Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 the acquisition, prediction, and processing units disclosed in claims 1 and 17.
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
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without reciting elements that amount to significant more than
the abstract idea. The rationale for this rejection, under MPEP § 2106, for this finding is
explained below.
Step 1: Under step 1, the claims are analyzed to determine if the claim is directed to a
process, machine, article of manufacture, or composition of matter. For the claims in question,
claims 1-14 and 17 are directed towards a machine and claims 15-16 are directed towards a process.
Step 2A, Prong 1: Under step 2A, prong 1, the claims are evaluated to determine if the
claim recites a judicial exception, which includes the laws of nature, physical phenomena, or an
abstract idea. For independent claim 1 (and corresponding independent claims 15-17), the limitations regarding using an image to extract a feature and consequently using the feature to identify a predicted specific region in an image is directed towards a mental process. An individual can reasonably look an image (for example an image obtained from a vehicle displaying a road and oncoming stop sign), identify specific features in an image (i.e., the oncoming stop sign in an image), and determine a point-by-point bounded region in an image where the vehicle should stop with regards to the stop sign (i.e., a region in front of the stop sign). Additionally, the limitations where a model is trained using a loss function is directed towards a mathematical calculation.
Step 2A, Prong 2: Under step 2A, prong 2, the claims are evaluated to determine
whether the claim as a whole integrates the recited judicial exception into a practical application
of the exception (see MPEP 2106.04(d)). The examiner notes that MPEP 2106.05(a) -(c) and (e)
generally concern limitations that are indicative of integration, whereas 2106.05(f)-(h) generally
concern limitations that are not indicative of integration.
In regards to independent claims 1 and 15-17, the limitations as described above are directed towards a mental process and/or a mathematical calculation. The additional limitations regarding utilizing processors and processing apparatuses are recited at a high level of generality and are generic computing components, which do not constitute integration into a practical application or significantly more. The limitation regarding acquiring an image is considered extra-solution activity and does not constitute integration into a practical application or significantly more.
In regards to dependent claims 2-14, the additional limitations are broadly recited and further disclose steps used to perform the judicial exception with any clear indication or detail which would indicate integration into a practical application as noted in MPEP 2106.05(a) or MPEP 2106.05(e). Furthermore, several dependent claims further disclose details regarding a loss function, which are directed towards a mathematical concept. Therefore, the additional limitations of claims 2-14 do not constitute integration into a practical application.
The examiner emphasizes MPEP 2106.05(a), which states that a limitation is indicative of integration into a practical application if the limitation identifies a manner in which an improvement is explicitly and specifically achieved and recited in the claims. The current claim language all are recited at a high level of generality which do not serve to integrate the limitations in view of MPEP 2106.05(f), and furthermore nothing precludes the current limitations from being interpreted under the mental processes grouping.
The examiner encourages the applicant to review the analysis of claim 2, example 47, of the most recent Patent Eligibility Guidance from July 17, 2024 (https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility, titled “2024 AI Examples 47 through 49”), wherein using an artificial neural network trained on input data to detect anomalies was deemed ineligible due to steps (b), (d), and (e) of claim 2 being related to a mental process. In regard to the instant claims of this application, they are directed towards what is otherwise/historically a mentally performed process of identifying features and associated regions, which has merely been made automatic by the use of a machine learning model, which cannot in itself serve to integrate at step 2A, prong 2, as an inventive concept (see MPEP 2106.05(f)).
Additionally, the Examiner notes the Ex Parte Desjardins decision issued on 09/26/2025 (https://www.uspto.gov/sites/default/files/documents/202400567-arp-rehearing-decision-20250926.pdf), and how the claims discussed in Desjardins are different from the claims currently presented. The invention pertains to a training method used to train a machine learning model and claim 1 specifically discloses the computation of several parameters associated with the machine learning model and consequently adjusting the parameters to optimize an objective function. While the limitations are directed towards a mathematical concept at prong one, the limitations are specific and furthermore the Appeals Review Panel directs the limitations towards an improvement to the training of the machine learning model (noting paragraph 21 of the specification of the invention). In contrast to the present application, the claims discussed in Desjardins are specific and recite a specific improvement which is supported by the specification (note Page 9 from the Appeals Review Panel decision). As presented, the current claims presented do not recite a specific improvement which is supported by the specification of the invention.
Step 2B: Under step 2B, the claims are evaluated as a whole to determine if it amounts to
significantly more than the recited exception (i.e., whether any additional element, or
combination of additional elements, adds an inventive concept to the claim). The considerations
of step 2A, prong 2 and step 2B overlap, but differ in that 2B also requires considering the claim
as a whole/combination of limitations, and with reference to MPEP 2106.05(d) whether the
claims feature any “specific limitation(s) other than what is well - understood, routine,
conventional activity in the field” (WURC). The examiner asserts that, even when considered in
combination, the additional elements of claims 1-17 represent mere instruction to apply a mental
process (identifying regions of an image point-by-point) at a high level of generality that is
generally linked to the field of applying machine learning techniques for identifying regions of interest in an image and therefore does not provide a specifically recited inventive concept.
Claim Rejections - 35 USC § 102
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 1 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al. (“PolyFormer: Referring Image Segmentation as Sequential Polygon Generation”, Publication Year: 2023, as noted by Document #2 from IDS filed 05/28/2025; hereinafter “Liu”.
Regarding Claim 1, Liu discloses an information processing apparatus comprising:
one or more processors; and a memory storing instructions which, when the instructions are executed by the one or more processors, cause the information processing apparatus to function as (4.2. Implementation Details, The Examiner notes that the computation and processing disclosed by Liu is performed on a computing device, which includes the claimed “processors”.):
an acquisition unit configured to acquire an image as input information (Fig. 2, 3. PolyFormer, 4.1. Datasets and Metrics, Liu discloses obtaining images.);
a prediction unit configured with one or more machine learning models that extract a feature from the input information and predict a specific region in the image based on the extracted feature (Figs. 1-2, 3. PolyFormer, Liu discloses using an encoder-decoder network that extracts image features and textual features in order to identify a region the input image.); and
a processing unit, wherein the prediction unit outputs a prediction result indicating the specific region including coordinates of a plurality of points surrounding the specific region and information indicating a next point after each of the plurality of points (Fig. 1-3, 3. PolyFormer, Liu discloses identifying a region on an input region based on a series of points
{
(
x
i
,
y
i
)
i
=
1
K
}
.), and
wherein the processing unit trains the one or more machine learning models using a loss function based on a difference between the prediction result including the coordinates of the plurality of points surrounding the specific region and information indicating the next point after each of the plurality of points and ground truth data for the prediction result (3.5. Training, Liu discloses training a machine learning model based on a loss function, wherein the L1 regression (LCOO) is conditionally based on information regarding the other predicted points and the cross-entropy loss Lcls is based on ground truth information. The Examiner notes that the limitation “the information indicating the next point after each of the plurality of points” is broadly recited and interpreted as any value/data/information that can be used to help determine/predict (i.e., “indicate”) what the following point should be. Liu does disclose that the PolyFormer model is autoregressive (see Fig. 2, Abstract, 3. PolyFormer) to generate a sequence of vertices. The Examiner asserts that since the model is autoregressive, any particular point (i.e.,
x
i
,
y
i
) is considered as “information indicating the next point” since point
x
i
,
y
i
) is used to determine the following point (i.e.,
x
i
+
1
,
y
i
+
1
).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 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.
Claims 1-2, 4, and 10-17 are rejected as being unpatentable over Liu in view of McGlade et al. (US 2023/0123300; hereinafter “McGlade”).
Regarding Claim 11, Liu discloses an information processing apparatus comprising:
one or more processors; and a memory storing instructions which, when the instructions are executed by the one or more processors, cause the information processing apparatus to function as (4.2. Implementation Details, The Examiner notes that the computation and processing disclosed by Liu is performed on a computing device, which includes the claimed “processors”.):
an acquisition unit configured to acquire an image as input information (Fig. 2, 3. PolyFormer, 4.1. Datasets and Metrics, Liu discloses obtaining images.);
a prediction unit configured with one or more machine learning models that extract a feature from the input information and predict a specific region in the image based on the extracted feature (Figs. 1-2, 3. PolyFormer, Liu discloses using an encoder-decoder network that extracts image features and textual features in order to identify a region the input image.); and
a processing unit, wherein the prediction unit outputs a prediction result indicating the specific region including coordinates of a plurality of points surrounding the specific region and information indicating a next point after each of the plurality of points (Fig. 1-3, 3. PolyFormer, Liu discloses identifying a region on an input region based on a series of points
{
(
x
i
,
y
i
)
i
=
1
K
}
.), and
wherein the processing unit trains the one or more machine learning models using a loss function based on a difference between the prediction result including the coordinates of the plurality of points surrounding the specific region (3.5. Training, Liu discloses training a machine learning model based on a loss function, wherein the L1 regression (LCOO) is conditionally based on information regarding the other predicted points and the cross-entropy loss Lcls is based on ground truth information.).
Liu does not explicitly disclose using a loss function based on a difference between the prediction result including the coordinates of the plurality of points surrounding the specific region (italicized for context) and information indicating the next point after each of the plurality of points (The Examiner notes Fig. 3 from Liu, wherein Liu does disclose a sequential way of representing polygons.).
McGlade discloses using a loss function based on a difference between the prediction result including the coordinates of the plurality of points surrounding the specific region (italicized for context) and information indicating the next point after each of the plurality of points ([0088], McGlade discloses defining a segmented polygonal region using a set of vectors.).
Liu and McGlade are considered to be analogous to the claimed invention as they are in the same field of identifying regions in an image or a map using a polygonal shape. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Liu such that the predicted region disclosed by Liu is further defined by a series of vectors as disclosed by McGlade, such that the point coordinate information (disclosed by Liu) and vector information (disclosed by McGlade) are incorporated together with the loss functions disclosed by Liu to train a machine learning model. The motivation for this combination being the ability to consider relative positions when training the machine learning model.
Claims 15-17 are the method and non-transitory computer readable storage medium claims corresponding to claim 1, and are similarly rejected (Regarding the method claims, note Fig. 2, 3. PolyFormer, and the ability to train a model and consequently use the model at the inference stage (i.e, generate). Regarding the non-transitory computer readable storage medium claim, note Liu’s computing device (as noted in the preamble of claim 1) which contains the claimed apparatus and units of claim 17.).
Regarding Claim 2, Liu in view of McGlade teaches the information processing apparatus according to claim 1,
wherein the acquisition unit further acquires language information including a designation of a place indicated in a natural language as the input information (Fig. 2, Figs. 6-7, Liu discloses obtaining textual information to encode alongside the image feature.), and
wherein the prediction unit predicts, as the specific region, a target region in the image corresponding to the designation of the place based on an image feature extracted from the image and a language feature extracted from the language information (Fig. 2, Fig. 6-7, Liu discloses using the image and textual features to generate a polygon encompassing a target region in an image.).
Regarding Claim 4, Liu in view of McGlade teaches the information processing apparatus according to claim 1, wherein the loss function includes a loss based on coordinates of a plurality of points surrounding the specific region and a loss based on similarity between vectors with respect to a vector indicating a next point after each of the plurality of points (3.5. Training, Liu discloses using L1 regression loss to train a function. Additionally, the Examiner notes the combination made in claim 1 regarding the usage of vectors to use a loss function to train a model.).
Regarding Claim 9, Liu in view of McGlade teaches the information processing apparatus according to claim 1, wherein information indicating the next point after each of the plurality of points is represented by a vector from each of the plurality of points to the next point (See Fig. 3, wherein Liu discloses a sequential way of representing polygons. Also see [0088], wherein McGlade discloses defining a segmented polygonal region using a set of vectors.).
Regarding Claim 10, Liu in view of McGlade teaches the information processing apparatus according to claim 2, wherein the prediction unit generates a fusion feature obtained by fusing the image feature and the language feature based on the input information, and predicts a specific region in the image based on the fusion feature (Fig. 2, 3. PolyFormer, Liu discloses concatenating image features and textual features, inputting the features into a multi-modal encoder-decoder network to identify a target region in an image.).
Regarding Claim 11, Liu in view of McGlade teaches the information processing apparatus according to claim 10, wherein the one or more machine learning models include a recursive machine learning model that inputs the generated fusion feature and outputs the prediction result indicating the specific region including the coordinates of the plurality of points surrounding the specific region and the information indicating the next point after each of the plurality of points (Fig. 2, Liu discloses the PolyFormer model which predicts in a autoregressive way.).
Regarding Claim 12, Liu in view of McGlade teaches the information processing apparatus according to claim 10, wherein the one or more machine learning models include a transformer decoder that inputs the generated fusion feature and outputs the prediction result indicating the specific region including the coordinates of the plurality of points surrounding the specific region (see Fig. 2, Liu) and the information indicating the next point after each of the plurality of points (See Fig. 2, wherein Liu discloses a transformer encoder and decoder network, wherein the decoder outputs a series of polygon vertices. The Examiner further notes the interpretation note made in the 35 U.S.C. 102(a)(1) rejection regarding the limitation “information indicating the next point after each of the plurality of points”.).
Regarding Claim 13, Liu in view of McGlade teaches the information processing apparatus according to claim 10, wherein the one or more machine learning models include an encoder that inputs the generated fusion feature and encodes the feature, and a decoder that inputs the encoded feature and outputs the prediction result indicating the specific region including the coordinates of the plurality of points surrounding the specific region and the information indicating the next point after each of the plurality of points (Fig. 2, Liu discloses a transformer encoder-decoder network to generate a plurality of points surrounding a specific region.).
Regarding Claim 14, Liu in view of McGlade teaches the information processing apparatus according to claim 13, wherein the encoder is a transformer encoder, and the decoder is a transformer decoder (see Fig. 2, Liu).
Claim 3 is rejected as being unpatentable over Liu in view of McGlade in view of Li et al. (“Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport”, Publication Year: 2023, DOI: 10.1109/ICCV51070.2023.00059; hereinafter “Li”).
Regarding Claim 3, Liu in view of McGlade teaches the information processing apparatus according to claim 1, wherein the loss function is for the plurality of points surrounding the specific region in the prediction result and a plurality of points surrounding the specific region in the ground truth data (5. Training, Liu discloses training a machine learning model based on a L1 regression loss, which is based on a comparison between the predicted points and the ground truth points.).
Liu in view of McGlade does not explicitly teach wherein the loss function is (italicized for context) based on an optimum transport cost.
Li discloses wherein the loss function is (italicized for context) based on an optimum transport cost (3.3. Pseudo-mask Generation by OT, Li discloses solving an optimum transport cost problem using a Sinkhorn based approach to generate image masks.).
Liu, McGlade, and Li are considered to be analogous to the claimed invention as they are in the same field of identifying regions in an image or a map. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Liu in view of McGlade such that the loss functions used to train a machine learning model utilize the optimum transport cost problem approach disclosed by Li. The motivation for this combination being the ability to utilize the constraints of the optimum transport cost problem to improve the results provided by the machine learning model.
Claims 5 and 8 are rejected as being unpatentable over Liu in view of McGlade in view of Cheng et al. (“Parallel Vertex Diffusion for Unified Visual Grounding”, Publication Year: 2023, as noted by Document #3 from IDS filed 05/28/2025; hereinafter “Cheng”).
Regarding Claim 5, Liu in view of McGlade teaches the information processing apparatus according to claim 1, wherein the processing unit outputs the prediction result including coordinates of a plurality of points surrounding the specific region, information indicating a next point after each of the plurality of points (Fig. 2, Liu discloses outputting a series of points representing a polygon surrounding a region. The Examiner notes the combination made in claim 1 regarding the usage of vectors to provide information regarding a next point.),
The combination of Liu in view of McGlade does not explicitly teach coordinates of a central point in the specific region.
Cheng discloses coordinates of a central point in the specific region (3.3. Center Anchor Mechanism, Cheng discloses identifying a center point of an object being identified.).
Liu, McGlade, and Cheng are considered to be analogous to the claimed invention as they are in the same field of identifying regions in an image or a map using a polygonal shape. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Liu in view of McGlade such that the region identified by Liu in view of McGlade further includes a center point of a region disclosed by Cheng. The motivation for this combination being the ability to have an anchor point from which the polygon is generated, improving the accuracy of the polygon that is generated.
Regarding Claim 8, Liu in view of McGlade in view of Cheng teaches the information processing apparatus according to claim 5, wherein the coordinates of the central point in the specific region are represented by information of a vector from a position of a target in the image to coordinates of the central point in the specific region (3.3. Center Anchor Mechanism, 3.4. Parallel Vertex Diffusion, Cheng discloses a vertex vector
V
^
which contains several individual vectors between a center point and a vertex.).
Conclusion
No claims are allowed. Claims 6-7 are free of the prior art. Final allowability is contingent on overcoming all presented rejections.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cui et al. (“End-to-End Vector Simplification for Building Contours via a Sequence Generation Model”, Publication Year: 2025, DOI: 10.3390/ijgi14030124)
Chen et al. (“PLOT: Prompt Learning With Optimal Transport For Vision-Language Models”, Publication Year: 2023, DOI: 10.48550/arXiv.2210.01253)
Borg et al. (US20230049168A1)
Zhao et al. (“Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework”, Publication Year: 2021, DOI: 10.1016/j.isprsjprs.2021.02.014)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PROMOTTO TAJRIAN ISLAM whose telephone number is (703)756-5584. The examiner can normally be reached Monday - Friday 8:30 am - 5:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PROMOTTO TAJRIAN ISLAM/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
1 The Examiner notes that Claim 1 is rejected under both 35 U.S.C. 102(a)(1) and 35 U.S.C. 103, wherein the 35 U.S.C. 103 rejection will be used as the basis for the remaining dependent claims.
Prosecution Insights