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 . This action is in response to the application filled 10/23/2023. Claims 1-24 are pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 2/13/25 and 4/15/25 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 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. Claims 13 and 17 use the word “means” along with functional language and are being interpreted under 35 U.S.C. 112(f).
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Claim 1 recites an apparatus comprising a combination of components, which falls into the statutory category of machine.
Step 2A Prong 1: Claim 1 recites multiple abstract ideas: compress the diffusion model by removing at least one of: one or more model parameters or one or more giga multiply-accumulate operations (GMACs), a mental process given a human being can reduce the parameters of a model in their mind; perform guidance conditioning to train the compressed diffusion model, the guidance conditioning combining a conditional output and an unconditional output from respective teacher models, a mental process given a human being can reasonably combine two outputs in their mind; perform, after the guidance conditioning, step distillation on the compressed diffusion model, a method of organizing human activity given distillation can be done between two humans in the form of teaching.
Step 2A Prong 2: Claim 1 does not integrate the abstract idea into a practical application since the additional elements of:
one or more processors; and one or more memories, are merely instructions to apply the abstract idea by a generic computer component.
to train the compressed diffusion, are merely instructions to apply the abstract idea of guidance conditioning by a generic computer component (a compressed diffusion model).
on the compressed diffusion model, are merely instructions to apply the abstract idea of step distillation by a generic computer component (a compressed diffusion model).
Step 2B: Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements are merely instructions to apply the abstract ideas by generic computer components. Therefore, claim 1 is not patent eligible.
Regarding claim 2, the rejection of claim 1 is incorporated, further the claim recites wherein: the conditional output is based on a text string received at a first teacher model; and the unconditional output is based on an empty string received at a second teacher model. This limitation amounts to specifics about the outputs used in the abstract idea of guidance conditioning and amounts to mere specifics of the judicial exception. Claim 2 is not patent eligible.
Regarding claim 3, the rejection of claim 1 is incorporated, further the claim recites wherein: the compressed diffusion model receives a guidance value from a guidance embedding during the guidance conditioning; and the guidance value modulates an output of the compressed diffusion model. This limitation amounts to specifics of how the generic computer applies the abstract idea of guidance conditioning by receiving a guidance value that modulates an output. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more that the judicial exception. Claim 3 is not patent eligible.
Regarding claim 4, the rejection of claim 1 is incorporated, further the claim recites wherein: the step distillation includes two sequential teacher models and the compressed diffusion model; and the step distillation comprises distilling two steps associated with the two sequential teacher models into one step of the compressed diffusion model. The limitation of includes two sequential teacher models and the compressed diffusion model amounts to generally linking the abstract idea of distillation to a particular technological environment (teacher and student models); meanwhile, the limitation of step distillation comprises distilling two steps associated with the two sequential teacher models into one step of the compressed diffusion model amounts to mere specifics of the abstract idea of distillation (that it turns two steps into one). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more that the judicial exception. Claim 4 is not patent eligible.
Regarding claim 5, the rejection of claim 1 is incorporated, further the claim recites wherein execution of the instructions further cause the apparatus to perform an epsilon to velocity conversion prior to compressing the diffusion model. This limitation amounts to a mathematical concept given the conversion is done using a function (Par 0080). As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more that the judicial exception. Claim 5 is not patent eligible.
Regarding claim 6, the rejection of claim 1 is incorporated, further the claim recites wherein the diffusion model comprises a UNet architecture. This limitation amounts to general specifics of the general computer (diffusion model) used to apply the abstract idea. As such the claim does not have any additional elements that amount to an integration of the judicial exception into a practical application, nor to significantly more that the judicial exception. Claim 6 is not patent eligible.
Regarding claims 7-12, given the claims are merely variations of claims 1-6 in which a method, which falls into the statutory category of process, is claimed instead of a machine, the rejections above are incorporated. Therefore, claims 7-12 are not patent eligible.
Regarding claim 13-18, given the claims are merely variations of claims 1-6 in which the means for completing the abstract ideas are the processors and program code used in claim 1 and claim 19. Therefore, claims 13-18 are not patent eligible.
Regarding claims 19-24, given the claims are merely variations of claims 1-6 in which a non-transitory CRM, which falls into the statutory category of manufacture, is claimed instead of a machine, the rejections above are incorporated. Therefore, claims 19-24 are not patent eligible.
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.
Claim(s) 1-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li (SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds).
Regarding claim 1, Li teaches an apparatus for training a diffusion model, comprising: one or more processors; and one or more memories coupled with the one or more processors and storing instructions operable, when executed by the one or more processors, to cause the apparatus to (Section 5, We use 16 or 32 nodes for most of the training. Each node has 8 NVIDIA A100 GPUs with 40GB or 80GB memory): compress the diffusion model by removing at least one of: one or more model parameters or one or more giga multiply-accumulate operations (GMACs) (Appendix B.2, The student, which is the compressed decoder, takes the latent from the teacher model as input and generates an output image, Section 3.2, The efficient image decoder is obtained by applying 50% uniform channel pruning to the original image decoder, resulting in a compressed efficient image decoder with approximately 1/4 size and MACs of the original one); perform guidance conditioning to train the compressed diffusion model, the guidance conditioning combining a conditional output and an unconditional output from respective teacher models (Figure 3, Section 4.2, As a remedy, this section introduces a classifier-free guidance-aware (CFG-aware) distillation loss objective function, which will be shown to improve the CLIP score significantly, CFG-Aware step distillation trains the student model by combining a conditional output, vη(t,zt,c), and unconditional output vη(t,zt,∅)); and perform, after the guidance conditioning, step distillation on the compressed diffusion model (Figure 3, Our Step Distillation, Section 4.1, Finally, we use the 16-step SD-v1.5 as the teacher to conduct step distillation on the efficient UNet).
Regarding claim 2, Li teaches wherein: the conditional output is based on a text string received at a first teacher model; and the unconditional output is based on an empty string received at a second teacher model (Section 2.1, LDM also explores the text-to-image generation, where a text prompt embedding c is fed into the diffusion model as the condition. When synthesizing images, an important technique, classifier-free guidance (CFG) [34], is adopted to improve quality…where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅, Section 4.2, We propose to perform classifier-free guidance to both the teacher and student before calculating the loss, the conditional output vη(t,zt,c) is based off c, which is a text prompt embedding. The unconditional output vη(t,zt,∅) is based off the null text).
Regarding claim 3, Li teaches wherein: the compressed diffusion model receives a guidance value from a guidance embedding during the guidance conditioning; and the guidance value modulates an output of the compressed diffusion model (Section 2.1, When synthesizing images, an important technique, classifier-free guidance (CFG)[34], is adopted to improve quality where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅. The guidance scale w can be adjusted to control the strength of conditional information on the generated images to achieve the trade-off between quality and diversity, equation 10 demonstrates the guidance value w (which is derived from the guidance embedding vt(s), a combination of the unconditional and conditional outputs). The guidance embedding is used during step distillation as shown in figure 3, which means the guidance value modulates the compressed diffusion model).
Regarding claim 4, Li teaches wherein: the step distillation includes two sequential teacher models and the compressed diffusion model; and the step distillation comprises distilling two steps associated with the two sequential teacher models into one step of the compressed diffusion model (Figure 3, right side shows two sequential teacher models distilling into the student model, Section 4, We follow the research direction of step distillation [33], where the inference steps are reduced by distilling the teacher, e.g., at 32 steps, to a student that runs at fewer steps, e.g., 16 steps. This way, the student enjoys 2× speedup against the teacher).
Regarding claim 5, Li teaches wherein execution of the instructions further cause the apparatus to perform an epsilon to velocity conversion prior to compressing the diffusion model (Section 4.1, Citing the wisdom from previous studies [33, 32], step distillation works best with the v-prediction type, i.e., UNet outputs velocity v [33] instead of the noise ϵ. Thus, we fine-tune SD-v1.5 to v prediction…. Eq (6) …… where v is the ground-truth target velocity, which can be derived analytically from the clean latent x and noise ϵ given time step t: v ≡ αtϵ − σtx).
Regarding claim 6, Li teaches wherein the diffusion model comprises a UNet architecture (Figure 3).
Regarding claim 7, Li teaches a method for training a diffusion model, comprising: compressing the diffusion model by removing at least one of: one or more model parameters or one or more giga multiply-accumulate operations (GMACs) (Appendix B.2, The student, which is the compressed decoder, takes the latent from the teacher model as input and generates an output image, Section 3.2, The efficient image decoder is obtained by applying 50% uniform channel pruning to the original image decoder, resulting in a compressed efficient image decoder with approximately 1/4 size and MACs of the original one); performing guidance conditioning to train the compressed diffusion model, the guidance conditioning combining a conditional output and an unconditional output from respective teacher models (Figure 3, Section 4.2, As a remedy, this section introduces a classifier-free guidance-aware (CFG-aware) distillation loss objective function, which will be shown to improve the CLIP score significantly, CFG-Aware step distillation trains the student model by combining a conditional output, vη(t,zt,c), and unconditional output vη(t,zt,∅)); and performing, after the guidance conditioning, step distillation on the compressed diffusion model (Figure 3, Our Step Distillation, Section 4.1, Finally, we use the 16-step SD-v1.5 as the teacher to conduct step distillation on the efficient UNet).
Regarding claim 8, Li teaches wherein: the conditional output is based on a text string received at a first teacher model; and the unconditional output is based on an empty string received at a second teacher model (Section 2.1, LDM also explores the text-to-image generation, where a text prompt embedding c is fed into the diffusion model as the condition. When synthesizing images, an important technique, classifier-free guidance (CFG) [34], is adopted to improve quality…where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅, Section 4.2, We propose to perform classifier-free guidance to both the teacher and student before calculating the loss, the conditional output vη(t,zt,c) is based off c, which is a text prompt embedding. The unconditional output vη(t,zt,∅) is based off the null text).
Regarding claim 9, Li teaches wherein: the compressed diffusion model receives a guidance value from a guidance embedding during the guidance conditioning; and the guidance value modulates an output of the compressed diffusion model (Section 2.1, When synthesizing images, an important technique, classifier-free guidance (CFG)[34], is adopted to improve quality where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅. The guidance scale w can be adjusted to control the strength of conditional information on the generated images to achieve the trade-off between quality and diversity, equation 10 demonstrates the guidance value w (which is derived from the guidance embedding vt(s), a combination of the unconditional and conditional outputs). The guidance embedding is used during step distillation as shown in figure 3, which means the guidance value modulates the compressed diffusion model).
Regarding claim 10, Li teaches wherein: the step distillation includes two sequential teacher models and the compressed diffusion model; and the step distillation comprises distilling two steps associated with the two sequential teacher models into one step of the compressed diffusion model (Figure 3, right side shows two sequential teacher models distilling into the student model, Section 4, We follow the research direction of step distillation [33], where the inference steps are reduced by distilling the teacher, e.g., at 32 steps, to a student that runs at fewer steps, e.g., 16 steps. This way, the student enjoys 2× speedup against the teacher).
Regarding claim 11, Li teaches wherein execution of the instructions further cause the apparatus to perform an epsilon to velocity conversion prior to compressing the diffusion model (Section 4.1, Citing the wisdom from previous studies [33, 32], step distillation works best with the v-prediction type, i.e., UNet outputs velocity v [33] instead of the noise ϵ. Thus, we fine-tune SD-v1.5 to v prediction…. Eq (6) …… where v is the ground-truth target velocity, which can be derived analytically from the clean latent x and noise ϵ given time step t: v ≡ αtϵ − σtx).
Regarding claim 12, Li teaches wherein the diffusion model comprises a UNet architecture (Figure 3).
Regarding claim 13, Li teaches an apparatus for training a diffusion model, comprising: (Section 5, We use 16 or 32 nodes for most of the training. Each node has 8 NVIDIA A100 GPUs with 40GB or 80GB memory): means for compressing the diffusion model by removing at least one of: one or more model parameters or one or more giga multiply-accumulate operations (GMACs) (Appendix B.2, The student, which is the compressed decoder, takes the latent from the teacher model as input and generates an output image, Section 3.2, The efficient image decoder is obtained by applying 50% uniform channel pruning to the original image decoder, resulting in a compressed efficient image decoder with approximately 1/4 size and MACs of the original one); means for performing guidance conditioning to train the compressed diffusion model, the guidance conditioning combining a conditional output and an unconditional output from respective teacher models (Figure 3, Section 4.2, As a remedy, this section introduces a classifier-free guidance-aware (CFG-aware) distillation loss objective function, which will be shown to improve the CLIP score significantly, CFG-Aware step distillation trains the student model by combining a conditional output, vη(t,zt,c), and unconditional output vη(t,zt,∅)); and means for performing, after the guidance conditioning, step distillation on the compressed diffusion model (Figure 3, Our Step Distillation, Section 4.1, Finally, we use the 16-step SD-v1.5 as the teacher to conduct step distillation on the efficient UNet).
Regarding claim 14, Li teaches wherein: the conditional output is based on a text string received at a first teacher model; and the unconditional output is based on an empty string received at a second teacher model (Section 2.1, LDM also explores the text-to-image generation, where a text prompt embedding c is fed into the diffusion model as the condition. When synthesizing images, an important technique, classifier-free guidance (CFG) [34], is adopted to improve quality…where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅, Section 4.2, We propose to perform classifier-free guidance to both the teacher and student before calculating the loss, the conditional output vη(t,zt,c) is based off c, which is a text prompt embedding. The unconditional output vη(t,zt,∅) is based off the null text).
Regarding claim 15, Li teaches wherein: the compressed diffusion model receives a guidance value from a guidance embedding during the guidance conditioning; and the guidance value modulates an output of the compressed diffusion model (Section 2.1, When synthesizing images, an important technique, classifier-free guidance (CFG)[34], is adopted to improve quality where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅. The guidance scale w can be adjusted to control the strength of conditional information on the generated images to achieve the trade-off between quality and diversity, equation 10 demonstrates the guidance value w (which is derived from the guidance embedding vt(s), a combination of the unconditional and conditional outputs). The guidance embedding is used during step distillation as shown in figure 3, which means the guidance value modulates the compressed diffusion model).
Regarding claim 16, Li teaches wherein: the step distillation includes two sequential teacher models and the compressed diffusion model; and the step distillation comprises distilling two steps associated with the two sequential teacher models into one step of the compressed diffusion model (Figure 3, right side shows two sequential teacher models distilling into the student model, Section 4, We follow the research direction of step distillation [33], where the inference steps are reduced by distilling the teacher, e.g., at 32 steps, to a student that runs at fewer steps, e.g., 16 steps. This way, the student enjoys 2× speedup against the teacher).
Regarding claim 17, Li teaches further comprising means for performing an epsilon to velocity conversion prior to compressing the diffusion model (Section 4.1, Citing the wisdom from previous studies [33, 32], step distillation works best with the v-prediction type, i.e., UNet outputs velocity v [33] instead of the noise ϵ. Thus, we fine-tune SD-v1.5 to v prediction…. Eq (6) …… where v is the ground-truth target velocity, which can be derived analytically from the clean latent x and noise ϵ given time step t: v ≡ αtϵ − σtx).
Regarding claim 18, Li teaches wherein the diffusion model comprises a UNet architecture (Figure 3).
Regarding claim 19, Li teaches non-transitory computer-readable medium having program code recorded thereon for training a diffusion model, the program code executed by a processor and comprising (Section 5, Our code is developed based on diffusers library…...We use 16 or 32 nodes for most of the training. Each node has 8 NVIDIA A100 GPUs with 40GB or 80GB memory, implies a CRM): program code to compress the diffusion model by removing at least one of: one or more model parameters or one or more giga multiply-accumulate operations (GMACs) (Appendix B.2, The student, which is the compressed decoder, takes the latent from the teacher model as input and generates an output image, Section 3.2, The efficient image decoder is obtained by applying 50% uniform channel pruning to the original image decoder, resulting in a compressed efficient image decoder with approximately 1/4 size and MACs of the original one); program code to perform guidance conditioning to train the compressed diffusion model, the guidance conditioning combining a conditional output and an unconditional output from respective teacher models (Figure 3, Section 4.2, As a remedy, this section introduces a classifier-free guidance-aware (CFG-aware) distillation loss objective function, which will be shown to improve the CLIP score significantly, CFG-Aware step distillation trains the student model by combining a conditional output, vη(t,zt,c), and unconditional output vη(t,zt,∅)); and program code to perform, after the guidance conditioning, step distillation on the compressed diffusion model (Figure 3, Our Step Distillation, Section 4.1, Finally, we use the 16-step SD-v1.5 as the teacher to conduct step distillation on the efficient UNet).
Regarding claim 20, Li teaches wherein: the conditional output is based on a text string received at a first teacher model; and the unconditional output is based on an empty string received at a second teacher model (Section 2.1, LDM also explores the text-to-image generation, where a text prompt embedding c is fed into the diffusion model as the condition. When synthesizing images, an important technique, classifier-free guidance (CFG) [34], is adopted to improve quality…where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅, Section 4.2, We propose to perform classifier-free guidance to both the teacher and student before calculating the loss, the conditional output vη(t,zt,c), generated by the first teacher as done in CFG, is based off c, which is a text prompt embedding. The unconditional output vη(t,zt,∅), generated by the second teacher as done in CFG, is based off the null text).
Regarding claim 21, Li teaches wherein: the compressed diffusion model receives a guidance value from a guidance embedding during the guidance conditioning; and the guidance value modulates an output of the compressed diffusion model (Section 2.1, When synthesizing images, an important technique, classifier-free guidance (CFG)[34], is adopted to improve quality where ϵθ(t,zt,∅) represents the unconditional output obtained by using null text ∅. The guidance scale w can be adjusted to control the strength of conditional information on the generated images to achieve the trade-off between quality and diversity, equation 10 demonstrates the guidance value w (which is derived from the guidance embedding vt(s), a combination of the unconditional and conditional outputs). The guidance embedding is used during step distillation as shown in figure 3, which means the guidance value modulates the compressed diffusion model).
Regarding claim 22, Li teaches wherein: the step distillation includes two sequential teacher models and the compressed diffusion model; and the step distillation comprises distilling two steps associated with the two sequential teacher models into one step of the compressed diffusion model (Figure 3, right side shows two sequential teacher models distilling into the student model, Section 4, We follow the research direction of step distillation [33], where the inference steps are reduced by distilling the teacher, e.g., at 32 steps, to a student that runs at fewer steps, e.g., 16 steps. This way, the student enjoys 2× speedup against the teacher).
Regarding claim 23, Li teaches wherein the program code further comprises program code to perform an epsilon to velocity conversion prior to compressing the diffusion model (Section 4.1, Citing the wisdom from previous studies [33, 32], step distillation works best with the v-prediction type, i.e., UNet outputs velocity v [33] instead of the noise ϵ. Thus, we fine-tune SD-v1.5 to v prediction…. Eq (6) …… where v is the ground-truth target velocity, which can be derived analytically from the clean latent x and noise ϵ given time step t: v ≡ αtϵ − σtx).
Regarding claim 24, Li teaches wherein the diffusion model comprises a UNet architecture (Figure 3).
Conclusion
The prior art made on record and not relied upon is considered pertinent to applicant's disclosure. Meng (On Distillation of Guided Diffusion Models). The prior art was used to gain insight into the state of the art and the general ideas of classifier-free guidance and step distillation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATNAEL A ASEGDEW whose telephone number is (571)270-0407. The examiner can normally be reached 7:30-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATNAEL A ASEGDEW/Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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