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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: “Information processing apparatus, method, program and system for training a second specialized recognizer based on output from a first recognizer.”
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses 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 limitation(s) is/are: “a conversion part” in claim 1 and “a learning device” in claim 18. Claims 1-15 and 18 are therefore being interpreted under 35 USC 112(f).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
Claim 17 recites a "An information processing program" which may be interpreted as a signal per se. A signal or program does not fall into the four statutory categories of a process, machine, manufacture, or composition of matter. Further, if the claim were corrected to recite "a tangible computer-readable medium storing instructions" in place of “an information processing program”, the claimed invention would still be directed to non-statutory subject matter (In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007)). Examiner suggests correction to include language to the effect of non-transitory computer-readable media as recommended in the Kappos memo on Subject Matter Eligibility of Computer Readable Media, February 23, 2010, 1351 OG 212.
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 3-16 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.
Claim 3 recites the limitation “wherein the conversion part trains the second recognizer by distillation”, in which the term “distillation” is vague and unclear. What information is part of the distillation? Is distillation a particular technique or a general idea of information sharing? What is being distilled, and how? Appropriate clarification is required. For purposes of examination, the limitation will be given its broadest reasonable interpretation in view of the available prior art. Claims 4-8 are additionally rejected for inheriting the deficiencies of claim 3.
Claim 9 recites “wherein the conversion part trains control for reading the second signal from the second sensor by distillation”, in which the term “distillation” is vague and unclear. What information is part of the distillation? Is distillation a particular technique or a general idea of information sharing? What is being distilled, and how? Appropriate clarification is required. For purposes of examination, the limitation will be given its broadest reasonable interpretation in view of the available prior art. Claims 10-15 are additionally rejected for inheriting the deficiencies of claim 9.
Claim 16 recites the limitation “the second recognizer performing a recognition process…”, but it is unclear from this recitation whether this performance by the second recognizer is a sequential step of the claimed method, or just a capability of the second recognizer, as the limitation is recited passively rather than actively. Appropriate clarification is required. For purposes of examination, the limitation will be given its broadest reasonable interpretation in view of the available prior art.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation” (hereinafter “Piewak”; applicant-submitted prior art, published 2018).
Regarding Claim 1, Piewak discloses an information processing apparatus (Piewak, Fig. 1, Fig. 4, Section 3-3.4) comprising
a conversion part configured to train a second recognizer based on an output of a first recognizer that performs a recognition process based on a first signal read from a first sensor (Piewak, Section 3.4, third-fourth paragraphs; “In the first step, a high-quality pixel-wise semantic labeling of the reference camera image is computed via state-of-the-art deep neural networks…The described fully automated procedure yields semantically labeled point clouds which can directly be used to train LiDAR-based semantic labeling networks such as LiLaNet.” The “deep neural networks” are read as the claimed “first recognizer” and “LiLaNet” is read here as the claimed “second recognizer”, wherein the LiLaNet network is trained on the semantic labels output by the deep neural networks),
the second recognizer performing a recognition process based on a second signal read from a second sensor having a characteristic different from the first sensor (Piewak, Section 3, first paragraph; “we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain”).
Regarding Claim 2, claim 1 is incorporated, and Piewak further discloses wherein a first reading unit that is a reading unit of the first sensor is one frame, and a second reading unit that is a reading unit of the second sensor is smaller than the one frame (Piewak, Section 3-3.4; a LIDAR point cloud is smaller (i.e., more sparse) than a reference image frame in terms of the amount of data contained in each).
Regarding Claim 3, claim 2 is incorporated, and Piewak further discloses wherein the conversion part trains the second recognizer by distillation using the output of the first recognizer (Piewak, Section 3, first paragraph; “we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain”).
Regarding Claim 4, claim 3 is incorporated, and Piewak further discloses wherein the conversion part trains the second recognizer using the first recognizer, the second recognizer, the first signal, and the second signal (Piewak, Section 3-3.4; “To obtain high output quality and retain efficiency at the same time, we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain. The cylindrical projection of a 360◦ point cloud captured with a state-of-the-art rotating LiDAR scanner is used as input to our networks. Training is boosted by an efficient automated cross-modal data generation process, which we refer to as Autolabeling.”).
Regarding Claim 5, claim 3 is incorporated, and Piewak further discloses wherein the conversion part: converts the first signal into a signal corresponding to the second signal (Piewak, Section 3.4, Fig. 5; “As illustrated in Fig. 5, the Autolabeling concept is based on the use of one or more reference cameras in conjunction with the LiDAR sensor capturing the point cloud data. The obtained reference camera images have to be registered to the LiDAR data in space and time.”); and
trains the second recognizer by using the first recognizer, the second recognizer, the first signal, and the signal obtained by converting the first signal (Piewak, Section 3-3.4; “To obtain high output quality and retain efficiency at the same time, we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain. The cylindrical projection of a 360◦ point cloud captured with a state-of-the-art rotating LiDAR scanner is used as input to our networks. Training is boosted by an efficient automated cross-modal data generation process, which we refer to as Autolabeling.”).
Regarding Claim 6, claim 3 is incorporated, and Piewak further discloses wherein the conversion part: converts the second signal into a signal corresponding to the first signal; and trains the second recognizer by using the first recognizer, the second recognizer, the first signal, and the signal obtained by converting the second signal (Piewak, Section 3.4, Fig. 6; “Second, the captured point cloud is projected into the reference image plane to transfer the semantic information of the image pixels to the corresponding LiDAR points…The described fully automated procedure yields semantically labeled point clouds which can directly be used to train LiDAR-based semantic labeling networks such as LiLaNet.”).
Regarding Claim 7, claim 3 is incorporated, and Piewak further discloses wherein the conversion part: generates a signal corresponding to the first signal based on the first recognizer; converts the signal corresponding to the first signal into a signal corresponding to the second signal (Piewak, Section 3.4, Fig. 5; “As illustrated in Fig. 5, the Autolabeling concept is based on the use of one or more reference cameras in conjunction with the LiDAR sensor capturing the point cloud data. The obtained reference camera images have to be registered to the LiDAR data in space and time.”); and
trains the second recognizer by using the first recognizer, the second recognizer, the signal corresponding to the first signal, and the signal corresponding to the second signal (Piewak, Section 3-3.4; “To obtain high output quality and retain efficiency at the same time, we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain. The cylindrical projection of a 360◦ point cloud captured with a state-of-the-art rotating LiDAR scanner is used as input to our networks. Training is boosted by an efficient automated cross-modal data generation process, which we refer to as Autolabeling.”).
Regarding Claim 8, claim 3 is incorporated, and Piewak further discloses wherein the conversion part: generates the second signal; converts the second signal generated into a signal corresponding to the first signal; and trains the second recognizer by using the first recognizer, the second recognizer, the signal corresponding to the first signal, and the second signal generated (Piewak, Section 3.4, Fig. 6; “Second, the captured point cloud is projected into the reference image plane to transfer the semantic information of the image pixels to the corresponding LiDAR points…The described fully automated procedure yields semantically labeled point clouds which can directly be used to train LiDAR-based semantic labeling networks such as LiLaNet.”).
Regarding Claim 9, claim 2 is incorporated, and Piewak further discloses wherein the conversion part trains control for reading the second signal from the second sensor by distillation using the output of the first recognizer (Piewak, Section 3, first paragraph; “we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain”).
Regarding Claim 10, claim 9 is incorporated, and Piewak further discloses wherein the conversion part trains the control by using the first recognizer, the second recognizer, the first signal, and the second signal (Piewak, Section 3-3.4; “To obtain high output quality and retain efficiency at the same time, we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain. The cylindrical projection of a 360◦ point cloud captured with a state-of-the-art rotating LiDAR scanner is used as input to our networks. Training is boosted by an efficient automated cross-modal data generation process, which we refer to as Autolabeling.”).
Regarding Claim 11, claim 9 is incorporated, and Piewak further discloses wherein the conversion part: converts the first signal into a signal corresponding to the second signal (Piewak, Section 3.4, Fig. 5; “As illustrated in Fig. 5, the Autolabeling concept is based on the use of one or more reference cameras in conjunction with the LiDAR sensor capturing the point cloud data. The obtained reference camera images have to be registered to the LiDAR data in space and time.”); and
trains the control by using the first recognizer, the second recognizer, the first signal, and the signal obtained by converting the first signal (Piewak, Section 3-3.4; “To obtain high output quality and retain efficiency at the same time, we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain. The cylindrical projection of a 360◦ point cloud captured with a state-of-the-art rotating LiDAR scanner is used as input to our networks. Training is boosted by an efficient automated cross-modal data generation process, which we refer to as Autolabeling.”).
Regarding Claim 12, claim 9 is incorporated, and Piewak further discloses wherein the conversion part: converts the second signal into a signal corresponding to the first signal; and trains the control by using the first recognizer, the second recognizer, the first signal, and the signal obtained by converting the second signal (Piewak, Section 3.4, Fig. 6; “Second, the captured point cloud is projected into the reference image plane to transfer the semantic information of the image pixels to the corresponding LiDAR points…The described fully automated procedure yields semantically labeled point clouds which can directly be used to train LiDAR-based semantic labeling networks such as LiLaNet.”).
Regarding Claim 13, claim 9 is incorporated, and Piewak further discloses wherein the conversion part: generates a signal corresponding to the first signal based on the first recognizer; converts the signal corresponding to the first signal into a signal corresponding to the second signal (Piewak, Section 3.4, Fig. 5; “As illustrated in Fig. 5, the Autolabeling concept is based on the use of one or more reference cameras in conjunction with the LiDAR sensor capturing the point cloud data. The obtained reference camera images have to be registered to the LiDAR data in space and time.”); and
trains the control by using the first recognizer, the second recognizer, the signal corresponding to the first signal, and the signal corresponding to the second signal (Piewak, Section 3-3.4; “To obtain high output quality and retain efficiency at the same time, we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain. The cylindrical projection of a 360◦ point cloud captured with a state-of-the-art rotating LiDAR scanner is used as input to our networks. Training is boosted by an efficient automated cross-modal data generation process, which we refer to as Autolabeling.”).
Regarding Claim 14, claim 9 is incorporated, and Piewak further discloses wherein the conversion part: generates the second signal; converts the second signal generated into a signal corresponding to the first signal; and trains the control by using the first recognizer, the second recognizer, the signal corresponding to the first signal, and the second signal generated (Piewak, Section 3.4, Fig. 6; “Second, the captured point cloud is projected into the reference image plane to transfer the semantic information of the image pixels to the corresponding LiDAR points…The described fully automated procedure yields semantically labeled point clouds which can directly be used to train LiDAR-based semantic labeling networks such as LiLaNet.”).
Regarding Claim 15, claim 9 is incorporated, and Piewak further discloses wherein the conversion part further trains the second recognizer by the distillation using the output of the first recognizer (Piewak, Section 3, first paragraph; “we aim to transfer lessons learned from image-based semantic labeling methods to the LiDAR domain”).
Claim 16 recites an information processing method having features corresponding to the elements recited in apparatus claim 1, the rejection of which is applicable here.
Claim 17 recites an information processing program having features corresponding to the elements recited in apparatus claim 1, the rejection of which is applicable here.
Claim 18 recites an information processing system having features corresponding to the elements recited in apparatus claim 1, the rejection of which is applicable here.
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 conflicting claims 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined 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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/719264 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claim of the instant application is an obvious variant of the claim of the reference application, as shown in the table below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application Claim 1
Reference Application Claim 1
Claim 1. An information processing apparatus comprising a conversion part configured to train a second recognizer based on an output of a first recognizer that performs a recognition process based on a first signal read from a first sensor, the second recognizer performing a recognition process based on a second signal read from a second sensor having a characteristic different from the first sensor.
Claim 1. An information processing apparatus comprising a conversion part configured to convert a first recognizer or a first dataset for performing a recognition process based on a first signal read from a first sensor having a first pixel characteristic or a first signal characteristic into a second recognizer or a second dataset for performing a recognition process based on a second pixel characteristic different from the first pixel characteristic or a second signal characteristic different from the first signal characteristic.
Claim 2 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of copending Application No. 18/719264 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 16 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 18 of copending Application No. 18/719264 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 17 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 of copending Application No. 18/719264 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 18 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 20 of copending Application No. 18/719264 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 1 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18/721898 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claim of the instant application is anticipated by the claim of the reference application, as shown in the table below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application Claim 1
Reference Application Claim 1
Claim 1. An information processing apparatus comprising a conversion part configured to train a second recognizer based on an output of a first recognizer that performs a recognition process based on a first signal read from a first sensor, the second recognizer performing a recognition process based on a second signal read from a second sensor having a characteristic different from the first sensor.
Claim 1. An information processing apparatus comprising a conversion part configured to convert, based on an output of a first recognizer that performs a recognition process based on a first signal read from a first sensor, a processing parameter related to a recognition process of a second recognizer that performs the recognition process based on a second signal read from a second sensor having a characteristic different from a characteristic of the first sensor, wherein the conversion part converts the processing parameter to approximate an output of the second recognizer to the output of the first recognizer.
Claim 2 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 2 of copending Application No. 18/721898 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 16 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 18 of copending Application No. 18/721898 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 17 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 19 of copending Application No. 18/721898 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim 18 is provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 20 of copending Application No. 18/721898 (reference application) for the same rationale as set forth above with respect to claim 1.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The additionally provided references pertain generally to knowledge distillation from teacher to student networks and/or training specialized networks from generic pre-trained networks.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMAH A BEG whose telephone number is (571)270-7912. The examiner can normally be reached M-F 9 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAMAH A BEG/Primary Examiner, Art Unit 2676
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