DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on September 8, 2024 and January 6, 2025 comply with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Citations which have not been considered, have not been considered because they do not comply with 37 CFR 1.98(b) which states “The date of publication supplied must include at least the month and year of publication, except that the year of publication (without the month) will be accepted if the applicant points out in the information disclosure statement that the year of publication is sufficiently earlier than the effective U.S. filing date and any foreign priority date so that the particular month of publication is not in issue”.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a processing component” for “formatting incoming data for feature extraction” in claim1; “a transformer backbone” for “multiplying formatted data with a positional embedding” in claim 1; “a single head incremental classifier” for “receiving the encoded vector and determining that the incoming data is in a new class, wherein upon determining that the incoming data is in a new class classification weight matrix is augmented with a new null-class weight vector” in claim 1; “an image processing component” for “extracting multiple flattened image patches from an input image” in claim 8; and “a transformer backbone” for “linearly transforming each of the multiple flattened patches, by a patch encoder layer, and mapping the linear transformation to a patch vector, multiplying each patch vector with a positional embedding” in claim 8.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim rejections - 35 U.S.C. §112(b)
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.
Claims 19 and 20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 19 and 20, which are directed to a “non-transitory computer-readable storage medium”, depend from claims 12 and 13 respectively, which are claims directed to a “system”. A claim cannot be both a non-transitory computer-readable storage medium and a system, making the claims vague and indefinite.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to:
http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending U.S. Patent Application No. 18/815,992. Although the conflicting claims are not identical, they are not patentably distinct from each other because both sets of claims are directed towards the common subject matter.
The claims in the present application define the invention differently from the claims in the copending U.S. Patent Application No. 18/815,992, however they are not patentably distinguishable from the claims in the other copending application. In re White et al., 160 USPQ 417, In re Thorington et al., 163 USPQ 644.
For example, comparing representative claim 1 of the present application with representative claim 1 of copending U.S. Patent Application No. 18/815,992. Claim 1 of the present application recites: A system for incrementally training a classifier to continuously learn new classes and classify incoming data, the system comprising (Claim 1 of copending U.S. Patent Application No. 18/815,992 recites: A system for incrementally training a classifier to continuously learn new classes and classify incoming data, the system comprising); a processing component for formatting incoming data for feature extraction (Claim 1 of copending U.S. Patent Application No. 18/815,992 recites: a processing component for formatting incoming data for feature extraction); a transformer backbone for multiplying formatted data with a positional embedding, transforming, by an encoder, formatted data with positional embedding to produce an encoded vector, appending a class token to the encoded vector (Claim 1 of copending U.S. Patent Application No. 18/815,992 recites: a feature extraction backbone for multiplying formatted data with a positional embedding, transforming, by an encoder, formatted data with positional embedding to produce an encoded vector appending a class token to the encoded vector); a single head incremental classifier trained on known classes including a classification weight matrix wk, where k denotes the kth training update for receiving the encoded vector and determining that the incoming data is in a new class, wherein upon determining that the incoming data is in a new class classification weight matrix is augmented with a new null-class weight vector Δwk , wherein the single head incremental classifier is trained on training data having feature samples corresponding to the incoming data directed to the new class (Claim 1 of copending U.S. Patent Application No. 18/815,992 recites: a single head incremental classifier trained on one or more known classes for receiving, by a classification weight matrix wk , where k denotes the kth training update, the encoded vector and determining that the incoming data is in a new class, augmenting the classification weight matrix with a new null-class weight vector Δwk , and training the incremental classifier on training data having feature samples corresponding to the incoming data directed to the new class).
As the comparison shows the claims recite common subject matter, and the differences relate to variations of the claimed limitations, and the processing is carried out on the data and/or elements in no way affects how the data would be received from an input, processed and output within the context of the claims. Therefore, the substitution of the different variations would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. While present claim 1 includes additional limitations that are not set forth in claim 1 of copending U.S. Patent Application No. 18/815,992, the use of transitional term "comprising" in claim 1 of copending U.S. Patent Application No. 18/815,992 fails to preclude the possibility of additional elements, so that claim 1 of copending U.S. Patent Application No. 18/815,992 fails to define an invention that is patentably distinct from present claim 1. Furthermore, the elements of claim 1 of copending U.S. Patent Application No. 18/815,992 are fully anticipated by the present claim, and anticipation is “the ultimate or epitome of obviousness (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Claims 2-20 of the present application recite limitations which are in most cases word for word the same limitations as found in claims 2-20 respectively of copending U.S. Patent Application No. 18/815,992.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. §103(a) which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 8, 11-13 and 18 are rejected under 35 U.S.C. §103(a) as being unpatentable over Khan et al. (U.S. Patent Application Publication No. US 2020/0410299 A1) (hereafter referred to as “Khan”) in view of Shim et al. (U.S. Patent Application Publication No. US 2021/0383158 A1) (hereafter referred to as “Shim”), Gao et al. (U.S. Patent Application Publication No. US 2020/0410299 A1) (hereafter referred to as “Gao”) or Zhang et al. (U.S. Patent Application Publication No. US 2020/0175384 A1) (hereafter referred to as “Zhang”)
The examiner would like to point out that the various “units” identified in section 6 hereinabove are being interpreted under 35 U.S.C. 112(f) as described in paragraphs [0103] through [0105].
Paragraphs [0103] through [0105] describe the hardware configuration of the system. The above-mentioned configuration of the system is a functional configuration achieved by cooperation of the hardware configuration described in paragraphs [0103] through [0105] and a program. As described in paragraphs [0103] through [0105], the system includes an integrated computer chip and includes graphics processing units, field programmable gate arrays, and application-specific integrated circuits and also includes memory for storing the data on which the logic functionality is implemented. Exemplary memory capabilities include dynamic random-access memory (DRAM).
With regard to claim 1, Khan describes a processing component for formatting incoming data for feature extraction (see Figure 6, element 602 and refer for example to paragraph [0052] which discusses detecting and cropping faces frame by frame, this corresponds to applicant’s “formatting incoming data”, paragraphs [0032] and [0033] discuss the extraction of features, and to paragraph [0052] which discusses that “the transformer architecture 600 uses both face images 602 and their UV texture maps 606 in the extraction of the image features); a transformer backbone for multiplying formatted data with a positional embedding (refer for example to paragraphs [0052], which discusses that the transformer includes a backbone, and to paragraphs [0055] and [0058] which discuss the “multiplying formatted data with a positional embedding”), transforming, by an encoder, formatted data with positional embedding to produce an encoded vector (refer for example to paragraphs [0054] through [0058]), appending a class token to the encoded vector (refer for example to paragraphs [0034], [0065] and [0066]), a single head incremental classifier trained on known classes including a classification weight matrix wk, where k denotes the kth training update for receiving the encoded vector and determining that the incoming data is in a new class, wherein upon determining that the incoming data is in a new class classification weight matrix is augmented with a new null-class weight vector Δwk (refer for example to paragraphs [0061] and [0062]), wherein the single head incremental classifier is trained on training data having feature samples corresponding to the incoming data directed to the new class (refer for example to paragraphs [0061] and [0062]).
Khan describes “an incremental learning strategy is used for fine tuning the models on different datasets incrementally to achieve state-of-the-art performance on new datasets while maintaining the performance on the previous datasets, thus improving generalization” (refer for example to paragraph [0035]) “incremental learning strategy improves the generalization capability of the video transformer. Experiments show that the video transformer model can achieve good performance on a new dataset, while maintaining their performance on previous dataset (refer for example to paragraph [0035]) and uses “an incremental learning strategy is used to fine-tune a pretrained transformer model on new datasets, without sacrificing its performance on previous datasets … the loss function in the incremental learning consists of two parts one part that measures the similarity between the weights from a new dataset and the old weights from the previous dataset, and the other one is to measure the accuracy of the training model on the new dataset”, however Khan does not expressly describe a feature correlation memory, including a ridge regression penalty applied for regularization, such a technique is well known and widely utilized in the prior art.
Shim discloses a class incremental continual learning system (refer for example the abstract) which describes an incremental classifier trained on known classes and determining that the incoming data is in a new class regularization (refer for example to paragraphs [0071] through [0073]), and provides for a feature correlation memory, including a ridge regression penalty applied for regularization (refer for example to paragraphs [0074], [0075] and [0078]).
Gao discloses an incremental learning system (refer for example the abstract) which describes an incremental classifier trained on known classes and determining that the incoming data is in a new class (refer for example to paragraphs [0047] through [0055]), and provides for a feature correlation memory, including a ridge regression penalty applied for regularization (refer for example to paragraphs [0055] and [0056]).
Zhang discloses a system for incremental learning (refer for example the abstract) which describes an incremental classifier trained on known classes and determining that the incoming data is in a new class (refer for example to paragraphs [0027] and [0031]), and provides for a feature correlation memory, including a ridge regression penalty applied for regularization (refer to paragraphs [0029] and [0030]).
Given the teachings of the cited references and the same environment of operation, namely that of systems that provide for incremental learning, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Khan system in the manner described by Shim, Gao and Zhang according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Shim (refer for example to paragraphs [0005] and [0006]), Gao (refer for example to paragraphs [0011] and [0012]) and Zhang (refer for example to paragraphs [0007] and [0008]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
As to claim 2, Khan describes wherein the transformer backbone is a pretrained, self-supervised, model (refer for example to paragraph [0051]).
In regard to claim 3, Khan describes wherein each column of the classification matrix estimates a likelihood of a different data class (refer for example to paragraphs [0155 and [0168]).
As to claim 8, Khan describes an image processing component for extracting multiple flattened image patches from an input image (see Figure 6, element 602 and refer for example to paragraph [0052] which discusses detecting and cropping faces frame by frame, this corresponds to applicant’s “formatting incoming data”, paragraphs [0032] and [0033] discuss the extraction of features, and to paragraph [0052] which discusses that “the transformer architecture 600 uses both face images 602 and their UV texture maps 606 in the extraction of the image features); a transformer backbone for linearly transforming each of the multiple flattened patches, by a patch encoder layer, and mapping the linear transformation to a patch vector, multiplying each patch vector with a positional embedding (refer for example to paragraphs [0052], which discusses that the transformer includes a backbone, and to paragraphs [0055] and [0058] which discuss the “multiplying formatted data with a positional embedding”), transforming, by an encoder, each patch vector with positional embedding to produce an encoded patch vector (refer for example to paragraphs [0054] through [0058]), appending a class token to the encoded patch vector (refer for example to paragraphs [0034], [0065] and [0066]), a single head incremental classifier trained on known image classes including a classification weight matrix wk , where k denotes the kth training update for receiving the encoded vector and determining that the input image is in a new class, wherein upon determining that the input image is in a new class augmenting the classification weight matrix with a new null-class weight vector Δwk ,(refer for example to paragraphs [0061] and [0062]), wherein the single head incremental classifier is trained on images having feature samples corresponding to the input image directed to the new class (refer for example to paragraphs [0061] and [0062]).
Khan describes “an incremental learning strategy is used for fine tuning the models on different datasets incrementally to achieve state-of-the-art performance on new datasets while maintaining the performance on the previous datasets, thus improving generalization” (refer for example to paragraph [0035]) “incremental learning strategy improves the generalization capability of the video transformer. Experiments show that the video transformer model can achieve good performance on a new dataset, while maintaining their performance on previous dataset (refer for example to paragraph [0035]) and uses “an incremental learning strategy is used to fine-tune a pretrained transformer model on new datasets, without sacrificing its performance on previous datasets … the loss function in the incremental learning consists of two parts one part that measures the similarity between the weights from a new dataset and the old weights from the previous dataset, and the other one is to measure the accuracy of the training model on the new dataset”, however Khan does not expressly describe a feature correlation memory, including a ridge regression penalty applied for regularization, such a technique is well known and widely utilized in the prior art.
Shim discloses a class incremental continual learning system (refer for example the abstract) which describes an incremental classifier trained on known classes and determining that the incoming data is in a new class regularization (refer for example to paragraphs [0071] through [0073]), and provides for a feature correlation memory, including a ridge regression penalty applied for regularization (refer for example to paragraphs [0074], [0075] and [0078]).
Gao discloses an incremental learning system (refer for example the abstract) which describes an incremental classifier trained on known classes and determining that the incoming data is in a new class (refer for example to paragraphs [0047] through [0055]), and provides for a feature correlation memory, including a ridge regression penalty applied for regularization (refer for example to paragraphs [0055] and [0056]).
Zhang discloses a system for incremental learning (refer for example the abstract) which describes an incremental classifier trained on known classes and determining that the incoming data is in a new class (refer for example to paragraphs [0027] and [0031]), and provides for a feature correlation memory, including a ridge regression penalty applied for regularization (refer to paragraphs [0029] and [0030]).
Given the teachings of the cited references and the same environment of operation, namely that of systems that provide for incremental learning, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Khan system in the manner described by Shim, Gao and Zhang according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Shim (refer for example to paragraphs [0005] and [0006]), Gao (refer for example to paragraphs [0011] and [0012]) and Zhang (refer for example to paragraphs [0007] and [0008]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
As to claim 11, Khan describes wherein the transformer backbone is a pretrained, self-supervised, model (refer for example to paragraph [0051]).
In regard to claim 12, Khan describes wherein the pretrained, self-supervised, model is a vision transformer model (refer for example to paragraph [0051]).
With regard to claim 13, Khan describes wherein each column of the classification matrix estimates a likelihood of a different image class (refer for example to paragraphs [0155 and [0168]).
In regard to claim 18, Khan describes a non-transitory computer-readable storage medium having computer-executable instructions stored thereon for predicting an image class, which when executed by one or more processors, cause the one or more processors to perform operations (refer for example to paragraph [0059]) comprising extracting multiple flattened image patches from an input image (see Figure 6, element 602 and refer for example to paragraph [0052] which discusses detecting and cropping faces frame by frame, this corresponds to applicant’s “extracting multiple image patches”; linearly transforming each of the multiple flattened patches, by a patch encoder layer, and mapping the linear transformation to a patch vector (refer for example to paragraphs [0032] and [0033] which discuss the extraction of features, and to paragraph [0052] which discusses that “the transformer architecture 600 uses both face images 602 and their UV texture maps 606 in the extraction of the image features, transforming and mapping the transformation to a vector); multiplying each patch vector with a positional embedding (refer for example to paragraphs [0052], which discusses that the transformer includes a backbone, and to paragraphs [0055] and [0058] which discuss the “multiplying formatted data with a positional embedding”); transforming, by an encoder, each patch vector with positional to produce an encoded patch vector (refer for example to paragraphs [0054] through [0058]); appending a class token to the encoded patch vector (refer for example to paragraphs [0034], [0065] and [0066]); receiving the encoded patch vector at a classifier and determining by a classification weight matrix that the image is in a new class, augmenting the classification weight matrix with a new null-class weight vector (refer for example to paragraphs [0061] and [0062]); and training the incremental classifier on images having feature samples corresponding to the input image directed to the new class (refer for example to paragraphs [0061] and [0062]).
Allowable Subject Matter
Claims 4-7, 9-10, 14-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 19 and 20 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b), set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grove, Jordan, Singh, Hsu, Zhao, Mo, Li and Chang all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
March 12, 2026
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