot, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Further, collecting input data via a chat bot amounts to mere data gathering, which is a form of insignificant extra-solution activity; the additional limitations are well-understood, routine, and conventional activity because the specification does not provide any indication that the additional elements are anything other than a conventional computer components. Furthermore, MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, INC., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Claim(s) 12 recite the limitation wherein the instructions, when executed by the computer system, further cause the computer system to: identify the decision tree from amongst a plurality of decision trees corresponding to sets of prescribed rules of different jurisdictions. The limitation(s), but for the additional elements of a computing system of claim 1, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Claim(s) 19 recite the limitation calculating a confidence level in association with the recommendation; and adding the recommendation and the associated confidence level to a data set for training a natural-language processing neural network for use in evaluating unstructured text. The limitation(s), but for the additional elements of a computing system of claim 1 and a neural network, are directed toward additional aspects of the judicial exception. The additional elements do not integrate the judicial exception into a practical application and are not sufficient to amount to significantly more than the judicial exception. The additional elements are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components.
Therefore, dependent claims 2-9, 11-12, 14-19 are not patent eligible under the same rationale as provided for in the rejection of the independent claim.
Therefore, claim(s) 1-9, 10-20 is/are ineligible under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1-3, 8, 11, 13-15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20210058844 (“Ringhiser”) in view of US 20210110484 (“Shalev-Shwartz”) and US 10417713 (“Brandmaier”).
As per claim(s) 1, 13, 20, Ringhiser discloses a computer system comprising:
a processor (see at least [0022]: processor);
a natural language processing engine (see at least [0032]: intents extracted from the user input via natural language processing);
a memory storing instructions that, when executed by the computer system (see at least [0026]: computer-readable storage media), cause the computer system to:
receive, by the processor, unstructured text (see at least [0028]: Each of the user devices 114, 118, and 122 can execute a dialogue client. The dialog client enables capture of text, auditory, and/or visual input from a user. The user input is transmitted to the bot 104 or the bot 105 across the network, [0032]: intents extracted from the user input via natural language processing);
identify, by the natural language processing engine and based on the unstructured text, an intent (see at least [0032]: intents extracted from the user input via natural language processing);
map the intent identified by the natural language processing engine to an internal node of a decision tree (see at least [0032]: intents extracted from the user input via natural language processing, [0036]: At each node in the example of FIG. 2, an intent monitor assesses the input from a user that resulted in a progression to the node within the bot logic);
successively prompt, via a computing device (see at least [0028]: Each of the user devices 114, 118, and 122 can execute a dialogue client, [0046]: in response to a bot prompting, “How can I help you?”, a user may input “won't load, non-functional, cannot use device.”), and receive, by the processor and from the computing device, input responsive to the prompting and corresponding to details of the unstructured text (see at least [0036]: At each node in the example of FIG. 2, an intent monitor assesses the input from a user that resulted in a progression to the node within the bot logic, [0055]: Based on the generated response from the bot at reference number 414, the user may input additional information at the dialogue client 402. Again, at reference number 416, the user input is intercepted or monitored by the intent monitor 204) and
identify, based on the input received from the computer device responsive to the prompting, a path through the decision tree starting from the internal node mapped using the intent mapper and ending at a leaf node corresponding to a wherein assessing the one or more factors includes a determination that a user frustration level exceeds a predetermined threshold); and
provide, by the processor, a recommendation as to a determination
Ringhiser discloses branches from each internal node, wherein each node may continue to split into one or more nodes until a leaf node is reached; however, should it be found that Ringhiser does not explicitly disclose identifying a path,
Shalev-Shwartz teaches identify a path through the decision tree starting from the internal node and ending at a leaf node corresponding to a rule (see at least abstract, [0213]-[0215], [0218]: Every leaf node should implement a policy that, based on the entire path from the root to the leaf, defines a set of Desires (e.g., a set of navigational goals for the host vehicle, [0219]: Plain road node 911 includes three child nodes: stay node 909, overtake left node 917, and overtake right node 915. Stay refers to a situation in which the host vehicle would like to keep driving in the same lane. The stay node is a leaf node (no outgoing edges/lines). Therefore, it the stay node defines a set of Desires, [0225]: driving policy module 803 may be required to choose a path from the root node to a leaf that goes through all critical nodes).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Shalev-Shwartz in order to help determine a safety action of the host vehicle and provide standardization of safety assurance (see at least Shalev [0004]). The combination would yield predictable results.
Ringhiser does not explicitly disclose a fault-determination rule; and a determination of fault based on the fault-determination rule.
However, Brandmaier teaches a fault-determination rule (see at least column 17 lines 4-46: fault determination module 438 may analyze the event data and the supplemental driving data to identify the at-fault participant and to determine the fault probability values (step 806). The fault determination module 438 may apply a fault determination ruleset 456 (step 808) when analyzing the event data and supplemental driving data. The fault determination ruleset may be configured, for example to apply the rules of the road to the vehicle telematics received from the vehicles…fault determination ruleset may be implemented, for example, as a decision tree or decision table, using conditional statements or switch statements, and combinations thereof); and
a determination of fault based on the fault-determination rule (see at least column 17 lines 46-55: Based on the fault determination ruleset 456, the fault determination module 438 may generate one or more fault probability values that respectively indicate the likelihood each participant is at fault for the collision (step 810). The fault determination module 438 may then attribute fault to one of the participants based on the fault probability values (step 812).).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Brandmaier with a reasonable expectation of success in order to provide an improved approach to processing insurance claims in response to a vehicle accident. The combination would yield predictable results.
As per claim(s) 2, 14, Ringhiser does not explicitly disclose wherein the instructions, when executed by the computer system, further cause the computer system to: initiate processing of an insurance claim related to a motor vehicle collision wherein responsibility for the motor vehicle collision is to be apportioned between one or more insurers based on the recommendation.
However, Brandmaier teaches wherein the instructions, when executed by the computer system, further cause the computer system to: initiate processing of an insurance claim related to a motor vehicle collision wherein responsibility for the motor vehicle collision is to be apportioned between one or more insurers based on a recommendation (see at least column 17 lines 46-55: Based on the fault determination ruleset 456, the fault determination module 438 may generate one or more fault probability values that respectively indicate the likelihood each participant is at fault for the collision (step 810). The fault determination module 438 may then attribute fault to one of the participants based on the fault probability values (step 812). As an example, the fault determination module 438 may attribute fault to the participant associated with the higher fault probability value, column 18 lines 35-57: if there is not a dispute over fault, then the fault determination module may automatically assign liability to the third party insurer, column 23 lines 5-10, 29-42: claims processing module 442 may additionally or alternatively be configured to allocate settlement payments between the insurer and third-party insurers. In some example embodiments, the insurance management system may charge a settlement fee when a claim is settled using the system. The insurance management system may also employ the fault determination probabilities to determine respective portions of a settlement payment shared between one insurer and another insurer. As an example, if the insurers agree that one of the drivers was 35% at fault and the other driver was 65% at fault, then one of the insurers may agree to provide 35% of the settlement payment, and the other insurer may agree to provide 65% of the settlement payment).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Brandmaier in order to provide an improved approach to processing insurance claims in response to a vehicle accident. The combination would yield predictable results.
As per claim(s) 3, 15, Ringhiser discloses wherein the prompting includes presenting prompts based on intermediate nodes along the path between the internal node and the leaf node of the decision tree (see at least [0035]: The branches from each internal node 208, 210, and 212 correspond to a further observation from their respective internal node. Each node may continue to split into one or more nodes based on further observations until a leaf node is reached. As illustrated, node 214, node 216, node 218, node 220, node 222 and, node 224 represent leaf nodes of the decision tree 200. In embodiments, a leaf node represents a final outcome based on series of user inputs in response to the bot response generation, [0055]: Based on the generated response from the bot at reference number 414, the user may input additional information at the dialogue client 402. Again, at reference number 416, the user input is intercepted or monitored by the intent monitor 204, [0056]: Based on the generated response at reference number 420, the user may input additional information at the dialogue client 402).
As per claim(s) 8, Ringhiser recognizing that a second bot or human support agent has a higher likelihood of satisfying a user need when compared to the current bot (see at least [0037]: a trigger may be when the current bot recognizes that a second bot or human support agent has a higher likelihood of satisfying a user need when compared to the current bot) but does not explicitly disclose wherein the instructions, when executed by the computer system, further cause the computer system to: calculate a confidence level in association with the recommendation.
However, Brandmaier teaches wherein the instructions, when executed by the computer system, further cause the computer system to: calculate a confidence level in association with the recommendation (see at least column 17 lines 46-55: Based on the fault determination ruleset 456, the fault determination module 438 may generate one or more fault probability values that respectively indicate the likelihood each participant is at fault for the collision (step 810). The fault determination module 438 may then attribute fault to one of the participants based on the fault probability values (step 812). As an example, the fault determination module 438 may attribute fault to the participant associated with the higher fault probability value, column 18 lines 35-57: if there is not a dispute over fault, then the fault determination module may automatically assign liability to the third party insurer, column 17 lines 55-67: fault determination module 438 may be configured to determine that fault cannot be accurately attributed to one participant over the other. For example, the fault determination module 438 may conclude that fault cannot be attributed where the fault probability values for each participant are equal or where the difference between the fault probability values does not exceed a predetermined threshold. As an example, the fault determination module 438 may conclude that fault can be accurately attributed where the respective fault values are 75% likelihood of fault versus 25% likelihood of fault—a fifty percentage point difference—but that fault cannot be accurately attributed where the respective fault values are 53% likelihood of fault versus 47% likelihood of fault—only a six percentage point difference).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Brandmaier in order to provide an improved approach to processing insurance claims in response to a vehicle accident. The combination would yield predictable results.
As per claim(s) 11, Ringhiser discloses wherein at least one of the unstructured text and the input received responsive to the prompting is received via a chat bot (see at least [0028]: Each of the user devices 114, 118, and 122 can execute a dialogue client. The dialog client enables capture of text, auditory, and/or visual input from a user. The user input is transmitted to the bot 104 or the bot 105 across the network, [0032]: intents extracted from the user input via natural language processing).
Claim(s) 4, 5, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ringhiser in view of Shalev-Shwartz and Brandmaier, and further in view of US 20160328799 (“Edwards”).
As per claim(s) 4, 16, Ringhiser does not explicitly disclose wherein the rule corresponds to a regulatory jurisdiction and wherein providing the recommendation includes mapping the rule to a corresponding rule of a second regulatory jurisdiction and providing the recommendation based on the corresponding rule.
However, Edwards teaches wherein the rule corresponds to a regulatory jurisdiction and wherein providing the recommendation includes mapping the rule to a corresponding rule of a second regulatory jurisdiction and providing the recommendation based on the corresponding rule (see at least abstract: computer system for efficient processing of rules-based data. Computer-readable instructions cause one or more processors to generate one or more user interface displays including prompts for data indicative of employee data and jurisdiction; based on user inputs received in response, generate questions; based on responses to the questions and jurisdiction data, [0040]: single table may include rules for differentially configuring user interfaces for different jurisdictions, claim 1: based on the received user inputs, via formulas embodied in the instructions, generate pay period gross wage data for the injured worker; based on the generated gross wage data, determine average weekly wage data for the injured worker; and responsive to determination of the average weekly wage data, generate a formatted state form incorporating the determined average weekly wage data for the injured worker).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Edwards in order to provide efficient processing of rules-based data. The combination would yield predictable results.
As per claim(s) 5, Ringhiser does not explicitly disclose wherein the instructions, when executed by the computer system, further cause the computer system to: identify the second regulatory jurisdiction from amongst a plurality of secondary regulatory jurisdictions.
However, Edwards teaches wherein the instructions, when executed by the computer system, further cause the computer system to: identify the second regulatory jurisdiction from amongst a plurality of secondary regulatory jurisdictions (see at least abstract: computer system for efficient processing of rules-based data. Computer-readable instructions cause one or more processors to generate one or more user interface displays including prompts for data indicative of employee data and jurisdiction; based on user inputs received in response, generate questions; based on responses to the questions and jurisdiction data, [0040]: single table may include rules for differentially configuring user interfaces for different jurisdictions, claim 1: based on the received user inputs, via formulas embodied in the instructions, generate pay period gross wage data for the injured worker; based on the generated gross wage data, determine average weekly wage data for the injured worker; and responsive to determination of the average weekly wage data, generate a formatted state form incorporating the determined average weekly wage data for the injured worker).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Edwards in order to provide efficient processing of rules-based data. The combination would yield predictable results.
Claim(s) 6, 7, 17, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ringhiser in view of Shalev-Shwartz, Brandmaier, and Edwards, and further in view of US 20170053461 (“Pal”).
As per claim(s) 6, Ringhiser does not explicitly disclose wherein the second regulatory jurisdiction is identified based on a current location of the computing device used to provide the input received responsive to the prompting.
However, Edwards teaches wherein the second regulatory jurisdiction is identified responsive to the prompting (see at least abstract: computer system for efficient processing of rules-based data. Computer-readable instructions cause one or more processors to generate one or more user interface displays including prompts for data indicative of employee data and jurisdiction; based on user inputs received in response, generate questions; based on responses to the questions and jurisdiction data, [0040]: single table may include rules for differentially configuring user interfaces for different jurisdictions, claim 1: based on the received user inputs, via formulas embodied in the instructions, generate pay period gross wage data for the injured worker; based on the generated gross wage data, determine average weekly wage data for the injured worker; and responsive to determination of the average weekly wage data, generate a formatted state form incorporating the determined average weekly wage data for the injured worker).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Edwards in order to provide efficient processing of rules-based data. The combination would yield predictable results.
However, Pal teaches wherein the second regulatory jurisdiction is identified based on a current location of the computing device used to provide the input received (see at least abstract, [0051]: actively collecting supplementary data (e.g., from presenting a digital survey to the user at the mobile computing device, etc…prompting, [0111]: different traffic scenarios (e.g., low traffic, high traffic, different traffic laws, single-lane traffic, multi-lane traffic, etc.), [0132]: Virtual assistants can include any one or more of: a chat bot, vocal input bot, media bot, analysis bot, technical support bot, decision support bot, and/or any other suitable virtual assistant, claim 1: extracting a vehicle motion characteristic from at least one of the location dataset and the motion dataset, wherein the vehicle motion characteristic describes the movement of the vehicle within a time window of the first time period; detecting the vehicular accident event with the accident detection model and at least one of the second location dataset and the second motion dataset; in response to detecting the vehicular accident event, automatically initiating an accident response action at the mobile computing device, claim 5: a traffic dataset describing traffic conditions proximal a vehicle location extracted from the second location dataset, wherein the traffic conditions comprise at least one of: a traffic level, a traffic law, and accident data, wherein detecting the vehicular accident event comprises detecting the vehicular accident event with the accident detection model, the traffic dataset, and the at least one of the second location dataset and the second motion dataset).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Pal in order to automatically initiate an accident response action and to provide prompt transmission of time sensitive information. The combination would yield predictable results.
As per claim(s) 7, 18, Ringhiser does not explicitly disclose wherein the unstructured text describes circumstances of a motor vehicle collision and is received from the computing device used to provide the input received responsive to the prompting.
However, Pal teaches wherein the unstructured text describes circumstances of a motor vehicle collision and is received from the computing device used to provide the input received responsive to the prompting (see at least abstract, [0051]: actively collecting supplementary data (e.g., from presenting a digital survey to the user at the mobile computing device, etc…prompting, [0111]: different traffic scenarios (e.g., low traffic, high traffic, different traffic laws, single-lane traffic, multi-lane traffic, etc.), [0132]: Virtual assistants can include any one or more of: a chat bot, vocal input bot, media bot, analysis bot, technical support bot, decision support bot, and/or any other suitable virtual assistant, claim 1: extracting a vehicle motion characteristic from at least one of the location dataset and the motion dataset, wherein the vehicle motion characteristic describes the movement of the vehicle within a time window of the first time period; detecting the vehicular accident event with the accident detection model and at least one of the second location dataset and the second motion dataset; in response to detecting the vehicular accident event, automatically initiating an accident response action at the mobile computing device, claim 5: a traffic dataset describing traffic conditions proximal a vehicle location extracted from the second location dataset, wherein the traffic conditions comprise at least one of: a traffic level, a traffic law, and accident data, wherein detecting the vehicular accident event comprises detecting the vehicular accident event with the accident detection model, the traffic dataset, and the at least one of the second location dataset and the second motion dataset).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Pal in order to automatically initiate an accident response action and to provide prompt transmission of time sensitive information. The combination would yield predictable results.
As per claim(s) 17, Ringhiser does not explicitly disclose identifying the second regulatory jurisdiction from amongst a plurality of secondary regulatory jurisdictions, wherein the second regulatory jurisdiction is identified based on a current location of the computing device used to provide the input received responsive to the prompting.
However, Edwards teaches identifying the second regulatory jurisdiction from amongst a plurality of secondary regulatory jurisdictions, wherein the second regulatory jurisdiction is identified responsive to the prompting (see at least abstract: computer system for efficient processing of rules-based data. Computer-readable instructions cause one or more processors to generate one or more user interface displays including prompts for data indicative of employee data and jurisdiction; based on user inputs received in response, generate questions; based on responses to the questions and jurisdiction data, [0040]: single table may include rules for differentially configuring user interfaces for different jurisdictions, claim 1: based on the received user inputs, via formulas embodied in the instructions, generate pay period gross wage data for the injured worker; based on the generated gross wage data, determine average weekly wage data for the injured worker; and responsive to determination of the average weekly wage data, generate a formatted state form incorporating the determined average weekly wage data for the injured worker).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Edwards in order to provide efficient processing of rules-based data. The combination would yield predictable results.
However, Pal teaches wherein the second regulatory jurisdiction is identified based on a current location of the computing device used to provide the input received (see at least abstract, [0051]: actively collecting supplementary data (e.g., from presenting a digital survey to the user at the mobile computing device, etc…prompting, [0111]: different traffic scenarios (e.g., low traffic, high traffic, different traffic laws, single-lane traffic, multi-lane traffic, etc.), [0132]: Virtual assistants can include any one or more of: a chat bot, vocal input bot, media bot, analysis bot, technical support bot, decision support bot, and/or any other suitable virtual assistant, claim 1: extracting a vehicle motion characteristic from at least one of the location dataset and the motion dataset, wherein the vehicle motion characteristic describes the movement of the vehicle within a time window of the first time period; detecting the vehicular accident event with the accident detection model and at least one of the second location dataset and the second motion dataset; in response to detecting the vehicular accident event, automatically initiating an accident response action at the mobile computing device, claim 5: a traffic dataset describing traffic conditions proximal a vehicle location extracted from the second location dataset, wherein the traffic conditions comprise at least one of: a traffic level, a traffic law, and accident data, wherein detecting the vehicular accident event comprises detecting the vehicular accident event with the accident detection model, the traffic dataset, and the at least one of the second location dataset and the second motion dataset).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Pal in order to automatically initiate an accident response action and to provide prompt transmission of time sensitive information. The combination would yield predictable results.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ringhiser in view of Shalev-Shwartz and Brandmaier, and further in view of US 20180338041 (McGann”) and US 20210035015 (“Edgar”).
As per claim(s) 9, Ringhiser recognizing that a second bot or human support agent has a higher likelihood of satisfying a user need when compared to the current bot (see at least [0037]: a trigger may be when the current bot recognizes that a second bot or human support agent has a higher likelihood of satisfying a user need when compared to the current bot) but does not explicitly disclose wherein the instructions, when executed by the computer system, further cause the computer system to: add the recommendation and the associated confidence level to a data set for training a natural-language processing neural network for use in evaluating unstructured text.
However, McGann teaches wherein the instructions, when executed by the computer system, further cause the computer system to: output the recommendation and the associated confidence level for use in evaluating unstructured text (see at least [0144]: act 560, the dialog engine outputs the response generated by the dialog engine based on the triggered behavior. For example, the response my simply be a prompt to be output by the dialog controller, taking of a specific action such as transferring the call to an agent, and/or identification of a target node along with a confidence value).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of McGann in order to expand the ability to understand user intent. The combination would yield predictable results.
However, Edgar teaches wherein the instructions, when executed by the computer system, further cause the computer system to: add a recommendation and the associated confidence level to a data set for training a natural-language processing neural network for use in evaluating unstructured text (see at least [0026]: deep neural networks (DNN)s, have recently shown impressive performance, sometimes exceeding humans, in various AI domains, including computer vision, speech, natural language processing (NPL), [0044]: model development module 108 can further use the high confidence annotated data samples 116 added to the annotated training data set 106 to further train and refine or optimize the machine learning model 110 (M1) model over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Edgar in order to further train, refine or optimize a model over time. The combination would yield predictable results.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ringhiser in view of Shalev-Shwartz and Brandmaier, and further in view of US 20180285773 (“Hsiao”).
As per claim(s) 12, Ringhiser discloses decision trees (see at least [0032]: decision tree); Ringhiser does not explicitly disclose wherein the instructions, when executed by the computer system, further cause the computer system to: identify a model from amongst a plurality of models corresponding to sets of prescribed rules of different jurisdictions.
However, Hsiao teaches wherein the instructions, when executed by the computer system, further cause the computer system to: identify the model from amongst a plurality of models corresponding to sets of prescribed rules of different jurisdictions (see at least [0024]: labels defined in the tax law of a jurisdiction that has authority to levy taxes from the user, [0060]: there are many different types of supervised classification models that can be used for the machine-learning model. Other examples of supervised classification models include neural networks…decision trees, [0069]: Each of the machine-learning models in the set has been trained using training data associated with a particular respective geographical region or a particular industry. In one embodiment, the selected machine-learning model has been trained using training data associated with the geographical region or industry that was specified in the API request).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Hsiao in order to provide a trained model trained with the geographical region or industry and provide improved labeling for a transaction. The combination would yield predictable results.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ringhiser in view of Shalev-Shwartz and Brandmaier, and further in view of McGann and Edgar.
As per claim(s) 19, Ringhiser recognizing that a second bot or human support agent has a higher likelihood of satisfying a user need when compared to the current bot (see at least [0037]: a trigger may be when the current bot recognizes that a second bot or human support agent has a higher likelihood of satisfying a user need when compared to the current bot) but does not explicitly disclose calculating a confidence level in association with the recommendation.
However, Brandmaier teaches calculating a confidence level in association with the recommendation (see at least column 17 lines 46-55: Based on the fault determination ruleset 456, the fault determination module 438 may generate one or more fault probability values that respectively indicate the likelihood each participant is at fault for the collision (step 810). The fault determination module 438 may then attribute fault to one of the participants based on the fault probability values (step 812). As an example, the fault determination module 438 may attribute fault to the participant associated with the higher fault probability value, column 18 lines 35-57: if there is not a dispute over fault, then the fault determination module may automatically assign liability to the third party insurer, column 17 lines 55-67: fault determination module 438 may be configured to determine that fault cannot be accurately attributed to one participant over the other. For example, the fault determination module 438 may conclude that fault cannot be attributed where the fault probability values for each participant are equal or where the difference between the fault probability values does not exceed a predetermined threshold. As an example, the fault determination module 438 may conclude that fault can be accurately attributed where the respective fault values are 75% likelihood of fault versus 25% likelihood of fault—a fifty percentage point difference—but that fault cannot be accurately attributed where the respective fault values are 53% likelihood of fault versus 47% likelihood of fault—only a six percentage point difference).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Brandmaier in order to provide an improved approach to processing insurance claims in response to a vehicle accident. The combination would yield predictable results.
However, McGann teaches outputting the recommendation and the associated confidence level for use in evaluating unstructured text (see at least [0144]: act 560, the dialog engine outputs the response generated by the dialog engine based on the triggered behavior. For example, the response my simply be a prompt to be output by the dialog controller, taking of a specific action such as transferring the call to an agent, and/or identification of a target node along with a confidence value).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of McGann in order to expand the ability to understand user intent. The combination would yield predictable results.
However, Edgar teaches adding a recommendation and the associated confidence level to a data set for training a natural-language processing neural network for use in evaluating unstructured text (see at least [0026]: deep neural networks (DNN)s, have recently shown impressive performance, sometimes exceeding humans, in various AI domains, including computer vision, speech, natural language processing (NPL), [0044]: model development module 108 can further use the high confidence annotated data samples 116 added to the annotated training data set 106 to further train and refine or optimize the machine learning model 110 (M1) model over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date to provide the invention as disclosed by Ringhiser by incorporating the teachings of Edgar in order to further train, refine or optimize a model over time. The combination would yield predictable results.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 9457754 (“Christensen”) (see at least Fig. 2A (10): portable computing device, (102): collision identification server, column 12 lines 66-67, column 13 lines 1-13: if the statistical model is a decision tree with each leaf node of the decision tree indicating a likelihood of a vehicle collision, the comparison module 402 may follow the branches of the decision tree which correspond to the current set of sensor data until reaching a leaf node. In particular, in another exemplary scenario, the current set of sensor data may include a maximum acceleration of five G, a sample rate of 50 Hz, and a maximum change in speed of 40 mph. The decision tree may include a branch for sample rates within a sample rate range of 30-60 Hz, followed by a branch for when the maximum acceleration is less than six G, followed by a branch for when the maximum change in speed is greater than 30 mph, followed by a leaf node indicating a likelihood of a vehicle collision of 95 percent);
US 20130304517 (“Florence”) (see at least abstract, [0003]: different states have different negligence rules that might alter the subrogation potential of insurance claims, [0046]-[0048]: negligence rule database or spreadsheet 1000 according to some embodiments. The table may include, for example, entries identifying all 50 states in the United States along with the District of Columbia and any other covered territory; For example, Alabama might implement a "contributory" negligence rule while Alaska implements a "comparative" rule. According to some embodiments, the negligence rule 1004 might comprise a numerical value representing how a subrogation potential score should be adjusted, claim 8).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELINA M SHUDY whose telephone number is (571)272-6757. The examiner can normally be reached M - F 10am - 6pm.
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Angelina Shudy
Primary Examiner
Art Unit 3668
/Angelina M Shudy/Primary Examiner, Art Unit 3668