DETAILED ACTION
Claims 1-20 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 U.S.C. § 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.
The invention, as taught in Claims 1-20, is directed to “mental steps” and “mathematical steps” without significantly more.
The claims recite:
• data associated with a plurality of sequences (i.e., mathematical
expressions)
• each sequence of the plurality of sequences comprises a plurality of
items (i.e., mathematical expressions)
• data associated with the plurality of sequences to produce a trained
sequential machine learning model (i.e., mathematical expressions)
• data associated with the plurality of sequences (i.e., mathematical
expressions)
• determining a plurality of sequential dependencies between items in the
plurality of sequences (i.e., mental steps)
• denoising the plurality of sequential dependencies to produce denoised
sequential dependencies, wherein denoising the plurality of sequential
dependencies comprises: (i.e., mathematical steps)
• applying at least one trainable binary mask to each self- attention layer of
the at least one self-attention layer (i.e., mathematical steps)
• training the at least one trainable binary mask to produce at least one
trained binary mask (i.e., mathematical steps)
• excluding one or more sequential dependencies in the plurality of
sequential dependencies (i.e., mental steps or mathematical steps)
• produce the denoised sequential dependencies based on the at least
one trained binary mask; (i.e., mathematical steps)
• denoised sequential dependencies (i.e., mathematical expressions)
• generating, with at least one processor, a prediction of an item
associated with a sequence of items based on the output of the trained
sequential machine learning model (i.e., mental steps)
Claim 1
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “1. A computer-implemented method comprising: …” Therefore, it is a “method” (or “process”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 1 that recite abstract ideas?
YES. The following limitations in Claim 1 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• data associated with a plurality of sequences (i.e., mathematical
expressions)
• each sequence of the plurality of sequences comprises a plurality of
items (i.e., mathematical expressions)
• data associated with the plurality of sequences to produce a trained
sequential machine learning model (i.e., mathematical expressions)
• data associated with the plurality of sequences (i.e., mathematical
expressions)
• determining a plurality of sequential dependencies between items in the
plurality of sequences (i.e., mental steps)
• denoising the plurality of sequential dependencies to produce denoised
sequential dependencies, wherein denoising the plurality of sequential
dependencies comprises: (i.e., mathematical steps)
• applying at least one trainable binary mask to each self- attention layer of
the at least one self-attention layer (i.e., mathematical steps)
• training the at least one trainable binary mask to produce at least one
trained binary mask (i.e., mathematical steps)
• excluding one or more sequential dependencies in the plurality of
sequential dependencies (i.e., mental steps or mathematical steps)
• produce the denoised sequential dependencies based on the at least
one trained binary mask; (i.e., mathematical steps)
• denoised sequential dependencies (i.e., mathematical expressions)
• generating, …, a prediction of an item associated with a sequence of
items based on the output of the trained sequential machine learning
model (i.e., mental steps)
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A “processor”
(2) A “receiving, with at least one processor”/“inputting … to at least one self-attention layer of the sequential machine learning model”
(3) A “sequential machine learning model”
(4) An “output of the trained sequential machine learning model”
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05(f) recites:
For claim limitations that do not amount to more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two…
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
This “processor” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(2) A “receiving, with at least one processor”/“inputting … to at least one self-attention layer of the sequential machine learning model”
is a broad term which is described at a high level. M.P.E.P. § 2106.05(g) recites:
2106.05(g) Insignificant Extra-Solution Activity [R-10.2019]
Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.
This “receiving, with at least one processor”/“inputting … to at least one self-attention layer of the sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(3) A “sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05 (f)(2) recites in part:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
This “sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(4) An “output of the trained sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(g) recites:
2106.05(g) Insignificant Extra-Solution Activity [R-10.2019]
Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.
This “output of the trained sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A “processor”
(2) A “receiving, with at least one processor”/“inputting … to at least one self-attention layer of the sequential machine learning model”
(3) A “sequential machine learning model”
(4) An “output of the trained sequential machine learning model”
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(2) A “receiving, with at least one processor”/“inputting … to at least one self-attention layer of the sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. 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); …
Further, M.P.E.P. § 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(3) A “sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(f) recites:
For claim limitations that do not amount to more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two…
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Therefore, simply using the “sequential machine learning model” to produce a result is not eligible.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(4) An “output of the trained sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. 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); …
Further, M.P.E.P. § 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 1 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 2
Claim 2 recites:
2. The computer-implemented method of claim 1, wherein training the sequential machine learning model comprises:
providing the plurality of sequential dependencies to at least one feed forward layer of the sequential machine learning model; and
generating, using the at least one feed forward layer, a plurality of weights associated with the plurality of sequential dependencies based on the plurality of sequential dependencies; and
wherein generating the prediction of the item associated with the sequence of items comprises:
generating the prediction of the item associated with the sequence of items based on the weights associated with the plurality of sequential dependencies.
Applicant’s Claim 2 merely teaches training a generic sequential machine learning model and the mental step of generating a prediction. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 2 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 3
Claim 3 recites:
3. The computer-implemented method of claim 1, further comprising:
receiving, with at least one processor, the sequence of items; and inputting, with at least one processor, the sequence of items to the trained sequential machine learning model;
wherein generating the prediction of the item associated with the sequence of items based on the output of the trained sequential machine learning model comprises:
generating the prediction of the item associated with the sequence of items using at least one prediction layer of the trained sequential machine learning model.
Applicant’s Claim 3 merely teaches generically receiving data, performing a mental step of prediction, and training a generic sequential machine learning model. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 3 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 4
Claim 4 recites:
4. The computer-implemented method of claim 3, the method further comprising:
generating, with at least one processor, a targeted advertisement based on the prediction of the item associated with the sequence of items; and
transmitting, with at least one processor, the targeted advertisement to a computing device of a user.
Applicant’s Claim 4 merely teaches generating and transmitting an advertisement (i.e., organizing human activity). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 4 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 5
Claim 5 recites:
5. The computer-implemented method of claim 3, further comprising:
receiving, with at least one processor, a transaction authorization request associated with a transaction;
determining, with at least one processor, a likelihood of fraud for the transaction authorization request based at least partly on a comparison of a transaction type of the transaction to a transaction type associated with the prediction of the item associated with the sequence of items;
determining, with at least one processor, that the likelihood of fraud satisfies a threshold; and
performing, with at least one processor, a fraud mitigation action in response to determining that the likelihood of fraud satisfies the threshold.
Applicant’s Claim 5 merely teaches the receipt of a transaction authorization request, the determination of a likelihood of fraud (i.e., mathematical or mental steps), the determination that that likelihood satisfies the threshold(i.e., mathematical or mental steps), and an unspecified fraud mitigation action which could simply be data output. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 5 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 6
Claim 6 recites:
6. The computer-implemented method of claim 3, wherein:
the sequence of items comprises a sequence of words;
wherein receiving the sequence of items comprises:
receiving the sequence of words from a computing device of a user; and
wherein generating the prediction of the item associated with the sequence of items comprises:
generating a prediction of a word associated with the sequence of words; and
the method further comprising:
transmitting, with at least one processor, the word to the computing device of the user.
Applicant’s Claim 6 merely teaches a sequence of words (i.e., mental linguistic expressions,) the generic receipt of that sequence of words, the generation of a prediction of a word associated with that same sequence (i.e., mental steps,) and the generic transmission of a word to another computing device. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 6 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 7
Claim 7 recites:
7. The computer-implemented method of claim 2, wherein at least one self-attention block comprises one or more self-attention layers of the at least one self-attention layer and the at least one feed forward layer, and wherein training the sequential machine learning model comprises:
stabilizing the sequential machine learning model against perturbations in the data, wherein stabilizing the sequential machine learning model comprises:
regularizing the at least one self-attention block.
Applicant’s Claim 7 merely teaches mathematical “regularization”. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 7 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 8
Claim 8 recites:
8. The computer-implemented method of claim 7, wherein regularizing the at least one self-attention block comprises:
regularizing the at least one self-attention block using a Jacobian regularization technique.
Applicant’s Claim 8 merely teaches mathematical “regularization”. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 8 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 9
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “9. A system comprising at least one processor programmed or configured to:…” Therefore, it is a “system” (or “apparatus”), which is a statutory category of invention. Therefore, the answer to the inquiry is: “YES.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 9 that recite abstract ideas?
YES. The following limitations in Claim 9 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• data associated with a plurality of sequences (i.e., mathematical
expressions)
• each sequence of the plurality of sequences comprises a plurality of
items (i.e., mathematical expressions)
• data associated with the plurality of sequences to produce a trained
sequential machine learning model (i.e., mathematical expressions)
• data associated with the plurality of sequences (i.e., mathematical
expressions)
• determining a plurality of sequential dependencies between items in the
plurality of sequences (i.e., mental steps)
• denoising the plurality of sequential dependencies to produce denoised
sequential dependencies, wherein denoising the plurality of sequential
dependencies comprises: (i.e., mathematical steps)
• applying at least one trainable binary mask to each self- attention layer of
the at least one self-attention layer (i.e., mathematical steps)
• training the at least one trainable binary mask to produce at least one
trained binary mask (i.e., mathematical steps)
• excluding one or more sequential dependencies in the plurality of
sequential dependencies (i.e., mental steps or mathematical steps)
• produce the denoised sequential dependencies based on the at least
one trained binary mask; (i.e., mathematical steps)
• denoised sequential dependencies (i.e., mathematical expressions)
• generating, …, a prediction of an item associated with a sequence of
items based on the output of the trained sequential machine learning
model (i.e., mental steps)
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A “processor”
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model”
(3) A “sequential machine learning model”
(4) An “output of the trained sequential machine learning model”
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05(f) recites:
For claim limitations that do not amount to more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two…
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
This “processor” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(g) recites:
2106.05(g) Insignificant Extra-Solution Activity [R-10.2019]
Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.
This “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(3) A “sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05 (f)(2) recites in part:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
This “sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(4) An “output of the trained sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(g) recites:
2106.05(g) Insignificant Extra-Solution Activity [R-10.2019]
Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.
This “output of the trained sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A “processor”
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model”
(3) A “sequential machine learning model”
(4) A “output of the trained sequential machine learning model”
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. 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); …
Further, M.P.E.P. § 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(3) A “sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(f) recites:
For claim limitations that do not amount to more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two…
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Therefore, simply using the “sequential machine learning model” to produce a result is not eligible.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(4) An “output of the trained sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. 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); …
Further, M.P.E.P. § 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 9 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 10
Claim 10 recites:
10. The system of claim 9, wherein, when training the sequential machine learning model, the at least one processor is programmed or configured to:
provide the plurality of sequential dependencies to at least one feed forward layer of the sequential machine learning model; and
generate, using the at least one feed forward layer, a plurality of weights associated with the plurality of sequential dependencies based on the plurality of sequential dependencies; and
wherein, when generating the prediction of the item associated with the sequence of items, the at least one processor is programmed or configured to:
generate the prediction of the item associated with the sequence of items based on the weights associated with the plurality of sequential dependencies.
Applicant’s Claim 10 merely teaches training a generic sequential machine learning model and the mental step of generating a prediction. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 10 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 11
Claim 11 recites:
11. The system of claim 9, wherein the at least one processor is further programmed or configured to:
receive the sequence of items; and
input the sequence of items to the trained sequential machine learning model;
wherein, when generating the prediction of the item associated with the sequence of items, the at least one processor is programmed or configured to:
generate the prediction of the item associated with the sequence of items using at least one prediction layer of the trained sequential machine learning model.
Applicant’s Claim 11 merely teaches generically receiving data, performing a mental step of prediction, and training a generic sequential machine learning model. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 11 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 12
Claim 12 recites:
12. The system of claim 11, wherein the at least one processor is further programmed or configured to:
generate a targeted advertisement based on the prediction of the item associated with the sequence of items; and
transmit the targeted advertisement to a computing device of a user.
Applicant’s Claim 12 merely teaches generating and transmitting an advertisement (i.e., organizing human activity). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 12 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 13
Claim 13 recites:
13. The system of claim 11, wherein the at least one processor is further programmed or configured to:
receive a transaction authorization request associated with a transaction;
determine a likelihood of fraud for the transaction authorization request based at least partly on a comparison of a transaction type of the transaction to a transaction type associated with the prediction of the item associated with the sequence of items;
determine that the likelihood of fraud satisfies a threshold; and
perform a fraud mitigation action in response to determining that the likelihood of fraud satisfies the threshold.
Applicant’s Claim 13 merely teaches the receipt of a transaction authorization request, the determination of a likelihood of fraud (i.e., mathematical or mental steps), the determination that that likelihood satisfies the threshold(i.e., mathematical or mental steps), and an unspecified fraud mitigation action which could simply be data output. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 13 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 14
Claim 14 recites:
14. The system of claim 11, wherein:
the sequence of items comprises a sequence of words;
wherein, when receiving the sequence of items, the at least one processor is programmed or configured to:
receive the sequence of words from a computing device of a user;
wherein, when generating the prediction of the item associated with the sequence of items, the at least one processor is programmed or configured to:
generate a prediction of a word associated with the sequence of words; and
wherein the at least one processor is further programmed or configured to:
transmit the word to the computing device of the user.
Applicant’s Claim 14 merely teaches a sequence of words (i.e., mental linguistic expressions,) the generic receipt of that sequence of words, the generation of a prediction of a word associated with that same sequence (i.e., mental steps,) and the generic transmission of a word to another computing device. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 14 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 15
Step 1 inquiry: Does this claim fall within a statutory category?
The preamble of the claim recites “15. A computer program product comprising…” Therefore, it is a “computer program product” (or “computer program,” since this part did not recite that it is a “non-transitory” product), which is a NOT statutory category of invention. Therefore, the answer to the inquiry is: “NO.”
Step 2A (Prong One) inquiry:
Are there limitations in Claim 15 that recite abstract ideas?
YES. The following limitations in Claim 15 recite abstract ideas that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, they are “mental steps” and “mathematical steps”:
• data associated with a plurality of sequences (i.e., mathematical
expressions)
• each sequence of the plurality of sequences comprises a plurality of
items (i.e., mathematical expressions)
• data associated with the plurality of sequences to produce a trained
sequential machine learning model (i.e., mathematical expressions)
• data associated with the plurality of sequences (i.e., mathematical
expressions)
• determining a plurality of sequential dependencies between items in the
plurality of sequences (i.e., mental steps)
• denoising the plurality of sequential dependencies to produce denoised
sequential dependencies, wherein denoising the plurality of sequential
dependencies comprises: (i.e., mathematical steps)
• applying at least one trainable binary mask to each self- attention layer of
the at least one self-attention layer (i.e., mathematical steps)
• training the at least one trainable binary mask to produce at least one
trained binary mask (i.e., mathematical steps)
• excluding one or more sequential dependencies in the plurality of
sequential dependencies (i.e., mental steps or mathematical steps)
• produce the denoised sequential dependencies based on the at least
one trained binary mask; (i.e., mathematical steps)
• denoised sequential dependencies (i.e., mathematical expressions)
• generating, …, a prediction of an item associated with a sequence of
items based on the output of the trained sequential machine learning
model (i.e., mental steps)
Step 2A (Prong Two) inquiry:
Are there additional elements or a combination of elements in the claim that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception?
Applicant’s claims contain the following “additional elements”:
(1) A “processor”
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model”
(3) A “sequential machine learning model”
(4) An “output of the trained sequential machine learning model”
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2106.05(f) recites:
For claim limitations that do not amount to more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two…
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
This “processor” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(g) recites:
2106.05(g) Insignificant Extra-Solution Activity [R-10.2019]
Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.
This “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(3) A “sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05 (f)(2) recites in part:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
This “sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
(4) An “output of the trained sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(g) recites:
2106.05(g) Insignificant Extra-Solution Activity [R-10.2019]
Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more in Step 2B is whether the additional elements add more than insignificant extra-solution activity to the judicial exception. The term “extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity.
This “output of the trained sequential machine learning model” limitation does not integrate the additional element into a practical application and represents “insignificant extra-solution activity”. (See, M.P.E.P. § 2106.05(I)(A)).
The answer to the inquiry is “NO”, no additional elements integrate the claimed abstract idea into a practical application.
Step 2B inquiry:
Does the claim provide an inventive concept, i.e., does the claim recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception in the claim?
Applicant’s claims contain the following “additional elements”:
(1) A “processor”
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model”
(3) A “sequential machine learning model”
(4) A “output of the trained sequential machine learning model”
(1) A “processor” is a broad term which is described at a high level and includes general purpose computers. M.P.E.P. § 2016.05(f) recites:
2106.05(f) Mere Instructions To Apply An Exception [R-10.2019]
Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do “‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’”. Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on “the draftsman’s art”).
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(2) A “receiving”/“inputting … to at least one self-attention layer of the sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. 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); …
Further, M.P.E.P. § 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(3) A “sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(f) recites:
For claim limitations that do not amount to more than a recitation of the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners should explain why they do not meaningfully limit the claim in an eligibility rejection. For example, an examiner could explain that implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two…
Further, M.P.E.P. § 2106.05(f)(2) recites:
(2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Therefore, simply using the “sequential machine learning model” to produce a result is not eligible.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
(4) An “output of the trained sequential machine learning model” is a broad term which is described at a high level. M.P.E.P. § 2106.05(d)(II) recites:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
i. 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); …
Further, M.P.E.P. § 2106.05(d)(I)(2) recites in part:
2. A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").
Merely using the conventional computer to receive data is well known, understood, and conventional. Thus, it adds nothing significantly more to the judicial exception.
Therefore, the claim as a whole does not amount to significantly more than the exception itself (i.e., there is no inventive concept in the claim). (See, M.P.E.P. § 2106.05(II)).
Therefore, the answer to the inquiry is “NO”, no additional elements provide an inventive concept that is significantly more than the claimed abstract ideas the claimed abstract idea into a practical application.
Claim 15 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 16
Claim 16 recites:
16. The computer program product of claim 15, wherein, the one or more instructions that cause the at least one processor to train the sequential machine learning model, cause the at least one processor to:
provide the plurality of sequential dependencies to at least one feed forward layer of the sequential machine learning model; and
generate, using the at least one feed forward layer, a plurality of weights associated with the plurality of sequential dependencies based on the plurality of sequential dependencies; and
wherein, the one or more instructions that cause the at least one processor to generate the prediction of the item associated with the sequence of items, cause the at least one processor to:
generate the prediction of the item associated with the sequence of items based on the weights associated with the plurality of sequential dependencies.
Applicant’s Claim 16 merely teaches training a generic sequential machine learning model and the mental step of generating a prediction. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 16 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 17
Claim 17 recites:
17. The computer program product of claim 15, wherein the one or more instructions further cause the at least one processor to:
receive the sequence of items; and
input the sequence of items to the trained sequential machine learning model;
wherein, the one or more instructions that cause the at least one processor to generate the prediction of the item associated with the sequence of items, cause the at least one processer to:
generate the prediction of the item associated with the sequence of items using at least one prediction layer of the trained sequential machine learning model.
Applicant’s Claim 17 merely teaches generically receiving data, performing a mental step of prediction, and training a generic sequential machine learning model. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 17 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 18
Claim 18 recites:
18. The computer program product of claim 17, wherein the one or more instructions further cause the at least one processor to:
generate a targeted advertisement based on the prediction of the item associated with the sequence of items; and
transmit the targeted advertisement to a computing device of a user.
Applicant’s Claim 18 merely teaches generating and transmitting an advertisement (i.e., organizing human activity). It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 18 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 19
Claim 19 recites:
19. The computer program product of claim 17, wherein the one or more instructions further cause the at least one processor to:
receive a transaction authorization request associated with a transaction;
determine a likelihood of fraud for the transaction authorization request based at least partly on a comparison of a transaction type of the transaction to a transaction type associated with the prediction of the item associated with the sequence of items;
determine that the likelihood of fraud satisfies a threshold; and
perform a fraud mitigation action in response to determining that the likelihood of fraud satisfies the threshold.
Applicant’s Claim 19 merely teaches the receipt of a transaction authorization request, the determination of a likelihood of fraud (i.e., mathematical or mental steps), the determination that that likelihood satisfies the threshold(i.e., mathematical or mental steps), and an unspecified fraud mitigation action which could simply be data output. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 19 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Claim 20
Claim 20 recites:
20. The computer program product of claim 17, wherein:
the sequence of items comprises a sequence of words;
wherein, the one or more instructions that cause the at least one processor to receive the sequence of items, cause the at least one processor to:
receive the sequence of words from a computing device of a user;
wherein, the one or more instructions that cause the at least one processor to generate the prediction of the item associated with the sequence of items, cause the at least one processor to:
generate a prediction of a word associated with the sequence of words; and
wherein the one or more instructions further cause the at least one processor to:
transmit the word to the computing device of the user.
Applicant’s Claim 20 merely teaches a sequence of words (i.e., mental linguistic expressions,) the generic receipt of that sequence of words, the generation of a prediction of a word associated with that same sequence (i.e., mental steps,) and the generic transmission of a word to another computing device. It does not integrate the abstract idea to a practical application, nor is it anything significantly more than the abstract idea. (See, 2106.05(a)(II).)
Claim 20 is, therefore, NOT ELIGIBLE subject matter under 35 U.S.C. § 101.
Reasons for Not Rejecting the Clams Under Art
Claims 1-20 are not rejected since when reading the claims in light of the Specification, as per MPEP § 2111.01, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 1. Specifically, the closest prior art of Amer, et al., Modeling Dynamic Construction Work Template From Existing Scheduling Records via Sequential Machine Learning, Advanced Engineering Informatics, Article: 101198, Vol. 47, JAN 2021, pp. 1-15 fails to expressly teach:
Claim 1's "...denoising sequential dependencies..."
Claim 1's "...trainable binary mask..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 9. Specifically, the closest prior art of Amer, et al. fails to expressly teach:
Claim 9's "...denoising sequential dependencies..."
Claim 9's "...trainable binary mask..."
Further, none of the references of record, whether taken alone or in combination, discloses or suggests the combination of limitations specified in independent Claim 15. Specifically, the closest prior art of Amer, et al. fails to expressly teach:
Claim 15's "...denoising sequential dependencies..."
Claim 15's "...trainable binary mask..."
Only to the extent that these limitations (specifically as defined above) are not found in the prior art of record is the present case not rejected over the prior art.
Conclusion
Any inquiries concerning this communication or earlier communications from the examiner should be directed to Wilbert L. Starks, Jr., who may be reached Monday through Friday, between 8:00 a.m. and 5:00 p.m. EST. or via telephone at (571) 272-3691 or email: Wilbert.Starks@uspto.gov.
If you need to send an Official facsimile transmission, please send it to (571) 273-8300.
If attempts to reach the examiner are unsuccessful the Examiner’s Supervisor (SPE), Kakali Chaki, may be reached at (571) 272-3719.
Hand-delivered responses should be delivered to the Receptionist @ (Customer Service Window Randolph Building 401 Dulany Street, Alexandria, VA 22313), located on the first floor of the south side of the Randolph Building.
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/WILBERT L STARKS/
Primary Examiner, Art Unit 2122
WLS
20 SEP 2026
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