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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/13/2022, 02/05/2024 and 03/09/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Interpretation
The Examiner notes that several of the claims comprise limitations which are not given patentable weight due to the limitations merely being an intended result. The limitation of “resulting in an optimal performance of a few-shot classifier on the query set” of claims 1, 15, and 18 is merely an intended result. Furthermore, the limitations of “such that the user is able to view the selected example” of claim 3 is merely an intended result. Furthermore, “such that the computer-implemented process is able to use information about the selected example to influence downstream processing” of claim 4 is not positively recited, is merely an intended result, and is merely reciting intended possible future use of the computer-implemented process. Furthermore, the limitation of “such that the adapted few-shot classifier is able operate for the new class” of claim 15 is not positively recited, is merely an intended result, and is merely reciting an intended possible future use of the few-shot classifier. Furthermore, the limitation of “in order to help the user locate the object of interest” of claim 17 is merely an intended result.
Claim Objections
Claim 15 is objected to because of the following informalities:
Claim 15 appears to contain a grammatical error and should be changed to: “such that the adapted few-shot classifier is able to operate for the new class.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, claim 1 recites the limitation of: “Obtaining a query set comprising a plurality of held out examples in a plurality of classes”. It is unclear if “held out” is meant to be understood as the examples are not within a class, or if “held out” is meant to be understood as examples that have been selected from a plurality of classes. For purposes of examination, the Examiner interprets the limitation to mean that the examples have been selected from a plurality of classes.
Claims 2-14 inherit the same deficiency as claim 1 based on dependence.
Regarding claim 2, claim 2 recites the limitation of: “providing the metadata to a user or an automated process”. The metes and bounds of the limitation is unclear as to the “automated process”. It is unclear if the “automated process” is meant to be understood as a process performed by an algorithm of some sort, or if it is meant to be understood as a process performed by a user. Furthermore, it is unclear if the “automated process” is meant to be understood as being any process which is automated, or if it meant to be limited by being related to any particular classification automated process.
Regarding claim 7, claim 7 recites the limitation of: “wherein the method comprises modifying the few-shot classifier so it has improved performance for one of the selected examples”. The metes and bounds of the limitation is indefinite because it merely recites a use, modifying a few-shot classifier) without any active, positive steps delimiting how the use is actually practiced, see MPEP 2173.05(q).
Furthermore, Regarding claims 7, and 8, the claims recite the limitation of: “where the optimal performance is a worst performance”. Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “optimal” in claim 7 is used by the claim to mean “worst,” while the accepted meaning is “best1” The term is indefinite because the specification does not clearly redefine the term.
Regarding claim 11, claim 11 recites the limitation of: “wherein only one projection step is used per class”. Claim 11 is dependent on claims 10 and 1. Claim 10 recites the limitations of “wherein the constrained optimization problem comprises a first step being a gradient ascent, and a second step being a projection step projecting a vector of the weights onto an l1 ball”. Claim 1 recites the limitations of “solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights”, and “selecting, using the optimal weights, an example per class from the pool”. The limitations of claim 1 describe that a projection step is used in solving the constrained optimization problem, specifically projected gradient ascent or descent. Claim 1 also describes that the solving of the constrained optimization problem results in optimal weights, and that optimal weights are used per class in the pool. Claim 10 limitations describe the constrained optimization problem comprising two steps, a gradient ascent step, and a second step being a projection step projecting a vector of the weights onto an l1 ball. It is unclear how the limitation of “wherein only one projection step is used per class” of claim 11, coincides with the limitations set forth in the claims 10, and 1 which it depends upon.
Regarding claim 15, claim 15 recites the limitations of: “obtaining a query set comprising a plurality of held out examples of the new class”. It is unclear if “held out” is meant to be understood as the examples are not within a class, or if “held out” is meant to be understood as examples that have been selected from the new class. For purposes of examination, the Examiner interprets the limitation to mean that the examples have been selected from the new class.
Furthermore, claim 15 recites the limitation of: “new class”. The bounds of the limitation is unclear because implicit in the limitation, “new class”, is that there is some other class or plurality of classes which are unclaimed.
Claims 16-17 inherit the same deficiency as claim 15 based on dependence.
Regarding claim 17, claim 17 recites the limitations of: “the adapted few-shot classifier is operable to recognize the object of interest in images depicting an environment of the user”. It is unclear if the limitation is meant to be understood as the adapted few-shot classifier is operable to recognize the object of interest in images depicting an environment that the user is in, or if it is meant to be understood as the adapted few-shot classifier is operable to recognize the object of interest in images depicting an environment that the user determines.
Regarding claim 18, claim 18 recites the limitations of: “obtaining a query set comprising a plurality of held out images”. It is unclear if “held out” is meant to be understood as the images are withheld images, or if “held out” is meant to be understood as images that have been selected. For purposes of examination, the Examiner interprets the limitation to mean that the images have been selected.
Claims 19-20 inherit the same deficiency as claim 18 based on dependence.
Regarding claim 19, claim 19 recites the limitations of: “wherein the images depict an object of a class not yet operable by the few-shot classifier.” It is unclear if “the images” is referring to the “pool of images”, the “plurality of held out images”, or the “selected images” of claim 18.
Furthermore, claim 19 recites the limitations of: “wherein the images depict an object of a class not yet operable by the few-shot classifier”. The metes and bounds of the limitation are unclear. The limitation of “not yet operable” implies that the few-shot classifier at some point in the future does become operable for these images, however, it is unclear if what is being claimed is merely the images depicting an object of a class not operable by the few-shot classifier, or if what is being claimed is the intended future operation of the few-shot classifier.
Claim 20 inherits the same deficiency as claim 19 based on dependence.
Regarding claim 20, claim 20 recites the limitations of: “wherein the operations comprise adapting the few-shot classifier using images from the pool excluding the selected images.” It is unclear if “the operations” is referring to all of the operations of claim 18, “direct the computing system to perform operations comprising: accessing a pool of images; obtaining a query set comprising a plurality of held out images; for each image in the pool, assigning a weight to the image and initializing the weight using a default or random value, accessing a constrained optimization problem; solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights resulting in an optimal performance of a few-shot classifier on the query set, where the few-shot classifier is trained using the images from the pool weighted by the optimal weights; selecting, using the optimal weights, an image per class from the pool; storing the selected images.” Or if “the operations” is meant to be a specific one of the operations, or if “the operations comprise” of claim 20 is meant to be a replacement to the list of operations of claim 18 (which claim 20 is dependent upon).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, under the Alice Framework Step 1, claim 1 falls within the four statutory categories of patentable subject matter identified by 35 USC 101: a process, machine, manufacture, or a composition of matter.
Under the Alice Framework Step 2A prong 1, claim 1 recites an abstract idea, including a mental process and mathematical concept. Specifically, claim 1 recites the following, mental process, and mathematical relationships, calculations, formulas:
A method comprising: accessing a pool of examples; a query set comprising a plurality of held out examples in a plurality of classes; for each example in the pool, assigning a weight to the example and initializing the weight using a default or random value, accessing a constrained optimization problem; solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights resulting in an optimal performance of a few-shot classifier on the query set, wherein the few-shot classifier is trained using the examples from the pool weighted by the optimal weights; selecting, using the optimal weights, an example per class from the pool; the selected examples.
Under the Alice Framework Step 2A prong 2 analysis, claim 1 recites the additional elements of, “computer-implemented”, “obtaining” and “storing”. The additional element of “computer-implemented” is generically recited as merely a generic computing device upon which the abstract idea is applied, see MPEP 2106.04(d)(I). Furthermore, the additional elements of “obtaining”, and “storing” are insignificant extra-solution activity, mere data gathering, see MPEP 2106.04(d)(I), 2106.05(g). For these reasons, claim 1 is not integrated into a practical application.
Under the Alice Framework Step 2B analysis, the additional element of claim 1, “computer-implemented” is describing merely a generic computing device upon which the abstract idea is applied, see MPEP 2106.05(f), and 2106.05(I)(A)(i). Furthermore, the additional elements of “obtaining”, and “storing” are well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i), 2106.05(d)(II)(iv). For these reasons, claim 1 is not amounting to significantly more than an abstract idea.
Claim 2 is rejected for at least the reasons set forth with respect to claim 1. Claim 2 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 2 recites an abstract idea, including a mathematical concept. Specifically, claim 2 recites the following mental process, and mathematical concepts:
further comprising, for one of the selected examples, metadata about content of the selected example.
The Examiner notes that metadata is merely a description of data used in a mathematical algorithm.
Under the Alice Framework Step 2A prong 2 analysis, claim 2 recites the further additional elements of “providing the metadata to a user or an automated process”. The additional element of “providing the metadata to a user or an automated process” is insignificant extra-solution activity, mere data gathering, see MPEP 2106.04(d)(I), 2106.05(g). For these reasons, claim 2 is not integrated into a practical application.
Under the Alice Framework Step 2B analysis, the additional element of claim 2, “providing the metadata to a user or an automated process” is well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i), furthermore, see MPEP 2106.05(I)(A)(iii), and see MPEP 2106.05(a)(vii), (viii), (vi) regarding providing data to a user as not sufficient to show an improvement in computer-functionality. For these reasons, claim 2 is not amounting to significantly more than an abstract idea.
Claim 3 is rejected for at least the reasons set forth with respect to claim 1. Claim 3 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 3 recites an abstract idea, including a mathematical concept. Specifically, claim 3 recites the following mental process, and mathematical concepts:
further comprising, the feedback comprising one of the selected examples, such that the user is able to view the selected example.
The Examiner notes that, as referenced above, the limitation of “such that the user is able to view the selected example” is merely an intended result of providing feedback to a user via a user interface.
Under the Alice Framework Step 2A prong 2 analysis, claim 3 recites the further additional element of “providing feedback to a user via a user interface”. The additional element of “providing feedback to a user via a user interface” is insignificant extra-solution activity, see MPEP 2106.04(d)(I), 2106.05(g). For these reasons, claim 3 is not integrated into a practical application.
Under the Alice Framework Step 2B analysis, the additional element of claim 3, “providing feedback to a user via a user interface” is well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i), furthermore, see MPEP 2106.05(I)(A)(iii), and see MPEP 2106.05(a)(vii), (viii), (vi) regarding providing data to a user, displaying data, as not sufficient to show an improvement in computer-functionality. For these reasons, claim 3 is not amounting to significantly more than an abstract idea.
Claim 4 is rejected for at least the reasons set forth with respect to claim 1. Claim 4 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 4 recites an abstract idea, including a mathematical concept. Specifically, claim 4 recites the following mental process, and mathematical concepts:
further comprising providing feedback to a process, the feedback comprising one of the selected examples, such that the process is able to use information about the selected example to influence downstream processing.
The Examiner notes that, as referenced above, the limitation of “such that the computer-implemented process is able to use information about the selected example to influence downstream processing” is not positively recited, is merely an intended result of providing the computer-implemented process with feedback, and is merely an intended possible future use, “computer-implemented process is able to use”.
Claim 4 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 5 is rejected for at least the reasons set forth with respect to claim 1. Claim 5 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 5 recites an abstract idea, including a mathematical concept. Specifically, claim 5 recites the following mental process, and mathematical concepts:
further comprising analyzing content of one of the selected examples to extract a characteristic of the selected example.
Claim 5 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 6 is rejected for at least the reasons set forth with respect to claim 1. Claim 6 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 6 recites an abstract idea, including a mathematical concept. Specifically, claim 6 recites the following mental process, and mathematical concepts:
further comprising creating a benchmark comprising one of the selected examples.
Claim 6 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 7 is rejected for at least the reasons set forth with respect to claim 1. Claim 7 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 7 recites an abstract idea, including a mathematical concept. Specifically, claim 7 recites the following mental process, and mathematical concepts:
where the optimal performance is a worst performance and wherein the method comprises modifying the few-shot classifier so it has improved performance for one of the selected examples.
Claim 7 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 8 is rejected for at least the reasons set forth with respect to claim 1. Claim 8 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 8 recites an abstract idea, including a mathematical concept. Specifically, claim 8 recites the following mental process, and mathematical concepts:
where the optimal performance is a worst performance and wherein the method comprises removing one of the selected examples from the pool and then training the few-shot classifier using a support set drawn from the pool.
Claim 8 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 9 is rejected for at least the reasons set forth with respect to claim 1. Claim 9 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 9 recites an abstract idea, including a mathematical concept. Specifically, claim 9 recites the following mathematical concepts:
wherein the constrained optimization problem is constrained using a sparsity constraint on the weights.
Claim 9 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 10 is rejected for at least the reasons set forth with respect to claim 1. Claim 10 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 10 recites an abstract idea, including a mathematical concept. Specifically, claim 10 recites the following mathematical concepts:
wherein the constrained optimization problem comprises a first step being a gradient ascent, and a second step being a projection step projecting a vector of the weights onto an l1 ball.
Claim 10 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 11 is rejected for at least the reasons set forth with respect to claim 10. Claim 11 merely further limits the mathematical concept set forth in claim 10.
Under the Alice Framework Step 2A prong 1, claim 11 recites an abstract idea, including a mathematical concept. Specifically, claim 11 recites the following mathematical concepts:
wherein only one projection step is used per class.
Claim 11 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 12 is rejected for at least the reasons set forth with respect to claim 10. Claim 12 merely further limits the mathematical concept set forth in claim 10.
Under the Alice Framework Step 2A prong 1, claim 12 recites an abstract idea, including a mathematical concept. Specifically, claim 12 recites the following mathematical concepts:
wherein the gradient ascent uses a high learning rate.
Claim 12 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 13 is rejected for at least the reasons set forth with respect to claim 10. Claim 13 merely further limits the mathematical concept set forth in claim 10.
Under the Alice Framework Step 2A prong 1, claim 13 recites an abstract idea, including a mathematical concept. Specifically, claim 13 recites the following mathematical concepts:
wherein the projection step is computed using a Lagrange multiplier2 computed to obtain a vector of the optimal weights by setting the vector of the optimal weights equal to a sign of the weight vector multiplied by the magnitude of the weight vector minus a constant.
Claim 13 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 14 is rejected for at least the reasons set forth with respect to claim 1. Claim 14 merely further limits the mathematical concept set forth in claim 1.
Under the Alice Framework Step 2A prong 1, claim 14 recites an abstract idea, including a mathematical concept. Specifically, claim 14 recites the following mathematical concepts:
wherein the examples are any of images, videos, speech signals, text, molecules, sensor data.
The Examiner notes that “images, videos, speech signals, text, molecules, sensor data” are merely descriptions of where the data used in the mathematical concepts is derived from.
Claim 14 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Regarding claim 15, under the Alice Framework Step 1, claim 15 falls within the four statutory categories of patentable subject matter identified by 35 USC 101: a process, machine, manufacture, or a composition of matter.
Under the Alice Framework Step 2A prong 1, claim 15 recites an abstract idea, including a mental process and mathematical concept. Specifically, claim 15 recites the following, mental process, and mathematical relationships, calculations, formulas:
An comprising: a method comprising: accessing a pool of examples of a new class, the examples being associated with a user; a query set comprising a plurality of held out examples of the new class; for each example in the pool, assigning a weight to the example and initializing the weight using a default or random value, accessing a constrained optimization problem; solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights resulting in an optimal performance of a few-shot classifier on the query set, where the few-shot classifier is trained using the examples from the pool weighted by the optimal weights; selecting, using the optimal weights, an example per class from the pool; adapting the few-shot classifier using the pool excluding the selected examples, to create an adapted few-shot classifier, such that the adapted few-shot classifier is able operate for the new class.
Under the Alice Framework Step 2A prong 2 analysis, claim 15 recites the additional elements of, “apparatus”, “processor”, “memory (508) storing instructions that, when executed by the processor (502), perform a method”, and “obtaining”. The additional elements of “apparatus”, “processor”, “memory (508) storing instructions that, when executed by the processor (502), perform a method” are generically recited as merely a generic computing device upon which the abstract idea is applied, see MPEP 2106.04(d)(I). Furthermore, the additional element of “obtaining” is insignificant extra-solution activity, mere data gathering, see MPEP 2106.04(d)(I), 2106.05(g). For these reasons, claim 15 is not integrated into a practical application.
Under the Alice Framework Step 2B analysis, the additional elements of claim 15, “apparatus”, “processor”, “memory (508) storing instructions that, when executed by the processor (502), perform a method” are describing merely a generic computing device upon which the abstract idea is applied, see MPEP 2106.05(f), and 2106.05(I)(A)(i). Furthermore, the additional element of “obtaining” is well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i). For these reasons, claim 15 is not amounting to significantly more than an abstract idea.
Claim 16 is rejected for at least the reasons set forth with respect to claim 15. Claim 16 merely further limits the mathematical concept set forth in claim 15.
Under the Alice Framework Step 2A prong 1, claim 16 recites an abstract idea, including a mathematical concept. Specifically, claim 16 recites the following mathematical concepts:
wherein the few-shot classifier operates for the new class in addition to at least one other class.
Claim 16 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 17 is rejected for at least the reasons set forth with respect to claim 15. Claim 17 merely further limits the mathematical concept set forth in claim 15.
Under the Alice Framework Step 2A prong 1, claim 17 recites an abstract idea, including a mathematical concept. Specifically, claim 17 recites the following mathematical concepts:
wherein the pool of examples are images depicting an object of interest to the user and wherein the adapted few-shot classifier is operable to recognize the object of interest in images depicting an environment of the user in order to help the user locate the object of interest.
The Examiner notes, as referenced above, the limitation of “in order to help the user locate the object of interest” is merely an intended result of the few-shot classifier being operable to recognize the object of interest in images depicting an environment of the user. Furthermore, the Examiner notes that “images depicting an object of interest to the user”, and “images depicting an environment of the user” are merely descriptive of the data used in the mathematical concepts.
Claim 17 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Regarding claim 18, under the Alice Framework Step 1, claim 18 falls within the four statutory categories of patentable subject matter identified by 35 USC 101: a process, machine, manufacture, or a composition of matter.
Under the Alice Framework Step 2A prong 1, claim 18 recites an abstract idea, including a mental process and mathematical concept. Specifically, claim 18 recites the following, mental process, and mathematical relationships, calculations, formulas:
comprising: accessing a pool of images; a query set comprising a plurality of held out images; for each image in the pool, assigning a weight to the image and initializing the weight using a default or random value, accessing a constrained optimization problem; solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights resulting in an optimal performance of a few-shot classifier on the query set, where the few-shot classifier is trained using the images from the pool weighted by the optimal weights; selecting, using the optimal weights, an image per class from the pool; the selected images.
The Examiner notes that “image”, and “images” merely describe data which is used in the mathematical concepts.
Under the Alice Framework Step 2A prong 2 analysis, claim 18 recites the additional elements of, “computer storage medium having computer-executable instructions that, when executed by a computing system, direct the computing system to perform operations”, “obtaining”, and “storing”. The additional element of “computer storage medium having computer-executable instructions that, when executed by a computing system, direct the computing system to perform operations” is generically recited as merely a generic computing device upon which the abstract idea is applied, see MPEP 2106.04(d)(I). Furthermore, the additional elements of “obtaining”, and “storing” are insignificant extra-solution activity, mere data gathering, see MPEP 2106.04(d)(I), 2106.05(g). For these reasons, claim 18 is not integrated into a practical application.
Under the Alice Framework Step 2B analysis, the additional element of claim 18, “computer storage medium having computer-executable instructions that, when executed by a computing system, direct the computing system to perform operations” is describing merely a generic computing device upon which the abstract idea is applied, see MPEP 2106.05(f), and 2106.05(I)(A)(i). Furthermore, the additional elements of “obtaining”, and “storing” are well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i), MPEP 2106.05(d)(II)(iv). For these reasons, claim 18 is not amounting to significantly more than an abstract idea.
Claim 19 is rejected for at least the reasons set forth with respect to claim 18. Claim 19 merely further limits the mathematical concept set forth in claim 18.
Under the Alice Framework Step 2A prong 1, claim 19 recites an abstract idea, including a mathematical concept. Specifically, claim 19 recites the following mathematical concepts:
wherein the images depict an object of a class not yet operable by the few-shot classifier.
Claim 19 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 20 is rejected for at least the reasons set forth with respect to claim 19. Claim 20 merely further limits the mathematical concept set forth in claim 19.
Under the Alice Framework Step 2A prong 1, claim 20 recites an abstract idea, including a mathematical concept. Specifically, claim 20 recites the following mathematical concepts:
wherein the operations comprise adapting the few-shot classifier using images from the pool excluding the selected images.
Claim 20 recites no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Indication of Allowable Subject Matter
Claims 1-22 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, and 35 U.S.C. 101 rejections, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter regarding claim 1-14:
With regards to claim 1, the applicant claims a computer-implemented method, wherein the computer-implemented method of claim 1 comprises:
A computer-implemented method comprising: accessing a pool of examples; obtaining a query set comprising a plurality of held out examples in a plurality of classes; for each example in the pool, assigning a weight to the example and initializing the weight using a default or random value, accessing a constrained optimization problem; solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights resulting in an optimal performance of a few-shot classifier on the query set, wherein the few-shot classifier is trained using the examples from the pool weighted by the optimal weights; selecting, using the optimal weights, an example per class from the pool; storing the selected examples.
The primary reason for indication of allowable subject matter is the above italicized claim limitations in combination with the remaining claim limitations including intervening claims.
The following is a statement of reasons for the indication of allowable subject matter regarding claim 15-17:
With regards to claim 15, the applicant claims an apparatus, wherein the apparatus of claim 15 comprises:
An apparatus comprising: a processor (502); a memory (508) storing instructions that, when executed by the processor (502), perform a method comprising: accessing a pool of examples of a new class, the examples being associated with a user; obtaining a query set comprising a plurality of held out examples of the new class; for each example in the pool, assigning a weight to the example and initializing the weight using a default or random value, accessing a constrained optimization problem; solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights resulting in an optimal performance of a few-shot classifier on the query set, where the few-shot classifier is trained using the examples from the pool weighted by the optimal weights; selecting, using the optimal weights, an example per class from the pool; adapting the few-shot classifier using the pool excluding the selected examples, to create an adapted few-shot classifier, such that the adapted few-shot classifier is able operate for the new class.
The primary reason for indication of allowable subject matter is the above italicized claim limitations in combination with the remaining claim limitations including intervening claims.
The following is a statement of reasons for the indication of allowable subject matter regarding claim 18-20:
With regards to claim 18, the applicant claims a computer storage medium having computer-executable instructions that, when executed by a computing system, direct the computing system to perform operations, wherein the performed operations of claim 18 comprises:
comprising: accessing a pool of images; obtaining a query set comprising a plurality of held out images; for each image in the pool, assigning a weight to the image and initializing the weight using a default or random value, accessing a constrained optimization problem; solving the constrained optimization problem using a projected gradient ascent or descent, the solving resulting in optimal weights resulting in an optimal performance of a few-shot classifier on the query set, where the few-shot classifier is trained using the images from the pool weighted by the optimal weights; selecting, using the optimal weights, an image per class from the pool; storing the selected images.
The primary reason for indication of allowable subject matter is the above italicized claim limitations in combination with the remaining claim limitations including intervening claims.
Liang et al. (U.S. 2022/0300823 A1), hereinafter, “Liang” discloses a few-shot classifier and method (Fig. 3), accessing a pool of examples ([0008]), obtaining a query set comprising a plurality of examples in a plurality of classes ([0010]), assigning and initializing a weight to each example in the pool of examples (Fig. 4 reference number 404; [0082] regarding initializing the autoencoder including the weights), solving an optimization problem using gradient descent ([0070] & [0080] regarding solving a problem to calculate optimized parameters (weights) using gradient descent). However, Liang fails to teach or suggest the italicized claim limitations in combination with the remaining claim limitations as referenced above, instead Liang discloses generating a prototype for each class by averaging the support set feature maps of each class, and uses these prototypes to update the classifier (Fig. 5 reference number 508). Liang does not teach or suggest selecting a single example/image per class from the pool of examples/images by using the optimized weights and storing the selected examples/images.
Kida (U.S. 2024/0330703 A1), hereinafter, “Kida” discloses a computer-implemented ([0015]) few-shot classifier and method (Fig. 1C; Fig. 6), accessing a pool of examples (Fig. 1A reference number 10), obtaining a query set comprising a plurality of examples in a plurality of classes (Fig. 1A reference number 10; Fig. 1C query set Q; [0040] regarding query set Q consisting of a dataset of samples of classes), for each example in the pool assigning and initializing a weight (Fig 1A reference numbers 10(dataset), and 24 (classification weight); [0035] regarding (24) representing a weight vector related to the dataset; [0012] regarding base class having an assigned weight), accessing a constrained optimization problem (Fig. 1C reference number 100; Fig. 6 reference number 60 (Meta-learning module); [0090] regarding the meta-learning module (60) with a feature optimization unit (64) and graph neural network (70) base calculations on a pre-trained model), solving the optimization problem using a gradient and resulting in optimal weights ([0090] regarding the meta-learning module calculating classification weights), the few-shot classifier trained using the examples from the pool weighted by the optimal weights (Fig. 6 reference numbers 60 (Meta-learning module), 72 (reconstruction classification weight), 80 (Base and novel class recognition module), 88 (Base and novel classification weight); Fig. 8 regarding updating of the meta-module). However, Kida fails to teach or suggest the italicized claim limitations in combination with the remaining claim limitations as referenced above, instead optimized parameters (weights) are determined and stored rather than individually selected examples per class. Kida does not teach or suggest selecting a single example/image per class from the pool of examples/images by using the optimized weights and storing the selected examples/images.
JI et al. (“Reweighting and information-guidance networks for Few-Shot Learning”, Neurocomputing, Vol. 423, pp. 13-23, October 20, 2020), hereinafter “JI” discloses a few-shot learning classification method (Fig. 2), creating a query set of examples (sections 3.1, 3.2 regarding creating query set), calculating optimized weights (section 3.3 equations (3), and (4)). However, Ji fails to teach or suggest the italicized claim limitations in combination with the remaining claim limitations as referenced above, instead, JI discloses creation of a prototype of each class by averaging feature vectors of embedded support points, and more powerful prototypes for each class are generated by extending the prototypical networks to reweighing networks (section 3.2). JI does not teach or suggest selecting a single example/image per class from the pool of examples/images by using the optimized weights and storing the selected examples/images.
AGARWAL et al. (“On Sensitivity of Meta-learning to Support Data”, In Proceedings of Advances in Neural Information Processing Systems Volume 34, December 6, 2021), hereinafter “AGARWAL” discloses Meta-learning for few-shot classification (section 2.1). AGARWAL discloses choosing selected examples post-adaptation for each class as support examples (section 3, algorithm 1 and section named “Visualizing the worst case support search”). However, AGARWAL discloses that the choosing of worst case support examples per class is in lieu of using weights of potential support examples. Therefore, AGARWAL fails to teach or suggest the italicized claim limitations in combination with the remaining claim limitations as referenced above. AGARWAL does not teach or suggest selecting a single example/image per class from the pool of examples/images by using the optimized weights and storing the selected examples/images.
YANG et al. (“Diagnosing Ensemble Few-shot classifiers”) hereinafter, “YANG” discloses learners for few-shot classifying (Fig. 1; Fig. 2). YANG further discloses selecting low quality shots, and recommending high quality shot replacement (Section 7.2.1; Fig. 11). However, YANG discloses that the choosing of low quality shot examples chosen by confidence scores rather than optimized weights, and does not explicitly teach/suggest that the chosen shots are one per class. Therefore, YANG fails to teach or suggest the italicized claim limitations in combination with the remaining claim limitations as referenced above. YANG does not teach or suggest selecting a single example/image per class from the pool of examples/images by using the optimized weights and storing the selected examples/images.
Conclusion
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/J.A.K./ Examiner, Art Unit 2182
/EMILY E LAROCQUE/ Primary Examiner, Art Unit 2182
1 Definition of optimum: Dictionary.com. dictionary.com. (2019, June). web.archive.org/web/20190621233717/https://www.dictionary.com/browse/optimum
The definition of optimal. Dictionary.com. (2018, February). web.archive.org/web/20180217052026/http://www.dictionary.com/browse/optimal?s=t
Regarding the definition of “optimal”
2 Lagrange multiplier. Calcworkshop. (2022, January 26). calcworkshop.com/partial-derivatives/lagrange-multiplier/
Regarding the Lagrange Multiplier as a mathematical concept, not hardware
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